Atmospheric pressure plasma chemical vapor deposition reactor for 100 mm wafers, optimized for minimum contamination at low gas flow rates

Gas discharge plasmas used for thinfilm deposition by plasma-enhanced chemical vapor deposition (PECVD) must be devoid of contaminants, like dust or active species which disturb the intended chemical reaction. In atmospheric pressure plasma systems employing an inert gas, the main source of such contamination is the residual air inside the system. To enable the construction of an atmospheric pressure plasma (APP) system with minimal contamination, we have carried out fluid dynamic simulation of the APP chamber into which an inert gas is injected at different mass flow rates. On the basis of the simulation results, we have designed and built a simple, scaled APP system, which is capable of holding a 100 mm substrate wafer, so that the presence of air (contamination) in the APP chamber is minimized with as low a flow rate of argon as possible. This is examined systematically by examining optical emission from the plasma as a function of inert gas flow rate. It is found that optical emission from the plasma shows the presence of atmospheric air, if the inlet argon flow rate is lowered below 300 sccm. That there is minimal contamination of the APP reactor built here, was verified by conducting an atmospheric pressure PECVD process under acetylene flow, combined with argon flow at 100 sccm and 500 sccm. The deposition of a polymer coating is confirmed by infrared spectroscopy. X-ray photoelectron spectroscopy shows that the polymer coating contains only 5% of oxygen, which is comparable to the oxygen content in polymer deposits obtained in low-pressure PECVD systems. (C) 2015 AIP Publishing LLC.

Evaluation of Precipitation Retrievals From Orbital Data Products of TRMM Over a Subtropical Basin in India

The spatial error structure of daily precipitation derived from the latest version 7 (v7) tropical rainfall measuring mission (TRMM) level 2 data products are studied through comparison with the Asian precipitation highly resolved observational data integration toward evaluation of the water resources (APHRODITE) data over a subtropical region of the Indian subcontinent for the seasonal rainfall over 6 years from June 2002 to September 2007. The data products examined include v7 data from the TRMM radiometer Microwave Imager (TMI) and radar precipitation radar (PR), namely, 2A12, 2A25, and 2B31 (combined data from PR and TMI). The spatial distribution of uncertainty from these data products were quantified based on performance metrics derived from the contingency table. For the seasonal daily precipitation over a subtropical basin in India, the data product of 2A12 showed greater skill in detecting and quantifying the volume of rainfall when compared with the 2A25 and 2B31 data products. Error characterization using various error models revealed that random errors from multiplicative error models were homoscedastic and that they better represented rainfall estimates from 2A12 algorithm. Error decomposition techniques performed to disentangle systematic and random errors verify that the multiplicative error model representing rainfall from 2A12 algorithm successfully estimated a greater percentage of systematic error than 2A25 or 2B31 algorithms. Results verify that although the radiometer derived 2A12 rainfall data is known to suffer from many sources of uncertainties, spatial analysis over the case study region of India testifies that the 2A12 rainfall estimates are in a very good agreement with the reference estimates for the data period considered.

Coupled Mechanisms of Precipitation and Atomization in Burning Nanofluid Fuel Droplets

Understanding the combustion characteristics of fuel droplets laden with energetic nanoparticles (NP) is pivotal for lowering ignition delay, reducing pollutant emissions and increasing the combustion efficiency in next generation combustors. In this study, first we elucidate the feedback coupling between two key interacting mechanisms, namely, secondary atomization and particle agglomeration; that govern the effective mass fraction of NPs within the droplet. Second, we show how the initial NP concentration modulates their relative dominance leading to a masterslave configuration. Secondary atomization of novel nanofuels is a crucial process since it enables an effective transport of dispersed NPs to the flame (a pre-requisite condition for NPs to burn). Contrarily, NP agglomeration at the droplet surface leads to shell formation thereby retaining NPs inside the droplet. In particular, we show that at dense concentrations shell formation (master process) dominates over secondary atomization (slave) while at dilute particle loading it is the high frequency bubble ejections (master) that disrupt shell formation (slave) through its rupture and continuous outflux of NPs. This results in distinct combustion residues at dilute and dense concentrations, thereby providing a method of manufacturing flame synthesized microstructures with distinct morphologies.

Common effect of chemical and external pressures on the magnetic properties of RCoPO (R = La, Pr, Nd, Sm). II.

We investigate the direct correspondence between Co band ferromagnetism and structural parameters in the pnictide oxides RCoPO for different rare-earth ions (R = La, Pr, Nd, Sm) by means of muon-spin spectroscopy and ab initio calculations, complementing our results published previously G. Prando et al., Common effect of chemical and external pressures on the magnetic properties of RCoPO (R = La, Pr), Phys. Rev. B 87, 064401 (2013)]. We find that both the transition temperature to the ferromagnetic phase T-C and the volume of the crystallographic unit cell V are conveniently tuned by the R ionic radius and/or external pressure. We report a linear correlation between T-C and V and our ab initio calculations unambiguously demonstrate a full equivalence of chemical and external pressures. As such, we show that R ions influence the ferromagnetic phase only via the induced structural shrinkage without involving any active role from the electronic f degrees of freedom, which are only giving a sizable magnetic contribution at much lower temperatures.

Synthesis of 3-dimensional porous graphene nanosheets using electron cyclotron resonance plasma enhanced chemical vapour deposition

Microwave plasma driven chemical vapour deposition was used to synthesize graphene nanosheets from a mixture of acetylene and hydrogen gas molecules. In this plasma, acetylene decomposes to carbon atoms that form nanostructures in the outlet plasma stream and get deposited on the substrate. The GNS consists of a few layers of graphene aligned vertically to the substrate. Graphene layers have been confirmed by high-resolution transmission electron microscopy, and Raman spectral studies were conducted to observe the defective nature of the sample. The growth of nanosheets in a vertical direction is assumed to be due to the effect of electric field and from the difference in the deposition rate in the axial and parallel directions. These vertical graphene sheets are attractive for various applications in energy storage and sensors.

Nickel substitution induced effects on gas sensing properties of cobalt ferrite nanoparticles

Ni2+ ion induced unusual conductivity reversal and an enhancement in the gas sensing properties of ferrites based gas sensors, is reported. The Co1-xNixFe2O4 (for x = 0, 0.5 and 1) nanoparticles were synthesized by wet chemical co-precipitation method and gas sensing properties were studied as a function of composition and temperature. The structural, morphological and microstructural characterization revealed crystallite size of in the range 10-20 nm with porous morphology consisting of nano-sized grains. The Energy Dispersive X-ray (EDX) mapping confirms homogeneous distribution of Co, Ni, Fe and O elements in the ferrites. The non-stoichiometry of the inverse spinel type ferrites and the relative concentration of Ni3+/Co3+ defects were studied using X-ray photoelectron spectroscopy. It is found that the addition of Ni2+ ions into cobalt ferrite shows preferred selectivity towards CO gas at high temperature (325 degrees C) and ethanol gas at low temperature (250 degrees C), unlike undoped cobalt ferrite or undoped nickel ferrite, which show similar response for both these gases. Moreover, an unusual conductivity reversal is observed, except cobalt ferrite due to the difference in reactivity of the gases as well as characteristic non-stoichiometry of ferrites. This behavior is highly gas ambient dependent and hence can be well-exploited for selective detection of gases. (C) 2015 Elsevier B.V. All rights reserved.

Ag-doped hydroxyapatite as efficient adsorbent for removal of Congo red dye from aqueous solution: Synthesis, kinetic and equilibrium adsorption isotherm analysis

In the present study a versatile and efficient adsorbent with high adsorption capacity for adsorption of Congo red dye in aqueous solution at ambient temperature without adjusting any pH is presented over the Ag modified calcium hydroxyapatite (CaHAp). CaHAp and Ag-doped CaHAp materials were synthesized using facile aqueous precipitation method. The physico-chemical properties of the materials were determined by powder X-ray diffraction (XRD), Fourier transform infrared (FT-IR) spectroscopy, Transmission electron microscopy (TEM), UV-Visible spectroscopy, N-2 physisorption and acidity was determined by n-butylamine titration and pyridine adsorption methods. XRD analysis confirmed all adsorbents exhibit hexagonal CaHAp structure with P6(3)/m space group. TEM analysis confirms the rod like morphology of the adsorbents and the average length of the rods were in the range of 40-45 nm. Pyridine adsorption results indicate increase in number of Lewis acid sites with Ag doping in CaHAp. Adsorption capacity of CaHAp was found increased with Ag content in the adsorbents. Ag (10): CaHAp adsorbent showed superior adsorption performance among all the adsorbents for various concentrations of Congo red (CR) dye in aqueous solutions. The amount of CR dye adsorbed on Ag (10): CaHAp was found to be 49.89-267.81 mg g(-1) for 50-300 ppm in aqueous solution. A good correlation between adsorption capacity and acidity of the adsorbents was observed. The adsorption kinetic data of adsorbents fitted well with pseudo second-order kinetic model with correlation coefficients ranged from 0.998 to 0.999. The equilibrium adsorption data was found to best fit to the Langmuir adsorption isotherm model. (C) 2015 Elsevier Inc. All rights reserved.

Comprehensive evaluation of multisatellite precipitation estimates over India using gridded rainfall data

This study presents a comprehensive evaluation of five widely used multisatellite precipitation estimates (MPEs) against 1 degrees x 1 degrees gridded rain gauge data set as ground truth over India. One decade observations are used to assess the performance of various MPEs (Climate Prediction Center (CPC)-South Asia data set, CPC Morphing Technique (CMORPH), Precipitation Estimation From Remotely Sensed Information Using Artificial Neural Networks, Tropical Rainfall Measuring Mission's Multisatellite Precipitation Analysis (TMPA-3B42), and Global Precipitation Climatology Project). All MPEs have high detection skills of rain with larger probability of detection (POD) and smaller ``missing'' values. However, the detection sensitivity differs from one product (and also one region) to the other. While the CMORPH has the lowest sensitivity of detecting rain, CPC shows highest sensitivity and often overdetects rain, as evidenced by large POD and false alarm ratio and small missing values. All MPEs show higher rain sensitivity over eastern India than western India. These differential sensitivities are found to alter the biases in rain amount differently. All MPEs show similar spatial patterns of seasonal rain bias and root-mean-square error, but their spatial variability across India is complex and pronounced. The MPEs overestimate the rainfall over the dry regions (northwest and southeast India) and severely underestimate over mountainous regions (west coast and northeast India), whereas the bias is relatively small over the core monsoon zone. Higher occurrence of virga rain due to subcloud evaporation and possible missing of small-scale convective events by gauges over the dry regions are the main reasons for the observed overestimation of rain by MPEs. The decomposed components of total bias show that the major part of overestimation is due to false precipitation. The severe underestimation of rain along the west coast is attributed to the predominant occurrence of shallow rain and underestimation of moderate to heavy rain by MPEs. The decomposed components suggest that the missed precipitation and hit bias are the leading error sources for the total bias along the west coast. All evaluation metrics are found to be nearly equal in two contrasting monsoon seasons (southwest and northeast), indicating that the performance of MPEs does not change with the season, at least over southeast India. Among various MPEs, the performance of TMPA is found to be better than others, as it reproduced most of the spatial variability exhibited by the reference.

On the detection of human influence in extreme precipitation over India

Climate change is expected to influence extreme precipitation which in turn might affect risks of pluvial flooding. Recent studies on extreme rainfall over India vary in their definition of extremes, scales of analyses and conclusions about nature of changes in such extremes. Fingerprint-based detection and attribution (D&A) offer a formal way of investigating the presence of anthropogenic signals in hydroclimatic observations. There have been recent efforts to quantify human effects in the components of the hydrologic cycle at large scales, including precipitation extremes. This study conducts a D&A analysis on precipitation extremes over India, considering both univariate and multivariate fingerprints, using a standardized probability-based index (SPI) from annual maximum one-day (RX1D) and five-day accumulated (RX5D) rainfall. The pattern-correlation based fingerprint method is used for the D&A analysis. Transformation of annual extreme values to SPI and subsequent interpolation to coarser grids are carried out to facilitate comparison between observations and model simulations. Our results show that in spite of employing these methods to address scale and physical processes mismatch between observed and model simulated extremes, attributing changes in regional extreme precipitation to anthropogenic climate change is difficult. At very high (95%) confidence, no signals are detected for RX1D, while for the RX5D and multivariate cases only the anthropogenic (ANT) signal is detected, though the fingerprints are in general found to be noisy. The findings indicate that model simulations may underestimate regional climate system responses to increasing human forcings for extremes, and though anthropogenic factors may have a role to play in causing changes in extreme precipitation, their detection is difficult at regional scales and not statistically significant. (C) 2015 Elsevier B.V. All rights reserved.

Multivariate PLS Modeling of Apicomplexan FabD-Ligand Interaction Space for Mapping Target-Specific Chemical Space and Pharmacophore Fingerprints

Biomolecular recognition underlying drug-target interactions is determined by both binding affinity and specificity. Whilst, quantification of binding efficacy is possible, determining specificity remains a challenge, as it requires affinity data for multiple targets with the same ligand dataset. Thus, understanding the interaction space by mapping the target space to model its complementary chemical space through computational techniques are desirable. In this study, active site architecture of FabD drug target in two apicomplexan parasites viz. Plasmodium falciparum (PfFabD) and Toxoplasma gondii (TgFabD) is explored, followed by consensus docking calculations and identification of fifteen best hit compounds, most of which are found to be derivatives of natural products. Subsequently, machine learning techniques were applied on molecular descriptors of six FabD homologs and sixty ligands to induce distinct multivariate partial-least square models. The biological space of FabD mapped by the various chemical entities explain their interaction space in general. It also highlights the selective variations in FabD of apicomplexan parasites with that of the host. Furthermore, chemometric models revealed the principal chemical scaffolds in PfFabD and TgFabD as pyrrolidines and imidazoles, respectively, which render target specificity and improve binding affinity in combination with other functional descriptors conducive for the design and optimization of the leads.

The electronic, chemical and electrocatalytic processes and intermediates on iron oxide surfaces during photoelectrochemical water splitting

We re-assess experimental soft X-ray absorption spectra of the oxygen K-shell which we recorded operando from iron oxide during photoelectrochemical water splitting in KOH electrolyte. In particular, we refer to recently reported transitional electron hole states which originate within the charge carrier depletion layer of the iron oxide and on the iron oxide surface. For the latter we find that an intermediate oxy-peroxo species is formed on the iron oxide with increasing bias potential, which disappears upon further polarization of the electrode, concomitantly with the evolution and disappearance of the aforementioned surface state. The oxygen spectra contain also the spectroscopic signatures of the electrolyte water, the position of which changes with increasing bias potential towards lower X-ray energies, revealing the breaking and formation of hydrogen bonds in the water during the experiment. Combined with potential dependent impedance spectroscopy data we are able to sketch the molecular structure of chemical intermediates and their charge carrier dynamics. (C) 2015 Elsevier B.V. All rights reserved.

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.

Chemical Shifts to Metabolic Pathways: Identifying Metabolic Pathways Directly from a Single 2D NMR Spectrum

Identifying cellular processes in terms of metabolic pathways is one of the avowed goals of metabolomics studies. Currently, this is done after relevant metabolites are identified to allow their mapping onto specific pathways. This task is daunting due to the complex nature of cellular processes and the difficulty in establishing the identity of individual metabolites. We propose here a new method: ChemSMP (Chemical Shifts to Metabolic Pathways), which facilitates rapid analysis by identifying the active metabolic pathways directly from chemical shifts obtained from a single two-dimensional (2D) C-13-H-1] correlation NMR spectrum without the need for identification and assignment of individual metabolites. ChemSMP uses a novel indexing and scoring system comprised of a ``uniqueness score'' and a ``coverage score''. Our method is demonstrated on metabolic pathways data from the Small Molecule Pathway Database (SMPDB) and chemical shifts from the Human Metabolome Database (HMDB). Benchmarks show that ChemSMP has a positive prediction rate of >90% in the presence of deduttered data and can sustain the same at 60-70% even in the presence of noise, such as deletions of peaks and chemical shift deviations. The method tested on NMR data acquired for a mixture of 20 amino acids shows a success rate of 93% in correct recovery of pathways. When used on data obtained from the cell lysate of an unexplored oncogenic cell line, it revealed active metabolic pathways responsible for regulating energy homeostasis of cancer cells. Our unique tool is thus expected to significantly enhance analysis of NMIR-based metabolomics data by reducing existing impediments.

Hydrogen energy future with formic acid: a renewable chemical hydrogen storage system

Formic acid, the simplest carboxylic acid, is found in nature or can be easily synthesized in the laboratory (major by-product of some second generation biorefinery processes); it is also an important chemical due to its myriad applications in pharmaceuticals and industry. In recent years, formic acid has been used as an important fuel either without reformation (in direct formic acid fuel cells, DFAFCs) or with reformation (as a potential chemical hydrogen storage material). Owing to the better efficiency of DFAFCs compared to several other PEMFCs and reversible hydrogen storage systems, formic acid could serve as one of the better fuels for portable devices, vehicles and other energy-related applications in the future. This perspective is focused on recent developments in the use of formic acid as a reversible source for hydrogen storage. Recent developments in this direction will likely give access to a variety of low-cost and highly efficient rechargeable hydrogen fuel cells within the next few years by the use of suitable homogeneous metal complex/heterogeneous metal nanoparticle-based catalysts under ambient reaction conditions. The production of formic acid from atmospheric CO2 (a greenhouse gas) will decrease the CO2 content and may be helpful in reducing global warming.

Tailoring of Electronic Structure and Thermoelectric Properties of a Topological Crystalline Insulator by Chemical Doping

Topological crystalline insulators (TCIs) are a new quantum state of matter in which linearly dispersed metallic surface states are protected by crystal mirror symmetry. Owing to its vanishingly small bulk band gap, a TCI like Pb0.6Sn0.4Te has poor thermoelectric properties. Breaking of crystal symmetry can widen the band gap of TCI. While breaking of mirror symmetry in a TCI has been mostly explored by various physical perturbation techniques, chemical doping, which may also alter the electronic structure of TCI by perturbing the local mirror symmetry, has not yet been explored. Herein, we demonstrate that Na doping in Pb0.6Sn0.4Te locally breaks the crystal symmetry and opens up a bulk electronic band gap, which is confirmed by direct electronic absorption spectroscopy and electronic structure calculations. Na doping in Pb0.6Sn0.4Te increases p-type carrier concentration and suppresses the bipolar conduction (by widening the band gap), which collectively gives rise to a promising zT of 1 at 856 K for Pb0.58Sn0.40Na0.02Te. Breaking of crystal symmetry by chemical doping widens the bulk band gap in TCI, which uncovers a route to improve TCI for thermoelectric applications.