The Reconstruction of Bi<sub>2</sub>Te<sub>4</sub>O<sub>11</sub> Nanorods for Efficient and pH‐universal Electrochemical CO<sub>2</sub> Reduction
Bibliographic record
Abstract
Abstract The electrochemical CO 2 reduction reaction (CO 2 RR) to generate chemical fuels such as formate presents a promising route to a carbon‐neutral future. However, its practical application is hindered by the competing CO production and hydrogen evolution reaction (HER), as well as the lack of pH‐universal catalysts. Here, Te‐modified Bi nanorods (Te−Bi NRs) were synthesized through in situ reconstruction of Bi 2 Te 4 O 11 NRs under the CO 2 RR condition. Our study illustrates that the complex reconstruction process of Bi 2 Te 4 O 11 NRs during CO 2 RR could be decoupled into three distinct steps, i.e., the destruction of Bi 2 Te 4 O 11 , the formation of Te/Bi phases, and the dissolution of Te. The thus‐obtained Te−Bi NRs exhibit remarkably high performance in CO 2 RR towards formate production, showing high activity, selectivity, and stability across all pH conditions (acidic, neutral, and alkaline). In a flow cell reactor under neutral, alkaline, or acidic conditions, the catalysts achieved HCOOH Faradaic efficiencies of up to 94.3 %, 96.4 %, and 91.0 %, respectively, at a high current density of 300 mA cm −2 . Density functional theory calculations, along with operando spectral measurements, reveal that Te manipulates the Bi sites to an electron‐deficient state, enhancing the adsorption strength of the *OCHO intermediate, and significantly suppressing the competing HER and CO production. This study highlights the substantial influence of catalyst reconstruction under operational conditions and offers insights into designing highly active and stable electrocatalysts towards CO 2 RR.
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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".