Mechanism of laser induced void array formation in polydimethylsiloxane (PDMS)
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Bibliographic record
Abstract
Abstract This study investigates multi-void formation 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. While multi-void formation has been studied in transparent dielectrics and polymers such as PMMA, this work is the first to systematically investigate and demonstrate void array generation in PDMS using tightly focused femtosecond laser pulses. Our experimental and numerical results show multi-void formation in PDMS occurred as a result of multi-pulse irradiation in the bulk of PDMS and that the number, size, and spacing of voids can be finely tuned by varying laser energy, writing speed (pulses per micron), and numerical aperture. A particularly novel aspect of this work is the use of Finite-Difference-Time-Domain simulations incorporating pre-existing voids with densified shells to model the stepwise formation of void arrays. This approach captures the interaction of the laser pulse and previously formed voids, which allows reproduction of the experimentally observed void array. Furthermore, by comparing simulations with and without Kerr nonlinearity, we demonstrate that the dominant mechanism governing multi-void formation in PDMS is linear rather than nonlinear self-focusing. 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 and provide a pathway for precise, repeatable, and controllable fabrication of 3D microstructures in polymers such as PDMS, with potential applications in photonic crystals, optical memory devices, and microfluidic sensors.
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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 it