The lithiation process and Li diffusion in amorphous SiO2 and Si from first-principles

Silicon is considered the next-generation, high-capacity anode for Li-ion energy storage applications, however, despite significant effort, there are still uncertainties regarding the bulk Si and surface SiO2 structural and chemical evolution as it undergoes lithiation and amorphization. In this paper, we present first-principles calculations of the evolution of the amorphous Si anode, including its oxide surface layer, as a function of Li concentration. We benchmark our methodology by comparing the results for the Si bulk to existing experimental evidence of local structure evolution, ionic diffusivity as well as electrochemical activity. Recognizing the important role of the surface Si oxide (either native or artificially grown), we undertake the same calculations for amorphous SiO2, analyzing its potential impact on the activity of Si anode materials. Derived voltage curves for the amorphous phases compare well to experimental results, highlighting that SiO2 lithiates at approximately 0.7 V higher than Si in the low Li concentration regime, which provides an important electrochemical fingerprint. The combined evidence suggests that i) the inherent diffusivity of amorphous Si is high (in the order 10−9cm2s−1 - 10−7cm2s−1), ii) SiO2 is thermodynamically driven to lithiate, such that Li–O local environments are increasingly favored as compared to Si–O bonding, iii) the ionic diffusivity of Li in LiySiO2 is initially two orders of magnitude lower than that of LiySi at low Li concentrations but increases rapidly with increasing Li content and iv) the final lithiation product of SiO2 is Li2O and highly lithiated silicides. Hence, this work suggests that - excluding explicit interactions with the electrolyte - the SiO2 surface layer presents a kinetic impediment for the lithiation of Si and a sink for Li inventory, resulting in non-reversible capacity loss through strong local Li–O bond formation

The Role of Exporters’ Emotional Intelligence in Building Foreign Customer Relationships

Despite the critical importance of emotional intelligence in effectively interacting with other people, its role has been overlooked in scholarly research on cross-border interorganizational relationships. Drawing on Emotion Regulation Theory, we propose a model that conceptualizes links among exporters’ emotional intelligence, key behavioral dimensions characterizing the atmosphere of the relationship with import buyers, and the resulting relational performance. We test the model with data collected from 262 Greek exporters using structural equation modeling. The results indicate that higher levels of exporter emotional intelligence enhances communication and social bonding with the importer, while diminishing distance and conflict in their working relationship. Relational performance is positively influenced by communication and social bonding, but negatively affected by distance and conflict. The results also reveal the moderating effect of both opportunism and interpartner incompatibility on the association between the exporter’s emotional intelligence and the behavioral atmosphere of the relationship with import buyers

Approximation by trigonometric polynomials in the variable exponent weighted Morrey spaces

In this paper we investigate the best approximation by trigonometric polynomials in the variable exponent weighted Morrey spaces ${\mathcal{M}}_{p(\cdot),\lambda(\cdot)}(I_{0},w)$, where $w$ is a weight function in the Muckenhoupt $A_{p(\cdot)}(I_{0})$ class. We get a characterization of $K$-functionals in terms of the modulus of smoothness in the spaces ${\mathcal{M}}_{p(\cdot),\lambda(\cdot)}(I_{0},w)$. Finally, we prove the direct and inverse theorems of approximation by trigonometric polynomials in the spaces ${\mathcal{\widetilde{M}}}_{p(\cdot),\lambda(\cdot)}(I_{0},w),$ the closure of the set of all trigonometric polynomials in ${\mathcal{M}}_{p(\cdot),\lambda(\cdot)}(I_{0},w)$

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

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

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

Riesz potential in the local Morrey-Lorentz spaces and some applications

In this paper, the necessary and sufficient conditions are found for the boundedness of the Riesz potential I α {I_{\alpha}} in the local Morrey-Lorentz spaces M p, q; λ loc ⢠(ââžÂ n) {M_{p,q;{\lambda}}{\mathrm{loc}}({\mathbb{R}{n}})}. This result is applied to the boundedness of particular operators such as the fractional maximal operator, fractional Marcinkiewicz operator and fractional powers of some analytic semigroups on the local Morrey-Lorentz spaces M p, q; λ loc ⢠(ââžÂ n) {M_{p,q;{\lambda}}{\mathrm{loc}}({\mathbb{R}{n}})}. © 2020 Walter de Gruyter GmbH, Berlin/Boston 2020