Emerging Directions in Bioelectrochemistry, Electrocatalysis and Intelligent Electrochemical Characterisation

Dear Colleagues,

Electrochemical science is advancing rapidly, fuelled by breakthroughs in biological electron-transfer systems, next-generation catalytic materials, and increasingly sophisticated analytical methodologies. At the same time, the emergence of AI-enabled data analysis, autonomous experimentation, and machine-learning-assisted material discovery is reshaping how we understand, predict, and optimise electrochemical behaviour. These developments are accelerating progress across energy technologies, sensing, biomedical interfaces, environmental remediation, and sustainable chemical transformations.

This Special Issue aims to bring together contributions that reflect this evolution. We welcome studies that explore bioelectrochemical mechanisms, develop innovative electrocatalysts, characterise functional materials using advanced electrochemical or spectroelectrochemical techniques, or that integrate AI, automation, informatics, and data-driven tools into electrochemical research.

Scope and Themes:

1. Bioelectrochemistry and biological interfaces:

• Electron-transfer processes in enzymes, proteins, and biomolecular assemblies;
• Electrode–biological interface engineering;
• Enzymatic and microbial electrochemical systems;
• Biosensors and wearable/implantable electrochemical devices;
• Bioelectrocatalysis and bioenergy conversion.

2. Electrocatalysis and catalytic material innovation:

• Catalysts for ORR, OER, HER, CO₂RR, NRR, and related processes;
• Nanostructured, hybrid, MXene-based, and MOF-based catalytic materials;
• In situ/operando mechanistic studies;
• Electrode degradation, kinetics, and interface stability;
• Photo- and photoelectrocatalytic processes.

3. Electrochemical analysis of functional materials:

• Voltammetric, impedance, spectroelectrochemical, and scanning probe methods;
• Electrochemical performance of energy materials (batteries, supercapacitors, and electrolyzers);
• Charge-storage mechanisms and interfacial processes;
• Functional polymers, ceramics, 2D materials, and composites;
• Micro-/nanofluidic electrochemical systems.

4. AI-enabled electrochemical and chemical analysis:

• Machine learning models predicting electrochemical behaviour and catalytic performance;
• Autonomous experiment platforms and robotic electrochemistry;
• Data-centric electrochemical analysis: Datasets, pattern discovery, and algorithmic optimisation;
• AI-assisted interpretation of voltammetry, spectroscopy, and impedance data;
• Accelerated material discovery using generative and predictive AI.

Dr. Shaneel Chandra
Guest Editor

Manuscript Submission Information

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