Nano-engineered 2D hBN-Based Materials for Environmental and Healthcare Applications
Bibliographic record
Abstract
The critical state of access to clean water is jeopardizing our health. According to a Cambridge University report, approximately 50,000 tons of dyes are discharged into global water systems from textile industries. "The world bank estimates that at least 20% of water pollution originates from textile dyeing." On the other hand, tanker spills account for about 10 percent of the oil in waters around the world, and regular operations of the shipping industry contribute about onethird. Chemicals and heavy metals such as mercury, lead, nickel, cobalt, cadmium, sulphur and arsenic are the additional leading causes of contaminated water creating neurological damage in young children." What can be done to remove these detrimental contaminants from the water? In this thesis, we report an environmentally benign and cost-effective preparation method of new adsorbents using few-layers of 2D hexagonal boron nitride nanosheets (hBNNs) incorporated in various forms of adsorbent systems such as membranes or sponges either alone or biofunctionalized with the enzyme laccase toalsobiodegradetheadsorbed pollutantsafterremoving them. Dueto thepolarity of BN bonds, high surface area, nanosheet structure, and specifically hydrophobicity property of porous hBN nanosheets, they have been proven to have several advantages over existing technologies for organic pollutant removal and clean-up of oil spillage. These adsorbent materials were also modified in terms of their structure and functional groups to remove alternative
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".