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Record W1974473328 · doi:10.1021/ic0349510

Silver-109 NMR Spectroscopy of Inorganic Solids

2004· article· en· W1974473328 on OpenAlexaff
Glenn H. Penner, Wenli Li

Bibliographic record

VenueInorganic Chemistry · 2004
Typearticle
Languageen
FieldChemistry
TopicAdvanced NMR Techniques and Applications
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsChemistryChemical shiftMagic angle spinningSpectral lineNMR spectra databaseAnalytical Chemistry (journal)Nuclear magnetic resonance spectroscopyMagic angleSingle crystalSpectroscopyRelaxation (psychology)CrystallographyPhysical chemistryStereochemistryChromatography

Abstract

fetched live from OpenAlex

In this study the (109)Ag NMR spectra of the following solid inorganic silver-containing compounds were investigated: AgNO(3), AgNO(2), Ag(2)SO(4), Ag(2)SO(3), AgCO(3), Ag(3)PO(4), AgCl, AgBr, AgI, AgSO(3)CH(3), silver p-toluenesulfonate, NaAg(CN)(2), KAg(CN)(2), K(3)Ag(CN)(4), Me(4)NAgCl(2), silver diethylthiocarbamate, silver lactate, silver acetate, silver citrate, and bis[(N,N(1)-di-tert-butylformamidinato)silver(I)]. The magic angle spinning (MAS) spectra of all compounds were obtained. In some cases, when protons were available, the (1)H to (109)Ag cross-polarization (CP) technique was used to enhance the signal and shorten the experimental relaxation delay. It was possible to obtain slow MAS (or CP/MAS) or nonspinning spectra for 10 samples, allowing the determination of the principal components of the (109)Ag chemical shift (CS) tensors. The isotropic chemical shifts and the CS tensors are discussed in light of the available crystal structures. The need for an accepted standard for referencing (109)Ag chemical shifts and the use of AgSO(3)CH(3) as a CP setup sample are also discussed.

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), Insufficient 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 score0.032
Threshold uncertainty score1.000

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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.008
GPT teacher head0.258
Teacher spread0.250 · 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

Citations46
Published2004
Admission routes1
Has abstractyes

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