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Record W2508269772 · doi:10.1021/acs.iecr.5b03360

Lignin Reverse Micelles for UV-Absorbing and High Mechanical Performance Thermoplastics

2015· article· en· W2508269772 on OpenAlexaff
Yong Qian, Xueqing Qiu, Xiaowen Zhong, Delang Zhang, Yonghong Deng, Dongjie Yang, Shiping Zhu

Bibliographic record

VenueIndustrial & Engineering Chemistry Research · 2015
Typearticle
Languageen
FieldEngineering
TopicLignin and Wood Chemistry
Canadian institutionsMcMaster University
FundersChina Postdoctoral Science FoundationFundamental Research Funds for the Central UniversitiesMinistry of Education of the People's Republic of ChinaNational Natural Science Foundation of China
KeywordsCyclohexaneHigh-density polyethyleneLigninMaterials scienceMicelleChemical engineeringMiscibilityModulusPolymer chemistryComposite materialPolymerPolyethyleneChemistryOrganic chemistryAqueous solution

Abstract

fetched live from OpenAlex

This work reports the preparation of a novel class of lignin reverse micelles (LRM) and their application as a UV-blocking additive for thermoplastics. It is found that when 7 vol % cyclohexane was added into alkali lignin (AL)/dioxane solution, LRM formed. When the cyclohexane amount increased, LRM tended to aggregate and separate from the solutions. LRM films had a water contact angle higher than 80°, in comparison to only 52° of the AL film control sample. Due to hydrophobicity, the miscibility of LRM with high density polyethylene (HDPE) was significantly improved from that of AL with HDPE. Since LRM retained the phenolic hydroxyl structure of AL, the HDPE/LRM samples showed an excellent UV-absorbing performance. Furthermore, the added LRM had little negative influence, but significantly improved the HDPE mechanical properties. With 5 wt % LRM loading, Young’s modulus increased from 1066 to 2104 MPa and the elongation at break increased from 671% to 1030%, respectively.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.040
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.109
GPT teacher head0.286
Teacher spread0.178 · 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

Citations95
Published2015
Admission routes1
Has abstractyes

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