Synthesis of magnetic nanofibers using femtosecond laser material processing in air

In this study, we report formation of weblike fibrous nanostructure and nanoparticles of magnetic neodymium-iron-boron (NdFeB) via femtosecond laser radiation at MHz pulse repetition frequency in air at atmospheric pressure. Scanning electron microscopy (SEM) analysis revealed that the nanostructure is formed due to aggregation of polycrystalline nanoparticles of the respective constituent materials. The nanofibers diameter varies between 30 and 70 nm and they are mixed with nanoparticles. The effect of pulse to pulse separation rate on the size of the magnetic fibrous structure and the magnetic strength was reported. X-ray diffraction (XRD) analysis revealed metallic and oxide phases in the nanostructure. The growth of magnetic nanostructure is highly recommended for the applications of magnetic devices like biosensors and the results suggest that the pulsed-laser method is a promising technique for growing nanocrystalline magnetic nanofibers and nanoparticles for biomedical applications.     

Development of high quantum efficiency CdS/ZnS core/shell structured photocatalyst for the enhanced solar hydrogen evolution

Development of co-catalyst free, core/shell structured photocatalyst with ultra-thin shell is of great importance towards the stable and continuous hydrogen (H₂) production, where the shell prevents photo-corrosion of the core for longer stability with continuous H₂ generation. Accordingly, herein, we report a one-step, surfactant free hydrothermal process for synthesis of high-efficient CdS/ZnS core/shell structured catalyst for H₂ evolution under natural solar light. The structural and morphological characterizations using XRD and TEM techniques revealed the formation of phase pure CdS/ZnS system, with core and shell thickness of 395 and 15 nm, respectively. XPS studies revealed that the constituted elements in system exist in their native oxidation states, which indicated the stable structural integrity of the individual phase in the core/shell structure. The synergistic optical properties of CdS/ZnS showed the absorption edge around 500 nm and the decreased PL intensity indicated the improved charge recombination resistance in the system. The parametric studies such as synthesis time, core diameters and shell thickness optimization were conducted to study the formation kinetics of the core/shell structure and their photocatalytic efficiencies. Accordingly, the optimized core/shell catalyst showed around 763 and 2.4 folds superior activity when compared to the pristine CdS and ZnS, respectively. Further, the catalyst showed excellent stability for over 100 h with quantum efficiency of 8.78% under the irradiation of 20 W LED light at 420 nm. Based on the obtained results, the observed improved photocatalytic quantum efficiency could be ascribed to their synergistic effects of CdS and ZnS towards increased charge separation and spatial distributions of the carriers due to their core/shell configuration of the materials.     

Scanning laser differential-heterodyne interferometer for flying-height measurement

With conventional optical interferometry flying-height testing, a stationary measurement beam and a two-axis moving stage are used to measure slider-disk spacing at different points on the slider. Pitch angle or roll angle is calculated on the basis of the measurement results. We report on a scanning differential-heterodyne interferometer, which measures the continuous flying-height variation along the edge of a slider with two continuously scanning laser beams. Pitch angle or roll angle can be obtained directly from the scanning measurement. The system can also measure points individually to obtain the absolute flying height at different locations on the slider. Experiments were performed to demonstrate the concept of scanning measurement. The flying-height variation along the slider edge was measured by continuous scan and by point-to-point moving. The measurement results from continuous scan coincided with those of conventional methods.     

Proteomics of hosts and pathogens in cystic fibrosis

Cystic fibrosis (CF) is a congenital disease that results in great morbidity and mortality mainly in the Caucasian population. Although CF is a monogenic disease caused by mutation in the CF conductance transmembrane regulator (CFTR) gene, most of the related mortality can be attributed to infection mediated by opportunistic bacterial and fungal pathogens. Over the past decade, advancements in the field of proteomics have helped to gain insight into the repertoire of host and pathogen proteins involved in CF pathophysiology. This review provides an overview of the contributions of proteomic studies in advancing our knowledge of the biology of CF and disease progression associated with pathogen infection and host defense responses.     

Backstepping Approach for Controlling a Quadrotor Using Lagrange Form Dynamics

The dynamics of a quadrotor are a simplified form of helicopter dynamics that exhibit the same basic problems of underactuation, strong coupling, multi-input/multi-output design, and unknown nonlinearities. Control design for the quadrotor is more tractable yet reveals corresponding approaches for helicopter and UAV control design. In this paper, a backstepping approach is used for quadrotor controller design. In contrast to most other approaches, we apply backstepping on the Lagrangian form of the dynamics, not the state space form. This is complicated by the fact that the Lagrangian form for the position dynamics is bilinear in the controls. We confront this problem by using an inverse kinematics solution akin to that used in robotics. In addition, two neural nets are introduced to estimate the aerodynamic components, one for aerodynamic forces and one for aerodynamic moments. The result is a controller of intuitively appealing structure having an outer kinematics loop for position control and an inner dynamics loop for attitude control. The control approach described in this paper is robust since it explicitly deals with unmodeled state-dependent disturbances and forces without needing any prior knowledge of the same. A simulation study validates the results obtained in the paper.     

A statistical learning theory approach for uncertain linear and bilinear matrix inequalities

In this paper, we consider the problem of minimizing a linear functional subject to uncertain linear and bilinear matrix inequalities, which depend in a possibly nonlinear way on a vector of uncertain parameters. Motivated by recent results in statistical learning theory, we show that probabilistic guaranteed solutions can be obtained by means of randomized algorithms. In particular, we show that the Vapnik–Chervonenkis dimension (VC-dimension) of the two problems is finite, and we compute upper bounds on it. In turn, these bounds allow us to derive explicitly the sample complexity of these problems. Using these bounds, in the second part of the paper, we derive a sequential scheme, based on a sequence of optimization and validation steps. The algorithm is on the same lines of recent schemes proposed for similar problems, but improves both in terms of complexity and generality. The effectiveness of this approach is shown using a linear model of a robot manipulator subject to uncertain parameters.