A new soft lithographic route for the facile fabrication of hydrophilic sandwich microchips
Bibliographic record
Abstract
Manufacturing materials are an essential element for the fabrication of microfluidic chips. PDMS, the most widely used polymeric material, is associated with apparent disadvantages such as hydrophobic nature, while other materials also suffer from some limitations. In this paper, a new soft lithographic route was proposed for the facile manufacturing of hydrophilic sandwich microchips, using bisphenol A based epoxy acrylate (BABEA) as a new patterning material. The BABEA copolymers are hydrophilic, highly transparent in visible range while highly untransparent when the wavelength is less than 290 nm, and of high replication fidelity. By combining with appropriate monomers, including glycidyl methacrylate, methylmethacrylate, and acrylic acid, the copolymers contain active functional groups, which allows for easy postmodification for desirable functional units. A fabrication procedure was proposed for manufacturing hybrid quartz/BABEA copolymer/quartz microchips. In the procedure, no micromachining equipments, wet etching, or imprinting techniques were involved, making the fabrication approach applicable in ordinary chemistry laboratories. The performance of the prepared microchips was demonstrated in terms of CIEF with UV-whole channel imaging detection. The hydrophilic microchannel ensures stable focusing while the polymeric middle layer acts as a perfectly aligned optical slit for whole channel UV absorbance detection.
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) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".