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Record W1983372383 · doi:10.1021/es048022z

Photodegradation of Dimethyl Sulfide (DMS) in Natural Waters:  Laboratory Assessment of the Nitrate-Photolysis-Induced DMS Oxidation

2005· article· en· W1983372383 on OpenAlexaff
René‐Christian Bouillon, William L. Miller

Bibliographic record

VenueEnvironmental Science & Technology · 2005
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric chemistry and aerosols
Canadian institutionsDalhousie University
Fundersnot available
KeywordsChemistryPhotodegradationDimethyl sulfideBicarbonatePhotochemistryPhotodissociationAqueous solutionSulfurSulfideBromideCarbonateRadicalInorganic chemistryEnvironmental chemistryPhotocatalysisCatalysisOrganic chemistry

Abstract

fetched live from OpenAlex

The interaction of sunlight and dissolved chromophoric matter produces reactive chemical species that are significant in the removal of dimethyl sulfide (DMS) in the surface ocean. Using artificial solar radiation, we examined the role of several inorganic components of seawater on the kinetics of NO3- -photolysis-induced DMS removal in aqueous solution. This study strongly suggests that NO3- photolysis products react significantly with DMS in aqueous solution possibly via an electrophilic attack on the electron-rich sulfur atom. This supports previous field observations that indicate that NO3- photolysis has a substantial control on DMS photochemistry in nutrient-rich waters. A key finding of this research is that the oxidation rate of DMS induced by NO3- photolysis is dramatically enhanced in the presence of bromide ion. Moreover, our results suggest that bicarbonate/carbonate ions are involved in free radical production/scavenging processes important for DMS photochemistry. These reactions are pH dependent. We propose that DMS removal by some selective free radicals derived from bromide and bicarbonate/carbonate ion oxidation is a potentially important and previously unrecognized pathway for DMS photodegradation in marine waters.

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 score0.068
Threshold uncertainty score0.400

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
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 head0.005
GPT teacher head0.211
Teacher spread0.206 · 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

Citations90
Published2005
Admission routes1
Has abstractyes

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