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Record W1995224949 · doi:10.1002/pen.21807

Clays for polymeric nanocomposites

2011· article· en· W1995224949 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.

Bibliographic record

VenuePolymer Engineering and Science · 2011
Typearticle
Languageen
FieldMaterials Science
TopicPolymer Nanocomposites and Properties
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsNanocompositeImpurityMaterials scienceComposition (language)Chemical engineeringChemical compositionContaminationChemistryNanotechnologyOrganic chemistryEngineeringBiology

Abstract

fetched live from OpenAlex

Abstract We discuss test methods and results for determining individual clay platelets shape, size, size distribution, elemental composition, and impurities. Commercial sodium salt varieties of natural, semisynthetic and synthetic clay (Cloisite®‐Na + , Somasif ME‐100, and Topy‐Na + , respectively) were analyzed. In this international collaboration, eight laboratories on three continents carried out the work within the VAMAS TWA‐33 activities. There are large differences between the three nanofillers as far as: (1) the platelet orthogonal dimensions, (2) chemical composition, and (3) contaminants (their diversity and quantity) are concerned. Elaborate purification of natural clays leaves behind 2–5 wt% of organic and mineral impurities, whose nature, shape, size, and chemistry depend on the clay origin. These contaminants affect nanocomposite performance, thus controlling their composition and quantity is essential. The article describes the developed methods, summarizes the preliminary results, discusses the encountered difficulties, and proposes methods for solving them. POLYM. ENG. SCI., 2011. © 2011 Society of Plastics Engineers

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)0.000
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 score0.007
Threshold uncertainty score0.514

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.019
GPT teacher head0.208
Teacher spread0.189 · 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