SpectIR-fluidics: completely customizable microfluidic cartridges for high sensitivity on-chip infrared spectroscopy with point-of-application studies on bacterial biofilms
Bibliographic record
Abstract
We present a generalizable fabrication method for a new class of analytical devices that merges virtually any microfluidic design with high-sensitivity on-chip attenuated total reflection (ATR) sampling using any standard Fourier transform infrared (FTIR) spectrometer. Termed "spectIR-fluidics", a major design feature is the integration of a multi-groove silicon ATR crystal into a microfluidic device, compared with previous approaches in which the ATR surface served as a structural support for the entire device. This was accomplished by the design, fabrication, and aligned bonding of a highly engineered ATR sensing layer, which con```tains a seamlessly embedded ATR crystal on the channel side and an optical access port that matched the spectrometer light path characteristics at the device exterior. The refocused role of the ATR crystal as a dedicated analytical element, combined with optimized light coupling to the spectrometer, results in limits of detection as low as 540 nM for a D-glucose solution, arbitrarily complex channel features that are fully enclosed, and up to 18 world-to-chip connections. Three purpose-built spectIR-fluidic cartridges are used in a series of validation experiments followed by several point-of-application studies on biofilms from the gut microbiota of plastic-consuming insects using a small portable spectrometer.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".