Two‐Dimensional Molybdenum Trioxide and Dichalcogenides

In the quest to discover the properties of planar semiconductors, two‐dimensional molybdenum trioxide and dichalcogenides have recently attracted a large amount of interest. This family, which includes molybdenum trioxide (MoO₃), disulphide (MoS₂), diselenide (MoSe₂) and ditelluride (MoTe₂), possesses many unique properties that make its compounds appealing for a wide range of applications. These properties can be thickness dependent and may be manipulated via a large number of physical and chemical processes. In this Feature Article, a comprehensive review is delivered of the fundamental properties, synthesis techniques and applications of layered and planar MoO₃, MoS₂, MoSe₂, and MoTe₂ along with their future prospects.     

Effect of Bio-Lubricant and Biodiesel-Contaminated Lubricant on Tribological Behavior of Cylinder Liner–Piston Ring Combination

This article addresses the issue of friction and wear characteristics of diesel engine cylinder liner–piston ring combinations under different lubricating conditions using a pin-on-disc wear tribometer. The discs were made out of actual engine cylinder liner material using a casting process. Pins were made out of top compression ring material. The tests were conducted on a pin-on-disc tribometer for wear and friction characteristics of the cylinder liner and piston ring combination with diesel-contaminated rapeseed oil–based bio-lubricant, diesel-contaminated commercial synthetic lubrication oil (SAE 20W40), biodiesel-contaminated commercial synthetic lubrication oil (SAE 20W40), and used (150 h) commercial synthetic lubrication oil (SAE 20W40). Experimental results demonstrated that the rapeseed oil–based bio-lubricant and biodiesel-contaminated synthetic lubricant exhibited better performance in terms of wear, friction, and frictional force under similar operating conditions. Thus, usage of newly formulated bio-lubricant and biodiesel in the long run may have a positive impact on engine life.     

A comparison of statistical relational learning and graph neural networks for aggregate graph queries

Statistical relational learning (SRL) and graph neural networks (GNNs) are two powerful approaches for learning and inference over graphs. Typically, they are evaluated in terms of simple metrics such as accuracy over individual node labels. Complex aggregate graph queries (AGQ) involving multiple nodes, edges, and labels are common in the graph mining community and are used to estimate important network properties such as social cohesion and influence. While graph mining algorithms support AGQs, they typically do not take into account uncertainty, or when they do, make simplifying assumptions and do not build full probabilistic models. In this paper, we examine the performance of SRL and GNNs on AGQs over graphs with partially observed node labels. We show that, not surprisingly, inferring the unobserved node labels as a first step and then evaluating the queries on the fully observed graph can lead to sub-optimal estimates, and that a better approach is to compute these queries as an expectation under the joint distribution. We propose a sampling framework to tractably compute the expected values of AGQs. Motivated by the analysis of subgroup cohesion in social networks, we propose a suite of AGQs that estimate the community structure in graphs. In our empirical evaluation, we show that by estimating these queries as an expectation, SRL-based approaches yield up to a 50-fold reduction in average error when compared to existing GNN-based approaches.

     

NEURAL-NETWORK-AIDED DESIGN OF AUTOMOBILE EXHAUST CATALYSTS

A priori design of catalysts is not yet possible. Such task would demand unavailable scientific knowledge of the correlations among synthesis parameters and resulting solid state and surface structures, on the one hand, and among those atomic-level structural details and their catalytic functions, on the other hand. To avoid testing every possible combination, therefore, the applied chemist or chemical engineer must identify empirical correlations underlying the existing experimental data base.

The ability of artificial neural networks to identify complex correlations and to predict the result of experiments has recently generated considerable interest in various areas of science and engineering. In this paper, neural networks are used to identify composition-performance relationships in automobile exhaust catalysts.

This work employs an artificial neural network technique to do a sensitivity analysis of the conversions of pollutant gases as a function of the catalyst composition and the operating conditions. This approach converges on the optimum catalyst composition and operating condition in order to produce specified conversions of carbon monoxide, hydrocarbons and nitrogen oxides, to carbon dioxide, water and di-nitrogen respectively.     

Computational study on metal hydride based three-stage hydrogen compressor

A mathematical model for predicting the performances of a three-stage metal hydride based hydrogen compressor (MHHC) is presented. The performance of the MHHC is predicted by solving the unsteady heat and mass transfer characteristics of the coupled metal hydride beds of cylindrical configuration. The governing equations for energy, momentum and mass conservations, and reaction kinetic equations are solved simultaneously using the finite volume method. Metal hydrides chosen for a three-stage MHHC are LaNi₅, MmNi_4.6Al_0.4 and Ti_0.99Zr_0.01V_0.43Fe_0.99Cr_0.05Mn_1.5. Numerical results obtained for a single-stage MHHC using MmNi_4.6Al_0.4 are in good agreement with the experimental data reported in the literature. Using three-stage compression, a maximum pressure ratio of 28 is achieved for the supply conditions of 20 °C absorption temperature and 2.5 bar supply pressure. A maximum delivery pressure of 100 bar is obtained for the operating conditions of 20 °C absorption temperature and 120 °C desorption temperature.     

Elemental Analogues of Graphene: Silicene,Germanene, Stanene,and Phosphorene

The fascinating electronic and optoelectronic properties of free‐standing graphene has led to the exploration of alternative two‐dimensional materials that can be easily integrated with current generation of electronic technologies. In contrast to 2D oxide and dichalcogenides, elemental 2D analogues of graphene, which include monolayer silicon (silicene), are fast emerging as promising alternatives, with predictions of high degree of integration with existing technologies. This article reviews this emerging class of 2D elemental materials – silicene, germanene, stanene, and phosphorene – with emphasis on fundamental properties and synthesis techniques. The need for further investigations to establish controlled synthesis techniques and the viability of such elemental 2D materials is highlighted. Future prospects harnessing the ability to manipulate the electronic structure of these materials for nano‐ and opto‐electronic applications are identified.     

Thermal decomposition of doped calcium hydroxide for chemical energy storage

Thermal decomposition of Ca(OH)₂ with and without additives has been experimentally investigated for its application as a thermochemical energy storage system. The homogeneous reaction model gives a satisfactory fit for the kinetic data on pure and Ni(OH)₂---, Zn(OH)₂--- and Al(OH)₃---doped Ca(OH)₂ and the order of reaction is 0.76 in all cases except for the Al(OH)₃-doped sample for which the decomposition is zero order. These additives are shown not only to enhance the reaction rate but also to reduce the decomposition temperature significantly. Some models for solid decomposition reactions, and possible mechanisms in the decomposition of solids containing additives, are also discussed.     

Nanotwin formation and its physical properties and effect on reliability of copper interconnects

Ultra-fine grained copper with a large amount of nano-scale twin boundaries has high mechanical strength and maintains normal electrical conductivity. The combination of these properties may lead to promising applications in future Si microelectronic technology, especially as interconnect material for air-gap and free-standing copper technologies. Based on first principles calculations of total energy and in-situ stress measurements, high stress followed by stress relaxation during the Cu film deposition seems to have contributed to nanotwin formation. Nanoindentation studies have shown a larger hardness for copper with a higher nanotwin density. The effect of Cu nanotwin boundaries on grain growth was investigated by scanning electron microscopy (SEM), electron backscatter diffraction (EBSD) and transmission electron microscopy (TEM). The presence of a high density of nanotwin boundaries may improve the reliability of Cu interconnects.     

Theorem Proving Guided Development of Formal Assertions in a Resource-Constrained Scheduler for High-Level Synthesis

This paper presents a formal specification and a proof of correctness for the widely-used Force-Directed List Scheduling (FDLS) algorithm for resource-constrained scheduling of data flow graphs in high-level synthesis systems. The proof effort is conducted using a higher-order logic theorem prover. During the proof effort many interesting properties of the FDLS algorithm are discovered. These properties are formally stated and proved in a higher-order logic theorem proving environment. These properties constitute a detailed set of formal assertions and invariants that should hold at various steps in the FDLS algorithm. They are then inserted as programming assertions in the implementation of the FDLS algorithm in a production-strength high-level synthesis system. When turned on, the programming assertions (1) certify whether a specific run of the FDLS algorithm produced correct schedules and, (2) in the event of failure, help discover and isolate programming errors in the FDLS implementation.We present a detailed example and several experiments to demonstrate the effectiveness of these assertions in discovering and isolating errors. Based on this experience, we discuss the role of the formal theorem proving exercise in developing a useful set of assertions for embedding in the scheduler code and argue that in the absence of such a formal proof checking effort, discovering such a useful set of assertions would have been an arduous if not impossible task.     

An Integrated Approach to Locality-Conscious Processor Allocation and Scheduling of Mixed-Parallel Applications

Complex parallel applications can often be modeled as directed acyclic graphs of coarse-grained application tasks with dependences. These applications exhibit both task and data parallelism, and combining these two (also called mixed parallelism) has been shown to be an effective model for their execution. In this paper, we present an algorithm to compute the appropriate mix of task and data parallelism required to minimize the parallel completion time (makespan) of these applications. In other words, our algorithm determines the set of tasks that should be run concurrently and the number of processors to be allocated to each task. The processor allocation and scheduling decisions are made in an integrated manner and are based on several factors such as the structure of the task graph, the runtime estimates and scalability characteristics of the tasks, and the intertask data communication volumes. A locality-conscious scheduling strategy is used to improve intertask data reuse. Evaluation through simulations and actual executions of task graphs derived from real applications and synthetic graphs shows that our algorithm consistently generates schedules with a lower makespan as compared to Critical Path Reduction (CPR) and Critical Path and Allocation (CPA), two previously proposed scheduling algorithms. Our algorithm also produces schedules that have a lower makespan than pure task- and data-parallel schedules. For task graphs with known optimal schedules or lower bounds on the makespan, our algorithm generates schedules that are closer to the optima than other scheduling approaches.