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Record W2301885296 · doi:10.1002/ceat.201500639

Palladium Supported on Carbon Nanotubes for Methane Catalytic Oxidation

2016· article· en· W2301885296 on OpenAlexfundno aff
Xiuhui Gao, Sheng Wang, Diannan Gao, Zhiping Chen, Weigang Liu, Mingzhe Wang, Shudong Wang

Bibliographic record

VenueChemical Engineering & Technology · 2016
Typearticle
Languageen
FieldMaterials Science
TopicCatalytic Processes in Materials Science
Canadian institutionsnot available
FundersNational Natural Science Foundation of ChinaOntario Council on Graduate Studies, Council of Ontario Universities
KeywordsCatalysisCarbon nanotubePalladiumMethaneDispersion (optics)Anaerobic oxidation of methaneChemical engineeringRedoxCarbon fibersOxygenMaterials scienceInorganic chemistryChemistryNanotechnologyComposite materialOrganic chemistryComposite number

Abstract

fetched live from OpenAlex

Abstract A series of palladium/multi‐walled carbon nanotube (Pd/MWCNT) catalysts were prepared for the total oxidation of methane. Their morphologies, thermal stabilities and redox properties were investigated using different analytical techniques. Their textural properties were also measured by the Brunauer‐Emmett‐Teller method. On this basis, the catalytic behaviors were tailored by pretreating with mixed acid solutions and changing the MWCNT diameters. The results show that higher MWCNT diameters improved the Pd dispersion and enhanced the catalytic properties of the Pd/MWCNT. A proper ratio of HNO3 to H2SO4 can provide moderately active sites such as oxygen‐containing groups (OCG) and defects, on which the Pd precursor can be attached or anchored. Excessive H2SO4 will deteriorate the carbon framework and lower the frequency of OCG decorated on the outer surface of the MWCNT. As a result, the activity of the Pd/MWCNT catalysts for methane oxidation is suppressed.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.008
GPT teacher head0.227
Teacher spread0.218 · 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 source (direct Gemma or distilled Codex), 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

Citations5
Published2016
Admission routes1
Has abstractyes

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