F 2 -laser microfabrication for integrating optical circuits with microfluidic biochips
Bibliographic record
Abstract
Lasers microprocessing is attractive for the custom fabrication of novel lab-on-a-chip designs. However, processing of glass biochips is challenging for most lasers because of the weak light interactions inherent in such transparent substrates. The F2-laser generates a high 7.9-eV photon energy that drives strong absorption in glasses, while the short 157-nm wavelength offers high-resolution patterning on the 100-nm scale. With these benefits, F2-laser ablation is well suited to the fabrication of high aspect ratio microfluidic channels and other biochip functions. F2-laser radiation also produces a strong photosensitivity response in fused silica and other glasses that enable the fabrication of buried optical waveguides, Bragg grating filters and other refractive index structures inside the glass. In this paper, we combine laser micromachining and refractive index profiling to enable single-step integration of photonic functions with microfluidic functions on a single chip. Optical waveguides were written to intercept microfluidic channels for optical sensing of cells and other bio-materials. An integrated biophotonic sensor is demonstrated for polystyrene spheres. The sensor is optically characterized for insertion loss, propagation loss, and particle sensitivity. The demonstration and analysis of this simple device offers insight into the capabilities and potential applications for laser fabricated glass lab-on-a-chip devices. Moreover, the groundwork is laid for rapid laser prototyping of custom-designed microfluidic biochips interlaced with integrated-optical circuits to define a new generation of highly functional bio-sensor and lab-on-a-chip devices.
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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) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".