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Record W2795541556 · doi:10.1038/s41598-018-20285-5

Fast modification on wheat straw outer surface by water vapor plasma and its application on composite material

2018· article· en· W2795541556 on OpenAlexaff
Weimin Chen, Yicheng Xu, Shukai Shi, Yizhong Cao, Minzhi Chen, Xiaoyan Zhou

Bibliographic record

VenueScientific Reports · 2018
Typearticle
Languageen
FieldMaterials Science
TopicSurface Modification and Superhydrophobicity
Canadian institutionsUniversity of Alberta
FundersDoctorate Fellowship Foundation of Nanjing Forestry UniversityQinglan Project of Jiangsu Province of ChinaGovernment of Jiangsu ProvincePriority Academic Program Development of Jiangsu Higher Education InstitutionsNatural Science Foundation of Jiangsu ProvinceNational Natural Science Foundation of ChinaNanjing Forestry University
KeywordsContact angleSurface modificationMaterials scienceStrawComposite materialSurface energySurface roughnessAdhesiveWaxDielectric barrier dischargeWater vaporComposite numberChemical engineeringDielectricChemistryLayer (electronics)Organic chemistry

Abstract

fetched live from OpenAlex

The presence of non-poplar extracts, cutin, and wax layer in the wheat straw outer surface (WOS) greatly limit its application in bio-composite preparation. In this study, a dielectric-barrier-discharge plasma using water vapor as feeding gas was used to fast modify the WOS. The morphology, free radical concentrations, surface chemical components, and contact angles of WOS before and after plasma modification were investigated. Wheat straw was further prepared into wheat straw-based composites (WSC) and its bonding strength was evaluated by a paper tension meter. The results showed that water vapor plasma leads to the appearance of surface roughness, the generation of massive free radicals, and the introduction of oxygen-containing groups. In addition, both initial and equilibrium contact angle and the surface total free energy were significantly increased after plasma modification. These results synergistically facilitate the spread and permeation of adhesive onto the WOS and thus improve the bonding strength of all prepared WSCs. A good linear relationship between bonding strength and surface roughness parameters, contact angles, and total free energy were observed. In general, this study provided a time-saving and cost-effective modification method to realize WSC manufacture.

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 categoriesInsufficient 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 score0.008
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.251
Teacher spread0.233 · 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

Citations18
Published2018
Admission routes1
Has abstractyes

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