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Record W2763070343 · doi:10.1021/acssuschemeng.7b02582

Oxidation of Kraft Lignin with Hydrogen Peroxide and its Application as a Dispersant for Kaolin Suspensions

2017· article· en· W2763070343 on OpenAlexafffund
Wenming He, Weijue Gao, Pedram Fatehi

Bibliographic record

VenueACS Sustainable Chemistry & Engineering · 2017
Typearticle
Languageen
FieldEngineering
TopicLignin and Wood Chemistry
Canadian institutionsLakehead University
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsCanada Foundation for InnovationNorthern Ontario Heritage Fund Corporation
KeywordsDispersantLignosulfonatesChemistryLigninKraft processHydrogen peroxideOrganosolvKraft paperOxidizing agentCelluloseZeta potentialFlocculationOrganic chemistryChemical engineeringPulp and paper industryNuclear chemistry

Abstract

fetched live from OpenAlex

Lignin is an underutilized byproduct of pulping and cellulosic ethanol production plants. However, if utilized efficiently, it can facilitate the development of sustainable processes. In this work, oxidized kraft lignin (OKL) was prepared via treating kraft lignin (KL) with hydrogen peroxide, an environmentally friendly and industrially attractive oxidizing agent, under alkaline conditions. The oxidized kraft lignin with a carboxylate group content of 1.53 mequiv/g was obtained under the optimal oxidation conditions of 80 °C, 2 h treatment, at a 0.77 molar ratio of NaOH/H 2 O 2, and 2.85 molar ratio of H 2 O 2 /lignin, which was then employed as an anionic dispersant for kaolin suspensions. The zeta potential, particle size, and specific surface area as well as the relative turbidity and flocculation index of the kaolin suspension were affected by the pH of the suspension. By increasing the dosage of OKL to 40 mg/L, the relative turbidity of the suspension was increased to 1.18 at pH 5 and the kaolin concentration of 4 g/L, which made its performance superior to that of commercially produced lignosulfonate.

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 score0.005
Threshold uncertainty score0.929

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 head0.004
GPT teacher head0.197
Teacher spread0.194 · 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

Citations98
Published2017
Admission routes2
Has abstractyes

Explore more

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