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Record W4411652487 · doi:10.5194/amt-19-3049-2026

Exploring biogenic secondary organic aerosol using a PTRMS-CHARON in laboratory experiments: characterization and fingerprint analysis

2025· preprint· en· W4411652487 on OpenAlexaff
Carolina Ramírez-Romero, Olatunde Murana, Hichem Bouzidi, Marina Jamar, Sébastien Dusanter, Alexandre Tomas, Ahmad Lahib, Layal Fayad, Véronique Riffault, Christopher Pöhlker, Stéphane Sauvage, Joël Brito

Bibliographic record

VenueAtmospheric measurement techniques · 2025
Typepreprint
Languageen
FieldEngineering
TopicAdvanced Chemical Sensor Technologies
Canadian institutionsUniversity of Toronto
FundersEuropean Regional Development FundMax-Planck-GesellschaftAgence Nationale de la RechercheBundesministerium für Bildung, Wissenschaft, Forschung und TechnologieBundesministerium für Bildung und ForschungUniversité de Lille
KeywordsFingerprint (computing)Characterization (materials science)AerosolEnvironmental scienceRemote sensingMineralogyGeologyPhysicsMaterials scienceComputer scienceMeteorologyNanotechnologyArtificial intelligence

Abstract

fetched live from OpenAlex

Abstract. Volatile Organic Compounds (VOC), particularly of biogenic origin emitted by vegetation and soils, play an important role in the global Organic Aerosol (OA) budget. The introduction of field-deployable Aerosol Mass Spectrometers in the early 2000s, combined with statistical analysis of their mass spectra, has significantly improved our understanding of the impact of secondary processes on fine-mode aerosol concentrations. While delivering innovative and significant insights, those analyses usually fail to explicitly identify precursors/mechanisms. In this context, this work focuses on laboratory-generated secondary OA (SOA) of biogenic VOC and its spectral analysis through a new generation of aerosol mass spectrometers, notably a Proton Transfer Reaction Mass Spectrometer coupled to a Chemical Analysis of AeRosol Online (PTRMS-CHARON) inlet. Aerosol particles were formed in the DouAir atmospheric chamber via isoprene (ISOP) OH oxidation, monoterpene O3 (limonene, MT), and sesquiterpene O3 (β-caryophyllene, SQT) oxidation. ISOP experiments targeted "low-NO" environments, typically remote forested tropical areas, via epoxidiols formation (ISOP-IEPOX-SOA), or through an alternative branching favored in the absence of acidic seed particles (ISOP-Non-IEPOX-SOA) and "high NO" environments, representative in urban and polluted regions (ISOP-NO-SOA). Experiments showed that those five SOA formation pathways (ISOP-IEPOX-SOA, ISOP-Non-IEPOX-SOA, ISOP-NO-SOA, and the ozonolysis reactions of MT and SQT) exhibited distinguishable spectra, with identifiable tracer ions, such as m/z 83.049 (C5H6O), m/z 119.07 (C5H10O3), m/z 137.081 (C5H12O4) for ISOP-IEPOX-SOA, C5H10O4 (m/z 135.070), C5H10O6 (m/z 167.055) for ISOP-Non-IEPOX-SOA, and m/z 85.028 (C4H4O2) for NO-SOA pathways, as well as molecules with C7-C10 and C7-C15 structures identified during MT and SQT oxidation experiments, respectively. These laboratory findings depict promising results for ambient near-real-time biogenic SOA source apportionment, notably in forested and/or urbanized areas.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.070
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.062
GPT teacher head0.254
Teacher spread0.191 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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