MétaCan
Menu
Back to cohort
Record W2951191290 · doi:10.1021/ic011045j

Preparation and Structural Characterization of (Me<sub>3</sub>SiNSN)<sub>2</sub>Se, a New Synthon for Sulfur−Selenium Nitrides

2002· article· en· W2951191290 on OpenAlexaff
J. Konu, Arto Maaninen, Katja Paananen, Petri Ingman, Risto S. Laitinen, T. Chivers, J. Valkonen

Bibliographic record

VenueInorganic Chemistry · 2002
Typearticle
Languageen
FieldChemistry
TopicInorganic Chemistry and Materials
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsChemistrySynthonSeleniumSulfurCharacterization (materials science)NitrideAluminosilicateStereochemistryNanotechnologyOrganic chemistry

Abstract

fetched live from OpenAlex

The reaction of (Me(3)SiN)(2)S with SeCl(2) (2:1 ratio) in CH(2)Cl(2) at -70 degrees C provides a route to the novel mixed selenium-sulfur-nitrogen compound (Me(3)SiNSN)(2)Se (1). Crystals of 1 are monoclinic and belong the space group P2(1)/c, with a = 7.236(1) A, b = 19.260(4) A, c = 11.436(2) A, beta = 92.05(3) degrees, V = 1592.7(5) A(3), Z = 4, and T = -155(2) degrees C. The NSNSeNSN chain in 1 consists of Se-N single bonds (1.844(3) A) and S=N double bonds (1.521(3)-1.548(3) A) with syn and anti geometry at the N=S=N units. The N-Se-N bond angle is 91.8(1) degrees. The EI mass spectrum shows a molecular ion with good agreement between the observed and calculated isotopic distributions. The (14)N NMR spectrum exhibits two resonances at -65 and -77 ppm. Both (13)C and (77)Se NMR spectra show single resonances at 0.83 and 1433 ppm, respectively. The reaction of 1 with an equimolar amount of SeCl(2) produces 1,5-Se(2)S(2)N(4) (2) in a good yield, and that of (Me(3)SiNSN)(2)S with SCl(2) affords S(4)N(4) (3), but the reactions of (Me(3)SiNSN)(2)Se with SCl(2) and (Me(3)SiNSN)(2)S with SeCl(2) result in the formation of a mixture of 2 and 3. A likely reaction pathway involves the intermediate formation of E(2)N(2) fragments (E = S, Se).

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 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 score0.002
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
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 head0.011
GPT teacher head0.221
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

Citations21
Published2002
Admission routes1
Has abstractyes

Explore more

Same venueInorganic ChemistrySame topicInorganic Chemistry and MaterialsFrench-language works237,207