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Record W4391072067 · doi:10.1016/j.fochx.2024.101149

Simultaneous ultrasound and microwave application in myosin-chlorogenic acid conjugation: Unlocking enhanced emulsion stability

2024· article· en· W4391072067 on OpenAlexaff
Zhiyu Li, Xiaomei Zhong, Cuirong Luan, Nanhua Wen, Chuanyang Shi, Shuji Liu, Yizhou Xu, Quan He, Yijing Wu, Jie Yang

Bibliographic record

VenueFood Chemistry X · 2024
Typearticle
Languageen
FieldMaterials Science
TopicElectrospun Nanofibers in Biomedical Applications
Canadian institutionsDalhousie University
FundersMinjiang UniversityNatural Science Foundation of Fujian Province
KeywordsMyosinEmulsionCreamingConjugateChemistryMicrowaveMaterials scienceChlorogenic acidHomogeneousUltrasoundChemical engineeringBiophysicsChromatographyOrganic chemistryBiochemistry

Abstract

fetched live from OpenAlex

This study investigated the grafting chlorogenic acid (CA) onto myosin, utilizing various techniques including conventional method, ultrasound, microwave, and combination of ultrasound and microwave (UM). The grafting efficiency was as follows: conventional method < microwave < ultrasound < UM. The UM technique manifested the highest CA-binding capacity (80.26 μmol/g myosin) through covalent bonding, and a much shorter time was required for conjugation than conventional method. The conjugation of polyphenol significantly increased the solubility of myosin with reduced aggregation behavior, which was accompanied by structural alterations from ordered structures (α-helix and β-sheet) to disordered forms. The emulsion stabilized by UM-myosin-CA conjugate exhibited the most homogeneous microstructure with favorable creaming stability. Moreover, the resulting emulsion presented strong oxidation resistance and storage stability. These results illustrate the promising potential of employing CA-grafted myosin, especially when processed using the UM technique, in the development of highly efficient emulsifiers.

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 score0.018
Threshold uncertainty score0.669

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 science0.0000.000
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 head0.006
GPT teacher head0.233
Teacher spread0.227 · 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

Citations9
Published2024
Admission routes1
Has abstractyes

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