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Selective CO<sub>2</sub> Capture by Activated Carbons: Evaluation of the Effects of Precursors and Pyrolysis Process

2015· article· en· W2485081120 on OpenAlexafffundabout
Sepideh Shahkarami, Ajay K. Dalai, Jafar Soltan, Yongfeng Hu, Dongniu Wang

Bibliographic record

VenueEnergy & Fuels · 2015
Typearticle
Languageen
FieldEngineering
TopicCarbon Dioxide Capture Technologies
Canadian institutionsCanadian Light Source (Canada)University of Saskatchewan
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of SaskatchewanCanadian Light Source
KeywordsSawdustAdsorptionPyrolysisActivated carbonCarbonizationChemistryPotassium hydroxideChemical engineeringSpecific surface areaStrawVolume (thermodynamics)Carbon fibersOrganic chemistryMaterials scienceInorganic chemistryCatalysis

Abstract

fetched live from OpenAlex

Activated carbons are produced from different Canadian waste biomasses including agricultural waste (wheat straw and flax straw), forest residue (sawdust and willow ring), and animal manure (poultry litter). The precursors are carbonized through the fast and slow pyrolysis processes and then activated with potassium hydroxide. A fixed-bed reactor is used for temperature swing adsorption of CO 2 in a gas mixture of N 2, O 2, and CO 2 to study the cyclic CO 2 adsorption capacity and selectivity of the produced activated carbons. The breakthrough adsorption capacity of the produced activated carbon is measured under a flue gas condition of 15 mol % of CO 2, 5 mol % of O 2, and 80% of N 2 at 25 °C and atmospheric pressure. Slow pyrolysis based activated carbon has a lower surface area and total pore volume but higher adsorption capacity in the presence of N 2 . Sawdust based activated carbon synthesized using the slow pyrolysis process creates the highest ultra-micropore volume of 0.36 cm 3 /g, and the highest adsorption capacity in N 2 (78.1 mg/g) but low selectivity (2.8) over O 2 because of the oxygen functional groups on the surface. Ultra-micropores and surface chemistry of adsorbents are far more important than particle size, total pore volume, and internal surface area of the adsorbents. All the samples fully recovered their initial adsorption capacity in each cycle (for up to 10 cycles). This work also demonstrates that adsorption capacity and selectivity of activated carbon can be controlled and optimized through the choice of starting material and carbonization conditions.

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)0.001
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 score0.007
Threshold uncertainty score0.567

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.007
GPT teacher head0.215
Teacher spread0.208 · 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

Citations36
Published2015
Admission routes3
Has abstractyes

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