On the adsorption kinetics and mechanism of enhanced photocatalytic activity of <scp>Fe<sub>3</sub>O<sub>4</sub>‐SiO<sub>2</sub>‐TiO<sub>2</sub></scp> core‐multishell nanoparticles against <i>E. coli</i>
Bibliographic record
Abstract
Abstract In the present study, a Fe3O4‐TiO2 (FT) core‐shell and a core‐multishell structure of Fe3O4‐SiO2‐TiO2 (FST) were synthesized, and their bactericidal capability was investigated on Escherichia coli (E. coli). Scanning electron microscopy (SEM), ultraviolet–visible spectroscopy (UV–vis), X‐ray diffraction, Brunauer–Emmett–Teller, zeta potential, and fluorimetry were carried out to characterize properties of synthesized nanoparticles. An efficiency of 98% adsorption and harsh bacterial damage was observed when E. coli was put in contact with FST. Weaker adsorption of bacteria in contact with FT demonstrated that heterojunction has destructive effects on nanostructure. Further investigation proved that more OH were produced on the surface of FST, which is a sign of its longer lifetime. Moreover, results revealed that the presence of SiO2 in the structure caused enhanced coverage, surface area, and porosity in TiO2 outer layer, all of which have positive effects on adsorption. However, UV–vis showed smaller band gap for FT. It suggests that although photoactivity of FST is less influenced by light absorption, it possesses more e/h lifetime for generation of reactive oxygen species. Results point to the importance of SiO2 as an obstacle of heterojunction on both adsorption and photoactivity. It was also proposed that increasing band gap in FST can be attributed to the porosity of SiO2 that causes suppression of TiO2 nanocrystallite growth.
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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".