Polymer template-assisted microemulsion synthesis of large surface area, porous Li2MnO3 and its characterization as a positive electrode material of Li-ion cells

Lithium-rich manganese oxide (Li2MnO3) is prepared by reverse microemulsion method employing Pluronic acid (P123) as a soft template and studied as a positive electrode material. The as-prepared sample possesses good crystalline structure with a broadly distributed mesoporosity but low surface area. As expected, cyclic voltammetry and charge-discharge data indicate poor electrochemical activity. However, the sample gains surface area with narrowly distributed mesoporosity and also electrochemical activity after treating in 4 M H2SO4. A discharge capacity of about 160 mAh g(-1) is obtained. When the acid-treated sample is heated at 300 A degrees C, the resulting porous sample with a large surface area and dual porosity provides a discharge capacity of 240 mAh g(-1). The rate capability study suggests that the sample provides about 150 mAh g(-1) at a specific discharge current of 1.25 A g(-1). Although the cycling stability is poor, the high rate capability is attributed to porous nature of the material.

On Whitespace Identification Using Randomly Deployed Sensors

This work considers the identification of the available whitespace, i.e., the regions that do not contain any existing transmitter within a given geographical area. To this end, n sensors are deployed at random locations within the area. These sensors detect for the presence of a transmitter within their radio range r(s) using a binary sensing model, and their individual decisions are combined to estimate the available whitespace. The limiting behavior of the recovered whitespace as a function of n and r(s) is analyzed. It is shown that both the fraction of the available whitespace that the nodes fail to recover as well as their radio range optimally scale as log(n)/n as n gets large. The problem of minimizing the sum absolute error in transmitter localization is also analyzed, and the corresponding optimal scaling of the radio range and the necessary minimum transmitter separation is determined.

Constrained Linear Spectral Unmixing Technique for Regional Land Cover Mapping Using MODIS Data

Over the last few decades, there has been a significant land cover (LC) change across the globe due to the increasing demand of the burgeoning population and urban sprawl. In order to take account of the change, there is a need for accurate and up-to-date LC maps. Mapping and monitoring of LC in India is being carried out at national level using multi-temporal IRS AWiFS data. Multispectral data such as IKONOS, Landsat-TM/ETM+, IRS-ICID LISS-III/IV, AWiFS and SPOT-5, etc. have adequate spatial resolution (similar to 1m to 56m) for LC mapping to generate 1:50,000 maps. However, for developing countries and those with large geographical extent, seasonal LC mapping is prohibitive with data from commercial sensors of limited spatial coverage. Superspectral data from the MODIS sensor are freely available, have better temporal (8 day composites) and spectral information. MODIS pixels typically contain a mixture of various LC types (due to coarse spatial resolution of 250, 500 and 1000 in), especially in more fragmented landscapes. In this context, linear spectral unmixing would be useful for mapping patchy land covers, such as those that characterise much of the Indian subcontinent. This work evaluates the existing unmixing technique for LC mapping using MODIS data, using end-members that are extracted through Pixel Purity Index (PPI), Scatter plot and N-dimensional visualisation. The abundance maps were generated for agriculture, built up, forest, plantations, waste land/others and water bodies. The assessment of the results using ground truth and a LISS-III classified map shows 86% overall accuracy, suggesting the potential for broad-scale applicability of the technique with superspectral data for natural resource planning and inventory applications. Index Terms-Remote sensing, digital

Effect of Crystallographic Structure of MnO2 on Its Electrochemical Capacitance Properties

MnO2 is currently under extensive investigations for its capacitance properties. MnO2 crystallizes into several crystallographic structures, namely, α, β, γ, δ, and λ structures. Because these structures differ in the way MnO6 octahedra are interlinked, they possess tunnels or interlayers with gaps of different magnitudes. Because capacitance properties are due to tercalation/deintercalation of protons or cations in MnO2, only some crystallographic structures, which possess sufficient gaps to accommodate these ions, are expected to be useful for capacitance studies. In order to examine the dependence of capacitance on crystal structure, the present study involves preparation of these various crystal phases of MnO2 in nanodimensions and to evaluate their capacitance properties. Results of α-MnO2 prepared by a microemulsion route (α-MnO2(m)) are also used for comparison. Spherical particles of about 50 nm, nanorods of 30−50 nm in diameter, or interlocked fibers of 10−20 nm in diameters are formed, which depend on the crystal structure and the method of preparation. The specific capacitance (SC) measured for MnO2 is found to depend strongly on the crystallographic structure, and it decreases in the following order: α(m) > α δ > γ > λ > β. A SC value of 297 F g-1 is obtained for α-MnO2(m), whereas it is 9 F g-1 for β-MnO2. A wide (4.6 Å) tunnel size and large surface area of α-MnO2(m) are ascribed as favorable factors for its high SC. A large interlayer separation (7 Å) also facilitates insertion of cations in δ-MnO2 resulting in a SC close to 236 F g-1. A narrow tunnel size (1.89 Å) does not allow intercalation of cations into β-MnO2. As a result, it provides a very small SC.

Inorganic Analogues of Graphene

The discovery of graphene has aroused great interest in the properties and phenomena exhibited by two-dimensional inorganic materials, especially when they comprise only a single, two or a few layers. Graphene-like MoS2 and WS2 have been prepared by chemical methods, and the materials have been characterized by electron microscopy, atomic force microscopy (AFM) and other methods. Boron nitride analogues of graphene have been obtained by a simple chemical procedure starting with boric acid and urea and have been characterized by various techniques that include surface area measurements. A new layered material with the composition BCN possessing a few layers and a large surface area discovered recently exhibits a large uptake of CO2.

Mining Land Cover Information Using Multilayer Perceptron and Decision Tree from MODIS Data

Land cover (LC) changes play a major role in global as well as at regional scale patterns of the climate and biogeochemistry of the Earth system. LC information presents critical insights in understanding of Earth surface phenomena, particularly useful when obtained synoptically from remote sensing data. However, for developing countries and those with large geographical extent, regular LC mapping is prohibitive with data from commercial sensors (high cost factor) of limited spatial coverage (low temporal resolution and band swath). In this context, free MODIS data with good spectro-temporal resolution meet the purpose. LC mapping from these data has continuously evolved with advances in classification algorithms. This paper presents a comparative study of two robust data mining techniques, the multilayer perceptron (MLP) and decision tree (DT) on different products of MODIS data corresponding to Kolar district, Karnataka, India. The MODIS classified images when compared at three different spatial scales (at district level, taluk level and pixel level) shows that MLP based classification on minimum noise fraction components on MODIS 36 bands provide the most accurate LC mapping with 86% accuracy, while DT on MODIS 36 bands principal components leads to less accurate classification (69%).

Amorphous, Monoclinic, and Tetragonal Porous Zirconia Through a Controlled Self-Sustained Combustion Route

Porous, large surface area, metastable zirconias, are of importance to catalytic, electrochemical, biological, and thermal insulation applications. Combustion synthesis is a very commonly used method for producing such zirconias. However, its rapid nature makes control difficult. A simple modification has been made to traditional solution combustion synthesis to address this problem. It involves the addition of starch to yield a starting mixture with a ``dough-like'' consistency. Just 5 wt% starch is seen to significantly alter the combustion characteristics of the ``dough.'' In particular, it helps to achieve better control over reaction zone temperature that is significantly lower than the one calculated by the adiabatic approximation typically used in self-propagating high-temperature synthesis. The effect of such control is demonstrated by the ability to tune dough composition to yield zirconias with different phase compositions from the relatively elusive ``amorphous'' to monoclinic (> 30 nm grain size) and tetragonal pure zirconia (< 30 nm grain size). The nature of this amorphous phase has been investigated using infrared spectroscopy. Starch content also helps tailor porosity in the final product. Zirconias with an average pore size of about 50 mu m and specific surface area as large as 110 m2/g have been obtained.

Studies on Cu/CeO2: A new NO reduction catalyst

Fine particle and large surface area Cu/CeO2 catalysts of crystallite sizes in the range of 100-200 Angstrom synthesized by the solution combustion method have been investigated for NO reduction. Five percent Cu/CeO2 catalyst shows nearly 100% conversion of NO by NH3 below 300 degrees C, whereas pure ceria and Zr, Y, and Ca doped ceria show 85-95% NO conversion above 600 degrees C. Similarly NO reduction by CO has been observed over 5% Cu/CeO2 with nearly 100% conversion below 300 degrees C. Hydrocarbon (n-butane) oxidation by NO to CO2, N-2, and H2O has also been demonstrated over this catalyst below 350 degrees C making Cu/CeO2 a new NO reduction catalyst in the low temperature window of 150-350 degrees C. Kinetics of NO reduction over 5% Cu/CeO2 have also been investigated. The rate constants are in the range of 1.4 x 10(4) to 2.3 x 10(4) cm(3) g(-1) s(-1) between 170 and 300 degrees C. Cu/CeO2 catalysts are characterized by X-ray diffraction, transmission electron microscopy, X-ray photoelectron spectroscopy, and electron paramagnetic resonance spectroscopy where Cu2+ ions are shown to be dispersed on the CeO2 surface. (C) 1999 Academic Press.

Synthesis and properties of willemite Zn2SiO4, and M2+: Zn2SiO4 (M = Co and Ni)

Fine particles of willemite, alpha -Zn2SiO4, were prepared by both solution combustion and sol-gel methods. Both processes yield single-phase, large-surface area (26- and 78-m(2)/g), sinteractive willemite powders. Thermal evolution of crystalline phases was studied using X-ray powder diffraction patterns. The combustion method favors low-temperature formation of willemite compared to the sol-gel method. The powders, when uniaxially pressed and sintered at 1300 degreesC, achieved 78-80% theoretical density. The microstructures of the sintered body show the presence of equiaxed 0.5- to 4-mum grains. Blue pigments of willemite doped with Co2+ and Ni2+ were also prepared by the combustion process.

Hotspots of solar potential in India

Solar hotspots are the regions characterized by an exceptional solar power potential suitable for decentralized commercial exploitation of energy. Identification of solar hotspots in a vast geographical expanse with dense habitations helps to meet escalating power demand in a decentralized, efficient and sustainable manner. This communication focuses on the assessment of resource potential with variability in India derived from high resolution satellite derived insolation data. Data analysis reveals that nearly 58% of the geographical area potentially represent the solar hotspots in the country with more than 5 kWh/m(2)/day of annual average Global insolation. A techno-economic analysis of the solar power technologies and a prospective minimal utilization of the land available within these solar hotspots demonstrate their immense power generation as well as emission reduction potential. The study evaluates the progress made in solar power generation in the country especially with the inception of an ambitious National Solar Mission (NSM) also termed as `Solar India'. The organizational aspects of solar power generation with focus on existing policy elements are also addressed so as to probe the actual potential of the identified solar hotspots in meeting the NSM targets and beyond. (C) 2011 Elsevier Ltd. All rights reserved.

Contrained linear spectral unmixing technique for regional land cover mapping using MODIS data

Over the last few decades, there has been a significant land cover (LC) change across the globe due to the increasing demand of the burgeoning population and urban sprawl. In order to take account of the change, there is a need for accurate and up- to-date LC maps. Mapping and monitoring of LC in India is being carried out at national level using multi-temporal IRS AWiFS data. Multispectral data such as IKONOS, Landsat- TM/ETM+, IRS-1C/D LISS-III/IV, AWiFS and SPOT-5, etc. have adequate spatial resolution (~ 1m to 56m) for LC mapping to generate 1:50,000 maps. However, for developing countries and those with large geographical extent, seasonal LC mapping is prohibitive with data from commercial sensors of limited spatial coverage. Superspectral data from the MODIS sensor are freely available, have better temporal (8 day composites) and spectral information. MODIS pixels typically contain a mixture of various LC types (due to coarse spatial resolution of 250, 500 and 1000 m), especially in more fragmented landscapes. In this context, linear spectral unmixing would be useful for mapping patchy land covers, such as those that characterise much of the Indian subcontinent. This work evaluates the existing unmixing technique for LC mapping using MODIS data, using end- members that are extracted through Pixel Purity Index (PPI), Scatter plot and N-dimensional visualisation. The abundance maps were generated for agriculture, built up, forest, plantations, waste land/others and water bodies. The assessment of the results using ground truth and a LISS-III classified map shows 86% overall accuracy, suggesting the potential for broad-scale applicability of the technique with superspectral data for natural resource planning and inventory applications.

Changes in seasonal snow cover in Hindu Kush-Himalayan region

The changes in seasonal snow covered area in the Hindu Kush-Himalayan (HKH) region have been examined using Moderate – resolution Imaging Spectroradiometer (MODIS) 8-day standard snow products. The average snow covered area of the HKH region based on satellite data from 2000 to 2010 is 0.76 million km2 which is 18.23% of the total geographical area of the region. The linear trend in annual snow cover from 2000 to 2010 is −1.25±1.13%. This is in consistent with earlier reported decline of the decade from 1990 to 2001. A similar trend for western, central and eastern HKH region is 8.55±1.70%, +1.66% ± 2.26% and 0.82±2.50%, respectively. The snow covered area in spring for HKH region indicates a declining trend (−1.04±0.97%). The amount of annual snowfall is correlated with annual seasonal snow cover for the western Himalaya, indicating that changes in snow cover are primarily due to interannual variations in circulation patterns. Snow cover trends over a decade were also found to vary across seasonally and the region. Snow cover trends for western HKH are positive for all seasons. In central HKH the trend is positive (+15.53±5.69%) in autumn and negative (−03.68±3.01) in winter. In eastern HKH the trend is positive in summer (+3.35±1.62%) and autumn (+7.74±5.84%). The eastern and western region of HKH has an increasing trend of 10% to 12%, while the central region has a declining trend of 12% to 14% in the decade between 2000 and 2010. Snow cover depletion curve plotted for the hydrological year 2000–2001 reveal peaks in the month of February with subsidiary peaks observed in November and December in all three regions of the HKH.

First principles calculations of H-storage in sorption materials

A review of various contributions of first principles calculations in the area of hydrogen storage, particularly for the carbon-based sorption materials, is presented. Carbon-based sorption materials are considered as promising hydrogen storage media due to their light weight and large surface area. Depending upon the hybridization state of carbon, these materials can bind the hydrogen via various mechanisms, including physisorption, Kubas and chemical bonding. While attractive binding energy range of Kubas bonding has led to design of several promising storage systems, in reality the experiments remain very few due to materials design challenges that are yet to be overcome. Finally, we will discuss the spillover process, which deals with the catalytic chemisorption of hydrogen, and arguably is the most promising approach for reversibly storing hydrogen under ambient conditions.

Porous Three Dimensional Arrays of Plasmonic Nanoparticles

Plasmonic interactions in a well-defined array of metallic nanoparticles can lead to interesting optical effects, such as local electric field enhancement and shifts in the extinction spectra, which are of interest in diverse technological applications, including those pertaining to biochemical sensing and photonic circuitry. Here, we report on a single-step wafer scale fabrication of a three-dimensional array of metallic nanoparticles whose sizes and separations can be easily controlled to be anywhere between fifty to a few hundred nanometers, allowing the optical response of the system to be tailored with great control in the visible region of the spectrum. The substrates, apart from having a large surface area, are inherently porous and therefore suitable for optical sensing applications, such as surface enhanced Raman scattering, containing a high density of spots with enhanced local electric fields arising from plasmonic couplings.

Multiple transmitter localization and communication footprint identification using sparse reconstruction techniques

This paper considers the problem of identifying the footprints of communication of multiple transmitters in a given geographical area. To do this, a number of sensors are deployed at arbitrary but known locations in the area, and their individual decisions regarding the presence or absence of the transmitters' signal are combined at a fusion center to reconstruct the spatial spectral usage map. One straightforward scheme to construct this map is to query each of the sensors and cluster the sensors that detect the primary's signal. However, using the fact that a typical transmitter footprint map is a sparse image, two novel compressive sensing based schemes are proposed, which require significantly fewer number of transmissions compared to the querying scheme. A key feature of the proposed schemes is that the measurement matrix is constructed from a pseudo-random binary phase shift applied to the decision of each sensor prior to transmission. The measurement matrix is thus a binary ensemble which satisfies the restricted isometry property. The number of measurements needed for accurate footprint reconstruction is determined using compressive sampling theory. The three schemes are compared through simulations in terms of a performance measure that quantifies the accuracy of the reconstructed spatial spectral usage map. It is found that the proposed sparse reconstruction technique-based schemes significantly outperform the round-robin scheme.