Real-Time Cell-Electronic Sensing of Coal Fly Ash Particulate Matter for Toxicity-Based Air Quality Monitoring
Bibliographic record
Abstract
The development of a unique bioassay for cytotoxicity analysis of coal fly ash (CFA) particulate matter (PM) and its potential application for air quality monitoring is described. Using human cell lines, A549 and SK-MES-1, as live probes on microelectrode-embedded 96-well sensors, impedance changes over time are measured as cells are treated with varying concentrations (1 μg/mL-20 mg/mL) of CFA samples. A dose-dependent impedance change is determined for each CFA sample, from which an IC50 histogram is obtained. The assay was successfully applied to examine CFA samples collected from three coal-fired power plants (CFPs) in China. The samples were separated into three size fractions: PM2.5 (<2.5 μm), PM10-2.5 (2.5 μm < x < 10 μm), and PM10 (>10 μm). Dynamic cell-response profiles and temporal IC50 histograms of all samples show that CFA cytotoxicity depends on concentration, exposure time (0-60 h), and cell-type (SK-MES-1 > A549). The IC50 values differentiate the cytotoxicity of CFA samples based on size fraction (PM2.5 ≈ PM10-2.5 ≫ PM10) and the sampling location (CFP2 > CFP1 ≈ CFP3). Differential cytotoxicity measurements of particulates in human cell lines using cell-electronic sensing provide a useful tool for toxicity-based air quality monitoring and risk assessment.
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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) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".