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Record W4382651842 · doi:10.1016/j.ccst.2023.100128

Effect of physicochemical activation on CO2 adsorption of activated porous carbon derived from pine sawdust

2023· article· en· W4382651842 on OpenAlexafffund
Himanshu Patel, Haftom Weldekidan, Amar K. Mohanty, Manjusri Misra

Bibliographic record

VenueCarbon Capture Science & Technology · 2023
Typearticle
Languageen
FieldEngineering
TopicCarbon Dioxide Capture Technologies
Canadian institutionsUniversity of Guelph
FundersOntario Agri-Food Innovation AllianceNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsAdsorptionSawdustPhysisorptionCarbonizationActivated carbonChemistryDesorptionSpecific surface areaBar (unit)Yield (engineering)BET theoryCarbon fibersChemical engineeringNuclear chemistryOrganic chemistryCatalysisMaterials science

Abstract

fetched live from OpenAlex

The study reports on cost-effective and novel method for synthesis of activated porous carbon (APC) suitable for post-combustion CO2 capture. Inexpensive and abundant pine sawdust was utilized as a precursor. A hybrid synthesis protocol of physicochemical activation utilizing KOH + CO2 as activating agents was proposed. Effectiveness of APC derived via physicochemical activation was compared with that derived via chemical (only KOH) and physical (only CO2) activation. Also, the effect of carbonization condition on APC performance was investigated. Yield of APC (from precursor to final product) ranged within 20-24 wt.%. The maximum BET surface area of 2216 m2/g and the maximum CO2 adsorption capacity of 6.35 mmol/g at 0 °C/1 bar, 3.82 mmol/g at 25 °C/1 bar and 2.81 mmol/g at 40 °C/1 bar was obtained. Following the ideal adsorbed solution theory, CO2/N2 selectivity was computed at 0 °C, 25 °C and 40 °C and found to be within 10-19. For all the APCs, the isosteric heat of adsorption < 40 kJ/mol indicated physisorption as dominating mechanism for CO2 adsorption. Even after five consecutive adsorption-desorption cycles, multi-cyclic recyclability > 99.5% was observed.

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 categoriesMeta-epidemiology (narrow)
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 score0.006
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.006
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
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 head0.226
Teacher spread0.220 · 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.

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

Citations39
Published2023
Admission routes2
Has abstractyes

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