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Record W3006222328 · doi:10.5194/acp-20-8123-2020

Temperature effects on optical properties and chemical composition of secondary organic aerosol derived from <i>n</i> -dodecane

2020· article· en· W3006222328 on OpenAlexaff
Junling Li, Weigang Wang, Kun Li, Wenyu Zhang, Chao Peng, Li Zhou, Bo Shi, Yan Chen, Mingyuan Liu, Hong Li, Maofa Ge

Bibliographic record

VenueAtmospheric chemistry and physics · 2020
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric chemistry and aerosols
Canadian institutionsEnvironment and Climate Change Canada
FundersNational Key Research and Development Program of ChinaNational Natural Science Foundation of China
KeywordsAerosolDodecaneChemical compositionChemistryRefractive indexComposition (language)WavelengthAnalytical Chemistry (journal)Environmental chemistryMaterials scienceOrganic chemistryOptoelectronics

Abstract

fetched live from OpenAlex

Environmental temperature plays a vital role in controlling chemical transformations that lead to the formation of secondary organic aerosol (SOA) and ultimately impact the composition and optical properties of the aerosol particles. In this study, we investigated optical properties of n -dodecane SOA under two temperature conditions: 5 and 25 ∘ C. It was shown that low-temperature conditions could enhance the real part of the refractive index (RI) of the SOA at wavelengths of 532 and 375 nm. Mass spectrometry analysis revealed that the molecular composition of n -dodecane SOA was significantly modified by temperature: a large amount of oligomers were formed under low-temperature conditions, which led to higher RI values. These findings will help improve our understanding of the chemical composition and optical properties of SOA under different temperature conditions and will provide one possible explanation for the low visibility in suburban areas during winter.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.002
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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

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 head0.007
GPT teacher head0.167
Teacher spread0.160 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations37
Published2020
Admission routes1
Has abstractyes

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