Bibliographic record
Abstract
Lab-on-a-chip devices are miniaturized bio-medical laboratories on a small glass/plastic plate. These lab chips can duplicate the specialized functions of their room-sized counterparts such as clinical diagnoses and tests. The key microfluidic functions required in various lab-on-a-chip devices include pumping and mixing liquids, controlling bio-reactions, dispensing samples and reagents, and separating molecules and cells/particles. Using electrokinetic microfluidics to realize these functions can make the devices fully automatic, independent of external support (e.g., tubing, valves and pump), and truly portable. Understanding, modeling and controlling of various electrokinetic microfluidic phenomena and the electrokinetic microfluidic processes are essential to systematic design and operation control of the lab-on-a-chip systems. This presentation will explain the principles of these electrokinetic microfluidic processes and how they are used in lab-on-a-chip devices. Some lab-on-a-chip devices such as real-time PCR chip, immunoassay chip and flow cytometer chip developed in Dr. Li’s lab will be introduced.
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 | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".