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Record W4412644007 · doi:10.1002/smll.202501435

High‐Resolution AFM Visualization of Nanocellulose Surface Grafting with Small Molecules

2025· article· en· W4412644007 on OpenAlexafffund
Lucas J. Andrew, Ayhan Yurtsever, Raksha Kandel, Seiya Ota, Keisuke Miyazawa, Takeshi Fukuma, Mark J. MacLachlan

Bibliographic record

VenueSmall · 2025
Typearticle
Languageen
FieldMaterials Science
TopicAdvanced Cellulose Research Studies
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of British ColumbiaCanada Research ChairsMinistry of Education, Culture, Sports, Science and TechnologyCanada Foundation for Innovation
KeywordsNanocelluloseSurface modificationGraftingMaterials scienceMoleculeNanotechnologyCovalent bondCyclodextrinChemical engineeringEpichlorohydrinCellulosePolymer chemistryChemistryPolymerOrganic chemistryComposite material

Abstract

fetched live from OpenAlex

Nanocelluloses are attractive for bio-based materials development due to their renewability, intrinsic properties, and ease of functionalization. However, characterization of nanocelluloses functionalized with small molecules at low grafting densities is challenging. Here, the use of high-resolution frequency modulation atomic force microscopy (FM-AFM) to directly visualize the covalent grafting of small molecules is demonstrated for the first time. Using epichlorohydrin as a linker, α-cyclodextrin, β-cyclodextrin, and tris(4-tert-butylphenyl)methanol are attached to the surface of bacterial nanocellulose. The presence of these groups is verified through spectroscopic measurements, and their covalent attachment is confirmed with FM-AFM. Furthermore, the retention of cyclodextrin host-guest activity after surface grafting is also demonstrated. This work represents a novel application of FM-AFM for the characterization of small molecule-grafted nanocelluloses, allowing for future development of advanced functionalization strategies.

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 score0.106
Threshold uncertainty score0.551

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 head0.022
GPT teacher head0.278
Teacher spread0.256 · 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
Published2025
Admission routes2
Has abstractyes

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