Fabrication of microfluidic chips using controlled dissolution of<scp>3D</scp>printed scaffolds
Bibliographic record
Abstract
Abstract Microfluidic chips are commonly fabricated using soft lithography, which often requires a clean room and micropatterning equipment. Recently, microfluidic chips are increasingly fabricated using 3D printing, but this technology is still limited in smallest channel printability, transparency, supports residue, and biocompatibility. In this work, a simple, fast, and inexpensive step is introduced to fabricate polydimethylsiloxane (PDMS) microfluidic chips using enhanced internal scaffold removal (eISR). It is found that final channel dimension decreases by 0.22 ± 0.02 μm/revolution with a 7% error using eISR. Surface topology is inspected after dissolution using scanning electron microscopy. A T‐junction device, bifurcation channels, and curved channels are fabricated to demonstrate the usability of eISR in multiple applications. Compared to previous methods, eISR provides acrylonitrile–butadiene–styrene dissolution before PDMS casting to achieve thinner and smoother channels produced using a commercial 3D printer.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".