Peculiarities of Electron-Beam Formation of Hydrophobic and Superhydrophobic Coatings Based on Hydrocarbons of Various Molecular Weights and PTFE
Bibliographic record
Abstract
The paper studies the possibility of superhydrophobic coatings formations at exposure of powder mixture of polytetrafluorethylene and hydrocarbons having various molecular weights to low-energy electron beam in vacuum. It is shown that paraffin and PTFE based thin composite coatings may be characterized by superhydrophobic properties. The superhydrophobic properties are attained due to low surface energy of the fluorine-containing component and structured surface due to peculiarities of composite layer formation. The chemical processes observed in electron beam exposed area determine the molecular structure, morphology and the contact angle of thin organic coatings deposited. It is shown that high-molecular-weight hydrocarbon compounds should not be recommended for vacuum electron-beam deposition of superhydrophobic thin coatings because of deep changes in the molecular structure exposed to electron beam. These processes are responsible for high degree of unsaturation of the thin layer formed and for occurrence of oxygen-containing polar groups. The influence of substrate temperature on molecular structure, morphology and hydrophobic properties of thin coatings deposited is investigated. Potentially such coatings may be applied for deposition on the surface of metal capillaries used in biotechnological analyzers.
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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".