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Record W4411455440 · doi:10.1021/acs.inorgchem.5c01177

Understanding Reactive Sulfur Species Storage from Hybrid S/N Crosstalk Species: Perthionitrite (SSNO<sup>–</sup>) and Thionitrite (SNO<sup>–</sup>) Activation by a Mononuclear Nonheme Fe<sup>II</sup> Complex

2025· article· en· W4411455440 on OpenAlexaff
Keyan Li, Juan Hernandez, George W. Piepgras, Lev N. Zakharov, Michael D. Pluth

Bibliographic record

VenueInorganic Chemistry · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicSulfur Compounds in Biology
Canadian institutionsImpact
FundersNational Science Foundation
KeywordsChemistrySulfurPolysulfideRedoxMetalRadicalReactivity (psychology)HomolysisHydrogen sulfideMoleculeSulfideReactive nitrogen speciesPhotochemistryNitric oxideInorganic chemistryOrganic chemistryPhysical chemistry

Abstract

fetched live from OpenAlex

S/N hybrid species, such as perthionitrite (SSNO – ) and thionitrite (SNO – ), play intricate roles in nitric oxide (NO • ) and hydrogen sulfide (H 2 S) biological signaling and transport pathways. Despite this emerging significance, the fundamental reactivities of these species remain largely unexplored. In particular, a significant gap remains in understanding how these S/N hybrid species react with redox active metal centers. Building from this gap, we report here the reactivity of SSNO – and SNO – toward tripodal Fe 2+ complex [Fe( 3 CF 3 -baTren)] − ( 1 ) and examine the associated reactive nitrogen and sulfur species output pathways from these reactions using both spectroscopic and chemical trapping techniques. Specifically, we observe that complex 1 facilitates S–N bond homolysis of SSNO – to give {Fe–NO} 7 complex [Fe( 3 CF 3 -baTren)(NO)] − ( 2 ) and polysulfide radicals, whereas SNO – reacts with 1 to undergo S-centered reduction to give S 2– and NO • . Taken together, these results advance our understanding of how small molecule S/N hybrids react with redox active metal centers and may contribute to metal nitrosyl 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)0.001
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 score0.075
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.001
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 head0.029
GPT teacher head0.240
Teacher spread0.211 · 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

Citations3
Published2025
Admission routes1
Has abstractyes

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