Stability of high temperature ceramics under corrosive environments

Thesis (Ph.D.)--Boston UniversityCurrently, ceramics are being used under increasingly demanding environments. This research involves the study of high-temperature stability of ceramic materials in two diverse applications. The first application involves the use of ceramic materials in gas turbines. SiC/SiC ceramic matrix composites (CMCs) are increasingly being used in the hot-sections of gas turbines; and they are subject to recession of their surface if exposed to a flow of high-velocity water vapor, and to hot-corrosion when exposed to alkali salts. This research involves developing a hybrid system containing an environmental barrier coating (EBC) for protection of the CMC from chemical attack and a thermal barrier coating (TBC) that allows a steep temperature gradient across it to lower the temperature of the CMC for increased lifetimes. The EBC coating is a functionally graded mullite (3Al2O3•2SiO2) deposited by chemical vapor deposition (CVD), the TBC layer is yttria-stabilized zirconia (YSZ) deposited by air plasma spray (APS). The hybrid coating system demonstrated excellent physical and chemical stability under severe thermal shock and exposure to an aggressive hot-corrosion environment. Finite element modeling showed that through-thickness cracks reduce the tensile stresses in the TBC, but also reduce the beneficial compressive stresses in the EBC, and may actually lead to the propagation of the vertical cracks into the EBC. The second application involves the formation of solar-grade silicon by an inexpensive and environmentally friendly electrochemical process using an YSZ solid oxide membrane (SOM) at elevated temperature (~1100°C). The SOM membrane is exposed to a complex fluoride flux with dissolved silica, which is then electrochemically separated into silicon and oxygen. Membrane stability is crucial to ensure high efficiency and long-term performance of the SOM process. A failure model of the SOM membrane by the formation of "inner cracks" was studied, and attributed to yttrium depletion in the YSZ, which leads to phase transformation from the cubic to tetragonal phase. A series of systematic experiments were designed and performed to understand the synergistic roles of silica and YF3 in the flux in membrane degradation. It was shown that silica attacks the SOM membrane, while YF3 in the flux slows down the attack. The mechanism of the yttria depleted layer (YDL) formation was attributed to grain boundary attack by the silica in the flux, which was the rate-controlling step. This led to rapid ingress of the flux into this attacked grain boundaries, and the out diffusion of Y from the cubic YSZ grains to the grain boundary. This depletion of the Y from the cubic grains transformed them into tetragonal. Once all of the cubic grains in the YDL region converted to tetragonal YSZ grains, no further diffusion occurred. Based on the stability test results, a new flux design was proposed and tested. The flux composition did not attack the SOM membrane, and successful separation of silica in the flux to phase pure Si crystals was demonstrated without apparent damage to the SOM membrane, thereby demonstrating the viability of the Si-SOM process

Turing instability and pattern formation of a fractional Hopfield reaction–diffusion neural network with transmission delay

It is well known that integer-order neural networks with diffusion have rich spatial and temporal dynamical behaviors, including Turing pattern and Hopf bifurcation. Recently, some studies indicate that fractional calculus can depict the memory and hereditary attributes of neural networks more accurately. In this paper, we mainly investigate the Turing pattern in a delayed reaction–diffusion neural network with Caputo-type fractional derivative. In particular, we find that this fractional neural network can form steadily spatial patterns even if its first-derivative counterpart cannot develop any steady pattern, which implies that temporal fractional derivative contributes to pattern formation. Numerical simulations show that both fractional derivative and time delay have influence on the shape of Turing patterns

Mediator-less immunodetection with voltage-controlled intrinsic amplification for ultrasensitive and rapid detection of microorganism pathogens

A mediator-less immunodetection method for microorganisms is realized by incorporating the newly developed field-effect enzymatic detection (FEED) technique with the conventional electrochemical immunosensing approach. The gating voltage of FEED facilitates the transduction of electrical signal through the bulky immune complex so that the detection does not rely on the use of mediators or other diffusional substances. The voltage-controlled intrinsic amplification provided by the detection system allows detection in low-concentration samples without target pre-enrichment, leading to ultrasensitive and rapid detection. The detection approach is demonstrated with E. coliO157:H7, a model microorganism, in milk with an estimated detection limit of 20 CFU mL−1 (where CFU is a colony-forming unit) without performing sample pre-enrichment and centrifugation of sample followed by the resuspension of the pellet in a buffer solution, resulting in a significantly shortened assay time of 67 min. Optimizing the gating voltage resulted in the detection of 12 CFU mL−1 of the bacterium in milk. The novel detection approach can be used as a detection platform for ultrasensitive, specific and rapid detection of microorganism pathogens

Optimal Stationary State Estimation Over Multiple Markovian Packet Drop Channels

In this paper, we investigate the state estimation problem over multiple Markovian packet drop channels. In this problem setup, a remote estimator receives measurement data transmitted from multiple sensors over individual channels. By the method of Markovian jump linear systems, an optimal stationary estimator that minimizes the error variance in the steady state is obtained, based on the mean-square (MS) stabilizing solution to the coupled algebraic Riccati equations. An explicit necessary and sufficient condition is derived for the existence of the MS stabilizing solution, which coincides with that of the standard Kalman filter. More importantly, we provide a sufficient condition under which the MS detectability with multiple Markovian packet drop channels can be decoupled, and propose a locally optimal stationary estimator but computationally more tractable. Analytic sufficient and necessary MS detectability conditions are presented for the decoupled subsystems subsequently. Finally, numerical simulations are conducted to illustrate the results on the MS stabilizing solution, the MS detectability, and the performance of the optimal and locally optimal stationary estimators

Voltage-Controlled Enzyme-Catalyzed Glucose–Gluconolactone Conversion Using A Field-Effect Enzymatic Detector

The field-effect enzymatic detection (FEED) technique was used to control the kinetics of the enzymatic conversion of glucose to gluconolactone. The glucose–gluconolactone conversion occurring at an enzyme-immobilized electrode, a well-studied process, was confirmed using mass spectrometry. Electrochemical studies showed that the glucose oxidation current depends on the gating voltage VG and the ion concentration of the sample solution. Additionally, the depletion of glucose in the sample also showed a dependence on VG. FEED was used to detect H2O2 on the zepto-molar level in order to show the ultrasensitive detection capability of the technique. These results, while providing evidence for the proposed mechanism of FEED, indicate that VG controls the conversion process. The effect of VG on the glucose–gluconolactone conversion was demonstrated by the observed VG-dependent kinetic parameters of the conversion process

In-Domain GAN Inversion for Faithful Reconstruction and Editability

Generative Adversarial Networks (GANs) have significantly advanced image synthesis through mapping randomly sampled latent codes to high-fidelity synthesized images. However, applying well-trained GANs to real image editing remains challenging. A common solution is to find an approximate latent code that can adequately recover the input image to edit, which is also known as GAN inversion. To invert a GAN model, prior works typically focus on reconstructing the target image at the pixel level, yet few studies are conducted on whether the inverted result can well support manipulation at the semantic level. This work fills in this gap by proposing in-domain GAN inversion, which consists of a domain-guided encoder and a domain-regularized optimizer, to regularize the inverted code in the native latent space of the pre-trained GAN model. In this way, we manage to sufficiently reuse the knowledge learned by GANs for image reconstruction, facilitating a wide range of editing applications without any retraining. We further make comprehensive analyses on the effects of the encoder structure, the starting inversion point, as well as the inversion parameter space, and observe the trade-off between the reconstruction quality and the editing property. Such a trade-off sheds light on how a GAN model represents an image with various semantics encoded in the learned latent distribution. Code, models, and demo are available at the project page: https://genforce.github.io/idinvert/