Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Microfluidic lab-on-a-chip devices are usually fabricated using replica molding, with poly(dimethylsiloxane) (PDMS) casting on a mold. Most common techniques used to fabricate microfluidic molds, such as photolithography and soft lithography, require costly facilities such as a cleanroom, and complicated steps, especially for the fabrication of three-dimensional (3D) features. For example, an often-desired 3D microchannel feature consists of intersecting channels with depth variations. This type of 3D flow focusing geometry has applications in flow cytometry and droplet generation. Various manufacturing techniques have recently been developed for the rapid fabrication of such 3D microfluidic features. In this paper, we describe a new method of mold fabrication that utilizes water jet cutting technology to fabricate free-standing structures on mild steel sheets to make a mold for PDMS casting. As a proof-of-concept, we use this fabrication technique to make a PDMS chip that has a 3D flow focusing junction, an inlet for the sample fluid, two inlets for the sheath fluid, and an outlet. The flow focusing junction is patterned into the PDMS slab with an abrupt, nearly stepwise change to the depth of the microchannel junction. We use confocal microscopy to visualize the 3D flow focusing of a sample flow using this geometry, and we also use the same geometry to generate water-in-oil droplets. This alternative approach to create microfluidic molds is versatile and may find utility in reducing the cost and complexity involved in fabricating 3D features in microfluidic devices.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.001 | 0.012 |
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 it