Cold Spray: Over 30 Years of Development Toward a Hot Future
Bibliographic record
Abstract
Cold Spray (CS) is a deposition process, part of the thermal spray family. In this method, powder particles are accelerated at supersonic speed within a nozzle; impacts against a substrate material triggers a complex process, ultimately leading to consolidation and bonding. CS, in its modern form, has been around for approximately 30 years and has undergone through exciting and unprecedented developmental steps. In this article, we have summarized the key inventions and sub-inventions which pioneered the innovation aspect to the process that is known today, and the key breakthroughs related to the processing of materials CS is currently mastering. CS has not followed a liner path since its invention, but an evolution more similar to a hype cycle: high initial growth of expectations, followed by a decrease in interest and a renewed thrust pushed by a number of demonstrated industrial applications. The process interest is expected to continue (gently) to grow, alongside with further development of equipment and feedstock materials specific for CS processing. A number of current applications have been identified the areas that the process is likely to be the most disruptive in the medium-long term future have been laid down.
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.
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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".