Hollow core photonic crystal fiber as a robust Raman biosensor
Bibliographic record
Abstract
The present work demonstrates the integration of hollow core photonic crystal fibers (HC-PCF), microfluidics, and statistical analysis for monitoring biomolecules using Raman spectroscopy. HC-PCF as a signal enhancer has been proven by many researchers. However, there have been challenges in using HC-PCF for practical applications due to limitations such as coupling, stability, evaporation, clogging, consistent filling, and reusing the same fiber. This limited the potential of HC-PCF to detect low concentrations of liquid samples, which is why HC-PCF still hasn’t transcended the lab barriers. The current device is based on an H-design lay-out which uses the pressure difference between the two ends of the fiber for filling and flushing the liquid samples. This mitigated several issues related to device performance by allowing us to fill the fiber with liquid samples consistently, rapidly and reproducibly. The resulting Raman signals were significantly more stable as various concentrations of ethanol in water were sequentially introduced into the fiber. The scheme also allowed us to overcome the barrier of predicting low concentrations by applying Partial Least Square (PLS) technique which was done for the first time using HC-PCF. Thus, the present scheme paves path for the inclusion of HC-PCF in the main stream point-of-care technology.
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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 | 0.000 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".