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Record W1888973713 · doi:10.1039/b302893c

Thermodynamics of protein model compounds: apparent molar volumes and isobaric heat capacities of selected cyclic dipeptides and their transfer properties from water to aqueous urea solutions at T = 298.15 K

2003· article· en· W1888973713 on OpenAlexaff
Andrew W. Hakin, Jin L. Liu, Meghan O'Shea, Brianne Zorzetti

Bibliographic record

VenuePhysical Chemistry Chemical Physics · 2003
Typearticle
Languageen
FieldChemical Engineering
TopicThermodynamic properties of mixtures
Canadian institutionsUniversity of Lethbridge
Fundersnot available
KeywordsChemistryAqueous solutionUreaIsobaric processDilutionThermodynamicsGlycylglycineHeat capacityMolarEnthalpyPhysical chemistryOrganic chemistryAmino acid

Abstract

fetched live from OpenAlex

The effects of added protein denaturant (urea) on the volumetric and thermochemical properties of several protein model compounds (cyclic dipeptides) have been investigated at T = 298.15 K and p = 0.1 MPa. Relative densities and specific heat capacities are reported for the cyclic dipeptides cyclo-glycylglycine, cyclo-alanylalanine and cyclo-sarcosylsarcosine in aqueous urea solutions in the concentration range 1 ≤ m(urea)/mol kg−1 ≤ 11. The measurements were performed using a Sodev O2D vibrating tube densimeter and a Picker dynamic microcalorimeter. Apparent molar volumes and heat capacities have been calculated and their concentration dependences have been modeled to give partial molar properties at infinite dilution. The partial molar properties have been used to calculate thermodynamic parameters describing the transfer of the cyclic dipeptides from water to aqueous urea solutions. Estimates of transfer properties for the glycyl group and the alanyl side chain have been obtained using the principles of group additivity. The interaction of urea with the investigated protein model compounds is discussed in terms of the role of urea as a protein denaturant.

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 score0.229
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.012
GPT teacher head0.177
Teacher spread0.165 · 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

Citations9
Published2003
Admission routes1
Has abstractyes

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