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Record W3043946796 · doi:10.1039/d0ra05333c

The use of fluorescent protein-tagged carbohydrate-binding modules to evaluate the influence of drying on cellulose accessibility and enzymatic hydrolysis

2020· article· en· W3043946796 on OpenAlexafffund
Drake Mboowa, Vinay Khatri

Bibliographic record

VenueRSC Advances · 2020
Typearticle
Languageen
FieldEngineering
TopicBiofuel production and bioconversion
Canadian institutionsVancouver Biotech (Canada)University of British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of British Columbia
KeywordsCelluloseEnzymatic hydrolysisMacroChemistryLimitingHydrolysisCarbohydrateFiberEnzymeFluorescenceCellulose fiberFibrilBiochemistryChemical engineeringOrganic chemistryComputer science

Abstract

fetched live from OpenAlex

The influence of drying on cellulose accessibility and enzymatic hydrolysis was assessed. Dissolving pulp was differentially dried by freeze-, air- and oven-drying at 50 °C and subsequently hydrolyzed using the commercial CTec 3 cellulase preparation. It was apparent that drying reduced the ease of enzymatic hydrolysis of all of the substrates with a pronounced reduction (48%) exhibited by the oven-dried pulp. To assess if the ease of hydrolysis was due to enzyme accessibility to the substrate, microscopy (SEM), FTIR spectroscopy, water retention value (WRV), fiber aspect ratio analysis, Simons' stain and the selective binding of Fluorescent Protein-tagged Carbohydrate Binding Modules (FP-CBMs): CBM3a (crystalline cellulose) and CBM17 (amorphous cellulose) in combination with confocal laser scanning microscopy (CLSM) were used. The combined methods indicated that, if the gross characteristics of the substrate limited enzyme accessibility, the cellulases, as represented by the FP-CBMs, could not in turn access the finer structural components of the cellulosic substrates.

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 score0.057
Threshold uncertainty score0.254

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 head0.027
GPT teacher head0.247
Teacher spread0.220 · 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

Citations18
Published2020
Admission routes2
Has abstractyes

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