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Record W2117840981 · doi:10.1177/0731684412449699

A comparative study of polymethylmethacrylate/cellulose nanocomposites prepared by in situ polymerization and ex situ dispersion techniques

2013· article· en· W2117840981 on OpenAlexaff
Sunanda Sain, Mousumi Bose, Dipa Ray, Aniruddha Mukhopadhyay, Suparna Sengupta, Tanusree Kar, Christopher J. Ennis, Pattanathu Rahman, Manjusri Misra

Bibliographic record

VenueJournal of Reinforced Plastics and Composites · 2013
Typearticle
Languageen
FieldMaterials Science
TopicAdvanced Cellulose Research Studies
Canadian institutionsUniversity of Guelph
FundersUniversity Grants Commission
KeywordsMaterials scienceNanocompositeCelluloseIn situ polymerizationNanocelluloseFourier transform infrared spectroscopyCastingComposite materialPolymerizationDispersion (optics)Chemical engineeringDispersityPolymer chemistryPolymer

Abstract

fetched live from OpenAlex

Polymethylmethacrylate/cellulose nanocomposites were prepared by in situ polymerization and ex situ dispersion techniques with 10 wt% loading of cellulose nanoparticles. Cellulose nanoparticles were prepared from jute fibers by acid hydrolysis. The suspension polymerization of methylmethacrylate was carried out in presence of cellulose nanoparticles, which were dispersed in water medium and in situ polymethylmethacrylate/cellulose nanocomposite granules were formed. These granules were dissolved in chloroform, sonicated and films were prepared by solution casting method (IPC). Polymethylmethacrylate granules were prepared by similar suspension polymerization process and made into films by solution casting method. Another set of polymethylmethacrylate/cellulose nanocomposite films were prepared by dispersing nanocellulose powder (10 wt%) in polymethylmethacrylate solution and casting into films (EPC). The unreinforced polymethylmethacrylate and polymethylmethacrylate extracted from IPC films were subjected to size exclusion chromatography and nuclear magnetic resonance study. The average molecular weights of neat polymethylmethacrylate and polymethylmethacrylate from IPC were quite close, but the ‘dispersity’ was slightly higher in IPC than that in neat polymethylmethacrylate. Fourier transform infrared spectroscopy revealed some shifts in EPC. X-ray diffraction study showed a similar nature of X-ray diffraction curves in all the samples. Transmission electron microscopy of IPC and EPC showed a better dispersion of fillers and formation of a network structure in IPC, whereas in EPC, the fillers were agglomerated. Surface morphology of the films was examined by field emission scanning electron microscopy and atomic force microscopy. IPC exhibited a much smoother surface compared to that of EPC indicating a more homogeneous dispersion of fillers. IPC showed a higher modulus of elasticity compared to PMMA and EPC. Differential scanning calorimetry showed a shift of glass transition temperature to a higher one (125°C) in IPC compared to that of polymethylmethacrylate (118°C). Thermogravimetric analysis was done to study the thermal degradation behavior of the 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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.011
GPT teacher head0.268
Teacher spread0.258 · 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 source (direct Gemma or distilled Codex), 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

Citations23
Published2013
Admission routes1
Has abstractyes

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