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Record W2587781535 · doi:10.1039/c7dt00035a

Lewis acidity quantification and catalytic activity of Ti, Zr and Al-supported mesoporous silica

2017· article· en· W2587781535 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueDalton Transactions · 2017
Typearticle
Languageen
FieldChemistry
TopicChemical Synthesis and Reactions
Canadian institutionsUniversité Laval
FundersFonds de recherche du Québec – Nature et technologies
KeywordsCatalysisLewis acids and basesMesoporous materialMesoporous silicaChemistryInorganic chemistryNuclear chemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Water-tolerant supported Lewis acids are actively sought after, in particular to address the challenging direct amidation reaction. To this aim, a versatile and easy synthesis of large pore silica materials with supported Ti-, Al-, Zr-Lewis acids, using acetyl acetonate as a metal-stabilizing agent, was accomplished. The formation of bulk metal oxides was not observed, even at high concentrations of metal species. The Lewis acidity was demonstrated using quantitative and qualitative titration techniques using a series of Hammett indicators, such as butter yellow, phenylazodiphenylphosphine and dicinnamalacetone. The optimal concentration of metals corresponding to the highest Lewis acidity of solids was found to be 4% for Al-SBA-15, 12-15% for Ti-SBA-15 and 7% for Zr-SBA-15 materials. The water-tolerance of the supported metal centers was explored by a pyridine adsorption-FTIR study before and after water addition. The metalated materials were used as water-tolerant heterogeneous catalysts for the amidation of electron-poor and bulky amines, such as substituted anilines and morpholine, obtaining 59-99% yield of the corresponding amides.

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.

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 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 score0.038
Threshold uncertainty score0.493

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.027
GPT teacher head0.273
Teacher spread0.246 · 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