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Record W2072843267 · doi:10.1021/ja405634k

Plant Biomass Recalcitrance: Effect of Hemicellulose Composition on Nanoscale Forces that Control Cell Wall Strength

2013· article· en· W2072843267 on OpenAlexaff
Rodrigo L. Silveira, Stanislav R. Stoyanov, Sergey Gusarov, Munir S. Skaf, Andriy Kovalenko

Bibliographic record

VenueJournal of the American Chemical Society · 2013
Typearticle
Languageen
FieldEngineering
TopicBiofuel production and bioconversion
Canadian institutionsUniversity of AlbertaNational Institute for Nanotechnology
Fundersnot available
KeywordsHemicelluloseChemistryCell wallLigninCelluloseGlucuronateSecondary cell wallLignocellulosic biomassBiomass (ecology)Chemical engineeringBiochemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Efficient conversion of lignocellulosic biomass to second-generation biofuels and valuable chemicals requires decomposition of resilient plant cell wall structure. Cell wall recalcitrance varies among plant species and even phenotypes, depending on the chemical composition of the noncellulosic matrix. Changing the amount and composition of branches attached to the hemicellulose backbone can significantly alter the cell wall strength and microstructure. We address the effect of hemicellulose composition on primary cell wall assembly forces by using the 3D-RISM-KH molecular theory of solvation, which provides statistical-mechanical sampling and molecular picture of hemicellulose arrangement around cellulose. We show that hemicellulose branches of arabinose, glucuronic acid, and especially glucuronate strengthen the primary cell wall by strongly coordinating to hydrogen bond donor sites on the cellulose surface. We reveal molecular forces maintaining the cell wall structure and provide directions for genetic modulation of plants and pretreatment design to render biomass more amenable to processing.

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 score0.327

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 head0.190
Teacher spread0.186 · 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

Citations134
Published2013
Admission routes1
Has abstractyes

Explore more

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