Citation: Jian Huang, Mingjue Zhang, Shangchu Ma, Jia Dong, Guanzi Wu, Aiming Wen, Zhuoliang Liu. Data-Driven Approach for the Determination of Chemical Reaction Rate Constant[J]. University Chemistry, ;2026, 41(1): 213-226. doi: 10.12461/PKU.DXHX202505110 shu

Data-Driven Approach for the Determination of Chemical Reaction Rate Constant

College of Science, National University of Defense Technology, Changsha 410003, China

Corresponding author: Jian Huang, Zhuoliang Liu,
Received Date: 30 May 2025
Revised Date: 23 September 2025

The measurement of rate constants for the reaction between magnesium ribbon and dilute sulfuric acid constitutes a fundamental experiment in inorganic chemistry curricula across numerous universities. However, when applying first-order kinetic modeling, systematic deviations are frequently observed in the latter stage of the reaction. To examine the side-reaction hypothesis, we incorporated pH monitoring and established a standardized, automated experimental protocol with systematic data acquisition. Additionally, we developed a dedicated data processing toolkit to facilitate comprehensive analysis of the reaction kinetics in the magnesium-sulfuric acid system. The experimental analysis revealed that the main reaction (Mg with H₂SO₄) follows first-order kinetics, with its rate constant significantly increasing as temperature rises. In contrast, the side reaction (Mg with H₂O) aligns with a second-order kinetic model, exhibiting an activation energy of 127.79 kJ‧mol^‒1. Through multidimensional data integration and automated data processing workflows, this study systematically cross-validated the kinetic parameters of both main and side reactions. This approach successfully explained the experimental deviations observed in conductivity data during the reaction’s later stages. By transforming traditional verification-based experiments into a research-oriented learning platform, this work bridges the “data-driven approach” paradigm with practical laboratory instruction, providing a practical framework for cultivating innovative thinking and digital problem-solving skills among undergraduate students in non-chemistry science and engineering disciplines.
- Rate constant of chemical reaction,
- Digital design,
- Data-driven research,
- Data processing program package
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