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Record W2892006938 · doi:10.1039/c8nr05630g

Molecular reorganization in bulk bottlebrush polymers: direct observation <i>via</i> nanoscale imaging

2018· article· en· W2892006938 on OpenAlexaff
Nikolay Borodinov, Alex Belianinov, Dongsook Chang, Jan‐Michael Y. Carrillo, Matthew J. Burch, Yuewen Xu, Kunlun Hong, Anton V. Ievlev, Bobby G. Sumpter, Olga S. Ovchinnikova

Bibliographic record

VenueNanoscale · 2018
Typearticle
Languageen
FieldPhysics and Astronomy
TopicForce Microscopy Techniques and Applications
Canadian institutionsKimberly-Clark (Canada)
Fundersnot available
KeywordsNanoscopic scaleMaterials sciencePolymerNanotechnologyDirect imagingPhysicsOpticsComposite material

Abstract

fetched live from OpenAlex

Bottlebrush polymers are important for a variety of applications ranging from drug delivery to electronics. The functional flexibility of the branched sidechains has unique assembly properties when compared to linear block polymer systems. However, reports of direct observation of molecular reorganization have been sparse. This information is necessary to enhance the understanding of the structure-property relationships in these systems and yield a rational design approach for novel polymeric materials. In this work, we report direct visualization of bottlebrush molecular organization and the formation of nematic-type ordering in an amorphous polymer bottlebrush system, captured with plasma etching and helium ion microscopy. By observing the unperturbed structure of this material at high resolution and quantifying image features, we were able to qualitatively link experimental results with structures predicted by coarse-grained molecular dynamics simulations. The direct visualization and computation workflow developed in this work can be applied to a broad variety of polymers with different architectures, linking imaging results with other, independent channels of information for better understanding and control of these classes of materials.

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 score0.182
Threshold uncertainty score0.690

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.005
GPT teacher head0.242
Teacher spread0.236 · 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

Citations16
Published2018
Admission routes1
Has abstractyes

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