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Record W4238002520 · doi:10.1002/ange.201915389

Controlled‐Atmosphere Flame Fusion Single‐Crystal Growth of Non‐Noble fcc, hcp, and bcc Metals Using Copper, Cobalt, and Iron

2020· article· en· W4238002520 on OpenAlexafffund
Fabian M. Schuett, Derek Esau, K. Liam Varvaris, Shelly Gelman, Jonas Björk, Johanna Rosén, Gregory Jerkiewicz, Timo Jacob

Bibliographic record

VenueAngewandte Chemie · 2020
Typearticle
Languageen
FieldEarth and Planetary Sciences
Topicnanoparticles nucleation surface interactions
Canadian institutionsQueen's University
FundersLinköpings UniversitetCanadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of CanadaDeutsche ForschungsgemeinschaftDeutscher Akademischer Austauschdienst
KeywordsSingle crystalMaterials scienceNoble metalElectrocatalystCopperCrystallographyCrystal (programming language)FusionNanotechnologyElectrochemistryMetalChemistryMetallurgyPhysical chemistryElectrode

Abstract

fetched live from OpenAlex

Abstract The growth of noble‐metal single crystals via the flame fusion method was developed in the 1980s. Since then, there have been no major advancements to the technique until the recent development of the controlled‐atmosphere flame fusion (CAFF) method to grow non‐noble Ni single crystals. Herein, we demonstrate the generality of this method with the first preparation of fcc Cu as well as the first hcp and bcc single crystals of Co and Fe, respectively. The high quality of the single crystals was verified using scanning electron microscopy and Laue X‐ray backscattering. Based on Wulff constructions, the equilibrium shapes of the single‐crystal particles were studied, confirming the symmetry of the fcc, hcp, and bcc single‐crystal lattices. The low cost of the CAFF method makes all kinds of high‐quality non‐noble single crystals independent of their lattice accessible for use in electrocatalysis, electrochemistry, surface science, and materials science.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.037
Threshold uncertainty score0.914

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)0.0010.000

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 head0.024
GPT teacher head0.228
Teacher spread0.204 · 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

Citations4
Published2020
Admission routes2
Has abstractyes

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