Computation of Thickness and Mechanical Properties of Interconnected Structures: Accuracy, Deviations, and Approaches for Correction

Richert, Claudia; Odermatt, Anton; Huber, N.

doi:10.3389/fmats.2019.00327

Cited by 13 publications

(22 citation statements)

References 49 publications

(142 reference statements)

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“…Richert et al have found that for nanoporous gold, the biggest-sphere Thickness algorithm systematically overpredicts the local thickness assigned to a certain skeleton voxel by up to 30%, which can lead to overprediction in the macroscopic stiffness and strength by up to 100% for concave ligament shapes [50,53]. This effect is pronounced for shapes, which have a strong evolution of local diameters along their axis.…”

Section: Thickness Computationmentioning

confidence: 99%

“…The reason for the underprediction is as follows: the distance is determined by counting voxels from the surface contour normal to an [44]. (c) Significant shift in the diameter distribution is observed using the Th and EDT algorithm on one tomography: average diameter d Th = 400 nm and d EDT = 308 nm by Richert et al [53]. Reprinted from [63] with permission from John Wiley and Sons; [44] with permission from Elsevier; the author's own work [53] published under Creative Commons Attribution License (CC BY 4.0).…”

Section: Thickness Computationmentioning

confidence: 99%

“…As discussed in Section 2.3.3, Richert et al have found for nanoporous gold that the biggest-sphere Thickness algorithm systematically overpredicts the local thickness assigned to a certain skeleton voxel. It is shown that the biggest-sphere Thickness algorithm tends to smear local thinning by propagating the ligament thickness from thicker into the thinner parts [50,53]. Furthermore, Richert et al have shown, with the help of artificially created model structures, that the Euclidean distance transform (EDT) systematically underestimates the local thickness.…”

Section: Ligament Diameter Distributionmentioning

confidence: 99%

“…Similarly, for the interpretation of the experimental data with Equations (1) and (2), we should keep in mind that for > 0.3 the free length of the ligament becomes less than the ligament thickness and that inaccuracies associated with the volumes of the nodes become severe [9]. [14] with the data from (a) Huber [80], Supplementary material, Data sheet 2, and (b) comparison with the data from Richert et al [53], Supplementary material, Tables S2 and S4 (thin dashed lines correspond to yield stress, thin solid lines are unpublished data and correspond to flow stress at 10% plastic strain). Slopes entered as numbers in the plots correspond to exponents / in the dependency ~/ .…”

Section: Evolution Of Network Connectivity During Coarseningmentioning

confidence: 99%

“…This underlying loss is caused by the beam model representation of the nodal mass as a 0-dimensional point compared to the massive nodes in a solid model. From [53], Figure 5 we learn that the loss in stiffness considerably differs from that in the strength if we plot the data versus the solid fraction. This is owning to the locking of the FE beam elements against localized plastic deformation in the massive nodes-this insight leads to the development of a more general nodal correction for parabolic ligament shapes [114].…”

Section: Evolution Of Network Connectivity During Coarseningmentioning

confidence: 99%

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A Review of Experimentally Informed Micromechanical Modeling of Nanoporous Metals: From Structural Descriptors to Predictive Structure–Property Relationships

Richert

Huber

2020

Materials

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Nanoporous metals made by dealloying take the form of macroscopic (mm- or cm-sized) porous bodies with a solid fraction of around 30%. The material exhibits a network structure of “ligaments” with an average ligament diameter that can be adjusted between 5 and 500 nm. Current research explores the use of nanoporous metals as functional materials with respect to electrochemical conversion and storage, bioanalytical and biomedical applications, and actuation and sensing. The mechanical behavior of the network structure provides the scope for fundamental research, particularly because of the high complexity originating from the randomness of the structure and the challenges arising from the nanosized ligaments, which can be accessed through an experiment only indirectly via the testing of the macroscopic properties. The strength of nanoscale ligaments increases systematically with decreasing size, and owing to the high surface-to-volume ratio their elastic and plastic properties can be additionally tuned by applying an electric potential. Therefore, nanoporous metals offer themselves as suitable model systems for exploring the structure–property relationships of complex interconnected microstructures as well as the basic mechanisms of the chemo-electro-mechanical coupling at interfaces. The micromechanical modeling of nanoporous metals is a rapidly growing field that strongly benefits from developments in computational methods, high-performance computing, and visualization techniques; it also benefits at the same time through advances in characterization techniques, including nanotomography, 3D image processing, and algorithms for geometrical and topological analysis. The review article collects articles on the structural characterization and micromechanical modeling of nanoporous metals and discusses the acquired understanding in the context of advancements in the experimental discipline. The concluding remarks are given in the form of a summary and an outline of future perspectives.

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Section: Thickness Computationmentioning

confidence: 99%

Section: Thickness Computationmentioning

confidence: 99%

Section: Ligament Diameter Distributionmentioning

confidence: 99%

Section: Evolution Of Network Connectivity During Coarseningmentioning

confidence: 99%

Section: Evolution Of Network Connectivity During Coarseningmentioning

confidence: 99%

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A Review of Experimentally Informed Micromechanical Modeling of Nanoporous Metals: From Structural Descriptors to Predictive Structure–Property Relationships

Richert

Huber

2020

Materials

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Deep learning‐based segmentation of knee MRI for fully automatic subregional morphological assessment of cartilage tissues: Data from the Osteoarthritis Initiative

Panfilov

Tiulpin

Nieminen

et al. 2021

Journal Orthopaedic Research

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Morphological changes in knee cartilage subregions are valuable imaging‐based biomarkers for understanding progression of osteoarthritis, and they are typically detected from magnetic resonance imaging (MRI). So far, accurate segmentation of cartilage has been done manually. Deep learning approaches show high promise in automating the task; however, they lack clinically relevant evaluation. We introduce a fully automatic method for segmentation and subregional assessment of articular cartilage, and evaluate its predictive power in context of radiographic osteoarthritis progression. Two data sets of 3D double‐echo steady‐state (DESS) MRI derived from the Osteoarthritis Initiative were used: first, n = 88; second, n = 600, 0‐/12‐/24‐month visits. Our method performed deep learning‐based segmentation of knee cartilage tissues, their subregional division via multi‐atlas registration, and extraction of subregional volume and thickness. The segmentation model was developed and assessed on the first data set. Subsequently, on the second data set, the morphological measurements from our and the prior methods were analyzed in correlation and agreement, and, eventually, by their discriminative power of radiographic osteoarthritis progression over 12 and 24 months, retrospectively. The segmentation model showed very high correlation (r > 0.934) and agreement (mean difference < 116 mm3) in volumetric measurements with the reference segmentations. Comparison of our and manual segmentation methods yielded r = 0.845–0.973 and mean differences = 262–501 mm3 for weight‐bearing cartilage volume, and r = 0.770–0.962 and mean differences = 0.513–1.138 mm for subregional cartilage thickness. With regard to osteoarthritis progression, our method found most of the significant associations identified using the manual segmentation method, for both 12‐ and 24‐month subregional cartilage changes. The method may be effectively applied in osteoarthritis progression studies to extract cartilage‐related imaging biomarkers.

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N-type H2-doped amorphous silicon layer for solar-cell application

Prayogi

Ayunis

Cahyono

et al. 2023

Mater Renew Sustain Energy

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In this work, we report that hydrogen (H2) doped in n-type a-Si:H thin films strongly influences the electronic correlation in increasing the conversion output power of solar cells. Type n a-Si:H thin films were grown using PECVD on ITO substrates with various H2-doping, to obtain various thin films for solar-cell applications. N-type a-Si:H thin films were prepared, and then characterized using ellipsometric spectroscopy, atomic force microscopy, Fourier transform infrared spectroscopy, and transmission electron microscopy. The addition of doped-H2 to the thin layer shows a decrease in optical conductivity, while the energy gap in the thin layer shows a significant increase in the a-Si:H-type thin layer. Our results show that H2 doping plays a very important role in the electronic structure, which is indicated by the significant energy gap difference. On the other hand, the bond structure of each H2-doped thin film showed a change from amorphous to nanocrystalline structures which were evenly distributed in each H2-doped bonding. Overall, we believe that the addition of doped-H2 to our findings could help increase the power conversion output of the solar cell due to the modification of the electronic structure.

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Computation of Thickness and Mechanical Properties of Interconnected Structures: Accuracy, Deviations, and Approaches for Correction

Cited by 13 publications

References 49 publications

A Review of Experimentally Informed Micromechanical Modeling of Nanoporous Metals: From Structural Descriptors to Predictive Structure–Property Relationships

A Review of Experimentally Informed Micromechanical Modeling of Nanoporous Metals: From Structural Descriptors to Predictive Structure–Property Relationships

Deep learning‐based segmentation of knee MRI for fully automatic subregional morphological assessment of cartilage tissues: Data from the Osteoarthritis Initiative

N-type H2-doped amorphous silicon layer for solar-cell application

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