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Record W3081322895 · doi:10.1002/cphc.202000671

Improving the Yield and Rate of Acid‐Catalyzed Deconstruction of Lignin by Mechanochemical Activation

2020· article· en· W3081322895 on OpenAlexaff
Darpan H. Patel, Dominik Marx, Allan L. L. East

Bibliographic record

VenueChemPhysChem · 2020
Typearticle
Languageen
FieldEngineering
TopicLignin and Wood Chemistry
Canadian institutionsUniversity of Regina
Fundersnot available
KeywordsChemistryMechanochemistryLigninCleavage (geology)Reaction mechanismCatalysisBond cleavageYield (engineering)Computational chemistryPhotochemistryChemical engineeringOrganic chemistryMaterials scienceComposite material

Abstract

fetched live from OpenAlex

Lignin is a potential biomass feedstock from plant material, but it is particularly difficult to economically process. Inspired by recent ball-milling results, state-of-the-art quantum mechanochemistry calculations have been performed to isolate and probe the purely mechanochemical stretching effect alone upon acid-catalyzed deconstruction of lignin. Effects upon cleavage of several exemplary simple ethers are examined first, and with low stretching force they all are predicted to cleave substantially faster, allowing for use of milder acids and lower temperatures. Effects upon an experimentally known lignin fragment model (containing the ubiquitous β-O-4 linkage) are next examined; this first required a mechanism refinement (3-step indirect cleavage, 1-step side reaction) and identification of the rate-limiting step under zero-force (thermal) conditions. Mechanochemical activation using very low stretching forces improves at first only yield, by fully shutting off the ring-closure side reaction. At only somewhat larger forces, in stark contrast, a switch in mechanism is found to occur, from 3-step indirect cleavage to the direct cleavage mechanism of simple ethers, finally strongly enhancing the cleavage rate of lignin. It is concluded that mechanochemical activation of the common β-O-4 link in lignin would improve the rate of its acidolysis via a mechanism switch past a low force threshold. Relevance to ball-milling experiments is discussed.

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 score0.003
Threshold uncertainty score0.329

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 head0.008
GPT teacher head0.179
Teacher spread0.171 · 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

Citations5
Published2020
Admission routes1
Has abstractyes

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