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Record W2943824423 · doi:10.1021/acsnano.8b08725

Quantifying Polymer Chain Orientation in Strong and Tough Nanofibers with Low Crystallinity: Toward Next Generation Nanostructured Superfibers

2019· review· en· W2943824423 on OpenAlexafffund
Dimitry Papkov, Nicolas Delpouve, Laurent Delbreilh, Steven Araujo, Taylor A. Stockdale, Sergey Mamedov, Kaspars Maleckis, Yan Zou, Mohammad Nahid Andalib, Éric Dargent, Vinayak P. Dravid, Martin V. Holt, Christian Pellerin, Yuris A. Dzenis

Bibliographic record

VenueACS Nano · 2019
Typereview
Languageen
FieldMaterials Science
TopicElectrospun Nanofibers in Biomedical Applications
Canadian institutionsUniversité de Montréal
FundersOffice of Naval ResearchNatural Sciences and Engineering Research Council of CanadaUniversity of Nebraska-LincolnNational Heart, Lung, and Blood InstituteU.S. Department of EnergyDivision of Materials ResearchOffice of ScienceDivision of Civil, Mechanical and Manufacturing InnovationNational Institutes of HealthNational Science Foundation
KeywordsNanofiberMaterials scienceCrystallinityNanoscopic scaleNanotechnologyCharacterization (materials science)PolymerElectrospinningToughnessBrittlenessComposite material

Abstract

fetched live from OpenAlex

Advanced fibers revolutionized structural materials in the second half of the 20th century. However, all high-strength fibers developed to date are brittle. Recently, pioneering simultaneous ultrahigh strength and toughness were discovered in fine (<250 nm) individual electrospun polymer nanofibers (NFs). This highly desirable combination of properties was attributed to high macromolecular chain alignment coupled with low crystallinity. Quantitative analysis of the degree of preferred chain orientation will be crucial for control of NF mechanical properties. However, quantification of supramolecular nanoarchitecture in NFs with low crystallinity in the ultrafine diameter range is highly challenging. Here, we discuss the applicability of traditional as well as emerging methods for quantification of polymer chain orientation in nanoscale one-dimensional samples. Advantages and limitations of different techniques are critically evaluated on experimental examples. It is shown that straightforward application of some of the techniques to sub-wavelength-diameter NFs can lead to severe quantitative and even qualitative artifacts. Sources of such size-related artifacts, stemming from instrumental, materials, and geometric phenomena at the nanoscale, are analyzed on the example of polarized Raman method but are relevant to other spectroscopic techniques. A proposed modified, artifact-free method is demonstrated. Outstanding issues and their proposed solutions are discussed. The results provide guidance for accurate nanofiber characterization to improve fundamental understanding and accelerate development of nanofibers and related nanostructured materials produced by electrospinning or other methods. We expect that the discussion in this review will also be useful to studies of many biological systems that exhibit nanofilamentary architectures and combinations of high strength and toughness.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.081
GPT teacher head0.325
Teacher spread0.244 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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

Citations93
Published2019
Admission routes2
Has abstractyes

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