Maximizing CO<sub>2</sub> Reduction Efficiency: Surface‐Regulated Highly Porous Ag‐Cu Alloy Aerogel Inserted With Multi‐Wall Carbon Nanotube Achieving Nearly Complete CO Selectivity
Bibliographic record
Abstract
Abstract The electrochemical reduction of CO2 (eCO2RR) holds promise in mitigating atmospheric greenhouse gas levels but is hindered by low reaction kinetics, high energy barriers, and poor selectivity. To address these challenges, we developed a novel series of cost‐effective AgCu‐containing metal‐aerogel catalysts with high electrochemical surface areas (ECSA) using a top‐down reduction approach. The Ag85Cu15 aerogel, with an ECSA of 27.41 cm2, achieved a Faraday efficiency (FE%) of 89.3 % for CO production at −0.9 V vs. RHE. Increasing the Cu content to over 50 % in the aerogel matrix produced small amounts of C2H4, with a maximum FE% of 12.9 % at −1.0 V vs. RHE. To further enhance CO2 reduction efficiency, multi‐walled carbon nanotubes (CNT) were incorporated into the Ag85Cu15 alloy aerogel via a hydrothermal treatment. The highly dispersed CNTs within the aerogel matrix increased the ECSA to 57.00 cm2 by forming a well‐defined porous structure through van‐der Waals interactions, improving CO selectivity, and achieving a FE% of 98.6 % at −0.7 V vs. RHE and a partial current density of 9.6 mA/cm2 in an H‐cell. 86 % of the initial FECO% was maintained during an 18 h test with continuous electrolysis.
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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) | 0.000 | 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".