MétaCan
Menu
Back to cohort
Record W2124403849 · doi:10.1186/1556-276x-9-614

Understanding catalyst behavior during in situ heating through simultaneous secondary and transmitted electron imaging

2014· article· en· W2124403849 on OpenAlexaff
Jane Y. Howe, Lawrence F. Allard, Wilbur C. Bigelow, Hendrix Demers, Steven H. Overbury

Bibliographic record

VenueNanoscale Research Letters · 2014
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced Electron Microscopy Techniques and Applications
Canadian institutionsMcGill UniversityHitachi (Canada)
FundersOak Ridge National LaboratoryOffice of Energy EfficiencyOffice of Energy Efficiency and Renewable EnergyU.S. Department of Energy
KeywordsMaterials scienceCoalescence (physics)NanochemistryNanoprobeScanning transmission electron microscopyCatalysisIn situHigh-resolution transmission electron microscopyTransmission electron microscopyScanning electron microscopeOxideChemical engineeringElectronNanotechnologyNanoparticleComposite materialChemistryMetallurgy

Abstract

fetched live from OpenAlex

By coupling techniques of simultaneous secondary (SE) and transmitted electron (TE) imaging at high resolution in a modern scanning transmission electron microscope (STEM), with the ability to heat specimens using a highly stable MEMS-based heating platform, we obtained synergistic information to clarify the behavior of catalysts during in situ thermal treatments. Au/iron oxide catalyst 'leached' to remove surface Au was heated to temperatures as high as 700°C. The Fe2O3 support particle structure tended to reduce to Fe3O4 and formed surface terraces; the formation, coalescence, and mobility of 1- to 2-nm particles on the terraces were characterized in SE, STEM-ADF, and TEM-BF modes. If combined with simultaneous nanoprobe spectroscopy, this approach will open the door to a new way of studying the kinetics of nano-scaled phenomena.

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: Methods · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.004

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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.023
GPT teacher head0.348
Teacher spread0.325 · 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
GenreMethods

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

Citations14
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueNanoscale Research LettersSame topicAdvanced Electron Microscopy Techniques and ApplicationsFrench-language works237,207