Transient electrochemistry: beyond simply temporal resolution
Bibliographic record
Abstract
Some physicochemical intrigues for which transient electrochemistry was necessary to solve the problem are summarized in this feature article. First, we highlight the main constraints to be aware of to access to low time scales, and particularly focus on the effects of stray capacitances. Then, the electron transfer rate constant measured for redox molecules in a self-assembled monolayer configuration is compared to the conductance measured through the same systems, but at the single molecule level. This evidences strong conformational changes when molecules are trapped in the nanogap created between both electrodes. We also report about dendrimers, for which a short electrochemical perturbation induces creation of a diffusion layer within the molecule, allowing the electron hopping rate to be measured and analyzed in terms of molecular motions of the redox centers. Finally, we show that transient electrochemistry provides also useful information when coupled to other methodologies. For example, when an ultrasonic field drives very fast movements of a bubble situated above the electrode surface, the motion can be detected indirectly through a modification of the diffusion flux. Another field concerns pulse radiolysis, and we describe how the reactivity (at the electrode or within the solution) of radicals created by a radiolytic pulse can be quantified, widening the possibilities of electrochemistry to operate in biological media.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.003 | 0.001 |
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".