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Critical discussion of light scattering and microscopy techniques for CNC particle sizing

2014· article· en· W2319648114 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueNordic Pulp & Paper Research Journal · 2014
Typearticle
Languageen
FieldEngineering
TopicAdvanced machining processes and optimization
Canadian institutionsFPInnovations
FundersNatural Resources CanadaArboraNano
KeywordsSizingParticle (ecology)MicroscopyMaterials scienceLight scatteringOpticsNanotechnologyComposite materialScatteringChemistryPhysicsGeology

Abstract

fetched live from OpenAlex

The determination of particle length and aspect ratio of acicular cellulose nanocrystals (CNCs) becomes crucial when CNCs are used as a catalyst or as a reinforcement additive or when their size is used as a quality control parameter for production. We measured particle dimensions and particle size distribution of CNCs using several experimental techniques based on electron and optical microscopy as well as static and dynamic light scattering. Electron microscopy can easily reveal the real dimensions (i.e. length and cross-section) of nonsymmetrical particles like CNCs while the lightscattering derived techniques cannot. With the latter, it is however possible to approximate the real dimensions using the appropriate theory. The different aspects of light scattering techniques and types of data that can be obtained are presented and detailed. For each technique used, measurements done with CNCs are compared to those obtained with spherical standard latex particles. The accuracy of the final data was also related to the time necessary to obtain it. This "accuracy over time" ratio is of importance when specific applications such as on-line quality control are targeted.

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.

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)0.001
metaresearch head score (Gemma)0.001
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: none
Teacher disagreement score0.853
Threshold uncertainty score0.245

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.368
Teacher spread0.346 · 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