Laser-assisted surface patterning of magnesium with silver nanoparticles: synthesis, characterization and modelling
Bibliographic record
Abstract
Abstract Surface patterning of biocompatible materials may improve cell adhesion, infiltration and proliferation. Magnesium is a biocompatible metal which has bioresorption capability, as well as a low elastic modulus. This unique combination of properties has attracted researchers to develop biodegradable magnesium devices. In this work, the patterning of magnesium with silver nanoparticles printed by the laser-assisted maskless microdeposition (LAMM) process was studied. The geometry and microstructure of the printed silver patterns and the diffusion zone at the Mg/Ag interface were studied using profilometry, scanning electron microscopy (SEM), energy dispersive x-ray spectroscopy (EDS) and transmission electron microscopy (TEM). The TEM analysis suggested that some intermetallic particles may form at the interface during laser sintering. The penetration curve of the Ag–Mg diffusion couple was obtained using EDS analysis. A finite element model is developed to simulate the LAMM process of silver nanoparticles on magnesium substrates. The model included a thermal analysis for determining the temperature history throughout the process, coupled with a diffusion model for evaluating compositional profiles across the interface. A comparison of the predicted profiles with the EDS results showed that the modelling and the experimental results qualitatively match. The analysis of SEM micrographs of the sintered nanoparticles, in light of the thermal modelling results, suggested that a minimum laser heat input was required for effective sintering of the nanoparticles.
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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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".