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Record W2079887974 · doi:10.1021/la9037028

Direct Visualization of the Enzymatic Digestion of a Single Fiber of Native Cellulose in an Aqueous Environment by Atomic Force Microscopy

2010· article· en· W2079887974 on OpenAlexaff
Amanda Quirk, Jacek Lipkowski, Chris Vandenende, Darrell Cockburn, Anthony J. Clarke, John Dutcher, Sharon G. Roscoe

Bibliographic record

VenueLangmuir · 2010
Typearticle
Languageen
FieldMaterials Science
TopicAdvanced Cellulose Research Studies
Canadian institutionsAcadia UniversityUniversity of Guelph
Fundersnot available
KeywordsAqueous solutionCelluloseAtomic force microscopyChemistryDigestion (alchemy)Bacterial celluloseFiberChemical engineeringEnzymeMicroscopyMaterials scienceNanotechnologyChromatographyOrganic chemistryOpticsPhysics

Abstract

fetched live from OpenAlex

Atomic force microscopy (AFM) was used to study native cellulose films prepared from a bacterial cellulose source, Acetobacter xylinum, using a novel application of the Langmuir-Blodgett technique. These films allowed high-resolution AFM images of single fibers and their microfibril structure to be obtained. Two types of experiments were performed. First, the fibers were characterized using samples that were dried after LB deposition. Next, novel protocols that allowed us to image single fibers of cellulose in films that were never dried were developed. This procedure allowed us to perform in situ AFM imaging studies of the enzymatic hydrolysis of single cellulose fibers in solution using cellulolytic enzymes. The in situ degradation of cellulose fibers was monitored over a 9 h period using AFM. These studies provided the first direct, real-time images of the enzymatic degradation of a single cellulose fiber. We have demonstrated the tremendous potential of AFM to study the mechanism of the enzymatic digestion of cellulose and to identify the most effective enzymes for the digestion of various cellulose structures or isomorphs.

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 score0.002
Threshold uncertainty score0.245

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 head0.011
GPT teacher head0.283
Teacher spread0.272 · 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

Citations29
Published2010
Admission routes1
Has abstractyes

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