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Record W3157153918 · doi:10.3390/nano11051179

The Effect of High Lignin Content on Oxidative Nanofibrillation of Wood Cell Wall

2021· article· en· W3157153918 on OpenAlexaff
Simon Jonasson, Anne Bünder, Linn Berglund, Magnus Hertzberg, Totte Niittylä, Kristiina Oksman

Bibliographic record

VenueNanomaterials · 2021
Typearticle
Languageen
FieldMaterials Science
TopicAdvanced Cellulose Research Studies
Canadian institutionsUniversity of Toronto
FundersKempestiftelsernaSvenska Forskningsrådet Formas
KeywordsLigninCelluloseDispersityMaterials scienceYield (engineering)PorosityChemical engineeringChemistryComposite materialNuclear chemistryOrganic chemistryPolymer chemistry

Abstract

fetched live from OpenAlex

Wood from field-grown poplars with different genotypes and varying lignin content (17.4 wt % to 30.0 wt %) were subjected to one-pot 2,2,6,6-Tetramethylpiperidin-1-yl)oxyl catalyzed oxidation and high-pressure homogenization in order to investigate nanofibrillation following simultaneous delignification and cellulose oxidation. When comparing low and high lignin wood it was found that the high lignin wood was more easily fibrillated as indicated by a higher nanofibril yield (68% and 45%) and suspension viscosity (27 and 15 mPa·s). The nanofibrils were monodisperse with diameter ranging between 1.2 and 2.0 nm as measured using atomic force microscopy. Slightly less cellulose oxidation (0.44 and 0.68 mmol·g−1) together with a reduced process yield (36% and 44%) was also found which showed that the removal of a larger amount of lignin increased the efficiency of the homogenization step despite slightly reduced oxidation of the nanofibril surfaces. The surface area of oxidized high lignin wood was also higher than low lignin wood (114 m2·g−1 and 76 m2·g−1) which implicates porosity as a factor that can influence cellulose nanofibril isolation from wood in a beneficial manner.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.001
Threshold uncertainty score0.411

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.021
GPT teacher head0.277
Teacher spread0.256 · 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

Citations14
Published2021
Admission routes1
Has abstractyes

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