A review of the mineralogical and chemical composition of nanoparticles associated with coal fires
Bibliographic record
Abstract
Abstract This review summarizes our current knowledge on the health and environmental impact as well as the mineralogical and geochemical composition of nanoparticles (NPs) associated with coal fires. It will furthermore recommend new sampling and characterization protocols to gain a better understanding of the various types of NPs that are formed either through high‐temperature nucleation and alteration processes or via low‐temperature dissolution‐reprecipitation and weathering processes. Coal fires affect the immediate environment of coal‐producing areas and produce positive and negative feedback to climate change through the emission of carbon‐ and sulfate‐bearing gases and aerosols, respectively. Nanoparticles form during and after coal fires. They are composed of mainly soot and tar particles as well as amorphous phases, minerals, and complex mixtures of amorphous phases and minerals. It is recommended that NPs for mineralogical studies should be collected using impactors (a new generation of collectors for particulate matter, such as the TPS100 NP sampler) or that borosilicate filters at the openings of pipes and chambers be used to collect and measure gases emitted by coal fires. Furthermore, assemblages of NPs occurring at the mouths of coal fire vents should be examined using a combination of focused ion beam (FIB) technology and transmission electron microscopy (TEM), and those containing ion‐ or electron‐beam sensitive phases should be examined with the corresponding cryo‐techniques, such as cryo‐FIB, cryo‐ion mill, and cryo‐TEM. The mineralogical and chemical composition of NP‐bearing bulk samples should be examined with spectroscopy techniques such as X‐ray photoelectron spectroscopy, 13 C nuclear magnetic resonance spectroscopy, or time‐of‐flight secondary ion mass spectroscopy.
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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) | 0.001 | 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 | 0.000 | 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".