Thermally driven spin injection from a ferromagnet into a non-magnetic metal

Creating, manipulating and detecting spin polarized carriers are the key elements of spin based electronics. Most practical devices use a perpendicular geometry in which the spin currents, describing the transport of spin angular momentum, are accompanied by charge currents. In recent years, new sources of pure spin currents, i.e., without charge currents, have been demonstrated and applied. In this paper, we demonstrate a conceptually new source of pure spin current driven by the flow of heat across a ferromagnetic/non-magnetic metal (FM/NM) interface. This spin current is generated because the Seebeck coefficient, which describes the generation of a voltage as a result of a temperature gradient, is spin dependent in a ferromagnet. For a detailed study of this new source of spins, it is measured in a non-local lateral geometry. We developed a 3D model that describes the heat, charge and spin transport in this geometry which allows us to quantify this process. We obtain a spin Seebeck coefficient for Permalloy of -3.8 microvolt/Kelvin demonstrating that thermally driven spin injection is a feasible alternative for electrical spin injection in, for example, spin transfer torque experiments

Observation of the inverse spin Hall effect in silicon

The spin–orbit interaction in a solid couples the spin of an electron to its momentum. This coupling gives rise to mutual conversion between spin and charge currents: the direct and inverse spin Hall effects. The spin Hall effects have been observed in metals and semiconductors. However, the spin/charge conversion has not been realized in one of the most fundamental semiconductors, silicon, where accessing the spin Hall effects has been believed to be difficult because of its very weak spin–orbit interaction. Here we report observation of the inverse spin Hall effect in silicon at room temperature. The spin/charge current conversion efficiency, the spin Hall angle, is obtained as 0.0001 for a p-type silicon film. In spite of the small spin Hall angle, we found a clear electric voltage due to the inverse spin Hall effect in the p-Si film, demonstrating that silicon can be used as a spin-current detector

Parameters influencing the size of chitosan-TPP nano- and microparticles

Chitosan nanoparticles, produced by ionic gelation, are among the most intensely studied nanosystems for drug delivery. However, a lack of inter-laboratory reproducibility and a poor physicochemical understanding of the process of particle formation have been slowing their potential market applications. To address these shortcomings, the current study presents a systematic analysis of the main polymer factors affecting the nanoparticle formation driven by an initial screening using systematic statistical Design of Experiments (DoE). In summary, we found that for a given chitosan to TPP molar ratio, the average hydrodynamic diameter of the particles formed is strongly dependent on the initial chitosan concentration. The degree of acetylation of the chitosan was found to be the second most important factor involved in the system's ability to form particles. Interestingly, viscosimetry studies indicated that the particle formation and the average hydrodynamic diameter of the particles formed were highly dependent on the presence or absence of salts in the medium. In conclusion, we found that by controlling two simple factors of the polymer solution, namely its initial concentration and its solvent environment, it is feasible to control in a reproducible manner the production and characteristics of chitosan particles ranging in size from nano- to micrometres

Spin-Dependent Transport in Fe/GaAs(100)/Fe Vertical Spin-Valves

The integration of magnetic materials with semiconductors will lead to the development of the next spintronics devices such as spin field effect transistor (SFET), which is capable of both data storage and processing. While the fabrication and transport studies of lateral SFET have attracted greatly attentions, there are only few studies of vertical devices, which may offer the opportunity for the future three-dimensional integration. Here, we provide evidence of two-terminal electrical spin injection and detection in Fe/GaAs/Fe vertical spin-valves (SVs) with the GaAs layer of 50 nanometers thick and top and bottom Fe electrodes deposited by molecular beam epitaxy. The spin-valve effect, which corresponds to the individual switching of the top and bottom Fe layers, is bias dependent and observed up to 20 K. We propose that the strongly bias-and temperature-dependent MR is associated with spin transport at the interfacial Fe/GaAs Schottky contacts and in the GaAs membranes, where balance between the barrier profiles as well as the dwell time to spin lifetime ratio are crucial factors for determining the device operations. The demonstration of the fabrication and spin injection in the vertical SV with a semiconductor interlayer is expected to open a new avenue in exploring the SFET

Nanoscale magnetic imaging of a single electron spin under ambient conditions

The detection of ensembles of spins under ambient conditions has revolutionized the biological, chemical and physical sciences through magnetic resonance imaging and nuclear magnetic resonance . Pushing sensing capabilities to the individual-spin level would enable unprecedented applications such as single-molecule structural imaging; however, the weak magnetic fields from single spins are undetectable by conventional far-field resonance techniques . In recent years, there has been a considerable effort to develop nanoscale scanning magnetometers , which are able to measure fewer spins by bringing the sensor in close proximity to its target. The most sensitive of these magnetometers generally require low temperatures for operation, but the ability to measure under ambient conditions (standard temperature and pressure) is critical for many imaging applications, particularly in biological systems. Here we demonstrate detection and nanoscale imaging of the magnetic field from an initialized single electron spin under ambient conditions using a scanning nitrogen-vacancy magnetometer. Real-space, quantitative magnetic-field images are obtained by deterministically scanning our nitrogen-vacancy magnetometer 50 nm above a target electron spin, while measuring the local magnetic field using dynamically decoupled magnetometry protocols. We discuss how this single-spin detection enables the study of a variety of room-temperature phenomena in condensed-matter physics with an unprecedented combination of spatial resolution and spin sensitivity

Combined artificial bee colony algorithm and machine learning techniques for prediction of online consumer repurchase intention

A novel paradigm in the service sector i.e. services through the web is a progressive mechanism for rendering offerings over diverse environments. Internet provides huge opportunities for companies to provide personalized online services to their customers. But prompt novel web services introduction may unfavorably affect the quality and user gratification. Subsequently, prediction of the consumer intention is of supreme importance in selecting the web services for an application. The aim of study is to predict online consumer repurchase intention and to achieve this objective a hybrid approach which a combination of machine learning techniques and Artificial Bee Colony (ABC) algorithm has been used. The study is divided into three phases. Initially, shopping mall and consumer characteristic’s for repurchase intention has been identified through extensive literature review. Secondly, ABC has been used to determine the feature selection of consumers’ characteristics and shopping malls’ attributes (with > 0.1 threshold value) for the prediction model. Finally, validation using K-fold cross has been employed to measure the best classification model robustness. The classification models viz., Decision Trees (C5.0), AdaBoost, Random Forest (RF), Support Vector Machine (SVM) and Neural Network (NN), are utilized for prediction of consumer purchase intention. Performance evaluation of identified models on training-testing partitions (70-30%) of the data set, shows that AdaBoost method outperforms other classification models with sensitivity and accuracy of 0.95 and 97.58% respectively, on testing data set. This study is a revolutionary attempt that considers both, shopping mall and consumer characteristics in examine the consumer purchase intention.N/

Analysis of the efficacy, safety, and regulatory status of novel forms of creatine

Creatine has become one of the most popular dietary supplements in the sports nutrition market. The form of creatine that has been most extensively studied and commonly used in dietary supplements is creatine monohydrate (CM). Studies have consistently indicated that CM supplementation increases muscle creatine and phosphocreatine concentrations by approximately 15–40%, enhances anaerobic exercise capacity, and increases training volume leading to greater gains in strength, power, and muscle mass. A number of potential therapeutic benefits have also been suggested in various clinical populations. Studies have indicated that CM is not degraded during normal digestion and that nearly 99% of orally ingested CM is either taken up by muscle or excreted in urine. Further, no medically significant side effects have been reported in literature. Nevertheless, supplement manufacturers have continually introduced newer forms of creatine into the marketplace. These newer forms have been purported to have better physical and chemical properties, bioavailability, efficacy, and/or safety profiles than CM. However, there is little to no evidence that any of the newer forms of creatine are more effective and/or safer than CM whether ingested alone and/or in combination with other nutrients. In addition, whereas the safety, efficacy, and regulatory status of CM is clearly defined in almost all global markets; the safety, efficacy, and regulatory status of other forms of creatine present in today’s marketplace as a dietary or food supplement is less clear

Combinatorial Effect of Non-Steroidal Anti-inflammatory Drugs and NF-κB Inhibitors in Ovarian Cancer Therapy

Several epidemiological studies have correlated the use of non-steroidal anti-inflammatory drugs (NSAID) with reduced risk of ovarian cancer, the most lethal gynecological cancer, diagnosed usually in late stages of the disease. We have previously established that the pro-apoptotic cytokine melanoma differentiation associated gene-7/Interleukin-24 (mda-7/IL-24) is a crucial mediator of NSAID-induced apoptosis in prostate, breast, renal and stomach cancer cells. In this report we evaluated various structurally different NSAIDs for their efficacies to induce apoptosis and mda-7/IL-24 expression in ovarian cancer cells. While several NSAIDs induced apoptosis, Sulindac Sulfide and Diclofenac most potently induced apoptosis and reduced tumor growth. A combination of these agents results in a synergistic effect. Furthermore, mda-7/IL-24 induction by NSAIDs is essential for programmed cell death, since inhibition of mda-7/IL-24 by small interfering RNA abrogates apoptosis. mda-7/IL-24 activation leads to upregulation of growth arrest and DNA damage inducible (GADD) 45 α and γ and JNK activation. The NF-κB family of transcription factors has been implicated in ovarian cancer development. We previously established NF-κB/IκB signaling as an essential step for cell survival in cancer cells and hypothesized that targeting NF-κB could potentiate NSAID-mediated apoptosis induction in ovarian cancer cells. Indeed, combining NSAID treatment with NF-κB inhibitors led to enhanced apoptosis induction. Our results indicate that inhibition of NF-κB in combination with activation of mda-7/IL-24 expression may lead to a new combinatorial therapy for ovarian cancer

Single-cell analysis tools for drug discovery and development

The genetic, functional or compositional heterogeneity of healthy and diseased tissues presents major challenges in drug discovery and development. Such heterogeneity hinders the design of accurate disease models and can confound the interpretation of biomarker levels and of patient responses to specific therapies. The complex nature of virtually all tissues has motivated the development of tools for single-cell genomic, transcriptomic and multiplex proteomic analyses. Here, we review these tools and assess their advantages and limitations. Emerging applications of single cell analysis tools in drug discovery and development, particularly in the field of oncology, are discussed