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Record W2991674994 · doi:10.1515/ntrev-2019-0036

Surface modification of nano-sized carbon black for reinforcement of rubber

2019· article· en· W2991674994 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

VenueNanotechnology Reviews · 2019
Typearticle
Languageen
FieldMaterials Science
TopicPolymer Nanocomposites and Properties
Canadian institutionsCarleton University
FundersDepartment of Education, Fujian Province
KeywordsCarbon blackNatural rubberInverse gas chromatographyTolueneAdsorptionMaterials scienceDispersion (optics)Carbon fibersEmulsionSurface modificationChemical engineeringComposite materialChemistrySurface energyOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract The standard sample IRB7# of nano-sized carbon black was reated by different methods, including washing by alcohol, toluene, water, the emulsion of toluene and water, and heating. Surface activity of the obtained nanoparticles was measured using inverse gas chromatography. The results showed that the dispersion free energy could be significantly increased after heating. The surface activity of three kinds of industrial carbon black was studied after heat treatment at different temperature. The results indicated that surface activity, iodine adsorption number and nitrogen surface area values of carbon black nanoparticles increased with the increase of temperature. However, the dibutyl phthalate adsorption value maintained nearly constant. The performance test of rubber showed that with the increasing heat treatment temperature of carbon black, 300% modulus of rubber increased before 450°C and decreased after 450°C.

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)0.000
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 score0.040
Threshold uncertainty score0.400

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.034
GPT teacher head0.279
Teacher spread0.245 · 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