A multireflection cell for enhanced absorbance detection in microchip-based capillary electrophoresis devices
Bibliographic record
Abstract
The design, fabrication and testing of a photolithographically fabricated, glass-based multireflection absorbance cell for microfluidic devices, in particular microchip-based capillary electrophoresis (CE) systems is described. A multireflection cell was fabricated lithographically using a three-mask process to pattern aluminum mirrors above and below a flow channel in a chip, with 30 microm diameter optical entrance or exit apertures (one in each mirror) positioned 200 microm apart. Source and detector were positioned on opposite sides, and the metal mirrors were made 1 cm square, to reduce stray light effects. Calibration curves using bromothymol blue (BTB) with a 633 nm source (He:Ne laser) were linear to at least 0.5 absorbance units, with typical r2 values of 0.9997, relative standard deviations in the slopes of +/- 1.3%, and intercepts of zero within experimental error. Effective optical pathlengths of 50-272 microm were achieved, compared to single-pass pathlengths of 10-30 microm, corresponding to sensitivity enhancements (i.e., optical path length increase) of 5 to 10-fold over single-pass devices. Baseline absorbance noise varied within a factor of two in almost all devices, depending only weakly on path length. This device can give much higher absorbance sensitivity, and should be much easier to manufacture than planar, glass-based devices previously reported.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".