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Record W2950118397 · doi:10.1139/v06-021

2005 Pure or Applied Inorganic Chemistry Award Lecture — Host–guest inclusion chemistry of electroactive, mesoporous transition metal oxides oxidation and 1-D confinement in one step and why amorphous is better

2006· article· en· W2950118397 on OpenAlexvenueno aff
Boris O. Skadtchenko, David M. Antonelli

Bibliographic record

VenueCanadian Journal of Chemistry · 2006
Typearticle
Languageen
FieldMaterials Science
TopicMesoporous Materials and Catalysis
Canadian institutionsnot available
Fundersnot available
KeywordsChemistryMesoporous materialMicroporous materialAmorphous solidTransition metalNanotechnologyMesoporous organosilicaOrganometallic chemistryNanomaterialsRedoxMesoporous silicaCatalysisChemical engineeringInorganic chemistryOrganic chemistry

Abstract

fetched live from OpenAlex

The flexible oxidation states of mesoporous Nb, Ta, and Ti oxides make them unique amongst porous materials allowing reaction pathways and cascades that are not possible for mesoporous silica or microporous materials such as zeolites. This electronic activity coupled with the 20–30 Å pores and the amorphous wall structure, which provides greater bandwidth (W) and hence an even greater range of redox potentials, leads to a rich variety of host–guest inclusion chemistry, which serves as an unprecedented 1-D analogue to layered 2-D host–guest inclusion reactions studied for decades. In this paper we survey a series of reactions between these mesoporous hosts and a wide variety of organic and organometallic guest species including alkali fullerides, cobaltocene, and other organometallic sandwhich species, and discuss the electronic and magnetic properties of the resulting composites.Key words: mesoporous materials, semiconductors, fullerides, superconductors, oxides, nanomaterials, metallocenes.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.152
Threshold uncertainty score0.510

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.1520.032

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 head0.005
GPT teacher head0.177
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 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

Citations4
Published2006
Admission routes1
Has abstractyes

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