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2025 Volume 40 Issue 9
2025, 40(9): 1-4
doi: 10.12461/PKU.DXHX202502013
Abstract:
The emergence of Artificial Intelligence (AI) is significantly changing the course of human social development, and education cannot remain unaffected. This article briefly discusses the potential impact of AI on education, drawing on the author’s recent experience of learning alongside ChatGPT-4, as well as the extreme importance of learning how to learn in the context of AI-powered education. Based on this foundation, suggestions are made for AI-enabled education, particularly in the field of chemistry education.
The emergence of Artificial Intelligence (AI) is significantly changing the course of human social development, and education cannot remain unaffected. This article briefly discusses the potential impact of AI on education, drawing on the author’s recent experience of learning alongside ChatGPT-4, as well as the extreme importance of learning how to learn in the context of AI-powered education. Based on this foundation, suggestions are made for AI-enabled education, particularly in the field of chemistry education.
2025, 40(9): 5-10
doi: 10.12461/PKU.DXHX202408132
Abstract:
In recent years, the application of artificial intelligence (AI) in education has garnered increasing attention. This paper analyzes the distinctive characteristics of university chemistry experiments, including their comprehensiveness and systematization, exploratory and innovative nature, focus on safety and standardization, as well as their foundational and challenging aspects. It reviews the current state of AI integration in chemistry laboratory teaching, highlighting developments in intelligent teaching assistant systems, smart learning platforms, and virtual reality applications. However, AI is still rarely employed in the core aspect of chemistry laboratory — the teaching of experimental operations. The paper also explores the potential future of AI in university chemistry education, identifying key development needs, such as understanding symbolic systems, capturing action details, enhancing comprehensive support capabilities, and ensuring laboratory safety. Finally, it provides an outlook on the evolving role of AI in enhancing university chemistry laboratory teaching.
In recent years, the application of artificial intelligence (AI) in education has garnered increasing attention. This paper analyzes the distinctive characteristics of university chemistry experiments, including their comprehensiveness and systematization, exploratory and innovative nature, focus on safety and standardization, as well as their foundational and challenging aspects. It reviews the current state of AI integration in chemistry laboratory teaching, highlighting developments in intelligent teaching assistant systems, smart learning platforms, and virtual reality applications. However, AI is still rarely employed in the core aspect of chemistry laboratory — the teaching of experimental operations. The paper also explores the potential future of AI in university chemistry education, identifying key development needs, such as understanding symbolic systems, capturing action details, enhancing comprehensive support capabilities, and ensuring laboratory safety. Finally, it provides an outlook on the evolving role of AI in enhancing university chemistry laboratory teaching.
2025, 40(9): 11-18
doi: 10.12461/PKU.DXHX202408123
Abstract:
This study explores the innovative application of generative artificial intelligence (AI) in the simulation of acid-base titration images. Through hands-on experimentation, we employed generative AI techniques to develop code for dynamic acid-base titration images, establishing a “code generation and optimization for experimental images” paradigm. This approach is tightly aligned with the practical needs of educational settings, aiming to offer cutting-edge technological support for chemistry educators in experimental teaching. It also aims to create intelligent, interactive learning environments to enhance students’ understanding of chemistry.
This study explores the innovative application of generative artificial intelligence (AI) in the simulation of acid-base titration images. Through hands-on experimentation, we employed generative AI techniques to develop code for dynamic acid-base titration images, establishing a “code generation and optimization for experimental images” paradigm. This approach is tightly aligned with the practical needs of educational settings, aiming to offer cutting-edge technological support for chemistry educators in experimental teaching. It also aims to create intelligent, interactive learning environments to enhance students’ understanding of chemistry.
An Improved Simulated Annealing Algorithm for Predicting the Molecular Formulas of Organic Compounds
2025, 40(9): 19-24
doi: 10.12461/PKU.DXHX202408095
Abstract:
Simulated annealing algorithm is an artificial intelligence combinatorial optimization algorithm. Building upon the classic simulated annealing algorithm, we propose an enhanced version for predicting the molecular formulas of organic compounds. The algorithm begins by using a genetic algorithm to calculate the fitness values of individuals in the population, selecting the optimal individual as the initial solution for the simulated annealing process. Based on this initial solution, new solutions are generated through random perturbation, and their fitness values are calculated. If the change in fitness is less than or equal to zero, the new solution is accepted. Otherwise, the Metropolis criterion is applied to determine whether the new solution should be accepted. As the annealing temperature gradually decreases, the algorithm’s termination condition is used to determine if the global optimal solution has been found. Experimental results show that this improved algorithm increases the success rate of finding the global optimal solution. When applied to predict the molecular formulas of organic compounds, it demonstrates significantly better convergence of the fitness function compared to the classical simulated annealing algorithm.
Simulated annealing algorithm is an artificial intelligence combinatorial optimization algorithm. Building upon the classic simulated annealing algorithm, we propose an enhanced version for predicting the molecular formulas of organic compounds. The algorithm begins by using a genetic algorithm to calculate the fitness values of individuals in the population, selecting the optimal individual as the initial solution for the simulated annealing process. Based on this initial solution, new solutions are generated through random perturbation, and their fitness values are calculated. If the change in fitness is less than or equal to zero, the new solution is accepted. Otherwise, the Metropolis criterion is applied to determine whether the new solution should be accepted. As the annealing temperature gradually decreases, the algorithm’s termination condition is used to determine if the global optimal solution has been found. Experimental results show that this improved algorithm increases the success rate of finding the global optimal solution. When applied to predict the molecular formulas of organic compounds, it demonstrates significantly better convergence of the fitness function compared to the classical simulated annealing algorithm.
2025, 40(9): 25-33
doi: 10.12461/PKU.DXHX202408096
Abstract:
Generative AI, with its remarkable capabilities in natural language processing and knowledge generation, is profoundly influencing educational reform. This paper explores the application of generative AI in analytical chemistry education, examining its multiple roles within the teaching process. It provides a detailed exploration of intelligent lesson planning, the design of personalized learning paths for students, and the analysis of exam results, all aimed at enhancing the learning experience and teaching efficiency for both students and instructors. This study offers new insights into the modernization of teaching methods. Additionally, the paper objectively assesses the challenges associated with AI integration in education and suggests strategies to address these challenges, providing valuable guidance for the deeper application of generative AI in analytical chemistry and other educational fields..
Generative AI, with its remarkable capabilities in natural language processing and knowledge generation, is profoundly influencing educational reform. This paper explores the application of generative AI in analytical chemistry education, examining its multiple roles within the teaching process. It provides a detailed exploration of intelligent lesson planning, the design of personalized learning paths for students, and the analysis of exam results, all aimed at enhancing the learning experience and teaching efficiency for both students and instructors. This study offers new insights into the modernization of teaching methods. Additionally, the paper objectively assesses the challenges associated with AI integration in education and suggests strategies to address these challenges, providing valuable guidance for the deeper application of generative AI in analytical chemistry and other educational fields..
2025, 40(9): 34-42
doi: 10.12461/PKU.DXHX202412002
Abstract:
In recent years, the integration of artificial intelligence (AI) technology and chemistry education has given rise to a new track for the development of professional chemistry education in higher education institutions. How to transform chemistry teaching content, innovate teaching methods, and promote curriculum construction has become one of the key focuses. This article introduces the overview of the course construction of “Chemical Informatics and AI Chemistry” at Tianjin University, discusses the necessity of carrying out the project construction of artificial intelligence chemistry course, summarizes the teaching objectives, knowledge areas and teaching content, characteristic innovations and operational effects of the course, and puts forward shortcomings and future improvement plans, hoping to provide reference for teaching peers in related fields.
In recent years, the integration of artificial intelligence (AI) technology and chemistry education has given rise to a new track for the development of professional chemistry education in higher education institutions. How to transform chemistry teaching content, innovate teaching methods, and promote curriculum construction has become one of the key focuses. This article introduces the overview of the course construction of “Chemical Informatics and AI Chemistry” at Tianjin University, discusses the necessity of carrying out the project construction of artificial intelligence chemistry course, summarizes the teaching objectives, knowledge areas and teaching content, characteristic innovations and operational effects of the course, and puts forward shortcomings and future improvement plans, hoping to provide reference for teaching peers in related fields.
2025, 40(9): 43-52
doi: 10.12461/PKU.DXHX202410097
Abstract:
This study addresses prevalent issues in the teaching of general education courses, particularly focusing on the course “Chemistry and Human Civilization”. It emphasizes the enhancement of the course’s “intelligence” through the construction of a knowledge graph, the establishment of AI teaching assistants, the innovation of the SCIENCE teaching model, the elevation of learning task challenges, the reform of evaluation methods, and the strengthening of learning process assessments. The knowledge graph and AI teaching assistant were employed to facilitate personalized learning navigation and precise assessment for students across various disciplines. Initial teaching practice results indicate that AI-enabled teaching reform effectively addresses critical issues in general education, alters both teachers’ and students’ perceptions of such courses, increases student engagement, and enhances scientific literacy and higher-order learning capabilities.
This study addresses prevalent issues in the teaching of general education courses, particularly focusing on the course “Chemistry and Human Civilization”. It emphasizes the enhancement of the course’s “intelligence” through the construction of a knowledge graph, the establishment of AI teaching assistants, the innovation of the SCIENCE teaching model, the elevation of learning task challenges, the reform of evaluation methods, and the strengthening of learning process assessments. The knowledge graph and AI teaching assistant were employed to facilitate personalized learning navigation and precise assessment for students across various disciplines. Initial teaching practice results indicate that AI-enabled teaching reform effectively addresses critical issues in general education, alters both teachers’ and students’ perceptions of such courses, increases student engagement, and enhances scientific literacy and higher-order learning capabilities.
2025, 40(9): 53-60
doi: 10.12461/PKU.DXHX202410064
Abstract:
This study integrates the Bohrium cloud computing platform with the domestic open-source software ABACUS to develop an innovative hybrid teaching model, applied in the “New Energy Materials and Chemistry” course at Tianjin University. The course design effectively combines theory and practice, encompassing topics such as self-consistent calculations, structural optimization, electronic density of states, band structure, and phonon characteristics. The introduction of the cloud computing platform significantly reduces operational complexity, enhances classroom interactivity, and fosters students’ independent learning and innovative thinking. This teaching approach has demonstrably improved instructional quality and offers a novel pathway for cultivating chemistry professionals with a global perspective and innovative capabilities.
This study integrates the Bohrium cloud computing platform with the domestic open-source software ABACUS to develop an innovative hybrid teaching model, applied in the “New Energy Materials and Chemistry” course at Tianjin University. The course design effectively combines theory and practice, encompassing topics such as self-consistent calculations, structural optimization, electronic density of states, band structure, and phonon characteristics. The introduction of the cloud computing platform significantly reduces operational complexity, enhances classroom interactivity, and fosters students’ independent learning and innovative thinking. This teaching approach has demonstrably improved instructional quality and offers a novel pathway for cultivating chemistry professionals with a global perspective and innovative capabilities.
2025, 40(9): 61-68
doi: 10.12461/PKU.DXHX202411002
Abstract:
As a vital theoretical branch of chemistry, physical chemistry is characterized by its rigorous logical framework and practical applications, making it an essential medium for implementing ideological and political education. This article proposes the integration of digital technology and artificial intelligence to create a resource library for ideological and political education within physical chemistry courses. This initiative aims to foster the convergence of ideological and political education with cutting-edge technological advancements. Additionally, it advocates for the innovative application of digital technology to develop contextual frameworks for ideological and political discussions in these courses, facilitating a multi-channel and multidimensional integration of these elements. Furthermore, digital technology will be utilized to establish a feedback mechanism for ideological and political education within the curriculum.
As a vital theoretical branch of chemistry, physical chemistry is characterized by its rigorous logical framework and practical applications, making it an essential medium for implementing ideological and political education. This article proposes the integration of digital technology and artificial intelligence to create a resource library for ideological and political education within physical chemistry courses. This initiative aims to foster the convergence of ideological and political education with cutting-edge technological advancements. Additionally, it advocates for the innovative application of digital technology to develop contextual frameworks for ideological and political discussions in these courses, facilitating a multi-channel and multidimensional integration of these elements. Furthermore, digital technology will be utilized to establish a feedback mechanism for ideological and political education within the curriculum.
2025, 40(9): 69-75
doi: 10.12461/PKU.DXHX202411067
Abstract:
With the rapid advancements in computational chemistry and artificial intelligence technologies, their integration into chemical education has become increasingly vital. This experimental course is specifically tailored for senior undergraduate and graduate chemistry students, providing a comprehensive platform that merges computational chemistry with machine learning methodologies to explore the bond dissociation energies of organic compounds in depth. The curriculum covers fundamental principles and operational techniques of quantum chemical calculation methods, as well as the construction, training, and validation of machine learning models. Through hands-on experience, students will learn to utilize advanced computational tools and algorithms to predict and analyze chemical bond energies, thereby deepening their understanding of chemical reaction mechanisms. The objective of the course is to equip students with proficient data processing and analysis skills, empowering them to independently apply these skills to research chemical problems, thus establishing a strong foundation for future scientific endeavors or interdisciplinary explorations.
With the rapid advancements in computational chemistry and artificial intelligence technologies, their integration into chemical education has become increasingly vital. This experimental course is specifically tailored for senior undergraduate and graduate chemistry students, providing a comprehensive platform that merges computational chemistry with machine learning methodologies to explore the bond dissociation energies of organic compounds in depth. The curriculum covers fundamental principles and operational techniques of quantum chemical calculation methods, as well as the construction, training, and validation of machine learning models. Through hands-on experience, students will learn to utilize advanced computational tools and algorithms to predict and analyze chemical bond energies, thereby deepening their understanding of chemical reaction mechanisms. The objective of the course is to equip students with proficient data processing and analysis skills, empowering them to independently apply these skills to research chemical problems, thus establishing a strong foundation for future scientific endeavors or interdisciplinary explorations.
2025, 40(9): 76-86
doi: 10.12461/PKU.DXHX202411058
Abstract:
This study addresses the challenges faced in the teaching of chemistry history amid the rapid advancement of information technology. By constructing a knowledge graph based on online course resources and implementing AI teaching assistants, we explore a novel pedagogical approach empowered by artificial intelligence. The integration of knowledge graphs and AI assistants facilitates personalized learning for students and enables teachers to accurately assess learning conditions. Preliminary results from teaching practice suggest that AI-enabled reforms in chemistry history education effectively address existing issues, enhance students’ interest in learning, and improve educational quality. Moreover, the data generated from the knowledge graph and AI teaching assistants serves as a valuable foundation for the continuous enhancement of intelligent curricula.
This study addresses the challenges faced in the teaching of chemistry history amid the rapid advancement of information technology. By constructing a knowledge graph based on online course resources and implementing AI teaching assistants, we explore a novel pedagogical approach empowered by artificial intelligence. The integration of knowledge graphs and AI assistants facilitates personalized learning for students and enables teachers to accurately assess learning conditions. Preliminary results from teaching practice suggest that AI-enabled reforms in chemistry history education effectively address existing issues, enhance students’ interest in learning, and improve educational quality. Moreover, the data generated from the knowledge graph and AI teaching assistants serves as a valuable foundation for the continuous enhancement of intelligent curricula.
2025, 40(9): 87-98
doi: 10.12461/PKU.DXHX202411062
Abstract:
Undergraduate experimental design typically requires extensive trial-and-error experimentation to identify optimal reaction conditions, a process that demands considerable time and resources. To simplify this complex experimental design process and to enhance students’ interest in advanced technologies and interdisciplinary fields, an intelligent reaction optimization model framework was introduced. This model is designed to predict the optimal combination of reaction conditions for achieving the highest yield, and this framework has been integrated into the undergraduate organic chemistry curriculum, specifically for the synthesis of acetylsalicylic acid. Herein, 1054 reaction data points were collected conforming to this reaction mechanism as training data for the model, mainly including product, reactant, catalyst, solvent, the main reaction reagents, reaction temperature and yield. Initially, a yield prediction model was pre-trained based on a chemical multi-modal transformer, which served as the objective function for subsequent reaction optimization. Then the Bayesian optimization algorithm was utilized to ascertain the optimal combination of reaction conditions, with the aim of minimizing the negative value of the yield (the maximizing yield). Finally, the model-predicted yields were experimentally validated against the actual yields, and in 100 model tests, the predicted yields consistently fell within the range of the actual yields, demonstrating the model’s excellent performance. The model also identified a reaction reagent combination that yielded up to 90.1%, providing a variety of experimental options for this undergraduate experiment.
Undergraduate experimental design typically requires extensive trial-and-error experimentation to identify optimal reaction conditions, a process that demands considerable time and resources. To simplify this complex experimental design process and to enhance students’ interest in advanced technologies and interdisciplinary fields, an intelligent reaction optimization model framework was introduced. This model is designed to predict the optimal combination of reaction conditions for achieving the highest yield, and this framework has been integrated into the undergraduate organic chemistry curriculum, specifically for the synthesis of acetylsalicylic acid. Herein, 1054 reaction data points were collected conforming to this reaction mechanism as training data for the model, mainly including product, reactant, catalyst, solvent, the main reaction reagents, reaction temperature and yield. Initially, a yield prediction model was pre-trained based on a chemical multi-modal transformer, which served as the objective function for subsequent reaction optimization. Then the Bayesian optimization algorithm was utilized to ascertain the optimal combination of reaction conditions, with the aim of minimizing the negative value of the yield (the maximizing yield). Finally, the model-predicted yields were experimentally validated against the actual yields, and in 100 model tests, the predicted yields consistently fell within the range of the actual yields, demonstrating the model’s excellent performance. The model also identified a reaction reagent combination that yielded up to 90.1%, providing a variety of experimental options for this undergraduate experiment.
2025, 40(9): 99-106
doi: 10.12461/PKU.DXHX202410085
Abstract:
This paper investigates innovative approaches to organizing chemistry instruction through the application of artificial intelligence (AI) technology, utilizing specific case studies to demonstrate how AI can transform traditional teaching methods. To address the shortcomings of conventional chemistry education, we propose three primary innovation pathways based on AI technology: adaptive learning systems, intelligent experimental platforms, virtual laboratories, and collaborative learning enhanced by intelligent coordination. Adaptive learning systems leverage personalized data analysis to dynamically tailor learning pathways for students, effectively addressing the limitations of “one-size-fits-all” teaching models. Virtual laboratories and intelligent experimental platforms transcend the constraints of physical experiments by providing students with a safe and flexible environment for conducting experiments. Additionally, intelligent collaboration tools optimize group dynamics in cooperative learning settings, enhancing learning efficiency through real-time feedback. Nevertheless, the implementation of AI technology in chemical education faces challenges, including the uneven distribution of educational resources and inadequate technological proficiency among educators. This paper recommends addressing these challenges by optimizing resource allocation, strengthening teacher training, and integrating virtual and traditional teaching methods to enhance the effectiveness of AI applications in chemistry education. Looking ahead, AI technology is poised to continue driving innovation in chemical education, facilitating the intelligent transformation of instructional methods, and playing a crucial role in promoting educational equity and improving teaching quality.
This paper investigates innovative approaches to organizing chemistry instruction through the application of artificial intelligence (AI) technology, utilizing specific case studies to demonstrate how AI can transform traditional teaching methods. To address the shortcomings of conventional chemistry education, we propose three primary innovation pathways based on AI technology: adaptive learning systems, intelligent experimental platforms, virtual laboratories, and collaborative learning enhanced by intelligent coordination. Adaptive learning systems leverage personalized data analysis to dynamically tailor learning pathways for students, effectively addressing the limitations of “one-size-fits-all” teaching models. Virtual laboratories and intelligent experimental platforms transcend the constraints of physical experiments by providing students with a safe and flexible environment for conducting experiments. Additionally, intelligent collaboration tools optimize group dynamics in cooperative learning settings, enhancing learning efficiency through real-time feedback. Nevertheless, the implementation of AI technology in chemical education faces challenges, including the uneven distribution of educational resources and inadequate technological proficiency among educators. This paper recommends addressing these challenges by optimizing resource allocation, strengthening teacher training, and integrating virtual and traditional teaching methods to enhance the effectiveness of AI applications in chemistry education. Looking ahead, AI technology is poised to continue driving innovation in chemical education, facilitating the intelligent transformation of instructional methods, and playing a crucial role in promoting educational equity and improving teaching quality.
2025, 40(9): 107-117
doi: 10.12461/PKU.DXHX202412135
Abstract:
DNA computing, an innovative technology that utilizes biological molecules to execute computational tasks such as data storage, problem-solving, and logical operations, offers novel pathways to transcend the limitations of conventional computing. As DNA computing continues to evolve, challenges pertaining to complexity, error rates, and computational efficiency have become increasingly pronounced. The advent of artificial intelligence (AI) presents significant opportunities to optimize and enhance DNA computing. Particularly in the realms of data analysis, model optimization, and error correction, the integration of AI technologies has markedly improved the efficiency, accuracy, and stability of DNA computing. This paper provides an in-depth review of AI algorithm classifications and examines how AI empowers DNA computing, with a particular emphasis on optimizing computational workflows, enhancing logic gate functionality, and refining error correction mechanisms. Additionally, we summarize the pivotal applications of AI and DNA computing at the forefront of bioinformatics, including genomics, protein structure prediction, disease diagnosis, and precision medicine. Looking ahead, the ongoing innovation in AI and DNA computing is anticipated to further broaden their application scope, positioning them as a transformative force in the advancement of biosciences.
DNA computing, an innovative technology that utilizes biological molecules to execute computational tasks such as data storage, problem-solving, and logical operations, offers novel pathways to transcend the limitations of conventional computing. As DNA computing continues to evolve, challenges pertaining to complexity, error rates, and computational efficiency have become increasingly pronounced. The advent of artificial intelligence (AI) presents significant opportunities to optimize and enhance DNA computing. Particularly in the realms of data analysis, model optimization, and error correction, the integration of AI technologies has markedly improved the efficiency, accuracy, and stability of DNA computing. This paper provides an in-depth review of AI algorithm classifications and examines how AI empowers DNA computing, with a particular emphasis on optimizing computational workflows, enhancing logic gate functionality, and refining error correction mechanisms. Additionally, we summarize the pivotal applications of AI and DNA computing at the forefront of bioinformatics, including genomics, protein structure prediction, disease diagnosis, and precision medicine. Looking ahead, the ongoing innovation in AI and DNA computing is anticipated to further broaden their application scope, positioning them as a transformative force in the advancement of biosciences.
2025, 40(9): 118-125
doi: 10.12461/PKU.DXHX202503062
Abstract:
This study investigates innovative approaches to AI-enhanced experimental teaching within the context of marine-themed integrated physical chemistry experiments. The research demonstrates the effective utilization of knowledge graphs for systematic learning and interdisciplinary knowledge integration. By incorporating DeepSeek and Python-based open-source models, the study significantly enhances intelligent data processing capabilities, thereby promoting the advancement of smart experimental teaching methodologies.
This study investigates innovative approaches to AI-enhanced experimental teaching within the context of marine-themed integrated physical chemistry experiments. The research demonstrates the effective utilization of knowledge graphs for systematic learning and interdisciplinary knowledge integration. By incorporating DeepSeek and Python-based open-source models, the study significantly enhances intelligent data processing capabilities, thereby promoting the advancement of smart experimental teaching methodologies.
2025, 40(9): 126-131
doi: 10.12461/PKU.DXHX202412151
Abstract:
Laboratory safety education constitutes a critical element in graduate training, playing a pivotal role in maintaining a secure academic and research environment. This study investigates an AI-enhanced laboratory safety education system implemented within the Forestry Engineering graduate program at the School of Materials Science, Northeast Forestry University. To address prevalent issues such as the disconnection between theoretical knowledge and practical application, as well as the limitations inherent in conventional teaching methodologies, the proposed system incorporates three integrated modules: theoretical instruction, virtual simulation, and practical training. The integration of AI technology facilitates immersive safety training experiences, enables intelligent assessment mechanisms, and provides personalized feedback, thereby optimizing the learning process. Furthermore, the practical training component enhances emergency response capabilities and elevates laboratory safety competencies. The implementation of this system has demonstrated significant improvements in students’ safety awareness and a reduction in laboratory incidents, offering valuable insights for the optimization and dissemination of laboratory safety education in higher education institutions.
Laboratory safety education constitutes a critical element in graduate training, playing a pivotal role in maintaining a secure academic and research environment. This study investigates an AI-enhanced laboratory safety education system implemented within the Forestry Engineering graduate program at the School of Materials Science, Northeast Forestry University. To address prevalent issues such as the disconnection between theoretical knowledge and practical application, as well as the limitations inherent in conventional teaching methodologies, the proposed system incorporates three integrated modules: theoretical instruction, virtual simulation, and practical training. The integration of AI technology facilitates immersive safety training experiences, enables intelligent assessment mechanisms, and provides personalized feedback, thereby optimizing the learning process. Furthermore, the practical training component enhances emergency response capabilities and elevates laboratory safety competencies. The implementation of this system has demonstrated significant improvements in students’ safety awareness and a reduction in laboratory incidents, offering valuable insights for the optimization and dissemination of laboratory safety education in higher education institutions.
2025, 40(9): 132-141
doi: 10.12461/PKU.DXHX202502004
Abstract:
The progression from data-driven methodologies to the establishment of chemical symbols represents a fundamental process in the advancement of chemical education. The integration of logical reasoning with model construction serves as a pivotal approach for comprehending abstract chemical symbols, constituting a cornerstone of chemistry bridging education. This study employs the Transformer model to simulate and compute Avogadro’s constant, while utilizing Scikit-learn’s integrated models to deduce the most probable distribution. Through the implementation of the triple representation teaching strategy, the research investigates the application of AI models in chemistry bridging education, with particular emphasis on elucidating the conceptual underpinnings of Avogadro’s constant as a proportionality factor. This exploration extends to Boltzmann’s constant, the Boltzmann formula (interpretation of entropy), and the definition and derivation of temperature formulas. This pedagogical approach facilitates students’ mastery and comprehension of chemical symbols, enabling them to develop a profound understanding of chemistry as a discipline fundamentally concerned with the study of aggregates.
The progression from data-driven methodologies to the establishment of chemical symbols represents a fundamental process in the advancement of chemical education. The integration of logical reasoning with model construction serves as a pivotal approach for comprehending abstract chemical symbols, constituting a cornerstone of chemistry bridging education. This study employs the Transformer model to simulate and compute Avogadro’s constant, while utilizing Scikit-learn’s integrated models to deduce the most probable distribution. Through the implementation of the triple representation teaching strategy, the research investigates the application of AI models in chemistry bridging education, with particular emphasis on elucidating the conceptual underpinnings of Avogadro’s constant as a proportionality factor. This exploration extends to Boltzmann’s constant, the Boltzmann formula (interpretation of entropy), and the definition and derivation of temperature formulas. This pedagogical approach facilitates students’ mastery and comprehension of chemical symbols, enabling them to develop a profound understanding of chemistry as a discipline fundamentally concerned with the study of aggregates.
2025, 40(9): 142-147
doi: 10.12461/PKU.DXHX202502035
Abstract:
With the rapid development of generative artificial intelligence (GAI), its applications in education have attracted widespread attention. Many universities have successively initiated explorations of AI-enhanced courses. This study, based on the Superstar platform, trained an AI teaching assistant using textbooks, course videos, and related materials to enable it to answer general questions in the “Medical Organic Chemistry” course. The implementation successfully addressed students’ needs for full-time Q&A support and provided valuable experience for improving AI teaching assistant functionalities.
With the rapid development of generative artificial intelligence (GAI), its applications in education have attracted widespread attention. Many universities have successively initiated explorations of AI-enhanced courses. This study, based on the Superstar platform, trained an AI teaching assistant using textbooks, course videos, and related materials to enable it to answer general questions in the “Medical Organic Chemistry” course. The implementation successfully addressed students’ needs for full-time Q&A support and provided valuable experience for improving AI teaching assistant functionalities.
2025, 40(9): 148-155
doi: 10.12461/PKU.DXHX202412099
Abstract:
Against the backdrop of rapid advancements in artificial intelligence (AI) technology worldwide, universities face significant challenges in addressing contemporary issues by integrating traditional scientific research with AI. This integration aims to broaden the perspectives of engineering graduate students, stimulate innovative thinking, enhance the efficiency of innovative outputs, and cultivate versatile talents for national and societal needs. This paper, using electrocatalytic carbon dioxide reduction (CO2RR) within the framework of carbon neutrality as a case study, underscores the importance of merging scientific research with AI to augment research output and accuracy. It highlights how AI computing facilitates the screening and prediction of high-performance catalysts, deepens the understanding of complex reaction mechanisms, optimizes electrolytes, and aids in experimental design. Furthermore, it promotes interdisciplinary collaboration and serves as a reference for engineering graduate students embarking on experimental research in universities.
Against the backdrop of rapid advancements in artificial intelligence (AI) technology worldwide, universities face significant challenges in addressing contemporary issues by integrating traditional scientific research with AI. This integration aims to broaden the perspectives of engineering graduate students, stimulate innovative thinking, enhance the efficiency of innovative outputs, and cultivate versatile talents for national and societal needs. This paper, using electrocatalytic carbon dioxide reduction (CO2RR) within the framework of carbon neutrality as a case study, underscores the importance of merging scientific research with AI to augment research output and accuracy. It highlights how AI computing facilitates the screening and prediction of high-performance catalysts, deepens the understanding of complex reaction mechanisms, optimizes electrolytes, and aids in experimental design. Furthermore, it promotes interdisciplinary collaboration and serves as a reference for engineering graduate students embarking on experimental research in universities.
2025, 40(9): 156-163
doi: 10.12461/PKU.DXHX202502107
Abstract:
With the rapid advancement of artificial intelligence (AI) technology, the limitations inherent in traditional educational paradigms are increasingly being recognized and addressed. This paper examines the current state of chemical education in higher institutions, offering a comprehensive analysis of the deficiencies and underlying causes in conventional university chemistry instruction. By leveraging the strengths of AI technology, the study proposes strategic measures from the perspective of AI-enhanced chemistry education. These initiatives aim to drive meaningful reform in university chemistry teaching methodologies and elevate the overall quality of chemical education in academic institutions.
With the rapid advancement of artificial intelligence (AI) technology, the limitations inherent in traditional educational paradigms are increasingly being recognized and addressed. This paper examines the current state of chemical education in higher institutions, offering a comprehensive analysis of the deficiencies and underlying causes in conventional university chemistry instruction. By leveraging the strengths of AI technology, the study proposes strategic measures from the perspective of AI-enhanced chemistry education. These initiatives aim to drive meaningful reform in university chemistry teaching methodologies and elevate the overall quality of chemical education in academic institutions.
2025, 40(9): 164-170
doi: 10.12461/PKU.DXHX202502087
Abstract:
In the context of “new quality productive forces”, the accelerating intelligent transformation of the chemical industry has created an urgent demand for high-quality chemical engineering professionals with intelligent technology literacy. The effective cultivation of such talents has become a critical focus in higher education reform. This paper, centered on the concept of “new quality productive forces”, thoroughly explores the pathways and strategies for developing intelligent chemical engineering talents, aiming to provide insights and recommendations for the educational reform in chemical engineering.
In the context of “new quality productive forces”, the accelerating intelligent transformation of the chemical industry has created an urgent demand for high-quality chemical engineering professionals with intelligent technology literacy. The effective cultivation of such talents has become a critical focus in higher education reform. This paper, centered on the concept of “new quality productive forces”, thoroughly explores the pathways and strategies for developing intelligent chemical engineering talents, aiming to provide insights and recommendations for the educational reform in chemical engineering.
2025, 40(9): 171-177
doi: 10.12461/PKU.DXHX202502109
Abstract:
This study employs the DeepSeek large model to conduct simulated learning and explores its effective elements in inorganic chemistry education. In the era of digital-intelligent empowerment, it is imperative to reconsider innovative approaches for chemistry curriculum teaching and learning, thereby addressing the evolving requirements and challenges posed by social development in the field of inorganic chemistry education.
This study employs the DeepSeek large model to conduct simulated learning and explores its effective elements in inorganic chemistry education. In the era of digital-intelligent empowerment, it is imperative to reconsider innovative approaches for chemistry curriculum teaching and learning, thereby addressing the evolving requirements and challenges posed by social development in the field of inorganic chemistry education.
2025, 40(9): 178-188
doi: 10.12461/PKU.DXHX202503010
Abstract:
The rapid advancement of Artificial Intelligence (AI) has brought forth significant opportunities for innovation in polymer chemistry education, while simultaneously presenting a range of challenges. This paper provides an in-depth analysis of AI’s innovative applications in various aspects of teaching, including curriculum content, pedagogical approaches, classroom dynamics, and the evolving roles of educators and students. Furthermore, it examines the associated challenges and proposes practical solutions through case studies, focusing on enhancing faculty competencies and optimizing educational resources. The study aims to facilitate the seamless integration of AI into polymer chemistry instruction, thereby comprehensively improving the quality of teaching and learning.
The rapid advancement of Artificial Intelligence (AI) has brought forth significant opportunities for innovation in polymer chemistry education, while simultaneously presenting a range of challenges. This paper provides an in-depth analysis of AI’s innovative applications in various aspects of teaching, including curriculum content, pedagogical approaches, classroom dynamics, and the evolving roles of educators and students. Furthermore, it examines the associated challenges and proposes practical solutions through case studies, focusing on enhancing faculty competencies and optimizing educational resources. The study aims to facilitate the seamless integration of AI into polymer chemistry instruction, thereby comprehensively improving the quality of teaching and learning.
2025, 40(9): 189-195
doi: 10.12461/PKU.DXHX202412072
Abstract:
In the context of the rapid advancement of artificial intelligence, smart teaching has emerged as an innovative approach to higher education curriculum reform. This study establishes a student-centered smart teaching framework through the strategic design of curriculum levels, refinement of competency development pathways, and deep integration of disciplinary knowledge with intelligent technologies. Using the organic chemistry course as a case study, the proposed model incorporates advanced technologies such as artificial intelligence and big data analytics to optimize the allocation and utilization of teaching resources, thereby achieving personalized and precise teaching processes. The implementation of virtual laboratories, intelligent Q&A systems, and online learning platforms enables students to engage comprehensively in pre-class, in-class, and post-class learning activities, significantly enhancing their autonomous learning capabilities and practical operational skills. Empirical teaching practices demonstrate that this model effectively improves students’ classroom engagement, comprehensive competencies, and learning outcomes.
In the context of the rapid advancement of artificial intelligence, smart teaching has emerged as an innovative approach to higher education curriculum reform. This study establishes a student-centered smart teaching framework through the strategic design of curriculum levels, refinement of competency development pathways, and deep integration of disciplinary knowledge with intelligent technologies. Using the organic chemistry course as a case study, the proposed model incorporates advanced technologies such as artificial intelligence and big data analytics to optimize the allocation and utilization of teaching resources, thereby achieving personalized and precise teaching processes. The implementation of virtual laboratories, intelligent Q&A systems, and online learning platforms enables students to engage comprehensively in pre-class, in-class, and post-class learning activities, significantly enhancing their autonomous learning capabilities and practical operational skills. Empirical teaching practices demonstrate that this model effectively improves students’ classroom engagement, comprehensive competencies, and learning outcomes.
2025, 40(9): 196-205
doi: 10.12461/PKU.DXHX202503029
Abstract:
This paper delineates the distinctive features of physical chemistry courses and identifies the limitations inherent in traditional teaching methodologies. It examines the synergistic advantages of integrating artificial intelligence (AI) with the Rain Classroom platform and details the implementation process of AI-enabled Rain Classroom in blended teaching of physical chemistry, encompassing pre-class, in-class, and post-class instructional design. Through empirical teaching practices and student feedback, the study investigates the impact of the blended teaching model on students’ learning outcomes, academic interest, autonomous learning capabilities, and innovative practical skills. The findings demonstrate significant improvements in students’ academic performance with no failing grades, a substantial increase in learning interest accompanied by approximately threefold engagement enhancement, and notable advancements in both learning and practical abilities. This research aims to provide innovative approaches and methodologies for the reform of physical chemistry education, facilitating the profound integration of educational technology with disciplinary instruction.
This paper delineates the distinctive features of physical chemistry courses and identifies the limitations inherent in traditional teaching methodologies. It examines the synergistic advantages of integrating artificial intelligence (AI) with the Rain Classroom platform and details the implementation process of AI-enabled Rain Classroom in blended teaching of physical chemistry, encompassing pre-class, in-class, and post-class instructional design. Through empirical teaching practices and student feedback, the study investigates the impact of the blended teaching model on students’ learning outcomes, academic interest, autonomous learning capabilities, and innovative practical skills. The findings demonstrate significant improvements in students’ academic performance with no failing grades, a substantial increase in learning interest accompanied by approximately threefold engagement enhancement, and notable advancements in both learning and practical abilities. This research aims to provide innovative approaches and methodologies for the reform of physical chemistry education, facilitating the profound integration of educational technology with disciplinary instruction.
2025, 40(9): 206-219
doi: 10.12461/PKU.DXHX202503069
Abstract:
Chemistry serves as a foundational discipline across multiple fields, including chemical engineering, materials science, environmental science, biology, energy, and pharmaceutical sciences. The rapid advancement of generative artificial intelligence (Generative AI, GAI) is significantly transforming the paradigm of chemistry education. This review focuses on the innovative application of GAI in chemistry teaching. We systematically examine the current status of GAI in chemistry education through three key dimensions: instructional design and curriculum development, personalized learning path design, and teaching method reform. Furthermore, we explore innovative approaches of GAI in chemistry teaching, particularly in the integration of multimodal visualization teaching and the implementation of personalized learning strategies for both teachers and students. Finally, we critically analyze the key challenges in GAI-driven innovative chemistry teaching and provide a detailed discussion on future directions, offering valuable insights for the deeper integration of GAI in chemistry education.
Chemistry serves as a foundational discipline across multiple fields, including chemical engineering, materials science, environmental science, biology, energy, and pharmaceutical sciences. The rapid advancement of generative artificial intelligence (Generative AI, GAI) is significantly transforming the paradigm of chemistry education. This review focuses on the innovative application of GAI in chemistry teaching. We systematically examine the current status of GAI in chemistry education through three key dimensions: instructional design and curriculum development, personalized learning path design, and teaching method reform. Furthermore, we explore innovative approaches of GAI in chemistry teaching, particularly in the integration of multimodal visualization teaching and the implementation of personalized learning strategies for both teachers and students. Finally, we critically analyze the key challenges in GAI-driven innovative chemistry teaching and provide a detailed discussion on future directions, offering valuable insights for the deeper integration of GAI in chemistry education.
2025, 40(9): 220-227
doi: 10.12461/PKU.DXHX202503124
Abstract:
To further enhance the application value of artificial intelligence (AI) in university chemistry education and strengthen students’ information literacy and technological proficiency, this study proposes a series of teaching reforms and practical measures. Based on an analysis of the current state of chemistry education, it explores the deep integration of AI, big data, and large models into chemistry teaching, emphasizing their role in improving students’ data processing, intelligent decision-making, and innovative thinking skills. Focusing on cutting-edge technologies such as adaptive learning platforms and AI-powered data analysis, this study proposes specific strategies, including the development of intelligent teaching platforms, the integration of advanced data analysis tools, the optimization of research processes, the encouragement of student participation in interdisciplinary competitions, and the promotion of cross-disciplinary integration. Practical applications have demonstrated that the deep integration of intelligent technology with teaching reforms significantly enhances students’ self-directed learning, scientific research innovation, and practical application abilities, contributing to the establishment of a modern chemistry education system that aligns with future technological advancements. This study provides valuable insights for the intelligent transformation of chemistry education in universities and outlines potential future research directions.
To further enhance the application value of artificial intelligence (AI) in university chemistry education and strengthen students’ information literacy and technological proficiency, this study proposes a series of teaching reforms and practical measures. Based on an analysis of the current state of chemistry education, it explores the deep integration of AI, big data, and large models into chemistry teaching, emphasizing their role in improving students’ data processing, intelligent decision-making, and innovative thinking skills. Focusing on cutting-edge technologies such as adaptive learning platforms and AI-powered data analysis, this study proposes specific strategies, including the development of intelligent teaching platforms, the integration of advanced data analysis tools, the optimization of research processes, the encouragement of student participation in interdisciplinary competitions, and the promotion of cross-disciplinary integration. Practical applications have demonstrated that the deep integration of intelligent technology with teaching reforms significantly enhances students’ self-directed learning, scientific research innovation, and practical application abilities, contributing to the establishment of a modern chemistry education system that aligns with future technological advancements. This study provides valuable insights for the intelligent transformation of chemistry education in universities and outlines potential future research directions.
2025, 40(9): 228-237
doi: 10.12461/PKU.DXHX202504052
Abstract:
In the context of educational digital transformation, this study addresses the challenges of fragmented knowledge, low resource integration, and insufficient personalized support in traditional inorganic chemistry experiment teaching. A “Knowledge Graph + AI” integrated model is proposed, which constructs a “concept-operation-resource” triple network to achieve structured mapping of essential elements such as experimental principles and operational standards. Through an intelligent tutoring system and dynamic reasoning algorithms, the model supports personalized learning path planning and formative assessment. Teaching practice demonstrates that this approach significantly enhances students’ knowledge integration efficiency and innovation capabilities, providing a novel pathway for the digital transformation of experimental education.
In the context of educational digital transformation, this study addresses the challenges of fragmented knowledge, low resource integration, and insufficient personalized support in traditional inorganic chemistry experiment teaching. A “Knowledge Graph + AI” integrated model is proposed, which constructs a “concept-operation-resource” triple network to achieve structured mapping of essential elements such as experimental principles and operational standards. Through an intelligent tutoring system and dynamic reasoning algorithms, the model supports personalized learning path planning and formative assessment. Teaching practice demonstrates that this approach significantly enhances students’ knowledge integration efficiency and innovation capabilities, providing a novel pathway for the digital transformation of experimental education.
2025, 40(9): 238-244
doi: 10.12461/PKU.DXHX202504069
Abstract:
The groundbreaking advancements in Generative Artificial Intelligence (GAI) technology have introduced novel perspectives for reforming traditional educational curricula. This study focuses on physical chemistry courses, presenting and implementing an AI-enhanced teaching reform strategy. By harnessing DeepSeek’s capabilities in natural language understanding and reasoning, we have developed an integrated approach incorporating Xmind, Mathematica, and JiMing AI. This integration facilitates the creation of a systematic, hierarchical mind map that enables dynamic knowledge connections and intelligent expansion. Furthermore, complex mathematical models are transformed into dynamic, interactive representations, effectively overcoming the challenges posed by abstract mathematical concepts. Additionally, key concepts are presented through high-precision diagrams and concise video demonstrations, achieving a “visual, interactive, and mobile” delivery of educational content. This approach provides a practical framework for the digital transformation of chemistry education.
The groundbreaking advancements in Generative Artificial Intelligence (GAI) technology have introduced novel perspectives for reforming traditional educational curricula. This study focuses on physical chemistry courses, presenting and implementing an AI-enhanced teaching reform strategy. By harnessing DeepSeek’s capabilities in natural language understanding and reasoning, we have developed an integrated approach incorporating Xmind, Mathematica, and JiMing AI. This integration facilitates the creation of a systematic, hierarchical mind map that enables dynamic knowledge connections and intelligent expansion. Furthermore, complex mathematical models are transformed into dynamic, interactive representations, effectively overcoming the challenges posed by abstract mathematical concepts. Additionally, key concepts are presented through high-precision diagrams and concise video demonstrations, achieving a “visual, interactive, and mobile” delivery of educational content. This approach provides a practical framework for the digital transformation of chemistry education.
2025, 40(9): 245-252
doi: 10.12461/PKU.DXHX202504085
Abstract:
The digital transformation of university education, which integrates traditional classroom teaching with digital intelligence technologies, has emerged as a predominant trend in contemporary pedagogical reform. Among the various applications of generative AI in higher education, the implementation of intelligent AI teaching assistant systems has demonstrated significant potential in reshaping educational spaces and instructional activities. This study investigates the application of the AI teaching assistant system, an enhanced feature of the Chaoxing Fanya platform, in the principles of general chemistry course, thereby establishing an AI-enhanced blended teaching model. Using the “Atomic Structure” module as a case study, this research examines the system’s capabilities in content generation, personalized learning, and intelligent academic support. The AI teaching assistant was systematically integrated into various instructional phases, including lesson preparation, teaching delivery, question answering, and learning analytics. This approach not only facilitates educational reform but also provides tailored learning assistance for students, ultimately enhancing the overall quality of instruction.
The digital transformation of university education, which integrates traditional classroom teaching with digital intelligence technologies, has emerged as a predominant trend in contemporary pedagogical reform. Among the various applications of generative AI in higher education, the implementation of intelligent AI teaching assistant systems has demonstrated significant potential in reshaping educational spaces and instructional activities. This study investigates the application of the AI teaching assistant system, an enhanced feature of the Chaoxing Fanya platform, in the principles of general chemistry course, thereby establishing an AI-enhanced blended teaching model. Using the “Atomic Structure” module as a case study, this research examines the system’s capabilities in content generation, personalized learning, and intelligent academic support. The AI teaching assistant was systematically integrated into various instructional phases, including lesson preparation, teaching delivery, question answering, and learning analytics. This approach not only facilitates educational reform but also provides tailored learning assistance for students, ultimately enhancing the overall quality of instruction.
2025, 40(9): 253-263
doi: 10.12461/PKU.DXHX202503020
Abstract:
In the context of developing new quality productive forces, artificial intelligence (AI) has become a powerful tool in chemical research and education. This study presents the reform of the “Learning-Understanding-Application” cultivation model in Materials Chemistry at Northeast Forestry University, integrating AI technologies through the establishment of an intelligent virtual simulation platform and an industry-education collaborative intelligent evaluation system. These innovations effectively address key challenges in traditional education: the theory-practice disconnect (with knowledge application rates below 42%), fragmented knowledge systems (exhibiting only 31.7% cross-course relevance), and delayed innovation commercialization (averaging over 18 months for implementation). Over three years of implementation, the project has demonstrated significant outcomes: the innovation-to-market cycle for student projects shortened to 5.8 months (a 67.8% improvement), while national competition awards (including those from the China International College Students’ Innovation Competition) increased by 147.8%. This research confirms the unique value of AI in resolving fundamental industry-education integration challenges and establishes a replicable “Technology Empowerment - Practice Reinforcement - Industry Feedback” educational ecosystem for materials chemistry education within the emerging engineering education framework.
In the context of developing new quality productive forces, artificial intelligence (AI) has become a powerful tool in chemical research and education. This study presents the reform of the “Learning-Understanding-Application” cultivation model in Materials Chemistry at Northeast Forestry University, integrating AI technologies through the establishment of an intelligent virtual simulation platform and an industry-education collaborative intelligent evaluation system. These innovations effectively address key challenges in traditional education: the theory-practice disconnect (with knowledge application rates below 42%), fragmented knowledge systems (exhibiting only 31.7% cross-course relevance), and delayed innovation commercialization (averaging over 18 months for implementation). Over three years of implementation, the project has demonstrated significant outcomes: the innovation-to-market cycle for student projects shortened to 5.8 months (a 67.8% improvement), while national competition awards (including those from the China International College Students’ Innovation Competition) increased by 147.8%. This research confirms the unique value of AI in resolving fundamental industry-education integration challenges and establishes a replicable “Technology Empowerment - Practice Reinforcement - Industry Feedback” educational ecosystem for materials chemistry education within the emerging engineering education framework.
2025, 40(9): 264-272
doi: 10.12461/PKU.DXHX202501006
Abstract:
Amidst rapid technological advancements, artificial intelligence (AI) is transforming educational paradigms at an unprecedented pace, significantly enriching teaching resources and methodologies in higher education. Within the framework of China’s “Emerging Engineering Education” initiative, this study examines the inorganic chemistry course as a representative case. Addressing limitations inherent in traditional teaching approaches, the course systematically incorporates information technologies to establish an AI-empowered blended learning model that combines online and offline instruction. This innovative approach fosters a comprehensive smart classroom ecosystem designed to enhance students’ learning motivation and self-directed engagement. Furthermore, the curriculum effectively integrates AI-empowered ideological and political education components, achieving synergistic alignment among value cultivation, knowledge acquisition, and competency development.
Amidst rapid technological advancements, artificial intelligence (AI) is transforming educational paradigms at an unprecedented pace, significantly enriching teaching resources and methodologies in higher education. Within the framework of China’s “Emerging Engineering Education” initiative, this study examines the inorganic chemistry course as a representative case. Addressing limitations inherent in traditional teaching approaches, the course systematically incorporates information technologies to establish an AI-empowered blended learning model that combines online and offline instruction. This innovative approach fosters a comprehensive smart classroom ecosystem designed to enhance students’ learning motivation and self-directed engagement. Furthermore, the curriculum effectively integrates AI-empowered ideological and political education components, achieving synergistic alignment among value cultivation, knowledge acquisition, and competency development.
2025, 40(9): 273-278
doi: 10.12461/PKU.DXHX202411018
Abstract:
The safety risk and time-consuming research project “Catalytic oxidation of hydrogen chloride and reaction kinetics” is designed as a comprehensive experiment in applied chemistry in the form of virtual simulation under the impetus of digitalization. The research project has been digitized into an experimental course. Applied chemistry is characterized by both chemistry and chemical engineering. It promotes the cultivation of innovative researcher. The experimental course involves the preparation of catalysts, crystal structure analysis, chemical reaction mechanism prediction, reaction kinetics testing, catalyst characterization and other multi-phase contents. Teaching in the form of digital virtual simulation greatly ensures the safety of the experiment, improves the efficiency of experimental teaching, with three-dimensional effect enhances the students’ sense of experience, and interactivity increases the students’ sense of participation. In the course time, students can experience the whole process of scientific research in catalysis from microscopic chemical structure to macroscopic kinetic processes, which can stimulate their interest in learning and enhance their scientific research literacy.
The safety risk and time-consuming research project “Catalytic oxidation of hydrogen chloride and reaction kinetics” is designed as a comprehensive experiment in applied chemistry in the form of virtual simulation under the impetus of digitalization. The research project has been digitized into an experimental course. Applied chemistry is characterized by both chemistry and chemical engineering. It promotes the cultivation of innovative researcher. The experimental course involves the preparation of catalysts, crystal structure analysis, chemical reaction mechanism prediction, reaction kinetics testing, catalyst characterization and other multi-phase contents. Teaching in the form of digital virtual simulation greatly ensures the safety of the experiment, improves the efficiency of experimental teaching, with three-dimensional effect enhances the students’ sense of experience, and interactivity increases the students’ sense of participation. In the course time, students can experience the whole process of scientific research in catalysis from microscopic chemical structure to macroscopic kinetic processes, which can stimulate their interest in learning and enhance their scientific research literacy.
2025, 40(9): 279-286
doi: 10.12461/PKU.DXHX202410094
Abstract:
Organic chemistry laboratory, as one of the four core laboratory courses for chemistry undergraduates, plays an irreplaceable role in enhancing students’ practical skills and fostering creative thinking. The use of English-language instruction significantly supports students in tracking cutting-edge technologies and engaging in international academic communication. Our university’s Organic Chemistry Laboratory English course adopts the “flipped classroom + MOOC (Massive Open Online Course)” teaching model. With a “student-centered and problem-oriented” approach, this model enhances students’ ability for self-directed learning and knowledge integration. The course effectively promotes deep learning, broadens the depth and scope of laboratory teaching, and improves the level of international talent development.
Organic chemistry laboratory, as one of the four core laboratory courses for chemistry undergraduates, plays an irreplaceable role in enhancing students’ practical skills and fostering creative thinking. The use of English-language instruction significantly supports students in tracking cutting-edge technologies and engaging in international academic communication. Our university’s Organic Chemistry Laboratory English course adopts the “flipped classroom + MOOC (Massive Open Online Course)” teaching model. With a “student-centered and problem-oriented” approach, this model enhances students’ ability for self-directed learning and knowledge integration. The course effectively promotes deep learning, broadens the depth and scope of laboratory teaching, and improves the level of international talent development.
2025, 40(9): 287-294
doi: 10.12461/PKU.DXHX202410026
Abstract:
This paper analyzes the current state of learning and teaching for the “specialized English for chemistry majors” course from both students’ and teachers’ perspectives. Based on the BOPPPS (Bridge-in, Objective, Pre-assessment, Participatory Learning, Post-assessment, Summary) model, an online and offline mixed teaching mode is explored. This mode significantly stimulates students’ enthusiasm for learning, improves teaching quality and efficiency, and enhances students’ abilities in self-study, teamwork, expression, and summarization. It truly embodies the teaching philosophy of “students at the center, teachers as guides, and learning for practical application”. This teaching mode has not only achieved positive outcomes in the “specialized English for chemistry majors” course, but is also applicable to the teaching of other courses, demonstrating its general applicability.
This paper analyzes the current state of learning and teaching for the “specialized English for chemistry majors” course from both students’ and teachers’ perspectives. Based on the BOPPPS (Bridge-in, Objective, Pre-assessment, Participatory Learning, Post-assessment, Summary) model, an online and offline mixed teaching mode is explored. This mode significantly stimulates students’ enthusiasm for learning, improves teaching quality and efficiency, and enhances students’ abilities in self-study, teamwork, expression, and summarization. It truly embodies the teaching philosophy of “students at the center, teachers as guides, and learning for practical application”. This teaching mode has not only achieved positive outcomes in the “specialized English for chemistry majors” course, but is also applicable to the teaching of other courses, demonstrating its general applicability.
2025, 40(9): 295-302
doi: 10.12461/PKU.DXHX202410095
Abstract:
The establishment and effective implementation of all-English chemistry courses play a crucial role in building world-class disciplines and cultivating talent with core competitive advantages. The teaching of all-English chemistry courses focuses on imparting fundamental professional knowledge, with the goal of broadening students’ international perspectives, enabling them to master the globally recognized chemical language, and showcasing their chemical research achievements on the international stage. This paper addresses the challenges of teaching analytical chemistry in English in a non-native environment. It considers the individual differences in students’ English language proficiency, career aspirations, and the alignment between their chemistry and English foundations. Based on these factors, the paper designs and implements an all-English curriculum, exploring teaching methods and strategies that are applicable to most students, ensuring that each student achieves significant progress and growth in the learning process.
The establishment and effective implementation of all-English chemistry courses play a crucial role in building world-class disciplines and cultivating talent with core competitive advantages. The teaching of all-English chemistry courses focuses on imparting fundamental professional knowledge, with the goal of broadening students’ international perspectives, enabling them to master the globally recognized chemical language, and showcasing their chemical research achievements on the international stage. This paper addresses the challenges of teaching analytical chemistry in English in a non-native environment. It considers the individual differences in students’ English language proficiency, career aspirations, and the alignment between their chemistry and English foundations. Based on these factors, the paper designs and implements an all-English curriculum, exploring teaching methods and strategies that are applicable to most students, ensuring that each student achieves significant progress and growth in the learning process.
2025, 40(9): 303-309
doi: 10.12461/PKU.DXHX202502027
Abstract:
Electrolyte solution is an important part of the solution system. This paper presents a comparative study of electrolyte solution in eight representative physical chemistry textbooks at home and abroad, and analyzes the contents, chapter settings as well as the exercises of representative textbooks in order to provide reference for textbook construction and domestic teaching.
Electrolyte solution is an important part of the solution system. This paper presents a comparative study of electrolyte solution in eight representative physical chemistry textbooks at home and abroad, and analyzes the contents, chapter settings as well as the exercises of representative textbooks in order to provide reference for textbook construction and domestic teaching.
2025, 40(9): 310-317
doi: 10.12461/PKU.DXHX202412098
Abstract:
A prevalent issue in local universities is the mismatch between the teaching models of instrumental analysis experiment courses and the actual requirements of students, which regrettably undermines the courses’ unique educational capabilities. In response to this issue, an innovative experimental teaching model anchored in the “sheep flock effect” with dual preview has been introduced. This model consciously cultivates “leading sheep” to significantly enhance students’ intrinsic motivation and unlock their learning potential, thereby facilitating an intersectional integration of theoretical knowledge with practical skills. Teaching practice has validated that this teaching model not only compensates for the limitations of insufficient class hours and inadequate academic foundations among students in local universities but also notably bolsters students’ higher-order thinking and their comprehensive capabilities in problem analysis and resolution. This model also lays a critical foundation for fostering high-quality chemical engineering professionals and offers a reliable reference for advancing and consolidating the pedagogical reforms in experiment courses.
A prevalent issue in local universities is the mismatch between the teaching models of instrumental analysis experiment courses and the actual requirements of students, which regrettably undermines the courses’ unique educational capabilities. In response to this issue, an innovative experimental teaching model anchored in the “sheep flock effect” with dual preview has been introduced. This model consciously cultivates “leading sheep” to significantly enhance students’ intrinsic motivation and unlock their learning potential, thereby facilitating an intersectional integration of theoretical knowledge with practical skills. Teaching practice has validated that this teaching model not only compensates for the limitations of insufficient class hours and inadequate academic foundations among students in local universities but also notably bolsters students’ higher-order thinking and their comprehensive capabilities in problem analysis and resolution. This model also lays a critical foundation for fostering high-quality chemical engineering professionals and offers a reliable reference for advancing and consolidating the pedagogical reforms in experiment courses.
2025, 40(9): 318-325
doi: 10.12461/PKU.DXHX202506072
Abstract:
This study focuses on the reform of analytical chemistry’s theoretical and experimental teaching mode. Using CiteSpace 6.3 R1 for bibliometrics, it analyzes relevant Chinese literature. Through visual analysis, the hotspots of the reform of analytical chemistry teaching mode, such as flipped classroom, teaching reform and emerging engineering education were identified. It also explores curriculum ideology and politics, emerging engineering education, frontier teaching mode of the integration of science and education, and online and offline mixed teaching, which aims to provide a theoretical basis for the reform of the teaching mode of analytical chemistry.
This study focuses on the reform of analytical chemistry’s theoretical and experimental teaching mode. Using CiteSpace 6.3 R1 for bibliometrics, it analyzes relevant Chinese literature. Through visual analysis, the hotspots of the reform of analytical chemistry teaching mode, such as flipped classroom, teaching reform and emerging engineering education were identified. It also explores curriculum ideology and politics, emerging engineering education, frontier teaching mode of the integration of science and education, and online and offline mixed teaching, which aims to provide a theoretical basis for the reform of the teaching mode of analytical chemistry.
2025, 40(9): 326-331
doi: 10.12461/PKU.DXHX202411008
Abstract:
Carbon is a crucial non-metallic element and serves as the foundation for all life on earth. Its chemical structure is typically governed by the “octet rule”. However, advancements in science have led to the discovery of various carbon species with unique valence electron configurations, such as radicals with seven valence electrons, carbocations, and carbenes with six valence electrons. These discoveries have significantly propelled the fields of organic synthesis, medicinal chemistry, and natural product chemistry. Conversely, carbon species with fewer than six valence electrons have been challenging to synthesize and characterize due to their inherent instability and the lack of suitable techniques. Recently, a breakthrough in this scientific challenge has been achieved through the diligent efforts of Bertrand and colleagues. This article primarily focuses on the synthesis, characterization, and reactivity of doubly oxidized carbenes with four valence electrons. It aims to provide students with insights into cutting-edge chemical research, stimulate their thinking, enhance their independent learning capabilities, and further ignite their interest in the study of chemistry.
Carbon is a crucial non-metallic element and serves as the foundation for all life on earth. Its chemical structure is typically governed by the “octet rule”. However, advancements in science have led to the discovery of various carbon species with unique valence electron configurations, such as radicals with seven valence electrons, carbocations, and carbenes with six valence electrons. These discoveries have significantly propelled the fields of organic synthesis, medicinal chemistry, and natural product chemistry. Conversely, carbon species with fewer than six valence electrons have been challenging to synthesize and characterize due to their inherent instability and the lack of suitable techniques. Recently, a breakthrough in this scientific challenge has been achieved through the diligent efforts of Bertrand and colleagues. This article primarily focuses on the synthesis, characterization, and reactivity of doubly oxidized carbenes with four valence electrons. It aims to provide students with insights into cutting-edge chemical research, stimulate their thinking, enhance their independent learning capabilities, and further ignite their interest in the study of chemistry.
2025, 40(9): 332-343
doi: 10.12461/PKU.DXHX202410072
Abstract:
Hydroxyl-rich polycations represent a class of positively charged polymeric materials characterized by abundant hydroxyl groups. These materials synergistically combine the low toxicity and excellent biocompatibility of hydroxyl moieties with the superior cellular affinity of cationic polymers. Current applications span multiple biomedical domains including gene therapy, drug delivery, antimicrobial treatments, and wound hemostasis. This review systematically summarizes existing categories of hydroxyl-rich polycations and their applications in life and health sciences, with the objective of advancing fundamental understanding and facilitating further development of these materials.
Hydroxyl-rich polycations represent a class of positively charged polymeric materials characterized by abundant hydroxyl groups. These materials synergistically combine the low toxicity and excellent biocompatibility of hydroxyl moieties with the superior cellular affinity of cationic polymers. Current applications span multiple biomedical domains including gene therapy, drug delivery, antimicrobial treatments, and wound hemostasis. This review systematically summarizes existing categories of hydroxyl-rich polycations and their applications in life and health sciences, with the objective of advancing fundamental understanding and facilitating further development of these materials.
2025, 40(9): 344-353
doi: 10.12461/PKU.DXHX202503066
Abstract:
With the introduction of the “dual carbon” target, hydrogen energy has garnered significant attention as a clean and efficient secondary energy source. The production of “green hydrogen” through the electrocatalytic hydrogen evolution reaction (HER) represents one of the most promising strategies for achieving a hydrogen-powered society. Single-atom catalysts (SACs) have demonstrated exceptional catalytic performance in HER, attributed to their maximized atomic utilization efficiency and distinctive electronic structures. This paper comprehensively reviews recent advancements in the synthesis of metal SACs and their applications in HER. Through detailed case analyses, this review aims to provide insights into the forefront of SAC research and elucidate their critical role in electrocatalytic hydrogen evolution.
With the introduction of the “dual carbon” target, hydrogen energy has garnered significant attention as a clean and efficient secondary energy source. The production of “green hydrogen” through the electrocatalytic hydrogen evolution reaction (HER) represents one of the most promising strategies for achieving a hydrogen-powered society. Single-atom catalysts (SACs) have demonstrated exceptional catalytic performance in HER, attributed to their maximized atomic utilization efficiency and distinctive electronic structures. This paper comprehensively reviews recent advancements in the synthesis of metal SACs and their applications in HER. Through detailed case analyses, this review aims to provide insights into the forefront of SAC research and elucidate their critical role in electrocatalytic hydrogen evolution.
2025, 40(9): 354-360
doi: 10.12461/PKU.DXHX202410086
Abstract:
Inorganic chemistry laboratory courses serve as an effective platform for fostering chemical literacy and promoting innovative education. The integration of ideological and political education into the curriculum, coupled with carefully designed teaching activities, not only enhances students’ experimental skills, research report writing abilities, and solidifies their theoretical knowledge of inorganic chemistry, but also cultivates their sense of national identity, scientific spirit, creative thinking, and environmental consciousness. This study uses the preparation and performance determination of polyferric sulfate as a case to explore how integrating ideological and political education with inorganic chemistry experiments can achieve a harmonious blend of skill development and ideological guidance. This approach aims to fulfill the educational goals of knowledge transmission, skill cultivation, and value orientation.
Inorganic chemistry laboratory courses serve as an effective platform for fostering chemical literacy and promoting innovative education. The integration of ideological and political education into the curriculum, coupled with carefully designed teaching activities, not only enhances students’ experimental skills, research report writing abilities, and solidifies their theoretical knowledge of inorganic chemistry, but also cultivates their sense of national identity, scientific spirit, creative thinking, and environmental consciousness. This study uses the preparation and performance determination of polyferric sulfate as a case to explore how integrating ideological and political education with inorganic chemistry experiments can achieve a harmonious blend of skill development and ideological guidance. This approach aims to fulfill the educational goals of knowledge transmission, skill cultivation, and value orientation.
2025, 40(9): 361-367
doi: 10.12461/PKU.DXHX202410062
Abstract:
Comprehensive organic chemistry experiments constitute a fundamental component of chemical education, serving to significantly enhance undergraduates’ basic laboratory skills while deepening their understanding of specialized knowledge. This foundation proves crucial for future academic pursuits and professional development. We present herein an integrated organic chemistry experiment involving the palladium-catalyzed tandem cyclization of 4-vinylbenzoxazinone and indene-2-carbaldehyde to synthesize 12-vinyl-7,12-dihydrobenzo[b]indeno[1,2-e]azepine. The product structure was characterized using high-resolution mass spectrometry (HRMS), along with 1H and 13C nuclear magnetic resonance spectroscopy.
Comprehensive organic chemistry experiments constitute a fundamental component of chemical education, serving to significantly enhance undergraduates’ basic laboratory skills while deepening their understanding of specialized knowledge. This foundation proves crucial for future academic pursuits and professional development. We present herein an integrated organic chemistry experiment involving the palladium-catalyzed tandem cyclization of 4-vinylbenzoxazinone and indene-2-carbaldehyde to synthesize 12-vinyl-7,12-dihydrobenzo[b]indeno[1,2-e]azepine. The product structure was characterized using high-resolution mass spectrometry (HRMS), along with 1H and 13C nuclear magnetic resonance spectroscopy.
2025, 40(9): 368-374
doi: 10.12461/PKU.DXHX202410084
Abstract:
With the rapid advancement of economic globalization, internationalized education has become both an inevitable and urgent requirement for the development of higher education in China. This paper takes the all-English specialized course “Chemical and Biochemical Sensors” for both international and domestic graduate students at Shanxi University as a case study. It discusses the course design philosophy, current status, challenges faced, and proposed solutions for postgraduate chemistry students in the context of internationalized higher education. The paper also summarizes the experience of teaching in English, highlights issues encountered in teaching and learning, and provides targeted suggestions for improving the effectiveness of English-medium instruction, based on the specific needs of the students and the course characteristics.
With the rapid advancement of economic globalization, internationalized education has become both an inevitable and urgent requirement for the development of higher education in China. This paper takes the all-English specialized course “Chemical and Biochemical Sensors” for both international and domestic graduate students at Shanxi University as a case study. It discusses the course design philosophy, current status, challenges faced, and proposed solutions for postgraduate chemistry students in the context of internationalized higher education. The paper also summarizes the experience of teaching in English, highlights issues encountered in teaching and learning, and provides targeted suggestions for improving the effectiveness of English-medium instruction, based on the specific needs of the students and the course characteristics.
2025, 40(9): 375-382
doi: 10.12461/PKU.DXHX202411015
Abstract:
Hydrogen-like orbitals represent a crucial topic in the education of structural chemistry; however, existing textbooks usually present simplified schematic diagrams of their images. This paper employs MATLAB codes to systematically generate visualizations of the wave functions of hydrogen-like orbitals, including detailed isosurfaces of their angular components, followed by an in-depth analysis. By comparing these visualized images, we clarify several common misconceptions encountered in teaching and highlight errors found in widely used software, such as Orbital Viewer. The findings of this study provide valuable resources for visual teaching and offer significant insights to enhance students’ understanding of the characteristics of hydrogen-like orbitals.
Hydrogen-like orbitals represent a crucial topic in the education of structural chemistry; however, existing textbooks usually present simplified schematic diagrams of their images. This paper employs MATLAB codes to systematically generate visualizations of the wave functions of hydrogen-like orbitals, including detailed isosurfaces of their angular components, followed by an in-depth analysis. By comparing these visualized images, we clarify several common misconceptions encountered in teaching and highlight errors found in widely used software, such as Orbital Viewer. The findings of this study provide valuable resources for visual teaching and offer significant insights to enhance students’ understanding of the characteristics of hydrogen-like orbitals.
