Critical role of hydrogen sorption kinetics in electrocatalytic CO2 reduction revealed by on-chip in situ transport investigations
Bibliographic record
Abstract
Abstract Precise understanding of interfacial metal−hydrogen interactions, especially under in operando conditions, is crucial to advancing the application of metal catalysts in clean energy technologies. To this end, while Pd-based catalysts are widely utilized for electrochemical hydrogen production and hydrogenation, the interaction of Pd with hydrogen during active electrochemical processes is complex, distinct from most other metals, and yet to be clarified. In this report, the hydrogen surface adsorption and sub-surface absorption (phase transition) features of Pd and its alloy nanocatalysts are identified and quantified under operando electrocatalytic conditions via on-chip electrical transport measurements, and the competitive relationship between electrochemical carbon dioxide reduction (CO 2 RR) and hydrogen sorption kinetics is investigated. Systematic dynamic and steady-state evaluations reveal the key impacts of local electrolyte environment (such as proton donors with different p K a ) on the hydrogen sorption kinetics during CO 2 RR, which offer additional insights into the electrochemical interfaces and optimization of the catalytic systems.
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 | 0.001 | 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".