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Record W4313424626 · doi:10.1002/anie.202216105

SASSY NMR: Simultaneous Solid and Solution Spectroscopy

2023· article· en· W4313424626 on OpenAlexafffund
Rajshree Ghosh Biswas, Ronald Soong, Amy Jenne, Monica Bastawrous, Myrna J. Simpson, André J. Simpson

Bibliographic record

VenueAngewandte Chemie International Edition · 2023
Typearticle
Languageen
FieldChemistry
TopicAdvanced NMR Techniques and Applications
Canadian institutionsThe Scarborough HospitalUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSolid-state nuclear magnetic resonanceSolid-stateSpectroscopyPhase (matter)SpectrometerReactivity (psychology)Nuclear magnetic resonance spectroscopyChemistryProcess (computing)Analytical Chemistry (journal)Materials scienceBiological systemComputer scienceChromatographyPhysical chemistryNuclear magnetic resonanceOrganic chemistryPhysicsOptics

Abstract

fetched live from OpenAlex

Abstract Synergism between different phases gives rise to chemical, biological or environmental reactivity, thus it is increasingly important to study samples intact. Here, SASSY (SimultAneous Solid and Solution spectroscopY) is introduced to simultaneously observe (and differentiate) all phases in multiphase samples using standard, solid‐state NMR equipment. When monitoring processes, the traditional approach of studying solids and liquids sequentially, can lead to information in the non‐observed phase being missed. SASSY solves this by observing the full range of materials, from crystalline solids, through gels, to pure liquids, at full sensitivity in every scan. Results are identical to running separate 13 C CP‐MAS solid‐state and 13 C solution‐state experiments back‐to‐back but requires only a fraction of the spectrometer time. After its introduction, SASSY is applied to process monitoring and finally to detect all phases in a living freshwater shrimp. SASSY is simple to implement and thus should find application across all areas of research.

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 score0.489
Threshold uncertainty score0.577

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)0.0010.000

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 head0.014
GPT teacher head0.306
Teacher spread0.292 · 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

Citations11
Published2023
Admission routes2
Has abstractyes

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