Deflecting Dendrites by Introducing Compressive Stress in Li7La3Zr2O12 Using Ion Implantation.

Lithium dendrites belong to the key challenges of solid-state battery research. They are unavoidable due to the imperfect nature of surfaces containing defects of a critical size that can be filled by lithium until fracturing the solid electrolyte. The penetration of Li metal occurs along the propagating crack until a short circuit takes place. It is hypothesized that ion implantation can be used to introduce stress states into Li6.4La3Zr1.4Ta0.6O12 which enables an effective deflection and arrest of dendrites. The compositional and microstructural changes associated with the implantation of Ag-ions are studied via atom probe tomography, electron microscopy, and nano X-ray diffraction indicating that Ag-ions can be implanted up to 1 µm deep and amorphization takes place down to 650-700 nm, in good agreement with kinetic Monte Carlo simulations. Based on diffraction results pronounced stress states up to -700 MPa are generated in the near-surface region. Such a stress zone and the associated microstructural alterations exhibit the ability to not only deflect mechanically introduced cracks but also dendrites, as demonstrated by nano-indentation and galvanostatic cycling experiments with subsequent electron microscopy observations. These results demonstrate ion implantation as a viable technique to design "dendrite-free" solid-state electrolytes for high-power and energy-dense solid-state batteries.

An end-to-end convolutional neural network for automated failure localisation and characterisation of 3D interconnects.

The advancement in the field of 3D integration circuit technology leads to new challenges for quality assessment of interconnects such as through silicon vias (TSVs) in terms of automated and time-efficient analysis. In this paper, we develop a fully automated high-efficient End-to-End Convolutional Neural Network (CNN) model, utilizing two sequentially linked CNN architectures, suitable to classify and locate thousands of TSVs as well as provide statistical information. In particular, we generate interference patterns of the TSVs by conducting a unique concept of Scanning Acoustic Microscopy (SAM) imaging. Scanning Electron Microscopy (SEM) is used to validate and also disclose the characteristic pattern in the SAM C-scan images. By comparing the model with semi-automated machine learning approaches its outstanding performance is illustrated, indicating a localisation and classification accuracy of 100% and greater than 96%, respectively. The approach is not limited to SAM-image data and presents an important step towards zero defect strategies.

Probing the composition dependence of residual stress distribution in tungsten-titanium nanocrystalline thin films.

Nanocrystalline alloy thin films offer a variety of attractive properties, such as high hardness, strength and wear resistance. A disadvantage is the large residual stresses that result from their fabrication by deposition, and subsequent susceptibility to defects. Here, we use experimental and modelling methods to understand the impact of minority element concentration on residual stresses that emerge after deposition in a tungsten-titanium film with different titanium concentrations. We perform local residual stress measurements using micro-cantilever samples and employ machine learning for data extraction and stress prediction. The results are correlated with accompanying microstructure and elemental analysis as well as atomistic modelling. We discuss how titanium enrichment significantly affects the stress stored in the nanocrystalline thin film. These findings may be useful for designing stable nanocrystalline thin films.

Mesoscale Models for Describing the Formation of Anisotropic Porosity and Strain-Stress Distributions during the Pressing Step in Electroceramics.

Porous ceramics are often produced by using pyrolisable additives to generate porosity during the sintering step. The examination of the experimental microstructures of the resulted porous ceramics revealed certain levels of anisotropy, even if the original soft additives used as pore forming agents were spherical. The paper shows that anisotropic porosity may result in ceramics when using equiaxed soft polymeric additives for generating porosity, due to the deformation of soft inclusions during the pressing step. It has been found, by means of analytical and numerical calculations, that uniaxial pressing of a mixture of solid particles with contrasting mechanical properties (hard/soft) generates modifications in the shape of the soft phase. As a result, anisotropic shape distribution of the soft inclusions in the green ceramic body and elongated porosity in the final ceramic product are obtained. The elongated pores are statistically oriented with the major axes perpendicular to the pressing direction and will generate anisotropy-related functional properties. Analytical calculations indicate the deformation of a single soft inclusion inside a continuum solid. Further, by finite element simulations performed in 2D planes along the transversal and radial directions of the pressing axis, a bimodal angular distribution of the long axes of the soft inclusions has been found.

Heterogeneity of the osteocyte lacuno-canalicular network architecture and material characteristics across different tissue types in healing bone.

Various tissue types, including fibrous connective tissue, bone marrow, cartilage, woven and lamellar bone, coexist in healing bone. Similar to most bone tissue type, healing bone contains a lacuno-canalicular network (LCN) housing osteocytes. These cells are known to orchestrate bone remodeling in healthy bone by sensing mechanical strains and translating them into biochemical signals. The structure of the LCN is hypothesized to influence mineralization processes. Hence, the aim of the present study was to visualize and match spatial variations in the LCN topology with mineral characteristics, within and at the interfaces of the different tissue types that comprise healing bone. We applied a correlative multi-method approach to visualize the LCN architecture and quantify mineral particle size and orientation within healing femoral bone in a mouse osteotomy model (26 weeks old C57BL/6 mice). This approach revealed structural differences across several length scales during endochondral ossification within the following regions: calcified cartilage, bony callus, cortical bone and a transition zone between the cortical and callus region analyzed 21 days after the osteotomy. In this transition zone, we observed a continuous convergence of mineral characteristics and osteocyte lacunae shape as well as discontinuities in the lacunae volume and LCN connectivity. The bony callus exhibits a 34% higher lacunae number density and 40% larger lacunar volume compared to cortical bone. The presented correlations between LCN architecture and mineral characteristics improves our understanding of how bone develops during healing and may indicate a contribution of osteocytes to bone (re)modeling.

Multi-scale quantification and modeling of aged nanostructured silicon-based composite anodes.

Advanced anode material designs utilizing dual phase alloy systems like Si/FeSi2 nano-composites show great potential to decrease the capacity degrading and improve the cycling capability for Lithium (Li)-ion batteries. Here, we present a multi-scale characterization approach to understand the (de-)lithiation and irreversible volumetric changes of the amorphous silicon (a-Si)/crystalline iron-silicide (c-FeSi2) nanoscale phase and its evolution due to cycling, as well as their impact on the proximate pore network. Scattering and 2D/3D imaging techniques are applied to probe the anode structural ageing from nm to µm length scales, after up to 300 charge-discharge cycles, and combined with modeling using the collected image data as an input. We obtain a quantified insight into the inhomogeneous lithiation of the active material induced by the morphology changes due to cycling. The electrochemical performance of Li-ion batteries does not only depend on the active material used, but also on the architecture of its proximity.

Analysis of Sn-Bi Solders: X-ray Micro Computed Tomography Imaging and Microstructure Characterization in Relation to Properties and Liquid Phase Healing Potential.

This work provides an analysis of X-ray micro computed tomography data of Sn-xBi solders with x = 20, 30, 35, 47, 58 wt.% Bi. The eutectic thickness, fraction of eutectic and primary phase are analyzed. Furthermore, the 3D data is evaluated by means of morphology parameters, such as, shape complexity, flatness, elongation and mean intercept length tensor. The investigated alloys are categorized in three groups based on their morphology, which are described as "complex dominant", "complex- equiaxed" and "mixed". The mechanical behavior of Sn-Bi alloys in the semi-solid configuration and the correlation with microstructural parameters are discussed. A varying degree of geometric anisotropy of the investigated alloys is found through the mean intercept length tensor. Representative volume element models for finite element simulations (RVE-FEM) are created from tomography data of each alloy to analyze a correlation of geometric and elastic anisotropy. The simulations reveal an elastic isotropic behavior due to the small difference of elastic constants of primary and eutectic phase. A discussion of properties in the semi-solid state and liquid phase healing is provided.

Quantitative measurements of N-linked glycoproteins in human plasma by SWATH-MS.

SWATH-MS is a data-independent acquisition method that generates, in a single measurement, a complete recording of the fragment ion spectra of all the analytes in a biological sample for which the precursor ions are within a predetermined m/z versus retention time window. To assess the performance and suitability of SWATH-MS-based protein quantification for clinical use, we compared SWATH-MS and SRM-MS-based quantification of N-linked glycoproteins in human plasma, a commonly used sample for biomarker discovery. Using dilution series of isotopically labeled heavy peptides representing biomarker candidates, the LOQ of SWATH-MS was determined to reach 0.0456 fmol at peptide level by targeted data analysis, which corresponds to a concentration of 5-10 ng protein/mL in plasma, while SRM reached a peptide LOQ of 0.0152 fmol. Moreover, the quantification of endogenous glycoproteins using SWATH-MS showed a high degree of reproducibility, with the mean CV of 14.90%, correlating well with SRM results (R(2) = 0.9784). Overall, SWATH-MS measurements showed a slightly lower sensitivity and a comparable reproducibility to state-of-the-art SRM measurements for targeted quantification of the N-glycosites in human blood. However, a significantly larger number of peptides can be quantified per analysis. We suggest that SWATH-MS analysis combined with N-glycoproteome enrichment in plasma samples is a promising integrative proteomic approach for biomarker discovery and verification.

Single-spin readout in a double quantum dot including a micromagnet.

We use photon-assisted tunneling (PAT) and an inhomogeneous Zeeman field to demonstrate spin-selective PAT readout with a double quantum dot. The inhomogeneous Zeeman field is generated by a proximal micromagnet, which provides different stray fields between the two dots, resulting in an energy difference between the interdot PAT of the up-spin state and that of the down-spin state. We apply various external magnetic fields to modify the relative filling weight between the up-spin and down-spin states and detect it by using a charge detection technique to probe the PAT induced charge delocalization in the double dot.