Controlling Selectivity in Electrochemical Conversion of Organic Mixtures through Dynamic Control of Electrode Microenvironments
Bibliographic record
Abstract
Organic electrosynthesis using renewable electricity offers a sustainable approach to chemical manufacturing. Among its promising applications, the selective transformation of complex organic mixtures presents a valuable opportunity to eliminate costly separation processes and directly convert heterogeneous feedstocks to valuable products. However, controlling selectivity in reaction mixtures remains challenging due to competing reaction pathways and varying reactivities among substrates. Here, we demonstrate how selectivity in mixed organic electrosynthesis can be systematically controlled through a balance of reaction kinetics and mass transport limitations. Using acrylonitrile and crotononitrile mixtures as a model substrate mixture, we established quantitative relationships between substrate compositions, current densities, and product distributions that reveal distinct kinetically limited and mass transport-limited reaction regimes that control selectivity. We further demonstrated how pulsed electrolysis can be used to strategically control these reaction regimes to drive selectivity toward specific products. These insights create opportunities for developing adaptive and dynamic chemical manufacturing processes capable of handling complex feedstocks.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| 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".