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2026 Volume 41 Issue 1
2026, 41(1): 1-8
doi: 10.12461/PKU.DXHX202505049
Abstract:
The rapid advancement of artificial intelligence (AI) technology has established AI for Science (AI4S) as an emerging paradigm in scientific research, particularly revolutionizing experimental methodologies in chemistry. This study innovatively introduces the concept of Self-Driving Laboratories (SDL) to address future challenges in chemical research and explore its application in AI-driven digital transformation of fundamental chemical experiments. Employing a recyclable solid acid catalyst for the esterification reaction between acetic acid and benzyl alcohol, we developed a cost-effective, education-oriented closed-loop system for reaction condition optimization. The integrated system comprises an automated reactor based on the Neodawn automatic synthesizer, a custom-designed liquid-phase automatic sampling and monitoring system, and a Python-based decision optimization algorithm. This work successfully overcomes existing challenges in hardware compatibility and algorithm optimization, providing a robust foundation for future innovations in chemical experimentation.
The rapid advancement of artificial intelligence (AI) technology has established AI for Science (AI4S) as an emerging paradigm in scientific research, particularly revolutionizing experimental methodologies in chemistry. This study innovatively introduces the concept of Self-Driving Laboratories (SDL) to address future challenges in chemical research and explore its application in AI-driven digital transformation of fundamental chemical experiments. Employing a recyclable solid acid catalyst for the esterification reaction between acetic acid and benzyl alcohol, we developed a cost-effective, education-oriented closed-loop system for reaction condition optimization. The integrated system comprises an automated reactor based on the Neodawn automatic synthesizer, a custom-designed liquid-phase automatic sampling and monitoring system, and a Python-based decision optimization algorithm. This work successfully overcomes existing challenges in hardware compatibility and algorithm optimization, providing a robust foundation for future innovations in chemical experimentation.
2026, 41(1): 9-19
doi: 10.12461/PKU.DXHX202503128
Abstract:
High-performance liquid chromatography (HPLC) experiment is an important instrumental analysis experiment for chemistry majors. However, conventional HPLC experiments designed for instructional purposes often employ oversimplified analytes, making them lack practicality, cutting-edge relevance, and engagement. Crucially, the quantitative chromatographic analysis in traditional teaching laboratories relies on physical/chemical separation, exhibiting suboptimal efficiency and universality while failing to incorporate digital technologies in higher education. To address these limitations, we developed an innovative chromatographic experiment integrating digital technology: the determination of benzenediol isomers in cosmetics through mathematical separation-enhanced HPLC. Conventional HPLC methods for detecting illegally added hydroquinone, catechol, and resorcinol in cosmetic products face challenges including peak overlap, unknown interferences, and poor method transferability. Our approach employs chemometric mathematical separation techniques to resolve HPLC data, eliminating the need for extensive sample pretreatment. This methodology successfully obtains fully selective signals for each component despite peak coelution and unknown matrix effects, enabling precise quantification of three isomers in complex cosmetic matrices. This digitally enhanced experiment fosters students’ innovative thinking and develops their digital literacy. The implementation of “mathematical separation” establishes a robust “3S+3A” analytical approach that not only enhances students’ comprehensive problem-solving abilities but also provides innovative ideas for establishing standard chromatographic detection methods. Rooted in real cosmetic analysis scenarios, it fosters students’ practical skills and professional competence while thoroughly implementing the concept of green analytical chemistry.
High-performance liquid chromatography (HPLC) experiment is an important instrumental analysis experiment for chemistry majors. However, conventional HPLC experiments designed for instructional purposes often employ oversimplified analytes, making them lack practicality, cutting-edge relevance, and engagement. Crucially, the quantitative chromatographic analysis in traditional teaching laboratories relies on physical/chemical separation, exhibiting suboptimal efficiency and universality while failing to incorporate digital technologies in higher education. To address these limitations, we developed an innovative chromatographic experiment integrating digital technology: the determination of benzenediol isomers in cosmetics through mathematical separation-enhanced HPLC. Conventional HPLC methods for detecting illegally added hydroquinone, catechol, and resorcinol in cosmetic products face challenges including peak overlap, unknown interferences, and poor method transferability. Our approach employs chemometric mathematical separation techniques to resolve HPLC data, eliminating the need for extensive sample pretreatment. This methodology successfully obtains fully selective signals for each component despite peak coelution and unknown matrix effects, enabling precise quantification of three isomers in complex cosmetic matrices. This digitally enhanced experiment fosters students’ innovative thinking and develops their digital literacy. The implementation of “mathematical separation” establishes a robust “3S+3A” analytical approach that not only enhances students’ comprehensive problem-solving abilities but also provides innovative ideas for establishing standard chromatographic detection methods. Rooted in real cosmetic analysis scenarios, it fosters students’ practical skills and professional competence while thoroughly implementing the concept of green analytical chemistry.
2026, 41(1): 20-28
doi: 10.12461/PKU.DXHX202506013
Abstract:
Current instrumental analysis experiments in nuclear magnetic resonance (NMR) spectroscopy frequently overlook the crucial aspects of teaching and training in spectral interpretation. This study presents the independent development of “AI NMR Assistant”, an intelligent NMR spectral interpretation system based on the DP5 software package, designed for evaluating undergraduate NMR spectral analysis training. The system introduces an innovative “AI + NMR instrumental analysis” teaching methodology. AI NMR Assistant combines four core functionalities: automated NMR spectrum recognition and peak annotation, user practice in spectral interpretation, AI-assisted result validation, and visual feedback mechanisms, thereby establishing a comprehensive platform for undergraduate spectral analysis training. Students utilize NMR spectra obtained either experimentally or from literature to propose molecular structures through the system. DP5 subsequently performs computational analysis, providing error quantification for each carbon atom in the proposed structure. Through iterative refinement and structural adjustment, students progressively deduce the correct molecular configuration, thereby gaining deeper insights into NMR spectral interpretation. The novel “spectral database selection-practical interpretation-personalized solution” teaching paradigm not only enhances students’ analytical skills but also employs AI to streamline teaching assessments. With further development, this approach holds potential for extension to other instrumental analysis experiments.
Current instrumental analysis experiments in nuclear magnetic resonance (NMR) spectroscopy frequently overlook the crucial aspects of teaching and training in spectral interpretation. This study presents the independent development of “AI NMR Assistant”, an intelligent NMR spectral interpretation system based on the DP5 software package, designed for evaluating undergraduate NMR spectral analysis training. The system introduces an innovative “AI + NMR instrumental analysis” teaching methodology. AI NMR Assistant combines four core functionalities: automated NMR spectrum recognition and peak annotation, user practice in spectral interpretation, AI-assisted result validation, and visual feedback mechanisms, thereby establishing a comprehensive platform for undergraduate spectral analysis training. Students utilize NMR spectra obtained either experimentally or from literature to propose molecular structures through the system. DP5 subsequently performs computational analysis, providing error quantification for each carbon atom in the proposed structure. Through iterative refinement and structural adjustment, students progressively deduce the correct molecular configuration, thereby gaining deeper insights into NMR spectral interpretation. The novel “spectral database selection-practical interpretation-personalized solution” teaching paradigm not only enhances students’ analytical skills but also employs AI to streamline teaching assessments. With further development, this approach holds potential for extension to other instrumental analysis experiments.
2026, 41(1): 29-40
doi: 10.12461/PKU.DXHX202506009
Abstract:
While conventional thermal catalytic CO2 conversion requires harsh high-temperature and high-pressure conditions, plasma technology enables CO2 hydrogenation to methanol under ambient conditions, representing a crucial approach for energy conservation, carbon reduction, and achieving “double carbon” goals. This study develops an innovative dielectric barrier discharge (DBD) plasma-liquid film reactor that successfully couples plasma reaction with separation technology, overcoming the traditional trade-off between high CO2 conversion and methanol selectivity. Addressing two key challenges in plasma processes—precise temperature control and complex parameter optimization—we implement digital solutions: Infrared thermography provides real-time, comprehensive temperature monitoring of discharge zones, surpassing conventional thermocouples that suffer from localized measurement and interference from high-voltage electrode discharge; An automated control module dynamically adjusts coolant parameters to maintain thermal equilibrium between plasma generation and dissipation. Furthermore, we employ artificial neural network (ANN) modeling for intelligent prediction of optimal conditions and process optimization. This interdisciplinary experiment integrates digital technologies with AI methodologies, fostering students’ innovative capabilities in alignment with emerging engineering education requirements.
While conventional thermal catalytic CO2 conversion requires harsh high-temperature and high-pressure conditions, plasma technology enables CO2 hydrogenation to methanol under ambient conditions, representing a crucial approach for energy conservation, carbon reduction, and achieving “double carbon” goals. This study develops an innovative dielectric barrier discharge (DBD) plasma-liquid film reactor that successfully couples plasma reaction with separation technology, overcoming the traditional trade-off between high CO2 conversion and methanol selectivity. Addressing two key challenges in plasma processes—precise temperature control and complex parameter optimization—we implement digital solutions: Infrared thermography provides real-time, comprehensive temperature monitoring of discharge zones, surpassing conventional thermocouples that suffer from localized measurement and interference from high-voltage electrode discharge; An automated control module dynamically adjusts coolant parameters to maintain thermal equilibrium between plasma generation and dissipation. Furthermore, we employ artificial neural network (ANN) modeling for intelligent prediction of optimal conditions and process optimization. This interdisciplinary experiment integrates digital technologies with AI methodologies, fostering students’ innovative capabilities in alignment with emerging engineering education requirements.
2026, 41(1): 41-56
doi: 10.12461/PKU.DXHX202506010
Abstract:
As a critical material in national defense, aerospace, and rail transportation sectors, carbon fiber has been included in China’s strategic development plan. The structural composition of its precursors, particularly copolymerized polyacrylonitrile, serves as the key factor of carbon fiber’s structure and performance. However, China currently faces technological bottlenecks in synthesizing and applying carbon fiber copolymer precursors, necessitating the cultivation of interdisciplinary talents with both professional knowledge and innovative capabilities through undergraduate teaching experiments. Presently, polymer chemistry laboratory courses predominantly focus on homopolymer radical polymerization experiments using single-variable controlled, non-exploratory approaches. The incorporation of copolymer synthesis experiments—which hold significant practical applications—into traditional curricula remains challenging due to time constraints, complex monomer selection and ratio determination, and limited instrument availability. The rapid advancement of digital technologies, particularly artificial intelligence (AI), offers promising solutions. This study designs a digital experimental teaching program for copolymer synthesis and application, leveraging open-source databases to train neural networks. Through AI-assisted predictions of various synthesis strategies, students can optimize parameters on a virtual platform to simulate the complete synthesis process and performance testing of polyacrylonitrile-based carbon fibers. These virtual experiments then guide physical laboratory investigations of carbon fiber precursor synthesis. The experimental data generated can be uploaded to the platform for fine-tuning pre-trained models, thereby progressively enhancing the AI’s predictive accuracy. Ultimately, by integrating with relevant virtual simulation experiments, this approach establishes a comprehensive modular experimental system encompassing the entire “synthesis-structure-property-application” workflow of carbon fiber precursors, providing students with a systematic, exploratory, and innovative digital integrated experiment that significantly improves talent development quality.
As a critical material in national defense, aerospace, and rail transportation sectors, carbon fiber has been included in China’s strategic development plan. The structural composition of its precursors, particularly copolymerized polyacrylonitrile, serves as the key factor of carbon fiber’s structure and performance. However, China currently faces technological bottlenecks in synthesizing and applying carbon fiber copolymer precursors, necessitating the cultivation of interdisciplinary talents with both professional knowledge and innovative capabilities through undergraduate teaching experiments. Presently, polymer chemistry laboratory courses predominantly focus on homopolymer radical polymerization experiments using single-variable controlled, non-exploratory approaches. The incorporation of copolymer synthesis experiments—which hold significant practical applications—into traditional curricula remains challenging due to time constraints, complex monomer selection and ratio determination, and limited instrument availability. The rapid advancement of digital technologies, particularly artificial intelligence (AI), offers promising solutions. This study designs a digital experimental teaching program for copolymer synthesis and application, leveraging open-source databases to train neural networks. Through AI-assisted predictions of various synthesis strategies, students can optimize parameters on a virtual platform to simulate the complete synthesis process and performance testing of polyacrylonitrile-based carbon fibers. These virtual experiments then guide physical laboratory investigations of carbon fiber precursor synthesis. The experimental data generated can be uploaded to the platform for fine-tuning pre-trained models, thereby progressively enhancing the AI’s predictive accuracy. Ultimately, by integrating with relevant virtual simulation experiments, this approach establishes a comprehensive modular experimental system encompassing the entire “synthesis-structure-property-application” workflow of carbon fiber precursors, providing students with a systematic, exploratory, and innovative digital integrated experiment that significantly improves talent development quality.
2026, 41(1): 57-63
doi: 10.12461/PKU.DXHX202505001
Abstract:
This project integrates microcontroller technology with stepper motors to achieve precise syringe control, establishing an artificial intelligence-based titration analysis system utilizing computer vision. The system implements an innovative visual approach employing the ResNet neural network classification algorithm to intelligently identify titration endpoints through indicator color changes. During the 23rd-level analytical chemistry laboratory course, 40 students successfully completed the experiment within a 4-hour session, demonstrating the system’s user-friendly operation and ease of learning. In the inaugural Intelligent Experiment Challenge competition, 19 teams comprising 57 participants not only completed the assigned tasks but also showcased innovative potential, with 4 teams independently developing novel hardware systems and software algorithms. To enhance cost-effectiveness and scalability, the project utilizes commercial components and 3D printing technology. This technological integration not only pioneers new directions for teaching innovation in fundamental analytical chemistry experiments but also establishes a robust foundation for future research in intelligent experimental systems.
This project integrates microcontroller technology with stepper motors to achieve precise syringe control, establishing an artificial intelligence-based titration analysis system utilizing computer vision. The system implements an innovative visual approach employing the ResNet neural network classification algorithm to intelligently identify titration endpoints through indicator color changes. During the 23rd-level analytical chemistry laboratory course, 40 students successfully completed the experiment within a 4-hour session, demonstrating the system’s user-friendly operation and ease of learning. In the inaugural Intelligent Experiment Challenge competition, 19 teams comprising 57 participants not only completed the assigned tasks but also showcased innovative potential, with 4 teams independently developing novel hardware systems and software algorithms. To enhance cost-effectiveness and scalability, the project utilizes commercial components and 3D printing technology. This technological integration not only pioneers new directions for teaching innovation in fundamental analytical chemistry experiments but also establishes a robust foundation for future research in intelligent experimental systems.
2026, 41(1): 64-75
doi: 10.12461/PKU.DXHX202505088
Abstract:
The synthesis and characterization of silver nanoparticles have been progressively incorporated into undergraduate chemistry laboratory curricula in recent years, demonstrating both comprehensive and cutting-edge characteristics. Conventional teaching methods predominantly employ chemical reduction approaches, where silver ions are reduced to nanoparticles using mild reducing agents in glass vessels with water bath heating. However, this traditional methodology presents several limitations, including low reagent efficiency, inconsistent particle size distribution, and poor controllability over particle dimensions, all of which adversely affect experimental outcomes and student learning experiences. To enhance pedagogical effectiveness, this project introduces an integrated approach combining microfluidic chip-based synthesis with the guidance of machine learning. Precise control of flow rate, reactant concentration, and temperature parameters within microfluidic channels improves reaction reproducibility and controllability, significantly enhancing particle size uniformity and synthesis efficiency. Furthermore, machine learning algorithms are employed to model and predict experimental data, enabling students to comprehend how different reaction conditions influence product characteristics and achieve targeted nanoparticle synthesis. This innovative teaching design incorporates both microfluidic technology and data-driven artificial intelligence into traditional nanoparticle synthesis instruction, not only enriching course content but also elevating requirements for students’ operational skills, data processing capabilities, and problem-solving competencies. Through this implementation, the project aims to stimulate student interest in modern chemical research methodologies, facilitate deeper understanding of nanomaterial reaction mechanisms, and enhance scientific literacy and comprehensive innovation abilities.
The synthesis and characterization of silver nanoparticles have been progressively incorporated into undergraduate chemistry laboratory curricula in recent years, demonstrating both comprehensive and cutting-edge characteristics. Conventional teaching methods predominantly employ chemical reduction approaches, where silver ions are reduced to nanoparticles using mild reducing agents in glass vessels with water bath heating. However, this traditional methodology presents several limitations, including low reagent efficiency, inconsistent particle size distribution, and poor controllability over particle dimensions, all of which adversely affect experimental outcomes and student learning experiences. To enhance pedagogical effectiveness, this project introduces an integrated approach combining microfluidic chip-based synthesis with the guidance of machine learning. Precise control of flow rate, reactant concentration, and temperature parameters within microfluidic channels improves reaction reproducibility and controllability, significantly enhancing particle size uniformity and synthesis efficiency. Furthermore, machine learning algorithms are employed to model and predict experimental data, enabling students to comprehend how different reaction conditions influence product characteristics and achieve targeted nanoparticle synthesis. This innovative teaching design incorporates both microfluidic technology and data-driven artificial intelligence into traditional nanoparticle synthesis instruction, not only enriching course content but also elevating requirements for students’ operational skills, data processing capabilities, and problem-solving competencies. Through this implementation, the project aims to stimulate student interest in modern chemical research methodologies, facilitate deeper understanding of nanomaterial reaction mechanisms, and enhance scientific literacy and comprehensive innovation abilities.
2026, 41(1): 76-84
doi: 10.12461/PKU.DXHX202506023
Abstract:
Microcolumn ion exchange chromatography demands rigorous standardization and operational proficiency from students. This study investigates a machine vision-based approach to capture time-series behavioral data during on-column operations, establishing a comprehensive experimental variable database. By integrating experimentally acquired data with machine learning algorithms, we elucidate underlying patterns and employ digital twin technology for real-time process monitoring. This methodology enables identification of error sources and critical factors influencing result stability. Implemented as an instructional experiment, this approach cultivates students’ ability to apply digital technologies to address analytical chemistry challenges.
Microcolumn ion exchange chromatography demands rigorous standardization and operational proficiency from students. This study investigates a machine vision-based approach to capture time-series behavioral data during on-column operations, establishing a comprehensive experimental variable database. By integrating experimentally acquired data with machine learning algorithms, we elucidate underlying patterns and employ digital twin technology for real-time process monitoring. This methodology enables identification of error sources and critical factors influencing result stability. Implemented as an instructional experiment, this approach cultivates students’ ability to apply digital technologies to address analytical chemistry challenges.
2026, 41(1): 85-94
doi: 10.12461/PKU.DXHX202506021
Abstract:
The potassium dichromate reflux method, as specified in the water quality monitoring standard (HJ 828-2017), serves as a conventional approach for chemical oxygen demand (COD) determination. However, its application in fundamental chemistry laboratory education has been constrained due to inherent safety hazards, high operational costs, and significant pollutant discharge. This study presents an innovative approach employing a color-sensitive camera to capture solution reaction images, with subsequent RGB data extraction through Python’s OpenCV library. By integrating machine learning-based cluster analysis, we achieved automated monitoring of both the heating reflux and titration processes. The incorporation of digital twin visualization technology into the potassium dichromate reflux method for COD measurement represents a novel advancement, with interactive operations significantly enhancing the effectiveness of simulated experimental instruction.
The potassium dichromate reflux method, as specified in the water quality monitoring standard (HJ 828-2017), serves as a conventional approach for chemical oxygen demand (COD) determination. However, its application in fundamental chemistry laboratory education has been constrained due to inherent safety hazards, high operational costs, and significant pollutant discharge. This study presents an innovative approach employing a color-sensitive camera to capture solution reaction images, with subsequent RGB data extraction through Python’s OpenCV library. By integrating machine learning-based cluster analysis, we achieved automated monitoring of both the heating reflux and titration processes. The incorporation of digital twin visualization technology into the potassium dichromate reflux method for COD measurement represents a novel advancement, with interactive operations significantly enhancing the effectiveness of simulated experimental instruction.
2026, 41(1): 95-106
doi: 10.12461/PKU.DXHX202505005
Abstract:
Hypergolic propellants represent a predominant development direction in propulsion systems due to their ignition device-free characteristic, with ignition delay time serving as their primary performance metric. This study employed a custom-designed drop-test apparatus to examine the ignition characteristics between propellant fuel and high-concentration hydrogen peroxide oxidizer, while optimizing the experimental procedure through digital design. To overcome the challenge of processing extensive image data in ignition delay time measurement, we developed Python-based software incorporating the YOLOv8 deep learning algorithm. This implementation of image recognition technology enables precise analysis of propellant self-ignition delay time, facilitating efficient and accurate experimental data processing. Furthermore, integrating this innovative experimental approach into the “University Chemistry” curriculum has demonstrated significant enhancement in student engagement and learning motivation.
Hypergolic propellants represent a predominant development direction in propulsion systems due to their ignition device-free characteristic, with ignition delay time serving as their primary performance metric. This study employed a custom-designed drop-test apparatus to examine the ignition characteristics between propellant fuel and high-concentration hydrogen peroxide oxidizer, while optimizing the experimental procedure through digital design. To overcome the challenge of processing extensive image data in ignition delay time measurement, we developed Python-based software incorporating the YOLOv8 deep learning algorithm. This implementation of image recognition technology enables precise analysis of propellant self-ignition delay time, facilitating efficient and accurate experimental data processing. Furthermore, integrating this innovative experimental approach into the “University Chemistry” curriculum has demonstrated significant enhancement in student engagement and learning motivation.
2026, 41(1): 107-113
doi: 10.12461/PKU.DXHX202506020
Abstract:
Conventional mixed alkali titration analysis suffers from subjective endpoint determination at the first equivalence point and significant variability in triplicate measurements. This study employs Color-Discriminable Artificial Intelligence (CDAI) for endpoint detection, combining precise hardware control with intelligent algorithms to establish an objective, quantifiable dynamic color discrimination model for phenolphthalein and methyl orange indicators. The developed system effectively resolves endpoint determination challenges and improves measurement reproducibility. This work demonstrates an innovative intelligent titration methodology, offering a scalable solution for the digital transformation of chemical laboratory education.
Conventional mixed alkali titration analysis suffers from subjective endpoint determination at the first equivalence point and significant variability in triplicate measurements. This study employs Color-Discriminable Artificial Intelligence (CDAI) for endpoint detection, combining precise hardware control with intelligent algorithms to establish an objective, quantifiable dynamic color discrimination model for phenolphthalein and methyl orange indicators. The developed system effectively resolves endpoint determination challenges and improves measurement reproducibility. This work demonstrates an innovative intelligent titration methodology, offering a scalable solution for the digital transformation of chemical laboratory education.
2026, 41(1): 114-121
doi: 10.12461/PKU.DXHX202505104
Abstract:
To overcome the challenges in effectively implementing experimental teaching of field emission scanning electron microscopy (FE-SEM), this study developed an algorithm that simulates real-time image variations resulting from parameter adjustments including astigmatism, focus, and brightness, while replicating the complete operational procedure. An interactive simulation console is developed to enable interactive operations, aiming to enhance students’ capabilities in parameter adjustment and image acquisition for FE-SEM. The proposed scheme achieves high-fidelity simulation in both experimental content and operational methods, with potential applications in teaching transmission electron microscopy and similar instrumentation courses.
To overcome the challenges in effectively implementing experimental teaching of field emission scanning electron microscopy (FE-SEM), this study developed an algorithm that simulates real-time image variations resulting from parameter adjustments including astigmatism, focus, and brightness, while replicating the complete operational procedure. An interactive simulation console is developed to enable interactive operations, aiming to enhance students’ capabilities in parameter adjustment and image acquisition for FE-SEM. The proposed scheme achieves high-fidelity simulation in both experimental content and operational methods, with potential applications in teaching transmission electron microscopy and similar instrumentation courses.
2026, 41(1): 122-132
doi: 10.12461/PKU.DXHX202505103
Abstract:
This study builds upon the cobalt ion coordination reaction from “Inorganic Chemistry Experiments”, developing a thermodynamics comprehensive experiment for second-year chemistry majors. By implementing digital transformation in thermodynamic parameter determination, we systematically investigate how temperature and concentration influence reaction equilibrium and thermodynamic properties, offering innovative approaches for experimental pedagogy. Departing from conventional spectrophotometric measurements with manual formula calculations, our methodology employs a digital image-based (DIB) approach integrated with an intelligent visual robot monitoring system. This cutting-edge system enables seamless information acquisition, continuous data recording, and efficient extraction of RGB color model data. The RGB color model—a fundamental system in digital displays and image processing—generates the visible color spectrum through precise adjustments of red, green, and blue intensities. For data processing, we implement advanced programming techniques (e.g., Python) to replace traditional manual computations. This four-credit experiment establishes a comprehensive virtual simulation platform emphasizing analytical, modeling, evaluative, and innovative competencies, facilitating digital experimentation with cloud-based innovation sharing. Comparative analysis of 15 experimental trials demonstrates superior stability of the DIB method versus conventional approaches, validating the feasibility of intelligent visual robotics combined with DIB methodology for thermodynamic parameter determination.
This study builds upon the cobalt ion coordination reaction from “Inorganic Chemistry Experiments”, developing a thermodynamics comprehensive experiment for second-year chemistry majors. By implementing digital transformation in thermodynamic parameter determination, we systematically investigate how temperature and concentration influence reaction equilibrium and thermodynamic properties, offering innovative approaches for experimental pedagogy. Departing from conventional spectrophotometric measurements with manual formula calculations, our methodology employs a digital image-based (DIB) approach integrated with an intelligent visual robot monitoring system. This cutting-edge system enables seamless information acquisition, continuous data recording, and efficient extraction of RGB color model data. The RGB color model—a fundamental system in digital displays and image processing—generates the visible color spectrum through precise adjustments of red, green, and blue intensities. For data processing, we implement advanced programming techniques (e.g., Python) to replace traditional manual computations. This four-credit experiment establishes a comprehensive virtual simulation platform emphasizing analytical, modeling, evaluative, and innovative competencies, facilitating digital experimentation with cloud-based innovation sharing. Comparative analysis of 15 experimental trials demonstrates superior stability of the DIB method versus conventional approaches, validating the feasibility of intelligent visual robotics combined with DIB methodology for thermodynamic parameter determination.
2026, 41(1): 133-143
doi: 10.12461/PKU.DXHX202506017
Abstract:
Ammonium perchlorate (AP) serves as an essential component in solid rocket propellants, with its thermal decomposition characteristics directly determining propellant combustion performance. Metal-organic frameworks (MOFs), currently at the forefront of catalytic research, demonstrate considerable potential for enhancing AP’s thermal decomposition properties. Incorporating undergraduate experiments involving aerospace-critical AP materials and cutting-edge MOF materials not only fosters students’ research interests and practical skills but also seamlessly integrates ideological elements of aerospace advancement into the curriculum. However, AP’s energetic nature presents explosion hazards, while conventional solvothermal MOF synthesis involves high-pressure conditions and prolonged reaction times. To address these safety concerns, this study implements a digital approach combining virtual simulation and machine learning. The experimental design comprises two modules: Materials of institute Lavoisier (MIL)-101(Fe) synthesis and evaluation of its catalytic effects on AP decomposition via differential scanning calorimetry (DSC) analysis. This virtual methodology enables safe exploration of AP and MOF materials while achieving comprehensive educational objectives encompassing knowledge acquisition, skill development, and value cultivation.
Ammonium perchlorate (AP) serves as an essential component in solid rocket propellants, with its thermal decomposition characteristics directly determining propellant combustion performance. Metal-organic frameworks (MOFs), currently at the forefront of catalytic research, demonstrate considerable potential for enhancing AP’s thermal decomposition properties. Incorporating undergraduate experiments involving aerospace-critical AP materials and cutting-edge MOF materials not only fosters students’ research interests and practical skills but also seamlessly integrates ideological elements of aerospace advancement into the curriculum. However, AP’s energetic nature presents explosion hazards, while conventional solvothermal MOF synthesis involves high-pressure conditions and prolonged reaction times. To address these safety concerns, this study implements a digital approach combining virtual simulation and machine learning. The experimental design comprises two modules: Materials of institute Lavoisier (MIL)-101(Fe) synthesis and evaluation of its catalytic effects on AP decomposition via differential scanning calorimetry (DSC) analysis. This virtual methodology enables safe exploration of AP and MOF materials while achieving comprehensive educational objectives encompassing knowledge acquisition, skill development, and value cultivation.
2026, 41(1): 144-158
doi: 10.12461/PKU.DXHX202505028
Abstract:
Glutathione (GSH) plays a crucial role in regulating intracellular reactive oxygen species (ROS) levels. Depletion of glutathione in cancer cells leads to elevated ROS levels, subsequently inducing cellular inactivation and apoptosis, thereby establishing GSH as a promising therapeutic target for cancer treatment. Capitalizing on the propensity of α,β-unsaturated carbonyl compounds to undergo Michael addition reactions with glutathione, we developed an innovative AI-driven virtual screening protocol using ChemDraw, Chem3D, Gaussian, and Chemprop software. This approach involved constructing a molecular database of anticancer compounds and computationally characterizing their reactivity with glutathione. The AI prediction results demonstrated remarkable consistency with experimentally reported data in the literature, validating the scientific rigor and reliability of our virtual screening methodology. Potential drug candidates identified through AI screening were subsequently evaluated via chemical kinetics and pharmacological assays, yielding lead compounds worthy of further investigation. This AI-enhanced drug discovery approach significantly reduces screening costs while improving both efficiency and accuracy. The experimental framework serves as an effective pedagogical tool for chemical biology or chemoinformatics courses, where AI technology facilitates deeper understanding of core concepts. This innovative teaching method enhances curriculum sophistication, stimulates student engagement, and cultivates critical thinking and innovative capacity, ultimately fostering the development of exceptional talents for future scientific challenges.
Glutathione (GSH) plays a crucial role in regulating intracellular reactive oxygen species (ROS) levels. Depletion of glutathione in cancer cells leads to elevated ROS levels, subsequently inducing cellular inactivation and apoptosis, thereby establishing GSH as a promising therapeutic target for cancer treatment. Capitalizing on the propensity of α,β-unsaturated carbonyl compounds to undergo Michael addition reactions with glutathione, we developed an innovative AI-driven virtual screening protocol using ChemDraw, Chem3D, Gaussian, and Chemprop software. This approach involved constructing a molecular database of anticancer compounds and computationally characterizing their reactivity with glutathione. The AI prediction results demonstrated remarkable consistency with experimentally reported data in the literature, validating the scientific rigor and reliability of our virtual screening methodology. Potential drug candidates identified through AI screening were subsequently evaluated via chemical kinetics and pharmacological assays, yielding lead compounds worthy of further investigation. This AI-enhanced drug discovery approach significantly reduces screening costs while improving both efficiency and accuracy. The experimental framework serves as an effective pedagogical tool for chemical biology or chemoinformatics courses, where AI technology facilitates deeper understanding of core concepts. This innovative teaching method enhances curriculum sophistication, stimulates student engagement, and cultivates critical thinking and innovative capacity, ultimately fostering the development of exceptional talents for future scientific challenges.
2026, 41(1): 159-168
doi: 10.12461/PKU.DXHX202506018
Abstract:
Building upon the conventional methyl orange synthesis experiment, this study incorporates a self-developed “automated feeding and digital temperature control” system into the experimental procedure. This advancement substantially improves experimental safety by addressing issues such as uneven reaction distribution and excessive side reactions resulting from manual feeding, while simultaneously streamlining the workflow. In addition to fulfilling the original pedagogical objectives, this modified approach enables students to concentrate on observing experimental phenomena and understanding fundamental principles, thereby placing greater emphasis on developing students’ comprehensive competencies.
Building upon the conventional methyl orange synthesis experiment, this study incorporates a self-developed “automated feeding and digital temperature control” system into the experimental procedure. This advancement substantially improves experimental safety by addressing issues such as uneven reaction distribution and excessive side reactions resulting from manual feeding, while simultaneously streamlining the workflow. In addition to fulfilling the original pedagogical objectives, this modified approach enables students to concentrate on observing experimental phenomena and understanding fundamental principles, thereby placing greater emphasis on developing students’ comprehensive competencies.
2026, 41(1): 169-178
doi: 10.12461/PKU.DXHX202504098
Abstract:
To enhance students’ experimental operational skills and facilitate their profound comprehension of organic synthetic chemistry, we developed a chemical analysis platform incorporating intelligent big data fusion and deep learning algorithms. This platform guides students through autonomous exploration of digital experimental design processes, allowing them to directly experience the interdisciplinary research appeal during practical implementation. Utilizing a well-established digital platform infrastructure, we employed the synthesis of nifedipine as a teaching case to demonstrate the workflow of digital experimental design and its pedagogical applications in undergraduate laboratory courses. This experiment integrates knowledge from both organic chemistry and computer science disciplines, encompassing learning components related to synthetic chemistry and medicinal chemistry. Suitable for modular experimental teaching to accommodate diverse instructional requirements, this approach aims to cultivate students’ innovative thinking and comprehensive competencies.
To enhance students’ experimental operational skills and facilitate their profound comprehension of organic synthetic chemistry, we developed a chemical analysis platform incorporating intelligent big data fusion and deep learning algorithms. This platform guides students through autonomous exploration of digital experimental design processes, allowing them to directly experience the interdisciplinary research appeal during practical implementation. Utilizing a well-established digital platform infrastructure, we employed the synthesis of nifedipine as a teaching case to demonstrate the workflow of digital experimental design and its pedagogical applications in undergraduate laboratory courses. This experiment integrates knowledge from both organic chemistry and computer science disciplines, encompassing learning components related to synthetic chemistry and medicinal chemistry. Suitable for modular experimental teaching to accommodate diverse instructional requirements, this approach aims to cultivate students’ innovative thinking and comprehensive competencies.
Measurement of Stress-Strain Curves of Polymeric Materials Using a Non-Contact Displacement Detector
2026, 41(1): 179-187
doi: 10.12461/PKU.DXHX202505101
Abstract:
This study presents the development of a non-contact displacement detection system for determining stress-strain curves in polymeric materials, overcoming the limitations of conventional mechanical extensometers while maintaining measurement accuracy. The proposed method employs a camera integrated with the YOLOv8 deep learning algorithm to track real-time positional changes of red marker lines on specimens during tensile testing, enabling accurate strain measurement. This contact-free approach eliminates potential specimen interference caused by mechanical extensometers. Comparative experiments were conducted by identifying the grips of mechanical extensometers, confirming the consistency between both methods in stress-strain curve determination. The results demonstrate remarkable similarity between stress-strain curves obtained from the non-contact system and traditional mechanical extensometers, validating the reliability and potential applications of this method for evaluating mechanical properties of polymeric materials. This digital innovation enhances experimental flexibility and offers an alternative solution for mechanical characterization of polymer materials.
This study presents the development of a non-contact displacement detection system for determining stress-strain curves in polymeric materials, overcoming the limitations of conventional mechanical extensometers while maintaining measurement accuracy. The proposed method employs a camera integrated with the YOLOv8 deep learning algorithm to track real-time positional changes of red marker lines on specimens during tensile testing, enabling accurate strain measurement. This contact-free approach eliminates potential specimen interference caused by mechanical extensometers. Comparative experiments were conducted by identifying the grips of mechanical extensometers, confirming the consistency between both methods in stress-strain curve determination. The results demonstrate remarkable similarity between stress-strain curves obtained from the non-contact system and traditional mechanical extensometers, validating the reliability and potential applications of this method for evaluating mechanical properties of polymeric materials. This digital innovation enhances experimental flexibility and offers an alternative solution for mechanical characterization of polymer materials.
2026, 41(1): 188-203
doi: 10.12461/PKU.DXHX202505090
Abstract:
This study centers on the scientific investigation titled “Data-Driven Preparation of Low-Cost, High-Efficiency Water Electrolysis Catalysts.” To address the challenges of intricate procedures, prolonged cycles, and costly equipment when incorporating such advanced research into undergraduate and postgraduate teaching, we innovatively integrated digital intelligence technologies. These include online platforms, data-driven methodologies, numerical simulations, human-computer interaction, virtual simulations, and remotely controlled automated systems. Through strategic implementation of these smart technologies across experimental design, operation execution, and data analysis phases, we fully harnessed the experiment’s cutting-edge nature, practical applicability, innovative features, interdisciplinary value, and multi-skill training potential, adapting it into a comprehensive chemistry experiment suitable for higher education. The incorporation of digital intelligence technologies reduced the experimental cycle by 65%, enabling students to complete this advanced research experience within limited course hours. Student feedback was overwhelmingly positive: all participants acknowledged the digital-intelligent approach enhanced their understanding of experimental principles and completion; 85% reported increased engagement; 52% gained deeper insight into research processes; and 60% expressed heightened interest in learning digital technologies. This practice demonstrates that digital-intelligent transformation effectively improves teaching efficiency and fosters students’ scientific thinking and experimental competencies, providing a novel approach for reforming chemical experiment education in universities.
This study centers on the scientific investigation titled “Data-Driven Preparation of Low-Cost, High-Efficiency Water Electrolysis Catalysts.” To address the challenges of intricate procedures, prolonged cycles, and costly equipment when incorporating such advanced research into undergraduate and postgraduate teaching, we innovatively integrated digital intelligence technologies. These include online platforms, data-driven methodologies, numerical simulations, human-computer interaction, virtual simulations, and remotely controlled automated systems. Through strategic implementation of these smart technologies across experimental design, operation execution, and data analysis phases, we fully harnessed the experiment’s cutting-edge nature, practical applicability, innovative features, interdisciplinary value, and multi-skill training potential, adapting it into a comprehensive chemistry experiment suitable for higher education. The incorporation of digital intelligence technologies reduced the experimental cycle by 65%, enabling students to complete this advanced research experience within limited course hours. Student feedback was overwhelmingly positive: all participants acknowledged the digital-intelligent approach enhanced their understanding of experimental principles and completion; 85% reported increased engagement; 52% gained deeper insight into research processes; and 60% expressed heightened interest in learning digital technologies. This practice demonstrates that digital-intelligent transformation effectively improves teaching efficiency and fosters students’ scientific thinking and experimental competencies, providing a novel approach for reforming chemical experiment education in universities.
2026, 41(1): 204-212
doi: 10.12461/PKU.DXHX202510037
Abstract:
Based on the principles of physical chemistry, this study integrates knowledge from organic chemistry, analytical chemistry, and computer science to develop an “Intelligent detection system for metal ions and pesticide residues” using a high-precision starch-based carbon dot sensor chip. The system employs a low-cost and safe hydrogel matrix, with corn starch serving as the raw material for chip fabrication. Combined with a self-developed Android/HarmonyOS application—written in Java within the Android Studio environment—the system enables efficient and economical intelligent analysis and monitoring of metal ions and pesticide residues. This approach is user-friendly, innovative, and practical, enhancing both student engagement and the educational value of experimental teaching, while offering a valuable case study for interdisciplinary teaching reform.
Based on the principles of physical chemistry, this study integrates knowledge from organic chemistry, analytical chemistry, and computer science to develop an “Intelligent detection system for metal ions and pesticide residues” using a high-precision starch-based carbon dot sensor chip. The system employs a low-cost and safe hydrogel matrix, with corn starch serving as the raw material for chip fabrication. Combined with a self-developed Android/HarmonyOS application—written in Java within the Android Studio environment—the system enables efficient and economical intelligent analysis and monitoring of metal ions and pesticide residues. This approach is user-friendly, innovative, and practical, enhancing both student engagement and the educational value of experimental teaching, while offering a valuable case study for interdisciplinary teaching reform.
2026, 41(1): 213-226
doi: 10.12461/PKU.DXHX202505110
Abstract:
The measurement of rate constants for the reaction between magnesium ribbon and dilute sulfuric acid constitutes a fundamental experiment in inorganic chemistry curricula across numerous universities. However, when applying first-order kinetic modeling, systematic deviations are frequently observed in the latter stage of the reaction. To examine the side-reaction hypothesis, we incorporated pH monitoring and established a standardized, automated experimental protocol with systematic data acquisition. Additionally, we developed a dedicated data processing toolkit to facilitate comprehensive analysis of the reaction kinetics in the magnesium-sulfuric acid system. The experimental analysis revealed that the main reaction (Mg with H2SO4) follows first-order kinetics, with its rate constant significantly increasing as temperature rises. In contrast, the side reaction (Mg with H2O) aligns with a second-order kinetic model, exhibiting an activation energy of 127.79 kJ‧mol‒1. Through multidimensional data integration and automated data processing workflows, this study systematically cross-validated the kinetic parameters of both main and side reactions. This approach successfully explained the experimental deviations observed in conductivity data during the reaction’s later stages. By transforming traditional verification-based experiments into a research-oriented learning platform, this work bridges the “data-driven approach” paradigm with practical laboratory instruction, providing a practical framework for cultivating innovative thinking and digital problem-solving skills among undergraduate students in non-chemistry science and engineering disciplines.
The measurement of rate constants for the reaction between magnesium ribbon and dilute sulfuric acid constitutes a fundamental experiment in inorganic chemistry curricula across numerous universities. However, when applying first-order kinetic modeling, systematic deviations are frequently observed in the latter stage of the reaction. To examine the side-reaction hypothesis, we incorporated pH monitoring and established a standardized, automated experimental protocol with systematic data acquisition. Additionally, we developed a dedicated data processing toolkit to facilitate comprehensive analysis of the reaction kinetics in the magnesium-sulfuric acid system. The experimental analysis revealed that the main reaction (Mg with H2SO4) follows first-order kinetics, with its rate constant significantly increasing as temperature rises. In contrast, the side reaction (Mg with H2O) aligns with a second-order kinetic model, exhibiting an activation energy of 127.79 kJ‧mol‒1. Through multidimensional data integration and automated data processing workflows, this study systematically cross-validated the kinetic parameters of both main and side reactions. This approach successfully explained the experimental deviations observed in conductivity data during the reaction’s later stages. By transforming traditional verification-based experiments into a research-oriented learning platform, this work bridges the “data-driven approach” paradigm with practical laboratory instruction, providing a practical framework for cultivating innovative thinking and digital problem-solving skills among undergraduate students in non-chemistry science and engineering disciplines.
2026, 41(1): 227-243
doi: 10.12461/PKU.DXHX202503107
Abstract:
The determination of liquid saturated vapor pressure is a key component of undergraduate physical chemistry laboratory instruction, facilitating students’ systematic understanding of material properties and phase-transition behavior. However, the conventional dynamic method suffers from cumbersome operation, substantial experimental error, limited openness, and safety concerns. The proposed digital laboratory scheme integrates a proportional-integral-derivative (PID)-based automated control system with a web-based virtual laboratory platform and applies machine learning for in-depth analysis of experimental data, thereby providing a safe, efficient, and precise instructional environment. Empirical results show that the mean relative measurement error is reduced from the traditional 8.7% to 0.5%, the instructional cycle is shortened from more than one week to within a week, the temperature measurement error is controlled within ±0.5 °C, and the pressure error within ±0.2 kPa. This approach optimizes conventional teaching practices and effectively remedies deficiencies in current experimental instruction.
The determination of liquid saturated vapor pressure is a key component of undergraduate physical chemistry laboratory instruction, facilitating students’ systematic understanding of material properties and phase-transition behavior. However, the conventional dynamic method suffers from cumbersome operation, substantial experimental error, limited openness, and safety concerns. The proposed digital laboratory scheme integrates a proportional-integral-derivative (PID)-based automated control system with a web-based virtual laboratory platform and applies machine learning for in-depth analysis of experimental data, thereby providing a safe, efficient, and precise instructional environment. Empirical results show that the mean relative measurement error is reduced from the traditional 8.7% to 0.5%, the instructional cycle is shortened from more than one week to within a week, the temperature measurement error is controlled within ±0.5 °C, and the pressure error within ±0.2 kPa. This approach optimizes conventional teaching practices and effectively remedies deficiencies in current experimental instruction.
2026, 41(1): 244-252
doi: 10.12461/PKU.DXHX202505056
Abstract:
The digital transformation of education presents both opportunities and challenges for chemistry laboratory instruction in higher education. Using the magnetic susceptibility determination experiment as a case study, this work demonstrates digital enhancement through animated videos that integrate microscopic principles with crystal structures and electron configurations, as well as a comprehensive WeChat mini-program incorporating experimental theory, data processing, and interactive feedback mechanisms. Empirical results indicate these innovations successfully achieve the visualization of abstract theoretical concepts, intuitive representation of microscopic structures, streamlined data processing, and enhanced spatiotemporal flexibility in instructor-student interaction. Consequently, these implementations significantly improve both the digital sophistication of experimental pedagogy and overall teaching effectiveness.
The digital transformation of education presents both opportunities and challenges for chemistry laboratory instruction in higher education. Using the magnetic susceptibility determination experiment as a case study, this work demonstrates digital enhancement through animated videos that integrate microscopic principles with crystal structures and electron configurations, as well as a comprehensive WeChat mini-program incorporating experimental theory, data processing, and interactive feedback mechanisms. Empirical results indicate these innovations successfully achieve the visualization of abstract theoretical concepts, intuitive representation of microscopic structures, streamlined data processing, and enhanced spatiotemporal flexibility in instructor-student interaction. Consequently, these implementations significantly improve both the digital sophistication of experimental pedagogy and overall teaching effectiveness.
2026, 41(1): 253-263
doi: 10.12461/PKU.DXHX202506019
Abstract:
As one of the most significant scientific achievements of the 21st century, the concept of metamaterials has revolutionized multiple disciplines including physics, materials science, optics, and acoustics. This study presents a novel approach to preparing 3D-printed metamaterials through molecular design, employing a micro-to-macro fabrication strategy. By constructing carbon cage molecular models at varying scales and utilizing computational simulation techniques, we successfully simulated and predicted both the fabrication process and performance characteristics of 3D-printed metamaterials. This computational approach guides practical design and manufacturing, enabling the translation from microscopic molecular models to macroscopic porous materials while investigating the structure-property relationship between carbon cage architectures and macroscopic metamaterial performance. Through precise digital design of molecular structures, we optimized the physical, chemical, and mechanical properties of metamaterials. By combining molecular structure optimization with 3D printing parameter adjustment, we fabricated structural components with enhanced strength and toughness suitable for demanding applications in aerospace, automotive, and high-end equipment industries. The digital design approach further enables customized metamaterial fabrication. This innovative methodology, integrating molecular-level microstructure design with macroscopic component fabrication via 3D printing, provides new perspectives for metamaterial development while advancing digital design capabilities in chemical experimentation.
As one of the most significant scientific achievements of the 21st century, the concept of metamaterials has revolutionized multiple disciplines including physics, materials science, optics, and acoustics. This study presents a novel approach to preparing 3D-printed metamaterials through molecular design, employing a micro-to-macro fabrication strategy. By constructing carbon cage molecular models at varying scales and utilizing computational simulation techniques, we successfully simulated and predicted both the fabrication process and performance characteristics of 3D-printed metamaterials. This computational approach guides practical design and manufacturing, enabling the translation from microscopic molecular models to macroscopic porous materials while investigating the structure-property relationship between carbon cage architectures and macroscopic metamaterial performance. Through precise digital design of molecular structures, we optimized the physical, chemical, and mechanical properties of metamaterials. By combining molecular structure optimization with 3D printing parameter adjustment, we fabricated structural components with enhanced strength and toughness suitable for demanding applications in aerospace, automotive, and high-end equipment industries. The digital design approach further enables customized metamaterial fabrication. This innovative methodology, integrating molecular-level microstructure design with macroscopic component fabrication via 3D printing, provides new perspectives for metamaterial development while advancing digital design capabilities in chemical experimentation.
2026, 41(1): 264-275
doi: 10.12461/PKU.DXHX202503096
Abstract:
Digital experimentation proves advantageous for chemical education due to its capabilities in convenient data acquisition, visualization, and intelligent processing. The indirect iodometric determination of copper content requires precise identification of three critical color transitions: the light yellow endpoint after adding starch indicator, the light blue transition after KSCN addition, and the final off-white titration endpoint. Through extensive experimentation, we established a comprehensive database to determine HSV (Hue-Saturation-Value) color thresholds for these key transitions and developed a supporting titration application. The digitally enhanced method demonstrated a relative average deviation of 0.11%, significantly improving measurement precision compared to visual observation while stimulating students’ interdisciplinary research interests.
Digital experimentation proves advantageous for chemical education due to its capabilities in convenient data acquisition, visualization, and intelligent processing. The indirect iodometric determination of copper content requires precise identification of three critical color transitions: the light yellow endpoint after adding starch indicator, the light blue transition after KSCN addition, and the final off-white titration endpoint. Through extensive experimentation, we established a comprehensive database to determine HSV (Hue-Saturation-Value) color thresholds for these key transitions and developed a supporting titration application. The digitally enhanced method demonstrated a relative average deviation of 0.11%, significantly improving measurement precision compared to visual observation while stimulating students’ interdisciplinary research interests.
2026, 41(1): 276-288
doi: 10.12461/PKU.DXHX202504104
Abstract:
Within the framework of digital-intelligent education, this study develops a digital teaching assistance system for analytical chemistry experiments by integrating machine learning and big data technologies, using water hardness determination as a representative example. The system captures titration images and employs color histograms combined with a support vector machine (SVM) model to determine titration endpoints, enabling automated result analysis, evaluation, and personalized learning feedback. This research successfully bridges traditional experimental methods with digital technologies, fostering students’ competencies in data analysis, interdisciplinary thinking, and practical skills. The proposed approach offers an innovative methodology for promoting digital transformation in university-level analytical chemistry laboratory education.
Within the framework of digital-intelligent education, this study develops a digital teaching assistance system for analytical chemistry experiments by integrating machine learning and big data technologies, using water hardness determination as a representative example. The system captures titration images and employs color histograms combined with a support vector machine (SVM) model to determine titration endpoints, enabling automated result analysis, evaluation, and personalized learning feedback. This research successfully bridges traditional experimental methods with digital technologies, fostering students’ competencies in data analysis, interdisciplinary thinking, and practical skills. The proposed approach offers an innovative methodology for promoting digital transformation in university-level analytical chemistry laboratory education.
2026, 41(1): 289-297
doi: 10.12461/PKU.DXHX202509083
Abstract:
Conductive hydrogel sensors demonstrate significant application potential in intelligent interaction systems due to their high sensitivity and flexible characteristics. This experimental study developed a composite hydrogel through free radical polymerization using methacrylic acid as the monomer and MXene as the conductive filler, subsequently constructing a machine learning-assisted handwriting recognition system. By integrating cutting-edge research with pedagogical practice and incorporating interdisciplinary design elements, this approach effectively enhances students’ innovative capacities while fostering their understanding of the crucial role digital design and intelligent technologies play in societal advancement.
Conductive hydrogel sensors demonstrate significant application potential in intelligent interaction systems due to their high sensitivity and flexible characteristics. This experimental study developed a composite hydrogel through free radical polymerization using methacrylic acid as the monomer and MXene as the conductive filler, subsequently constructing a machine learning-assisted handwriting recognition system. By integrating cutting-edge research with pedagogical practice and incorporating interdisciplinary design elements, this approach effectively enhances students’ innovative capacities while fostering their understanding of the crucial role digital design and intelligent technologies play in societal advancement.
2026, 41(1): 298-309
doi: 10.12461/PKU.DXHX202506034
Abstract:
Advancing green energy technologies constitutes a core component of sustainable development strategies and has emerged as a crucial educational module in university chemistry curricula. Organic solar cells, with their distinctive advantages of lightweight construction, material flexibility, and scalable manufacturing potential, have developed into a significant technological pathway in renewable energy. This study presents an innovative experimental project that employs digital methodologies integrating artificial intelligence, high-throughput optical simulations, and digital twin technology. The project facilitates students’ comprehensive understanding of fundamental operational mechanisms in organic solar cells while promoting wider pedagogical adoption of such experiments. Through digitized human-computer interactions involving AI-assisted instruction, experimental parameter design, and data analysis, students acquire specialized knowledge of organic solar cells through hands-on practice, thereby enhancing autonomous learning and research capabilities. This approach contributes to talent development for the digitalized renewable energy sector. The experiment not only incorporates contemporary digital design elements but also provides a digital teaching framework for cultivating chemistry professionals.
Advancing green energy technologies constitutes a core component of sustainable development strategies and has emerged as a crucial educational module in university chemistry curricula. Organic solar cells, with their distinctive advantages of lightweight construction, material flexibility, and scalable manufacturing potential, have developed into a significant technological pathway in renewable energy. This study presents an innovative experimental project that employs digital methodologies integrating artificial intelligence, high-throughput optical simulations, and digital twin technology. The project facilitates students’ comprehensive understanding of fundamental operational mechanisms in organic solar cells while promoting wider pedagogical adoption of such experiments. Through digitized human-computer interactions involving AI-assisted instruction, experimental parameter design, and data analysis, students acquire specialized knowledge of organic solar cells through hands-on practice, thereby enhancing autonomous learning and research capabilities. This approach contributes to talent development for the digitalized renewable energy sector. The experiment not only incorporates contemporary digital design elements but also provides a digital teaching framework for cultivating chemistry professionals.
2026, 41(1): 310-320
doi: 10.12461/PKU.DXHX202506016
Abstract:
This study designed and implemented a programmable heating control system coupled with a wireless temperature-sensing stir bar to optimize experimental procedures for Hofmann degradation reactions. The integrated system incorporates Internet of Things (IoT)-based wireless temperature monitoring, a combined heating/cooling device, and Proportional-Integral-Derivative (PID) algorithm-based precision temperature control to improve the success rate and product purity in temperature-sensitive organic chemistry experiments. The wireless temperature sensing technology overcomes the limitations of conventional thermometers and thermocouples in organic solvent environments, enabling real-time, accurate internal temperature monitoring. The dual-function heating/cooling unit eliminates the need for traditional ice baths, facilitating fully automated temperature regulation throughout the experimental process while enhancing operational efficiency and measurement accuracy. Utilizing PID control algorithms, the system dynamically adjusts heating power according to the difference between target and actual temperatures, maintaining thermal stability and preventing temperature fluctuations, thereby improving reaction success rates and product purity. This automated design significantly reduces manual operation errors while improving experimental reproducibility and safety, offering novel approaches and technical support for digital education in organic chemistry experiments.
This study designed and implemented a programmable heating control system coupled with a wireless temperature-sensing stir bar to optimize experimental procedures for Hofmann degradation reactions. The integrated system incorporates Internet of Things (IoT)-based wireless temperature monitoring, a combined heating/cooling device, and Proportional-Integral-Derivative (PID) algorithm-based precision temperature control to improve the success rate and product purity in temperature-sensitive organic chemistry experiments. The wireless temperature sensing technology overcomes the limitations of conventional thermometers and thermocouples in organic solvent environments, enabling real-time, accurate internal temperature monitoring. The dual-function heating/cooling unit eliminates the need for traditional ice baths, facilitating fully automated temperature regulation throughout the experimental process while enhancing operational efficiency and measurement accuracy. Utilizing PID control algorithms, the system dynamically adjusts heating power according to the difference between target and actual temperatures, maintaining thermal stability and preventing temperature fluctuations, thereby improving reaction success rates and product purity. This automated design significantly reduces manual operation errors while improving experimental reproducibility and safety, offering novel approaches and technical support for digital education in organic chemistry experiments.
2026, 41(1): 321-331
doi: 10.12461/PKU.DXHX202504072
Abstract:
Adsorption represents a fundamental physicochemical process, and adsorption experiments are being increasingly integrated into undergraduate laboratory curricula. However, conventional teaching approaches often confine students to passively executing predetermined experimental procedures, limiting their comprehension of critical aspects such as adsorbent selection and microscopic adsorption mechanisms. To address these limitations, we propose an AI-enhanced digital experimental framework that combines generative AI models with molecular dynamics (MD) simulations. This innovative approach enables students to predict adsorption performance pre-experiment and engage in interactive pre- and post-experimental discussions through human-computer dialogue. The experimental workflow begins with students utilizing ChatGPT as an intelligent assistant for literature review, data organization, adsorbent screening, and personalized preparation via interactive dialogue. Subsequently, MD simulations are employed to predict adsorbent performance and investigate adsorption mechanisms. Experimental validation then follows to examine adsorption kinetics and thermodynamics in comparison with simulation predictions. Finally, students can engage in reflective discussions with ChatGPT regarding experimental observations and conceptual questions. This comprehensive digital teaching methodology encompasses interactive preparation, numerical simulation of adsorption processes, experimental verification, and AI-assisted reflection, effectively transforming students from passive executors into active learners and researchers. The approach presents a novel paradigm for modernizing and digitizing traditional experimental pedagogy.
Adsorption represents a fundamental physicochemical process, and adsorption experiments are being increasingly integrated into undergraduate laboratory curricula. However, conventional teaching approaches often confine students to passively executing predetermined experimental procedures, limiting their comprehension of critical aspects such as adsorbent selection and microscopic adsorption mechanisms. To address these limitations, we propose an AI-enhanced digital experimental framework that combines generative AI models with molecular dynamics (MD) simulations. This innovative approach enables students to predict adsorption performance pre-experiment and engage in interactive pre- and post-experimental discussions through human-computer dialogue. The experimental workflow begins with students utilizing ChatGPT as an intelligent assistant for literature review, data organization, adsorbent screening, and personalized preparation via interactive dialogue. Subsequently, MD simulations are employed to predict adsorbent performance and investigate adsorption mechanisms. Experimental validation then follows to examine adsorption kinetics and thermodynamics in comparison with simulation predictions. Finally, students can engage in reflective discussions with ChatGPT regarding experimental observations and conceptual questions. This comprehensive digital teaching methodology encompasses interactive preparation, numerical simulation of adsorption processes, experimental verification, and AI-assisted reflection, effectively transforming students from passive executors into active learners and researchers. The approach presents a novel paradigm for modernizing and digitizing traditional experimental pedagogy.
2026, 41(1): 332-345
doi: 10.12461/PKU.DXHX202505108
Abstract:
With the growing penetration of automation and artificial intelligence (AI) technologies in scientific research, chemical laboratory education, must keep pace with the times by integrating cutting-edge technologies, enabling students to familiarize themselves with advanced tools and methodologies in modern chemical research. This experiment integrates automated equipment and an AI teaching assistant built on Retrieval-Augmented Generation (RAG) technology, showcasing the intelligent transformation trends of future chemical research while achieving a dual enhancement of students’ data analysis capabilities and experimental efficiency. Focusing on the chemical reactivity of sulfosalicylic acid (HO3SC6H3(OH)CO2H, H3R), the study centers on two core objectives. First, determining its second and third ionization equilibrium constants (pKa2 and pKa3); second, investigating the composition and stability constants of its copper(II) complexes. Automated titration equipment works in synergy with pH meters to accurately monitor pH changes throughout the titration process for curve plotting, while ultraviolet-visible (UV-Vis) spectroscopy provides complementary characterization. The AI teaching assistant supports voice-interactive Q&A to address students’ queries, and the supporting AI tools facilitate rapid experimental data processing and accurate calculation of ionization equilibrium constants. For the investigation of complexes, the experiment adopts the method of continuous variations with equimolar ratios. A series of reaction solutions with varying molar ratios are precisely prepared via an automated platform, enabling systematic exploration of the compositional rules and stability of the complexes formed by H3R and Cu(II). This approach not only ensures students solidly grasp core chemical knowledge, but also effectively cultivates their innovative thinking and intelligent technology application capabilities through immersive exposure to automation and AI systems, realizing the organic integration of knowledge imparting and capability development.
With the growing penetration of automation and artificial intelligence (AI) technologies in scientific research, chemical laboratory education, must keep pace with the times by integrating cutting-edge technologies, enabling students to familiarize themselves with advanced tools and methodologies in modern chemical research. This experiment integrates automated equipment and an AI teaching assistant built on Retrieval-Augmented Generation (RAG) technology, showcasing the intelligent transformation trends of future chemical research while achieving a dual enhancement of students’ data analysis capabilities and experimental efficiency. Focusing on the chemical reactivity of sulfosalicylic acid (HO3SC6H3(OH)CO2H, H3R), the study centers on two core objectives. First, determining its second and third ionization equilibrium constants (pKa2 and pKa3); second, investigating the composition and stability constants of its copper(II) complexes. Automated titration equipment works in synergy with pH meters to accurately monitor pH changes throughout the titration process for curve plotting, while ultraviolet-visible (UV-Vis) spectroscopy provides complementary characterization. The AI teaching assistant supports voice-interactive Q&A to address students’ queries, and the supporting AI tools facilitate rapid experimental data processing and accurate calculation of ionization equilibrium constants. For the investigation of complexes, the experiment adopts the method of continuous variations with equimolar ratios. A series of reaction solutions with varying molar ratios are precisely prepared via an automated platform, enabling systematic exploration of the compositional rules and stability of the complexes formed by H3R and Cu(II). This approach not only ensures students solidly grasp core chemical knowledge, but also effectively cultivates their innovative thinking and intelligent technology application capabilities through immersive exposure to automation and AI systems, realizing the organic integration of knowledge imparting and capability development.
2026, 41(1): 346-353
doi: 10.12461/PKU.DXHX202506015
Abstract:
This study designs a comprehensive molecular simulation experiment for predicting the antifouling performance of polymer materials using machine learning approaches. By compiling existing polymer structure-antifouling performance data from literature and calculating molecular descriptors through molecular simulations, we employ various machine learning methods—including genetic function approximation, neural network modeling, multiple linear regression, and partial least squares regression analysis—to establish quantitative structure-property relationship (QSPR) models for antifouling polymer materials. The predictive capabilities and goodness-of-fit of these models are systematically evaluated. This experiment enables students to acquire fundamental knowledge of machine learning principles and operational techniques, gain insights into recent advancements in marine antifouling materials research, and develop problem-solving skills in chemical applications through the integration of machine learning and molecular simulation methodologies.
This study designs a comprehensive molecular simulation experiment for predicting the antifouling performance of polymer materials using machine learning approaches. By compiling existing polymer structure-antifouling performance data from literature and calculating molecular descriptors through molecular simulations, we employ various machine learning methods—including genetic function approximation, neural network modeling, multiple linear regression, and partial least squares regression analysis—to establish quantitative structure-property relationship (QSPR) models for antifouling polymer materials. The predictive capabilities and goodness-of-fit of these models are systematically evaluated. This experiment enables students to acquire fundamental knowledge of machine learning principles and operational techniques, gain insights into recent advancements in marine antifouling materials research, and develop problem-solving skills in chemical applications through the integration of machine learning and molecular simulation methodologies.
2026, 41(1): 354-362
doi: 10.12461/PKU.DXHX202506001
Abstract:
This study introduces an advanced research protocol from current literature—employing nano covalent organic framework (COF) materials to regulate intracellular Ca²⁺ concentration for enhanced photodynamic therapy (PDT)—as a scalable, multi-level comprehensive chemistry experiment for undergraduate education. We developed a digital virtual simulation platform for experimental teaching and implemented it in undergraduate chemistry curricula. The platform incorporates mathematical, physical, and database models of the experimental system, utilizing digital technology to virtually simulate experimental conditions, thereby achieving high-fidelity replication of experimental procedures and visual presentation of results. Compared with traditional laboratory experiments, the digital platform features modular design that enables students to independently investigate the effects of experimental variables, deepening their subject knowledge while avoiding limitations such as excessive time requirements, high costs, and safety concerns. The teaching process consistently adheres to the student-centered Outcome-Based Education (OBE) approach. Teaching practice demonstrates that this experiment improves students’ instrumental operation skills, data analysis capabilities, and critical thinking, while effectively stimulating learning motivation and creativity. This approach offers new perspectives for optimizing experimental resource allocation and advancing chemistry laboratory education reform.
This study introduces an advanced research protocol from current literature—employing nano covalent organic framework (COF) materials to regulate intracellular Ca²⁺ concentration for enhanced photodynamic therapy (PDT)—as a scalable, multi-level comprehensive chemistry experiment for undergraduate education. We developed a digital virtual simulation platform for experimental teaching and implemented it in undergraduate chemistry curricula. The platform incorporates mathematical, physical, and database models of the experimental system, utilizing digital technology to virtually simulate experimental conditions, thereby achieving high-fidelity replication of experimental procedures and visual presentation of results. Compared with traditional laboratory experiments, the digital platform features modular design that enables students to independently investigate the effects of experimental variables, deepening their subject knowledge while avoiding limitations such as excessive time requirements, high costs, and safety concerns. The teaching process consistently adheres to the student-centered Outcome-Based Education (OBE) approach. Teaching practice demonstrates that this experiment improves students’ instrumental operation skills, data analysis capabilities, and critical thinking, while effectively stimulating learning motivation and creativity. This approach offers new perspectives for optimizing experimental resource allocation and advancing chemistry laboratory education reform.
2026, 41(1): 363-372
doi: 10.12461/PKU.DXHX202506055
Abstract:
To align with the educational objectives of Energy Chemical Engineering and enhance undergraduates’ research interest while cultivating their scientific thinking and methodology, we have incorporated faculty research achievements into specialized experimental teaching. By employing digital tools to support the experimental learning process, we have developed a comprehensive and innovative undergraduate laboratory course. This digitally-enhanced experiment employs CO2-assisted oxidative dehydrogenation of ethane to ethylene as a probe reaction, encompassing the complete workflow of catalyst preparation, characterization, performance evaluation, and data analysis. This approach significantly deepens students’ understanding of catalytic processes in energy conversion. The integration of digital teaching methods during experimental preparation and data processing not only promotes autonomous learning but also enables efficient data classification and organization. Furthermore, utilizing our experimental database, decision tree models with optimized depth can identify critical input variables and provide targeted guidance for catalyst design. The experiment skillfully combines core elements from inorganic chemistry, physical chemistry, and instrumental analysis, creating an innovative and well-rounded experimental platform. This methodology not only develops students’ fundamental laboratory skills but also stimulates their enthusiasm for chemical experimentation while comprehensively enhancing their operational, analytical, innovative, and collaborative competencies.
To align with the educational objectives of Energy Chemical Engineering and enhance undergraduates’ research interest while cultivating their scientific thinking and methodology, we have incorporated faculty research achievements into specialized experimental teaching. By employing digital tools to support the experimental learning process, we have developed a comprehensive and innovative undergraduate laboratory course. This digitally-enhanced experiment employs CO2-assisted oxidative dehydrogenation of ethane to ethylene as a probe reaction, encompassing the complete workflow of catalyst preparation, characterization, performance evaluation, and data analysis. This approach significantly deepens students’ understanding of catalytic processes in energy conversion. The integration of digital teaching methods during experimental preparation and data processing not only promotes autonomous learning but also enables efficient data classification and organization. Furthermore, utilizing our experimental database, decision tree models with optimized depth can identify critical input variables and provide targeted guidance for catalyst design. The experiment skillfully combines core elements from inorganic chemistry, physical chemistry, and instrumental analysis, creating an innovative and well-rounded experimental platform. This methodology not only develops students’ fundamental laboratory skills but also stimulates their enthusiasm for chemical experimentation while comprehensively enhancing their operational, analytical, innovative, and collaborative competencies.
2026, 41(1): 373-381
doi: 10.12461/PKU.DXHX202504048
Abstract:
The preparation of cis(trans)-diglycine copper complexes represents a classic experiment in geometric isomer synthesis. To address the challenges of comprehending microscopic mechanisms, controlling reaction endpoints, and improving yields—despite the easily observable experimental phenomena—we have innovatively integrated quantum chemical calculations and color recognition technologies. Using Gaussian 09 with its visualization tool GaussView, we performed density functional theory (DFT) calculations to analyze geometric structures, energies, dipole moments, and electrostatic potentials of both cis- and trans-diglycine copper(II) complexes. This approach enables students to apply quantum chemical methods for investigating geometric isomer properties while gaining fundamental insights into their structural differences at the molecular level. Concurrently, we developed a machine vision-based color recognition system using C++ programming and OpenCV library. This system employs camera-captured reaction images analyzed through HSV color space modeling to monitor real-time solution color changes during the cis-to-trans isomerization process, thereby achieving precise endpoint determination. The synergistic incorporation of quantum chemical computation and color recognition technology into experimental teaching not only enhances students’ understanding of structure-property relationships but also improves experimental accuracy and reliability. Furthermore, this integrated approach effectively cultivates students’ scientific reasoning, interdisciplinary application skills, and digital intelligence competencies.
The preparation of cis(trans)-diglycine copper complexes represents a classic experiment in geometric isomer synthesis. To address the challenges of comprehending microscopic mechanisms, controlling reaction endpoints, and improving yields—despite the easily observable experimental phenomena—we have innovatively integrated quantum chemical calculations and color recognition technologies. Using Gaussian 09 with its visualization tool GaussView, we performed density functional theory (DFT) calculations to analyze geometric structures, energies, dipole moments, and electrostatic potentials of both cis- and trans-diglycine copper(II) complexes. This approach enables students to apply quantum chemical methods for investigating geometric isomer properties while gaining fundamental insights into their structural differences at the molecular level. Concurrently, we developed a machine vision-based color recognition system using C++ programming and OpenCV library. This system employs camera-captured reaction images analyzed through HSV color space modeling to monitor real-time solution color changes during the cis-to-trans isomerization process, thereby achieving precise endpoint determination. The synergistic incorporation of quantum chemical computation and color recognition technology into experimental teaching not only enhances students’ understanding of structure-property relationships but also improves experimental accuracy and reliability. Furthermore, this integrated approach effectively cultivates students’ scientific reasoning, interdisciplinary application skills, and digital intelligence competencies.
2026, 41(1): 382-393
doi: 10.12461/PKU.DXHX202506008
Abstract:
Coordination titration serves as a fundamental teaching module in analytical chemistry laboratories, extensively employed for quantitative metal ion analysis. EDTA, the most widely used titrant, exhibits pKa distribution characteristics that facilitate selective titration through pH modulation—a principle thoroughly demonstrated in classical teaching experiments. Commercial self-heating food packets contain precisely formulated calcium-aluminum binary systems (CaO and metallic Al as primary active components). This study designs a stepwise titration protocol enabling simultaneous training in coordination titration techniques under two characteristic pH conditions (alkaline and acidic media) within a single experiment, combining strong pedagogical representativeness with inherent student engagement. Addressing limitations in conventional teaching methods, we developed a digital experimental learning system through this self-heating pack component analysis case study. Guided by student-centered pedagogy, the system seamlessly integrates digital technologies with traditional titration workflows, featuring: real-time feasibility feedback for experimental design, automated titration operation and monitoring, intelligent real-time data acquisition and analysis, and comprehensive student performance evaluation. This integrated approach significantly strengthens students’ experimental competencies while enhancing logical reasoning, analytical judgment, and problem-solving skills, thereby markedly improving experimental teaching efficacy.
Coordination titration serves as a fundamental teaching module in analytical chemistry laboratories, extensively employed for quantitative metal ion analysis. EDTA, the most widely used titrant, exhibits pKa distribution characteristics that facilitate selective titration through pH modulation—a principle thoroughly demonstrated in classical teaching experiments. Commercial self-heating food packets contain precisely formulated calcium-aluminum binary systems (CaO and metallic Al as primary active components). This study designs a stepwise titration protocol enabling simultaneous training in coordination titration techniques under two characteristic pH conditions (alkaline and acidic media) within a single experiment, combining strong pedagogical representativeness with inherent student engagement. Addressing limitations in conventional teaching methods, we developed a digital experimental learning system through this self-heating pack component analysis case study. Guided by student-centered pedagogy, the system seamlessly integrates digital technologies with traditional titration workflows, featuring: real-time feasibility feedback for experimental design, automated titration operation and monitoring, intelligent real-time data acquisition and analysis, and comprehensive student performance evaluation. This integrated approach significantly strengthens students’ experimental competencies while enhancing logical reasoning, analytical judgment, and problem-solving skills, thereby markedly improving experimental teaching efficacy.
2026, 41(1): 394-405
doi: 10.12461/PKU.DXHX202506011
Abstract:
Chemical oxygen demand (COD) serves as a crucial parameter for assessing organic pollution in water and represents a routine analytical requirement. Building upon the standard method HJ/T399-2007 (“Water Quality-Determination of Chemical Oxygen Demand-Rapid Digestion Spectrophotometric Method”), this study innovatively developed a smartphone-based real-time visual detection system for COD analysis. The system utilizes smartphone color recognition to extract RGB values from digested water samples and establishes a ternary multiple regression model based on these values. A dedicated WeChat mini-program was developed to enable online visualization and analysis of COD, facilitating rapid on-site measurement of multiple water samples. This approach demonstrates advantages including operational simplicity, cost-effectiveness, and rapid analysis. Educational practice has shown that this technology not only finds application in environmental monitoring but also effectively enhances analytical chemistry pedagogy. It promotes outdoor teaching activities for undergraduates, stimulates students’ interest in chemistry, fosters scientific thinking and innovation awareness, and cultivates rigorous scientific attitudes.
Chemical oxygen demand (COD) serves as a crucial parameter for assessing organic pollution in water and represents a routine analytical requirement. Building upon the standard method HJ/T399-2007 (“Water Quality-Determination of Chemical Oxygen Demand-Rapid Digestion Spectrophotometric Method”), this study innovatively developed a smartphone-based real-time visual detection system for COD analysis. The system utilizes smartphone color recognition to extract RGB values from digested water samples and establishes a ternary multiple regression model based on these values. A dedicated WeChat mini-program was developed to enable online visualization and analysis of COD, facilitating rapid on-site measurement of multiple water samples. This approach demonstrates advantages including operational simplicity, cost-effectiveness, and rapid analysis. Educational practice has shown that this technology not only finds application in environmental monitoring but also effectively enhances analytical chemistry pedagogy. It promotes outdoor teaching activities for undergraduates, stimulates students’ interest in chemistry, fosters scientific thinking and innovation awareness, and cultivates rigorous scientific attitudes.
2026, 41(1): 406-412
doi: 10.12461/PKU.DXHX202504022
Abstract:
In this work, we developed an interactive digital teaching model for experimental chemistry education. The approach employs digital technologies to perform retrosynthetic analysis of target compounds while evaluating different synthetic routes based on cost, safety, and yield parameters to identify optimal pathways for teaching purposes. Each route is systematically optimized to address potential operational challenges, with multiple troubleshooting solutions incorporated. The final output is presented as an interactive video-based digital experiment. Within this framework, instructors can utilize digital tools to design experimental pathways and generate randomized events, thereby guiding students through problem identification, solution development, and experimental completion. This interactive teaching paradigm represents a significant advancement over traditional linear virtual experiments, offering enhanced flexibility, personalization, real-time feedback, and improved differentiation capabilities. The methodology demonstrates considerable potential for broader application in experimental chemistry education.
In this work, we developed an interactive digital teaching model for experimental chemistry education. The approach employs digital technologies to perform retrosynthetic analysis of target compounds while evaluating different synthetic routes based on cost, safety, and yield parameters to identify optimal pathways for teaching purposes. Each route is systematically optimized to address potential operational challenges, with multiple troubleshooting solutions incorporated. The final output is presented as an interactive video-based digital experiment. Within this framework, instructors can utilize digital tools to design experimental pathways and generate randomized events, thereby guiding students through problem identification, solution development, and experimental completion. This interactive teaching paradigm represents a significant advancement over traditional linear virtual experiments, offering enhanced flexibility, personalization, real-time feedback, and improved differentiation capabilities. The methodology demonstrates considerable potential for broader application in experimental chemistry education.
