Integration of metallic TaS<sub>2</sub> Co‐catalyst on carbon nitride photoharvester for enhanced photocatalytic performance
Bibliographic record
Abstract
Abstract The efficient separation of photogenerated electron‐hole (e−‐h+) pairs is the key point for photocatalytic reactions. The integration of co‐catalysts, especially noble‐metals, can improve the e−‐h+ separation efficiency dramatically. However, the high‐cost would limit their large‐scale application. Therefore, the exploration of noble‐metal‐free co‐catalysts is still important for the development of photocatalysis. In this work, we report a series of metallic TaS2 based catalysts that can effectively boost the photocatalytic performance of a metal‐free semiconductor (2D‐C3N4). The advantages of the as‐prepared catalysts were demonstrated as follows: (i) noble‐metal‐free; (ii) outstanding electrical conductivity; and (iii) superior stability. As demonstrated in a photocatalytic degradation reaction, the optimal removal rate of pollutant Rhodamine B (RhB) reached 92 % after 100 min of irradiation, which is enhanced by almost 25 % more than pure two‐dimensional graphitic carbon nitride nanosheets (2D‐C3N4). This work may provide insight into finding new low‐cost metallic materials as co‐catalysts to promote phototcatalytic performance.
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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".