Comparison of segmentation algorithms for FIB-SEM tomography of porous polymers: Importance of image contrast for machine learning segmentation

Čalkovský, Martin; Müller, Erich; Meffert, Matthias; Firman, Nadejda; Mayer, Frederik; Wegener, Martin; Gerthsen, D.

doi:10.1016/j.matchar.2020.110806

Cited by 14 publications

(7 citation statements)

References 29 publications

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“…It should be noted that comparable 3D datasets at high resolution (5 nm pixel size) might also be created by FIBSEM, [ 10 ] but this method would not allow comprehensive characterization of a large structure, since the volume that can be milled and imaged is limited due to technical reasons. With conventional FIBSEM instruments a volume of about 1000 µm 3 at 5–8 nm voxel size is routinely achievable.…”

Section: Resultsmentioning

confidence: 99%

Deconstructing 3D Structured Materials by Modern Ultramicrotomy for Multimodal Imaging and Volume Analysis across Length Scales

Wacker,

Curticean,

Ryklin

et al. 2023

Adv Funct Materials

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Based on the rapid advances in additive manufacturing, micro‐patterned heterostructures of soft materials have become available that need to be characterized down to the nanoscale. Advanced function‐structure relationships are designed by direct 3D structuring of the object and – in the future – fine control over material functionality in 3D will produce complex functional objects. To control their design, fabrication and final structure, morphological and spectroscopical imaging in 3D at nanometer resolution are critically required. With examples of carbon‐based objects, it is demonstrated how serial ultramicrotomy, that is, cutting a large number of successive ultrathin sections, can be utilized to gain access to the interior of 3D objects. Array tomography, hierarchical imaging and correlative light and electron microscopy can bridge length scales over several orders of magnitude and provide multimodal information of the sample's inner structure. Morphology data derived from scanning electron microscopy are correlated with spectroscopy in analytical transmission electron microscopy and probe microscopy at nanometer resolution, using TEM‐electron energy loss spectroscopy and infrared‐scanning‐near‐field microscopy. The correlation of different imaging modalities and spectroscopy of carbon‐based materials in 3D provides a powerful toolbox of complementary techniques for understanding emerging functions from nanoscopic structuring.

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Section: Resultsmentioning

confidence: 99%

Deconstructing 3D Structured Materials by Modern Ultramicrotomy for Multimodal Imaging and Volume Analysis across Length Scales

Wacker,

Curticean,

Ryklin

et al. 2023

Adv Funct Materials

Self Cite

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“…Recently, there has been a growing interest in the application of segmentation algorithms in combination with machine learning in order to increase the reliability of the segmented data sets, for instance, the pore volumes of highly porous systems, which is frequently underestimated. [152,[157][158][159] This enables an enhanced semantic segmentation and subsequent classification of multiple phases within the FIB/SEM dataset, improving the overall quality of the results.…”

Section: Focused Ion Beam/scanning Electron Microscopy (Fib/sem) Tomo...mentioning

confidence: 99%

Multiscale Investigation of Sodium‐Ion Battery Anodes: Analytical Techniques and Applications

Schäfer,

Hankins,

Allion

et al. 2024

Advanced Energy Materials

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The anode/electrolyte interface behavior, and by extension, the overall cell performance of sodium‐ion batteries is determined by a complex interaction of processes that occur at all components of the electrochemical cell across a wide range of size‐ and timescales. Single‐scale studies may provide incomplete insights, as they cannot capture the full picture of this complex and intertwined behavior. Broad, multiscale studies are essential to elucidate these processes. Within this perspectives article, several analytical and theoretical techniques are introduced, and described how they can be combined to provide a more complete and comprehensive understanding of sodium‐ion battery (SIB) performance throughout its lifetime, with a special focus on the interfaces of hard carbon anodes. These methods target various length‐ and time scales, ranging from micro to nano, from cell level to atomistic structures, and account for a broad spectrum of physical and (electro)chemical characteristics. Specifically, how mass spectrometric, microscopic, spectroscopic, electrochemical, thermodynamic, and physical methods can be employed to obtain the various types of information required to understand battery behavior will be explored. Ways are then discussed how these methods can be coupled together in order to elucidate the multiscale phenomena at the anode interface and develop a holistic understanding of their relationship to overall sodium‐ion battery function.

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“…31 Additionally, the data processing and reconstruction step requires multicomponent mixtures or porous single-component systems to be sufficiently different visually to allow the reconstruction algorithm to correctly identify the different materials or pores: a nontrivial step that may require specific development of more advanced segmentation algorithms. 32 …”

Section: Introductionmentioning

confidence: 99%

Computational Reverse-Engineering Analysis for Scattering Experiments of Assembled Binary Mixture of Nanoparticles

Heil

Jayaraman

2021

ACS Mater. Au

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In this paper, we describe a computational method for analyzing results from scattering experiments on dilute solutions of supraparticles, where each supraparticle is created by the assembly of nanoparticle mixtures. Taking scattering intensity profiles and nanoparticle mixture composition and size distributions in each supraparticle as input, this computational approach called computational reverse engineering analysis for scattering experiments (CREASE) uses a genetic algorithm to output information about the structure of the assembled nanoparticles (e.g., real space pair correlation function, extent of nanoparticle mixing/segregation, sizes of domains) within a supraparticle. We validate this method by taking as input in silico scattering intensity profiles from coarse-grained molecular simulations of a binary mixture of nanoparticles, forming a close-packed structure and testing if our computational method can correctly reproduce the nanoparticle structure observed in those simulations. We test the strengths and limitations of our method using a variety of in silico scattering intensity profiles obtained from simulations of a spherical or a cubic supraparticle comprising binary nanoparticle mixtures with varying chemistries, with and without dispersity in sizes, that exhibit well-mixed to strongly segregated structures. The strengths of the presented method include its capability to analyze scattering intensity profiles even when the wavevector q range is limited, to handily provide all of the pairwise radial distribution functions, and to correctly determine the extent of segregation/mixing of the nanoparticles assembled in complex geometries.

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Comparison of segmentation algorithms for FIB-SEM tomography of porous polymers: Importance of image contrast for machine learning segmentation

Cited by 14 publications

References 29 publications

Deconstructing 3D Structured Materials by Modern Ultramicrotomy for Multimodal Imaging and Volume Analysis across Length Scales

Deconstructing 3D Structured Materials by Modern Ultramicrotomy for Multimodal Imaging and Volume Analysis across Length Scales

Multiscale Investigation of Sodium‐Ion Battery Anodes: Analytical Techniques and Applications

Computational Reverse-Engineering Analysis for Scattering Experiments of Assembled Binary Mixture of Nanoparticles

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