Polymer Metallization by Cold Spray Deposition of Polyamide-Copper Composite Coatings
Bibliographic record
Abstract
Abstract Cold spray metallization of polymers is a promising surface engineering technique that enables the deposition of metal coatings onto polymer substrates at low process temperatures, resulting in improved surface properties, thus enhanced functionality of the polymeric material. However, deposition of well-adhering metallic coatings without causing surface damage to the polymer substrate is still a challenge. In this work, copper-polyamide composite coatings with different copper concentrations between 30 and 75 vol.% in the starting powders were deposited on polyamide substrates using a low-pressure cold spray system with two nozzle geometries of short and long diverging sections. The spray parameters were first developed for the deposition of polyamide powder (at gas temperature of 260 °C and gas pressures ranging from 0.41 to 1.37 MPa), and then used to spray the composite powder mixtures where the polyamide particles were acting as a binder for copper particles. Inflight and impact particle characteristics (velocity and temperature) of the polyamide powder were simulated to better understand the deposition properties. Considering that the selected conditions were suboptimal for the deposition of copper particles, no surface damage was caused as no penetration of the copper particles into the polymer substrate occurred. The results show that increasing the copper content in the powder mixtures significantly improved the resulting coating uniformity and the retained copper content. In addition, the coating deposited by spraying the powder mixture with a higher copper content of 75 vol.% and using the longer nozzle yielded the highest cohesion strength. To further improve coatings cohesion, two post-spray processing methods of furnace heating and hot pressing were used, and the effect of each process on coatings properties was investigated.Please confirm if the author names are presented accurately and in the correct sequence (given name, middle name/initial, family name). Given name: [Kintak Raymond] Last name [Yu]. Also, kindly confirm the details in the metadata are correct.The author names are now correct: Kintak Raymond given name and Yu last name All other details are corrects
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How this classification was reachedexpand
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) | 0.000 | 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) | 0.000 | 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 teacher head, 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".