Dynamic Activation of Edge-Hosted Co–N<sub>4</sub> Sites for Energy-Efficient Electrochemical CO<sub>2</sub> Reduction at Industry-Level Current Density
Bibliographic record
Abstract
Achieving energy-efficient electrochemical CO 2 reduction (ECR) at an industry-level current density is both critically important and highly challenging, as it requires simultaneously maintaining high product selectivity and low overpotential under such demanding conditions. In this study, we present a single-atom Co–N–C catalyst with superior selectivity and activity for CO production at a low overpotential in ECR, enabled by the design of edge-hosted Co–N 4 sites. The combined results of theoretical calculations and in situ characterizations reveal that axial *CO coordination forms dynamically on edge-hosted Co–N 4 sites (CoN 4 C 8 –CO) during the ECR process. The CoN 4 C 8 –CO structure as an active site modulates the density of states of the Co atom, reduces the free energy barrier for *COOH formation, and also makes the CO desorption easy, thereby promoting CO 2 -to-CO conversion. As a result, the catalyst demonstrates a CO selectivity exceeding 99% at current densities of 600 mA cm –2 in a flow cell and 500 mA cm –2 in a membrane electrode assembly (MEA), respectively. Impressively, the MEA device demonstrates a high cell energy efficiency of up to 70.8% at 200 mA cm –2 and 55.7% at 500 mA cm –2 toward CO production, demonstrating great advantages compared with other state-of-the-art electrocatalysts.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".