Maximizing the Formate Formation of CO<sub>2</sub> Electroreduction Via Boosting Charge Transfer Ability
Bibliographic record
Abstract
The electrochemical reduction reaction of CO2 (CO2RR) is an attractive strategy for achieving carbon-neutral sustainability while the highly active and selective reaction for formate formation remains challenging. In addition, the thermodynamic inertness of CO2 usually leads to a high energy barrier for CO2RR, resulting in a preference for the competitive hydrogen evolution reaction. It is known that CO2RR is a proton-coupled electron transfer (PCET) process and the complicated multi-electron transfer steps occur on the catalyst surface. Therefore, improving the charge transfer ability is considered as an effective approach to maximizing the electrocatalytic activity and selectivity for CO2RR. Interface engineering has been proven as an effective method to prompt charge transfer by constructing interfaces within the catalysts that is widely used in many electrochemical reactions. The introduced interfaces would benefit the electronic interaction at the interface and assist the electron redistribution, thus optimizing the electronic structure and boosting the interfacial charge transfer. Herein, we report the heterostructure of Bi2S3-Bi2O3 nanosheets (BS-BO NSs) with substantial interfaces for the efficient CO2-to-formate conversion. The rapid-interfacial charge transfer induced by the abundant interfaces not only optimizes electronic structure, but also accelerates the kinetics of CO2RR and improves the electrocatalytic activity and selectivity. Compared with the separate Bi2O3 and Bi2S3 electrocatalysts, BS-BO NSs shows desirable selectivity to formate with a maximum Faradaic efficiency of 93.8 % at a moderate potential. The CO2RR performance is further boosted by using a flow cell system. The high selectivity with large current density makes BS-BO NSs a promising candidate for the practical application of CO2RR in the formate formation.
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".