Single-Particle Analysis for Structure and Iron Chemistry of Atmospheric Particulate Matter
Bibliographic record
Abstract
As a representative transition metal, iron plays a key role in chemical activities of atmospheric particulate matter (PM), being involved in particle-related free radical generation and adverse health effects. However, limited understanding of the structure and properties of individual micrometer-sized particulates obscures investigating the contributions of iron toward chemical activities. Here, we describe multidimensional analytical strategies to characterize the mass, spatial distribution, and chemical forms of iron in single haze particles using synchrotron radiation techniques. We first used X-ray fluorescence imaging to quantify the masses of multiple metals and yielded distribution maps of transition metals, which revealed the types of elements that tend to occur together. Additionally, we employed nanocomputed tomography to assess the spatial distribution of iron and observed that iron exists as small aggregates and is concentrated primarily in subsurface regions. We also combined X-ray absorption near structures with scanning transmission X-ray microscopy to quantify the ferrous and ferric forms and mapped their distributions in individual particles, which probably attribute chemical activity of iron. In conclusion, we demonstrated the power of synchrotron radiation-based techniques to study heretofore inaccessible chemical information in single haze particles, which may provide important clues about iron chemistry as a source of Fenton reactions and health effects. The multifaceted analytical approaches exhibit high sensitivity (subfemtogram per particle or ∼0.2 fg/μm 2 ) toward multiple elements and are promising to be used for studying other concepts such as the solubility of aerosol iron, the heterogeneous oxidation of organic matters and SO 2, and the formation and the aging of haze particles.
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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 | 0.001 | 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) | 0.001 | 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".