Intermetallic Nanoarchitectures for Efficient Electrocatalysis

Intermetallic structures whose regular atomic arrays of constituent elements present unique catalytic properties have attracted considerable attention as efficient electrocatalysts for energy conversion reactions. Further performance enhancement in intermetallic catalysts hinges on constructing catalytic surfaces possessing high activity, durability, and selectivity. In this Perspective, we introduce recent endeavors to boost the performance of intermetallic catalysts by generating nanoarchitectures, which have well-defined size, shape, and dimension. We discuss the beneficial effects of nanoarchitectures compared with simple nanoparticles in catalysis. We highlight that the nanoarchitectures have high intrinsic activity owing to their inherent structural factors, including controlled facets, surface defects, strained surfaces, nanoscale confinement effects, and a high density of active sites. We next present notable examples of intermetallic nanoarchitectures, namely, facet-controlled intermetallic nanocrystals and multidimensional nanomaterials. Finally, we suggest the future research directions of intermetallic nanoarchitectures

25th annual computational neuroscience meeting: CNS-2016

The same neuron may play different functional roles in the neural circuits to which it belongs. For example, neurons in the Tritonia pedal ganglia may participate in variable phases of the swim motor rhythms [1]. While such neuronal functional variability is likely to play a major role the delivery of the functionality of neural systems, it is difficult to study it in most nervous systems. We work on the pyloric rhythm network of the crustacean stomatogastric ganglion (STG) [2]. Typically network models of the STG treat neurons of the same functional type as a single model neuron (e.g. PD neurons), assuming the same conductance parameters for these neurons and implying their synchronous firing [3, 4]. However, simultaneous recording of PD neurons shows differences between the timings of spikes of these neurons. This may indicate functional variability of these neurons. Here we modelled separately the two PD neurons of the STG in a multi-neuron model of the pyloric network. Our neuron models comply with known correlations between conductance parameters of ionic currents. Our results reproduce the experimental finding of increasing spike time distance between spikes originating from the two model PD neurons during their synchronised burst phase. The PD neuron with the larger calcium conductance generates its spikes before the other PD neuron. Larger potassium conductance values in the follower neuron imply longer delays between spikes, see Fig. 17.Neuromodulators change the conductance parameters of neurons and maintain the ratios of these parameters [5]. Our results show that such changes may shift the individual contribution of two PD neurons to the PD-phase of the pyloric rhythm altering their functionality within this rhythm. Our work paves the way towards an accessible experimental and computational framework for the analysis of the mechanisms and impact of functional variability of neurons within the neural circuits to which they belong

Crystal engineering of composite materials

© The Royal Society of Chemistry 2016101sciescopu

Hydrocarbon Ligand Derivatives of the Dicarbido Decaruthenium and Related Cluster Frameworks

177 p.Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 1997.Thermal reaction of \rm \lbrack Ru\sb{10}C\sb2(CO)\sb{24}\rbrack\sp{2-} with C\sb{60} forms \rm \lbrack Ru\sb{10}C\sb2(CO)\sb{22}(C\sb{60})\rbrack\sp{2-}, in which the C\sb{60} ligand bridges two apical ruthenium atoms in the $\mu$-\eta\sp2{:}\eta\sp2 bonding mode.U of I OnlyRestricted to the U of I community idenfinitely during batch ingest of legacy ETD

Development of the Architecture and Reconfiguration Methods for the Smart, Self-Reconfigurable Manufacturing System

Over recent decades, the demand for smarter and more intelligent manufacturing systems has increased in order to meet the growing requirements of customers. Manufacturing systems are termed as smart manufacturing systems (SMSs); these systems are capable of fully integrated autonomous operation. Specifically, the concept of autonomous systems and functions has been adopted for next generation manufacturing systems (NGMSs). Among these NGMSs, the fractal manufacturing system (FrMS) exhibits several characteristics that are similar to those of SMSs. Therefore, in this paper, a smart, self-reconfigurable manufacturing system (SSrMS) based on the FrMS is proposed. The proposed SSrMS architecture was designed for realizing self-reconfiguration functions based on the FrMS concept. SSrMS exhibits a fractal structure, which enables the distribution of control features; this also constitutes the fundamental basis of autonomous operation and reconfiguration between each fractal. SSrMS architecture includes the use of big data, digital facilities, and simulations. Furthermore, we introduce three reconfiguration methods to conduct system reconfiguration, which are a goal decision model, a negotiation model, and a sustainability assessment method. The goal decision model was developed to determine a goal of each fractal to achieve the system’s goal. In other words, each fractal can decide a goal to achieve the system’s goal, such as maximizing productivity or profit, or minimizing cost, and others. The negotiation model was adopted to perform partial process optimization by reassigning tasks and resources between the fractals, based on the goal of coping with the changes in the system’s condition. The sustainability assessment method was designed to simultaneously evaluate sustainability with respect to the system’s goals. The proposed architecture of SSrMS with goal decision model, negotiation model, and sustainability assessment method has the features of self-optimization, self-organization, and self-reconfiguration in order to achieve fully autonomous operations for the manufacturing system. The proposed architecture including three methods are expected to provide a fundamental study of the autonomous operations. The main findings of in this study is the development of a new architecture for fully autonomous operations of the smart manufacturing system with reconfiguration methods of goal-oriented manufacturing processes

Process Improvement for PDM/PLM Systems by Using Process Mining

Process mining is a useful methodology that can be used for extracting user patterns in log files in order to discover efficient or inefficient processes in organizations. In general, it is used to find and reduce differences between pre-defined processes and actually executed processes in an organization. In this paper, we propose a method to improve processes in PDM/PLM systems based on process mining. In order to improve and detect the inefficient processes, we gathered event logs from PDM/PLM systems and derived process models using several process mining techniques such as ??-algorithm mining, heuristics mining, and fuzzy miner. By comparing original process models with process mining results, it is possible to detect differences between predefined processes and real ones; thereby we can build improved process models for future application.clos

Digital Twin Approach for Solving Reconfiguration Planning Problems in RMS

Part 5: Industry 4.0 - Digital TwinInternational audienceReconfigurable Manufacturing System (RMS) appeared as a solution to high variation in customer demands allowing manufacturers to satisfy different amount of demands in different period. RMS satisfies demands by rapidly reconfiguring its hardware and software components, in order to quickly adjust its production capacity and functionality within a part family in response to sudden market changes or intrinsic system change depends on the demand of every single period. This reconfiguration process brings a critical issue within the RMS that is called as reconfiguration planning problems (RPP) introduced in this paper. With the rise of digital twin that has been a global issue, many companies or manufacturers are trying to integrate it into their production systems. There is a need of RMS to apply digital twin in order to hopefully improve the effectiveness and efficiency so that RPP can be automatically solved and controlled. The goal of this paper is to address the importance and requirement of the integration of digital twin simulation into RMS by providing comparison study between normal simulation program and digital twin simulation program. A case study is presented for comparison of two simulation models. Plant Simulation 11 is used for a normal simulation model, while Visual Components is used to build a digital twin simulation model