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ALKALIZING AGENT INFLUENCE ON THE CeO2 CATALYST SYNTHESIS AND PROPERTIES

2016· article· en· W3197175055 on OpenAlexaff
Matheus José Cunha de Oliveira, Max Rocha Quirino, Magna Silmara Oliveira DE ARAÚJO, Óscar Melo, Lucianna Gama

Bibliographic record

VenuePERIÓDICO TCHÊ QUÍMICA · 2016
Typearticle
Languageen
FieldMaterials Science
TopicCatalytic Processes in Materials Science
Canadian institutionsMicrosemi (Canada)
Fundersnot available
KeywordsCatalysisCerium oxideUreaCeriumChemistryOxideInorganic chemistryElectrolyteNuclear chemistryHydrothermal circulationReducing agentChemical engineeringOrganic chemistry

Abstract

fetched live from OpenAlex

Cerium oxide is an important catalyst used in several reactions as electrolytes in fuel cells, UV absorber and oxygen sensors. The objective of the present work is CeO2 catalyst synthesis by microwave hydrothermal method and then evaluate the influence of NaOH, NH4OH and urea as alkalizing agents in the catalyst properties. The results showed that it was possible to obtain the monophasic catalyst only when using NaOH and NH4OH as precursors; the first presenting greater surface area (141.09 m2.g-1) and higher pore volume. It was concluded that using urea as alkalizing agent synthesis conditions were not sufficient for monophasic cerium production.

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)0.002
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 score0.005
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.025
GPT teacher head0.231
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

Citations0
Published2016
Admission routes1
Has abstractyes

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