Advances in powder nano-photocatalysts as pollutant removal and as emerging contaminants in water: Analysis of pros and cons on health and environment
Bibliographic record
Abstract
Photocatalysis is an advanced oxidation process where light exposure triggers a semiconducting nanomaterial (nano-photocatalyst) to generate electron-hole (e − /h + ) pairs and free radicals . This phenomenon is widely used for the photocatalysis-assisted removal of organic and other contaminants using wide range of nano-photocatalysts, offering an efficient approach to environmental remediation. However, the introduction of powdered nano-photocatalysts into water systems often leads to unintended secondary pollution in the form of residual nano-photocatalysts, ion leaching, free radicals, toxic by-products etc. Such practices potentially introduce emerging secondary contaminants into aquatic environments, posing risks to both aquatic life and human health. The resulting chemical by-products and intermediates can effectively induce chronic toxicity , neurological and developmental disorders, cardiovascular defects, and intestinal ailments in humans and aquatic species. Despite having a range of health and environmental consequences, this dark side of nano-photocatalysts has been comparatively less explored and discussed in the literature. In this review, the pros and cons of powder nano-photocatalysts are discussed in view of their advantages as well as disadvantages in wastewater treatment . The discussion encompasses their classification based on composition , dimensions, structure, and activity, as well as recent advancements in improving their photocatalytic efficiency. The article also explores the recent advances on their applications in photocatalytic removal of various water pollutants/contaminants of emerging concern (i.e., organic pollutants , micro/nano plastics, heavy ions , disinfections, etc.) Furthermore, an emphasis on the role of such nano-photocatalysts as emerging (secondary) contaminants in water system, along with a thorough discussion of latest studies related to the health and environmental issues, has been discussed. Additionally, it addresses critical issues in applying powder nano-photocatalysts for wastewater detoxification and explores potential solutions to these challenges followed by future prospects.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".