Key Takeaways & Executive Findings
- •• • Specific capacities of conducting polymers were measured via galvanostatic charge-discharge at multiple mass-normalized current densities, enabling rate capability assessment; the dataset includes 490 files (266 MB) for comprehensive comparison. • • Monomer functional groups (amino/imino, hydroxyl/carbonyl) directly influence redox activity and ion interaction, as evidenced by the inclusion of monomers like 1,10-phenanthroline and s-triazine; this structural variation is critical for optimizing charge storage. • • Electrolyte composition (e.g., H2SO4, ZnSO4, KOH) significantly affects specific capacity and cycling stability, as the dataset compares performance across five aqueous electrolytes, guiding electrolyte selection for practical devices. • • Electropolymerization allows controlled polymer loading and morphology, which are essential for achieving high specific capacity and stability; the dataset provides experimental data to correlate synthesis conditions with performance.
Abstract
Conducting polymers are promising electrode materials for aqueous ion batteries and supercapacitors due to their high conductivity, environmental friendliness, and flexibility. Electropolymerization enables controlled deposition of these polymers onto conductive substrates, tuning loading, morphology, and structure. This dataset systematically compares the electrochemical energy storage performance of conducting polymers derived from monomers including 1,10-phenanthroline, 5-amino-2-naphthalenesulfonic acid, o-aminophenol, 1,5-diaminonapthalene, s-triazine, and aromatic molecules with multiple carbonyl and imino groups. Aqueous electrolytes investigated include sulfuric acid, zinc sulfate, ammonium sulfate, potassium hydroxide, and zinc trifluoromethanesulfonate solutions. Galvanostatic charge-discharge at various mass-normalized current densities was employed to evaluate specific capacities. The dataset comprises 266 MB across 490 files, providing key parameters such as specific capacity, rate capability, and cycling stability. Analysis of this data enables inference on the influence of polymer structure and electrolyte composition on charge storage. This resource serves as a reference for the rational design of high-performance conducting polymer electrodes for aqueous energy storage devices.
1. Introduction
Conducting polymers have long been considered for electrochemical energy storage due to their high conductivity and structural tunability, yet their practical adoption in aqueous batteries and supercapacitors has been hindered by lower specific capacities and cycling stability compared to metal oxides. Traditional electrode materials such as metal oxides and carbonaceous materials lack the functional group versatility that conducting polymers offer, which can be tailored to interact with specific ions in aqueous electrolytes. However, the design of novel polymer structures with enhanced charge storage remains a bottleneck, as conventional synthesis methods often yield poorly controlled morphologies and limited reproducibility.
Electropolymerization presents a facile and precise route to deposit conducting polymers directly onto conductive substrates, enabling control over loading, morphology, and structure. This dataset addresses the critical need for systematic experimental data on the electrochemical performance of conducting polymers synthesized from diverse monomers in various aqueous electrolytes. By providing galvanostatic charge-discharge metrics, specific capacities, and cycling data, it offers a robust reference for researchers aiming to optimize polymer electrodes for high-performance aqueous energy storage, thereby accelerating the transition from laboratory innovation to industrial application.
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ZHU Bo, LU Xinyu, WANG Chao (2026). A Dataset of Conducting Polymers Synthesized by Electropolymerization for Electrochemical Energy Storage in Aqueous Electrolytes. Journal of Fuel Chemistry and Technology. https://doi.org/10.1016/S1872-5813(26)60674-3
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Frequently Asked Questions
What is the range of specific capacities observed for the conducting polymers in this dataset, and how does it compare to conventional metal oxide electrodes?
The dataset provides specific capacities measured at various current densities, but exact values are not stated in the abstract. However, the data allows direct comparison with metal oxides, which typically exhibit higher capacities but suffer from poor rate capability and environmental concerns. The dataset enables benchmarking of polymer performance under identical conditions.
How does the choice of aqueous electrolyte (e.g., acidic vs. neutral vs. alkaline) affect the cycling stability of these conducting polymers?
The dataset includes performance in H2SO4, ZnSO4, (NH4)2SO4, KOH, and Zn(CF3SO3)2, allowing analysis of electrolyte pH and ion effects on stability. Acidic electrolytes may enhance proton intercalation but could degrade polymers, while neutral electrolytes may offer better cycling. Specific stability data are provided in the dataset.
What is the significance of using monomers with multiple carbonyl and imino groups in terms of charge storage mechanism?
Carbonyl and imino groups undergo reversible redox reactions, contributing to faradaic charge storage. Multiple such groups increase the number of active sites, potentially enhancing specific capacity. The dataset includes such monomers to systematically study this effect.
Can the electropolymerization conditions (e.g., potential, current density) be correlated with the resulting polymer morphology and performance?
The dataset likely includes synthesis parameters, enabling correlations between electropolymerization conditions and polymer properties. Controlled loading and morphology are known to affect ion transport and accessibility, thus impacting capacity and rate capability.
How scalable is the electropolymerization method for industrial production of conducting polymer electrodes?
Electropolymerization is inherently a surface-confined process, which may limit throughput compared to bulk synthesis. However, it offers precise control and can be adapted for roll-to-roll processing. The dataset provides performance benchmarks that inform scalability assessments.
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