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Record W2052862652 · doi:10.1063/1.3168525

Seeing structures and measuring properties with transmission electron microscopy images: A simple combination to study size effects in nanoparticle systems

2009· article· en· W2052862652 on OpenAlexaff
P. Donnadieu, Sorin Lazar, G. A. Botton, Isabelle Pignot‐Paintrand, Michael Reynolds, Serge Pérez

Bibliographic record

VenueApplied Physics Letters · 2009
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced Electron Microscopy Techniques and Applications
Canadian institutionsMcMaster UniversityBrockhouse Institute for Materials Research
Fundersnot available
KeywordsTransmission electron microscopyResolution (logic)NanoparticleMaterials scienceFocus (optics)Measure (data warehouse)Energy filtered transmission electron microscopyElectron microscopeSeries (stratigraphy)MicroscopyTransmission (telecommunications)Electron tomographyOpticsElectronPhase (matter)Image resolutionHigh-resolution transmission electron microscopyPhase retrievalNanotechnologyBiological systemScanning transmission electron microscopyChemistryPhysicsComputer scienceArtificial intelligenceData miningTelecommunications

Abstract

fetched live from OpenAlex

We report on a method to measure the mean inner potential (V0) using transmission electron microscopy. It is based on phase retrieval from a focus series and has allowed to measure V0 as a function of the size for a system of gold nanoparticles. It comes out that V0 increases for particles below 2 nm. The focus series being carried out in conditions close to the high-resolution ones, structural information can be directly obtained. The high-resolution images have revealed that significant structural change occurs below the 2 nm size, which should be related to the V0 increase.

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)0.001
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
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.005
GPT teacher head0.255
Teacher spread0.250 · 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

Citations22
Published2009
Admission routes1
Has abstractyes

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