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Record W4408407814 · doi:10.1021/acsapm.4c03971

Preparation of Citric Acid-Modified Cellulose Composites and Elucidation of Their Toughening Mechanism

2025· article· en· W4408407814 on OpenAlexfundno aff
Yuta Joka, Kenji Yamaoka, Ryohei Ikura, Takeru Komyo, Chao Luo, Akihide Sugawara, Hiroshi Uyama, Yasutomo UETSUJI, Yoshinori Takashima

Bibliographic record

VenueACS Applied Polymer Materials · 2025
Typearticle
Languageen
FieldMaterials Science
TopicAdvanced Cellulose Research Studies
Canadian institutionsnot available
FundersCore Research for Evolutional Science and TechnologyJapan Society for the Promotion of ScienceCo-creation place formation support programYazaki Memorial Foundation for Science and TechnologyIketani Science and Technology FoundationInternational Polyurethane Technology FoundationDavid Suzuki FoundationAsahi Glass Foundation
KeywordsTougheningCitric acidCelluloseComposite materialMechanism (biology)Materials scienceChemistryOrganic chemistryToughness

Abstract

fetched live from OpenAlex

Cellulose is a plant-based and highly abundant biobased resource and widely used as a filler for polymer composite materials because cellulose fillers have a high aspect ratio and high crystal modulus. Introducing high contents of cellulose fillers into polymer composites reduces the use of petroleum-derived synthetic polymers, increases the mechanical strength, and decreases the toughness due to the aggregation of fillers. In this study, we introduced hydrogen bonds between the polymer matrix and cellulose fillers. Citric acid-modified cellulose (CAC) has many carboxyl groups and forms hydrogen bonds with polymers that have hydroxy groups. The interactions between the polymer matrix and the CAC fillers were evaluated by the glass transition temperature, Fourier transform infrared spectroscopy, and a simulation study based on first-principles calculations. Noncovalent interactions between the polymer matrix and CAC fillers improved the toughness of the CAC composites and enabled mechanical recycling at a high CAC content. This study contributes to the reduced use of petroleum-derived synthetic polymers and longer lifetimes of the materials.

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 score0.051
Threshold uncertainty score0.661

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 head0.014
GPT teacher head0.291
Teacher spread0.276 · 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

Citations2
Published2025
Admission routes1
Has abstractyes

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