Water clustering modulates activity and enables hydrogenated product formation during carbon monoxide electroreduction in aprotic media
Bibliographic record
Abstract
Water solvation plays a critical role in a wide range of electrochemical transformations, but its role is often convoluted since it is typically used as both solvent and proton source. Here, we experimentally control water speciation and activity using aprotic solvent media during carbon monoxide reduction reaction (CORR). Remarkably, we show that aprotic solvents that support microheterogeneous water-water clusters leads to significant amounts of CORR products (methane and ethylene) with a maximum ethylene Faradaic efficiency of 22% in acetonitrile (χH2O = 0.2); the first report of heterogeneous CORR to C2+ products using water as a proton source in an aprotic solvent. In contrast, microhomogeneous systems – where water integrates into the solvents’ intermolecular binding network and has lower activity – primarily support undesired hydrogen evolution reaction (HER). Insights gained expand our understanding of water activity and nonaqueous electrolyte design for other important transformations reactions beyond CO reduction such as CO2RR and HER.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".