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Record W2332175292 · doi:10.1021/la302982s

Cellulose Nanocrystal Iridescence: A New Model

2012· article· en· W2332175292 on OpenAlexaff
Gilles Picard, David Simon, Y. Kadiri, J. D. LeBreux, F Ghozayel

Bibliographic record

VenueLangmuir · 2012
Typearticle
Languageen
FieldMaterials Science
TopicAdvanced Cellulose Research Studies
Canadian institutionsCollege Ahuntsic
Fundersnot available
KeywordsIridescenceMaterials scienceLamella (surface anatomy)ColloidEvaporationDiffractionNanotechnologyNanocrystalLiquid crystalDispersion (optics)CelluloseChemical engineeringOpticsComposite materialOptoelectronics

Abstract

fetched live from OpenAlex

A homogeneous aqueous dispersion of cellulose nanocrystals (CNs) that is left to evaporate in a Petri dish self-organizes into smectic liquid crystals that are actually liquid multilamellar structures. As evaporation proceeds, the liquid multilamellar structures solidify to become a solid multilamellar film. Each solid lamella is in the submicrometer range, and its iridescence is easily explained by classical light interference. A careful inspection of each solid lamella revealed long, oriented arrays of colloids. Interestingly, the array orientation is generally the same for each superposed layer. This is exceptional because the stratification appears first in the liquid, and the solid colloids are formed in each stratum at the very end of the process. Our findings are supported by optical, atomic force, and electron microscope observations and by laser diffraction observations. The multilamellar solid film model is easier to engineer than the helical model currently used to explain the iridescence and optical activities of CN solid films. This new understanding should promote the industrial production of colorful CN coatings and inks as a green alternative for decades to come.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.039
GPT teacher head0.305
Teacher spread0.266 · 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 designSimulation or modeling
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

Citations34
Published2012
Admission routes1
Has abstractyes

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