The field of electrochemical sensing is undergoing transformative change as advances in nanotechnology and artificial intelligence (AI) come together to create new possibilities for environmental monitoring, biomedical diagnostics, and energy storage. Electrochemical sensors—comprising devices like supercapacitors, biosensors, and photoelectrochemical interfaces—have traditionally been at the cutting edge of analytical and diagnostic technology. However, the ever-increasing need for sensitivity, selectivity, scalability, and adaptability in real-world applications has exposed limitations in conventional approaches. Recent studies show how AI, through techniques such as machine learning, neural network algorithms, and predictive modeling, is accelerating materials discovery, optimizing device performance, and revolutionizing the analysis of large, complex datasets generated by modern electrochemical systems. Despite these advances, significant challenges remain in consistently achieving integrated, scalable, and intelligent systems that leverage both nanomaterials and AI in seamless harmony.
This Research Topic aims to catalyze progress by assembling pioneering studies and perspectives at the interface of AI, nanotechnology, and electrochemical science. The main objective is to illuminate how intelligent computational methods can drive the design, optimization, and deployment of next-generation electrochemical systems, including but not limited to sensors, supercapacitors, and diagnostic devices. We seek to address critical questions such as: Which machine learning frameworks best support real-time monitoring and control? What breakthroughs can nanostructured materials offer when paired with AI-driven analysis? How can artificial intelligence enable adaptive or self-healing sensing systems? The goal is to inspire interdisciplinary innovation and foster solutions that move beyond isolated proofs of concept, emphasizing translational research with practical impact.
To gather further insights in the integration of AI and nanotechnology within electrochemical sensing, we welcome articles addressing, but not limited to, the following themes:
- AI-driven modeling and simulation of electrochemical systems
- Nanomaterials for electrochemical sensors and biosensors
- Machine learning algorithms for photoelectrochemical and electrochemical analysis
- Neural network-based optimization and advanced signal processing for sensing applications
- AI-assisted discovery and design of cutting-edge electrode materials
- Smart diagnostics with electrochemical biosensors for personalized healthcare
- Real-time data analysis, predictive modeling, and control in electrochemical devices
- Integration of AI with nanomaterials for enhanced energy storage and supercapacitor technologies
- Adaptive, self-healing, and sustainable electrochemical sensing solutions
- Quantum computing and its emerging roles in electrochemical nanoscience
Article types and fees
This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:
- Editorial
- FAIR² Data
- FAIR² DATA Direct Submission
- Mini Review
- Original Research
- Perspective
- Review
Articles that are accepted for publication by our external editors following rigorous peer review incur a publishing fee charged to Authors, institutions, or funders.
Keywords: AI-Enhanced Sensing, Electrochemical Nanomaterials, Smart Diagnostics, Machine Learning Algorithms, Nanotechnology Integration, Energy Storage Systems
Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
