Cost‐effective template development for the microfluidic device
Bibliographic record
Abstract
Photolithography, typically used to create microchannel networks on silicon to fabricate the template for microfluid devices, has the drawback of requiring sophisticated instruments, available only in few premier fabrication units. Template fabrication thus was a privilege of few researchers. Through the years, researchers economised the process of device development using a three‐dimensional (3D) printer which directly projected non‐planar structures on to a photo‐curable resin. Devices thus built lacked the versatility of polydimethylsiloxane (PDMS). The novelty of this work is to use the 3D printing resin for template fabrication and subsequent device development with PDMS. In this way, cost reduction and ease of template generation are substantially enhanced while retaining the advantages of a PDMS device. Unlike directly printed devices that are formed from ultraviolet curable photopolymer, this method fabricates the master with cured photopolymer used in 3D printing. The master pattern is transferred to PDMS for subsequent processing to construct the device. Compared to devices built on silicon templates, PDMS on polymer templates necessitate careful curing at a lower temperature. Low‐temperature PDMS–substrate bonding has also been studied in this work. Fabricated device has channel dimensions in the order of 200–300 μm and has been used to study various oil–water emulsions.
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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.005 |
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".