Wood combustion nanoparticles emitted by conventional and advanced technology cordwood boilers, and their interactions in vitro with human lung epithelial monolayers
Bibliographic record
Abstract
Biomass-burning boilers and stoves are widely used in many parts of the world, producing combustion emissions linked with health risks. Combustion emission nanoparticles (NPs) were collected from four representative wood burning boilers using oak cordwood at specific times in the burn cycle. The morphology and composition of the NPs was characterized using transmission electron microscopy and energy dispersive X-ray analysis. To determine the degree of NP cytotoxicity with human lung tissue, the combustion NPs were introduced to incubated lung bronchial epithelial monolayers (NCI-H292) in vitro at doses of 0.1 × 10-6 and 3.0 × 10-6 kg/L for 2 and 4 h. Histochemical analysis showed that cell death increased by a factor of 3.5 for both doses after 4 h when compared to the control. Ultrapure NPs prepared by wet chemical methods were also introduced to the epithelial lung cells for similar doses and exposure times and the cultures exhibited significantly reduced mortality. Electron microscopy was used to study the mechanism of cell mortality for the synthesized and combustion-based NPs by examining how the NP byproducts interacted with individual cell organelles. It was found that cell survival was strongly correlated with the absence of contaminants (salts, heavy metals, poly aromatic hydrocarbons) associated with the NPs entering the cells. Synthesized NPs consisting of pure carbon were relatively well tolerated and could be excreted without damaging the cell ultrastructure. Thus, careful removal of extraneous contaminants by controlling the burn cycle with a catalyst is essential to minimize the health and environmental effects of wood biofuel combustion. In better words, optimized advanced technology wood-burning boilers and stoves can provide a CO2-neutral energy source and significantly contribute to a future where fossil fuels have a reduced role.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".