Parameterization and quantification of two key operando physio-chemical descriptors for water-assisted electro-catalytic organic oxidation
Bibliographic record
Abstract
Electro-selective-oxidation using water as a green oxygen source demonstrates promising potential towards efficient and sustainable chemical upgrading. However, surface micro-kinetics regarding co-adsorption and reaction between organic and oxygen intermediates remain unclear. Here we systematically study the electro-oxidation of aldehydes, alcohols, and amines on Co/Ni-oxyhydroxides with multiple characterizations. Utilizing Fourier transformed alternating current voltammetry (FTacV) measurements, we show the identification and quantification of two key operando parameters (ΔIharmonics/IOER and ΔVharmonics) that can be fundamentally linked to the altered surface coverage ( $$\Delta {\theta }_{{{{{\rm{OH}}}}}^{*}}/{\theta }_{{{{{\rm{OH}}}}}^{*}}^{{{{\rm{OER}}}}}$$ ) and the changes in adsorption energy of vital oxygenated intermediates ( $${\Delta G}_{{{{\rm{OH}}}}*}^{{{{\rm{EOOR}}}}}-{\Delta G}_{{{{\rm{OH}}}}*}^{{{{\rm{OER}}}}}$$ ), under the influence of organic adsorption/oxidation. Mechanistic analysis based on these descriptors reveals distinct optimal oxyhydroxide surface states for each organics, and elucidates the critical catalyst design principles: balancing organic and M3+δ−OH* coverages and fine-tuning ΔG for key elementary steps, e.g., via precise modulation of chemical compositions, crystallinity, defects, electronic structures, and/or surface bimolecular interactions. Water-assisted electro-catalytic selective oxidation is promising for sustainable production of value-added chemicals. Here the authors quantify two key physio-chemical parameters for efficient mechanistic investigation and rational catalyst design.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".