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Record W2759358671 · doi:10.5539/jmsr.v6n4p96

Influence of Water Activity on Protease Adsorbed on Biochar in Organic Solvents

2017· article· en· W2759358671 on OpenAlexvenueno aff
Hidetaka Noritomi, Jumpei Nishigami, Nobuyuki Endo, Satoru Kato, Katsumi Uchiyama

Bibliographic record

VenueJournal of Materials Science Research · 2017
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicEnzyme Catalysis and Immobilization
Canadian institutionsnot available
Fundersnot available
KeywordsTransesterificationAdsorptionHydrolysisCatalysisAcetonitrileNuclear chemistryMaterials scienceChemistryOrganic chemistry

Abstract

fetched live from OpenAlex

We have found that the organic solvent-resistance of Alpha-chymotrypsin (Alpha-CT) is enhanced by adsorbing Alpha-CT onto bamboo charcoal powder (BCP), which is obtained by pyrolyzing bamboo waste under nitrogen atmosphere, and is markedly dependent on the thermodynamic water activity (aw) in organic solvents. When BCP-adsorbed Alpha-CT was immersed in acetonitrile at an appropriate water activity, it effectively enhanced the transesterification of N-acetyl-L-tyrosine ethyl ester (N-Ac-Tyr-OEt) with n-butanol (BuOH) to produce N-acetyl-L-tyrosine butyl ester (N-Ac-Tyr-OBu), compared to the hydrolysis of N-Ac-Tyr-OEt with water to give N-acetyl-L-tyrosine (N-Ac-Tyr-OH). When the water activity was 0.28, the initial rate of transesterification catalyzed by BCP-adsorbed Alpha-CT was about sixty times greater than that catalyzed by free Alpha-CT. Regarding the reaction selectivity which is defined as a ratio of the initial rate of transesterification to that of hydrolysis, BCP-adsorbed α-CT was much superior to free Alpha-CT. The catalytic activity of BCP-adsorbed Alpha-CT was markedly dependent on the reaction temperature. Furthermore, concerning the thermal stability at 50 oC, the half-life of BCP-adsorbed Alpha-CT exhibited 3.8-fold, compared to that of free Alpha-CT.

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)0.004
metaresearch head score (Gemma)0.001
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 score0.033
Threshold uncertainty score0.174

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
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 science0.0010.000
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 head0.043
GPT teacher head0.376
Teacher spread0.333 · 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

Citations5
Published2017
Admission routes1
Has abstractyes

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