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Record W4408344803 · doi:10.1002/adfm.202424591

Recent Advances in Green and Efficient Cellulose Utilization Through Structure Deconstruction and Regeneration

2025· article· en· W4408344803 on OpenAlexaff
Chengling Huang, Hou–Yong Yu, Youjie Gao, Yi Chen, Somia Yassin Hussain Abdalkarim, Kam Chiu Tam

Bibliographic record

VenueAdvanced Functional Materials · 2025
Typearticle
Languageen
FieldMaterials Science
TopicAdvanced Cellulose Research Studies
Canadian institutionsUniversity of Waterloo
FundersKey Research and Development Program of Zhejiang ProvinceNational Natural Science Foundation of China
KeywordsMaterials scienceDeconstruction (building)Regeneration (biology)CelluloseNanotechnologyPolymer scienceChemical engineeringWaste managementEngineeringCell biology

Abstract

fetched live from OpenAlex

Abstract From the invention of papermaking in ancient times to the wide range of modern applications in the fields of textiles, medicine, food, and nanotechnology, the development of cellulose‐based materials reflects humanity's ongoing exploration and utilization of renewable resources. The production of cellulose‐based materials is highly dependent on the solvents which can deconstruct and regenerate the structure of cellulose. However, the solubility, processing technology, and application development of cellulose materials based on structure deconstruction and regeneration require further research and breakthroughs. Here, structural characteristics, solvent system, modification methods, and degradation performance of cellulose‐based materials are briefly introduced. Moreover, the life cycle assessment is discussed to improve the evaluation system and to further demonstrate the environmental friendliness and potential application of cellulose‐based materials. Finally, several key technologies and strategies that can assist cellulose‐based materials in meeting the performance requirements of bioplastics are emphasized, with a view to increasing recognition of their advantages and potential as biodegradable 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 score0.046
Threshold uncertainty score0.772

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 communication0.0000.001
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 head0.021
GPT teacher head0.295
Teacher spread0.274 · 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

Citations52
Published2025
Admission routes1
Has abstractyes

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