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Record W1539798615 · doi:10.1002/app.42723

Potato protein isolate‐based biopolymers

2015· article· en· W1539798615 on OpenAlexafffund
Yicheng Du, Fangping Chen, Yachuan Zhang, Curtis Rempel, Michael R. Thompson, Qiang Liu

Bibliographic record

VenueJournal of Applied Polymer Science · 2015
Typearticle
Languageen
FieldMaterials Science
Topicbiodegradable polymer synthesis and properties
Canadian institutionsUniversity of ManitobaAgriculture and Agri-Food CanadaMcMaster UniversityResearch ManitobaMinistry of Agriculture, Food and Rural Affairs
FundersAgriculture and Agri-Food Canada
KeywordsPlasticizerBiopolymerFourier transform infrared spectroscopyDifferential scanning calorimetryUltimate tensile strengthMaterials scienceSodium dodecyl sulfateGlycerolChemical engineeringChemistryComposite materialPolymerChromatographyOrganic chemistry

Abstract

fetched live from OpenAlex

ABSTRACT Thermal processing of two potato protein isolates (PPIs) with glycerol as a plasticizer was explored in this study. The PPIs were pretreated by alkali or alkali under reducing conditions. The PPIs before and after pretreatment were analyzed by sodium dodecyl sulfate polyacrylamide gel electrophoresis, differential scanning calorimetry, and Fourier transform infrared spectroscopy. The effects of plasticizer content and pretreatment on mechanical and thermo‐mechanical properties of the compression‐molded biopolymers were studied. The highest tensile strengths obtained were 20–25 MPa and the biopolymer can be brittle or ductile depending on the plasticizer contents. The molecular weight and protein structure of the PPIs markedly affected the resultant biopolymers’ static and dynamic mechanical properties. The pretreatment of PPIs caused distinctly different changes in the mechanical properties of the two PPIs. © 2015 Wiley Periodicals, Inc. J. Appl. Polym. Sci.2015,132, 42723.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

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 integrity0.0000.001
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 head0.030
GPT teacher head0.242
Teacher spread0.213 · 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 source (direct Gemma or distilled Codex), 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

Citations9
Published2015
Admission routes2
Has abstractyes

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