Sticker molds and stencils for low-cost rapid-fabrication of polymer and textile-based biomedical devices
Bibliographic record
Abstract
SU-8 molds are used to prototype polydimethylsiloxane (PDMS) devices but require specialized skill, time, and costly photolithographic equipment to fabricate. Low-cost polymer alternatives such as laser-ablated polymethylmethacrylate (PMMA) molds have been explored, but are limited by the surface roughness of the ablated PMMA which transfers to molded PDMS devices. In addition, screens and stencils are widely used as ink masks for direct on-textile fabrication, but may suffer from seepage underneath their edges, resulting in lower pattern resolution. In this work we present “sticker molds” for ultra-rapid prototyping. For fabrication of PDMS devices, these molds are comprised of a glass substrate with layers of Uline Industrial Tape S-423 patterned by laser ablation. We examine the geometry and surface roughness of these molds and the resultant PDMS devices using microscopy and profilometry. Channels dimensions as small as 100 μm wide and 50 μm deep are achieved, with a W-shaped kerf. These molds have low material cost (< 10 cents Canadian per mold), require under 10 minutes to fabricate, and have low surface roughness, resulting in smooth PDMS devices that readily bond. For devices directly printed on textiles, “sticker stencils” developed from simple sticky paper can also be patterned using laser ablation, and adhered to the textile or existing ink patterns. The low barrier to entry makes sticker molds and stencils ideal for rapid proof-of-concept work where designs are frequently changing, or a classroom setting. Furthermore, we employ sticker stencils for development of electronic and microfluidic structures directly on textiles for wearable biomedical 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".