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Record W4415061367 · doi:10.1007/s10973-025-14738-z

Pyrolysis of spruce wood, wheat straw, switchgrass, miscanthus and swine manure using catalysts containing alkali and alkaline earth metals: thermogravimetric analysis and kinetic modeling

2025· article· en· W4415061367 on OpenAlexafffund
Olivier Fischer, R. Lemaire, Ammar Bensakhria

Bibliographic record

VenueJournal of Thermal Analysis and Calorimetry · 2025
Typearticle
Languageen
FieldEngineering
TopicThermochemical Biomass Conversion Processes
Canadian institutionsÉcole de Technologie Supérieure
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsThermogravimetric analysisPyrolysisCatalysisAlkali metalDecompositionBiomass (ecology)MiscanthusThermal decompositionRaw material

Abstract

fetched live from OpenAlex

This paper examines the pyrolysis of 5 biomass types (spruce wood, wheat straw, switchgrass, miscanthus and swine manure) when being catalyzed by additives (NaCl, KCl, CaCl 2 and MgCl 2 ) containing alkali and alkaline earth metals (AAEMs). Thermogravimetric analyses (TGA) were carried out with raw samples and with catalyzed ones prepared by wet impregnation. 4 different heating rates (5, 10, 15 and 30 K min −1 ) were used, and the associated experimental data were modeled by implementing 3 isoconversional approaches (Kissinger–Akahira–Sunose (KAS), Flynn–Wall–Ozawa (OFW) and Friedman). The inferred rate constant parameters were then used to compute the variations of the conversion degree of the fuels versus the temperature while considering different reaction mechanisms commonly employed in the literature, including order-based, diffusion, geometrical, nucleation and power law models. As highlights, the results obtained revealed that AAEM catalysts shift the decomposition process to lower temperatures. Besides, conversion profiles computed using each tested modeling approach were found to properly reproduce the TGA results as long as order-based models were selected. An analysis of the kinetic parameters estimated when implementing the three above-listed isoconversional methods showed that the rate constants tend to increase when adding catalysts, with CaCl 2 and MgCl 2 being found to exhibit a stronger capacity to increase pyrolysis rates as compared to NaCl and KCl. Finally, a sensitivity analysis focusing on the impact of the catalyst load on the pyrolysis kinetics revealed a so-called saturation phenomenon indicating that the AAEMs tend to promote the conversion of biomass more effectively when the concentration of metal cation in the impregnating solution is relatively low (not higher than ⁓0.15 mol L −1 in the present 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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.005
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

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 head0.009
GPT teacher head0.230
Teacher spread0.221 · 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 source (direct Gemma or distilled Codex), 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

Citations4
Published2025
Admission routes2
Has abstractno

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