3D thermoplastic elastomer microfluidic devices for biological probe immobilization
Bibliographic record
Abstract
Microfluidics has emerged as a valuable tool for the high-resolution patterning of biological probes on solid supports. Yet, its widespread adoption as a universal biological immobilization tool is still limited by several technical challenges, particularly for the patterning of isolated spots using three-dimensional (3D) channel networks. A key limitation arises from the difficulties to adapt the techniques and materials typically used in prototyping to low-cost mass-production. In this paper, we present the fabrication of thin thermoplastic elastomer membranes with microscopic through-holes using a hot-embossing process that is compatible with high-throughput manufacturing. The membranes provide the basis for the fabrication of highly integrated 3D microfluidic devices with a footprint of only 1 × 1 cm(2). When placed on a solid support, the device allows for the immobilization of up to 96 different probes in the form of a 10 × 10 array comprising isolated spots of 50 × 50 μm(2). The design of the channel network is optimized using 3D simulations based on the Lattice-Boltzmann method to promote capillary action as the sole force distributing the liquid in the device. Finally, we demonstrate the patterning of DNA and protein arrays on hard thermoplastic substrates yielding spots of excellent definition that prove to be highly specific in subsequent hybridization experiments.
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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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".