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Record W4410519014 · doi:10.1002/mame.202500117

Melt Processing of Cellulose Acetate for Controlled Release Applications – A Review

2025· review· en· W4410519014 on OpenAlexaff
Thabang N. Mphateng, António Benjamim Mapossa, Teboho Clement Mokhena, Suprakas Sinha Ray, Uttandaraman Sundararaj

Bibliographic record

VenueMacromolecular Materials and Engineering · 2025
Typereview
Languageen
FieldMaterials Science
TopicAdvanced Cellulose Research Studies
Canadian institutionsUniversity of Calgary
FundersDepartment of Science and Technology, Ministry of Science and Technology, IndiaUniversity of PretoriaPaper Manufacturers Association of South AfricaNational Research Foundation
KeywordsMaterials scienceCellulose acetateCelluloseMaterials processingControlled releasePolymer scienceChemical engineeringProcess engineeringNanotechnologyEngineering

Abstract

fetched live from OpenAlex

Abstract Cellulose acetate (CA) has garnered considerable industrial and research interest due to its sustainable properties, such as biodegradability and biocompatibility. Despite these attractive properties, CA is difficult to process using traditional melt processing techniques. This is due to its high crystallinity and a glass transition temperature that exceeds the thermal degradation temperature. Therefore, different additives have been explored to overcome these issues. This review explores recent trends in the use of melt‐processed CA materials for encapsulating and controlling the release of active compounds. It highlights the advancements made over the past decade in processing CA‐based materials using thermoplastic techniques. Additionally, the review discusses the properties of these materials, including biodegradation, photodegradation, and solubility, which are important for delivering active agents. Finally, it provides an overview of the challenges and prospects for CA‐based materials processed through thermoplastic processing methods.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.837
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.000
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 head0.016
GPT teacher head0.311
Teacher spread0.295 · 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.

Study designNot applicable
Domainnot available
GenreReview

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

Citations5
Published2025
Admission routes1
Has abstractyes

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