MOF-derived C, N-In2O3 with GdFeO3 Z-scheme heterostructure for the photocatalytic removal of tetracycline
Bibliographic record
Abstract
Abstract Metal oxides derived from metal–organic frameworks have received considerable attention for water treatment. Herein, MIL-68 (In)–derived rod-like carbon (C) and nitrogen (N) codoped In2O3–modified GdFeO3 (CN–InO/GdF) Z-scheme heterostructures were rationally constructed through simple pyrolysis and a wet-chemical route. The synthesized CN–InO/GdF catalysts were characterized using several physicochemical techniques. Moreover, the obtained CN–InO/GdF exhibited excellent photocatalytic performance toward tetracycline (TC) degradation. Impressively, the optimized CN–InO/GdF-3 heterostructure catalyst displayed a boosted TC removal efficiency of 99.06% within 60 min of irradiation, which was 7.1 and 9.7 times higher than those of bare CN–InO and GdFeO3, respectively. The remarkably enhanced photoactivity of CN–InO/GdF was principally attributed to the synergistically combined interfaces hindering the recombination of photoinduced carriers, and the maintained redox capacity via Z-scheme spatial charge transfer. Finally, the tentative degradation mechanism was determined on the basis of density functional theory calculations and intermediates analysis. This work elucidated the design of highly efficient catalysts for various applications.
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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) | 0.000 | 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".