For decades, employing cyclic voltammetry for mechanistic investigation has demanded manual inspection of voltammograms. Here, we report a deep-learning-based algorithm that automatically analyzes cyclic voltammograms and designates a probable electrochemical mechanism among five of the most common ones in homogeneous molecular electrochemistry. The reported algorithm will aid researchers’ mechanistic analyses, utilize otherwise elusive features in voltammograms, and experimentally observe the gradual mechanism transitions encountered in electrochemistry. An automated voltammogram analysis will aid the analysis of complex electrochemical systems and promise autonomous high-throughput research in electrochemistry with minimal human interference.

Neural networks, trained on data generated by a microkinetic model and finite-element simulations, expand explorable parameter space by significantly accelerating the predictions of electrocatalytic performance. In addition to modeling electrode reactivity, we use micro/nanowire arrays as a well-defined, easily tuned, and experimentally relevant exemplary morphology for electrochemical nitrogen fixation. This model system provides the data necessary for training neural networks which are subsequently exploited for electrocatalytic material morphology optimizations and explorations into the influence of geometry on nitrogen fixation electrodes, feats untenable without large-scale simulations, on both a global and a local basis.

Nanoparticles have been conjugated to biological systems for numerous applications such as self-assembly, sensing, imaging, and therapy. Development of more reliable and robust biosensors that exhibit high response rate, increased detection limit, and enhanced useful lifetime is in high demand. We have developed a sensing platform by the conjugation of β-galactosidase, a crucial enzyme, with lab-synthesized gel-like carbon dots (CDs) which have high luminescence, photostability, and easy surface functionalization. We found that the conjugated enzyme exhibited higher stability towards temperature and pH changes in comparison to the native enzyme. This enriched property of the enzyme was distinctly used to develop a stable, reliable, robust biosensor. The detection limit of the biosensor was found to be 2.9 × 10−4 M, whereas its sensitivity was 0.81 µA·mmol−1·cm−2. Further, we used the Langmuir monolayer technique to understand the surface properties of the conjugated enzyme. It was found that the conjugate was highly stable at the air/subphase interface which additionally reinforces the suitability of the use of the conjugated enzyme for the biosensing applications.

A fundamental understanding of extracellular microenvironments of O 2 and reactive oxygen species (ROS) such as H 2 O 2 , ubiquitous in microbiology, demands high-throughput methods of mimicking, controlling, and perturbing gradients of O 2 and H 2 O 2 at microscopic scale with high spatiotemporal precision. However, there is a paucity of high-throughput strategies of microenvironment design, and it remains challenging to achieve O 2 and H 2 O 2 heterogeneities with microbiologically desirable spatiotemporal resolutions. Here, we report the inverse design, based on machine learning (ML), of electrochemically generated microscopic O 2 and H 2 O 2 profiles relevant for microbiology. Microwire arrays with suitably designed electrochemical catalysts enable the independent control of O 2 and H 2 O 2 profiles with spatial resolution of ∼10 1 μm and temporal resolution of ∼10° s. Neural networks aided by data augmentation inversely design the experimental conditions needed for targeted O 2 and H 2 O 2 microenvironments while being two orders of magnitude faster than experimental explorations. Interfacing ML-based inverse design with electrochemically controlled concentration heterogeneity creates a viable fast-response platform toward better understanding the extracellular space with desirable spatiotemporal control.

For decades, employing cyclic voltammetry for mechanistic investigation demands manual inspection of voltammograms. Here we report a deep-learning-based algorithm that automatically analyzes cyclic voltammograms and designates a electrochemical probable mechanism among five of the most common ones in homogenous molecular electrochemistry. The reported algorithm will aid researchers’ mechanistic analysis, utilize otherwise elusive features in voltammograms, and experimentally observe the gradual mechanism transitions encountered in electrochemistry. An automated voltammogram analysis will aid the analysis of complex electrochemical systems and promise autonomous high-throughput research in electrochemistry with minimal human interference.