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Synthesis of a Stable Metallic Niobium Oxide Molecular Sieve and Subsequent Room Temperature Activation of Dinitrogen

2002· article· en· W1983007965 on OpenAlexaff
M. Vettraino, Xiao He, Michel L. Trudeau, John E. Drake, David M. Antonelli

Bibliographic record

VenueAdvanced Functional Materials · 2002
Typearticle
Languageen
FieldMaterials Science
TopicCatalytic Processes in Materials Science
Canadian institutionsHydro-QuébecUniversity of Windsor
Fundersnot available
KeywordsMaterials scienceMesoporous materialOxideMolecular sieveNiobiumNitrideChemical engineeringNanoclustersMetalPorosityInorganic chemistryCatalysisComposite materialNanotechnologyMetallurgyOrganic chemistryLayer (electronics)Chemistry

Abstract

fetched live from OpenAlex

Mesoporous niobium oxide was treated at room temperature with bis(toluene) niobium to make a new black material that exhibits metallic properties. The metallic behavior is attributed to low-valent NbII in the walls of the porous structure and is fully supported by strong emissions at the Fermi level, variable temperature resistivity measurements, and temperature-independent paramagnetism. These materials possess higher conductivities than reported for any molecular sieve and also represent the first example of a metallic oxide-based molecular sieve. For comparison, the conductivity is almost 10 000 times greater than the highest value measured for an open structured mesoporous material. Treatment at room temperature with dinitrogen leads to formation of a thin nitride coat on the surface. Cleavage of dinitrogen is an extremely rare and important reaction that typically requires very forcing conditions in the solid state (high temperatures in excess of several hundred degrees, argon plasmas, etc.) or low-valent coordinatively stressed metal centers in the homogeneous phase. This is the first example of a molecular sieve mediating this process and because of the controlled porosity and high surface areas of these materials they are ideal candidates for model studies for nitrogen reduction and catalytic nitrogen incorporation into organic compounds. Since porous materials are often the catalytic support of choice for industrial processes, these materials may lead to the development of commercial nitrogen incorporation processes.

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 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 score0.004
Threshold uncertainty score0.998

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 communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.013
GPT teacher head0.220
Teacher spread0.207 · 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

Citations23
Published2002
Admission routes1
Has abstractyes

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