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Record W4411668368 · doi:10.3390/waste3030021

Extrusion-Biodelignification Approach for Biomass Pretreatment

2025· article· en· W4411668368 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueWaste · 2025
Typearticle
Languageen
FieldEngineering
TopicBiofuel production and bioconversion
Canadian institutionsUniversité LavalUniversité de SherbrookeInstitut National de la Recherche Scientifique
FundersNatural Sciences and Engineering Research Council of CanadaInstitut national de la recherche scientifique
KeywordsExtrusionBiomass (ecology)Pulp and paper industryChemistryMaterials scienceAgronomyComposite materialEngineeringBiology

Abstract

fetched live from OpenAlex

This work presents a new approach for lignocellulosic biomass pretreatment. The process is a sequential combination of extrusion (Ex) and semi-solid fermentation (SSF). To assess the Ex-SSF pretreatment efficiency, black spruce chips (wood residues) and corn stover (crop residues) were subjected to the process. The negative controls were the pretreatment of both residues with SSF alone without extrusion. Lignin peroxidase was the main ligninolytic enzyme contributing to the delignification in the negative controls. High lignin peroxide (LiP) activities were recorded for raw black spruce (53.7 ± 2.7 U/L) and corn stover (16.4 ± 0.8 U/L) compared to the Ex-SSF pretreated biomasses where the highest LiP activity recorded was 6.0 ± 0.3 U/L (corn residues). However, with the negative controls, only a maximum of 17% delignification was achieved for both biomasses. As for the Ex-SSF process, the pretreatments were preceded by the optimization of the extrusion (Ex) step and the semi-solid fermentation (SSF) step via experimental designs. The Ex-SSF pretreatments led to interesting results and offered cost-effective advantages compared to existing pretreatments. Biomass delignification reached 59.1% and 65.4% for black spruce and corn stover, respectively. For the analyses performed, it was found that manganese peroxidase (MnP) was the main contributor to delignification during the SSF step. MnP activity was up to 13.8 U/L for Ex-SSF pretreated black spruce, and 32.0 U/L for Ex-SSF pretreated corn stover, while the maximum MnP recorded in the negative controls was 1.4 ± 0.1 U/L. Ex-SSF pretreatment increased the cellulose crystallinity index (CrI) by 13% for black spruce and 4% for corn stover. But enzymatic digestibility of the Ex-SSF pretreated biomasses with 0.25 mL/g of enzyme led to 7.6 mg/L sugar recovery for black spruce, which is 2.3 times the raw biomass yield. The Ex-SSF pretreated corn stover led to 17.0 mg/L sugar recovery, which is a 44% improvement in sugar concentration compared to raw corn stover. However, increasing the enzyme content from 0.25 mL/g to 0.50 mg/L and 0.75 mg/L generated lower hydrolysis efficiency (the sugar recovery decreased).

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.

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: none
Teacher disagreement score0.856
Threshold uncertainty score0.232

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 head0.013
GPT teacher head0.220
Teacher spread0.207 · 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