MétaCan
Menu
Back to cohort
Record W2324710178 · doi:10.1021/ie3001666

Preparation and Characterization of Plasticized Starch/Carbon Black-Oxide Nanocomposites

2012· article· en· W2324710178 on OpenAlexaff
Dayan Qian, Peter R. Chang, Pengwu Zheng, Xiaofei Ma

Bibliographic record

VenueIndustrial & Engineering Chemistry Research · 2012
Typearticle
Languageen
FieldMaterials Science
TopicPolymer Nanocomposites and Properties
Canadian institutionsAgriculture and Agri-Food Canada
FundersNational Natural Science Foundation of China
KeywordsCarbon blackNanocompositeComposite materialDistilled waterUltimate tensile strengthStarchMaterials scienceAqueous solutionDispersion (optics)OxideChemical engineeringChemistryOrganic chemistryMetallurgy

Abstract

fetched live from OpenAlex

The carbon black-oxide (CBO) particles were prepared by oxidizing carbon black (CB) in a modified Hummer’s method to improve the dispersion of CBO in water. The carboxylic acid, epoxide groups, and hydroxyl groups were formed in the resulting golden CBO particles, which in the size of about 30–50 nm were smaller than CB particles. And the dispersion of CBO particles was so good in distilled water that CBO particles in aqueous solution followed the Lambert–Beer’s law well. The nanocomposites were also prepared using CBO or CB particles as the fillers in glycerol-plasticized starch (GPS) matrix by the casting process. CBO fillers had good dispersion in GPS matrix and exhibited an obvious reinforcing effect. Both tensile strength and Youngs modulous of GPS/CBO composites were higher than GPS/CB composites. In views of the values of water vapor permeability (WVP), GPS/CBO composites exhibited better water resistance than GPS/CB composites.

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 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 score0.002
Threshold uncertainty score0.508

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.050
GPT teacher head0.302
Teacher spread0.252 · 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 teacher head, 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

Citations13
Published2012
Admission routes1
Has abstractyes

Explore more

Same venueIndustrial & Engineering Chemistry ResearchSame topicPolymer Nanocomposites and PropertiesFrench-language works237,207