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Record W2024066646 · doi:10.1021/cm020057f

Compositional Studies on the Electronic and Magnetic Properties of Potassium Fulleride Mesoporous Niobium Oxide Composites

2002· article· en· W2024066646 on OpenAlexaff
Bing Ye, Michel L. Trudeau, David M. Antonelli

Bibliographic record

VenueChemistry of Materials · 2002
Typearticle
Languageen
FieldChemistry
TopicFullerene Chemistry and Applications
Canadian institutionsUniversity of WindsorHydro-Québec
Fundersnot available
KeywordsMaterials scienceMesoporous materialOxideNiobiumNiobium oxidePotassiumX-ray photoelectron spectroscopyInorganic chemistryComposite materialChemical engineeringChemistryMetallurgyOrganic chemistry

Abstract

fetched live from OpenAlex

Mesoporous niobium oxide potassium fulleride composites were synthesized by the treatment of mesoporous niobium oxide with 0.3, 0.6, or 1.0 equiv of potassium naphthalene followed by stirring with either excess K 3 C 60 or C 60 . The reaction of potassium-reduced mesoporous niobium oxide with neutral C 60 is the first reported example of a mesoporous oxide functioning as an electron donor to a guest molecule. These new routes allow for greater flexibility in the K:Nb and C:Nb ratios in the composite than our previous method and for this reason enables a more comprehensive study of the effect of absolute carbon and potassium content on electron transport properties. Materials were characterized by elemental analysis, nitrogen adsorption, XRD, XPS, SQUID magnetometry, and room-temperature conductivity measurements in an effort to relate density of states at the Fermi level, temperature-independent paramagnetism, and conductivity patterns to the composition of the composite. Variable temperature resistivity measurements showed that the composites are metallic, semiconducting, or insulating, depending on the composition. The main factors governing conductivity were the absolute carbon content and the oxidation state of the intercalated fulleride, while the absolute potassium content had little effect on the electronic properties.

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 categoriesInsufficient payload (model declined to judge)
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 score0.005
Threshold uncertainty score0.999

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.025
GPT teacher head0.217
Teacher spread0.192 · 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

Citations13
Published2002
Admission routes1
Has abstractyes

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