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Record W4402699161 · doi:10.1021/acs.iecr.4c01398

Development of Alcalase-Polyacrylonitrile Nanofibrous Biocatalytic Membranes for Protein Hydrolysis

2024· article· en· W4402699161 on OpenAlexafffund
Aotian Li, Xinyue Wang, Julian T. Dafoe, Trent Chunzhong Yang, Christopher Q. Lan

Bibliographic record

VenueIndustrial & Engineering Chemistry Research · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicEnzyme Catalysis and Immobilization
Canadian institutionsNational Research Council CanadaUniversity of Ottawa
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPolyacrylonitrileHydrolysisMembraneChemistryElectrospinningPolymer scienceChemical engineeringPolymer chemistryOrganic chemistryPolymerBiochemistryEngineering

Abstract

fetched live from OpenAlex

Biocatalytic membranes (BM) combine the benefits of enzymes and membranes and have found a wide variety of applications. This study focused on the development of BM by immobilizing alcalase onto electrospun polyacrylonitrile nanofibrous membranes (PANMs) modified with 1-ethyl-3-(3-dimethylamino-propyl) carbodiimide/ N -hydroxy succinimide through covalent bonding. Scanning electron microscopy and Fourier transform infrared spectroscopy were used to characterize the alcalase-functionalized PAN nanofibrous biocatalytic membrane (PAN-BM). The PAN-BM demonstrated enhanced pH and thermal stability compared to free alcalase. The BM retained 45% of its initial activity after 10 repeated uses under optimum conditions (i.e., 50 °C and pH 9.0). A BM reactor was successfully demonstrated for continuous hydrolysis of model substrate azo-casein (0.5% w/v), with its extent of hydrolysis significantly affected by both the number of layers of PAN-BM and the flux of feed. A five-layer PAN-BM was also successfully tested for continuous hydrolysis of the raw soy proteins (1.4% w/v) extracted from commercial soybean meals, showing a promising potential of application.

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)0.001
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 score0.081
Threshold uncertainty score0.668

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.053
GPT teacher head0.316
Teacher spread0.263 · 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

Citations2
Published2024
Admission routes2
Has abstractyes

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