Hard Coating is Because of Oppositely Worked Force-Energy Behaviors of Atoms
Bibliographic record
Abstract
Coatings of specific materials in few atoms of thickness to several microns on certain substrate is the basic need of society and they attend the regular attention of scientific community working in different domains; decorative and protective coatings, transparent and insulating coatings, coating medical implants and surgical instruments, coatings for drug delivery and security purposes, ultra-precision machine coatings, coating cutting tools, coatings for MEMS and NEMS, and so on. Different coatings develop under significant composition of atoms where certain force-energy behaviors provide the provision for electrons (of gas atoms) to deal double clamping of energy knots of unfilled states (of solid atoms). Under certain process conditions, different nature atoms oppositely-switch force-energy behaviors to the ones originally owned where they locate common mid-points of their ground points at accommodating level resulting into deal binding. Because of adjusting contraction-expansion of clamping energy knots under varying potential energy of electrons, they develop structure of their atoms termed as hard coating, which is known since antiquity. Different properties and characteristics of hard coatings like hardness, adhesion, roughness and friction coefficient, etc. are emerged under attained mid-points of transformed atoms in evolving structure. This work describes the science of depositing hard coating opening several new areas. Keywords: Hard coating; TiN; Atomic nature; Expansion and contraction of clamping energy knot; Potential energy of electron; Ground point; Structure evolution
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.003 | 0.001 |
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".