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Sustainable high yield production of cellulose nanomaterials for easy-cleaning surfaces of cellulose-based materials

2019· article· en· W3010516840 on OpenAlexafffund
Wangxia Wang, Nanxun Sun, Zhaosheng Cai, Kaijin Sun, Feng Gu, Yongcan Jin, Huining Xiao

Bibliographic record

VenueBioResources · 2019
Typearticle
Languageen
FieldMaterials Science
TopicAdvanced Cellulose Research Studies
Canadian institutionsUniversity of New Brunswick
FundersNatural Sciences and Engineering Research Council of CanadaNatural Science Foundation of Jiangsu ProvinceGovernment of Jiangsu ProvinceChina Postdoctoral Science FoundationJiangsu Province Biomass Energy and Material LaboratoryNational Natural Science Foundation of China
KeywordsNanocelluloseOxalic acidMaterials scienceCelluloseNanomaterialsCoatingChemical engineeringYield (engineering)Sulfuric acidZeta potentialNanotechnologyComposite materialChemistryOrganic chemistryNanoparticleMetallurgy

Abstract

fetched live from OpenAlex

Cellulose nanomaterials with high yield and desired properties were sustainably produced using a facile recyclable acid treatment (oxalic acid) with mineral acid catalysis at ambient pressure. The resultant nanocellulose was uniform in dimensions (diameter and length distributions) and highly dispersible in the aqueous phase. The nanocellulose with yield up to 33.9%, a zeta potential of -53.9 mV, and 100% volume stability (24 h) was achieved via oxalic acid treatment in conjunction with sulfuric acid addition. The coating of such nanocellulose on paper created a uniform and dense layer on the surface, which lowered Gurley air permeability (i.e., prolonging the time required for air flow from 3.9 to 681.9 s per 100 mL). Moreover, the coated paper showed a complete grease barrier after 48 h and presented easy-cleaning behavior. The approach developed in this work offers an adoptable guidance to design green and sustainable easy-cleaning surfaces. In turn, this approach will provide potential applications of nanocellulose for green based packaging and environmental protection.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
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 score0.006
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0010.000

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 head0.019
GPT teacher head0.253
Teacher spread0.235 · 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 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

Citations12
Published2019
Admission routes2
Has abstractyes

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