First in-flight synchrotron X-ray absorption and photoemission study of carbon soot nanoparticles
Bibliographic record
Abstract
Many studies have been conducted on the environmental impacts of combustion generated aerosols. Due to their complex composition and morphology, their chemical reactivity is not well understood and new developments of analysis methods are needed. We report the first demonstration of in-flight X-ray based characterizations of freshly emitted soot particles, which is of paramount importance for understanding the role of one of the main anthropogenic particulate contributors to global climate change. Soot particles, produced by a burner for several air-to-fuel ratios, were injected through an aerodynamic lens, focusing them to a region where they interacted with synchrotron radiation. X-ray photoelectron spectroscopy and carbon K-edge near-edge X-ray absorption spectroscopy were performed and compared to those obtained for supported samples. A good agreement is found between these samples, although slight oxidation is observed for supported samples. Our experiments demonstrate that NEXAFS characterization of supported samples provides relevant information on soot composition, with limited effects of contamination or ageing under ambient storage conditions. The highly surface sensitive XPS experiments of airborne soot indicate that the oxidation is different at the surface as compared to the bulk probed by NEXAFS. We also report changes in soot's work function obtained at different combustion conditions.
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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 | 0.000 | 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".