Yuma Iwasaki

Flexible heat-flow sensing sheets based on the longitudinal spin Seebeck effect using one-dimensional spin-current conducting films

Heat-flow sensing is expected to be an important technological component of smart thermal management in the future. Conventionally, the thermoelectric (TE) conversion technique, which is based on the Seebeck effect, has been used to measure a heat flow by converting the flow into electric voltage. However, for ubiquitous heat-flow visualization, thin and flexible sensors with extremely low thermal resistance are highly desired. Recently, another type of TE effect, the longitudinal spin Seebeck effect (LSSE), has aroused great interest because the LSSE potentially offers favourable features for TE applications such as simple thin-film device structures. Here we demonstrate an LSSE-based flexible TE sheet that is especially suitable for a heat-flow sensing application. This TE sheet contained a Ni0.2Zn0.3Fe2.5O4 film which was formed on a flexible plastic sheet using a spray-coating method known as “ferrite plating”. The experimental results suggest that the ferrite-plated film, which has a columnar crystal structure aligned perpendicular to the film plane, functions as a unique one-dimensional spin-current conductor suitable for bendable LSSE-based sensors. This newly developed thin TE sheet may be attached to differently shaped heat sources without obstructing an innate heat flux, paving the way to versatile heat-flow measurements and management.

Comparison of dissimilarity measures for cluster analysis of X-ray diffraction data from combinatorial libraries

Machine learning techniques have proven invaluable to manage the ever growing volume of materials research data produced as developments continue in high-throughput materials simulation, fabrication, and characterization. In particular, machine learning techniques have been demonstrated for their utility in rapidly and automatically identifying potential composition–phase maps from structural data characterization of composition spread libraries, enabling rapid materials fabrication-structure-property analysis and functional materials discovery. A key issue in development of an automated phase-diagram determination method is the choice of dissimilarity measure, or kernel function. The desired measure reduces the impact of confounding structural data issues on analysis performance. The issues include peak height changes and peak shifting due to lattice constant change as a function of composition. In this work, we investigate the choice of dissimilarity measure in X-ray diffraction-based structure analysis and the choice of measure’s performance impact on automatic composition-phase map determination. Nine dissimilarity measures are investigated for their impact in analyzing X-ray diffraction patterns for a Fe–Co–Ni ternary alloy composition spread. The cosine, Pearson correlation coefficient, and Jensen–Shannon divergence measures are shown to provide the best performance in the presence of peak height change and peak shifting (due to lattice constant change) when the magnitude of peak shifting is unknown. With prior knowledge of the maximum peak shifting, dynamic time warping in a normalized constrained mode provides the best performance. This work also serves to demonstrate a strategy for rapid analysis of a large number of X-ray diffraction patterns in general beyond data from combinatorial libraries.Machine learning: Spying enhanced materials with x-ray visionUsing algorithms to automatically spot variations in massive X-ray diffraction data sets may improve design of multi-component alloys. Having three or more metals in an alloy can lead to overwhelming combinations of possible materials, each with different properties. A. Gilad Kusne from the National Institute of Standards and co-workers examined how machine learning techniques could simplify alloy discovery through ‘dissimilarity measures’ that quantify how key structural data points, such as the positions and intensities of X-ray peaks, change with sample makeup. The team fabricated a compositional spread of iron–cobalt–nickel thin film alloys, and then evaluated different software approaches to finding X-ray dissimilarities for both processing speed and accuracy. Several algorithms suitable for high-throughput generation of color-coded maps that display relations between alloy composition and phase in both two and three-dimensions were identified.

Virtual integration technology of distributed energy storages

Widely spread renewable energies (REs) will destabilize electricity supply-demand balancing because of their uncontrollability and unpredictability. Therefore effective balancing technology is required. We focus on distributed energy storages (ESs) and propose a novel balancing technology using distributed ESs, “Virtual Integration (VI) of distributed ESs”. VI enables us to control multiple ESs as a concentrated single ES: VI can make ESs respond quicker, control electric-charge/discharge of ESs precisely, and achieve reliable property of ESs. The feature of VI is that by regarding the difference in dynamic status of each ES as a functional characteristic of ES, a virtually integrated ES is built to create new functions by arranging functional characteristics appropriately. In other words, VI technology properly assigns real-time charging/discharging power to each ES on the basis of each characteristic. For an example of reliable control of ESs, we numerically demonstrated VI suppresses capacity degradation of lithium Ion batteries by 20%.

Spin-Seebeck thermoelectric converter

Thermoelectric conversion (TEC) technologies, which convert heat into electricity, have received a great attention, because they are expected to be a powerful approach to utilize wasted thermal energy. Here we present novel thermoelectric converters based on the spin Seebeck effect (SSE), and show their scaling law which is largely different from that of conventional TEC devices. We experimentally demonstrate that the TEC output signals straightforwardly increase with the size of the converters. This scaling law enables us to implement simple-structured thermoelectric converters by using productive film-coating methods. Such coating-based TEC techniques may pave the way for a wide range of applications using a variety of heat sources.