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Record W4392316465 · doi:10.1080/02773813.2024.2314454

Aqueous ethanol fractionation of softwood and hardwood kraft lignins: Impact on purity and properties

2024· article· en· W4392316465 on OpenAlexaff
Daniel Beaudoin, Sophie Langis‐Barsetti, Alain Gagné, Ernest Paluš, John P. W. Inwood, Mohan K.R. Konduri

Bibliographic record

VenueJournal of Wood Chemistry and Technology · 2024
Typearticle
Languageen
FieldEngineering
TopicLignin and Wood Chemistry
Canadian institutionsFPInnovations
Fundersnot available
KeywordsSoftwoodChemistryHardwoodKraft paperAqueous solutionFractionationPulp and paper industryEthanolOrganic chemistryChromatographyBotany

Abstract

fetched live from OpenAlex

Industrial kraft lignins are mixtures of macromolecular components variable in both structure and composition. To maximize their value in commercial applications, they often need to be homogenized and purified. Several fractionation methods have been reported to improve the properties of kraft lignins, but these reports have been mostly limited to kraft lignins of single origins. In this study, the physicochemical properties of fractions from four industrial kraft lignins were compared. Fractionation was performed on both softwood and hardwood lignins by partial dissolution in aqueous ethanol followed by precipitation with water. The yields of each fraction varied greatly between the lignins, with differences reaching up to 35% for a single fraction. All fractions were characterized which showed that fractions having remarkably similar properties and compositions can be obtained from different lignins. Organic and inorganic impurities were found to concentrate in specific fractions which allowed the isolation of highly purified fractions of kraft lignin. These results highlight the importance of matching individual kraft lignins with suitable applications.

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 score0.014
Threshold uncertainty score0.465

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 head0.005
GPT teacher head0.210
Teacher spread0.205 · 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

Citations3
Published2024
Admission routes1
Has abstractyes

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