Light-addressable electrochemistry at semiconductor electrodes: redox imaging, mask-free lithography and spatially resolved chemical and biological sensing
Bibliographic record
Abstract
Spatial confinement of electrochemical reactions at solid/liquid interfaces is a mature area of research, and a central theme from cell biology to analytical chemistry. Monitoring or manipulating the kinetics of a charge transfer reaction in 2D is generally achieved using scanning electrochemical microscopy or multielectrode arrays, techniques that rely on moving physical probes or on a network of electrical connections. This tutorial is introducing concepts and instruments to confine faradaic electrochemical reactions in 2D without resorting to the mechanical movement of a probe, and with the simple design of one semiconducting electrode, one electrical lead and a single-channel potentiostat. We provide a theoretical background of semiconductor electrochemistry, and describe the use of localised visible light stimuli on photoconductor/liquid and semiconductor/liquid interfaces to address electrical conductivity - hence chemical reactivity - only at one specific site defined by the experimentalist. This enables shifting of the tenet of one electrode/one wire towards one wire/many electrodes. We discuss the applications of this emerging platform in the context of surface chemistry patterning, redox imaging, chemical and biological sensing, generating chemical gradients, electrocatalysis, nanotechnology and cell biology.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".