Bolaamphiphilic microstructural polyphenol flavonoids as sustainable high efficacy coating for aluminium surface in aqueous solution
Bibliographic record
Abstract
Abstract Bolaamphiphilic polyphenol flavonoids were successfully revealed as a sustainable coating at the solid/liquid interface of an aluminium surface in an aqueous solution. Polyphenol flavonoids extracted from brown onions demonstrated the presence of bolaamphiphiles above 600 ppm. Characterization of the polyphenol flavonoids coating was performed using spectroscopic 1 H‐nuclear magnetic resonance and attenuated total reflectance Fourier‐transform infrared techniques for chemical identification, UV–vis and optical microscopy techniques were used for bolaamphiphilic microstructures assessment, and a scanning electron microscope was used for the assessment of surface morphology. Variant operating conditions used to show best coating efficacy were as follows: concentration = 600 ppm, solution pH = 10 in the presence of PO 4 3− ion cross‐linker, operating temperature = 10°C, microwave pre‐irradiation time = 5 s, and turbulent flow of the solution = 300 rpm. Maximum coating efficacy showed a coating efficacy of 97%. The suitability of several adsorption isotherms, like Langmuir, Temkin, and Freundlich, was tested to fit our data. Equilibrium constant values were in favour of successful coating, especially at lower temperatures (20°C). Spontaneous (negative Δ G °) and high affinities of coating material to the surface were revealed from thermodynamic parameters (Δ H ° and Δ S °). Conclusively, such research is meant to emphasize our continuous support for the use of plant waste in artificial sectors such as coating of metals, and for their economic feasibility and low cost as high efficacy renewable materials.
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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".