Microscopy, 2014, Vol. 63, No. S1
ORAL SESSION Three-dimensional microscopy for multi-scale imaging: from nano to macro Hiroshi Jinnai Institute for Materials Chemistry and Engineering (IMCE), Kyushu University, 744 Motooka, Nishi-ku, Fukuoka 819-0395, Japan
References 1. Keller A (1955) The spherulitic structure of crystalline polymers Part I Investigations with the polarizing microscope. J. Polym. Sci. 17: 291–308. 2. Lawrence MC (1992) Least-squares method of alignment using markers. In: Frank J (ed.), Least squares method of alignment using markers, pp 197–204 (New York, London, Plenum Press). 3. Jinnai H, Nishikawa Y, Spontak RJ, Smith SD, Agard DA, Hashimoto T (2000) Direct measurement of interfacial couvature distributions in a bicontinuous block copolymer. Phys. Rev. Lett. 84: 518–521.
doi: 10.1093/jmicro/dfu072
3D/4D analyses of damage and fracture behaviours in structural materials via synchrotron X-ray tomography Hiroyuki Toda Department of Mechanical Engineering /3D/4D structural Materials Research Centre, Kyushu University, 744, Motooka, Fukuoka, 819-0395, Japan, [email protected] X-ray microtomography has been utilized for the in-situ observation of various structural metals under external loading. Recent advances in X-ray microtomography provide remarkable tools to image the interior of materials. In-situ X-ray microtomography provides a unique possibility to access the 3D character of internal microstructure and its time evolution behaviours non-destructively, thereby enabling advanced techniques for measuring local strain distribution. Local strain mapping is readily enabled by processing such high-resolution tomographic images either by the particle tracking technique or the digital image correlation technique [1]. Procedures for tracking microstructural features which have been developed by the authors [2], have been applied to analyse localised deformation and damage evolution in a material [3]. Typically several tens of thousands of microstructural features, such as particles and pores, are tracked in a tomographic specimen (0.2 - 0.3 mm3 in volume). When a sufficient number of microstructural features is dispersed in 3D space, the Delaunay tessellation algorithm is used to obtain local strain distribution. With these techniques, 3D strain fields can be measured with reasonable accuracy. Even local crack driving forces, such as local variations in the stress intensity factor, crack tip opening displacement and J integral along a crack front line, can be measured from discrete crack tip displacement fields [4]. In the present presentation, complicated crack initiation and growth behaviour and the extensive formation of micro cracks ahead of a crack tip are introduced as examples. A novel experimental method has recently been developed by amalgamating a pencil beam X-Ray diffraction (XRD) technique with
Downloaded from http://jmicro.oxfordjournals.org/ at University of California, Davis on January 20, 2015
The structures studied in soft material science and biology are often hierarchical. One typical example of such hierarchical structures in polymer physics is a semi-crystalline region inside non-branched linear polymers, called spherulites [1]. Transmission electron microtomography (or simply electron tomography, ET) is known to be a very powerful tool in three-dimensional (3D) structural studies [2]. It has been extensively used to investigate polymer nano-morphologies [3,4], biological specimens [5], metallic alloys [6], etc. One of the biggest drawbacks of ET is that their observable thickness of specimens for 3D that is limited to less than ∼200 nm (in the case of accelerating voltage of 100–200 kV). In other words, structural elements with sizes on the order of several tens of nanometers are most suitable for ET. 3D observation of larger entities with sizes on the order of several hundreds of nanometers in hierarchical structures requires larger volume, i.e. a specimen with a greater thickness, i.e. the volume with a few micrometers. Recently, it has been shown that electron tomography with a scanning transmission mode, i.e. scanning transmission electron microscopy (STEM), is useful for the 3D observation of micrometer-thick specimens or ‘meso-scale’ volumes [7]. Hereafter, we refer to electron microtomography with STEM as scanning transmission electron microtomography (scanning electron tomography, SET). Although the resolution degradation due to the chromatic aberration is less pronounced because of the absence of an imaging system in STEM, however, multiple scattering of the electrons inside specimens commonly occurs both in STEM and TEM. The process of multiple scattering and accompanying beam broadening in STEM is rather complicated, particularly in thick specimens. Though the beam broadening could be another major source of the resolution degradation, this effect has not been discussed. As multiple scattering (and thus the beam broadening) strongly depends on the scattering angle, the detection angle (of the detector) should be a key parameter in SET. In the present talk, we discuss the detection-angle dependence of SET with particular emphasis on the beam broadening using a polymeric resin of thickness with ∼1 µm as a standard sample. Exactly the same volume of the polymeric specimen was observed by three different electron tomography modes. The difference between ET and SET together with some other 3D techniques has also been investigated [8].
4. Higuchi T, Sugimori H, Jiang X, Hong S, Matsunaga K, Kaneko T, Abetz V, Takahara A, Jinnai H (2014) Morphological Control of Helical Structures of an ABC-Type Triblock Terpolymer by Distribution Control of a Blending Homopolymer in a Block Copolymer Microdomain. Macromolecules 46(13): 6991–6997. 5. Beck M, Lucic V, Förster F, Baumeister W, Medalia O (2007) Snapshots of nuclear pore complexes in action taken by cryoelectron tomography. Nature 449: 611–615. 6. Kaneko K, Inoke K, Sato K, Kitawaki K, Higashida H, Arslan I, Midgley PA (2007) TEM characterization of Ge precipitates in an al-1.6 at% Ge alloy. Ultramicroscopy 108: 210–220. 7. Loos J, Sourty E, Lu K, Freitag B, Tang D, Wall D (2009) Electron tomography on micrometer-thick specimens with nanometer resolution. Nano. Lett. 9: 1704–1708. 8. Motoki S, Kaneko T, Aoyama Y, Nishioka H, Okura Y, Kondo Y, Jinnai H (2010) Dependence of beam broadening on detection angle in scanning transmission electron microtomography. J. Electron. Microsc. 59(S1): S45–S53.
Microscopy, 2014, Vol. 63, No. S1
i4 the microstructural tracking technique [5]. The technique provides information about individual grain orientations and 1-micron-level grain morphologies in 3D together with high-density local strain mapping. The application of this technique to the deformation behavior of a polycrystalline aluminium alloy will be demonstrated in the presentation [6]. The synchrotron-based microtomography has been mainly utilized to light materials due to their good X-ray transmission. In the present talk, the application of the synchrotron-based microtomography to steels will be also introduced. Degradation of contrast and spatial resolution due to forward scattering could be avoided by selecting appropriate experimental conditions in order to obtain superior spatial resolution close to the physical limit even in ferrous materials [7].
References
Downloaded from http://jmicro.oxfordjournals.org/ at University of California, Davis on January 20, 2015
1. Toda H., Maire E., Aoki Y., Kobayashi M.: J. Strain Anal. Eng., 46(2011), 549–561. 2. Kobayashi M., Toda H., Kawai Y., Ohgaki T., Uesugi K., et al. Acta Mater., 56 (2008), 2167–2181. 3. Toda H., Minami K., Koyama K., Ichitani K., et al. Acta Mater., 57 (2009), 4391–4403. 4. Toda H., Yamamoto S., Kobayashi M., Uesugi K., Zhang H.: Acta Mater., 56(2008), 6027–6039. 5. Toda H., Ohkawa Y., Kamiko T., Naganuma T., Uesugi K., et al. Acta Mater., 61(2013), 5535–5548. 6. LeClere D.J., Kamiko T., Mizuseki Y., Suzuki Y., Toda H., et al. Proc. of ICAA13 (2012), 9–14. 7. Seo D., Tomizato F., Toda H., Uesugi K., Takeuchi A., et al. Appl. Phys. Lett., 101 (2012) 261901. doi: 10.1093/jmicro/dfu038
High dimensional data driven statistical mechanics Yoshitaka Adachi and Sunao Sadamatsu Department of Mechanical Engineering, Graduate School of Science and Engineering, Kagoshima University, Korimoto 1-21-24, Kagoshima, 890-0065, Japan In “3D4D materials science”, there are five categories such as (a) Image acquisition, (b) Processing, (c) Analysis, (d) Modelling, and (e) Data sharing. This presentation highlights the core of these categories [1]. Analysis and modelling A three-dimensional (3D) microstructure image contains topological features such as connectivity in addition to metric features. Such more microstructural information seems to be useful for more precise property prediction. There are two ways for microstructure-based property prediction (Fig. 1A). One is 3D image data based modelling such as micromechanics or crystal plasticity finite element method. The other one is a numerical microstructural features driven machine learning approach such as artificial neural network or Bayesian estimation method. It is the key to convert the 3D image data into numerals in order to apply the dataset to property prediction. As a numerical feature of microstructures, grain size, number of density, of particles, connectivity of particles, grain boundary connectivity, stacking degree, clustering etc. should be taken into consideration. These microstructural features are so-called “materials genome”. Among those materials genome, we have to find out dominant factors to determine a focused property. The dominant factorzs are defined as “descriptor(s)” in high dimensional data driven statistical mechanics. Image acquisition It is important for researchers to choice a 3D microscope from various microscopes depending on a length-scale of a focused microstructure. There is a long term request to acquire a 3D microstructure image
Fig. 1. (a) A concept of 3D4D materials science. (b) Fully-automated serial sectioning 3D microscope “Genus_3D”. (c) Materials Genome Archive (JSPS).
ORAL SESSION Three-dimensional microscopy for multi-scale imaging: from nano to macro Hiroshi Jinnai Institute for Materials Chemistry and Engineering (IMCE), Kyushu University, 744 Motooka, Nishi-ku, Fukuoka 819-0395, Japan
References 1. Keller A (1955) The spherulitic structure of crystalline polymers Part I Investigations with the polarizing microscope. J. Polym. Sci. 17: 291–308. 2. Lawrence MC (1992) Least-squares method of alignment using markers. In: Frank J (ed.), Least squares method of alignment using markers, pp 197–204 (New York, London, Plenum Press). 3. Jinnai H, Nishikawa Y, Spontak RJ, Smith SD, Agard DA, Hashimoto T (2000) Direct measurement of interfacial couvature distributions in a bicontinuous block copolymer. Phys. Rev. Lett. 84: 518–521.
doi: 10.1093/jmicro/dfu072
3D/4D analyses of damage and fracture behaviours in structural materials via synchrotron X-ray tomography Hiroyuki Toda Department of Mechanical Engineering /3D/4D structural Materials Research Centre, Kyushu University, 744, Motooka, Fukuoka, 819-0395, Japan, [email protected] X-ray microtomography has been utilized for the in-situ observation of various structural metals under external loading. Recent advances in X-ray microtomography provide remarkable tools to image the interior of materials. In-situ X-ray microtomography provides a unique possibility to access the 3D character of internal microstructure and its time evolution behaviours non-destructively, thereby enabling advanced techniques for measuring local strain distribution. Local strain mapping is readily enabled by processing such high-resolution tomographic images either by the particle tracking technique or the digital image correlation technique [1]. Procedures for tracking microstructural features which have been developed by the authors [2], have been applied to analyse localised deformation and damage evolution in a material [3]. Typically several tens of thousands of microstructural features, such as particles and pores, are tracked in a tomographic specimen (0.2 - 0.3 mm3 in volume). When a sufficient number of microstructural features is dispersed in 3D space, the Delaunay tessellation algorithm is used to obtain local strain distribution. With these techniques, 3D strain fields can be measured with reasonable accuracy. Even local crack driving forces, such as local variations in the stress intensity factor, crack tip opening displacement and J integral along a crack front line, can be measured from discrete crack tip displacement fields [4]. In the present presentation, complicated crack initiation and growth behaviour and the extensive formation of micro cracks ahead of a crack tip are introduced as examples. A novel experimental method has recently been developed by amalgamating a pencil beam X-Ray diffraction (XRD) technique with
Downloaded from http://jmicro.oxfordjournals.org/ at University of California, Davis on January 20, 2015
The structures studied in soft material science and biology are often hierarchical. One typical example of such hierarchical structures in polymer physics is a semi-crystalline region inside non-branched linear polymers, called spherulites [1]. Transmission electron microtomography (or simply electron tomography, ET) is known to be a very powerful tool in three-dimensional (3D) structural studies [2]. It has been extensively used to investigate polymer nano-morphologies [3,4], biological specimens [5], metallic alloys [6], etc. One of the biggest drawbacks of ET is that their observable thickness of specimens for 3D that is limited to less than ∼200 nm (in the case of accelerating voltage of 100–200 kV). In other words, structural elements with sizes on the order of several tens of nanometers are most suitable for ET. 3D observation of larger entities with sizes on the order of several hundreds of nanometers in hierarchical structures requires larger volume, i.e. a specimen with a greater thickness, i.e. the volume with a few micrometers. Recently, it has been shown that electron tomography with a scanning transmission mode, i.e. scanning transmission electron microscopy (STEM), is useful for the 3D observation of micrometer-thick specimens or ‘meso-scale’ volumes [7]. Hereafter, we refer to electron microtomography with STEM as scanning transmission electron microtomography (scanning electron tomography, SET). Although the resolution degradation due to the chromatic aberration is less pronounced because of the absence of an imaging system in STEM, however, multiple scattering of the electrons inside specimens commonly occurs both in STEM and TEM. The process of multiple scattering and accompanying beam broadening in STEM is rather complicated, particularly in thick specimens. Though the beam broadening could be another major source of the resolution degradation, this effect has not been discussed. As multiple scattering (and thus the beam broadening) strongly depends on the scattering angle, the detection angle (of the detector) should be a key parameter in SET. In the present talk, we discuss the detection-angle dependence of SET with particular emphasis on the beam broadening using a polymeric resin of thickness with ∼1 µm as a standard sample. Exactly the same volume of the polymeric specimen was observed by three different electron tomography modes. The difference between ET and SET together with some other 3D techniques has also been investigated [8].
4. Higuchi T, Sugimori H, Jiang X, Hong S, Matsunaga K, Kaneko T, Abetz V, Takahara A, Jinnai H (2014) Morphological Control of Helical Structures of an ABC-Type Triblock Terpolymer by Distribution Control of a Blending Homopolymer in a Block Copolymer Microdomain. Macromolecules 46(13): 6991–6997. 5. Beck M, Lucic V, Förster F, Baumeister W, Medalia O (2007) Snapshots of nuclear pore complexes in action taken by cryoelectron tomography. Nature 449: 611–615. 6. Kaneko K, Inoke K, Sato K, Kitawaki K, Higashida H, Arslan I, Midgley PA (2007) TEM characterization of Ge precipitates in an al-1.6 at% Ge alloy. Ultramicroscopy 108: 210–220. 7. Loos J, Sourty E, Lu K, Freitag B, Tang D, Wall D (2009) Electron tomography on micrometer-thick specimens with nanometer resolution. Nano. Lett. 9: 1704–1708. 8. Motoki S, Kaneko T, Aoyama Y, Nishioka H, Okura Y, Kondo Y, Jinnai H (2010) Dependence of beam broadening on detection angle in scanning transmission electron microtomography. J. Electron. Microsc. 59(S1): S45–S53.
Microscopy, 2014, Vol. 63, No. S1
i4 the microstructural tracking technique [5]. The technique provides information about individual grain orientations and 1-micron-level grain morphologies in 3D together with high-density local strain mapping. The application of this technique to the deformation behavior of a polycrystalline aluminium alloy will be demonstrated in the presentation [6]. The synchrotron-based microtomography has been mainly utilized to light materials due to their good X-ray transmission. In the present talk, the application of the synchrotron-based microtomography to steels will be also introduced. Degradation of contrast and spatial resolution due to forward scattering could be avoided by selecting appropriate experimental conditions in order to obtain superior spatial resolution close to the physical limit even in ferrous materials [7].
References
Downloaded from http://jmicro.oxfordjournals.org/ at University of California, Davis on January 20, 2015
1. Toda H., Maire E., Aoki Y., Kobayashi M.: J. Strain Anal. Eng., 46(2011), 549–561. 2. Kobayashi M., Toda H., Kawai Y., Ohgaki T., Uesugi K., et al. Acta Mater., 56 (2008), 2167–2181. 3. Toda H., Minami K., Koyama K., Ichitani K., et al. Acta Mater., 57 (2009), 4391–4403. 4. Toda H., Yamamoto S., Kobayashi M., Uesugi K., Zhang H.: Acta Mater., 56(2008), 6027–6039. 5. Toda H., Ohkawa Y., Kamiko T., Naganuma T., Uesugi K., et al. Acta Mater., 61(2013), 5535–5548. 6. LeClere D.J., Kamiko T., Mizuseki Y., Suzuki Y., Toda H., et al. Proc. of ICAA13 (2012), 9–14. 7. Seo D., Tomizato F., Toda H., Uesugi K., Takeuchi A., et al. Appl. Phys. Lett., 101 (2012) 261901. doi: 10.1093/jmicro/dfu038
High dimensional data driven statistical mechanics Yoshitaka Adachi and Sunao Sadamatsu Department of Mechanical Engineering, Graduate School of Science and Engineering, Kagoshima University, Korimoto 1-21-24, Kagoshima, 890-0065, Japan In “3D4D materials science”, there are five categories such as (a) Image acquisition, (b) Processing, (c) Analysis, (d) Modelling, and (e) Data sharing. This presentation highlights the core of these categories [1]. Analysis and modelling A three-dimensional (3D) microstructure image contains topological features such as connectivity in addition to metric features. Such more microstructural information seems to be useful for more precise property prediction. There are two ways for microstructure-based property prediction (Fig. 1A). One is 3D image data based modelling such as micromechanics or crystal plasticity finite element method. The other one is a numerical microstructural features driven machine learning approach such as artificial neural network or Bayesian estimation method. It is the key to convert the 3D image data into numerals in order to apply the dataset to property prediction. As a numerical feature of microstructures, grain size, number of density, of particles, connectivity of particles, grain boundary connectivity, stacking degree, clustering etc. should be taken into consideration. These microstructural features are so-called “materials genome”. Among those materials genome, we have to find out dominant factors to determine a focused property. The dominant factorzs are defined as “descriptor(s)” in high dimensional data driven statistical mechanics. Image acquisition It is important for researchers to choice a 3D microscope from various microscopes depending on a length-scale of a focused microstructure. There is a long term request to acquire a 3D microstructure image
Fig. 1. (a) A concept of 3D4D materials science. (b) Fully-automated serial sectioning 3D microscope “Genus_3D”. (c) Materials Genome Archive (JSPS).
