Polydimethylsiloxane (PDMS)/Carbon Nanofiber Nanocomposite with Piezoresistive Sensing Functions

Flexible material that can be deployed for sensing a wide range of pressure and strain is an active research area due to potential applications in engineering and biomedical devices. Current load sensing materials such as metals, semiconductor, and piezo ceramics have limitations in certain applications, due to their heavy density and small maximum measurable strain. In order to overcome those issues, this thesis delves into an alternative material class based on polydimethyl siloxane (PDMS) and carbon nanofiber (CNF) nanocomposites. Although silica and carbon nanoparticles have been traditionally used to reinforce mechanical properties in PDMS matrix nanocomposites, this study focuses on novel sensing systems with high sensitivity and wide load ranges. A series of nanocomposites with different CNF and silica concentrations were synthesized and characterized to understand their thermal, electrical, and sensing capabilities. The thermal properties, such as thermal stability and thermal diffusivity, of the developed nanocomposites were studied using thermogravimetirc and laser flash techniques, respectively. The electrical volume conductivity of each type of nanocomposite was measured using the four-probe method to eliminate the effects of contact electrical resistance during measurement. Scanning electron microscopy (SEM) was used at different length scales which showed uniform dispersion. Experimental results showed that both CNFs and silica were able to impact on the overall properties of the synthesized PDMS/CNF nanocomposites. The pressure sensing functions were achieved by correlating the piezoresistance variations of the materials to the applied load on the sensing area. Due to the conductive network formed by carbon nanofibers (CNFs) and the tunneling effect between neighboring CNFs, the experimental results showed a clear correlation between piezoresistance and the loading conditions. The proposed nanocomposite based sensor materials were experimentally characterized under both quasi-static and cyclic tensile and compressive loading conditions. The optimal nanocomposite formulation was identified by choosing materials with the highest sensing gauge factors under the required load ranges. The ideal material were employed to sense strain as high as 30% and pressures up to 50, 100, and 150 psi, which was a significant improvement compared to current off-the-shelf similar sensors. The sensing capability and sensitivity of the identified nanocomposites were further optimized using advanced optimization algorithms and finite element analysis method. Three different shapes including cylinder, conical, and truncated pyramid shaped sensing units were designed, fabricated, and characterized. Cyclic compression tests verified that the optimized sensor units enhanced the sensing capability by obtaining higher gauge factors. Finally, optimized sensing units were assembly in array forms for the continuous monitoring of pressure in a large area. The prototypes of sensor arrays successfully demonstrated their sensing capability under both static and cyclic pressure conditions in the desired pressure range

Mechanical behavior and strain engineering in thin film materials and interfaces

Thesis (Ph. D.)--University of Rochester. Department of Mechanical Engineering, 2023.Two dimensional (2D) materials are a class of layered material with merely a single to few atomic layers thickness. Due to the ultrathin structure and high surface-to-volume ratio, 2D materials have favorable mechanical properties such as large in-plane stiffness, high strength, and low flexural rigidity. Their weak interlayer interactions also allow fine control over stacking order and orientations of individual layers. Besides, they offer tunable electronic properties ranging from metallic to semiconducting and intensive optical emission at a broad wavelength from ultraviolet to near-infrared. Due to the strong relationship between mechanical and opto-electronic properties, they can be used to manufacture opto-electronic devices with unique characteristics where mechanical deformation is the driving factor. However, at the small length scales involved, many aspect of deformation mechanism, and impact of various interfaces are unknown. In this research, we aim to use multiscale computational modeling methods to reveal unknown mechanical behavior of a number of 2D materials and other types of thin films. We start with a single layer 2D material MoTe2 which offer superior properties, albeit little was known about their mechanical behavior. Using an evolutionary algorithm, an accurate interatomic potential for molecular dynamics simulation is developed. Based on structural analysis, and molecular dynamics simulation, pseudoelastic behavior is found to be their underlying deformation mechanism. Directional anisotropy with orientation of applied uniaxial deformation is found for both fracture and the pseudoelastic behavior. Besides the intrinsic deformation mechanism, strain can be engineered in 2D materials which are only weakly connected between layers. This allows strain to be applied locally to individual layers through other thin film interfaces. The layer by layer transfer of strain in different multilayered 2D materials are computationally predicted and validated by raman spectroscopy measurements. The length scale of strain propagation across layers are found to be material dependent and failure limit due to interlayer slippage is detected. Existence of different interfaces in thin film devices are unavoidable, and can even offer beneficial properties. Impacts of interfaces on the strain engineering applications are investigated with a closed-loop model based on density functional theory, microscale finite element analysis, and traction-separation law with no free-parameter. The model is applied to predict interface behavior consisting metallic thin film and substrate that is commonly used in 2D materials based nanoelectronics. The stress transfer through the interface is described with a shear traction separation law. The resultant large strain gradient in the substrate is shown to produce significant electric field polarization through flexoelectric effect and can be used in memory devices. As interfaces are an important aspect of thin film materials, a general continuum model for inhomogeneity interface is developed. The interlayer stacking of 2D materials provides an important control on their structure and properties. Twisted bilayer graphene, where two layers are rotated at an angle, shows structural variations at the large scale as well as well local atomic scale. Using atomistic simulations, local variations of interlayer stacking and atomic reconstructions even in twisted bilayer graphene is detected at large twist angle, which was previously beyond the experimental limit. By combining twist angle and applied strain, the atomic reconstructions is shown to be magnified. Finally, using a framework based on slip vector analysis of interlayer dislocations, we validate and provide the governing mechanics of atomic reconstructions measurements

Mechanical Properties and Strain Transfer Behavior of Molybdenum Ditelluride (MoTe2) Thin Films

Abstract Transition metal dichalcogenides (TMDs) offer superior properties over conventional materials in many areas such as in electronic devices. In recent years, TMDs have been shown to display a phase switching mechanism under the application of external mechanical strain, making them exciting candidates for phase change transistors. Molybdenum ditelluride (MoTe2) is one such material that has been engineered as a strain-based phase change transistor. In this work, we explore various aspects of the mechanical properties of this material by a suite of computational and experimental approaches. First, we present parameterization of an interatomic potential for modeling monolayer as well as multilayered MoTe2 films. For generating the empirical potential parameter set, we fit results from density functional theory calculations using a random search algorithm known as particle swarm optimization. The potential closely predicts structural properties, elastic constants, and vibrational frequencies of MoTe2 indicating a reliable fit. Our simulated mechanical response matches earlier larger scale experimental nanoindentation results with excellent prediction of fracture points. Simulation of uniaxial tensile deformation by molecular dynamics shows the complete non-linear stress-strain response up to failure. Mechanical behavior, including failure properties, exhibits directional anisotropy due to the variation of bond alignments with crystal orientation. Furthermore, we show the deterioration of mechanical properties with increasing temperature. Finally, we present computational and experimental evidence of an extended c-axis strain transfer length in MoTe2 compared to TMDs with smaller chalcogen atoms.</jats:p

Mechanical Properties and Strain Transfer Behavior of Molybdenum Ditelluride (MoTe2) Thin Films

Transition metal dichalcogenides (TMDs) offer superior properties over conventional materials in many areas such as in electronic devices. In recent years, TMDs have been shown to display a phase switching mechanism under the application of external mechanical strain, making them exciting candidates for phase change transistors. Molybdenum ditelluride (MoTe2) is one such material that has been engineered as a strain-based phase change transistor. In this work, we explore various aspects of the mechanical properties of this material by a suite of computational and experimental approaches. First, we present parameterization of an interatomic potential for modeling monolayer as well as multilayered MoTe2 films. For generating the empirical potential parameter set, we fit results from density functional theory calculations using a random search algorithm known as particle swarm optimization. The potential closely predicts structural properties, elastic constants, and vibrational frequencies of MoTe2 indicating a reliable fit. Our simulated mechanical response matches earlier larger scale experimental nanoindentation results with excellent prediction of fracture points. Simulation of uniaxial tensile deformation by molecular dynamics shows the complete non-linear stress-strain response up to failure. Mechanical behavior, including failure properties, exhibits directional anisotropy due to the variation of bond alignments with crystal orientation. Furthermore, we show the deterioration of mechanical properties with increasing temperature. Finally, we present computational and experimental evidence of an extended c-axis strain transfer length in MoTe2 compared to TMDs with smaller chalcogen atoms

An Atomistic Insight into Moiré Reconstruction in Twisted Bilayer Graphene beyond the Magic Angle

Twisted bilayer graphene exhibits electronic properties strongly correlated with the size and arrangement of moiré patterns. While rigid rotation of the two graphene layers results in a moiré interference pattern, local rearrangements of atoms due to interlayer van der Waals interactions result in atomic reconstruction within the moiré cells. Manipulating these patterns by controlling the twist angle and externally applied strain provides a promising route to tuning their properties. Atomic reconstruction has been extensively studied for angles close to or smaller than the magic angle (θm = 1.1°). However, this effect has not been explored for applied strain and is believed to be negligible for high twist angles. Using interpretive and fundamental physical measurements, we use theoretical and numerical analyses to resolve atomic reconstruction in angles above θm. In addition, we propose a method to identify local regions within moiré cells and track their evolution with strain for a range of representative high twist angles. Our results show that atomic reconstruction is actively present beyond the magic angle, and its contribution to the moiré cell evolution is significant. Our theoretical method to correlate local and global phonon behavior further validates the role of reconstruction at higher angles. Our findings provide a better understanding of moiré reconstruction in large twist angles and the evolution of moiré cells under the application of strain, which might be potentially crucial for twistronics-based applications

Automated Grain Boundary (GB) Segmentation and Microstructural Analysis in 347H Stainless Steel Using Deep Learning and Multimodal Microscopy

Austenitic 347H stainless steel offers superior mechanical properties and corrosion resistance required for extreme operating conditions such as high temperature. The change in microstructure due to composition and process variations is expected to impact material properties. Identifying microstructural features such as grain boundaries thus becomes an important task in the process-microstructure-properties loop. Applying convolutional neural network (CNN) based deep-learning models is a powerful technique to detect features from material micrographs in an automated manner. Manual labeling of the images for the segmentation task poses a major bottleneck for generating training data and labels in a reliable and reproducible way within a reasonable timeframe. In this study, we attempt to overcome such limitations by utilizing multi-modal microscopy to generate labels directly instead of manual labeling. We combine scanning electron microscopy (SEM) images of 347H stainless steel as training data and electron backscatter diffraction (EBSD) micrographs as pixel-wise labels for grain boundary detection as a semantic segmentation task. We demonstrate that despite producing instrumentation drift during data collection between two modes of microscopy, this method performs comparably to similar segmentation tasks that used manual labeling. Additionally, we find that na\"ive pixel-wise segmentation results in small gaps and missing boundaries in the predicted grain boundary map. By incorporating topological information during model training, the connectivity of the grain boundary network and segmentation performance is improved. Finally, our approach is validated by accurate computation on downstream tasks of predicting the underlying grain morphology distributions which are the ultimate quantities of interest for microstructural characterization