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Record W4308532087 · doi:10.3390/molecules27227662

Thermogravimetric Analysis and Kinetic Modeling of the AAEM-Catalyzed Pyrolysis of Woody Biomass

2022· article· en· W4308532087 on OpenAlexaff
Wei Wang, R. Lemaire, Ammar Bensakhria, Denis Luart

Bibliographic record

VenueMolecules · 2022
Typearticle
Languageen
FieldEngineering
TopicThermochemical Biomass Conversion Processes
Canadian institutionsÉcole de Technologie Supérieure
Fundersnot available
KeywordsThermogravimetric analysisPyrolysisCatalysisAlkali metalChemistryBiomass (ecology)DiffusionDecompositionThermal decompositionChemical engineeringKineticsWork (physics)ThermodynamicsOrganic chemistryGeologyPhysics

Abstract

fetched live from OpenAlex

at four heating rates (5, 10, 15 and 30 °C/min). Obtained results showed that AAEM compounds promote the decomposition of biomass by reducing the initial and peak pyrolysis temperatures. More specifically, the catalytic effect of the alkaline earth metal was shown to be stronger than that of alkali metals. To further interpret the obtained trends, a kinetic modeling of measured data was realized using two isoconversional methods (the Ozawa-Flynn-Wall (OFW) and Kissinger-Akahira-Sunose (KAS) models). With a view to identifying a suitable reaction model, model fitting and master plot methods were considered to be coupled with the isoconversional modeling approaches. The 3-D diffusion reaction model has been identified as being well suited to properly simulate the evolution of the conversion degree of each sample as a function of the temperature. Furthermore, the kinetic parameters derived from the present modeling work highlighted significant decreases of the activation energies when impregnating wood with AAEM chlorides, thus corroborating the existence of catalytic effects shifting the decomposition process to lower temperatures. A survey of the speculated pathways allowing to account for the impact of AAEMs on the thermal degradation of woody biomass is eventually proposed to better interpret the trends identified in this work.

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 score0.067
Threshold uncertainty score0.328

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.003
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 head0.006
GPT teacher head0.185
Teacher spread0.178 · 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

Citations25
Published2022
Admission routes1
Has abstractyes

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