Güç kaynakları ve otomotiv elektroniği uygulamaları için bor tabanlı kalın kesitli metalik cam / nanokristal manyetik malzemelerin geliştirilmesi

TÜBİTAK MAG Proje01.06.2008This study is pertinent to setting a connection between glass forming ability (GFA) and topology of Fe-B based metallic glasses, identifying atomic effect order of elements increasing GFA and developing soft magnetic bulk metallic glasses (BMG) / bulk nanocrystalline alloys (BNCA) for industrial applications by combining intimate investigations on spatial atomic arrangements conducted via solid computer simulations with experimentations on high GFA bulk metallic glasses. In order to construct a theoretical framework, the nano-scale phase separation encountered in metallic glasses is investigated for amorphous Fe80B20 and Fe83B17 alloys via Monte Carlo and Reverse Monte Carlo simulations. All topological aspects revealed by developed analysis tools are compiled into a new model called Two-Dimensional Projection Model for predicting contributions to short and medium range order (MRO) and corresponding spacing relations. The outcome geometrically involves proportions approximating golden ratio. Soft magnetic Fe-Co-Nb-B-Si BMG and FeCo-Nb-B-Si-Cu BMG/BNCAs are produced with a totally conventional route, thermally characterized and their magnetic properties are measured. Influences of alloying elements that increase GFA and promote nanocristalization, on structural units and crystallization modes are identified by the developed model and radial distributions. While Co atoms substitute for Fe atoms, Nb and Si atoms deform trigonal prismatic units to provide local compactions at the outset of MRO. The GFA can be described by a new parameter quantifying the MRO compaction, cited as Φ. Moreover, after annealing Fe-Co-Nb-B-Si-Cu BMG alloy at 873 K for 300 s., the the precipitation is altered from Fe23B6 meta-sTablo phase to α-Fe nanocrystals, BNCAs are produced and this phenomenon is investigated structurally. It has been shown that developed Fe-B based BMGs and BNCAs show very good soft magnetic properties and optimum alloy composition is determined as (Fe36Co36B19.2Si4.8Nb4)99.25Cu0.75 with 3 mm thickness, 1.58 T saturation induction and 0.148 Oe coercivity

Closed-loop optimization of fast-charging protocols for batteries with machine learning.

Simultaneously optimizing many design parameters in time-consuming experiments causes bottlenecks in a broad range of scientific and engineering disciplines1,2. One such example is process and control optimization for lithium-ion batteries during materials selection, cell manufacturing and operation. A typical objective is to maximize battery lifetime; however, conducting even a single experiment to evaluate lifetime can take months to years3-5. Furthermore, both large parameter spaces and high sampling variability3,6,7 necessitate a large number of experiments. Hence, the key challenge is to reduce both the number and the duration of the experiments required. Here we develop and demonstrate a machine learning methodology  to efficiently optimize a parameter space specifying the current and voltage profiles of six-step, ten-minute fast-charging protocols for maximizing battery cycle life, which can alleviate range anxiety for electric-vehicle users8,9. We combine two key elements to reduce the optimization cost: an early-prediction model5, which reduces the time per experiment by predicting the final cycle life using data from the first few cycles, and a Bayesian optimization algorithm10,11, which reduces the number of experiments by balancing exploration and exploitation to efficiently probe the parameter space of charging protocols. Using this methodology, we rapidly identify high-cycle-life charging protocols among 224 candidates in 16 days (compared with over 500 days using exhaustive search without early prediction), and subsequently validate the accuracy and efficiency of our optimization approach. Our closed-loop methodology automatically incorporates feedback from past experiments to inform future decisions and can be generalized to other applications in battery design and, more broadly, other scientific domains that involve time-intensive experiments and multi-dimensional design spaces

Benchmarking the Acceleration of Materials Discovery by Sequential Learning

Sequential learning (SL) strategies, i.e. iteratively updating a machine learning model to guide experiments, have been proposed to significantly accelerate materials discovery and research. Applications on computational datasets and a handful of optimization experiments have demonstrated the promise of SL, motivating a quantitative evaluation of its ability to accelerate materials discovery, specifically in the case of physical experiments. The benchmarking effort in the present work quantifies the performance of SL algorithms with respect to a breadth of research goals: discovery of any “good” material, discovery of all “good” materials, and discovery of a model that accurately predicts the performance of new materials. To benchmark the effectiveness of different machine learning models against these goals, we use datasets in which the performance of all materials in the search space is known from high-throughput synthesis and electrochemistry experiments. Each dataset contains all pseudo-quaternary metal oxide combinations from a set of six elements (chemical space), the performance metric chosen is the electrocatalytic activity (overpotential) for the oxygen evolution reaction (OER). A diverse set of SL schemes is tested on four chemical spaces, each containing 2121 catalysts. The presented work suggests that research can be accelerated by up to a factor of 20 compared to random acquisition in specific scenarios. The results also show that certain choices of SL models are ill-suited for a given research goal resulting in substantial deceleration compared to random acquisition methods. The results provide quantitative guidance on how to tune an SL strategy for a given research goal and demonstrate the need for a new generation of materials-aware SL algorithms to further accelerate materials discovery

Stacking-Dependent Band Gap and Quantum Transport in Trilayer Graphene

In a multi-layer electronic system, stacking order provides a rarely-explored degree of freedom for tuning its electronic properties. Here we demonstrate the dramatically different transport properties in trilayer graphene (TLG) with different stacking orders. At the Dirac point, ABA-stacked TLG remains metallic while the ABC counterpart becomes insulating. The latter exhibits a gap-like dI/dV characteristics at low temperature and thermally activated conduction at higher temperatures, indicating an intrinsic gap ~6 meV. In magnetic fields, in addition to an insulating state at filling factor {\nu}=0, ABC TLG exhibits quantum Hall plateaus at {\nu}=-30, \pm 18, \pm 9, each of which splits into 3 branches at higher fields. Such splittings are signatures of the Lifshitz transition induced by trigonal warping, found only in ABC TLG, and in semi-quantitative agreement with theory. Our results underscore the rich interaction-induced phenomena in trilayer graphene with different stacking orders, and its potential towards electronic applications.Comment: minor revision; published versio

Online interprofessional education facilitation : a scoping review

<p><b>Introduction:</b> The use of online media to deliver interprofessional education (IPE) is becoming more prevalent across health professions education settings. Facilitation of IPE activities is known to be critical to the effective delivery of IPE, however, specifics about the nature of online IPE facilitation remains unclear.</p> <p><b>Aim:</b> To explore the health professions education literature to understand the extent, range and nature of research on online IPE facilitation.</p> <p><b>Methods:</b> Scoping review methodology was used to guide a search of four electronic databases for relevant papers. Of the 2095 abstracts initially identified, after screening of both abstracts and full-text papers, 10 studies were selected for inclusion in this review. Following abstraction of key information from each study, a thematic analysis was undertaken.</p> <p><b>Results:</b> Three key themes emerged to describe the nature of the IPE facilitation literature: (1) types of online IPE facilitation contributions, (2) the experience of online IPE facilitation and (3) personal outcomes of online IPE facilitation. These IPE facilitation themes were particularly focused on facilitation of interprofessional student teams on an asynchronous basis.</p> <p><b>Discussion:</b> While the included studies provide some insight into the nature of online IPE facilitation, future research is needed to better understand facilitator contributions, and the facilitation experience and associated outcomes, both relating to synchronous and asynchronous online environments.</p

The Materials Research Platform: Defining the Requirements from User Stories

A recent meeting focused on accelerated materials design and discovery examined user requirements for a general, collaborative, integrative, and on-demand materials research platform

Changing perspectives on the internationalization of R&D and innovation by multinational enterprises: a review of the literature

Internationalization of R&D and innovation by Multinational Enterprises (MNEs) has undergone a gradual and comprehensive change in perspective over the past 50 years. From sporadic works in the late 1950s and in the 1960s, it became a systematically analysed topic in the 1970s, starting with pioneering reports and “foundation texts”. Our review unfolds the theoretical and empirical evolution of the literature from dyadic interpretations of centralization versus decentralization of R&D by MNEs to more comprehensive frameworks, wherein established MNEs from Advanced Economies still play a pivotal role, but new players and places also emerge in the global generation and diffusion of knowledge. Hence views of R&D internationalization increasingly rely on concepts, ideas and methods from IB and other related disciplines such as industrial organization, international economics and economic geography. Two main findings are highlighted. First, scholarly research pays an increasing attention to the network-like characteristics of international R&D activities. Second, different streams of literature have emphasized the role of location- specific factors in R&D internationalization. The increasing emphasis on these aspects has created new research opportunities in some key areas, including inter alia: cross-border knowledge sourcing strategies, changes in the geography of R&D and innovation, and the international fragmentation of production and R&D activities