A photonic‐microfluidic integrated device for reliable fluorescence detection and counting
Bibliographic record
Abstract
A photonic-microfluidic integrated device is demonstrated with excellent and reliable fluorescence detection performance. CV values of 8% for 2.5-μm beads and 14% for 6-μm beads were achieved through the correct deployment of carefully formed excitation beam shapes via integrated on-chip optics even without the use of 3D hydrodynamic focusing or a high-quality laser source and single mode beam propagation. The devices are fabricated in a monolithic planar fashion using a system of microlenses and waveguides integrated with microfluidic channels on-chip and packaged using a high-quality and low-cost channel sealing and high-performance interconnecting technology developed from our earlier works. Beam geometry in the excitation region is shown to affect the variation of fluorescence intensity from specimens, hence configurations of beam geometry targeted for a specific bead sizes are examined to ensure proper deployment of the lens designs. The formed high-quality optical excitation regions ensure reliable detection even with relaxed hydrodynamic focusing to ensure applicability with multiple specimen sizes. Device performance with each bead size was found to be acceptable for a range of beam geometries with a different ideal configuration for each bead size. These device designs help to form a device that will supplement conventional flow cytometry in point-of-care and remote detection applications by performing specific detections with an inexpensive and replaceable device.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".