On-Chip 3-D Imaging and Counting of Particles Transported in a Microfluidic Channel Based on Light-Field Microscopy
Bibliographic record
Abstract
Rapid acquisition of 3-D volumetric information and accurate counting are significant in observing and monitoring specimens moving in microfluidic channels. This study introduces a novel approach of 3-D image flow cytmetry (IFC) using light-field microscopy that images the microfluidic channel with a microlens array (MLA) and reconstructs a complete 3-D volume from a single frame. To create the custom MLA, a cost-effective method is developed, combining gray-scale photolithography, nickel electroplating, and nanoimprinting. Furthermore, a calibration procedure is devised to rectify aberrations within the imaging system prior to 3-D reconstruction. The counting process employs an algorithm based on local intensity peak identification to quantify and locate particles moving in a microchannel, achieving counting accuracies around 95%. In comparison to existing 3-D IFCs, our method offers distinct advantages, including the capability for high throughput data capture and 3-D imaging fast moving samples without necessitating complex microchannel designs.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".