Production of fine-grained particles

A method of producing particles having nano-sized grains comprises the steps of: (a) prepg. a soln. contg. one or more metal cations; (b) mixing the soln. from step (a) with one or more surfactants to form a surfactant/liq. mixt. and (c) heating the mixt. from step (b) above to form the particles. [on SciFinder(R)]

Production of fine-grained particles

A method of producing particles having nano-sized grains comprises the steps of: (a) prepg. a soln. contg. one or more metal cations; (b) mixing the soln. from step (a) with one or more surfactants to form a surfactant/liq. mixt. and (c) heating the mixt. from step (b) above to form the particles. [on SciFinder(R)]

Ultra-fine Grain Materials by Severe Plastic Deformation: Application to Steels

Severe plastic deformation techniques are known to produce grain sizes up to submicron level. This leads to conventional Hall-Petch strengthening of the as-processed materials. In addition, the microstructures of severe plastic deformation processed materials are characterized by relatively lower dislocation density compared to the conventionally processed materials subjected to the same amount of strain. These two aspects taken together lead to many important attributes. Some examples are ultra-high yield and fracture strengths, superplastic formability at lower temperatures and higher strain rates, superior wear resistance, improved high cycle fatigue life. Since these processes are associated with large amount of strain, depending on the strain path, characteristic crystallographic textures develop. In the present paper, a detailed account of underlying mechanisms during SPD has been discussed and processing-microstructure-texture-property relationship has been presented with reference to a few varieties of steels that have been investigated till date.

Genetic model of the hydrate system in the fine grain sediments in the northern continental slope of South China Sea

Gas hydrate samples were obtained firstly in China by drilling on the northern margin of South China Sea (SCS). To understand the formation mechanism of this unique accumulation system, this paper discusses the factors controlling the formation of the system by accurate geophysical interpretation and geological analysis, based on the high precision 2-D and 3-D multichannel seismic data in the drilling area. There are three key factors controlling the accumulation of the gas hydrate system in fine grain sediment: (1) large volume of fluid bearing methane gas Joins the formation of gas hydrate. Active fluid flow in the northern South China Sea makes both thermal gas and/or biogenic gas migrate into shallow strata and form hydrate in the gas hydrate stability zone (GHSZ). The fluid flow includes mud diapir and gas chimney structure. They are commonly characterized by positive topographic relief, acoustic turbidity and push-down, and low reflection intensity on seismic profiles. The gas chimneys can reach to GHSZ, which favors the development of BSRs. It means that the active fluid flow has a close relationship with the formation and accumulation of gas hydrate. (2) The episodic process of fracture plays an important role in the generation of gas hydrate. It may provide the passage along which thermogenic or biogenic gas migrated into gas hydrate stability zone (GHSZ) upward. And it increases the pore space for the growth of hydrate crystal. (3) Submarine landslide induced the anomalous overpressure activity and development of fracture in the GHSZ. The formation model of high concentration gas hydrate in the drilling sea area was proposed on the basis of above analysis.

ALEA: Fine-Grain Energy Profiling with Basic Block Sampling

Energy efficiency is an essential requirement for all contemporary computing systems. We thus need tools to measure the energy consumption of computing systems and to understand how workloads affect it. Significant recent research effort has targeted direct power measurements on production computing systems using on-board sensors or external instruments. These direct methods have in turn guided studies of software techniques to reduce energy consumption via workload allocation and scaling. Unfortunately, direct energy measurements are hampered by the low power sampling frequency of power sensors. The coarse granularity of power sensing limits our understanding of how power is allocated in systems and our ability to optimize energy efficiency via workload allocation.
We present ALEA, a tool to measure power and energy consumption at the granularity of basic blocks, using a probabilistic approach. ALEA provides fine-grained energy profiling via sta- tistical sampling, which overcomes the limitations of power sens- ing instruments. Compared to state-of-the-art energy measurement tools, ALEA provides finer granularity without sacrificing accuracy. ALEA achieves low overhead energy measurements with mean error rates between 1.4% and 3.5% in 14 sequential and paral- lel benchmarks tested on both Intel and ARM platforms. The sampling method caps execution time overhead at approximately 1%. ALEA is thus suitable for online energy monitoring and optimization. Finally, ALEA is a user-space tool with a portable, machine-independent sampling method. We demonstrate two use cases of ALEA, where we reduce the energy consumption of a k-means computational kernel by 37% and an ocean modelling code by 33%, compared to high-performance execution baselines, by varying the power optimization strategy between basic blocks.