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Record W1981989462 · doi:10.1002/star.200800715

Aliphatic Amidediol and Glycerol as a Mixed Plasticizer for the Preparation of Thermoplastic Starch

2008· article· en· W1981989462 on OpenAlexaff
J.Y. Zhang, Peter R. Chang, Ying Wu, Jiu‐gao Yu, Xiaofei Ma

Bibliographic record

VenueStarch - Stärke · 2008
Typearticle
Languageen
FieldMaterials Science
Topicbiodegradable polymer synthesis and properties
Canadian institutionsAgriculture and Agri-Food Canada
Fundersnot available
KeywordsPlasticizerGlycerolStarchThermoplasticDynamic mechanical analysisGlass transitionFourier transform infrared spectroscopyMaterials scienceChemical engineeringScanning electron microscopePolymer chemistryNuclear chemistryChemistryComposite materialOrganic chemistryPolymer

Abstract

fetched live from OpenAlex

Abstract The synthesis of 2‐hydroxy‐N‐[2‐(2‐hydroxy‐propionylamino)‐ethyl]propionamide (“aliphatic amidediol”) is described. Aliphatic amidediol and glycerol were used as a novel mixed plasticizer for corn starch to prepare thermoplastic starch. Fourier transform infrared (FT‐IR) spectroscopy proved that the mixture of aliphatic amidediol and glycerol could form more stable and strong hydrogen bonds with starch molecules than glycerol alone. By scanning electron microscopy (SEM) and X‐ray diffraction (XRD) it was proven that native starch granules and crystalline structures were broken and starch was plasticized. Tensile testing revealed that TPS plasticized by aliphatic amidediol and glycerol (AGPTPS) showed a better mechanical properties than TPS plasticized by glycerol (GPTPS). Furthermore, the water resistance of AGPTPS was better than that of GPTPS. In addition, dynamic mechanical thermal analysis (DMTA) showed that both storage modulus and glass transition temperature (Tg) of AGPTPS were higher than those of GPTPS.

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 score0.003

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 integrity0.0000.000
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 head0.040
GPT teacher head0.276
Teacher spread0.237 · 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

Citations22
Published2008
Admission routes1
Has abstractyes

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