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Record W4306194626 · doi:10.1126/sciadv.abq0160

Molecular insights on the crystalline cellulose-water interfaces via three-dimensional atomic force microscopy

2022· article· en· W4306194626 on OpenAlexaff
Ayhan Yurtsever, Pei‐Xi Wang, Fabio Priante, Ygor Morais Jaques, Keisuke Miyazawa, Mark J. MacLachlan, Adam S. Foster, Takeshi Fukuma

Bibliographic record

VenueScience Advances · 2022
Typearticle
Languageen
FieldMaterials Science
TopicAdvanced Cellulose Research Studies
Canadian institutionsUniversity of British Columbia
FundersAalto-YliopistoMinistry of Education, Culture, Sports, Science and TechnologyKanazawa University
KeywordsCelluloseBiopolymerMolecular dynamicsAtomic force microscopyMoleculeMaterials scienceChemical physicsNanotechnologyChemical engineeringBacterial celluloseAnisotropyChemistryComputational chemistryPolymerOrganic chemistryComposite material

Abstract

fetched live from OpenAlex

Cellulose, a renewable structural biopolymer, is ubiquitous in nature and is the basic reinforcement component of the natural hierarchical structures of living plants, bacteria, and tunicates. However, a detailed picture of the crystalline cellulose surface at the molecular level is still unavailable. Here, using atomic force microscopy (AFM) and molecular dynamics (MD) simulations, we revealed the molecular details of the cellulose chain arrangements on the surfaces of individual cellulose nanocrystals (CNCs) in water. Furthermore, we visualized the three-dimensional (3D) local arrangement of water molecules near the CNC surface using 3D AFM. AFM experiments and MD simulations showed anisotropic water structuring, as determined by the surface topologies and exposed chemical moieties. These findings provide important insights into our understanding of the interfacial interactions between CNCs and water at the molecular level. This may allow the establishment of the structure-property relationship of CNCs extracted from various biomass sources.

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 categoriesScience and technology studies
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 score0.020
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.002
Scholarly communication0.0000.001
Open science0.0020.002
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.014
GPT teacher head0.286
Teacher spread0.271 · 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.

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

Citations52
Published2022
Admission routes1
Has abstractyes

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