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 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 | 0.000 |
| 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".