Tantalum Based High-Pressure Cold Spray Coatings on Stainless Steel Substrate
Bibliographic record
Abstract
Tantalum as a transition element possesses good corrosion resistant properties, along with ductility and hardness. It is also one of the best heat-resistant material (melting point 2996°C) and is known for its high heat and electrical conductivity. In this research, Tantalum is deposited on stainless steel substrate using high-pressure cold spray (HPCS) method. Cold spray coating technology enables the deposition of powder feedstock without melting. Feedstock particles are propelled through a nozzle at supersonic velocities and they deform plastically on impact, resulting in good bonding strength to the substrate. The low temperature and solid-state deposition associated with cold spray allows refractory materials such as Ta, Mo, and W to be deposited without high temperature requirements. The objective of this work is to achieve a dense and nonporous coating microstructure with a high deposition efficiency. The hardness of as-received tantalum particles is found to be 279 HV0.3and the microstructure is very dense. Tensile testing carried on the sample coated at a stagnation gas pressure of 50 bar and gas inlet temperature of 900°C exhibited an ultimate tensile strength of 442 MPa and adhesion strength of 77 MPa. Further mechanical properties of the coating in terms of hardness is carried out by nanoindentation. These results will be correlated with microstructural imaging and elemental analysis including morphology and composition using scanning electron microscopy and X-ray diffraction techniques.
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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".