Coupling solid phase microextraction to integrated optical sensors with microfluidic open interface
Bibliographic record
Abstract
• Simple integration of microfluidic open interface with UV/Vis absorption detection. • Rapid sensor readout for solid-phase microextraction devices. • Cost-effective system for screening of dyes in water enhancer solution. Increasing number of regulations today is very demanding, so many fields need to rely to screening techniques to separate samples whose content need to be confirmed with that field's gold standard approach. Therefore, the development of simple and fast approaches is very much needed. This manuscript presents the development of a microfluidic open interface integrated with ultraviolet-visible (UV–Vis) absorption detection for rapid screening applications. The system was designed as a rapid sensor readout for compounds enriched by solid-phase microextraction devices. Two azo dyes from a liquid water enhancer solution served as model analytes. These two dyes were extracted with the solid phase microextraction fiber and introduced to the system for analysis. Experimental results demonstrated that the system could effectively detect dyes with absorbance at 403 nm, indicating its potential for quantitating analytes with UV–Vis chromophores. Although this sensing method exhibits limited selectivity, it offers a cost-effective, straightforward approach to rapid screening that can be conveniently miniaturized for on-site applications. Further advances could focus on integrating low-cost sensors with specific responses in place of the UV–Vis unit, as well as simplification and miniaturization of the system to improve on-site analysis. Additionally, incorporating autosampler systems could enable high-throughput determinations, broadening the method's applicability. The developed solid phase microextraction method reliably samples and enriches small molecules from complex systems, delivering clean extracts to the selective sensor for readout without interference from the investigated sample matrix.
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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 | 0.001 |
| 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) | 0.001 | 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".