A Microfluidic Colorimetric Biosensor Integrated with Water-Soluble Nonwoven Fabrics for Collection and Detection of Bacterial Aerosols
Bibliographic record
Abstract
Bioaerosols constitute a major route of disease transmission, necessitating effective detection methods for public health protection. In this study, a microfluidic colorimetric biosensor integrated with the water-soluble nonwoven fabrics (WS-NWFs) was innovatively developed for the collection and detection of bacterial aerosols. The WS-NWFs made of poly(vinyl alcohol) with good biocompatibility and water solubility were used to collect the bacterial aerosols, which eliminated the need for mechanical or chemical elution, enabling direct release of collected bacteria, thereby maximizing the recovery rate. The immune magnetic nanobeads (MNBs) and Pd/Pt nanozymes were employed to specifically separate and label the collected bacteria, achieving enrichment of the collected bacteria and amplification of the biological signal. Meanwhile, the microfluidic colorimetric biosensor with a multivortex micromixer integrated bacterial separation, labeling, washing, catalysis, and detection, realizing the quantitative determination of bacterial aerosols. The proposed biosensor provides a rapid, specific, sensitive, and in-field solution for real-time monitoring of pathogenic bioaerosols in public health settings.
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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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".