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Record W2475255795 · doi:10.1007/978-1-59745-519-0_14

Fourier Transform Infrared Spectroscopy in the Study of Lipid Phase Transitions in Model and Biological Membranes

2007· review· en· W2475255795 on OpenAlexafffund
Ruthven N.A.H. Lewis, Ronald N. McElhaney

Bibliographic record

VenueMethods in molecular biology · 2007
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicLipid Membrane Structure and Behavior
Canadian institutionsUniversity of Alberta
FundersCanadian Institutes of Health ResearchFondation pour la Recherche Médicale
KeywordsFourier transform infrared spectroscopyCharacterization (materials science)Fourier transformMembraneSpectroscopyBiological membraneInfraredInfrared spectroscopyFourier transform spectroscopyPhase (matter)ChemistryAnalytical Chemistry (journal)Materials scienceLipid bilayerNanotechnologyChemical engineeringChromatographyOpticsPhysicsOrganic chemistryEngineeringBiochemistry

Abstract

fetched live from OpenAlex

Fourier transform infrared (FTIR) spectroscopy is a powerful, nonperturbing technique that has been used to good effect for the detection and characterization of lipid phase transitions in model and natural membranes. The technique is also quite versatile, covering a wide range of sophisticated applications, from which fairly detailed information about the structure and organization of membranes and other lipid assemblies can be obtained. In this chapter, an introduction to this particular application of FTIR spectroscopy is presented. Special emphasis is put on how the technique can be used to study lipid phase transitions under biologically relevant conditions. The chapter is intended to give an overview of the capabilities of FTIR spectroscopy in the field of lipid and biomembrane research, and provide the reader with some practical guidelines for the design and execution of simple FTIR spectroscopic experiments suitable for the detection and characterization of lipid phase transitions in hydrated lipid bilayers.

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)0.002
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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.809
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
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 head0.075
GPT teacher head0.465
Teacher spread0.390 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations75
Published2007
Admission routes2
Has abstractyes

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