Scalable optical annealing of microfluidic droplets via whispering gallery mode geometry and infrared illumination
Bibliographic record
Abstract
This work presents a solution to limitations on scalability in traditional on-chip optofluidic polymerase chain reaction (PCR) methods that are based on infrared annealing and droplet-based microfluidics. The scalability in these PCR optofluidic methods is limited by the optical penetration depth of light in a fluid droplet. Traditionally, such an implementation has minimal absorption when the droplet diameter is scaled well below the optical penetration depth due to the small interaction length. In the presented whispering gallery mode (WGM) optofluidic method, a WGM wave is created through total internal reflection, where light is trapped within a droplet. The effect of the trapped light can extend the interaction length beyond the penetration depth, even for small diameter droplets. Thus, this WGM wave permits the use of droplets with diameters scaled below the penetration depth of the light. A theoretical analysis of traditional optical annealing and of the WGM optofluidic method is conducted using finite-difference time-domain analyses. The WGM wave optofluidic method is also demonstrated experimentally, providing higher annealing temperatures than traditional optical annealing. It is envisioned that the presented work will allow for scalable PCR devices implemented on-chip.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".