Synthesis Of Graphene Nanomaterials And Their Application In Electrochemical Energy Storage

The need to store and use energy on diverse scales in a modern technological society necessitates the design of large and small energy systems, among which electrical energy storage systems such as batteries and capacitors have attracted much interest in the past several decades. Supercapacitors, also known as ultracapacitors, or electrochemical capacitors, with fast power delivery and long cycle life are complementing or even replacing batteries in many applications. The rapid development of miniaturized electronic devices has led to a growing need for rechargeable micro-power sources with high performance. Among different sources, electrochemical micro-capacitors or micro-supercapacitors provide higher power density than their counterparts and are gaining increased interest from the research and engineering communities. Rechargeable Li ion batteries with high energy and power density, long cycling life, high charge-discharge rate (1C - 3C) and safe operation are in high demand as power sources and power backup for hybrid electric vehicles and other applications. In the present work, graphene-based graphene materials have been designed and synthesized for electrochemical energy storage applications, e.g., conventional supercapacitors (macro-supercapacitors), microsupercapacitors and lithium ion batteries. Factors influencing the formation and structure of graphitic petals grown by microwave plasma-enhanced chemical vapor deposition on oxidized silicon substrates were investigated through process variation and materials analysis. Insights gained into the growth mechanism of these graphitic petals suggest a simple scribing method can be used to control both the location and formation of petals on flat Si substrates. Transitional metal oxides and conducting polymers have been coated on the graphitic petal-based electrodes by facile chemical methods for multifunctional energy storage applications. Detailed electrochemical characterization (e.g., cyclic voltammetry and constant galvanostatic charge/discharge) has been carried out to evaluate the performance of electrodes

Thin Electrical Double Layer Simulation of Micro-electrochemical Supercapacitors

The deteriorating state of the environment has drawn many people to hybrid electric vehicles. Electrochemical micro-supercapacitors are of interest in this field because of their high power density relative to other micro-power sources. However, little is known about how the properties of the electrolyte used affect the performance of such devices. The first step of this investigation was to use thermoreflectance microscopy to measure the temperature change of the electrodes while charging and discharging supercapacitor samples. The components of these samples were graphitic petal electrodes with a Ti/Au covering (for enhanced light reflectance) on a SiO2 base, with a PVA and H2SO4 polymer gel electrolyte. These experiments showed cooling of over 10°C. In order to better understand these results and the underlying mechanism of supercapacitors, a model to predict their behavior was needed. Therefore, a description of the dynamic and equilibrium behavior of ions in thin electrical double layers was constructed. The most accurate model was found to be the Poisson-Boltzmann equation modified to account for steric effects. MATLAB code was written and tested against previous theoretical research and will be published on nanoHUB. This will later be expanded to account for other supercapacitor features such as pseudocapacitance and high surface area of activated carbon electrodes

A New Open Loop Approach for Identifying the Initial Rotor Position of a Permanent Magnet Synchronous Motor

The precision of initial rotor position detection is critical for the start and running performance of permanent magnet synchronous motor (PMSM). This work describes a new open loop approach for identifying the initial position of a PMSM with an incremental encoder, even when a constant load torque is being applied. By giving a testing current with high frequency to the stator winding, the initial rotor position of a PMSM can be detected with reasonable accuracy. The rotor almost does not move during the process of identification. The FFT algorithms are used to remove the phase bias effects in identification. Our approach is quicker and simpler than the conventional approaches

Transcriptome and pan-cancer system analysis identify PM2.5-induced stanniocalcin 2 as a potential prognostic and immunological biomarker for cancers

Epidemiological studies have shown that air pollution and particulate matter (PM) are closely related to the occurrence of cancer. However, the potential prognostic and immunological biomarkers for air pollution related cancers are lacking. In this study, we proved PM2.5 exposure was correlated with lung cancer through transcriptome analysis. Importantly, we identified STC2 as a key gene regulated by PM2.5, whose expression in epithelial cells was significantly increased after PM2.5 treatment and validated by using RT-qPCR and immunofluorescence. Kaplan-Meier OS curves suggested that high STC2 expression positively correlated with a poor prognosis in lung cancer. Furthermore, we discovered that STC2 was associated with multiple cancers and pathways in cancer. Next, Pan-Cancer Expression Landscape of STC2 showed that STC2 exhibited inconsistent expression across 26 types of human cancer, lower in KIRP in cancer versus adjacent normal tissues, and significantly higher in another cancers. Cox regression results suggested that STC2 expression was positively or negatively associated with prognosis in different cancers. Moreover, STC2 expression was associated with clinical phenotypes including age, gender, stage and grade. Mutation features of STC2 were also analyzed, in which the highest alteration frequency of STC2 was presented in KIRC with amplification. Meanwhile, the effects of copy number variation (CNV) on STC2 expression were investigated across various tumor types, suggesting that STC2 expression was significantly correlated with CNV in tumors. Additionally, STC2 was closely related to tumor heterogeneity, tumor stemness and tumor immune microenvironment like immune cell infiltration. In the meantime, we analyzed methylation modifications and immunological correlation of STC2. The results demonstrated that STC2 expression positively correlated with most RNA methylation genes and immunomodulators across tumors. Taken together, the findings revealed that PM2.5-induced STC2 might be a potential prognostic and immunological biomarker for cancers related to air pollution