A simple, ultrahigh vacuum compatible scanning tunneling microscope for use at variable temperatures

We present the construction of a very compact scanning tunneling microscope (STM) which can be operated at temperatures between 4 and 350 K. The tip and a tiny tip holder are the only movable parts, whereas the sample and the piezoscanner are rigidly attached to the body of the STM. This leads to an excellent mechanical stability. The coarse approach system relies on the slip-stick principle and is operated by the same piezotube which is used for scanning. As an example of the performance of the device, images of a NbSe2 surface with atomic resolution are obtained

Compositional optimization of hard-magnetic phases with machine-learning models

Machine Learning (ML) plays an increasingly important role in the discovery and design of new materials. In this paper, we demonstrate the potential of ML for materials research using hard-magnetic phases as an illustrative case. We build kernel-based ML models to predict optimal chemical compositions for new permanent magnets, which are key components in many green-energy technologies. The magnetic-property data used for training and testing the ML models are obtained from a combinatorial high-throughput screening based on density-functional theory calculations. Our straightforward choice of describing the different configurations enables the subsequent use of the ML models for compositional optimization and thereby the prediction of promising substitutes of state-of-the-art magnetic materials like Nd$_2$Fe$_{14}$B with similar intrinsic hard-magnetic properties but a lower amount of critical rare-earth elements.Comment: 12 pages, 6 figure

Quantificação de caulinita em latossolo por difração de raios-X.

bitstream/item/31910/1/CPATU-BP39.pd

Exploring scholarly data with Rexplore.

Despite the large number and variety of tools and services available today for exploring scholarly data, current support is still very limited in the context of sensemaking tasks, which go beyond standard search and ranking of authors and publications, and focus instead on i) understanding the dynamics of research areas, ii) relating authors ‘semantically’ (e.g., in terms of common interests or shared academic trajectories), or iii) performing fine-grained academic expert search along multiple dimensions. To address this gap we have developed a novel tool, Rexplore, which integrates statistical analysis, semantic technologies, and visual analytics to provide effective support for exploring and making sense of scholarly data. Here, we describe the main innovative elements of the tool and we present the results from a task-centric empirical evaluation, which shows that Rexplore is highly effective at providing support for the aforementioned sensemaking tasks. In addition, these results are robust both with respect to the background of the users (i.e., expert analysts vs. ‘ordinary’ users) and also with respect to whether the tasks are selected by the evaluators or proposed by the users themselves