Metal–organic frameworks‐derived <scp> TiO <sub>2</sub> </scp> for photocatalytic degradation of tetracycline hydrochloride
Bibliographic record
Abstract
Abstract This work will change the common understanding that C doping of MIL‐125(Ti)‐derived TiO 2 is a key factor in improving its photocatalytic performance, and it can also help to understand the internal relationship between the structure and performance of photocatalytic materials deeply. It provides a simple synthesis method for the wider application of TiO 2 in the field of photocatalysis. Compared with previous studies, this article uses the titanium‐based metal‐organic framework MIL‐125(Ti) to prepare the semiconductor photocatalyst M‐TiO 2 by calcination in the air at a lower temperature and shorter time. After analyzing the M‐TiO 2 prepared in the experiment, the results can be received that there is no obvious agglomeration and the morphology is almost unchanged, as the frame structure does not collapse at the same time. As a result, the advantages of the large specific surface area and porousness of metal–organic frameworks (MOF) as precursor derivatives are preserved. As for the changes in the micro‐morphology, pore structure, and specific surface area of M‐TiO 2 compared with the precursor, they are investigated seriatim. The results show that, compared with commercial TiO 2 ‐P25, the performance of M‐TiO 2 photocatalytic degradation of tetracycline hydrochloride is 5.7 times that of the precursor metal‐organic framework MIL‐125(Ti) and 2.2 times that of P25, and has good cycle stability.
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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".