Hard Coating is Because of Oppositely Worked Force-Energy Behaviors of Atoms
Bibliographic record
Abstract
Coatings of suitable materials having thickness of few atoms to several microns on a substrate have caught the regular attention of scientific community working in various fields of science and technology. Decorative and protective coatings, transparent and insulating coatings, coatings of medical implants and surgical instruments, coatings for drug delivery, ultra-precision machine-tool coatings and coatings for miscellaneous uses are in the routine demand. Different hard coatings develop under the significant composition of suitably different natured atoms where their force-energy behaviors, when in their certain transition states, provide the provision to bind (adhere). In the binding mechanism of different nature suitable atoms, electron (of outer ring) belonging to filled state gaseous nature atom takes another clamp of energy knot (of outer ring) belonging to unfilled state solid-natured atom. Set conditions of the process provide the provision of binding different nature atoms in a technique or method meant for it. Different natures of atoms develop structure in the form of hard coating by locating ground points between their original ones where gaseous nature atoms increase potential energy under the decreasing levitational force at electron-level while the solid atoms decrease potential energy under the decreasing gravitational force at electron-level. Ti–Ti binding occurs through the difference of expansion of their energy knots nets when one atom just lands on the already landed atom while the adhered nitrogen atom incorporates at their interstitial position. Under suitable set parameters, differently natured atoms deposit in the form of coating at substrate surface under the given conditions. The rate of solid-natured atoms ejecting or dissociating from the source depend on its nature, process parameters and the processing technique or approach. In random arc-based vapor deposition system, depositing differently natured atoms at substrate surface depends on the input power. In addition to intrinsic nature of atoms, different properties and characteristics of coatings emerge as per engaged forces under the involved energy. The present study sets new trends in the field of coatings involving the diversified class of materials and their counterparts.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".