Mechanism of laser induced self-organized void array formation in Polydimethylsiloxane (PDMS)
Bibliographic record
Abstract
Abstract This study investigated the formation of multi-voids in polydimethylsiloxane (PDMS) using a multi-pulse irradiation method and explored the impact of laser energy, number of pulses per micron (writing speed), and laser spot size on the process. The experimental results revealed that multi-void formation occurred due to multi-pulse irradiation in the bulk of PDMS. Additionally, increasing laser energy led to an increase in the number of voids, while the number of voids did not change with an increase in the number of pulses per micron for a fixed laser parameter. However, the size of the voids increased with the number of pulses per micron, and tighter focusing conditions resulted in smaller voids with a shorter distance between them.Furthermore, Finite-Difference-Time-Domain (FDTD) simulations reproduced the generation of void arrays in PDMS using a similar multi-laser pulse approach. By modeling the voids as concentric spheres with densified shells and simulating the laser interaction with the voids, we showed that void array generation in PDMS is a linear mechanism. This study provides valuable insight into the mechanism behind the formation of void arrays in PDMS. The simulation results agree with the experimental results to further validate the model and gain a better understanding of the physical processes involved in the generation of void arrays in PDMS.
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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 | 0.000 | 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".