Dual Catalytic Sites of Alloying Effect Bloom CO<sub>2</sub> Catalytic Conversion for Highly Stable Li–CO<sub>2</sub> Battery
Bibliographic record
Abstract
Abstract Owing to the ingenious utilization of CO2 conversion electrochemistry, rechargeable Li–CO2 batteries, have attracted more and more attention. However, the large potential polarization resulting from the sluggish CO2 reduction/evolution electrochemistry degrades energy efficiency and cycling performance. One possibility to break the kinetic bottlenecks of ‐Li–CO2 batteries is to design high‐efficiency catalysts with flexible geometric and electronic structures. Herein, an efficient synergistic catalyst with unique alloyed dual catalytic sites composed of uniformly ultrafine Ir–Ru alloyed nanoparticles modified nitrogen‐doped carbon nanotube composite (denoted as IrRu/N‐CNT) is synthesized. Combining the synergistic effect between the remarkably enhanced catalytic activity of Ir–Ru dual catalytic sites, the Li–CO2 battery delivers a high discharge capacity of 6228 mAh g−1 and outstanding stability over 7660 h. Density functional theory (DFT) calculation results uncover that the excellent electrochemical performance is ascribed to the novel dual catalytic sites on the surface of IrRu nanoalloys, which effectively modify its electronic structures and shorten the electron transfer pathway, leading to the deposition of film‐like Li2CO3 products. This study highlights the novel view of building a dual catalytic site and provides some new insights for understanding the catalytic mechanism of an alloying‐type bifunctional catalyst toward realizing high‐performance Li–CO2 batteries.
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".