Experimental probes of silver metal nanoparticle formation kinetics: Comparing indirect versus more direct methods

The kinetics of noble metal nanoparticle formation in bottom-up syntheses are important for controlling and optimizing these methods. Hence, experimental probes that are easily accessible to most laboratories are also of interest. We collected kinetic curves for the formation of silver nanoparticles in a modified Turkevich method with citrate acting as the reducing and stabilizing agent by (i) measuring the change in silver nanoparticle surface plasmon resonance by UV-visible spectroscopy, a somewhat indirect method, and then also by (ii) measuring the change in silver ion concentration by ion-selective electrode potentiometry and/or atomic absorption spectroscopy, two more direct methods. The resulting sigmoidal kinetic curves were curvefitted with the Finke-Watzky two-step kinetic model of slow, continuous nucleation and fast autocatalytic growth to extract average rate constants. We found that the kinetic curves obtained by following the change in silver ion concentration were apparent mirror images of those constructed by following the change in nanoparticle surface plasmon resonance, and that their respective curvefits displayed the same sigmoidal characteristics. The resultant values of the rate constants for nucleation and growth overlapped within experimental error between the methods and showed similar trends over the range of citrate concentrations studied. The use of multiple probes in this work to follow the kinetics of nanoparticle formation helps fill a need for the comparison and evaluation of different methods available to scientists, particularly those considered easily accessible.     

Switching plasticity in compensated ferrimagnetic multilayers for neuromorphic computing

Current-induced multilevel magnetization switching in ferrimagnetic spintronic devices is highly pursued for the application in neuromorphic computing. In this work, we demonstrate the switching plasticity in Co/Gd ferrimagnetic multilayers where the binary states magnetization switching induced by spin-orbit toque can be tuned into a multistate one as decreasing the domain nucleation barrier. Therefore, the switching plasticity can be tuned by the perpendicular magnetic anisotropy of the multilayers and the in-plane magnetic field. Moreover, we used the switching plasticity of Co/Gd multilayers for demonstrating spike timing-dependent plasticity and sigmoid-like activation behavior. This work gives useful guidance to design multilevel spintronic devices which could be applied in high-performance neuromorphic computing.     

Application of the Šesták-Berggren Equation to Organic and Inorganic Materials of Practical Interest

Reactions that have an initial acceleratory period are common in both organic and inorganic systems. The Šesták-Berggren equation, dx/dt= -kx ⁿ(1-x)^m[-ln(x)]^p, with p set to zero (also called the extended Prout-Tompkins (PT) equation) is an excellent empirical kinetic law for many of these systems. In this work, it is shown to fit both isothermal and constant heating rate pyrolysis data for a well-preserved algal kerogen in a petroleum source rock and two synthetic polymers (polycarbonate and poly-ether-etherketone), dehydration of calcium oxalate monohydrate, decomposition of ammonium percholorate, and diffusive release of gas implanted in materials. Activation energies derived by non-linear regression to multiple experiments are consistent with those derived by simple isoconversional methods. Errors caused by misapplication of first-order kinetics to single-heating-rate data are discussed briefly. This revised version was published online in August 2006 with corrections to the Cover Date.     

Note on a Density Question for Neural Networks

We prove a uniform density result for three-layer feedforward sigma-pi neural networks of hyperbolic type.     

Approximation by neural networks with sigmoidal functions

In this paper, we introduce a type of approximation operators of neural networks with sigmodal functions on compact intervals, and obtain the pointwise and uniform estimates of the ap- proximation. To improve the approximation rate, we further introduce a type of combinations of neurM networks. Moreover, we show that the derivatives of functions can also be simultaneously approximated by the derivatives of the combinations. We also apply our method to construct approximation operators of neural networks with sigmodal functions on infinite intervals.     

The construction and approximation of some neural networks operators

In this paper,the technique of approximate partition of unity is used to construct a class of neural networks operators with sigmoidal functions.Using the modulus of continuity of function as a metric,...     

A restricted b‐spline basis for S‐shaped calibration curves

S‐shaped curves arise in many calibration assays. They are typically modeled using an empirically chosen nonlinear function. Nonparametric alternatives usually perform poorly because there are often insufficient data points and there are difficulties in enforcing the required asymptotic behavior. We propose an alternative approach using a b‐spline basis adapted to produce lower and upper asymptotes in the fitted curves. Its main advantage is that, with pre‐determined knot positions, the model is linear in the parameters and so is more numerically stable than a nonlinear model; this has advantages in, for example, fitting a mixed model to multiple curves. We explore the performance of this approach using data from a single radioimmunoassay for cortisol and from a set of 96 ELISA assays for the herbicide atrazine. Copyright © 2012 John Wiley & Sons, Ltd.     

小波函数的电路合成方法

针对国内对小波的研究主要集中在算法和应用方面,小波的电路实现方法研究还鲜见的现状,提出了一种小波函数的电路实现方法。小波函数使用反曲型母小波模拟,电路由双极型晶体管、可控放大器和线性无源电子元件等组成。根据小波分解的多分辨率近似特性,有限能量多变量函数能使用这个模拟电路近似表示,结合实例,PSpice仿真实验表明,小波函数的电路合成方法是有效、可靠的。     

Neural networks for of continuous optimal approximation functions in R^d

Using some regular matrices we present a method to express any multivariate algebraic polynomial of total order n in a normal form. Consequently, we prove constructively that, to approximate continuous target functions defined on some compact set of ? ^d , neural networks are at least as good as algebraic polynomials.     

Chemorheology of photopolymerizable acrylates using a modified Boltzmann sigmoidal model

Experiments were conducted to evaluate the influence of ambient photoconversion on rheology for a range of photopolymerizable urethane dimethacrylate (UDMA) resins containing varying amounts of three comonomers including 1,6 hexane diol‐dimethacrylate (HDDMA), an alkoxylated cyclohexane dimethanol diacrylate monomer (CD‐582), and hydroxyethyl methacrylate (HEMA). Experiments were performed both as a function of composition and time‐dependent dose varying the intensity using a photorheometer. A semilog‐based sigmoidal model allowed the determination of four physical model parameters to define the relationship between reaction kinetics and its dynamic influence on viscosity. We have observed induction times and viscosity changes associated with the model that shows a trend in reaction kinetics in the following order from most to least reactive: UDMA > CD582 > HDDMA > HEMA. With increasing amounts of reactive diluent included in the formulation, the kinetics of reaction was more sluggish. The value of this sigmoidal model is that it could help define formulation and process conditions most likely to control crosslinking to maximize dimensional stability or other thermophysical properties. © 2008 Wiley Periodicals, Inc. J Polym Sci Part B: Polym Phys 46: 2319–2325, 2008