Toward an ab Initio Description of Adsorbate Surface Dynamics

The advent of machine learning potentials (MLPs) provides a unique opportunity to access simulation time scales and to directly compute physicochemical properties that are typically intractable using density functional theory (DFT). In this study, we use an active learning curriculum to train a generalizable MLP using the DeepMD-kit architecture. By using sufficiently long MLP-based molecular dynamics (MD) simulations, which provide DFT-level accuracy, we investigate the diffusion of key surface-bound adsorbates on a Ag(111) facet. Detailed analysis of the MLP/MD-calculated diffusivities sheds light on the potential shortcomings of using DFT-based nudged elastic band to estimate surface diffusion barriers. More generally, while this study is focused on a specific system, we anticipate that the underlying workflows and the resulting models can be extended to other adsorbates and other materials in the future

Assessing the Performance of 1D-Convolution Neural Networks to Predict Concentration of Mixture Components from Raman Spectra

An emerging application of Raman spectroscopy is monitoring the state of chemical reactors during biologic drug production. Raman shift intensities scale linearly with the concentrations of chemical species and thus can be used to analytically determine real-time concentrations using non-destructive light irradiation in a label-free manner. Chemometric algorithms are used to interpret Raman spectra produced from complex mixtures of bioreactor contents as a reaction evolves. Finding the optimal algorithm for a specific bioreactor environment is challenging due to the lack of freely available Raman mixture datasets. The RaMix Python package addresses this challenge by enabling the generation of synthetic Raman mixture datasets with controllable noise levels to assess the utility of different chemometric algorithm types for real-time monitoring applications. To demonstrate the capabilities of this package and compare the performance of different chemometric algorithms, 48 datasets of simulated spectra were generated using the RaMix Python package. The four tested algorithms include partial least squares regression (PLS), a simple neural network, a simple convolutional neural network (simple CNN), and a 1D convolutional neural network with a ResNet architecture (ResNet). The performance of the PLS and simple CNN model was found to be comparable, with the PLS algorithm slightly outperforming the other models on 83\% of the data sets. The simple CNN model outperforms the other models on large, high noise datasets, demonstrating the superior capability of convolutional neural networks compared to PLS in analyzing noisy spectra. These results demonstrate the promise of CNNs to automatically extract concentration information from unprocessed, noisy spectra, allowing for better process control of industrial drug production. Code for this project is available at github.com/DexterAntonio/RaMix.Comment: 7 pages, 7 figure

CMAS Reactive Coatings for TBCs

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Engineered Nanostructures for High Thermal Conductivity Substrates

In the DARPA Thermal Ground Plane (TGP) program[1],we are developing a new thermal technology that will enable a monumental thermal technological leap to an entirely new class of electronics, particularly electronics for use in high-tech military systems. The proposed TGP is a planar, thermal expansion matched heat spreader that is capable of moving heat from multiple chips to a remote thermal sink. DARPA’s final goals require the TGP to have an effective conductivity of 20,000 W/mK, operate at 20g, with minimal fluid loss of less than 0.1%/year and in a large ultra-thin planar package of 10cmx20cm, no thicker than 1mm. The proposed TGP is based on a heat pipe architecture[2], whereby the enhanced transport of heat is made possible by applying nanoengineered surfaces to the evaporator, wick, and condenser surfaces. Ultra-low thermal resistances are engineered using superhydrophilic and superhydrophobic nanostructures on the interior surfaces of the TGP envelope. The final TGP design will be easily integrated into existing printed circuit board manufacturing technology. In this paper, we present the transport design, fabrication and packaging techniques, and finally a novel fluorescence imaging technique to visualize the capillary flow in these nanostructured wicks.United States. Defense Advanced Research Projects Agency (SSC SD Contract No. N66001-08-C-2008

Atomistic Characterization and Continuum Modeling of Novel Thermomechanical Behaviors of Zinc Oxide Nanostructures

ZnO nanowires and nanorods are a new class of one-dimensional nanomaterials with a wide range of applications in NEMS. The motivation for this work stems from the lack of understanding and characterization of their thermomechanical behaviors essential for their incorporation in nanosystems. The overall goal of this work is to develop a fundamental understanding of the mechanisms controlling the responses of these nanostructures with focus on: (1) development of a molecular dynamics based framework for analyzing thermomechanical behaviors, (2) characterization of the thermal and mechanical behaviors in ZnO nanowires and (3) development of models for pseudoelasticity and thermal conductivity. The thermal response analyses show that the values of thermal conductivity are one order of magnitude lower than that for bulk ZnO due to surface scattering of phonons. A modified equation for phonon radiative transport incorporating the effects of surface scattering is used to model the thermal conductivity as a function of wire size and temperature. Quasistatic tensile loading of wires show that the elastic moduli values are 68.2-27.8% higher than that for bulk ZnO. Previously unknown phase transformations from the initial wurtzite (WZ) structure to graphitic (HX) and body-centered-tetragonal (BCT-4) phases are discovered in nanowires which lead to a more complete understanding of the extent of polymorphism in ZnO and its dependence on load triaxiality. The reversibility of the WZ-to-HX transform gives rise to a novel pseudoelastic behavior with recoverable strains up to 16%. A micromechanical continuum model is developed to capture the major characteristics of the pseudoelastic behavior accounting for size and temperature effects. The effect of the phase transformations on the thermal properties is characterized. Results obtained show that the WZ→HX phase transformation causes a novel transition in thermal response with the conductivity of HX wires being 20.5-28.5% higher than that of the initial WZ-structured wires. The results obtained here can provide guidance and criteria for the design and fabrication of a range of new building blocks for nanometer-scale devices that rely on thermomechanical responses.Ph.D.Committee Chair: Zhou, Min; Committee Member: Gall, Kenneth; Committee Member: Graham, Samuel; Committee Member: Limpijumnong, Sukit; Committee Member: Qu, Jianmin; Committee Member: Thadhani, Nares

Development of an EV drivetrain for a small car

Electrical vehicles (EVs) have a significant role in reducing transportation emissions and dependence on fossil fuels. This research has focused on energy efficient in-wheel switch reluctance motor (SRM) based drivetrain for a small car. The mechanical design optimisation and performance analyses have been conducted using finite element, virtual and augmented reality methods to develop high power density motor, light weight rim, light weight brake, ride comfortable suspension, and improved vehicle handling. The newly developed in-wheel SRM drivetrain is expected to be 75-80% efficient compared with a conventional EV drivetrain efficiency of 55-60%

Multiscale modeling of nanoporous materials for adsorptive separations

The detrimental effects of rising CO₂ levels on the global climate have made carbon abatement technologies one of the most widely researched areas of recent times. In this thesis, we first present a techno-economic analysis of a novel approach to directly capture CO₂ from air (Air Capture) using highly selective adsorbents. Our process modeling calculations suggest that the monetary cost of Air Capture can be reduced significantly by identifying adsorbents that have high capacities and optimum heats of adsorption. The search for the best performing material is not limited to Air Capture, but is generally applicable for any adsorption-based separation. Recently, a new class of nanoporous materials, Metal-Organic Frameworks (MOFs), have been widely studied using both experimental and computational techniques. In this thesis, we use a combined quantum chemistry and classical simulations approach to predict macroscopic properties of MOFs. Specifically, we describe a systematic procedure for developing classical force fields that accurately represent hydrocarbon interactions with the MIL-series of MOFs using Density Functional Theory (DFT) calculations. We show that this force field development technique is easily extended for screening a large number of complex open metal site MOFs for various olefin/paraffin separations. Finally, we demonstrate the capability of DFT for predicting MOF topologies by studying the effect of ligand functionalization during CuBTC synthesis. This thesis highlights the versatility and opportunities of using multiscale modeling approach that combines process modeling, classical simulations and quantum chemistry calculations to study nanoporous materials for adsorptive separations.Ph.D

Design of materials by microstructural optimization

In applications where the performance of engineered systems may be limited by the properties and performance of the materials, substantial improvement can be achieved by developing a design methodology to synthesize the optimal microstructure that will satisfy macroscopic user defined design criteria. Developing such a systematic design procedure entails the development of models or simulations tor expressing the relationships between the microstructure and macroscopic properties. Such correlations can then serve as inputs to determine the optimal microstructure. To develop the vision of design of materials by microstructural optimization, the first paper presents a methodology to tailor the microstructure of alloys. A genetic algorithm is used to optimize the microstructure of an Al-Mg-Sc-Zr alloy to satisfy user defined requirements on low temperature strength, ductility and high temperature strength. In the second paper, the focus is on simulating the motion of dislocation using known dislocation-particle interaction physics. Particle size distribution effects, neglected in the theoretical strength expressions, are considered in the simulation. Shear stress results are presented for an Al-Mg-Sc-Zr alloy and compared with analytical values. Efforts, such as those considered in the study, aimed at resolving the challenge of converting from a deductive cause/effect approach to inductive goal based approach will be of much practical value to materials developers and system designers. Advanced materials can be developed in significantly shorter time and at much lower cost by employing systematic design procedures instead of relying on heuristics --Abstract, page iv