THE APPLICATION OF IRON-BASED MATERIALS IN AQUEOUS ENERGY STORAGE

Aqueous energy storage has been an important part of battery research for its cost-effective, environmentally benign, and robust nature. Iron-based materials, including iron hydroxides and iron oxides, are widely investigated as electrode materials for their extremely low cost and sufficient discharge capacity. Iron-based electrode materials were often operated in strong alkaline electrolytes, experiencing either slow reaction kinetics, severe side reactions, or significant capacity loss over cycling. This research focused on the application of iron-based materials in aqueous electrolytes with low alkalinity. This research showed that the synthesized γ-FeOOH measured with a cocktail electrolyte of sodium sulfate and sodium hydroxide demonstrated an enhanced discharge capacity and improved capacity retention, compared with the results measured in sodium hydroxide electrolyte. The investigation on the charge storage mechanism using in-situ XRD, XPS, as well as electrochemical methods showed that a green rust phase formed in the discharge stage in the cocktail electrolyte played an important role in the enhancing of electrochemical performance of γ-FeOOH, promoting Fe2+/Fe3+ one-electron transfer reaction with an enhanced capacity. The green rust phase also reduced the formation of the electrochemically inert Fe3O4 phase during the discharge process, promoting cycling performance. This research on the performance of iron-based materials in cocktail electrolytes opens up a new field in utilizing iron-based materials for aqueous battery applications

THE APPLICATION OF IRON-BASED MATERIALS IN AQUEOUS ENERGY STORAGE

Aqueous energy storage has been an important part of battery research for its cost-effective, environmentally benign, and robust nature. Iron-based materials, including iron hydroxides and iron oxides, are widely investigated as electrode materials for their extremely low cost and sufficient discharge capacity. Iron-based electrode materials were often operated in strong alkaline electrolytes, experiencing either slow reaction kinetics, severe side reactions, or significant capacity loss over cycling. This research focused on the application of iron-based materials in aqueous electrolytes with low alkalinity. This research showed that the synthesized γ-FeOOH measured with a cocktail electrolyte of sodium sulfate and sodium hydroxide demonstrated an enhanced discharge capacity and improved capacity retention, compared with the results measured in sodium hydroxide electrolyte. The investigation on the charge storage mechanism using in-situ XRD, XPS, as well as electrochemical methods showed that a green rust phase formed in the discharge stage in the cocktail electrolyte played an important role in the enhancing of electrochemical performance of γ-FeOOH, promoting Fe2+/Fe3+ one-electron transfer reaction with an enhanced capacity. The green rust phase also reduced the formation of the electrochemically inert Fe3O4 phase during the discharge process, promoting cycling performance. This research on the performance of iron-based materials in cocktail electrolytes opens up a new field in utilizing iron-based materials for aqueous battery applications

Enhancing Pseudocapacitive Process for Energy Storage Devices: Analyzing the Charge Transport Using Electro-kinetic Study and Numerical Modeling

Supercapacitors are a class of energy storage devices that store energy by either ionic adsorption via an electrochemical double layer capacitive process or fast surface redox reaction via a pseudocapacitive process. Supercapacitors display fast charging and discharging performance and excellent chemical stability, which fill the gap between high energy density batteries and high-power-density electrostatic capacitors. In this book chapter, the authors have presented the current studies on improving the capacitive storage capacity of various electrode materials for supercapacitors, mainly focusing on the metal oxide electrode materials. In particular, the approaches that mathematically simulate the behavior of interaction between electrode materials and charge carriers subject to potentiodynamic conditions (e.g., cyclic voltammetry) have been described. These include a general relationship between current and voltage to describe overall electrokinetics during the charge transfer process and a more comprehensive numerical modeling that studies ionic transport and electrokinetics within a spherical solid particle. The two aforementioned types of mathematical analyses can provide fundamental understanding of the parameters governing the electrode reaction and mass transfer in the electrode material, and thus shed light on how to improve the storage capacity of supercapacitors

Combination model of heterogeneous data for security measurement

Measuring security is a core step for guaranteeing security of network and information systems. Due to massiveness and heterogeneity of measurement data, it is difficult toclassify and combinethem on demand. In thispaper, consideringimplication relationship of metrics, we propose a combination model and combination policy for security measurement. Several examples demonstrate the effectiveness of our model

Let-7b expression determines response to chemotherapy through the regulation of Cyclin D1 in Glioblastoma

BACKGROUND: Glioblastoma is the most common type of primary brain tumors. Cisplatin is a commonly used chemotherapeutic agent for Glioblastoma patients. Despite a consistent rate of initial responses, cisplatin treatment often develops chemoresistance, leading to therapeutic failure. Cellular resistance to cisplatin is of great concern and understanding the molecular mechanisms is an utter need. METHODS: Glioblastoma cell line U251 cells were exposed to increasing doses of cisplatin for 6 months to establish cisplatin-resistant cell line U251R. The differential miRNA expression profiles in U251 and U251R cell lines were identified by microarray analysis and confirmed by Q-PCR. MiRNA mimics were transfected into U251R cells, and cellular response to cisplatin-induced apoptosis and cell cycle distribution were examined by FACS analysis. RESULTS: U251R cells showed 3.1-fold increase in cisplatin resistance compared to its parental U251 cells. Microarray analysis identified Let-7b and other miRNAs significantly down-regulated in U251R cells compared to U251 cells. Transfection of Let-7b mimics greatly re-sensitized U251R cells to cisplatin, while transfection of other miRNAs has no effect or slightly effect. Cyclin D1 is predicted as a target of Let-7b through bioinformatics analysis. Over-expression of Let-7b mimics suppressed cyclin D1 protein expression and inhibited cyclin D1-3’-UTR luciferase activity. Knockdown of cyclin D1 expression significantly increased cisplatin-induced G1 arrest and apoptosis. CONCLUSIONS: Collectively, our results indicated that cisplatin treatment leads to Let-7b suppression, which in turn up-regulates cyclin D1 expression. Let-7b may serve as a marker of cisplatin resistance, and can enhance the therapeutic benefit of cisplatin in glioblastoma cells

Combination Model of Heterogeneous Data for Security Measurement

Measuring security is a core step for guaranteeing security of network and information systems. Due to massiveness and heterogeneity of measurement data, it is difficult to classify and combine them on demand. In this paper, considering implication relationship of metrics, we propose a combination model and combination policy for security measurement. Several examples demonstrate the effectiveness of our model

The effect of gradient nonlinearities on fiber orientation estimates from spherical deconvolution of diffusion MRI data

Gradient nonlinearities in magnetic resonance imaging (MRI) cause spatially varying mismatches between the imposed and the effective gradients and can cause significant biases in rotationally invariant diffusion MRI measures derived from, for example, diffusion tensor imaging. The estimation of the orientational organization of fibrous tissue, which is nowadays frequently performed with spherical deconvolution techniques ideally using higher diffusion weightings, can likewise be biased by gradient nonlinearities. We explore the sensitivity of two established spherical deconvolution approaches to gradient nonlinearities, namely constrained spherical deconvolution (CSD) and damped Richardson‐Lucy (dRL). Additionally, we propose an extension of dRL to take into account gradient imperfections, without the need of data interpolation. Simulations show that using the effective b‐matrix can improve dRL fiber orientation estimation and reduces angular deviations, while CSD can be more robust to gradient nonlinearity depending on the implementation. Angular errors depend on a complex interplay of many factors, including the direction and magnitude of gradient deviations, underlying microstructure, SNR, anisotropy of the effective response function, and diffusion weighting. Notably, angular deviations can also be observed at lower b‐values in contrast to the perhaps common assumption that only high b‐value data are affected. In in vivo Human Connectome Project data and acquisitions from an ultrastrong gradient (300 mT/m) scanner, angular differences are observed between applying and not applying the effective gradients in dRL estimation. As even small angular differences can lead to error propagation during tractography and as such impact connectivity analyses, incorporating gradient deviations into the estimation of fiber orientations should make such analyses more reliable