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Record W2969696791 · doi:10.3390/polym11091378

Investigation on the Preparation and Properties of CMC/magadiite Nacre-Like Nanocomposite Films

2019· article· en· W2969696791 on OpenAlexaff
Mingliang Ge, Yueying Li, Yinye Yang, Yanwu Wang, Guodong Liang, Guoqing Hu, Jahangir Alam S.M.

Bibliographic record

VenuePolymers · 2019
Typearticle
Languageen
FieldMaterials Science
TopicCalcium Carbonate Crystallization and Inhibition
Canadian institutionsUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
Fundersnot available
KeywordsMaterials scienceNanocompositeThermal stabilityUltimate tensile strengthComposite materialScanning electron microscopeCastingChemical engineering

Abstract

fetched live from OpenAlex

The layered hydrated sodium salt-magadiite (MAG), which has special interpenetrating petals structure, was used as a functional filler to slowly self-assemble with sodium carboxy-methylcellulose (CMC), in order to prepare nacre-like nanocomposite film by solvent evaporation method. The structure of prepared nacre-like nanocomposite film was characterized by Scanning Electron Microscope (SEM) and X-ray diffraction (XRD) analysis; whereas, it was indicated that CMC macromolecules were inserted between the layers of MAG to increase the layer spacing of MAG by forming an interpenetrating petals structure; in the meantime, the addition of MAG improved the thermal stability of CMC. The tensile strength of CMC/MAG was significantly improved compared with pure CMC. The tensile strength of CMC/MAG reached the maximum value at 1.71 MPa when the MAG content was 20%, to maintaining high transparency. Due to the high content of inorganic filler, the flame retarding performance and the thermal stability were also brilliant; hence, the great biocompatibility and excellent mechanical properties of the bionic nanocomposite films with the unique interpenetrating petals structure provided a great probability for these original composites to be widely applied in material research, such as tissue engineering in biomedical research.

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)0.000
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 score0.003
Threshold uncertainty score0.384

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 head0.025
GPT teacher head0.218
Teacher spread0.193 · 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

Citations8
Published2019
Admission routes1
Has abstractyes

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