PHOTOPATTERNING OF PDMS VIA BENZOPHENONE-MEDIATED HYDROSILYLATION FOR SOFT MATERIAL MICROFABRICATION
Bibliographic record
Abstract
This thesis presents a mold-free photopatterning strategy for polydimethylsiloxane (PDMS) and bottlebrush elastomers (BBEs) via benzophenone-mediated UV-induced hydrosilylation. Conventional PDMS patterning methods rely heavily on soft lithography and mold demolding, which are incompatible with ultrasoft materials due to deformation or tearing. Here, benzophenone is used as a photoinitiator to achieve selective crosslinking under UV exposure, enabling high-resolution negative-tone patterning without rigid molds. Mechanistic validation using (_^1)H NMR confirms that hydrogen abstraction by UV-excited benzophenone initiates radical-driven network formation. Process parameters were optimized for Sylgard 184 and extended to HMS- and VDT-based BBEs. The resulting patterned films exhibit features as small as 20 μm, vertical sidewalls, and minimal swelling. Mechanical characterization shows significant reduction in Young’s modulus under photochemical curing, demonstrating compatibility with soft electronics and neural interfaces. Demonstration devices including a microfluidic mixer and a stretchable conductor validate the method’s practical utility for bio-integrated soft systems.
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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) | 0.003 | 0.001 |
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".