Universal Technology Corporation (United States)

Abstract Chemical sensors based on solution‐processed 2D nanomaterials represent an extremely attractive approach toward scalable and low‐cost devices. Through the implementation of real‐time impedance spectroscopy and development of a three‐element circuit model, redox exfoliated MoS2 nanoflakes demonstrate an ultrasensitive empirical detection limit of NO2 gas at 1 ppb, with an extrapolated ultimate detection limit approaching 63 ppt. This sensor construct reveals a more than three orders of magnitude improvement from conventional direct current sensing approaches as the traditionally dominant interflake interactions are bypassed in favor of selectively extracting intraflake doping effects. This same approach allows for an all solution‐processed, flexible 2D sensor to be fabricated on a polyimide substrate using a combination of graphene contacts and drop‐casted MoS2 nanoflakes, exhibiting similar sensitivity limits. Finally, a thermal annealing strategy is used to explore the tunability of the nanoflake interactions and subsequent circuit model fit, with a demonstrated sensitivity improvement of 2× with thermal annealing at 200 °C.

Dynamic mechanical analysis (DMA) provides insight into polymer viscoelastic behavior and its dependence on temperature. In that DMA is applied to a bulk sample, however, inferences about the connection among bulk and molecular behavior are generally speculative. In this work, a strain history similar to that of DMA is applied to atomistic polymer systems in silico. This method, termed Virtual DMA, can provide the data necessary to relate molecular information to bulk response. For example, strain amplitude can be varied to explore the effect of the corresponding small-scale perturbations on the stability of non-bond interactions. In some polymers, statistically significant differences in the glass transition temperature as measured by differential scanning calorimetry and DMA are found in physical experiments. When Virtual DMA results are compared with those from quasi-equilibrium cooling, the differing effects of imposed randomized and deterministic atomic velocities on molecular structure and local order can be identified.

The thermal reshaping of gold nanorods in a polymer matrix is an important phenomenon for many potential applications. However, a fundamental understanding of the various mechanisms that govern the nanorod reshaping dynamics is still lacking. Here, we provide evidence for a phenomenological model of the gold nanorod shape transformation based on the measurements and detailed analysis of the time-resolved thermal reshaping for a variety of gold nanorods having different geometries (aspect ratio, volume, diameter) in a cross-linked epoxy matrix at application relevant temperatures (120-220 °C). Our analysis suggests that (a) the nanorod reshaping dynamics consist of two temporal regimes that are governed by different phenomena and (b) the ultimate amount of reshaping at a given temperature depends strongly on the initial particle geometry and the mechanical stiffness of its surroundings. At short times, the shape transformation is dominated by a curvature-induced surface diffusion process, in which the activation energy for diffusion depends on curvature. At long times, however, the surrounding environment plays a key role in slowing the diffusion and stabilizing the nanorod shape. We show that the long-time behavior can be well described using a modified surface diffusion model that takes into account the slowing of atomic diffusivity as a result of external forces arising from mechanical constraints. The ability to tune both the final shape and the reshaping dynamics in nanocomposites opens up new possibilities in tailoring the optical properties of these materials.

As-manufactured rotors behave quite differently than nominal, as-designed rotors due to small geometric and material property deviations in the rotor, referred to as mistuning. The mistuning of a 20 bladed, integrally bladed rotor (IBR) will be evaluated via analytical methods, bench-top testing, and using a rotating compressor research facility. Analytical methods consist of the development of an as-manufactured model based on geometry measurements from a high fidelity optical scanning system. Benchtop testing of the IBR is done using a traveling wave excitation (TWE) system that simulates engine order excitation in stationary bladed disks for the purpose of determining potentially high responding blades due to mistuning. The compressor research facility utilizes blade tip timing (BTT) to measure the blade vibration of the IBR. The resonant response of the IBR at various modes and harmonic excitations is investigated. A comprehensive mistuning and force amplification comparison between the as-manufactured model, TWE, and the compressor rig is performed. Mistuning of each method is evaluated using three different methods. First, the tuned absorber factor (TAF), which is a metric to determine potential high responding blades, is determined for each system. Next, mistuning is analyzed by isolating individual blades both experimentally on the bench and analytically to determine the mistuning patterns. Lastly, the mistuning determined by each system will be evaluated using a reduced-order model, namely the Fundamental Mistuning Model Identification (FMM ID). It will be shown that TAF shows variability between each method providing indications TAF may not be the best approach of force amplification predictions. Basic mistuning agreements exist when isolating blades both experimentally and analytically exhibiting as-manufactured models are capable of representing full experiments. System ID methods provide a basic agreement between both the mistuning pattern and the mistuning amplification for all three methods analyzed. This ultimately shows the importance and the ability to use as-manufactured models to help increase detailed understanding of IBR’s.

Thermal energy transfer across physically interacting single-wall carbon nanotube (SWCNT) interconnects has been investigated using non-equilibrium molecular dynamics simulations. The role of various geometrical and structural (length, diameter, chirality) as well as external (deformation and strain) carbon nanotube (CNT) parameters has been explored to estimate total as well as area-normalized thermal conductance across cross-contact interconnects. It is shown that the CNT aspect ratio and degree of lateral as well as tensile deformation play a significant role in determining the extent of thermal energy exchange across CNT contacts, while CNT chirality has a negligible influence on thermal transport. Depending on the CNT diameter, aspect ratio, and degree of deformation at the contact interface, the thermal conductance values can vary significantly –by more than an order of magnitude for total conductance and a factor of 3 to 4 for area-normalized conductance. The observed trends are discussed from the perspective of modulation in number of low frequency out-of-plane (transverse, flexural, and radial) phonons that transmit thermal energy across the contact and govern the conductance across the interface. The established general dependencies for phonon governed thermal transport at CNT contacts are anticipated to help design and performance prediction of CNT-based flexible nanoelectronic devices, where CNT-CNT contact deformation and strain are routinely encountered during device operations.

Effective entrapment of enzymes in solid phase materials is critical to their practical application. The entrapment generally stabilizes biological activity compared to soluble molecules and the material simplifies catalyst integration compared to other methods. A silica sol-gel process based upon biological mechanisms of inorganic material formation (biomineralization) supports protein immobilization reactions within minutes. The material has high protein binding capacity and the catalytic activity of the enzyme is retained. We have demonstrated that both oligopeptides and selected proteins will mediate the biomineralization of silica and allow effective co-encapsulation of other proteins present in the reaction mixture. The detailed methods described here provide a simple and effective approach for molecular biologists, biochemists and bioengineers to create stable, solid phase biocatalysts that may be integrated within sensors, synthetic processes, reactive barriers, energy conversion, and other biotechnology concepts.

Grid convergence in finite element analysis (FEA), despite a wide variety of tools available to date, remains an elusive and challenging task. Due to the complex and time-consuming process of remeshing and solving the finite element model (FEM), convergence studies can be a part of the most arduous portion of the modeling process and can even be impossible with FEMs unassociated with CAD. Existing a posteriori methods, such as relative error in the energy norm, provide a near arbitrary indication of the model convergence for eigenfrequencies. This paper proposes a new approach to evaluate the harmonic convergence of an existing model without conducting a convergence study. Strain energy superconvergence (SES) takes advantage of superconvergence points within a FEM and accurately recovers the strain energy within the model using polyharmonic splines, thus providing a more accurate estimate of the system's eigenfrequencies without modification of the FEM. Accurate eigenfrequencies are critical for designing for airfoil resonance avoidance and mistuned rotor response prediction. Traditional error estimation strategies fail to capture harmonic convergence as effectively as SES, potentially leading to a less accurate airfoil resonance and rotor mistuning prediction.

The effective optical and thermal properties as well as the opto-thermal response of composite materials are investigated. A stochastic method is developed which implements a random microstructure generation (RMG) algorithm to produce a synthetic microstructure representing the geometry of unidirectional continuous fiber reinforced polymer matrix composites (PMCs). Simulation of the synthetic microstructure determines effective thermal properties, effective optical properties, and the thermal response to optical energy. The micromechanics framework developed herein is also applied to unit cell analysis and compared with analytical solutions. The metrics of effective transverse thermal conductivity, effective attenuation coefficient, and temperature response are studied as a function of fiber volume fraction. We show that some composites’ effective thermal properties can be adequately predicted using unit cell analysis or analytic techniques; however, effective optical properties and thermal response to optical input depend strongly the specific random arrangement of the fibers.

Model calibration is often performed with a limited number of data points due to the significant cost of high fidelity simulations or experiments. To capture specific events, such as failure, optimal data collection methods can aid in achieving globally accurate models that can also predict targeted events. In this research, the expected information gain criterion for experimental design is used to determine the most informative designs for sequential calibration of uncertain model parameters. For accurate prediction of events of interest, the Targeted Information Gain for Error Reduction (TIGER) method is introduced to balance the placement of exploration points in the design space based on model accuracy and capturing the event of interest. This approach was compared to using sequential and all-at-once random data collection methods. The comparison of global and local prediction errors indicated that this is a feasible approach based on an analytical two-dimensional example. The method was also successful in a classification problem for flutter and critical limit cycle oscillation amplitude for a panel in hypersonic flow.