3 resultados para PH electrodes

em Duke University


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Quantitative models are required to engineer biomaterials with environmentally responsive properties. With this goal in mind, we developed a model that describes the pH-dependent phase behavior of a class of stimulus responsive elastin-like polypeptides (ELPs) that undergo reversible phase separation in response to their solution environment. Under isothermal conditions, charged ELPs can undergo phase separation when their charge is neutralized. Optimization of this behavior has been challenging because the pH at which they phase separate, pHt, depends on their composition, molecular weight, concentration, and temperature. To address this problem, we developed a quantitative model to describe the phase behavior of charged ELPs that uses the Henderson-Hasselbalch relationship to describe the effect of side-chain ionization on the phase-transition temperature of an ELP. The model was validated with pH-responsive ELPs that contained either acidic (Glu) or basic (His) residues. The phase separation of both ELPs fit this model across a range of pH. These results have important implications for applications of pH-responsive ELPs because they provide a quantitative model for the rational design of pH-responsive polypeptides whose transition can be triggered at a specified pH.

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For efficient use of metal oxides, such as MnO(2) and RuO(2), in pseudocapacitors and other electrochemical applications, the poor conductivity of the metal oxide is a major problem. To tackle the problem, we have designed a ternary nanocomposite film composed of metal oxide (MnO(2)), carbon nanotube (CNT), and conducting polymer (CP). Each component in the MnO(2)/CNT/CP film provides unique and critical function to achieve optimized electrochemical properties. The electrochemical performance of the film is evaluated by cyclic voltammetry, and constant-current charge/discharge cycling techniques. Specific capacitance (SC) of the ternary composite electrode can reach 427 F/g. Even at high mass loading and high concentration of MnO(2) (60%), the film still showed SC value as high as 200 F/g. The electrode also exhibited excellent charge/discharge rate and good cycling stability, retaining over 99% of its initial charge after 1000 cycles. The results demonstrated that MnO(2) is effectively utilized with assistance of other components (fFWNTs and poly(3,4-ethylenedioxythiophene)-poly(styrenesulfonate) in the electrode. Such ternary composite is very promising for the next generation high performance electrochemical supercapacitors.

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Simultaneous neural recordings taken from multiple areas of the rodent brain are garnering growing interest due to the insight they can provide about spatially distributed neural circuitry. The promise of such recordings has inspired great progress in methods for surgically implanting large numbers of metal electrodes into intact rodent brains. However, methods for localizing the precise location of these electrodes have remained severely lacking. Traditional histological techniques that require slicing and staining of physical brain tissue are cumbersome, and become increasingly impractical as the number of implanted electrodes increases. Here we solve these problems by describing a method that registers 3-D computerized tomography (CT) images of intact rat brains implanted with metal electrode bundles to a Magnetic Resonance Imaging Histology (MRH) Atlas. Our method allows accurate visualization of each electrode bundle's trajectory and location without removing the electrodes from the brain or surgically implanting external markers. In addition, unlike physical brain slices, once the 3D images of the electrode bundles and the MRH atlas are registered, it is possible to verify electrode placements from many angles by "re-slicing" the images along different planes of view. Further, our method can be fully automated and easily scaled to applications with large numbers of specimens. Our digital imaging approach to efficiently localizing metal electrodes offers a substantial addition to currently available methods, which, in turn, may help accelerate the rate at which insights are gleaned from rodent network neuroscience.