Fluctuation Microscopy in the STEM

Voyles, Paul M.; Bogle, Stephanie N.; Abelson, John R.

doi:10.1007/978-1-4419-7200-2_18

Cited by 16 publications

(39 citation statements)

References 79 publications

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“…FEM analysis reveals heterogeneities of the atomic structure and establishes the medium-range atomic order in amorphous materials [18] [19]. It has been applied e.g.…”

Section: Introductionmentioning

confidence: 99%

Radial distribution function imaging by STEM diffraction: Phase mapping and analysis of heterogeneous nanostructured glasses

Wang

Feng

et al. 2016

Ultramicroscopy

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To overcome some of the challenges in characterizing heterogeneous nanostructured amorphous materials, we developed a new TEM method, RDF imaging, combining scanning transmission electron microscopy diffraction mapping with radial distribution function (RDF) analysis followed by hyperspectral analysis, to enable phase analysis and mapping of heterogeneous amorphous structures purely based on their short-and medium range atomic order. We applied this method to an amorphous zirconium oxide and zirconium iron multilayer system, demonstrating an extreme sensitivity of the method to small atomic packing variations. This approach has great potential to understand local structure variations in glassy composite materials and to provide new insights to correlate structure and properties of glasses.

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“…FEM analysis reveals heterogeneities of the atomic structure and establishes the medium-range atomic order in amorphous materials [18] [19]. It has been applied e.g.…”

Section: Introductionmentioning

confidence: 99%

Radial distribution function imaging by STEM diffraction: Phase mapping and analysis of heterogeneous nanostructured glasses

Wang

Feng

et al. 2016

Ultramicroscopy

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“…Forward simulation from a family of computational structural models has yielded clear trends in the variance as a function of the size and volume fraction of ordered regions (Bogle et al, 2007; Yi & Voyles, 2012). Based on these developments, the FEM technique has been used to identify differences in nanoscale order in thin films of a-Si (Gibson et al, 1998; Cheng et al, 2001; Voyles et al, 2001 a , 2001 b ; Nittala et al, 2005; Bogle et al, 2010), a-Ge (Gibson & Treacy, 1997; Voyles & Muller, 2002), phase change chalcogenide materials (Kwon et al, 2007; Lee et al, 2009; Darmawikarta et al, 2013), and various amorphous metals (Stratton et al, 2004; Stratton et al, 2005). In these experiments, qualitative differences in FEM variance were observed, and convincingly attributed to fundamental physical phenomena such as differences in film deposition condition (Voyles et al, 2001 a ), existence, and thermal ripening of subcritical nuclei that precede crystallization (Lee et al, 2009; Darmawikarta et al, 2013), and effect of alloying in crystallization kinetics (Darmawikarta et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

Quantitative Fluctuation Electron Microscopy in the STEM: Methods to Identify, Avoid, and Correct for Artifacts

Bogle

Abelson

2014

Microsc Microanal

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Fluctuation electron microscopy can reveal the nanoscale order in amorphous materials via the statistical variance in the scattering intensity as a function of position, scattering vector, and resolution. However, several sources of experimental artifacts can seriously affect the magnitude of the variance peaks. The use of a scanning transmission electron microscope for data collection affords a convenient means to check whether artifacts are present. As nanodiffraction patterns are collected in serial, any spatial or temporal dependence of the scattering intensity across the series can easily be detected. We present examples of the major types of artifact and methods to correct the data or to avoid the problem experimentally. We also re-cast the statistical formalism used to identify sources of noise in view of the present results. The present work provides a basis on which to perform fluctuation electron microscopy with a high level of reliability and confidence in the quantitative magnitude of the data.

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“…The relative error in the measured normalized intensity variance due to Poisson noise in the individual diffraction patterns was below 0.02 [73]. The error in the normalized intensity variance was calculated as the standard error from the four scanned areas.…”

Section: Area Selectedmentioning

confidence: 99%

A multi-method study of the transformation of the carbonaceous skeleton of a polymer-based nanoporous carbon along the activation pathway

Hu¹,

Liu²,

Weyland³

et al. 2015

Carbon

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Fluctuation Microscopy in the STEM

Cited by 16 publications

References 79 publications

Radial distribution function imaging by STEM diffraction: Phase mapping and analysis of heterogeneous nanostructured glasses

Radial distribution function imaging by STEM diffraction: Phase mapping and analysis of heterogeneous nanostructured glasses

Quantitative Fluctuation Electron Microscopy in the STEM: Methods to Identify, Avoid, and Correct for Artifacts

A multi-method study of the transformation of the carbonaceous skeleton of a polymer-based nanoporous carbon along the activation pathway

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