Upper bounds on the superfluid stiffness and superconducting TcT_c: Applications to twisted-bilayer graphene and ultra-cold Fermi gases

Understanding the material parameters that control the superconducting transition temperature $T_c$ is a problem of fundamental importance. In many novel superconductors, phase fluctuations determine $T_c$, rather than the collapse of the pairing amplitude. We derive rigorous upper bounds on the superfluid phase stiffness for multi-band systems, valid in any dimension. This in turn leads to an upper bound on $T_c$ in two dimensions (2D), which holds irrespective of pairing mechanism, interaction strength, or order-parameter symmetry. Our bound is particularly useful for the strongly correlated regime of low-density and narrow-band systems, where mean field theory fails. For a simple parabolic band in 2D with Fermi energy $E_F$, we find that $k_BT_c \leq E_F/8$, an exact result that has direct implications for the 2D BCS-BEC crossover in ultra-cold Fermi gases. Applying our multi-band bound to magic-angle twisted bilayer graphene (MA-TBG), we find that band structure results constrain the maximum $T_c$ to be close to the experimentally observed value. Finally, we discuss the question of deriving rigorous upper bounds on $T_c$ in 3D.Comment: Revised figures, includes estimates from another model of MA-TBG, published version of manuscrip

Genetic Algorithms: Usefulness and Effectiveness for Pattern Recognition

Genetic Algorithms have been gaining much interest since the early 1970\u27s and have intrigued people from the fields of machine learning, artificial intelligence, neural networks and operations research. This paper describes the approach of genetic algorithms applied to neural networks. The experiments were conducted using various functions such as XOR,AND,SINE and different network sizes. Based on the experimental data, we concluded that for small network architectures represented by the functions (SINE,ENCODE,etc), genetic algorithms were not effective and the desired results were not achieved within a reasonable period of time

Nucleic Acid Detection of Live Pathogens on Contaminated Foods

The goal is to develop a point-of-care biosensor for the detection of live pathogens contaminating beef products. Biosensing of live pathogens is based on isothermal amplification of nucleic acid on a paper-based device. A colorimetric dye is employed as an indicator of the amplification product for visual result. The assay incorporates a compound Propidium monoazide (PMA) that makes the DNA from dead cells inaccessible for amplification. This approach is especially applicable for pathogens that can enter a viable but non-culturable state (VBNC)

Effect of Carbohydrates on the Gut Microbiome

The microbiome within the gut is directly linked to biological processes within a person, influencing factors such as metabolism, signaling pathways, and available nutrients. Long term dieting is known to alter ecological conditions within the gut, allowing certain types of microbes to flourish. Therefore, the overall health of an individual is ultimately influenced by shifts in the microbial community state caused by persistent dieting. This study investigates the connection between diet and the microbiome and draws an understanding of how common carbohydrates in food can affect bacterial composition. Using KBase software, anaerobic bacterial growth was investigated for bacteria subject to a defined media with distinct sugars. Common bacteria found in young children were studied as microbiome development begins post-partum. The results show that only certain carbohydrates have a crucial impact on bacterial growth while others are inert. In future studies, it is recommended that co-cultures of bacteria are studied in the sugar additive media to determine relative abundance and how different bacterial strains can dominate one another

Developing a Thermometallurgical Model and Furnace Optimization for Austenitization of Al-Si Coated 22MnB5 Steel in a Roller Hearth Furnace

Lightweighting of vehicles while preserving crash-worthiness, in order to satisfy stringent restrictions imposed by the government on the automotive industry, has become a sought after solution which can be realized via hot-forming die quenching (HFDQ). HFDQ is a process where boron-manganese steel blanks, a grade of ultra-high strength steels with a thin eutectic Al-Si coating, are heated beyond TAc3 to achieve a fully austenitic microstructure, a precursor for martensite. Heat treatment is performed using 30 to 40 meter long roller hearth furnaces, comprised of multiple heating zones, with two key objectives: (1) ensure complete austenitization of blanks and (2) transformation of the Al-Si coating into a protective Al-Si-Fe intermetallic coating. Blank heating rates are controlled by the roller speed and zone set-point temperatures, which are currently set by trial-and-error procedures. Therefore, a thorough understanding of the furnace parameters and the industrial objectives are essential. Patched blanks, with spatially varying thickness, leads to inhomogenous heating, making this relationship elusive. Previous furnace-based energy models only focused on simulating the sensible energy of the load with no explicit information about the latent energy associated with austenitization. Consequentially, the latent term had been incorporated into the sensible energy term thereby defining an effective specific heat. In order to realize how blank heating rate influences microstructural and Al-Si layer evolution, a model coupling heating and austenite kinetics is necessary. This integrated model serves as means for optimizing the heating process. In this work a thermometallurgical model is developed, combining a heat transfer submodel with two austenite kinetic submodels, an empirical first-order kinetics model and a constitutive kinetics model, via the latent heat of austenitization. The models simultaneously predict the heating and austenitization curves, for unpatched/patched blanks heated within a roller hearth furnace. Validation studies showed that the first-order kinetics model reliably estimated heating and transformation kinetics compared to the constitutive model. The validated models are then used to optimize the zone set-point temperatures, roller speed, and cycle length for a 12-zone roller hearth furnace whilst minimizing the cycle time in a deterministic setting. A gradient-based interior point method and hybrid scheme were used to assess the constrained multivariate minimization problem with two alternative austenitization constraints imposed: a soak-time based and explicitly modeled requirement. In both cases, the most savings in cycle time were achieved using the explicitly modeled phase fraction austenite constraint, with reductions of approximately 2 to 3 times from the nominal settings

Colorimetric Detection of Pathogenic Bacteria Using Morphology-Controlled Gold Nanoparticles

Simple and rapid detection of pathogens is crucial for preventing and treating infectious diseases. Conventional methods for pathogen detection are based on cell cultures and could require several days. The use of nanotechnology and specifically, gold nanoparticles has facilitated the development of biosensors that can potentially be used at the point-of-care because they provide a colorimetric output. A systematic literature review demonstrates that most instances of gold nanoparticles are in the detection of amplified nucleic acids but these methods require specialized equipment. There is a growing drive for making the biosensors simpler and more sensitive such that they could be employed outside the laboratory. This thesis focuses on the development of a gold nanoparticle-based biosensor that has the potential to rapidly detect and identify pathogens at the point-of-care. The biosensor consists of cationic gold nanoparticles that aggregate around target bacteria and produce a color change, which can be observed visually and quantified spectrophotometrically. Combining nanoparticles with various sizes and shapes creates a “chemical nose” biosensor, which uses a unique combination of responses to represent each target of interest, in a manner similar to the human sense of smell. This “chemical nose” biosensor can discriminate between bacterial species based on their cell wall components. This approach produces a versatile biosensor that can be deployed for a variety of applications as opposed to biofunctionalized nanoparticles, which are typically limited to a single target. Development of the biosensor begins with the synthesis of gold nanostars because this shape allows control over size and degree of branching, both of which govern the optical properties of the solutions. Gold nanostars are synthesized by a surfactant-assisted seed-mediated growth method. Increasing the surfactant concentration increases the degree of branching while increasing the amount of seed decreases the particle size. The cationic surface facilitates electrostatic aggregation of the nanostars on the negatively charged bacterial cell wall. This aggregation allows a rapid visual detection of Staphylococcus aureus, a model Gram-positive pathogen. The colorimetric response of gold nanostars depends on the intrinsic size and morphology of particles. Discriminating between bacteria of different species is important for accurate diagnosis. The ability of gold nanostars to identify the species of bacteria is explored by targeting ocular pathogens that are currently affecting contact lens wearers. Using two different degrees of branching of gold nanostars, a “chemical nose” biosensor is developed, where colorimetric response from each type of nanostar is different for each bacterial species. The biosensor is able to discriminate between saline control and four species of bacteria at the same concentration with 99% accuracy. Transmission electron microscopy demonstrates that this discrimination in colorimetric responses is because of different degrees and patterns of aggregation of gold nanostars around bacteria. In addition to identifying the species of bacteria, some applications require detection at various concentrations. Thus, the “chemical nose” was tested for the detection of eight species of bacteria at three different concentrations and an accuracy of 89% was obtained by analyzing the absorption spectra of the gold nanoparticles. Additionally, the potential of the “chemical nose” to detect polymicrobial infections was demonstrated by measuring the colorimetric response of mixtures of bacteria. The “chemical nose” was able to discriminate between Staphylococcus aureus, Escherichia coli, Pseudomonas aeruginosa, and their binary and tertiary mixtures with 100% accuracy. Implementation of the “chemical nose” biosensor at the point-of-care requires a rapid response. This is possible by acquiring absorption spectra at a faster rate. Using a portable charge-coupled device spectrophotometer, the “chemical nose” was able to distinguish between mixtures of bacteria within two minutes of data acquisition. This was possible by exploiting the kinetics of color change, which is unique for each bacterial species and their mixtures. Additionally, within each mixture, the bacteria seem to maintain their patterns and extent of aggregation of gold nanoparticles as confirmed by transmission electron microscopy. Finally, the effect of morphology was further studied using two Gram-positive and two Gram-negative bacteria. Gold nanoparticles with different shapes – nanospheres, nanostars, nanocubes, and nanorods – were incubated with the bacteria to obtain a concentration dependent response. While the responses were similar for Gram-positive bacteria, there were significant differences for Gram-negative ones with the order of decreasing response being: nanostars> nanocubes> nanospheres > nanorods. Additionally, the concentration of gold nanoparticles determines the range of concentration of bacteria that can be detected. This thesis demonstrates that detection, identification, and quantification of bacteria could be possible using gold nanoparticles for applications in food, water, and environmental contamination. In these applications, gold nanoparticles have exploited intrinsic properties of the nanoparticles and analytes to provide specific responses. Thus, gold nanoparticles exemplify the tremendous potential offered by nanotechnology.4 month

Raman Spectroscopy - An Analytical Tool for Biologics

Raman Spectroscopy (RS) is a non-invasive technique that analyses biomolecules qualitatively and quantitatively. Raman spectroscopy measures the inelastic scattering of light due to molecular vibrations. It can be applied to liquid, solid, or semi-solid forms of the biological sample reducing the sample preparation measures. The minimal sample preparation and non-invasive nature of Raman Spectroscopy can be applied in developing a Process analytical technology (PAT) tool and as a diagnostic tool. We demonstrated qualitative and quantitative measurements of biologics with Raman spectroscopy through our previous studies. Our results indicate that RS distinguishes various Gram-positive and Gram-negative bacteria, fungi, and a mixture of microbes and Chinese hamster ovary (CHO) cells. In addition, RS can determine the concentration of viral samples. We aim to optimize and refine Raman spectroscopy sensitivity by developing in-line probes and acoustic devices. Our future works also involve coupling Raman data with machine learning tools for accurate in-line measurements

Automatic Railway track Inspection for early warning using Real time image Processing with GPS

Railroad assessment assumes an indispensable part for the correct working of the rail line frameworks; in the past its done manual investigation yet has a few ambiguities. Modified vision based audit systems are engaged to look at the stipulation of rail track. Thusly system fabricates the capability of audit, reduces the required time and giving a more exact and ceaseless information of the railroad track. To give the continuous screens and assistant condition for railroad track using "vision based" and "vibration based" strategy for wellbeing reason. Thusly we can bolster exactness, adequacy and steadfastness. PC vision frameworks have been especially made to be used with the model. The structure delineated in this suggestion makes use of different standard and balanced picture get ready systems, not simply to facilitate the necessities for manual examinations, furthermore to allow steady checking and taking after of any blemishes or varieties from the standard in a rail track. Hereafter to keep up a vital separation from deferments, our propose structure will thusly survey the railroad track by using vision based and vibration based technique. The system gives continuous watching and essential condition for railroad track using vision based procedure and change in accordance with look for the inadequacy range on the track. An examination join perceiving disfigurements on tracks, missing shocks, stay, tie plate and fastens et cetera