Production and structural characterization of BN/TiN multilayers
Bibliographic record
Abstract
A pulsed excimer laser was used to evaporate targets of boron nitride and titanium nitride in an attempt to produce hard thin films on crystalline silicon substrates. The films were either pure TiN or BN layers, as well as alternating multilayers and mixed layers. Deposition could be assisted by ion bombardment. The films were characterized by Auger electron spectroscopy, Fourier transform infrared spectroscopy (FTIR), and x-ray diffraction. A selection of films was also studied by profilometry in order to determine deposition rate and the type of stress present. The level of stress in TiN films was also a function of the deposition temperature and could be varied with the use of ion bombardment. Amorphous, cubic, and hexagonal BN films were produced and the effect of the stress of the substrate on these layers was investigated. Multilayers were stressed, having alternating layers of nanocrystalline TiN and amorphous BN. Mixtures consisted of nanometer-sized regions of crystalline TiN and sp2 coordinated boron nitride. FTIR spectra and high-resolution transmission electron microscope pictures suggested that in the mixtures, boron nitride planes tended to parallel the surface of the TiN grains. No sign of stress-driven formation of cubic BN was observed in the multilayers nor in the nanosized mixtures, regardless of the stress level present in them; neither was there any sign of titanium borides or other structures that might increase the hardness of the films.
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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".