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Record W2328491920 · doi:10.1021/bm1010905

Controlling the Reflection Wavelength of Iridescent Solid Films of Nanocrystalline Cellulose

2010· article· en· W2328491920 on OpenAlexaff
Stephanie Beck, Jean Bouchard, Richard Berry

Bibliographic record

VenueBiomacromolecules · 2010
Typearticle
Languageen
FieldMaterials Science
TopicAdvanced Cellulose Research Studies
Canadian institutionsFPInnovations
Fundersnot available
KeywordsNanocrystalline materialSuspension (topology)Reflection (computer programming)SonicationWavelengthIridescenceMaterials scienceCelluloseLiquid crystalElectrolyteChemical engineeringOpticsOptoelectronicsChemistryNanotechnologyChromatographyOrganic chemistryPhysical chemistryPhysics

Abstract

fetched live from OpenAlex

Nanocrystalline cellulose (NCC) self-assembles in suspension to form iridescent chiral nematic films upon drying that can reflect circularly polarized light at specific wavelengths. Ultrasound treatment has now been found to increase the chiral nematic pitch in suspension and red-shift the reflection wavelength of NCC films as the applied energy increases. Sonication and electrolyte addition combined allow the reflective properties of the film to be predictably tuned. The effects of sonicating an NCC suspension are cumulative and permanent. Suspensions sonicated with different energy inputs may be mixed to give an NCC film having a reflection band intermediate between those obtained from the individual suspensions. The data suggest that the ultrasound-induced red-shift is electrostatic in nature.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.018
GPT teacher head0.300
Teacher spread0.282 · 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 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

Citations367
Published2010
Admission routes1
Has abstractyes

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