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Record W2505995905 · doi:10.1002/cjce.22595

Comparative study between physicochemical characterization of biochar and metal organic frameworks (MOFs) as gas adsorbents

2016· article· en· W2505995905 on OpenAlexafffundvenue
Hanieh Bamdad, Kelly Hawboldt

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2016
Typearticle
Languageen
FieldChemistry
TopicMetal-Organic Frameworks: Synthesis and Applications
Canadian institutionsMemorial University of Newfoundland
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsBiocharPyrolysisAdsorptionHardwoodMetal-organic frameworkChemistryChemical engineeringSoftwoodCarbon fibersTorrefactionOrganic chemistryMaterials scienceComposite numberComposite materialBotany

Abstract

fetched live from OpenAlex

Abstract In this study, biochar produced from three different woody biomasses, softwood (shaving, bark (Balsam fir)), and hardwood (Ash wood), are compared in terms of chemical and physical properties. The biochar was produced via fast pyrolysis at 450 °C in a 4 kg/h capacity auger reactor. The produced biochars were characterized for elemental composition, surface area, morphology, proximate analysis, and thermal properties. The biochars were compared with a Metal Organic Framework (MOF) with respect to properties key to adsorbent applications. All biochars were basic (pH 8.9–10.7), while MOFs were acidic. The morphologies of biochar and MOF‐5 differ in pore size, chemistry, and structure. Biochar has higher carbon content and more aromatic functional groups than MOFs, which could play an important role for adsorption of acidic gases from natural/produced gas.

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 score0.529

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 head0.012
GPT teacher head0.223
Teacher spread0.211 · 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

Citations26
Published2016
Admission routes3
Has abstractyes

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