Application of Engineered Si Nanoparticles in Light-Induced Advanced Oxidation Remediation of a Water-Borne Model Contaminant
Bibliographic record
Abstract
Surface-engineered amphiphilic polymer-coated silicon nanoparticles (SiNPs) were employed as photocatalysts to capture and degrade a model organic contaminant (methanol) in water. This study represents the first time SiNPs have been employed in the initiation of advanced oxidation processes that are commonly used to degrade organic constituents in industrial wastewaters. The quantum yield of photocatalytic methanol oxidation and the corresponding yield factor for the generation of active OH radicals are reported. The size and surface defect dependent photocatalytic activity of SiNPs was investigated. The yield factors (η) decreased with increasing particle size and reached impressive values that exceeded that of equivalent TiO2 nanoparticle systems by 3-4 times and are comparable to the robust UV/Cl2 and UV/H2O2 systems. The higher photocatalytic efficiency of SiNPs is attributed to the combined effects of quantum confinement, effective band gap, and surface states, among which surface states play a dominant role. SiNPs provide a potentially tunable, biologically inert, and robust nanoparticle system for photocatalytic oxidation of wastewater contaminants.
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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".