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Record W2324192831 · doi:10.1021/ie201565x

Synthesis of Mesoporous Carbons from Bituminous Coal Tar Pitch Using Combined Nanosilica Template and KOH Activation

2011· article· en· W2324192831 on OpenAlexafffund
Shitang Tong, Lei Mao, Xiaohua Zhang, Charles Q. Jia

Bibliographic record

VenueIndustrial & Engineering Chemistry Research · 2011
Typearticle
Languageen
FieldMaterials Science
TopicMesoporous Materials and Catalysis
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaNational Science Foundation
KeywordsMesoporous materialMacroporeMaterials scienceCarbon fibersChemical engineeringCoal tarSpecific surface areaPorosityAdsorptionMorphology (biology)Activated carbonCarbonizationScanning electron microscopeCatalysisCoalOrganic chemistryChemistryComposite materialComposite number

Abstract

fetched live from OpenAlex

Mesoporous carbons (MCs) facilitate mass transport in pore networks and enhance the performance of porous carbon. By combining a nanosilica template with KOH activation, highly porous MCs with BET specific surface areas ( S BET ) greater than 1000 m 2 /g were synthesized from unmodified commercial coal tar pitch. The MCs had a honeycomblike morphology with randomly arranged macropores. An increase in template amount resulted in an increase in pores larger than 20 nm, the size of silica template. An MC sample activated at 850 °C for 2 h with a KOH-to-carbon ratio of 2.7:1 and a pitch-to-template ratio of 3:1 had an S BET value of 1366 m 2 /g, 65 vol % mesopores, 19 vol % macropores, and an average pore diameter of 25 nm. Whereas the silica template was largely responsible for macropores and large mesopores, KOH created new micropores and enlarged existing pores through KOH–carbon reactions. Moreover, KOH enhanced the formation of carbonyl, carboxyl, and lactonic groups on the carbon surface. The feasibility of synthesizing MCs from an unmodified commercial carbon precursor with a simpler route was demonstrated.

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)0.001
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 score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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)0.0010.000

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 head0.119
GPT teacher head0.290
Teacher spread0.172 · 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

Citations11
Published2011
Admission routes2
Has abstractyes

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