Recent Advances in Plasmonic Chemically Modified Bioactive Membrane Applications for the Removal of Water Pollution
Bibliographic record
Abstract
Population growth has reduced the available freshwater resources and increased water pollution, leading to a severe global freshwater crisis. The decontamination and reuse of wastewater is often proposed as a solution for water scarcity worldwide. Membrane technology is a promising solution to the problems currently facing the water and wastewater treatment industry. However, another problem is the high energy costs required to operate systems which use membranes for water treatment. In addition, membranes need to be replaced frequently due to fouling and biofouling, which negatively affect water flow through the membranes. To address these problems, the researchers proposed membrane modification as a solution. One of the exciting applications of plasmonic nanoparticles (NPs) is that they can be used to modify the surface of membranes to yield various properties. Positive feedback was reported on plasmonic-modified membranes as means of wastewater treatment. However, a fundamental gap exists in studies of plasmonic membranes’ performance and applications. Given the importance of membrane technology for water and wastewater treatment, this paper reviews recent advances in the development of plasmonic chemically modified bioactive membranes and provides a perspective for future researchers interested in investigating modified membranes.
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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 | 0.000 |
| 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".