Characteristics of CVD Grown Diamond Films on Langasite Substrates
Bibliographic record
Abstract
Surface acoustic wave (SAW) devices consist of a piezoelectric substrate with interdigitated (IDT) electrodes. These devices can be used to fabricate wireless and passive sensors that can be mounted in remote and/or inaccessible places. If encapsulated with CVD diamond, the SAW devices can be made to operate under extremely hostile conditions. The piezoelectric layer (AlN, ZnO etc.) deposited on the diamond or an inverse system can increase the frequency of the SAW device. Most piezoelectric materials (such as quartz) show phase transition temperatures below diamond deposition temperature (650o-1100ºC), preventing their use as a substrate for diamond growth. Langasite La3Ga5SiO14 (LGS) is recently fabricated piezoelectric material that can withstand high temperatures without being deteriorated. LGS does not have phase transitions up to its melting point of 1470 °C. Here we report the deposition of diamond films by microwave plasma CVD in methane-hydrogen gas mixtures on polished and rough surfaces of the LGS substrates seeded with nanodiamonds. No buffer layer between the substrate and the coating had been used. The effect of substrate pretreatment (PT) was also investigated on the growth behaviour of diamond films on LGS. The resulting films are characterised by Raman spectroscopy, X-ray diffraction (XRD), field-emission scanning electron microscopy (FESEM), X-ray photoelectron spectroscopy (XPS). The effect of substrate roughness on the growth behaviour was found to favour bigger grain sizes on the unpolished substrates. Whereas, the effect of substrate pretreatment (PT) was found to produce unique microstructural features with better polycrystalline diamond (PCD) quality than on the substrates without PT. Raman signals confirm the deposition of PCD in all the cases but the X-ray results interestingly show new phase formation of hcp and rhombohedral diamond lattice structures under CVD growth environment.
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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) | 0.003 | 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".