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Preparation of an amphoteric lignin copolymer and its value in the papermaking industry

2020· article· en· W3096086117 on OpenAlexaff
Zhongming Liu, Chao Gao, Linlin Chen, Xinyu Dong, Fangong Kong, Shoujuan Wang, Magdi E. Gibril, Lucian A. Lucia, Pedram Fatehi

Bibliographic record

VenueBioResources · 2020
Typearticle
Languageen
FieldEngineering
TopicLignin and Wood Chemistry
Canadian institutionsLakehead University
FundersNatural Science Foundation of Shandong ProvinceNational Natural Science Foundation of China
KeywordsLigninMonomerCationic polymerizationCopolymerPapermakingFourier transform infrared spectroscopyMaterials sciencePolymer chemistryPolymerizationGraftingSoftwoodChemical engineeringOrganic chemistryChemistryPolymerComposite material

Abstract

fetched live from OpenAlex

Lignin biomass is an important renewable woody material that can be converted into high value-added products through physical and chemical reactions, such as paper strength additives. In this study, a cationic methacryloyloxyethyl trimethylammonium chloride monomer (DMC) and anionic acrylic monomer (AA) were grafted onto softwood kraft lignin through free radical polymerization to prepare an amphoteric lignin copolymer. Fourier transform infrared spectroscopy (FTIR), proton nuclear magnetic resonance spectroscopy (1H NMR), elemental analysis, and charge density analysis methods confirmed that the anionic and cationic monomers were successfully grafted onto the lignin. The grafting ratios of AA and DMC monomer in the lignin-DMC-AA copolymer were 62.4% and 51.3%, respectively. The application of lignin-DMC-AA copolymer as a paper additive for enhancing the physical properties of paper sheets was studied in the papermaking industry. The results indicated that the copolymer had a maximum increase in physical strength at around 2 wt% lignin-DMC-AA. The amount absorbed on the fibers was 18.5 mg/g, and the retention of the lignin-DMC-AA copolymer was over 90%.

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 score0.011
Threshold uncertainty score0.208

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 head0.015
GPT teacher head0.242
Teacher spread0.227 · 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

Citations1
Published2020
Admission routes1
Has abstractyes

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