Sustainable pollutant removal and wastewater remediation using TiO2-based nanocomposites: A critical review
Bibliographic record
Abstract
Rapid industrialization and urbanization emphasized water purification and the production of carbon-neutral fuels. State-of-the-art technology that is low-cost, long-lasting, and easy to implement is required to address these issues. In this context, substantial developments in green synthesis technology have provided a more efficient, effective, and affordable procedure. One possible paradigm worth considering is a circular system that purifies water and produces biomass. Titanium dioxide-based photocatalysts (TPs) can be a great tool due to their outstanding properties, which can be modified for improving photocatalytic activities in various advanced oxidation process applications. In recent years, there has been a significant increase in the attention given to the development of environmentally sustainable TPs. A comprehensive analysis of nearly 300 academic articles in this study has revealed significant shortcomings in effectively utilizing TPs. The primary objective of this paper was to address these gaps that need to be filled in order to ensure the efficient and sustainable utilization of TPs in wastewater treatment. Accordingly, this paper comprehensively reviewed TiO2's features, including structure, properties, synthesis methods, photocatalytic activities, mechanism, structure modifications, applications, treatment cost analysis, and catalysts reuse in wastewater treatment. Finally, in order to ensure the sustainability of this wastewater treatment technique and achieve the goal of the circular economy, the concept of waste-to-resource has been introduced. The implementation of the aforementioned approach may serve as a valuable means of achieving environmentally sustainable outcomes in the field of wastewater treatment.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".