Advancements in multifunctional nanomaterials for synergistic photocatalytic and adsorptive water treatment processes
Bibliographic record
Abstract
Wastewater generated from different anthropogenic activities often poses health risks to both human and aquatic lives which necessitate the development of advanced remediation technologies using multifunctional nanocomposites. There is an increasing interest in the application of multifunctional nanocomposites for wastewater treatment due to their tendency to be used in combined photocatalytic and adsorption process. The multifunctional nanocomposites offer synergistic effects which provide opportunities for efficient capture of the contaminants and subsequently degrading them under various environmental conditions. The recent advances in the applications of multifunctional nanocomposites include the design of photocatalysts that could be applied under visible light irradiation, surface modified adsorbents, and heterojunction nanomaterials. Multifunctional nanocomposites have displayed noteworthy performance in the removal of organic contaminants such as dyes, pharmaceuticals residues, phenols as well as heavy metals with enhanced stability, reusability and scalability. Key advancements in the application of the multifunctional nanocomposite, and the various mechanism in the adsorption and photocatalytic process have been highlighted in this review. The review presented a future perspective with an emphasis on the necessity of cost-effectiveness and environmentally sustainable nanomaterials to ensure sustainable wastewater treatment technologies.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".