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Production of Atmospheric Organosulfates via Mineral-Mediated Photochemistry

2019· article· en· W2917680124 on OpenAlexafffund
Mario Schmidt, Shawn M. Jansen van Beek, Maya Abou‐Ghanem, Anton O. Oliynyk, Andrew J. Locock, Sarah A. Styler

Bibliographic record

VenueACS Earth and Space Chemistry · 2019
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric chemistry and aerosols
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaCanada Foundation for Innovation
KeywordsMethacroleinSulfateMineralChemistryMineral dustPhotochemistryCounterionAerosolCatalysisEnvironmental chemistryInorganic chemistryOrganic chemistryIon

Abstract

fetched live from OpenAlex

Although organosulfates (ROSO3–) comprise a significant component of secondary organic aerosol (SOA) mass, their atmospheric formation mechanisms are not fully understood. Here, using methacrolein as a model organosulfate precursor, we present a new, mineral-mediated photochemical pathway for organosulfate formation. First, we describe studies of TiO2-catalyzed formation of the atmospherically important organosulfate hydroxyacetone sulfate from methacrolein as a function of illumination time, catalyst loading, sulfate concentration, counterion identity, and methacrolein concentration. Then, we propose a sulfate radical-mediated mechanism for organosulfate formation consistent with these observations. Finally, we show that natural Ti-containing minerals and road dust not only catalyze the formation of comparable amounts of hydroxyacetone sulfate to those formed in the presence of commercial TiO2 but also facilitate the production of additional organosulfate species. These results highlight the complex nature of photochemistry at the surface of natural mineral samples and underscore the need for further study of the role of mineral–organic interactions in atmospheric organosulfate formation.

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 categoriesInsufficient payload (model declined to judge)
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 score0.161
Threshold uncertainty score0.997

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)0.0040.000

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 head0.004
GPT teacher head0.169
Teacher spread0.165 · 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

Citations15
Published2019
Admission routes2
Has abstractyes

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