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Record W2889110832 · doi:10.1002/slct.201801837

Topochemical Understanding of Lignin Distribution During Hydrothermal Flowthrough Pretreatment

2018· article· en· W2889110832 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.

Bibliographic record

VenueChemistrySelect · 2018
Typearticle
Languageen
FieldEngineering
TopicLignin and Wood Chemistry
Canadian institutionsUniversity of British Columbia
FundersCenter for Bioenergy Innovation
KeywordsLigninCelluloseBiomass (ecology)Hydrothermal circulationChemistryChemical engineeringScanning electron microscopeLignocellulosic biomassMaterials scienceOrganic chemistryComposite materialGeology

Abstract

fetched live from OpenAlex

Abstract Changes in surface properties during biomass pretreatments are important parameters to understand and engineer biological biomass conversion processes. In particular, different influences on the surface of biomass are expected between flowthrough and batch pretreatments. For a better understanding of biomass surface changes by hydrothermal flowthrough pretreatment, the mechanism by which the surface of biomass is altered in terms of cellulose and lignin was investigated using time‐of‐flight secondary ion mass spectrometry (ToF‐SIMS) and compared with the bulk chemical composition and the results from scanning electron microscope (SEM). ToF‐SIMS analysis results provide semi‐quantitative information of cellulose and lignin and support the other observation from SEM and bulk compositional analyses. In brief, more lignin was observed on the surface of biomass at the early stage hydrothermal pretreatment, while the lignin mainly located at the cell corners was reduced by extended pretreatment time. Unlike batch pretreatment, pseudo‐lignin formation was not observed on the poplar surface during the flowthrough process.

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: Empirical
Teacher disagreement score0.031
Threshold uncertainty score0.795

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 head0.010
GPT teacher head0.202
Teacher spread0.192 · 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