Substrate Diffusion Electrodes for Electrochemical Hydrogenation: Influence of Material Choice and Process Conditions
Bibliographic record
Abstract
Abstract Synthetic electrochemistry enables selective chemical transformations without the need for stoichiometric reactants, yet the reliance on electrolytes and substrate dilution often limits its feasibility. Addressing this, we previously introduced the substrate diffusion electrode (SDE) as a flexible and modular platform for the conversion of pure substrate feeds. Herein, we examine the impact of the separation layer material, adjacent electrolyte, process conditions, and catalyst properties on electrochemical hydrogenation (EChH) efficiency and crossover rates. A linear correlation between the choice of hydrophobic transport layers and local substrate concentration was demonstrated, highlighting substrate concentration as a key factor for semi‐hydrogenations. Our results reveal that the ion exchange capacity (IEC) of the ionomer transport layer significantly influences semi‐hydrogenation efficiency, with a notable improvement in faradaic efficiency (FE) when switching from lower‐IEC to higher‐IEC membranes. However, the trade‐off between high FE and low electrolyte crossover persists, with the optimal conditions varying based on electrolyte pH and cation concentrations. Additionally, we pioneered operando spectroscopy in this context confirming that the local substrate concentration is a dynamic, tunable factor. The presented results reveal how the investigated parameters influence the local substrate‐electrolyte concentration balance and how they can be tuned to obtain optimum conditions for the valorization of discrete substrate feeds.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.001 |
| 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".