Carburizing of tantalum by radio-frequency plasma assisted chemical vapor deposition
Bibliographic record
Abstract
Tantalum carbide (TaC) has great potential as an alternative to tantalum and tantalum oxide for applications requiring thermal stability and corrosion resistance. In this study TaC layers were produced by inductive rf plasma-assisted chemical vapor deposition that combines diffusion with chemical vapor deposition. The maximum temperature of the tantalum substrates measured during a 6 h processing time was 900 °C using Ar–CH4 or Ar–CH4–H2 gas mixtures. The microstructure of the layers was characterized by X-ray diffraction and Auger electron spectroscopy, and the mechanical properties were studied by micro- and nanoindentation and by microscratch techniques. A close correlation among the carburizing parameters, the microstructure, the mechanical behavior of the layers, and the corrosion resistance was found. The best performing films, several μm thick, consisting of TaC phase with the highest hardness (∼25 GPa), were obtained under the following conditions: input power of 1400 W, pressure of 40–60 mbar, and substrate located at the center of the rf coil. The effect of gas composition, gas pressure, and substrate temperature on the layer composition, the TaC/Ta2C phase ratio, and the mechanical characteristics and chemical stability is presented and discussed. A mechanism of carburizing of tantalum in an inductive rf plasma is proposed.
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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".