Selective laser-assisted deposition of silver nanoparticles on a Mg-2wt.%Ca substrate
Bibliographic record
Abstract
Abstract Magnesium alloys appear as one of the most promising materials for many applications such as degradable implants. However, their mechanical, corrosion and integration behaviors need to be optimized to comply with this application. The present paper focuses on laser-assisted maskless microdeposition (LAMM) of silver nanoparticles, a surface treatment aiming to modify the surface characteristics for better integration of the magnesium implant. The LAMM process parameters for obtaining desirable depositions are reported. The impacts of the LAMM process on the deposit and the substrate microstructure have been investigated using various characterization techniques. The results show that laser processing, which can lead to particle sintering in the deposit, can be fine-tuned to achieve necking between nano particles, while the nano-scale characteristics of the deposited layer is retained. Microstructural characterization reveals significant grain refinement in the immediate vicinity of the surface, providing evidence for the thermal impact of the laser process on the substrate. The thermal profiles of the deposit and the substrate during processing are further investigated by developing a 3D finite element modelling method. The implementation of the model allows us to better understand the origin of the fine-grained sublayer as well as the overall thermal impact of the current laser processing method on the substrate.
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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".