Enhanced CO2-to-CH4 conversion via grain boundary oxidation effect in CuAg systems
Bibliographic record
Abstract
• Ag enrichment at grain boundaries induces significant oxidation effects. • Enhanced performance stems from stabilized Cu δ+ species and unique electron transfer. • The CO 2 -to-CH 4 pathway and the sluggish quenching of intermediates are revealed. • Grain boundary oxidation effect on CH 4 conversion is twice that of conventional effects. The authentic active sites of oxide-derived copper (OD-Cu), namely grain boundaries (GBs) and oxidized Cu δ+ species, is still debatable, and their role in governing CH 4 conversion remains unclear. Herein, this study answers these questions using bimetallic catalysts by novel electro-shock strategy with controllable GBs for the oxidization of Cu δ+ species by modulating Ag loading. The Ag enrichment at the GBs facilitates the bonding of oxygen with the uncoordinated Cu atoms, resulting in GB oxidation effect. The obtained CH 4 selectivity is twice that of GBs or nanoalloy effect. The enhanced performance is attributed to the stable Cu δ+ species and unique electron transfer mechanism from GB oxidation structure. Operando attenuated-total-reflection Fourier-transform-infrared-spectroscopy unveils the reaction pathway of CO 2 -to-CH 4 and the sluggish reversible quenching processes of intermediates. Theoretical calculations indicate that the weak *CO adsorption on GB oxidation structure facilitates *CO hydrogenation, promoting CO 2 -to-CH 4 conversion.
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".