In-situ deposition of β-FeOOH nanoparticles on commercially available filter paper for fast and efficient removal of antibiotic
Bibliographic record
Abstract
Enhancing the dispersibility and recoverability of powdered catalysts is essential for developing efficient and cost-effective photocatalytic systems. Herein, β -FeOOH nanoparticles were in-situ deposited on commercially available filter paper (FP) to construct paper-based composite material ( β -FeOOH@FP). Results showed that the rod-like β -FeOOH nanoparticles were uniformly distributed in the FP matrix without destroying the crystalline structure of cellulose. The resulting β -FeOOH synthesized at 3 h presented the highest photoelectrochemical response and exhibited better suppression of electron–hole recombination, allowing more photogenerated electrons to participate in the reaction. The β -FeOOH@FP catalyst achieved a 94.1% photocatalytic degradation rate of tetracycline (TC) within 120 min compared to the pure β -FeOOH (42.2%) and FP (20.1%) under simulated visible light irradiation. Photocatalytic degradation kinetics also demonstrated that the rate constant of β -FeOOH@FP was 9.6 × 10 −3 min −1 , much higher than that of others. In addition, the resulting β -FeOOH@FP composite material exhibited excellent stability and reusability with a photocatalytic efficiency of over 90% after five cycles. These findings provide a simple and cost-effective strategy to improve the degradation performance of powdered semiconductor catalysts and pave a new way to develop cellulose-based nanocomposites with high photocatalytic efficiency.
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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".