Compositional Studies on the Electronic and Magnetic Properties of Potassium Fulleride Mesoporous Niobium Oxide Composites
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".