Microenvironment engineering by targeted delivery of Ag nanoparticles for boosting electrocatalytic CO2 reduction reaction
Bibliographic record
Abstract
Creating and maintaining a favorable microenvironment for electrocatalytic CO2 reduction reaction (eCO2RR) is challenging due to the vigorous interactions with both gas and electrolyte solution during the electrocatalysis. Herein, to boost the performance of eCO2RR, a unique synthetic method that deploys the in situ reduction of precoated precursors is developed to produce activated Ag nanoparticles (NPs) within the gas diffusion layer (GDL), where the thus-obtained Ag NPs-Skeleton can block direct contact between the active Ag sites and electrolyte. Specifically, compared to the conventional surface loading mode in the acidic media, our freestanding and binder free electrode can achieve obvious higher CO selectivity of 94%, CO production rate of 23.3 mol g−1 h−1, single-pass CO2 conversion of 58.6%, and enhanced long-term stability of 8 hours. Our study shows that delivering catalysts within the GDL does not only gain the desired physical protection from GDL skeleton to achieve a superior local microenvironment for more efficient pH-universal eCO2RR, but also manifests the pore structures to effectively address gas accumulation and flood issues. Creating and maintaining a superior microenvironment is challenging for electrocatalytic CO2 reduction reaction. Here the authors report an in situ activation method to deliver silver nanoparticles within the gas diffusion layer, constructing a feasible and stable interfacial microenvironment.
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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 | 0.000 |
| 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".