Metal–Organic-Frameworks-Derived Cu/Cu<sub>2</sub>O Catalyst with Ultrahigh Current Density for Continuous-Flow CO<sub>2</sub> Electroreduction
Bibliographic record
Abstract
The electrochemical reduction of carbon dioxide (ECR-CO 2 ) to produce low-carbon fuels and high-value industrial chemicals has been proven to be a viable solution to energy sustainability. However, the energy efficiency of electrocatalytic CO 2 reduction is seriously limited by both poor electrocatalyst with insufficient activity, selectivity, and stability and ineffective electrochemical reactors. In this work, the electroreduction of CO 2 to CO is highly improved by the design of copper–metal–organic-frameworks-derived nanoparticle (Cu–MOF/NP) catalysts, in which Cu/Cu 2 O particles form a porous octahedral structure containing tunable Cu 0 and Cu + catalytic active sites. The ECR-CO 2 can be realized with a high current density of 25.15 mA cm –2 at a very low applied potential of mere 0.79 V RHE even in an H-type cell, owing to the high-surface-area porous structure with optimal surface chemistry of exposed Cu cations. Notably, a new flow electrochemical reactor integrated with a membrane electrode assembly (MEA) is designed to not only largely reduce the applied potential (∼200 mV) but also prompt the sensitivity of the reactor for identifying and quantifying reaction products. Accordingly, the Cu–MOF/NP catalyst enables an ultrahigh current density beyond 230 mA cm –2 at a low applied potential of −0.86 V RHE in the flow MEA reactor and the ethanol product (often undetectable in the traditional H-type cell) to be harvested.
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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".