A critical review on micro‐ and nanomotors: Application towards wastewater treatment
Bibliographic record
Abstract
Abstract Micro‐ and nanomotors are synthetic devices that can transform various sources of energy into motion. These devices perform specialized tasks as they propel themselves in response to stimuli. The application of self‐propelled micro‐ and nanomachines in wastewater treatment has been of prime importance in the last decade. Compared to static decontamination systems, micro‐ and nanomachines can remove or degrade water pollutants in a much more rapid way owing to higher diffusion rates and fluxes. The present review focuses on the recent progress of micro‐ and nanomachines in wastewater treatment and provides an overview of their structural features, synthesis procedures, and propulsion mechanisms. We reviewed the applications of micro‐ and nanomachines to remove heavy metals, dyes, and organic pollutants from wastewater. We also discussed the challenges micro‐ and nanomotors face during wastewater treatment, thus providing a holistic approach to the article. This article highlights the shortcomings as well as the opportunities for micro‐ and nanomotors‐based technology in 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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".