Autochthonous self-assembly of nature's nanomaterials: green, parsimonious and antibacterial carbon nanofilms on glass
Bibliographic record
Abstract
The development of thin film coatings has been a very important development in materials science for the modification of native material surface properties. Thin film coatings are enabled through the use of sophisticated instruments and technologies that demand expertise and huge initial and running costs. Nano-thin films are yet a furtherance of thin films which require more expertise and much more sophistication. In this work for the first time we present a one-pot straightforward carbon thin film coating methodology for glass substrates. There is novelty in every single aspect of the method, with the carbon used in the nanofilm being obtained from turmeric soot, the coating technique consisting of a basic immersion technique, a dip-dry method, in combination with the phytosoot-derived carbon's inherent ability to self-assemble to form a uniform and continuous stable coating. The carbon nanofilm has been characterized using field emission scanning electron microscopy (FESEM), Energy Dispersive X-ray (EDAX) analysis, a goniometer and X-ray diffraction (XRD). This study for the first time opens a new school of thought of using such naturally available free nanomaterials as eco-friendly green coatings. The amorphous porous carbon film can be coated on any hydrophilic substrate and is not substrate specific. Its added advantages of being transparent and antibacterial in spite of being green and parsimonious are meant to realize its utility as ideal choices for solar panels, medical implants and other construction applications.
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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) | 0.000 | 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".