Selective colorimetric detection of nanomolar Cr (VI) in aqueous solutions using unmodified silver nanoparticles

In this study we present a colorimetric detection method for Cr (VI) in aqueous solution based on as synthesized silver nanoparticles (Ag NPs) without surface functionalization. The method principle involves reduction of Cr (VI) to Cr (III) by excess reductant present in as synthesized Ag NP dispersion, and subsequent aggregation of Ag NPs by Cr (III) leading to red-shift of the surface plasmon resonance (SPR) peak. The UV-vis absorption spectra. Zeta potentials, dynamic light scattering measurements, and scanning electron microscopy (SEM) confirmed the aggregation of the Ag NPs. Under the optimized conditions, a good linear relationship (correlation coefficient r=0.981) was obtained between the ratio of the absorbance at 550 nm to that at 390 nm (A(550/390)) and the concentration of Cr (VI) over the range of 10(-3)-10(-9) M 50 mg/L to 50 ng/L]. The reported probe has a limit of detection down to 1 nM, which, to the best of our knowledge, is the lowest ever reported for the colorimetric detection of Cr (VI). Furthermore, a remarkable feature of this method is that it involves a simple technique exhibiting high selectivity to Cr (VI) over other tested heavy metal ions. (C) 2012 Elsevier BM. All rights reserved.

Cascaded walks in protein sequence space: use of artificial sequences in remote homology detection between natural proteins

Over the past two decades, many ingenious efforts have been made in protein remote homology detection. Because homologous proteins often diversify extensively in sequence, it is challenging to demonstrate such relatedness through entirely sequence-driven searches. Here, we describe a computational method for the generation of `protein-like' sequences that serves to bridge gaps in protein sequence space. Sequence profile information, as embodied in a position-specific scoring matrix of multiply aligned sequences of bona fide family members, serves as the starting point in this algorithm. The observed amino acid propensity and the selection of a random number dictate the selection of a residue for each position in the sequence. In a systematic manner, and by applying a `roulette-wheel' selection approach at each position, we generate parent family-like sequences and thus facilitate an enlargement of sequence space around the family. When generated for a large number of families, we demonstrate that they expand the utility of natural intermediately related sequences in linking distant proteins. In 91% of the assessed examples, inclusion of designed sequences improved fold coverage by 5-10% over searches made in their absence. Furthermore, with several examples from proteins adopting folds such as TIM, globin, lipocalin and others, we demonstrate that the success of including designed sequences in a database positively sensitized methods such as PSI-BLAST and Cascade PSI-BLAST and is a promising opportunity for enormously improved remote homology recognition using sequence information alone.

Carbon dioxide detection by boron nitride nanotubes

The effect of gas molecule adsorption is investigated on the density of states of (9,0) zigzag boron nitride nanotube within a random tight-binding Hamiltonian model. The Green function approach and coherent potential approximation have been implemented. The results show that the adsorption of carbon dioxide gas molecules by boron atoms only leads to a donor type semiconductor while the adsorption by nitrogen atoms only leads to an acceptor. Since the gas molecules are adsorbed by both boron and nitrogen atoms, a reduction of the band gap is found. In all cases, increasing the gas concentration causes an increase in the height of the peaks in the band gap. This is due to an increasing charge carrier concentration induced by adsorbed gas molecules.

Green synthesis of biopolymer-silver nanoparticle nanocomposite: An optical sensor for ammonia detection

Biopolymer used for the production of nanoparticles (NPs) has attracted increasing attention. In the presence article we use aqueous solution of polysaccharide Cyamopsis tetragonaloba commonly known as guar gum (GG), from plants. GG acts as reductive preparation of silver nanoparticles which are found to be <10. nm in size. The uniformity of the NPs size was measured by the SEM and TEM, while a face centered cubic structure of crystalline silver nanoparticles was characterized using powder X-ray diffraction technique. Aqueous ammonia sensing study of polymer/silver nanoparticles nanocomposite (GG/AgNPs NC) was performed by optical method based on surface plasmon resonance (SPR). The performances of optical sensor were investigated which provide the excellent result. The response time of 2-3. s and the detection limit of ammonia solution, 1. ppm were found at room temperature. Thus, in future this room temperature optical ammonia sensor can be used for clinical and medical diagnosis for detecting low ammonia level in biological fluids, such as plasma, sweat, saliva, cerebrospinal liquid or biological samples in general for various biomedical applications in human. © 2012 Elsevier B.V.

Pulsed radio emission from the Fermi-LAT pulsar J1732-3131: Search and a possible detection at 34.5 MHz

We report our search for and a possible detection of periodic radio pulses at 34.5 MHz from the Fermi Large Area Telescope pulsar J1732-3131. The candidate detection has been possible in only one of the many sessions of observations made with the low-frequency array at Gauribidanur, India, when the otherwise radio weak pulsar may have apparently brightened many folds. The candidate dispersion measure along the sight line, based on the broad periodic profiles from �20min of data, is estimated to be 15.44 ± 0.32 pccc -1. We present the details of our periodic and single-pulse search, and discuss the results and their implications relevant to both, the pulsar and the intervening medium. © 2012 RAS.

Simultaneous Detection of Two Triplets: A Time-Resolved Resonance Raman Study

Solvents are known to affect the triplet state structure and reactivity. In this paper, we have employed time-resolved resonance Raman (TR3) spectroscopy to understand solvent-induced subtle structural changes in the lowest excited triplet state of thioxanthone. Density functional theory (DFT) combined with the self-consistent reaction field (SCRF) implicit solvation model has been used to calculate the vibrational frequencies in the solvents. Here, we report a unique observation of the coexistence of two triplets, which has been substantiated by the probe wavelength-dependent Raman experiments. The coexistence of two triplets has been further supported by photoreduction experiments carried out at various temperatures.

Detection of sugar-lectin interactions by multivalent dendritic sugar functionalized single-walled carbon nanotubes

We show that single walled carbon nanotubes (SWNTs) decorated with sugar functionalized poly (propyl ether imine) (PETIM) dendrimer is a very sensitive platform to quantitatively detect carbohydrate recognizing proteins, namely, lectins. The changes in electrical conductivity of SWNT in field effect transistor device due to carbohydrate-protein interactions form the basis of present study. The mannose sugar attached PETIM dendrimers undergo charge-transfer interactions with the SWNTs. The changes in the conductance of the dendritic sugar functionalized SWNT after addition of lectins in varying concentrations were found to follow the Langmuir type isotherm, giving the concanavalin A (Con A)-mannose affinity constant to be 8.5 x 10(6) M-1. The increase in the device conductance observed after adding 10 nM of Con A is same as after adding 20 mu M of a non-specific lectin peanut agglutinin, showing the high specificity of the Con A-mannose interactions. The specificity of sugar-lectin interactions was characterized further by observing significant shifts in Raman modes of the SWNTs. (C) 2012 American Institute of Physics. http://dx.doi.org/10.1063/1.4739793]

Zinc Oxide Modified Au Electrode as Sensor for an Efficient Detection of Hydrazine

ZnO nanoparticles (ZnO NPs) prepared by microwave heating technique are used to modify a gold electrode (ZnO/Au) for the hydrazine detection study. The synthesized product is well characterized by various techniques. Detailed electrochemical investigation of the oxidation of hydrazine on the ZnO/Au electrode in 0.02 M phosphate buffer solution (PBS) of pH 7.4 was carried out. A very low detection limit of 66 nM (S/N=4) and a wide linearity in current for a concentration range from 66.0X10-3 to 415 mu M was achieved by amperometry. The electrode was found to be stable for over a month when preserved in PBS.

Multilayer assemblies of polyelectrolyte-gold nanoparticles for the electrocatalytic oxidation and detection of arsenic(III)

Multilayers of poly(diallyldimethylammonium chloride) (PDDA) and citrate capped Au nanoparticles (AuNPs) anchored on sodium 3-mercapto-1-propanesulfonate modified gold electrode by electrostatic layer-by-layer assembly (LbL) technique are shown to be an excellent architecture for the direct electrochemical oxidation of As(III) species. The growth of successive layers in the proposed LbL architecture is followed by atomic force microscopy, UV-vis spectroscopy, quartz crystal microbalance with energy dissipation, and electrochemistry. The first bilayer is found to show rather different physico-chemical characteristics as compared to the subsequent bilayers, and this is attributed to the difference in the adsorption environments. The analytical utility of the architecture with five bilayers is exploited for arsenic sensing via the direct electrocatalytic oxidation of As(III), and the detection limit is found to be well below the WHO guidelines of 10 ppb. When the non-redox active PDDA is replaced by the redoxactive Os(2,2'-bipyridine)(2)Cl-poly(4-vinylpyridine) polyelectrolyte (PVPOs) in the LbL assembly, the performance is found to be inferior, demonstrating that the redox activity of the polyelectrolyte is futile as far as the direct electro-oxidation of As(III) is concerned. (C) 2012 Elsevier Inc. All rights reserved.

Derating Based Hardware Optimizations in Soft Error Tolerant Designs

Ensuring reliable operation over an extended period of time is one of the biggest challenges facing present day electronic systems. The increased vulnerability of the components to atmospheric particle strikes poses a big threat in attaining the reliability required for various mission critical applications. Various soft error mitigation methodologies exist to address this reliability challenge. A general solution to this problem is to arrive at a soft error mitigation methodology with an acceptable implementation overhead and error tolerance level. This implementation overhead can then be reduced by taking advantage of various derating effects like logical derating, electrical derating and timing window derating, and/or making use of application redundancy, e. g. redundancy in firmware/software executing on the so designed robust hardware. In this paper, we analyze the impact of various derating factors and show how they can be profitably employed to reduce the hardware overhead to implement a given level of soft error robustness. This analysis is performed on a set of benchmark circuits using the delayed capture methodology. Experimental results show upto 23% reduction in the hardware overhead when considering individual and combined derating factors.

Stochastic Simulations Suggest that HIV-1 Survives Close to Its Error Threshold

The use of mutagenic drugs to drive HIV-1 past its error threshold presents a novel intervention strategy, as suggested by the quasispecies theory, that may be less susceptible to failure via viral mutation-induced emergence of drug resistance than current strategies. The error threshold of HIV-1, mu(c), however, is not known. Application of the quasispecies theory to determine mu(c) poses significant challenges: Whereas the quasispecies theory considers the asexual reproduction of an infinitely large population of haploid individuals, HIV-1 is diploid, undergoes recombination, and is estimated to have a small effective population size in vivo. We performed population genetics-based stochastic simulations of the within-host evolution of HIV-1 and estimated the structure of the HIV-1 quasispecies and mu(c). We found that with small mutation rates, the quasispecies was dominated by genomes with few mutations. Upon increasing the mutation rate, a sharp error catastrophe occurred where the quasispecies became delocalized in sequence space. Using parameter values that quantitatively captured data of viral diversification in HIV-1 patients, we estimated mu(c) to be 7 x 10(-5) -1 x 10(-4) substitutions/site/replication, similar to 2-6 fold higher than the natural mutation rate of HIV-1, suggesting that HIV-1 survives close to its error threshold and may be readily susceptible to mutagenic drugs. The latter estimate was weakly dependent on the within-host effective population size of HIV-1. With large population sizes and in the absence of recombination, our simulations converged to the quasispecies theory, bridging the gap between quasispecies theory and population genetics-based approaches to describing HIV-1 evolution. Further, mu(c) increased with the recombination rate, rendering HIV-1 less susceptible to error catastrophe, thus elucidating an added benefit of recombination to HIV-1. Our estimate of mu(c) may serve as a quantitative guideline for the use of mutagenic drugs against HIV-1.

Factor graph based joint detection/decoding for LDPC coded large-MIMO systems

In this paper, we employ message passing algorithms over graphical models to jointly detect and decode symbols transmitted over large multiple-input multiple-output (MIMO) channels with low density parity check (LDPC) coded bits. We adopt a factor graph based technique to integrate the detection and decoding operations. A Gaussian approximation of spatial interference is used for detection. This serves as a low complexity joint detection/decoding approach for large dimensional MIMO systems coded with LDPC codes of large block lengths. This joint processing achieves significantly better performance than the individual detection and decoding scheme.

On the basin-scale detection and attribution of human-induced climate change in monsoon precipitation and streamflow

Detecting and quantifying the presence of human-induced climate change in regional hydrology is important for studying the impacts of such changes on the water resources systems as well as for reliable future projections and policy making for adaptation. In this article a formal fingerprint-based detection and attribution analysis has been attempted to study the changes in the observed monsoon precipitation and streamflow in the rain-fed Mahanadi River Basin in India, considering the variability across different climate models. This is achieved through the use of observations, several climate model runs, a principal component analysis and regression based statistical downscaling technique, and a Genetic Programming based rainfall-runoff model. It is found that the decreases in observed hydrological variables across the second half of the 20th century lie outside the range that is expected from natural internal variability of climate alone at 95% statistical confidence level, for most of the climate models considered. For several climate models, such changes are consistent with those expected from anthropogenic emissions of greenhouse gases. However, unequivocal attribution to human-induced climate change cannot be claimed across all the climate models and uncertainties in our detection procedure, arising out of various sources including the use of models, cannot be ruled out. Changes in solar irradiance and volcanic activities are considered as other plausible natural external causes of climate change. Time evolution of the anthropogenic climate change ``signal'' in the hydrological observations, above the natural internal climate variability ``noise'' shows that the detection of the signal is achieved earlier in streamflow as compared to precipitation for most of the climate models, suggesting larger impacts of human-induced climate change on streamflow than precipitation at the river basin scale.