Key Takeaways & Executive Findings
- •• • The dataset contains 120 validated LSV curves from both non-precious metal (MNC) and platinum-based (Pt-MC) catalysts, enabling direct benchmarking of ORR activity under standardized conditions (O2-saturated 0.5 mol L−1 H2SO4). • • Quality control thresholds are strictly defined: relative standard deviation ≤2% for half-wave potential (E1/2) and ≤5% for limiting current density, ensuring high reproducibility and reliability for comparative analysis. • • All measurements were performed at controlled rotation speeds using a rotating disk electrode, allowing extraction of kinetic and mass transport parameters essential for mechanistic studies and catalyst screening. • • The dataset is openly accessible via Science Data Bank (DOI: 10.57760/sciencedb.j00125.00224), providing a structured resource for machine learning applications in electrocatalysis and facilitating community-wide benchmarking.
Abstract
A standardized dataset of linear sweep voltammetry (LSV) curves is presented for evaluating the oxygen reduction reaction (ORR) performance of carbon-supported catalysts in acidic media. All electrochemical tests were conducted in O2-saturated 0.5 mol L−1 H2SO4 at controlled rotation speeds using a rotating disk electrode. The dataset comprises 120 validated entries from both non-precious metal (MNC) and platinum-based (Pt-MC) catalysts, including original LSV curves and extracted performance parameters such as onset potential, half-wave potential, and limiting current densities at different rotation speeds. Data processing involved potential conversion to the reversible hydrogen electrode (RHE) scale, background subtraction, outlier removal, and reproducibility checks with defined quality control thresholds (relative standard deviation ≤2% for E1/2 and ≤5% for limiting current). The standardized collection serves as a reliable benchmark for catalyst performance comparison, supports kinetic and mass transport analysis, and provides a structured data source for machine learning applications in electrocatalysis. The dataset is openly available via Science Data Bank, with a DOI, and is intended as a dynamic resource for the ORR electrocatalysis community.
1. Introduction
The commercialization of hydrogen fuel cells is critically hindered by the sluggish kinetics and high overpotential of the cathodic oxygen reduction reaction (ORR). Carbon-supported catalysts, both platinum-based and non-precious metal alternatives, are promising but their performance evaluation often suffers from inconsistent testing protocols, making cross-laboratory comparisons unreliable. This lack of standardized benchmarking impedes rational catalyst development and the integration of machine learning for accelerated discovery.
To address this bottleneck, the present work establishes a rigorously standardized dataset of linear sweep voltammetry (LSV) curves for ORR on carbon-supported catalysts in acidic media. By fixing the electrolyte concentration (0.5 mol L−1 H2SO4), saturation (O2), and employing controlled rotation speeds, the dataset ensures high reproducibility and comparability. The inclusion of both non-precious metal and platinum-based catalysts, along with strict quality control thresholds, provides a reliable foundation for kinetic analysis, mass transport studies, and the development of predictive models in electrocatalysis.
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Zeng Zifeng, Zhao Zhenxin, Wang Meiling, Wang Xiaomin (2026). A Standardized Dataset of Linear Sweep Voltammetry Curves for the Acidic Oxygen Reduction Reaction on Carbon-Supported Catalysts in Sulfuric Acid Medium. New Carbon Materials. https://doi.org/10.1016/S1872-5805(26)61116-X
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Frequently Asked Questions
What are the exact experimental conditions under which the LSV curves were recorded, and how do they ensure comparability across different catalysts?
All LSV measurements were performed in O2-saturated 0.5 mol L−1 H2SO4 at controlled rotation speeds using a rotating disk electrode. The potential was converted to the reversible hydrogen electrode (RHE) scale. These standardized conditions minimize variability due to electrolyte concentration, oxygen saturation, and mass transport, enabling direct comparison of catalyst performance.
How were the quality control thresholds (RSD ≤2% for E1/2 and ≤5% for limiting current) established, and what is their significance for data reliability?
The thresholds were defined based on reproducibility checks across multiple measurements. They ensure that the reported half-wave potentials and limiting current densities are highly reproducible, with relative standard deviations within 2% and 5%, respectively. This level of precision is critical for detecting subtle differences in catalyst activity and for building reliable machine learning models.
What types of catalysts are included in the dataset, and how does the inclusion of both non-precious metal and platinum-based catalysts benefit benchmarking?
The dataset includes 120 validated entries from non-precious metal carbon-supported catalysts (MNC) and platinum-based catalysts (Pt-MC). This dual inclusion allows direct comparison of the performance gap between precious and non-precious metal systems, facilitating the development of cost-effective alternatives while maintaining a reference to established platinum benchmarks.
Can the dataset be used to extract kinetic parameters such as the Tafel slope or electron transfer number, and if so, how?
Yes, the LSV curves at multiple rotation speeds enable Koutecky-Levich analysis to determine the electron transfer number and kinetic current densities. The standardized data with controlled mass transport conditions allow extraction of Tafel slopes from the kinetic region, providing insights into the reaction mechanism and catalyst activity.
How can this dataset be integrated with machine learning for catalyst discovery, and what specific features are provided?
The dataset provides original LSV curves and extracted performance parameters (onset potential, half-wave potential, limiting current densities) for each catalyst. These features, combined with catalyst composition and structural descriptors, can be used to train models predicting ORR activity. The standardized format ensures that the data are directly usable for supervised learning tasks, such as regression of E1/2 or classification of catalyst families.
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