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The occurrence of strain is inevitable for the growth of lattice mismatched heterostructures. It affects greatly the mechanical, electrical and optical properties of nano-objects. It is also the case for nanowires which are characterized by a high surface to volume ratio. Thus, the knowledge of the strain distribution in nano-objects is critically important for their implementation into devices. This paper presents an experimental data for II-VI semiconductor system. Scanning nanobeam electron diffraction strain mapping technique for hetero-nanowires characterized by a large lattice mismatch (>6% in the case of CdTe/ZnTe) and containing segments with nano-twins has been described. The spatial resolution of about 2 nm is 10 times better than obtained in synchrotron nanobeam systems. The proposed approach allows us to overcome the difficulties related to nanowire thickness variations during the acquisition of the nano-beam electron diffraction data. In addition, the choice of optimal parameters used for the acquisition of nano-beam diffraction data for strain mapping has been discussed. The knowledge of the strain distribution enables, in our particular case, the improvement of the growth model of extremely strained axial nanowires synthetized by vapor-liquid solid growth mechanism. However, our method can be applied for the strain mapping in nanowire heterostructures grown by any other method. [ABSTRACT FROM AUTHOR]

Residual strains in a punched electrical steel sheet increase the iron loss in the steel sheet. To accurately estimate the effect of residual strain on iron loss, the residual strain distribution in a punched electrical steel sheet should be evaluated. In this study, we demonstrated the two-dimensional imaging of the residual strain distribution in a punched electrical steel sheet using the neutron Bragg-edge transmission imaging method. To improve the accuracy of strain measurement with minimal deterioration of spatial resolution, we applied a process of superposing many specimen images. The tensile strain near the punched edge and the compressive strain inside the core were experimentally confirmed using this method. Finally, the neutron Bragg-edge imaging results and those obtained from kernel average misorientation map using electron backscattered diffraction were compared to verify the validity of the proposed method. [ABSTRACT FROM AUTHOR]

Neutron diffraction is a useful technique for mapping residual strains in dense metal objects. The technique works by placing an object in the path of a neutron beam, measuring the diffracted signals and inferring the local lattice strain values from the measurement. In order to map the strains across the entire object, the object is stepped one position at a time in the path of the neutron beam, typically in raster order, and at each position a strain value is estimated. Typical dwell times at neutron diffraction instruments result in an overall measurement that can take several hours to map an object that is several tens of centimeters in each dimension at a resolution of a few millimeters, during which the end users do not have an estimate of the global strain features and are at risk of incomplete information in case of instruments outages. In this paper, we propose an object adaptive sampling strategy to measure the significant points first. We start with a small initial uniform set of measurement points across the object to be mapped, compute the strain in those positions and use a machine learning technique to predict the next position to measure in the object. Specifically, we use a Bayesian optimization based on a Gaussian process regression method to infer the underlying strain field from a sparse set of measurements and predict the next most informative positions to measure based on estimates of the mean and variance in the strain fields estimated from the previously measured points. We demonstrate our real-time measure-infer-predict workflow on additively manufactured steel parts—demonstrating that we can get an accurate strain estimate even with 30%–40% of the typical number of measurements—leading the path to faster strain mapping with useful real-time feedback. We emphasize that the proposed method is general and can be used for fast mapping of other material properties such as phase fractions from time-consuming point-wise neutron measurements.