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Record W2965437643 · doi:10.3389/fchem.2019.00578

Acidic Ionic Liquid as Both Solvent and Catalyst for Fast Chemical Esterification of Industrial Lignins: Performances and Regioselectivity

2019· article· en· W2965437643 on OpenAlexaff
Éric Husson, Lise Hulin, Caroline Hadad, Chaima Boughanmi, Tatjana Stevanović, Catherine Sarazin

Bibliographic record

VenueFrontiers in Chemistry · 2019
Typearticle
Languageen
FieldEngineering
TopicLignin and Wood Chemistry
Canadian institutionsUniversité Laval
FundersCentre National de la Recherche Scientifique
KeywordsLigninOrganosolvChemistryOrganic chemistryIonic liquidKraft paperCelluloseRegioselectivityCatalysisSoftwoodChemical engineeringMaterials science

Abstract

fetched live from OpenAlex

Lignin can be considered as an essential under-exploited polymer from lignocellulosic biomass representing a key for a profitable biorefinery. One way of lignin valorization could be the improvement of physico-chemical properties by esterification to enhance miscibility in apolar polyolefin matrices, thereby helping the production of bio-based composites. The present work describes for the first time a succeeded chemical esterification of industrial lignins with maleic anhydride in an acidic ionic liquid: 1-butyl-3-methyl imidazolium hydrogen sulfate without additional catalyst. This efficient strategy was applied to four industrial lignins: two softwood Kraft lignins (Indulin AT, Wayagamack), one hardwood Kraft lignin (Windsor) and one softwood organosolv lignin (Lignol), distinct in origin, extraction process and thus chemical structure. The chemical, structural and thermal properties of modified lignins were characterized by 31P nuclear magnetic resonance, infrared spectroscopy and thermal analyses then compared to those of unmodified lignins. After 4 h of reaction, between 30 to 52 % of the constitutive hydroxyls were esterified depending on the type of lignin sample. The regioselectivity of the reaction was demonstrated to be preferentially orientated toward aliphatic hydroxyls for three out of four lignins (66.6, 65.5, and 83.6 % for Indulin AT, Windsor and Lignol, respectively versus 51.7 % for Wayagamack). The origin and the extraction process of the polymer would thus influence the efficiency and the regioselectivity of this reaction. Finally, we demonstrated that the covalent grafting of maleyl chain on lignins did not significantly affect thermal stability and increased significantly the solubility in polar and protic solvent probably due to additional exposed carboxylic groups resulted from mono-acylation independently of H/G/S ratio. Blending with polyolefins could then be considered in regard of compatibility with the obtained physico-chemical properties.

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 score0.625

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 head0.007
GPT teacher head0.198
Teacher spread0.191 · 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

Citations29
Published2019
Admission routes1
Has abstractyes

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