Bacterial Bioaerosol-Specific Capture and In Situ Detection Using an Immune ZIF-8-Melamine Foam-Functionalized Colorimetric Biosensor
Bibliographic record
Abstract
Bioaerosol infections containing pathogenic viruses and bacteria have resulted in significant economic losses and posed a serious threat to public health, as evidenced by outbreaks of coronavirus disease and avian influenza. Consequently, the sampling and screening of bioaerosols are crucial for the prevention of bioaerosol-borne diseases. In this study, an ultrasensitive biosensor based on zeolitic imidazolate framework-8-melamine foam (ZIF-8-MF) was innovatively developed for the specific capture and in situ detection of bioaerosols. The bacterial bioaerosols were collected by a wet cyclone into phosphate-buffered saline (PBS) buffer at a high collection rate, achieving a satisfactory collection efficiency of ∼80% within 10 min. The target bacteria collected in the PBS buffer were specifically captured and effectively concentrated using immune ZIF-8-MF. The gold@platinum nanozymes (GPNs) were employed to specifically label the captured target bacteria and efficiently amplify the biological signal. And the resulting colorimetric signal was analyzed via a self-developed smartphone application (App). This biosensor demonstrated the capability to detect bioaerosols containing Salmonella typhimurium in the range of 1.6 × 10 2 –1.6 × 10 5 CFU/m 3 within 1.5 h, with a detection limit as low as 100 CFU/m 3 . Compared with other bioaerosol detection methods, the biosensor offered advantages such as high collection rate, specific capture, efficient concentration, and in situ detection, positioning it as a highly promising and practical tool for the monitoring of bioaerosols containing diverse pathogenic bacteria.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".