Bulletin of the American Physical Society
APS March Meeting 2023
Volume 68, Number 3
Las Vegas, Nevada (March 5-10)
Virtual (March 20-22); Time Zone: Pacific Time
Session D47: Undergraduate Research IVUndergrad Friendly
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Sponsoring Units: SPS Chair: Kayla Stephens, AIP Room: Room 313 |
Monday, March 6, 2023 3:00PM - 3:12PM |
D47.00001: Bioethanol production from banana and cassava agro-industrial wastes to obtain a third generation biofuel adaptable to a liquid/hybrid combustion rocket engine. Julio Alberto A Zermeño Pérez In recent years, ethanol production from the abundance of low-cost cellulose biomass or agricultural wastes has grown in importance, due to the hope of reducing the cost of ethanol production and benefiting the global environment. The application of the use of banana and cassava residues for ethanol production could be of great advantage to a country's economy; therefore, this study was conducted to determine the possibility of bioethanol production from banana and cassava peels as a cheaper source of bioethanol. |
Monday, March 6, 2023 3:12PM - 3:24PM |
D47.00002: Improving the efficiency of Tin-Based Perovskite using WxAMPS Venise Jan Castillon, Vincent De Castro, Paul Chionchio, Juan Echavez, Mehmet A Sahiner Perovskite solar cells (PSC), containing hybrid organic-inorganic layers, have shown promising properties that enhances the performance of thin films in photovoltaics. Lead (Pb)- based halide perovskites have made significant progress in improving the efficiency of metal halide perovskites to 25.5% in the recent years due to its unique optical properties and tolerance to defects in its crystallized structure. However, despite its promising properties, it contains lead which is intrinsically unstable and toxic. Due to this, tin- based perovskites have been investigated as an alternative to toxic lead perovskite, which can be attributed to its bandgap close to the optimal photovoltaic property. In this research, the efficiency and optical properties of tin-based (CH3NH3SnI3) perovskites using TiO2 ETL and Spiro:OMeTAD HTL will be explored using Widget Provided Analysis of Microelectronic and Photonic Structure (WxAMPS). The addition of other metals as a back contact electrode such as Pt, Au, and Ag, will also be evaluated. The optical layer properties including the thickness and deposition parameters will be varied to determine the optimum efficiency for the film. |
Monday, March 6, 2023 3:24PM - 3:36PM |
D47.00003: Development & optimization of low-cost TENGs using bioderived materials Noah Hann-Deschaine, Ramesh Adhikari Triboelectric nanogenerators (TENGs) allow for harvesting oft-overlooked sources of mechanical energy, albeit large-scale production of TENGs is currently impractical due in part to the inherent environmental cost of the electronic components. Rubber-based triboelectric nanogenerators (R-TENGs) are a welcome recourse; they address concerns intrinsic to their nonrenewable counterparts by replacing unsustainable materials with inexpensive, biodegradable, and ubiquitous ones such as rubber or paper. We investigated the effectiveness of contact-separation R-TENGs by considering several factors namely elapsed time, material combinations, frequency, electrode size, and configurations. The ideal configuration of the R-TENG was able to produce an open circuit voltage of 33 V, a short circuit current of 3.2 µA, and an effective capacitor charging rate for capacitors of different sizes. Such research provides both a framework for honing the utility of such devices, and evidence for the use of rubber and paper as viable alternatives to unsustainable, expensive materials. |
Monday, March 6, 2023 3:36PM - 3:48PM |
D47.00004: Use of a polarized light source to enhance machine learning based identification of graphene Alexander T Kanell, Sagar Bhandari The application of machine learning in physics research has been on the rise recently. Combining physics with machine learning has led to creating more efficient systems. We propose to use machine learning combined with polarization dependent optical properties of graphene to enhance the identification of monolayer graphene device. In graphene, the reflection of light depends on the polarization of light incident on it and the thickness of graphene. In this talk, we present our work on using retrofitted optical microscope in conjunction with a polarized light source with rotational degree of freedom for autonomous identification of graphene. The integrated system consists of motor control board, stepper motors, custom designed mounts for motor control and rotational control of the polarizing filters. The system collects reflected light from samples at various polarization angle and then automatically store it to a database for training the machine learning algorithm. The system will dictate the movement and lighting of the microscope based on the sample, find and log the locations of graphene and photograph it. The algorithms currently being implemented are object detection, image processing, and then data classification and storage. |
Monday, March 6, 2023 3:48PM - 4:00PM |
D47.00005: Integrating LASSO machine learning algorithm with LLG spin dynamics Alexander J Brady, Trinanjan Datta Machine learning, which is part of artificial intelligence, has become an invaluable tool to manipulate, analyze, predict, and reveal trends and associations hidden within big data. Machine learning algorithms build a mathematical model of sample data in order to make predictions or decisions, including recommending products in a search bar or discovering fundamental laws of physics. We apply the “Least Absolute Shrinkage and Selection Operator” (LASSO) method of data analysis which determines the relationship, or lack thereof, between variables, and allows for the removal of irrelevant noisy features. The LASSO module is implemented using Python’s sci-kit-learn. The method is first applied to a generic system of differential equations before showcasing its usage for a chaotic Lorenz oscillator and a 1D classical Heisenberg spin chain. For the chaotic system with synthetic Gaussian noisy data, LASSO successfully reproduces the clean Lorenz attractor solution. For the 1D spin chain, noisy data generated from unequilibrated Monte Carlo simulations were processed with LASSO. We show that the LASSO machine learning technique integrated with the LLG spin dynamics algorithm can remove irrelevant noise in spin dynamics simulations. |
Monday, March 6, 2023 4:00PM - 4:12PM |
D47.00006: Machine Learning to Predict Quasi TE011 Mode Resonance of Dielectric Resonators in Free Space Daniel J King, John S Colton Dielectric resonators, which are used to create standing waves in the microwave band, have been an important topic of research for decades due to their usefulness as antennae, filters, enhancing electron spin resonance, and a variety of other applications. For all these uses it is important to know the resonant frequency of a given resonator, but predicting this frequency is difficult, as only approximate formulas exist and performing precise numerical calculations to determine the frequency is computationally expensive. In previous work, we demonstrated that a neural network can be trained to accurately predict the quasi TE011 mode frequency and field modes of a pair of dielectric resonators inside a cylindrical microwave category, with the training data created for a variety of different configurations using the finite element method. Here, we have extended this method to similarly determine the frequency and field modes for the quasi TE011 mode for a single dielectric resonator in free space, by applying the appropriate modifications to our boundary conditions in our finite element modeling. |
Monday, March 6, 2023 4:12PM - 4:24PM |
D47.00007: Predictive Modeling of the Mechanical Properties in Triblock and Dual Triblock Copolymer Organogels Matthew Vallely, Kenneth P Mineart The purpose of our research is to predict the mechanical behavior of triblock and dual triblock organogels by developing a comprehensive model that is informed by a large collection of experimental mechanical property data. Block copolymer gels have applications in various sectors such as ballistics gels, model surgery materials, and are even used in transoceanic cable filler. The gels that are used in our experimentation and modeling are formulated with varying compositions of ABA triblock copolymers and aliphatic mineral oil. These gels either contain a single ABA triblock copolymer or a combination of two unique ones. We conduct quasi-static tensile tests on our formulated gels and produce nonlinear, elastic stress-extension curves. We then extract the crosslinked network and chain entanglement modulus contributions from the curves using the slip-tube network (STN) model. These modulus contributions help to describe the molecular structure within the gels, and are influenced by polymer molecular weight, "A"-block fraction, and polymer concentration. Using our experimental data and these formulation parameters, we have created an empirically-corrected STN model which allows us to predict stress-strain behavior for any triblock or dual triblock copolymer gels. |
Monday, March 6, 2023 4:24PM - 4:36PM |
D47.00008: Use Simple Pasco Experiments to Stimulate Computational Thinking Holden a Hankerson, Mina S Perez, Xiuping Tao
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Monday, March 6, 2023 4:36PM - 4:48PM |
D47.00009: Discovery of Copper-Catalyzed Click Chemistry Reaction Intermediates in Nanoreactor by Accelerated MD Shafat Mubin, Walker T Hayes, Todd J Martinez Click chemistry (Nobel Prize in Chemistry 2022) is characterized by small molecules acting as building blocks that can be joined to larger systems to build more complex molecules, with the copper-catalyzed azide-alkyne click reaction being the most prominent example. While catalysts are known to accelerate click reactions, the underlying reaction mechanisms and reaction intermediates have not been conclusively resolved. We approached this problem by constructing a first-principles based molecular dynamics simulation of the copper-catalyzed methylazide – propyne click reaction using the simulation platform Nanoreactor, which offers GPU-based capability in simulating dynamics and reaction events. Furthermore, the simulation was accelerated by an adaptive hyperdynamics algorithm to sample reactions over a larger time scale. By running the simulation at experimental conditions, we identified instances of reactions and subsequently identified species that could serve as reaction intermediates, including those not listed in literature. |
Monday, March 6, 2023 4:48PM - 5:00PM |
D47.00010: Distribution and scaling of complexity in Conway's Game of Life, and connections to nonequilibrium dynamics. Grennon J Gurney, Nathan L Harshman, Philip R Johnson We present results for the scaling properties of lifetimes as a function of grid size for Conway’s Game of Life (GOL). We discuss the implications for both fundamental questions in computational complexity and the connection to phase transitions including nonequilibrium dynamics for 2D Ising models. It is well-known that Turing complete cellular automata are capable of universal computing. We take the view of the initial state on a n x n GOL grid as a program and its evolution as a computation. Assuming periodic boundary conditions, the rules of GOL are invariant under both translations and the symmetries of a square. Consequently, initial states belong to equivalence classes which can each be identified with a unique shape, corresponding program, and computation. We implement Polya’s Enumeration Theorem to determine the number of symmetry equivalent states per shape (i.e. per program), and we analyze the statistical distribution of GOL evolution lifetimes over the set of unique shapes. We use a novel history-dependent definition of lifetime that treats the evolution as a non-Markovian process. |
Monday, March 6, 2023 5:00PM - 5:12PM |
D47.00011: Three-State Opinion Dynamics for Financial Markets on Complex Networks Mateus F. B Granha, Bernardo J Zubillaga Herrera, André L. M Vilela, Chao Wang, H E Stanley The recession due to the COVID-19 pandemic and the rise in inflation rates owing to the Ukraine war are current economic scenarios that demonstrate the fundamental impacts of collective behavior in the evolution of economic systems and financial markets. In this work, we propose to investigate the rich dynamics of financial markets by a heterogeneous agent-based opinion formation model. We divide the network of economic interactions into two sets of trader strategies, noise traders and fundamentalists. The former invests based on its local majority, whereas the latter acts based on the market index. Each agent is represented as a node in a complex network and may assume at a given time three distinct option states regarding buying, selling, or holding an asset. We investigate the model in several complex networks: random graphs, scale-free, and small-world networks. The model presents such fundamental qualitative and quantitative real-world market features as the distribution of logarithmic returns with fat-tails, clustered volatility, and long-term correlation of returns. We fit the histograms of logarithmic returns by using Student's t distributions, showing the gradual shift from a leptokurtic to a mesokurtic regime, depending on the fraction of fundamentalist agents. We also qualitatively compare our results with the distribution of logarithmic returns of several real-world financial indices. |
Monday, March 6, 2023 5:12PM - 5:24PM |
D47.00012: Critical behavior effects of Fractal Networks on the Majority-vote Model Dynamics Igor G Oliveira, André L M. Vilela, H. Eugene Stanley In this work, we investigate the impacts of Sierpinski fractal social networks with periodic boundary conditions in the dynamics and critical behavior of the two-state majority-vote model with noise. Here, each individual of a social community agrees with the majority of their neighbors with probability 1 – q and opposes them with the complementary chance q. The parameter q is the noise parameter and functions as a social temperature promoting the social disorder of the complex system. Similarly, the fractal dilution of the network of social interactions generates social-heat traps that favor nonconformist opinions against the majority of society. We perform Monte Carlo simulations to evaluate the order parameter or magnetization, the magnetic susceptibility, and the Binder fourth-order cumulant in three Sierpinski fractal networks with different sizes. We find that the model undergoes a second-order phase transition for a critical value of the noise parameter qc, which depends on the fractal structure. We estimate the critical social temperature parameter qc and the critical exponents β/ν, γ/ν and 1/ν of the majority-vote model on each fractal lattice. |
Monday, March 6, 2023 5:24PM - 5:36PM |
D47.00013: Majority-vote model with limited visibility on scale-free networks Giuliano G Porciúncula, André L M. Vilela, Luiz Felipe C. Pereira, H. Eugene Stanley Social internet networks are an essential part of humanity. With the advance of technology and the rise of algorithms and AI, content is now filtered systematically and facilitates the formation of filter bubbles. This work investigates the social influence under limited visibility in the two-state majority-vote model on scale-free networks. In the majority-vote evolution, each individual assimilates the opinion of the majority of their neighbors with probability 1−q. They also go against with chance q, known as the noise parameter. We define the visibility parameter V of an individual as the probability of considering the opinion of one of his neighbors. The parameter V enables us to model the limited visibility phenomenon in the dynamics of the majority-vote model. We build the social network of interactions starting from a fully connected network with z + 1 nodes. New nodes are connected to z neighbors with probability proportional to each node's connectivity until we reach N nodes. We employ Monte Carlo simulations to calculate the critical noise parameter as a function of the visibility V and the growth parameter z and obtain the phase diagram of the model. Applying finite-size scaling analysis, we find the critical exponents β/ν and γ/ν of the model associated with the magnetization and susceptibility and validate the unitary relation. |
Monday, March 6, 2023 5:36PM - 5:48PM |
D47.00014: Spectral Holography: Imaging Complex Systems with Broadband Sound Aashay R Pai, Matthew K Gronert, David G Grier Conventional holograms record the structure of scattered waves at many spatial positions, but just one frequency. These recordings encode information about the positions, sizes and material properties of the scatterers that can be extracted either by numerically reconstructing the three-dimensional wave or by analyzing the hologram with a generative model as an inverse problem. Much of the same information can be captured in the frequency dependence of the amplitude and phase of scattered waves recorded at a very small number of positions. While such spectral recording would be challenging in optical holography, it is a natural fit for acoustic holography. We introduce spectral holography by demonstrating Lorenz-Mie tracking and characterization of macroscopic objects with broadband sound. |
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