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Record W2032305503 · doi:10.1002/app.23026

Contributions to characterization of poly(vinyl chloride)–lignin blends

2006· article· en· W2032305503 on OpenAlexaff
D. Banu, A. El‐Aghoury, D. Feldman

Bibliographic record

VenueJournal of Applied Polymer Science · 2006
Typearticle
Languageen
FieldEngineering
TopicLignin and Wood Chemistry
Canadian institutionsConcordia University
Fundersnot available
KeywordsPlasticizerOrganosolvVinyl chlorideLigninMaterials sciencePolymerPolyvinyl chloridePolymer chemistryKraft paperChemical engineeringOrganic chemistryComposite materialChemistry

Abstract

fetched live from OpenAlex

Abstract The present study evaluates the impact of blending organosolv and kraft lignins, which are natural polymer by‐products in the pulp and paper industry, with plasticized poly(vinyl chloride) (PVC) in flooring formulations. Also examined is the impact of replacing dioctyl phthalate, a PVC industry general‐purpose plasticizer, with diethylene glycol dibenzoate (Benzoflex 2‐45), tricresyl phosphate (Lindol), or alkyl sulfonic phenyl ester (Mesamoll) in these formulations. The influence of the different types of lignins and plasticizers on the processibility, thermal, and mechanical properties of the blends is discussed. These properties demonstrate that partial replacement of PVC (20 parts) with different lignins is feasible for some formulations that can be successfully used as matrices for a high level of calcium carbonate filler in flooring products. In addition, the data demonstrate that the presence of certain plasticizers, which interfere with the intramolecular interactions existing in lignins, may allow the lignin molecules to have more molecular mobility. The morphology and the properties of PVC plasticized lignin blends are strongly influenced by the degree and mode of the lignin plasticization and its dispersion within the PVC matrix. © 2006 Wiley Periodicals, Inc. J Appl Polym Sci 101: 2732–2748, 2006

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 score0.017
Threshold uncertainty score0.350

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.003
GPT teacher head0.208
Teacher spread0.205 · 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

Citations44
Published2006
Admission routes1
Has abstractyes

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