Development of a Duplex Sn-Cu Coating on Carbon Fiber-Reinforced Polymers (CFRPs) using Cold Spray and Electrodeposition Processes
Bibliographic record
Abstract
Abstract Direct cold spray deposition of Cu was not possible on carbon fiber-reinforced polymer composites (CFRPs) with thermosetting polymer as the matrix material due to substrate erosion. In a recent study, an epoxy-CFRP was successfully metallized through a hybrid coating process that involves three consecutive coating steps: (i) electroless deposition, followed by (ii) electrodeposition, and finally (iii) cold spray. In this present study, for the reduction of the coating process steps, a duplex metallic coating was developed on an epoxy-CFRPs by cold spray deposition of tin (Sn) to fabricate a continuous metallic interlayer, followed by Cu electrodeposition (i.e., SnCS-CuEP). The tensile adhesion bond strength and the electrical resistivity of the duplex coating were investigated. It was found that cold-sprayed Sn coating failed adhesively in the absence of the electrodeposited Cu coating. After the electrodeposition of Cu, cohesive failure of the cold-sprayed Sn coating took place. A “dissolution-deposition” mechanism has been established to explain the cohesive failure of the coldsprayed Sn coating after electrodeposition. The cohesive strength of the Sn coating is slightly higher than that of the previously fabricated three-step coating system. The electrical conductivity of the electrodeposited Cu coating was found to be 90% of bulk Cu. These results suggest that a duplex SnCS-CuEP coating can be fabricated on epoxy-CFRPs with relatively high electrical conductivity and slightly enhanced adhesion properties as compared to multilayered coatings fabricated using a three-step electroless deposition-electrodeposition-cold spray process.
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 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 | 0.001 |
| 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".