Kinetically stabilized C-60-toluene solvate nanostructures with a discrete absorption edge enabling supramolecular topotactic molecular exchange

Nanosized fullerene solvates have attracted widespread research attention due to recent interesting discoveries. A particular type of solvate is limited to a fixed number of solvents and designing new solvates within the same family is a fundamental challenge. Here we demonstrate that the hexagonal closed packed (HCP) phase of C-60 solvates, formed with m-xylene, can also be stabilized using toluene. Contrary to the notion on their instability, these can be stabilized from minutes up to months by tuning the occupancy of solvent molecules. Due to high stability, we could record their absorption edge, and measure excitonic life-time, which has not been reported for any C-60 solvate. Despite being solid, absorbance spectrum of the solvates is similar in appearance to that of C-60 in solution. A new absorption band appears at 673 nm. The fluorescence lifetime at 760 nm is similar to 1.2 ns, suggesting an excited state unaffected by solvent-C-60 interaction. Finally, we utilized the unstable set of HCP solvates to exchange with a second solvent by a topotactic exchange mechanism, which rendered near permanent stability to the otherwise few minutes stable solvates. This is also the first example of topotactic exchange in supramolecular crystal, which is widely known in ionic solids. (C) 2014 Elsevier Ltd. All rights reserved.

Healing time for the growth of thin films on patterned substrates

The healing times for the growth of thin films on patterned substrates are studied using simulations of two discrete models of surface growth: the Family model and the Das Sarma-Tamborenea (DT) model. The healing time, defined as the time at which the characteristics of the growing interface are ``healed'' to those obtained in growth on a flat substrate, is determined via the study of the nearest-neighbor height difference correlation function. Two different initial patterns are considered in this work: a relatively smooth tent-shaped triangular substrate and an atomically rough substrate with singlesite pillars or grooves. We find that the healing time of the Family and DT models on aL x L triangular substrate is proportional to L-z, where z is the dynamical exponent of the models. For the Family model, we also analyze theoretically, using a continuum description based on the linear Edwards-Wilkinson equation, the time evolution of the nearest-neighbor height difference correlation function in this system. The correlation functions obtained from continuum theory and simulation are found to be consistent with each other for the relatively smooth triangular substrate. For substrates with periodic and random distributions of pillars or grooves of varying size, the healing time is found to increase linearly with the height (depth) of pillars (grooves). We show explicitly that the simulation data for the Family model grown on a substrate with pillars or grooves do not agree with results of a calculation based on the continuum Edwards-Wilkinson equation. This result implies that a continuum description does not work when the initial pattern is atomically rough. The observed dependence of the healing time on the substrate size and the initial height (depth) of pillars (grooves) can be understood from the details of the diffusion rule of the atomistic model. The healing time of both models for pillars is larger than that for grooves with depth equal to the height of the pillars. The calculated healing time for both Family and DT models is found to depend on how the pillars and grooves are distributed over the substrate. (C) 2014 Elsevier B.V. All rights reserved.

Approximation of Spatio-Temporal Random Processes Using Tensor Decomposition

A new representation of spatio-temporal random processes is proposed in this work. In practical applications, such processes are used to model velocity fields, temperature distributions, response of vibrating systems, to name a few. Finding an efficient representation for any random process leads to encapsulation of information which makes it more convenient for a practical implementations, for instance, in a computational mechanics problem. For a single-parameter process such as spatial or temporal process, the eigenvalue decomposition of the covariance matrix leads to the well-known Karhunen-Loeve (KL) decomposition. However, for multiparameter processes such as a spatio-temporal process, the covariance function itself can be defined in multiple ways. Here the process is assumed to be measured at a finite set of spatial locations and a finite number of time instants. Then the spatial covariance matrix at different time instants are considered to define the covariance of the process. This set of square, symmetric, positive semi-definite matrices is then represented as a third-order tensor. A suitable decomposition of this tensor can identify the dominant components of the process, and these components are then used to define a closed-form representation of the process. The procedure is analogous to the KL decomposition for a single-parameter process, however, the decompositions and interpretations vary significantly. The tensor decompositions are successfully applied on (i) a heat conduction problem, (ii) a vibration problem, and (iii) a covariance function taken from the literature that was fitted to model a measured wind velocity data. It is observed that the proposed representation provides an efficient approximation to some processes. Furthermore, a comparison with KL decomposition showed that the proposed method is computationally cheaper than the KL, both in terms of computer memory and execution time.

Time resolved terahertz spectroscopy of low frequency electronic resonances and optical pump-induced terahertz photoconductivity in reduced graphene oxide membrane

Towards ultrafast optoelectronic applications of single and a few layer reduced graphene oxide (RGO), we study time domain terahertz spectroscopy and optical pump induced changes in terahertz conductivity of self-supported RGO membrane in the spectral window of 0.5-3.5 THz. The real and imaginary parts of conductivity spectra clearly reveal low frequency resonances, attributed to the energy gaps due to the van Hove singularities in the density of states flanking the Dirac points arising due to the relative rotation of the graphene layers. Further, optical pump induced terahertz conductivity is positive, pointing to the dominance of intraband scattering processes. The relaxation dynamics of the photo-excited carriers consists of three cooling pathways: the faster (similar to 450 fs) one due to optical phonon emission followed by disorder mediated large momentum and large energy acoustic phonon emission with a time constant of a few ps (called the super-collision mechanism) and a very large time (similar to 100 ps) arising from the deep trap states. The frequency dependence of the dynamic conductivity at different delay times is analyzed in term of Drude-Smith model. (C) 2014 Published by Elsevier Ltd.

Discrete-Rate Adaptation and Selection in Energy Harvesting Wireless Systems

In a system with energy harvesting (EH) nodes, the design focus shifts from minimizing energy consumption by infrequently transmitting less information to making the best use of available energy to efficiently deliver data while adhering to the fundamental energy neutrality constraint. We address the problem of maximizing the throughput of a system consisting of rate-adaptive EH nodes that transmit to a destination. Unlike related literature, we focus on the practically important discrete-rate adaptation model. First, for a single EH node, we propose a discrete-rate adaptation rule and prove its optimality for a general class of stationary and ergodic EH and fading processes. We then study a general system with multiple EH nodes in which one is opportunistically selected to transmit. We first derive a novel and throughput-optimal joint selection and rate adaptation rule (TOJSRA) when the nodes are subject to a weaker average power constraint. We then propose a novel rule for a multi-EH node system that is based on TOJSRA, and we prove its optimality for stationary and ergodic EH and fading processes. We also model the various energy overheads of the EH nodes and characterize their effect on the adaptation policy and the system throughput.

Using long time series of agricultural-derived nitrates for estimating catchment transit times

The estimation of water and solute transit times in catchments is crucial for predicting the response of hydrosystems to external forcings (climatic or anthropogenic). The hydrogeochemical signatures of tracers (either natural or anthropogenic) in streams have been widely used to estimate transit times in catchments as they integrate the various processes at stake. However, most of these tracers are well suited for catchments with mean transit times lower than about 4-5 years. Since the second half of the 20th century, the intensification of agriculture led to a general increase of the nitrogen load in rivers. As nitrate is mainly transported by groundwater in agricultural catchments, this signal can be used to estimate transit times greater than several years, even if nitrate is not a conservative tracer. Conceptual hydrological models can be used to estimate catchment transit times provided their consistency is demonstrated, based on their ability to simulate the stream chemical signatures at various time scales and catchment internal processes such as N storage in groundwater. The objective of this study was to assess if a conceptual lumped model was able to simulate the observed patterns of nitrogen concentration, at various time scales, from seasonal to pluriannual and thus if it was relevant to estimate the nitrogen transit times in headwater catchments. A conceptual lumped model, representing shallow groundwater flow as two parallel linear stores with double porosity, and riparian processes by a constant nitrogen removal function, was applied on two paired agricultural catchments which belong to the Research Observatory ORE AgrHys. The Global Likelihood Uncertainty Estimation (GLUE) approach was used to estimate parameter values and uncertainties. The model performance was assessed on (i) its ability to simulate the contrasted patterns of stream flow and stream nitrate concentrations at seasonal and inter-annual time scales, (ii) its ability to simulate the patterns observed in groundwater at the same temporal scales, and (iii) the consistency of long-term simulations using the calibrated model and the general pattern of the nitrate concentration increase in the region since the beginning of the intensification of agriculture in the 1960s. The simulated nitrate transit times were found more sensitive to climate variability than to parameter uncertainty, and average values were found to be consistent with results from others studies in the same region involving modeling and groundwater dating. This study shows that a simple model can be used to simulate the main dynamics of nitrogen in an intensively polluted catchment and then be used to estimate the transit times of these pollutants in the system which is crucial to guide mitigation plans design and assessment. (C) 2015 Elsevier B.V. All rights reserved.

Self-Assembled Discrete Molecules for Sensing Nitroaromatics

Efficient sensing of trace amount nitroaromatic (NAC) explosives has become a major research focus in recent time due to concerns over national security as well as their role as environment pollutants. NO2-containing electron-deficient aromatic compounds, such as picric acid (PA), trinitrotoluene (TNT), and dinitrotoluene (DNT), are the common constituents of many commercially available chemical explosives. In this article, we have summarized our recent developments on the rational design of electron-rich self-assembled discrete molecular sensors and their efficacy in sensing nitroaromatics both in solution as well as in vapor phase. Several p-electron-rich fluorescent metallacycles (squares, rectangles, and tweezers/pincers) and metallacages (trigonal and tetragonal prisms) have been synthesized by means of metal-ligand coordination-bonding interactions, with enough internal space to accommodate electron-deficient nitroaromatics at the molecular level by multiple supramolecular interactions. Such interactions subsequently result in the detectable fluorescence quenching of sensors even in the presence of trace quantities of nitroaromatics. The fascinating sensing characteristics of molecular architectures discussed in this article may enable future development of improved sensors for nitroaromatic explosives.

Use of Data Driven Techniques for Short Lead Time Streamflow Forecasting in Mahanadi basin

Streamflow forecasts at daily time scale are necessary for effective management of water resources systems. Typical applications include flood control, water quality management, water supply to multiple stakeholders, hydropower and irrigation systems. Conventionally physically based conceptual models and data-driven models are used for forecasting streamflows. Conceptual models require detailed understanding of physical processes governing the system being modeled. Major constraints in developing effective conceptual models are sparse hydrometric gauge network and short historical records that limit our understanding of physical processes. On the other hand, data-driven models rely solely on previous hydrological and meteorological data without directly taking into account the underlying physical processes. Among various data driven models Auto Regressive Integrated Moving Average (ARIMA), Artificial Neural Networks (ANNs) are most widely used techniques. The present study assesses performance of ARIMA and ANNs methods in arriving at one-to seven-day ahead forecast of daily streamflows at Basantpur streamgauge site that is situated at upstream of Hirakud Dam in Mahanadi river basin, India. The ANNs considered include Feed-Forward back propagation Neural Network (FFNN) and Radial Basis Neural Network (RBNN). Daily streamflow forecasts at Basantpur site find use in management of water from Hirakud reservoir. (C) 2015 The Authors. Published by Elsevier B.V.

Direct Observation of Thermal Equilibrium of Excited Triplet States of 9,10-Phenanthrenequinone. A Time-Resolved Resonance Raman Study

The photochemistry of aromatic ketones plays a key role in various physicochemical and biological processes, and solvent polarity can be used to tune their triplet state properties. Therefore, a comprehensive analysis of the conformational structure and the solvent polarity induced energy level reordering of the two lowest triplet states of 9,10-phenanthrenequinone (PQ) was carried out using nanosecond-time-resolved absorption (ns-TRA), time-resolved resonance Raman (TR3) spectroscopy, and time dependent-density functional theory (TD-DFT) studies. The ns-TRA of PQ in acetonitrile displays two bands in the visible range, and these two bands decay with similar lifetime at least at longer time scales (mu s). Interestingly, TR3 spectra of these two bands indicate that the kinetics are different at shorter time scales (ns), while at longer time scales they followed the kinetics of ns-TRA spectra. Therefore, we report a real-time observation of the thermal equilibrium between the two lowest triplet excited states of PQ assigned to n pi* and pi pi* of which the pi pi* triplet state is formed first through intersystem crossing. Despite the fact that these two states are energetically close and have a similar conformational structure supported by TD-DFT studies, the slow internal conversion (similar to 2 ns) between the T-2(1(3)n pi*) and T-1(1(3)pi pi*) triplet states indicates a barrier. Insights from the singlet excited states of PQ in protic solvents J. Chem. Phys. 2015, 142, 24305] suggest that the lowest n pi* and pi pi* triplet states should undergo hydrogen bond weakening and strengthening, respectively, relative to the ground state, and these mechanisms are substantiated by TD-DFT calculations. We also hypothesize that the different hydrogen bonding mechanisms exhibited by the two lowest singlet and triplet excited states of PQ could influence its ISC mechanism.

Ultrafast Chemical Dynamics in Time Domain Through Fluorescence Spectroscopy

While absorption and emission spectroscopy have always been used to detect and characterize molecules and molecular complexes, the availability of ultrashort laser pulses and associated computer-aided optical detection techniques allowed study of chemical processes directly in the time domain at unprecedented time scales, through appearance and disappearance of fluorescence from participating chemical species. Application of such techniques to chemical dynamics in liquids, where many processes occur with picosecond and femtosecond time scales lead to the discovery of a host of new phenomena that in turn led to the development of many new theories. Experiment and theory together provided new and valuable insight into many fundamental chemical processes, like isomerization dynamics, electron and proton transfer reactions, vibrational energy and phase relaxation, photosynthesis, to name just a few. In this article, we shall review a few of such discoveries in attempt to provide a glimpse of the fascinating research employing fluorescence spectroscopy that changed the field of chemical dynamics forever.

Probing Photoexcited Carriers in a Few-Layer MoS2 Laminate by Time-Resolved Optical Pump-Terahertz Probe Spectroscopy

We report the dynamics of photoinduced carriers in a free-standing MoS2 laminate consisting of a few layers (1-6 layers) using time-resolved optical pump-terahertz probe spectroscopy. Upon photoexcitation with the 800 nm pump pulse, the terahertz conductivity increases due to absorption by the photoinduced charge carriers. The relaxation of the non-equilibrium carriers shows fast as well as slow decay channels, analyzed using a rate equation model incorporating defect-assisted Auger scattering of photoexcited electrons, holes, and excitons. The fast relaxation time occurs due to the capture of electrons and holes by defects via Auger processes, resulting in nonradiative recombination. The slower relaxation arises since the excitons are bound to the defects, preventing the defect-assisted Auger recombination of the electrons and the holes. Our results provide a comprehensive understanding of the non-equilibrium carrier kinetics in a system of unscreened Coulomb interactions, where defect-assisted Auger processes dominate and should be applicable to other 2D systems.

Gauge invariance in the theoretical description of time-resolved angle-resolved pump/probe photoemission spectroscopy

Nonequilibrium calculations in the presence of an electric field are usually performed in a gauge, and need to be transformed to reveal the gauge-invariant observables. In this work, we discuss the issue of gauge invariance in the context of time-resolved angle-resolved pump/probe photoemission. If the probe is applied while the pump is still on, one must ensure that the calculations of the observed photocurrent are gauge invariant. We also discuss the requirement of the photoemission signal to be positive and the relationship of this constraint to gauge invariance. We end by discussing some technical details related to the perturbative derivation of the photoemission spectra, which involve processes where the pump pulse photoemits electrons due to nonequilibrium effects.

Characteristic Dimensionless Numbers in Multi-Scale and Rate-Dependent Processes

For this sake, the macroscopic equations of mechanics and the kinetic equations of the microstructural transformations should form a unified set that be solved simultaneously. As a case study of coupling length and time scales, the trans-scale formulation

Tractable nonparametric bayesian inference in poisson processes with Gaussian process intensities

The inhomogeneous Poisson process is a point process that has varying intensity across its domain (usually time or space). For nonparametric Bayesian modeling, the Gaussian process is a useful way to place a prior distribution on this intensity. The combination of a Poisson process and GP is known as a Gaussian Cox process, or doubly-stochastic Poisson process. Likelihood-based inference in these models requires an intractable integral over an infinite-dimensional random function. In this paper we present the first approach to Gaussian Cox processes in which it is possible to perform inference without introducing approximations or finitedimensional proxy distributions. We call our method the Sigmoidal Gaussian Cox Process, which uses a generative model for Poisson data to enable tractable inference via Markov chain Monte Carlo. We compare our methods to competing methods on synthetic data and apply it to several real-world data sets. Copyright 2009.

Tractable nonparametric Bayesian inference in Poisson processes with Gaussian process intensities

The inhomogeneous Poisson process is a point process that has varying intensity across its domain (usually time or space). For nonparametric Bayesian modeling, the Gaussian process is a useful way to place a prior distribution on this intensity. The combination of a Poisson process and GP is known as a Gaussian Cox process, or doubly-stochastic Poisson process. Likelihood-based inference in these models requires an intractable integral over an infinite-dimensional random function. In this paper we present the first approach to Gaussian Cox processes in which it is possible to perform inference without introducing approximations or finite-dimensional proxy distributions. We call our method the Sigmoidal Gaussian Cox Process, which uses a generative model for Poisson data to enable tractable inference via Markov chain Monte Carlo. We compare our methods to competing methods on synthetic data and apply it to several real-world data sets.