Pt nanoparticles supported in nanoporous Al2O3 catalyst are prepared by reduction of K2PtCl4 solution using H2 in the presence of Al2O3 and poly(acrylic acid) as capping material. After thorough washing with water to remove Pt nanoparticles located on the external surface of the Al2O3 and drying at 70 °C for 12 h, they were used in propene hydrogenation to evaluate catalytic activity as measured by the value of the activation energy in the temperature range between 30 and 90 °C. The Pt nanoparticles are characterized by using transmission electron microscopy (TEM). The particles in Pt/Al2O3 are found to be encapsulated and uniformly dispersed inside the Al2O3; however, the size and shapes are not clearly seen. After extraction of the Pt nanoparticles from the Al2O3 channels by using an ethanol-diluted HF solution, various shapes such as truncated octahedral, cubic, tetrahedral, and spherical with a size around 5 nm are observed. The encapsulated particles have various shapes but are smaller in size than those prepared in K2PtCl4 solution with polyacrylate in the absence of Al2O3. Using FT-IR studies, the capping material initially used in Pt/Al2O3 is not found in the Al2O3 channels. This might be due to the fact that the polymer (average MW 2100) is too large to be accommodated within the Al2O3 pores. The nanopores of Al2O3 have several roles in the synthesis of these nanoparticles. It allows for uniform dispersion and encapsulation of Pt nanoparticles. It controls the Pt sizes with narrow distribution that is determined by the pore dimension (5.8 nm). It protects against metal particle aggregation and produces various shapes even in the absence of the capping material. Using these Pt nanoparticles, the catalysis of hydrogenation of propene gas was studied. The initial rates, reaction order, rate constants, and activation energy for the hydrogenation are determined by use of mass spectrometric techniques. The activation energy is found to be 5.7 kcal/mol, which is about one-half that previously reported for catalysis by Pt metal deposited in SiO2 and TiO2 synthesized by using H2PtCl6 and Pt(allyl)2 by impregnation method.

The binding of gold nanoparticles to biological systems and its potential use in diagnostics [1] as well as medical treatment is becoming an active field of nanomaterial research. For example, Hamad-Schifferli and co-workers [2] recently showed that inductive coupling of a radiofrequency magnetic field to a metal nanoparticle covalently linked to DNA increases the local temperature of the bound DNA, thereby inducing denaturation while leaving the surrounding molecules relatively unaffected. They showed high spatial localization of the denaturation, which might allow, in the future, portions of proteins or nucleic acids to be controlled while the rest of the molecule and neighboring species remain relatively unaffected. Due to the strong visible absorption of the surface plasmon absorption of gold nanoparticles and its convenient wavelength, laser photothermal treatment is also expected to be of wide future application. In material applications, laser pulses have been shown to be useful in shaping noble metal nanoparticles [3±5] and narrowing their size distribution.[6] For these reasons, it is important to understand the mechanisms involved in the transfer of the excitation energy from the nanoparticle into the environment. Here, we report a study on the electron relaxation dynamics and the thermal cooling of colloidal gold nanorods and truncated tetrahedra, in air as well as in water, after excitation with femtosecond laser pulses. It is found that that the local energy exchange with the surrounding medium for gold nanoparticles in water occurs on the few picosecond time scale, comparable with the electron±phonon relaxation, while a slow heat dissipation by water ensures that the particles remain heated for hundreds of picoseconds. Gold, and more generally speaking noble metal nanoparticles larger than a few nanometers, show a strong absorption band in the visible, which is due to the coherent excitation of the conduction band (ªfreeº) electrons. This absorption band is known as the surface plasmon resonance and depends on the particle size, and even more importantly, on the particle shape. [3,7] For nearly spherical particles only one resonance located around 520 nm is seen for gold nanoparticles. For gold nanorods this plasmon absorption splits into two bands, known as the transverse and longitudinal plasmon resonances, which are polarized perpendicular and parallel to the long axis of the rod, respectively. While the transverse mode is relatively insensitive to the nanorod dimensions, usually defined by the aspect ratio (length divided by the width), the longitudinal plasmon absorption band red-shifts with increasing the aspect ratio. [3,7] Figure 1A (top spectrum, dashed line) shows the absorption spectrum of gold nanorods in aqueous solution having an aspect ratio of 2.9 (length: 35 nm; width: 12 nm).While the absorption spectrum of nanorods is a rather old problem and was solved a long time ago by Gans, [8] the development of preparation methods allowing size-selective synthesis of many particle shapes...

Abstract-Information transmission in biological signaling circuits has often been described using the metaphor of a noise filter. Cellular systems need accurate, real-time data about their environmental conditions, but the biochemical reaction networks that propagate, amplify, and process signals work with noisy representations of that data. Biology must implement strategies that not only filter the noise, but also predict the current state of the environment based on information delayed due to the finite speed of chemical signaling. The idea of a biochemical noise filter is actually more than just a metaphor: we describe recent work that has made an explicit mathematical connection between signaling fidelity in cellular circuits and the classic theories of optimal noise filtering and prediction that began with Wiener, Kolmogorov, Shannon, and Bode. This theoretical framework provides a versatile tool, allowing us to derive analytical bounds on the maximum mutual information between the environmental signal and the real-time estimate constructed by the system. It helps us understand how the structure of a biological network, and the response times of its components, influences the accuracy of that estimate. The theory also provides insights into how evolution may have tuned enzyme kinetic parameters and populations to optimize information transfer.

Evolutionary graph theory models the effects of natural selection and random drift on structured populations of competing mutant and non-mutant individuals. Recent studies have found that fixation times in such systems often have right-skewed distributions. Little is known, however, about how these distributions and their skew depend on mutant fitness. Here we calculate the fitness dependence of the fixation-time distribution for the Moran Birth-death process in populations modeled by two extreme networks: the complete graph and the one-dimensional ring lattice, obtaining exact solutions in the limit of large network size. We find that with non-neutral fitness, the Moran process on the ring has normally distributed fixation times, independent of the relative fitness of mutants and non-mutants. In contrast, on the complete graph, the fixation-time distribution is a fitness-weighted convolution of two Gumbel distributions. When fitness is neutral the fixationtime distribution jumps discontinuously and becomes highly skewed on both the complete graph and the ring. Even on these simple networks, the fixation-time distribution exhibits rich fitness dependence, with discontinuities and regions of universality. Extensions of our results to two-fitness Moran models, times to partial fixation, and evolution on random networks are discussed.

We derive a mean-field approximation for the macroscopic dynamics of large networks of pulse-coupled theta neurons in order to study the effects of different network degree distributions and degree correlations (assortativity). Using the ansatz of Ott and Antonsen [Chaos 18, 037113 (2008)], we obtain a reduced system of ordinary differential equations describing the mean-field dynamics, with significantly lower dimensionality compared with the complete set of dynamical equations for the system. We find that, for sufficiently large networks and degrees, the dynamical behavior of the reduced system agrees well with that of the full network. This dimensional reduction allows for an efficient characterization of system phase transitions and attractors. For networks with tightly peaked degree distributions, the macroscopic behavior closely resembles that of fully connected networks previously studied by others. In contrast, networks with highly skewed degree distributions exhibit different macroscopic dynamics due to the emergence of degree dependent behavior of different oscillators. For nonassortative networks (i.e., networks without degree correlations), we observe the presence of a synchronously firing phase that can be suppressed by the presence of either assortativity or disassortativity in the network. We show that the results derived here can be used to analyze the effects of network topology on macroscopic behavior in neuronal networks in a computationally efficient fashion.