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Record W2990183953 · doi:10.3390/polym11121929

Lignin Redistribution for Enhancing Barrier Properties of Cellulose-Based Materials

2019· article· en· W2990183953 on OpenAlex
Wangxia Wang, Tianyu Guo, Kaiyong Sun, Yongcan Jin, Feng Gu, Huining Xiao

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenuePolymers · 2019
Typearticle
Languageen
FieldEngineering
TopicLignin and Wood Chemistry
Canadian institutionsUniversity of New Brunswick
FundersChina Postdoctoral Science FoundationNatural Science Foundation of Jiangsu ProvinceNational Natural Science Foundation of China
KeywordsLigninCelluloseRedistribution (election)Materials sciencePolymer scienceChemical engineeringChemistryOrganic chemistryEngineering

Abstract

fetched live from OpenAlex

Renewable cellulose-based materials have gained increasing interest in food packaging because of its favorable biodegradability and biocompatibility, whereas the barrier properties of hydrophilic and porous fibers are inadequate for most applications. Exploration of lignin redistribution for enhancing barrier properties of paper packaging material was carried out in this work. The redistribution of nanolized alkali lignin on paper surface showed excellent water, grease, and water vapor barrier. It provided persisted water (contact angle decrease rate at 0.05°/s) and grease (stained area undetectable at 72 h) resistance under long-term moisture or oil direct contact conditions, which also inhibited the bacterial growth to certain degree. Tough water vapor transmission rate can be lowered 82% from 528 to 97 g/m2/d by lignin redistribution. The result suggests that alkali lignin, with multiple barrier properties, has great potential in bio-based application considering the biodegradability, biocompatibility, and recyclability.

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.

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 score0.003
Threshold uncertainty score0.331

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 head0.007
GPT teacher head0.183
Teacher spread0.176 · 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