A comparative review on cold gas dynamic spraying processes and technologies
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Cold gas dynamic spraying (CGDS) is a relatively new technology of cold spraying techniques that uses converging-diverging (De Laval) nozzle at a supersonic velocity to accelerate different solid powders towards a substrate where it plastically deforms on the substrate. This deformation results in adhesion to the surface. Several materials with viable deposition capability have been processed through cold spraying, including metals, ceramics, composite materials, and polymers, thereby creating a wide range of opportunities towards harnessing various properties. CGDS is one of the innovative cold spraying processes with fast-growing scientific interests and industrial applications in the field of aerospace, automotive and biotechnology, over the past years. Cold gas spraying with a wide range of materials offers corrosion protection and results in increases in mechanical durability and wear resistance. It creates components with different thermal and electrical conductivities than that substrates would yield, or produces coatings on the substrate components as thermal insulators and high fatigue-strength coatings, and for clearance control, restoration and repairing, or prostheses with improved wear, and produces components with attractive appearances. This review extensively exploits the latest developments in the experimental analysis of CGDS processes. Cold gas dynamic spraying system, coating formation and deposit development, description of process parameter and principles, are summarized. Industrial applications and prospectives of CGDS in future research are also commented.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it