Characterization of Laser Assisted Maskless Micro-Deposition Technology for Fabrication of Conductive Micro Systems
Bibliographic record
Abstract
Laser assisted maskless micro-deposition (LAMM) is a recently developed technology for deposition of thin films with micro-scale feature sizes. It can be used for deposition of a large number of materials such as, electronic inks/pastes, conductive polymers, insulators, adhesives, and biological materials. This process is a combination of liquid atomization, micro-deposition and laser sintering. The present study is concerned with the process characterization for the deposition of silver thin films on silica. The deposited material is in the form of nano-particles dispersed in liquid, known as nano-past. In the present research work, deposition patterns are produced with different process parameters. Afterwards, the effects of process parameters, e.g., laser power and laser scanning speed, are investigated by examining the quality of the deposited material.
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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".