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-CO2) 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 CO2 reduction is seriously limited by both poor electrocatalyst with insufficient activity, selectivity, and stability and ineffective electrochemical reactors. In this work, the electroreduction of CO2 to CO is highly improved by the design of copper–metal–organic-frameworks-derived nanoparticle (Cu–MOF/NP) catalysts, in which Cu/Cu2O particles form a porous octahedral structure containing tunable Cu0 and Cu+ catalytic active sites. The ECR-CO2 can be realized with a high current density of 25.15 mA cm–2 at a very low applied potential of mere 0.79 VRHE 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 VRHE 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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 teacher head, 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".