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Record W4407568754 · doi:10.1038/s42004-025-01449-7

Measurement of phospholipid lateral diffusion at high pressure by in situ magic-angle spinning NMR spectroscopy

2025· article· en· W4407568754 on OpenAlexfundno aff
Thomas M. Osborn Popp, M. Karthikeyan, Eliot M. Herman, Andrew C. Dufur, Costantino Vetriani, Andrew J. Nieuwkoop

Bibliographic record

VenueCommunications Chemistry · 2025
Typearticle
Languageen
FieldPhysics and Astronomy
TopicNMR spectroscopy and applications
Canadian institutionsnot available
FundersNational Institute of General Medical SciencesNational Institutes of HealthUniversity of TorontoFoundation for the National Institutes of Health
KeywordsMagic angle spinningNuclear magnetic resonance spectroscopyMagic angleMaterials scienceSpectroscopyDiffusionPhospholipidIn situSpinningNuclear magnetic resonanceAnalytical Chemistry (journal)ChemistryPhysicsComposite materialChromatographyOrganic chemistryThermodynamicsMembrane

Abstract

fetched live from OpenAlex

The development of experimental methodologies that enable investigations of biochemistry at high pressure promises to yield significant advances in our understanding of life on Earth and its origins. Here, we introduce a method for studying lipid membranes at thermodynamic conditions relevant for life at deep sea hydrothermal vents. Using in situ high pressure magic-angle spinning solid state nuclear magnetic resonance spectroscopy (NMR), we measure changes in the fluidity of model microbial membranes at pressures up to 28 MPa. We find that the fluid-phase lateral diffusion of phospholipids at high pressure is significantly affected by the stoichiometric ratio of lipids in the membrane. Our results were facilitated by an accessible pressurization strategy that we have developed to enable routine preparation of solid state NMR rotors to pressures of 30 MPa or greater.

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 score0.120
Threshold uncertainty score0.623

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)0.0000.000

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 head0.009
GPT teacher head0.295
Teacher spread0.286 · 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

Citations1
Published2025
Admission routes1
Has abstractyes

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