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Record W2961783096 · doi:10.14288/1.0379859

Characterization of lignin molar mass and molecular conformation by multi-angle light scattering

2019· article· en· W2961783096 on OpenAlexaff
Lun Ji

Bibliographic record

VenuecIRcle (University of British Columbia) · 2019
Typearticle
Languageen
FieldEngineering
TopicLignin and Wood Chemistry
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMolar massCharacterization (materials science)LigninChemistryLight scatteringMolecular massMolarScatteringCrystallographyMaterials scienceOrganic chemistryOpticsPhysicsNanotechnologyPolymerGeology

Abstract

fetched live from OpenAlex

There are many obstacles that hinder the understanding and hence the utilization of softwood kraft lignin (SKL). The lack of reliable measurements for lignin molecular weight and the corresponding molecular conformation hampers proper elucidation of structure-property relationships. Conventional gel permeation chromatography (GPC) is unable to robustly measure the molecular weight because of a lack of calibration standards with a similar structure to lignin. Further, the potential for interactions between lignin and the column gel packing delays separation, changing the mechanism from a strict hydrodynamic radius interpretation. In the present work, the determination of absolute molar mass of technical lignin was conducted utilizing gel permeation chromatography (GPC) combined with multi-angle light scattering (MALS). In order to clarify the light scattering profile, six SKL fractions, homogeneous in both structure and size were obtained by a combination of ultrafiltration and organic solvent fractionation. Further information on the molecular structure was studied utilizing a differential viscometer combined with quantitative 1D and 2D nuclear magnetic resonance spectroscopy (NMR) methods for chemical and structural analysis of functional groups and interunit linkages, respectively. Separated lignin fractions were used to enhance the clarity of light scattering profiles by narrowing the molecular weight distribution of lignin fractions so the larger polymers would not dominate the scattering. For solvent fractionated materials, the acetone soluble fraction had a lower molecular weight than the acetone insoluble fraction. In addition, hydrodynamic behaviour was acquired based on viscosity and molecular mass of fractionated samples. Acetone soluble lignin was found to possess a more compact structure relative to the acetone insoluble fraction, due to a significantly lower “α” value in the Mark-Houwink-Sakurada (MHS) plot. This compact geometry was supported by the structural analysis from NMR showing the acetone soluble part contained fewer native linkages and aliphatic side chains, which suggests the samples were considerably degraded. The relative degree of compactness (branching degree) was quantified by comparing hydrodynamic behaviour of SKL fractions with a “linear” lignin reference, as represented by enzymatic milled acidolysis lignin (EMAL), and it was found that lower molecular mass samples contained more branches than higher molecular mass fractions.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.002
GPT teacher head0.133
Teacher spread0.130 · 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 source (direct Gemma or distilled Codex), 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

Citations4
Published2019
Admission routes1
Has abstractyes

Explore more

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