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Record W4319334995 · doi:10.1016/j.heliyon.2023.e13500

Transforming micropores to mesopores by heat cycling KOH activated petcoke for improved kinetics of adsorption of naphthenic acids

2023· article· en· W4319334995 on OpenAlexaff
Oliver K.L. Strong, Elmira Nazari, Tyler Roy, Kevin M. Scotland, Paul R. Pede, Andrew J. Vreugdenhil

Bibliographic record

VenueHeliyon · 2023
Typearticle
Languageen
FieldChemistry
TopicPetroleum Processing and Analysis
Canadian institutionsTrent University
Fundersnot available
KeywordsAdsorptionMicroporous materialActivated carbonAcetic acidKineticsChemistryMesoporous materialChemical engineeringNaphthenic acidPetroleum cokeCarbon fibersRaw materialCokeInorganic chemistryMaterials scienceNuclear chemistryOrganic chemistryCatalysisComposite material

Abstract

fetched live from OpenAlex

Formation of activated carbon from petroleum coke by KOH, results in high specific surface area materials that are predominantly microporous. This initial microporosity means that the adsorption kinetics of target species are not as rapid as they could be, thus limiting environmental remediation applications for the material. To address this problem a series of additional heat cycles with no additional chemical inputs were applied after activation but prior to the removal of activating agents. This process resulted in the oxidation of residual potassium metal from the initial activation which allows it to function again as an activating agent for the subsequent cycles. The heat cycling resulted in an increase in mesoporosity by 10–25% with each successive cycle independent of the KOH to feedstock ratio. This was shown to be demonstrably different than equivalently extended heating times, thus identifying the importance of thermal cycling. Adsorption kinetics of three model naphthenic acids showed faster kinetics for the pore widened activated carbon. The t 1/2 times dropped from 20 to 6.6 min for diphenyl acetic acid, 34.3 to 4.5 min for cyclohexane acetic acid, and 51.4 to 12.0 min for heptanoic acid.

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 score0.007
Threshold uncertainty score0.620

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 head0.265
Teacher spread0.253 · 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

Citations18
Published2023
Admission routes1
Has abstractyes

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