MétaCan
Menu
Back to cohort
Record W2806665122 · doi:10.18331/brj2018.5.2.4

Magnetically recyclable nanocatalysts based on magnetite: an environmentally friendly and recyclable catalyst for esterification reactions

2018· article· en· W2806665122 on OpenAlexvenueno aff
Vannia Cristina dos Santos, Tailor Machado Peruzzolo, Geani Maria Ucoski, Luiz Pereira Ramos, Shirley Nakagaki

Bibliographic record

VenueBiofuel Research Journal · 2018
Typearticle
Languageen
FieldChemistry
TopicChemical Synthesis and Reactions
Canadian institutionsnot available
FundersUniversidade Federal do ParanáConselho Nacional de Desenvolvimento Científico e TecnológicoCoordenação de Aperfeiçoamento de Pessoal de Nível Superior
KeywordsCatalysisNanomaterial-based catalystMagnetiteMethanolEnvironmentally friendlyChemical engineeringNanoparticleMagnetic nanoparticlesMaterials scienceMagnetite NanoparticlesChemistryNuclear chemistryOrganic chemistryNanotechnologyMetallurgy

Abstract

fetched live from OpenAlex

Solid magnetic nanoparticles (magnetite = Mag) composed of Fe3O4 and magnetite coated with silica (Fe3O4/SiO2 = Mag/Si) were prepared from inexpensive starting materials. The catalytic activity of the solids was investigated for palmitic acid esterification with methanol under solvothermal conditions. Both pure Fe3O4 (Mag) and silica-coated (Mag/Si) nanoparticles exhibited high catalytic activities and were easy to recover from the reaction environment using an external magnet. Furthermore, the magnetic nanoparticle catalysts were reused without significant loss of catalytic activity and showed high durability in typical acid-catalyzed reactions. XRD and SEM analyses were conducted before and after esterification, showing almost identical particle distribution in both fresh and reused catalysts.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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 head0.050
GPT teacher head0.341
Teacher spread0.290 · 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

Citations21
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueBiofuel Research JournalSame topicChemical Synthesis and ReactionsFrench-language works237,207