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Record W4381436564 · doi:10.3390/cryst13060973

Atomic-Scale Imaging of Organic-Inorganic Hybrid Perovskite Using Transmission Electron Microscope

2023· article· en· W4381436564 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueCrystals · 2023
Typearticle
Languageen
FieldEngineering
TopicPerovskite Materials and Applications
Canadian institutionsMcMaster University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCharacterization (materials science)Transmission electron microscopyAtomic unitsMaterials sciencePerovskite (structure)LimitingNanotechnologyElectron tomographyElectron microscopeElectronTransmission (telecommunications)Scanning transmission electron microscopyOptoelectronicsComputer scienceOpticsChemical engineeringPhysicsEngineering

Abstract

fetched live from OpenAlex

Transmission electron microscope (TEM) is thought as one powerful tool to imaging the atomic-level structure of organic inorganic hybrid perovskite (OIHP) materials, which provides valuable and essential guidance toward high performance OIHP-related devices. However, these OIHPs exhibit poor electron beam stability, severely limiting their practical applications in TEM. Here in this article, the application of TEM to obtain atomic-scale image of OIHPs, main obstacles in identifying the degradation product and future prospects of TEM in the characterization of OIHP materials are reviewed and presented. Three potential strategies (sample protection, low temperature technology, and low-dose technologies) are also proposed to overcome the current drawback of TEM technology.

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.

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 score0.017
Threshold uncertainty score0.663

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)0.0000.000

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 head0.007
GPT teacher head0.228
Teacher spread0.221 · 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