Bias Enhanced Electro-Photocatalysis on TiO2 Nanoporous Materials for Decomposition of Forever Chemicals in Saltwater
Bibliographic record
Abstract
In this study, a TiO2-based bias-enhanced photocatalytic system was prepared with the aim of achieving high photocatalytic activity in deionized as well as in saltwater systems. Highly ordered TiO2 nanoporous materials were fabricated via a double-anodization method. A small potential bias was applied that suppresses electron-hole recombination and deters interference from the ions present in the solution while minimizing electrochemical reactions. Photodegradation reactions involving Rhodamine B (RhB) were conducted to evaluate the efficacy of the nanomaterials with and without the applied bias. Our experimental results revealed that the synthesized TiO2 nanomaterials possessed high photochemical activity under UVA light, substantially enhanced with the applied bias. A very significant enhancement was observed when the Bias Enhanced Electrolytic Photocatalysis (BEEP) system was used in saline solutions. Higher photocatalytic efficiency was achieved with the increase in the salinity level in the reaction mixture. These bias-enhanced nanomaterials were successfully tested for the degradation of per- and polyfluorinated substances (PFAS), recognized as a significant threat to the environment and human health. The results indicated that the presented technology could eliminate a wide range of fluorinated molecules. The simplicity, efficacy and scalability of the new BEEP approach described in this study make the use of TiO2-based advanced oxidation process (AOP) possible in designing high-performance water purification technologies applicable to fresh and salt water.
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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".