Rapid and cheap prototyping of a microfluidic cell sorter
Bibliographic record
Abstract
Development of a microfluidic device is generally based on fabrication-design-fabrication loop, as, unlike the microelectronics design, there is no rigorous simulation-based verification of the chip before fabrication. This usually results in extremely long, and hence expensive, product development cycle if micro/nano fabrication facilities are used from the beginning of the cycle. Here, we illustrate a novel approach of device prototyping that is fast, cheap, reliable, and most importantly, this technique can be adopted even if no state-of-the-art microfabrication facility is available. A water-jet machine is used to cut the desired microfluidic channels into a thin steel plate which is then used as a template to cut the channels into a thin sheet of a transparent and cheap polymer material named Surlyn® by using a Hot Knife™. The feature-inscribed Surlyn sheet is bonded in between two microscope glass slides by utilizing the techniques which has been being used in curing polymer film between dual layer automotive glasses for years. Optical fibers are inserted from the sides of chip and are bonded by UV epoxy. To study the applicability of this prototyping approach, we made a basic microfluidic sorter and tested its functionalities. Sample containing microparticles is injected into the chip. Light from a 532-nm diode laser is coupled into the optical fiber that delivers light to the interrogation region in the channel. The emitted light from the particle is collected by a photodiode (PD) placed over the detection window. The device sorts the particles into the sorted or waste outlets depending on the level of the PD signal. We used fluorescent latex beads to test the detection and sorting functionalities of the device. We found that the system could detect all the beads that passed through its geometric observation region and could sort almost all the beads it detected.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".