Synthesis of Three-Dimensional β-In<sub>2</sub>S<sub>3</sub> Nanoflowers with a Tunable Surface Area for Boosted Photocatalytic Degradation of Tetracycline and Rhodamine B
Bibliographic record
Abstract
Three-dimensional (3D) β-In 2 S 3 nanoflowers with a tunable surface area were successfully synthesized by a simple hydrothermal method. Their growth mechanism, observed through transmission electron microscopy (TEM) and scanning electron microscopy (SEM), revealed the formation of In 2 S 3 flowers by the assembly of 2D In 2 S 3 nanosheets. This unique 3D structure enhances optical absorption and tailors the band gap, as evidenced by UV–vis DRS and photoluminescence (PL) analyses. XRD and Raman spectroscopy confirm the β-phase of the synthesized In 2 S 3 nanoflowers. The tunable surface area of the samples was confirmed by Brunauer–Emmett–Teller (BET) analysis. As a result, the prepared material exhibits an enhanced degradation efficiency to tetracycline (TC) and Rhodamine B (RhB), reaching up to 85.4 and 99.4% after 240 and 60 min under irradiation by low-power household LED (60 W), respectively, which has not been reported yet. Radical trapping experiments indicated that O 2 •– was the primary reactive species responsible for the photocatalytic degradation of RhB and TC molecules in the β-In 2 S 3 system. The excellent photocatalytic properties and high structural stability of β-In 2 S 3 make it a promising material for degrading antibiotics and persistent textile pollutants.
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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".