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Record W4386070049 · doi:10.3390/catal13091229

Catalytic Transfer Hydrogenation Performance of Magnesium-Doped ZrO2 Solid Solutions

2023· article· en· W4386070049 on OpenAlexaff
Ewa M. Iwanek, Donald W. Kirk, Marek Gliński, Zbigniew Kaszkur

Bibliographic record

VenueCatalysts · 2023
Typearticle
Languageen
FieldEngineering
TopicCatalysis and Hydrodesulfurization Studies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMagnesiumCubic zirconiaCatalysisSolid solutionInorganic chemistryChemistryAcetophenoneIonYield (engineering)Materials scienceNuclear chemistryOrganic chemistryMetallurgyCeramic

Abstract

fetched live from OpenAlex

This is the first study to investigate the activity of a solid solution containing magnesium ions in a zirconia matrix in the catalytic transfer hydrogenation (CTH) of acetophenone with 2-pentanol. The results have shown that magnesium oxide is very highly active in CTH when physically mixed with zirconia. However, the same concentration of Mg2+ ions (Mg:Zr = 3:97) inserted into a zirconia lattice did not yield high activity in CTH. A higher concentration of Mg2+ ions (5%) was also tested in the two types of systems, i.e., a physical mixture of oxides and a solid solution. The increase in the concentration of Mg2+ ions in the physical mixture led to a pronounced increase in the activity of the system, whereas in the case of the solid solution it led to a slight decrease in activity. The impact of the zirconyl salt used in the synthesis was also examined, but showed little effect on the properties and activity of the systems. The study has also shown that the increase of the concentration of magnesium ions caused a decrease in the m-ZrO2 to t-ZrO2 ratio. Nevertheless, the rate of heating had an even bigger effect on this ratio.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.190
Threshold uncertainty score0.604

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.020
GPT teacher head0.225
Teacher spread0.205 · 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.

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

Citations2
Published2023
Admission routes1
Has abstractyes

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