Nanomaterials and Nanotechnologies for Marine and Membrane Antifouling Applications
Bibliographic record
Abstract
The major responsibility of the marine industry includes the global transportation of goods, materials, and people. To cater the longstanding challenges like degradation of materials and biofouling, it has embraced nanotechnology solutions. Nano-technology offered numerous products such as nano-ZnO, nano alumina, and nano silica, etc. to deal with corrosion in a cost-effective manner. Similarly, to address the biofouling in the aquatic environment, hybrid nanocomposites of organic-inorganic materials, photocatalytic nanomaterials, metal and metal oxide nanomaterials (nanoparticles, nanowires, nanorods), etc. are employed as viable agents to create non-toxic or low-toxic antifouling coatings. On the other hand, membrane separation technology plays a significant role in various industries including water treatment plants, food, medicine, pharmacy, biotechnology, etc. in addition to the domestic arena for the purification of drinking water. Such a wonderful technology is being totally disturbed by a troublesome problem and a predominant barrier called membrane fouling, which drastically limits the commercialization of the membranes and the whole membrane industrial technology as well. Hence, this review exclusively throws light on the role of nanomaterials and nanotechnologies developed for the prevention of fouling that occurs on submerged structures and membranes as well and to give possible solutions with increased resilience against challenges to come.
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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) | 0.000 | 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".