Unique copper and reduced graphene oxide nanocomposite toward the efficient electrochemical reduction of carbon dioxide
Bibliographic record
Abstract
Abstract The electrochemical reduction of CO2to useful chemicals and fuels has garnered a keen and broad interest. Herein, we report a unique nanocomposite consisting of Cu nanoparticles (NPs) and reduced graphene oxide (rGO) supported on a Cu substrate with a high catalytic activity for CO2reduction. The nanocomposite was optimized in terms of the composition of Cu NPs and rGO as well as the overall amount. A gas chromatograph was employed to analyze the gaseous products, whereas a chemical oxygen demand (COD) method was proposed and utilized to quantify the overall liquid products. The optimized nanocomposite could effectively reduce CO2to CO, HCOOH and CH4with a Faradaic efficiency (FE) of 76.6% at −0.4 V (vs. RHE) in a CO2saturated NaHCO3solution. The remarkable catalytic activity, high FE, and excellent stability make this Cu-rGO nanocomposite promising for the electrochemical reduction of CO2to value-added products to address the pressing environmental and energy challenges.
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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".