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Record W2895874784 · doi:10.1002/cssc.201802073

Pyrolysis of the Cellulose Fraction of Biomass in the Presence of Solid Acid Catalysts: An Operando Spectroscopy and Theoretical Investigation

2018· article· en· W2895874784 on OpenAlexaff
Christopher J. Keturakis, Olga B. Lapina, Aleksandr A. Shubin, Victor V. Terskikh, Evgeniy S. Papulovskiy, Ivan V. Yudaev, E. A. Paukshtis, Israel E. Wachs

Bibliographic record

VenueChemSusChem · 2018
Typearticle
Languageen
FieldEngineering
TopicThermochemical Biomass Conversion Processes
Canadian institutionsUniversity of Ottawa
FundersFonds National de la Recherche LuxembourgRussian Foundation for Basic ResearchNational Science Foundation
KeywordsPyrolysisBiomass (ecology)CatalysisCelluloseChemistrySelectivityChemical engineeringRaw materialInfrared spectroscopySpectroscopyHydrocarbonRaman spectroscopyOrganic chemistryInorganic chemistryMaterials science

Abstract

fetched live from OpenAlex

Abstract Biomass pyrolysis by solid acid catalysts is one of many promising technologies for sustainable production of hydrocarbon liquid fuels and value‐added chemicals, but these complex chemical transformations are still poorly understood. A series of well‐defined model SiO 2 ‐supported alumina catalysts were synthesized and molecularly characterized, under dehydrated conditions and during biomass pyrolysis, with the aim of establishing fundamental catalyst structure–activity/selectivity relationships. The nature and corresponding acidity of the supported AlO x nanostructures on SiO 2 were determined with 27 Al/ 1 H NMR and IR spectroscopy of chemisorbed CO, and DFT calculations. Operando time‐resolved IR–Raman–MS spectroscopy studies revealed the molecular transformations taking place during biomass pyrolysis. The molecular transformations during biomass pyrolysis depended on both the domain size of the AlO x cluster and molecular nature of the biomass feedstock. These new insights allowed the establishment of fundamental structure–activity/selectivity relationships during biomass pyrolysis.

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 score0.001
Threshold uncertainty score0.261

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 studies0.0000.001
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 head0.007
GPT teacher head0.225
Teacher spread0.217 · 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

Citations10
Published2018
Admission routes1
Has abstractyes

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