Complex Metal-Intermetallic Composites by Cold Spray Deposition: A Copper-Indium-Gallium (CIG) Example
Bibliographic record
Abstract
Cold spray offers unique possibilities in processing intermetallic-forming or phase-segregating metallic alloys, which are difficult to address by classical melt-metallurgical processes such as casting or thermal spraying due to the formation of large brittle regions or low melting point and volatile eutectics. An example is the production of thick coatings of the copper-indium-gallium (CIG) system on plates or tubes, as they are required in the fabrication of the CIGS(Se) absorber layer of thin film photovoltaic solar modules on an industrial-scale. At the relevant compositions, extended solidification intervals, such as in casting, create material segregation into large brittle CuGa2-type regions intermixed in an indium-rich matrix rendering the cast non-uniform and fragile with high internal stresses. Shrinkage effect further promote porosity. The equilibrium phase diagrams even allow the retention of pure Ga and the In-Ga eutectic, essentially liquid materials at room temperature and highly undesirable for the application. On the other hand, using rapid solidification by gas atomizing the Cu-In-Ga to produce cold spray powder promotes a fine-scaled and uniform phase distribution and microstructure of Cu(In,Ga), CuIn and In. By decoupling the material synthesis from the forming process, this phase distribution can be fully preserved in the cold spray deposition process to advantageously fabricate the coatings for that application.
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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 | 0.001 |
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 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".