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Record W2023301111 · doi:10.1016/j.envpol.2014.06.018

Decreasing effect and mechanism of FeSO 4 seed particles on secondary organic aerosol in α -pinene photooxidation

2014· article· en· W2023301111 on OpenAlexafffund
Biwu Chu, Yongchun Liu, Junhua Li, Hideto Takekawa, John Liggio, Shao‐Meng Li, Jingkun Jiang, Jiming Hao, Hong He

Bibliographic record

VenueEnvironmental Pollution · 2014
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric chemistry and aerosols
Canadian institutionsEnvironment and Climate Change Canada
FundersClean Air Regulatory AgendaToyota Motor CorporationChinese Academy of Sciences
KeywordsPineneChemistryRadicalRelative humidityAerosolParticle (ecology)HumiditySaturation (graph theory)Water vaporEnvironmental chemistryNOxChemical engineeringPhotochemistryCombustionOrganic chemistry

Abstract

fetched live from OpenAlex

α-Pinene/NOx and α-pinene/HONO photooxidation experiments at varying humidity were conducted in smog chambers in the presence or absence of FeSO4 seed particles. FeSO4 seed particles decrease SOA mass as long as water was present on the seed particle surface, but FeSO4 seed particles have no decreasing effect on SOA under dryer conditions at 12% relative humidity (RH). The decreasing effect of FeSO4 seed particles on the SOA mass is proposed to be related to oxidation processes in the surface layer of water on the seed particles. Free radicals, including OH, can be formed from catalytic cycling of Fe(2+) and Fe(3+) in the aqueous phase. These radicals can react further with the organic products of α-pinene oxidation on the seed particles. The oxidation may lead to formation of smaller molecules which have higher saturation vapor pressures and favor repartitioning to the gas phase, and therefore, reduces SOA mass.

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 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 score0.398
Threshold uncertainty score0.693

Codex and Gemma teacher scores by category

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 head0.003
GPT teacher head0.164
Teacher spread0.161 · 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.

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

Citations32
Published2014
Admission routes2
Has abstractyes

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