Spin-resolved magnetic studies of focused ion beam etched nano-sized magnetic structures

Scanning ion microscopy with polarization analysis (SIMPA) is used to study the spin-resolved surface magnetic structure of nano-sized magnetic systems. SIMPA is utilized for in situ topographic and spin-resolved magnetic domain imaging as well as for focused ion beam (FIB) etching of desired structures in magnetic or non-magnetic systems. Ultra-thin Co films are deposited on surfaces of Si(1 0 0) substrates, and ultra-thin, tri-layered, bct Fe(1 0 0)/Mn/bct Fe(1 0 0) wedged magnetic structures are deposited on fcc Pd(1 0 0) substrates. SIMPA experiments clearly show that ion-induced electrons emitted from magnetic surfaces exhibit non-zero electron spin polarization (ESP), whereas electrons emitted from non-magnetic surfaces such as Si and Pd exhibit zero ESP, which can be used to calibrate sputtering rates in situ. We report on new, spin-resolved magnetic microstructures, such as magnetic “C” states and magnetic vortices, found at surfaces of FIB patterned magnetic elements. It is found that FIB milling has a negligible effect on surface magnetic domain and domain wall structures. It is demonstrated that SIMPA can evolve into an important and efficient tool to study magnetic domain, domain wall and other structures as well as to perform magnetic depth profiling of magnetic nano-systems to be used in ultra-high density magnetic recording and in magnetic sensors.     

A Flexible Framework for Fault Tolerance in the Grid

This paper presents a failure detection service (FDS) and a flexible failure handling framework (Grid-WFS) as a fault tolerance mechanism on the Grid. The FDS enables the detection of both task crashes and user-defined exceptions. A major challenge in providing such a generic failure detection service on the Grid is to detect those failures without requiring any modification to both the Grid protocol and the local policy of each Grid node. This paper describes how to overcome the challenge by using a notification mechanism which is based on the interpretation of notification messages being delivered from the underlying Grid resources. The Grid-WFS built on top of FDS allows users to achieve failure recovery in a variety of ways depending on the requirements and constraints of their applications. Central to the framework is flexibility in handling failures. This paper describes how to achieve the flexibility by the use of workflow structure as a high-level recovery policy specification, which enables support for multiple failure recovery techniques, the separation of failure handling strategies from the application code, and user-defined exception handlings. Finally, this paper presents an experimental evaluation of the Grid-WFS using a simulation, demonstrating the value of supporting multiple failure recovery techniques in Grid applications to achieve high performance in the presence of failures.     

Electrodiffusion Model Simulation of Ionic Channels: 1D Simulations

The drift-diffusion (Poisson-Nernst-Planck) model is applied to ionic channels in biological membranes plus surrounding solution baths. Simulations of the K channel in KCl solutions using the TRBDF2 method are presented which show significant boundary layers at the ends of the channel. The computed current-voltage curve for the K channel shows excellent agreement with experimental measurements.     

Solution of a Well-Field Design Problem with Implicit Filtering

Problems involving the management of groundwater resources occur routinely, and management decisions based upon optimization approaches offer the potential to save substantial amounts of money. However, this class of application is notoriously difficult to solve due to non-convex objective functions with multiple local minima and both nonlinear models and nonlinear constraints. We solve a subset of community test problems from this application field using MODFLOW, a standard groundwater flow model, and IFFCO, an implicit filtering algorithm that was designed to solve problems similar to those of focus in this work. While sampling methods have received only scant attention in the groundwater optimization literature, we show encouraging results that suggest they are deserving of more widespread consideration for this class of problems. In keeping with our objectives for the community problems, we have packaged the approaches used in this work to facilitate additional work on these problems by others and the application of implicit filtering to other problems in this field. We provide the data for our formulation and solution on the web.     

Automated Quality Assessment of Stone Aggregates Based on Laser Imaging and a Neural Network

An automated quality assessment technique is proposed for rapidly detecting excessive size variations during the production of stone aggregates. The system uses a laser profiler to scan collections of aggregate particles and obtain three-dimensional data points on the particle surfaces. For computational efficiency, the resulting data are converted into digital images. Wavelet transforms are then applied to the images to extract features indicative of the material gradation. These wavelet-based features are used as inputs to an artificial neural network, which is trained to classify the aggregate sample. Taken together, these components form a neural network-based classification system that can determine whether or not an aggregate product is in compliance with a given specification. Verification tests show that this approach could potentially help to determine, in an accurate and fast (real-time) manner, when adjustments or repairs to the production equipment are needed.     

A general compartment‐based population balance model for particle coating and layered granulation

In this work, a general multidimensional population balance (PB) model is developed to predict the coating volume distribution on polydisperse particles as a function of time during particle coating in a paddle mixer. The model adopts a compartmental approach to account for coating variation caused by particle flow heterogeneity. Simulations show that for a realistic range of seed particle size polydispersity and coating mass applied, the coating volume distribution depends on the growth rate exponent and seed particle size distribute on, with the coating volume coefficient of variance (CoV) approaching an asymptotic value as the coating‐to‐particle volume ratio increases. These effects cannot be predicted by simpler one‐dimensional models. However, the full two‐dimensional PB and simpler one‐dimensional models such as Mann's equation do predict similar sensitivity of coating volume CoV to the variation in the compartment model parameters, i.e., to changes in the particle mixing behavior in the vessel. © 2011 American Institute of Chemical Engineers AIChE J, 2012     

Editorial: Biodegradable Materials

This Special Issue “Biodegradable Materials” features research and review papers concerning recent advances on the development, synthesis, testing and characterisation of biomaterials. These biomaterials, derived from natural and renewable sources, offer a potential alternative to existing non-biodegradable materials with application to the food and biomedical industries amongst many others. In this Special Issue, the work is expanded to include the combined use of fillers that can enhance the properties of biomaterials prepared as films. The future application of these biomaterials could have an impact not only at the economic level, but also for the improvement of the environment.     

Evaluation of Agronomic and Seed Characteristics in Elevated Oleic Acid Soybean Lines in the South-Eastern US

Increasing oleic acid, a monounsaturated fatty acid, is reported to strike the best balance between cold flow properties and oxidative stability in soybean seed oil to enhance biodiesel and produce a better burning fuel. In addition, it is important that elevated oleic acid soybeans have the agronomic traits of local cultivars and maintain oleic acid stability across environments. Research was conducted in 2007–2008 to evaluate six Roundup Ready^® soybean recombinant inbred lines exhibiting enhanced levels of oleic acid. The six elevated oleic lines averaged a 55% increase in oleic acid and a 43% decrease in linolenic acid over the two commercial cultivars (AG3906 and AG4103). Some elevated oleic acid genotypes fulfilled the linear regression definition of a stable genotype. TN03-93RR was the best genotype because of its oleic acid content (397 g kg^?1) and desirable regression estimates for stability. Iodine value (IV), peroxide value (PV), and induction period (IP) were used to evaluate the fuel properties of the two lines with the highest oleic acid content and the two commercial cultivars. The elevated oleic acid lines had significantly better IP, PV and IV for better biodiesel properties and oxidative stability than the two commercial cultivars.