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Record W2588557390 · doi:10.1017/s1431927600035145

Assessment of Deformation using the Focused Ion Beam Technique

2000· article· en· W2588557390 on OpenAlexaff
M.G. Burke, Pedro Duda, Gianluigi A. Botton, M. W. Phaneuf

Bibliographic record

VenueMicroscopy and Microanalysis · 2000
Typearticle
Languageen
FieldMaterials Science
TopicMicrostructure and mechanical properties
Canadian institutionsFibics (Canada)
Fundersnot available
KeywordsFocused ion beamMaterials scienceTransmission electron microscopyMicrostructureIon milling machineCharacterization (materials science)Ion beamSurface micromachiningSample preparationBeam (structure)Acceleration voltageAlloyComposite materialScanning electron microscopeIonNanotechnologyAnalytical Chemistry (journal)Cathode rayFabricationOpticsElectronChemistry

Abstract

fetched live from OpenAlex

Abstract Focused Ion Beam (FIB) micromachining techniques have gained significant attention over the past few years as a promising method for the preparation of a variety of metallic and nonmetallic materials for subsequent characterization using transmission electron microscopy (TEM) The advantage of the FIB in terms of site specificity and speed for the preparation of uniform electron transparent sections has opened a wide range of potential applications in materials characterization. The ability to image the sample in the FIB can also provide important microstructural data for materials analysis. In this study, both conventionally electropolished and FIB-ed specimens were prepared in order to characterize the microstructure of a commercially-produced tube of Alloy 600 (approximately Ni-15 Cr-10 Fe- 0.05 C). The electropolished samples were prepared using a solution of 20% HClO4 - 80% CH3OH at ∼-40°C. The FIB sections were obtained from a cross-section of the tube that had been mechanically thinned to ∼100 μm. The section was thinned in a Micrion 2500 FIB system with a Ga ion beam at 50 kV accelerating voltage.

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 score0.004
Threshold uncertainty score0.984

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 head0.014
GPT teacher head0.276
Teacher spread0.263 · 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

Citations1
Published2000
Admission routes1
Has abstractyes

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