Matching Items (4)
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- All Subjects: Quantum Mechanics
- All Subjects: Machine Learning
- Status: Published
Description
In a pure spin current, electrons of opposite spins flow in opposite directions, thus information is conveyed by spin current without any charge current. This process almost causes no power consumption, which has the potential to realize ultra-low-power-consumption electronics. Recently, thermal effects in magnetic materials have attracted a great deal of attention because of its potential to generate pure spin currents using a thermal gradient (∇T), such as the spin Seebeck effect. However, unlike electric potential, the exact thermal gradient direction is experimentally difficult to control, which has already caused misinterpretation of the thermal effects in Py and Py/Pt films. In this work, we show that a well-defined ∇T can be created by two thermoelectric coolers (TECs) based on Peltier effect. The ∇T as well as its sign can be accurately controlled by the driven voltage on the TECs. Using a square-wave driven potential, thermal effects of a few μV can be measured. Using this technique, we have measured the anomalous Nernst effect in magnetic Co/Py and Py/Pt layers and determined their angular dependence. The angular dependence shows the same symmetry as the anomalous Hall effect in these films.
This work has been carried out under the guidance of the author’s thesis advisor, Professor Tingyong Chen.
This work has been carried out under the guidance of the author’s thesis advisor, Professor Tingyong Chen.
ContributorsSimaie, Salar (Author) / Chen, Tingyon (Thesis director) / Alizadeh, Iman (Committee member) / Barrett, The Honors College (Contributor) / Mechanical and Aerospace Engineering Program (Contributor) / Department of Physics (Contributor)
Created2015-05
Description
The longstanding issue of how much time it takes a particle to tunnel through quantum barriers is discussed; in particular, the phenomenon known as the Hartman effect is reviewed. A calculation of the dwell time for two successive rectangular barriers in the opaque limit is given and the result depends on the barrier widths and hence does not lead to superluminal tunneling or the Hartman effect.
ContributorsMcDonald, Scott (Author) / Davies, Paul (Thesis director) / Comfort, Joseph (Committee member) / McCartney, M. R. (Committee member) / Barrett, The Honors College (Contributor)
Created2009-05
Description
This thesis attempts to explain Everettian quantum mechanics from the ground up, such that those with little to no experience in quantum physics can understand it. First, we introduce the history of quantum theory, and some concepts that make up the framework of quantum physics. Through these concepts, we reveal why interpretations are necessary to map the quantum world onto our classical world. We then introduce the Copenhagen interpretation, and how many-worlds differs from it. From there, we dive into the concepts of entanglement and decoherence, explaining how worlds branch in an Everettian universe, and how an Everettian universe can appear as our classical observed world. From there, we attempt to answer common questions about many-worlds and discuss whether there are philosophical ramifications to believing such a theory. Finally, we look at whether the many-worlds interpretation can be proven, and why one might choose to believe it.
ContributorsSecrest, Micah (Author) / Foy, Joseph (Thesis director) / Hines, Taylor (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
Description
Scientific research encompasses a variety of objectives, including measurement, making predictions, identifying laws, and more. The advent of advanced measurement technologies and computational methods has largely automated the processes of big data collection and prediction. However, the discovery of laws, particularly universal ones, still heavily relies on human intellect. Even with human intelligence, complex systems present a unique challenge in discerning the laws that govern them. Even the preliminary step, system description, poses a substantial challenge. Numerous metrics have been developed, but universally applicable laws remain elusive. Due to the cognitive limitations of human comprehension, a direct understanding of big data derived from complex systems is impractical. Therefore, simplification becomes essential for identifying hidden regularities, enabling scientists to abstract observations or draw connections with existing knowledge. As a result, the concept of macrostates -- simplified, lower-dimensional representations of high-dimensional systems -- proves to be indispensable. Macrostates serve a role beyond simplification. They are integral in deciphering reusable laws for complex systems. In physics, macrostates form the foundation for constructing laws and provide building blocks for studying relationships between quantities, rather than pursuing case-by-case analysis. Therefore, the concept of macrostates facilitates the discovery of regularities across various systems. Recognizing the importance of macrostates, I propose the relational macrostate theory and a machine learning framework, MacroNet, to identify macrostates and design microstates. The relational macrostate theory defines a macrostate based on the relationships between observations, enabling the abstraction from microscopic details. In MacroNet, I propose an architecture to encode microstates into macrostates, allowing for the sampling of microstates associated with a specific macrostate. My experiments on simulated systems demonstrate the effectiveness of this theory and method in identifying macrostates such as energy. Furthermore, I apply this theory and method to a complex chemical system, analyzing oil droplets with intricate movement patterns in a Petri dish, to answer the question, ``which combinations of parameters control which behavior?'' The macrostate theory allows me to identify a two-dimensional macrostate, establish a mapping between the chemical compound and the macrostate, and decipher the relationship between oil droplet patterns and the macrostate.
ContributorsZhang, Yanbo (Author) / Walker, Sara I (Thesis advisor) / Anbar, Ariel (Committee member) / Daniels, Bryan (Committee member) / Das, Jnaneshwar (Committee member) / Davies, Paul (Committee member) / Arizona State University (Publisher)
Created2023