Irreversible bonding of polyimide and polydimethylsiloxane (PDMS) based on a thiol-epoxy click reaction
Bibliographic record
Abstract
Polyimide is one of the most popular substrate materials for the microfabrication of flexible electronics, while polydimethylsiloxane (PDMS) is the most widely used stretchable substrate/encapsulant material. These two polymers are essential in fabricating devices for microfluidics, bioelectronics, and the internet of things; bonding these materials together is a crucial challenge. In this work, we employ click chemistry at room temperature to irreversibly bond polyimide and PDMS through thiol-epoxy bonds using two different methods. In the first method, we functionalize the surfaces of the PDMS and polyimide substrates with mercaptosilanes and epoxysilanes, respectively, for the formation of a thiol-epoxy bond in the click reaction. In the second method, we functionalize one or both surfaces with mercaptosilane and introduce an epoxy adhesive layer between the two surfaces. When the surfaces are bonded using the epoxy adhesive without any surface functionalization, an extremely small peel strength (<0.01 N mm −1 ) is measured with a peel test, and adhesive failure occurs at the PDMS surface. With surface functionalization, however, remarkably higher peel strengths of ~0.2 N mm −1 (method 1) and >0.3 N mm −1 (method 2) are observed, and failure occurs by tearing of the PDMS layer. We envision that the novel processing route employing click chemistry can be utilized in various cases of stretchable and flexible device fabrication.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".