Browsing by Subject "Computation"

Now showing 1 - 5 of 5
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    Computational investigation of nucleic acids.
    (2009-08) Moser, Adam Thomas
    In this work, various computational chemistry models are applied to problems of biochemical interest, with emphasis on nucleic acids. First various density functionals and multilevel methods are benchmarked against experimental proton affinities and gas-phase basicities. Then prediction of biologically relevant values of nucleic acids, amino acids, RNA sugar, and phosphates are made. In applied work, density functional theory is employed to help elucidate topics in lesion formation in nucleic acids. In particular the role of C5 methyl cytosine substitution is investigated. through the use of various analogues and explaining NMR spectra of specific adenine lesions formed by 1,2,3,4-diepoxybutane. Finally, two works related to parameterization are given. The first is CHARMM molecular mechanical force field parameter development for the reactive intermediates of native and thio-substituted ribozymes. This work provides modifications necessary to reproduce structural aspects of transition state structures during phosphate transesterification. The second is an investigation into the appropriate solvation free energy for phosphoric acid and its anions. This includes both a review of the currently used and available data as well as a benchmark of various computational solvation models.
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    E-points for diagonal games II
    (University of Minnesota. Institute for Mathematics and Its Applications, 2008-04) Marchi, Ezio
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    A Framework for Computing Discrete-Time Systems and Functions using DNA
    (2017-07) Salehi, sayed ahmad
    Due to the recent advances in the field of synthetic biology, molecular computing has emerged as a non-conventional computing technology. A broad range of computational processes has been considered for molecular implementation. In this dissertation, we investigate the development of molecular systems for performing the following computations: signal processing, Markov chains, polynomials, and mathematical functions. First, we present a \textit{fully asynchronous} framework to design molecular signal processing algorithms. The framework maps each delay unit to two molecular types, i.e., first-type and second-type, and provides a 4-phase scheme to synchronize data flow for any multi-input/multi-output signal processing system. In the first phase, the input signal and values stored in all delay elements are consumed for computations. Results of computations are stored in the first-type molecules corresponding to the delay units and output variables. During the second phase, the values of the first-type molecules are transferred to the second-type molecules for the output variable. In the third phase, the concentrations of the first-type molecules are transferred to the second-type molecules associated with each delay element. Finally, in the fourth phase, the output molecules are collected. The method is illustrated by synthesizing a simple finite-impulse response (FIR) filter, an infinite-impulse response (IIR) filter, and an 8-point real-valued fast Fourier transform (FFT). The simulation results show that the proposed framework provides faster molecular signal processing systems compared to prior frameworks. We then present an overview of how continuous-time, discrete-time and digital signal processing systems can be implemented using molecular reactions. We also present molecular sensing systems where molecular reactions are used to implement analog-to-digital converters (ADCs) and digital-to-analog converters (DACs). These converters can be used to design mixed-signal processing molecular systems. A complete example of the addition of two molecules using digital implementation is described where the concentrations of two molecules are converted to digital by two 3-bit ADCs, and the 4-bit output of the digital adder is converted to analog by a 4-bit DAC. Furthermore, we describe implementation of other forms of molecular computation. We propose an approach to implement any first-order Markov chain using molecular reactions in general and DNA in particular. The Markov chain consists of two parts: a set of states and state transition probabilities. Each state is modeled by a unique molecular type, referred to as a data molecule. Each state transition is modeled by a unique molecular type, referred to as a control molecule, and a unique molecular reaction. Each reaction consumes data molecules of one state and produces data molecules of another state. The concentrations of control molecules are initialized according to the probabilities of corresponding state transitions in the chain. The steady-state probability of the Markov chain is computed by the equilibrium concentration of data molecules. We demonstrate our method for the Gambler’s Ruin problem as an instance of the Markov chain process. We analyze the method according to both the stochastic chemical kinetics and the mass-action kinetics model. Additionally, we propose a novel {\em unipolar molecular encoding} approach to compute a certain class of polynomials. In this molecular encoding, each variable is represented using two molecular types: a \mbox{type-0} and a \mbox{type-1}. The value is the ratio of the concentration of type-1 molecules to the sum of the concentrations of \mbox{type-0} and \mbox{type-1} molecules. With the new encoding, CRNs can compute any set of polynomial functions subject only to the limitation that these polynomials can be expressed as linear combinations of Bernstein basis polynomials with positive coefficients less than or equal to 1. The proposed encoding naturally exploits the expansion of a power-form polynomial into a Bernstein polynomial. We present molecular encoders for converting any input in a standard representation to the fractional representation, as well as decoders for converting the computed output from the fractional to a standard representation. Lastly, we expand the unipolar molecular encoding for bipolar molecular encoding and propose simple molecular circuits that can compute multiplication and scaled addition. Using these circuits, we design molecular circuits to compute more complex mathematical functions such as $e^{-x}$, $\sin (x)$, and sigmoid$(x)$. According to this approach, we implement a molecular perceptron as a simple artificial neural network.
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    Graphene Lateral Spin Valves For Computing And Magnetic Field Sensing Applications
    (2019-01) Hu, Jiaxi
    The current complementary metal–oxide–semiconductor (CMOS) technologies are facing greater-than-ever challenges as the Moore’s law approaches to its physical limits. The search for future electronic devices began decades ago. Spintronics, which utilizes the properties of electron spins, is indeed one of the most promising solutions for the beyond-CMOS era. Over the past years, spintronics has been very successful in Hard-disc drives (HDDs) and has significantly increased the storage areal-density. Recently, because of its built-in non-volatility, spintronics has also demonstrated its potential in memory applications. On the other hand, graphene, which is a monolayer of carbon atoms arranged in hexagonal order, is very attractive as the material for spin transport. For example, graphene has the longest spin diffusion length and spin lifetime at room temperature. Therefore, as the device that combines the unique properties from both sides, the graphene lateral spin valve can be useful in many applications. This dissertation mainly explores the use of graphene lateral spin valves for future computing and magnetic field sensing applications. This thesis firstly discusses the spin-circuit model, which is capable of simulating the dc, ac and transient behavior spintronic devices. Using the spin-circuit model, the scaling and energy consumption of all-spin logic devices is quantitatively studied. As one of the original proposals for spin-based computing, ASL utilizes lateral spin valves to process information in the spin domain. By using the physics-based spin-circuit model, the simulations suggest the effect of output-input isolation may be the fundamental challenges that prevent ASL from competing with CMOS in the scheme of conventional Boolean-computing. Next, this thesis explores the application of graphene lateral spin valves in non-Boolean computing and presents an implementation of spintronic Cellular Neural Networks (CNNs). In the graphene-based spintronic CNNs, weights are programmed as spin currents. Because of the tunable spin diffusion length in graphene, the weights can be controlled as local gate voltages, which can tune the weight values over a wide range. The simulation results show that the graphene-based spintronic CNNs have significantly improved scalability, particularly as the number and accuracy of synapses increases. In the last part of this thesis, the width scaling of graphene spin channels is experimentally studied, which is crucial for both the computing and magnetic field sensing applications. By using the graphene deposited by chemical vapor deposition (CVD) and a dedicated fabrication process, a large number of graphene lateral spin valves with consistent interface properties but different channel aspect ratios are fabricated on a single chip. The experimental results show that, as the channel width is scaled from 10 µm to 0.5 µm, the change in the nonlocal spin resistance matches the theory of contact-induced spin relaxation with the interface spin polarization, P, of 3 – 5 %, and spin diffusion length, λs, of 1.5 – 2.5 µm. Meanwhile, the spin-independent baseline resistance dramatically decreases due to the reduction in charge current spreading. However, we find that a remnant baseline remains due to the thermoelectric effects of graphene. By using the gate-voltage and bias-dependent analyses, we attribute the remnant baseline signal to the Joule-heating induced Seebeck voltage. These results suggest that in lateral spin valve design, to avoid any background signals, both the charge and thermal equilibrium conditions should be satisfied.
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    Mesoscopic Distinct Element Method for Computational Design of Carbon Nanotube Materials
    (2017-07) Wang, Yuezhou
    Carbon Nanotubes (CNTs) are hollow molecular cylinders conceptually formed by rolling single or multiple layers of graphene into tubes. CNT materials have become an attractive research subject during the last decades owning to the superior mechanical and electronic properties of individual CNTs. Developing applications, such as structural materials, supercapacitors, batteries or nanomechanical devices, depend on our ability to understand, model, and design the structure and properties of realistic CNT assemblies. Toward this goal, here we have applied a recently developed mesoscale computational method, titled the mesoscopic distinct element method (MDEM) that makes it possible to simulate the formation, stability, and mechanics of CNT aggregates and ultrathin CNT films. We first combine experiments and distinct element method simulations to understand the stability of rings and rackets formed by single-walled carbon nanotubes assembled into ropes. The obtained agreement validates MDEM and indicates that the stability of the experimental aggregates can be largely explained by the competition between bending and van der Waals adhesion energies. Next, we have considered the geometry and internal packing in twisted CNT ropes. Compared to the state of the art, MDEM accounts in a computationally tractable manner for both the deformation of the fiber and the distributed van der Waals cohesive energy between fibers. These features enable us to investigate the torsional response in a new regime where the twisted rope develops packing rearrangements and aspect-ratio-dependent geometric nonlinearities, in agreement with phenomenological models. Finally, we have performed MDEM simulations and developed an atomic-scale picture of the CNT network stress relaxation. On this basis, we put forward the concept of mesoscale design by the addition of excluded-volume interactions. Silicon nanoparticles are integrated into the model and the nanoparticle-filled networks present superior stability and mechanical response relative to those of pure films. The approach opens new possibilities for tuning the network microstructure in a manner that is compatible with flexible electronics applications. As a distinct direction, MDEM was explored for modeling the mechanics of nanocrystalline particles. Simulations that rely on the fitting of the peak stress, strain, and failure mode on the experimental testing of Au and CdS hollow nanocrystalline particles illustrate the promising potential of MDEM for bridging the atomistic-scale simulations with experimental testing data.