Modeling the liquid–gas interface using self-assembled monolayers

Stochastic classical trajectory simulations were used to study the efficiency of the energy exchange at the gas–liquid interface. Self-assembled monolayers (SAM) of long-chain functionalized molecules were used to mimic the liquid surface. Since the molecules in the monolayers are anchored by only one end, they retain some of the mobility that they have in the liquid but lose all their fluidity. The corrugation of the surface and the stiffness of the interface were tuned by varying the length of the molecules in the monolayers. The use of longer molecules leads to increased corrugation of the surface and provides additional dissipation channels that promote more efficient momentum and energy accommodation, increase the translational–rotational energy interconversion and enhance trapping. However, this “length effect” appears to saturate, as no further significant changes are observed in those properties when the monolayer's molecules's length is elongated from six to nine carbons. This saturation effect suggests that, even though monolayers can provide some of the mobility observed in liquid surfaces, they lack the energy dissipation channel provided by the fluidity of the liquid.     

Mean Field Analysis of Large-Scale Interacting Populations of Stochastic Conductance-Based Spiking Neurons Using the Klimontovich Method

We investigate the dynamics of large-scale interacting neural populations, composed of conductance based, spiking model neurons with modifiable synaptic connection strengths, which are possibly also subjected to external noisy currents. The network dynamics is controlled by a set of neural population probability distributions (PPD) which are constructed along the same lines as in the Klimontovich approach to the kinetic theory of plasmas. An exact non-closed, nonlinear, system of integro-partial differential equations is derived for the PPDs. As is customary, a closing procedure leads to a mean field limit. The equations we have obtained are of the same type as those which have been recently derived using rigorous techniques of probability theory. The numerical solutions of these so called McKean–Vlasov–Fokker–Planck equations, which are only valid in the limit of infinite size networks, actually shows that the statistical measures as obtained from PPDs are in good agreement with those obtained through direct integration of the stochastic dynamical system for large but finite size networks. Although numerical solutions have been obtained for networks of Fitzhugh–Nagumo model neurons, which are often used to approximate Hodgkin–Huxley model neurons, the theory can be readily applied to networks of general conductance-based model neurons of arbitrary dimension.     

Intrinsic fiber Fabry-Perot temperature sensor with fiber Bragg grating mirrors

A fiber Fabry-Perot interferometer (FFPI) sensor is formed with broadband (~3 nm, 3-dB bandwidth) fiber Bragg grating (FBG) mirrors. Repetitively modulating a distributed-feedback laser produces chirping that modulates the reflectance of the FFPI. Because the reflectance of the FBG mirrors varies with optical frequency, the fringes in the sensor reflectance modulation are distinguishable, making it possible to extend the sensor dynamic range versus that of a FFPI sensor with conventional wavelength-dependent mirrors. An ambient temperature is determined in the range from 25 to 170 degrees C with a resolution of 0.005 degrees C.     

Distinguishing between the interior pressures induced from two independent sources within a room using the probabilistic approach

In this paper, a probabilistic approach is introduced and used to distinguish between the interior pressures that are induced from two independent sound sources within a rectangular room. One source is a vibrating wall of the room and the other is an interior point source. The model is set up using the modal analysis method. In the probabilistic method, the probability densities of the uncertain acoustic model parameters are computed. The values of highest probability density are identified and assigned to the uncertain parameters in the acoustic model. The contributions of the sound sources to the total pressure are then obtained from the acoustic model input with the identified parameters. The effects of the modal truncations, the modeling errors, and the measurement locations on the accuracies of the identification process are studied.     

Spectroscopic imaging with volume selection by unpaired adiabatic pi pulses: theory and application

In NMR spectroscopy, volume selection can be advantageously achieved using adiabatic pi pulses, which enable high bandwidth and B(1) insensitivity. In order to avoid the generation of non-linear phase profiles and the subsequent signal loss caused by incoherent averaging, adiabatic pi pulses are usually used in pairs for volume selection in each spatial dimension. Alternatively, when performing spectroscopic imaging (SI), a high enough spatial resolution results in negligible phase dispersion within each pixel. This allows using only one pulse per selected spatial dimension, resulting in a reduced echo-time and reduced power deposition. In this work, the feasibility of such an approach is explored theoretically and numerically, allowing the derivation of explicit conditions to obtain SI images without artifact. Adequate spatial and spectral post-processing procedures are described to compensate for the effect of non-linear phase profiles. These developments are applied to SI in the rat brain at 9.4 T, using a new adiabatic sequence named Pseudo-LASER.