Shell (India)

Selective partial oxidation of methane to methanol suffers from low efficiency. Here, we report a heterogeneous catalyst system for enhanced methanol productivity in methane oxidation by in situ generated hydrogen peroxide at mild temperature (70°C). The catalyst was synthesized by fixation of AuPd alloy nanoparticles within aluminosilicate zeolite crystals, followed by modification of the external surface of the zeolite with organosilanes. The silanes appear to allow diffusion of hydrogen, oxygen, and methane to the catalyst active sites, while confining the generated peroxide there to enhance its reaction probability. At 17.3% conversion of methane, methanol selectivity reached 92%, corresponding to methanol productivity up to 91.6 millimoles per gram of AuPd per hour.

ABSTRACT: The Cenozoic evolution of SE Asia records a diverse array of tectonic processes with rifting, subduction, terrane collision and large-scale continental strike-slip faulting occurring in spatially and temporally complex relations. Oligocene seafloor spreading and rift propagation in the South China Sea are critical tectonic events that overprint an earlier phase of regional extension. Two end-member models proposed to explain the opening of the South China Sea differ in the relative importance of extrusion versus subduction as the driving mechanism. This paper treats the South China Sea region as a large multi-phase continental rift basin. Synthesizing recently published studies and using filtered Bouguer gravity data, we make a series of observations and possible interpretations to advance the notion that a hybrid tectonic models need to be proposed and tested. We present an example from the Phu Khanh Basin where flexural backstripping supports our interpretation that an ‘out-of-sequence ’ rifting event was of sufficient magnitude to completely attenuate the continental crust in the ultra deep water part of the basin. The complex rift history of the region leads us to believe that future frontier hydrocarbon exploration will carry large uncertainties from basin to basin.

[1] Organic-rich sediments are the salient marine sedimentation product in the mid-Cretaceous of the ocean basins formed in the Mesozoic. Oceanic anoxic events (OAEs) are discrete and particularly organic-rich intervals within these mid-Cretaceous organic-rich sequences and are defined by pronounced carbon isotope excursions. Marine productivity during OAEs appears to have been enhanced by the increased availability of biolimiting nutrients in seawater due to hydrothermal alteration of submarine basalts in the Pacific and proto-Indian oceans. The exact mechanisms behind the deposition of organic-rich sediments in the mid-Cretaceous are still a matter of discussion, but a hypothesis which is often put forward is that their deposition was a consequence of the coupling of a particular paleogeography with changes in ocean circulation and nutrient supply. In this study, we used a global coupled climate model to investigate oceanic processes that affect the interbasinal exchange of nutrients as well as their spatial distribution and bioavailability. We conclude that the mid-Cretaceous North Atlantic was a nutrient trap as a consequence of an estuarine circulation with respect to the Pacific. Organic-rich sediments in the North Atlantic were deposited below regions of intense upwelling. We suggest that enhanced productivity during OAEs was a consequence of upwelling of Pacific-derived nutrient-rich seawater associated with submarine igneous events.

In India, half the population depends on agriculture for a livelihood. Microbial diseases are a significant threat to food security, but their rapid identification remains difficult due to limited infrastructure. With AI, automatic detection of plant diseases from raw images is possible using deep learning and transfer learning. This paper aims to detect and classify Grapes and Mango leaf diseases, employing a dataset of 8,438 images of diseased and healthy leaves collected from the Plant Village dataset and acquired locally. The deep convolutional neural network (CNN) is trained to identify diseases or their absence. A pre-trained CNN architecture called AlexNet is modeled for automatic feature extraction and classification. The system is developed with MATLAB achieves an accuracy rate of the detection of 99% and 89% for Grape leaves and Mango leaves respectively. An app named "JIT CROPFIX" is developed to implement the same on an Android Smartphone.

For the successful development and application of lubricants, a full understanding of their complex nanoscale behavior under a wide range of external conditions is required, but this is difficult to obtain experimentally. Nonequilibrium molecular dynamics (NEMD) simulations can be used to yield unique insights into the atomic-scale structure and friction of lubricants and additives; however, the accuracy of the results depend on the chosen force-field. In this study, we demonstrate that the use of an accurate, all-atom force-field is critical in order to; (i) accurately predict important properties of long-chain, linear molecules; and (ii) reproduce experimental friction behavior of multi-component tribological systems. In particular, we focus on n-hexadecane, an important model lubricant with a wide range of industrial applications. Moreover, simulating conditions common in tribological systems, i.e., high temperatures and pressures (HTHP), allows the limits of the selected force-fields to be tested. In the first section, a large number of united-atom and all-atom force-fields are benchmarked in terms of their density and viscosity prediction accuracy of n-hexadecane using equilibrium molecular dynamics (EMD) simulations at ambient and HTHP conditions. Whilst united-atom force-fields accurately reproduce experimental density, the viscosity is significantly under-predicted compared to all-atom force-fields and experiments. Moreover, some all-atom force-fields yield elevated melting points, leading to significant overestimation of both the density and viscosity. In the second section, the most accurate united-atom and all-atom force-field are compared in confined NEMD simulations which probe the structure and friction of stearic acid adsorbed on iron oxide and separated by a thin layer of n-hexadecane. The united-atom force-field provides an accurate representation of the structure of the confined stearic acid film; however, friction coefficients are consistently under-predicted and the friction-coverage and friction-velocity behavior deviates from that observed using all-atom force-fields and experimentally. This has important implications regarding force-field selection for NEMD simulations of systems containing long-chain, linear molecules; specifically, it is recommended that accurate all-atom potentials, such as L-OPLS-AA, are employed.

We have conducted two pilot surveys for radio pulsars and fast transients with the Low-Frequency Array (LOFAR) around 140 MHz and here report on the first low-frequency fast-radio burst limit and the discovery of two new pulsars. The first survey, the LOFAR Pilot Pulsar Survey (LPPS), observed a large fraction of the northern sky, ~ 1.4 × 104 deg2, with 1 h dwell times. Each observation covered ~75 deg2 using 7 independent fields formed by incoherently summing the high-band antenna fields. The second pilot survey, the LOFAR Tied-Array Survey (LOTAS), spanned ~600 deg2, with roughly a 5-fold increase in sensitivity compared with LPPS. Using a coherent sum of the 6 LOFAR “Superterp” stations, we formed 19 tied-array beams, together covering 4 deg2 per pointing. From LPPS we derive a limit on the occurrence, at 142 MHz, of dispersed radio bursts of < 150 day-1 sky-1, for bursts brighter than S> 107 Jy for the narrowest searched burst duration of 0.66 ms. In LPPS, we re-detected 65 previously known pulsars. LOTAS discovered two pulsars, the first with LOFAR or any digital aperture array. LOTAS also re-detected 27 previously known pulsars. These pilot studies show that LOFAR can efficiently carry out all-sky surveys for pulsars and fast transients, and they set the stage for further surveying efforts using LOFAR and the planned low-frequency component of the Square Kilometer Array.

The manuscript firstly describes the data collection and validation process for the European Hydrogen Incidents and Accidents Database (HIAD 2.0), a public repository tool collecting systematic data on hydrogen-related incidents and near-misses. This is followed by an overview of HIAD 2.0, which currently contains 706 events. Subsequently, the approaches and procedures followed by the authors to derive lessons learned and formulate recommendations from the events are described. The lessons learned have been divided into four categories including system design; system manufacturing, installation and modification; human factors and emergency response. An overarching lesson learned is that minor events which occurred simultaneously could still result in serious consequences, echoing James Reason's Swiss Cheese theory. Recommendations were formulated in relation to the established safety principles adapted for hydrogen by the European Hydrogen Safety Panel, considering operational modes, industrial sectors, and human factors. This workprovide an important contribution to the safety of systems involving hydrogen, benefitting technical safety engineers, emergency responders and emergency services. The lesson learned and the discussion derived from the statistics can also be used in training and risk assessment studies, being of equal importance to promote and assist the development of sound safety culture in organisations.

In the paradigm of virtual high-throughput screening for materials, we have developed a semiautomated workflow or “recipe” that can help a material scientist to start from a raw data set of materials with their properties and descriptors, build predictive models, and draw insights into the governing mechanism. We demonstrate our recipe, which employs machine learning tools and statistical analysis, through application to a case study leading to identification of descriptors relevant to catalysts for CO2 electroreduction, starting from a published database of 298 catalyst alloys. At the heart of our methodology lies the Bootstrapped Projected Gradient Descent (BoPGD) algorithm, which has significant advantages over commonly used machine learning (ML) and statistical analysis (SA) tools such as the regression coefficient shrinkage-based method (LASSO) or artificial neural networks: (a) it selects descriptors with greater stability and transferability, with a goal to understand the chemical mechanism rather than fitting data, and (b) while being effective for smaller data sets such as in the test case, it employs clustering of descriptors to scale far more efficiently to large size of descriptor sets in terms of computational speed. In addition to identifying the descriptors that parametrize the d-band model of catalysts for CO2 reduction, we predict work function to be an essential and relevant descriptor. Based on this result, we propose a modification of the d-band model that includes the chemical effect of work function, and show that the resulting predictive model gives the binding energy of CO to catalyst fairly accurately. Since our scheme is general and particularly efficient in reducing a set of large number of descriptors to a minimal one, we expect it to be a versatile tool in obtaining chemical insights into complex phenomena and development of predictive models for design of materials.

OBJECTIVES: To review the physiologic approach to setting mechanical ventilation in acute lung injury/acute respiratory distress syndrome. DATA SOURCES: MEDLINE search from 1979 to the present. DATA SELECTION: Personal selection of some articles we believe relevant for understanding acute lung injury/acute respiratory distress syndrome physiopathology and its physiologic management. DATA SUMMARY: Knowing the underlying pathology is key to estimating the potential for recruitment. The potential for recruitment is rather low when the consolidation of pulmonary units exceeds collapse, as in diffuse pneumonia. In contrast, when pulmonary unit collapse exceeds consolidation, as in acute lung injury/acute respiratory distress syndrome from extrapulmonary origin, the potential for recruitment may be high. To exploit the potential for recruitment, a transpulmonary pressure greater than the opening pressure must be applied to the lung. To do so, chest wall elastance must be measured or estimated. To avoid collapse after recruitment, a positive end-expiratory pressure greater than the compressive forces operating on the lung and an alveolar ventilation sufficient to prevent absorption atelectasis must be provided. Indeed, avoidance of stretch (low airway plateau pressure) and prevention of cyclic collapse and reopening (adequate positive end-expiratory pressure and alveolar ventilation) are the physiologic cornerstones of mechanical ventilation in acute lung injury/acute respiratory distress syndrome. When considering all the randomized clinical trials reported so far, it is tempting to speculate that transpulmonary pressure and stresses, rather than tidal volume per se, are the key factors that may have an impact on mortality. CONCLUSIONS: The majority of physiologic, experimental, and clinical trial data converge on one simple concept: treat the lung gently.

The International LOFAR Telescope is an interferometer with stations spread across Europe. With baselines of up to ~2000 km, LOFAR has the unique capability of achieving sub-arcsecond resolution at frequencies below 200 MHz. However, it is technically and logistically challenging to process LOFAR data at this resolution. To date only a handful of publications have exploited this capability. Here we present a calibration strategy that builds on previous high-resolution work with LOFAR. It is implemented in a pipeline using mostly dedicated LOFAR software tools and the same processing framework as the LOFAR Two-metre Sky Survey (LoTSS). We give an overview of the calibration strategy and discuss the special challenges inherent to enacting high-resolution imaging with LOFAR, and describe the pipeline, which is publicly available, in detail. We demonstrate the calibration strategy by using the pipeline on P205+55, a typical LoTSS pointing with an 8 h observation and 13 international stations. We perform in-field delay calibration, solution referencing to other calibrators in the field, self-calibration of these calibrators, and imaging of example directions of interest in the field. We find that for this specific field and these ionospheric conditions, dispersive delay solutions can be transferred between calibrators up to ~1.5° away, while phase solution transferral works well over ~1°. We also demonstrate a check of the astrometry and flux density scale with the in-field delay calibrator source. Imaging in 17 directions, we find the restoring beam is typically ~0.3′′ ×0.2′′ although this varies slightly over the entire 5 deg 2 field of view. We find we can achieve ~80–300 μJy bm −1 image rms noise, which is dependent on the distance from the phase centre; typical values are ~90 μJy bm −1 for the 8 h observation with 48 MHz of bandwidth. Seventy percent of processed sources are detected, and from this we estimate that we should be able to image roughly 900 sources per LoTSS pointing. This equates to ~ 3 million sources in the northern sky, which LoTSS will entirely cover in the next several years. Future optimisation of the calibration strategy for efficient post-processing of LoTSS at high resolution makes this estimate a lower limit.

The protein requirement of juvenile mud crab Scylla serrata (body weight=0.25±0.051 g, carapace width=9.3±0.04 mm) fed with different iso-energetic, iso-lipidic diets with graded protein levels (15–55% crude protein at 5% intervals) was determined. The feeding trial was conducted for a period of 63 days to determine the minimum and optimum protein requirement of juvenile S. serrata. The crabs fed with 15% and 20% dietary protein levels showed 100% and 12.5% of mortalities respectively. The mortalities observed in the above treatments were associated with the prolonged intermoult duration (46 and 32 days respectively). All other treatments recorded 100% survival. The best growth performance as well as the nutrient turn-over was recorded in crabs fed with 45% crude protein in the diet. Second-order polynomial regression of specific growth rate (SGR) as well as body protein gain vs. dietary protein levels suggested that 46.9–47.03% dietary protein is required for the best growth response and protein deposition in juvenile S. serrata. An extrapolation of ‘SGR’ and ‘daily protein gain’ upon the ‘dietary protein level’ axis (Y=0) showed that 14.7–16.2% dietary protein is necessary for the minimum maintenance metabolism.

Context. Type II radio bursts are evidence of shocks in the solar atmosphere and inner heliosphere that emit radio waves ranging from sub-meter to kilometer lengths. These shocks may be associated with coronal mass ejections (CMEs) and reach speeds higher than the local magnetosonic speed. Radio imaging of decameter wavelengths (20–90 MHz) is now possible with the Low Frequency Array (LOFAR), opening a new radio window in which to study coronal shocks that leave the inner solar corona and enter the interplanetary medium and to understand their association with CMEs. Aims. To this end, we study a coronal shock associated with a CME and type II radio burst to determine the locations at which the radio emission is generated, and we investigate the origin of the band-splitting phenomenon. Methods. Thetype II shock source-positions and spectra were obtained using 91 simultaneous tied-array beams of LOFAR, and the CME was observed by the Large Angle and Spectrometric Coronagraph (LASCO) on board the Solar and Heliospheric Observatory (SOHO) and by the COR2A coronagraph of the SECCHI instruments on board the Solar Terrestrial Relation Observatory(STEREO). The 3D structure was inferred using triangulation of the coronographic observations. Coronal magnetic fields were obtained from a 3D magnetohydrodynamics (MHD) polytropic model using the photospheric fields measured by the Heliospheric Imager (HMI) on board the Solar Dynamic Observatory (SDO) as lower boundary. Results. The type II radio source of the coronal shock observed between 50 and 70 MHz was found to be located at the expanding flank of the CME, where the shock geometry is quasi-perpendicular with θ Bn ~ 70°. The type II radio burst showed first and second harmonic emission; the second harmonic source was cospatial with the first harmonic source to within the observational uncertainty. This suggests that radio wave propagation does not alter the apparent location of the harmonic source. The sources of the two split bands were also found to be cospatial within the observational uncertainty, in agreement with the interpretation that split bands are simultaneous radio emission from upstream and downstream of the shock front. The fast magnetosonic Mach number derived from this interpretation was found to lie in the range 1.3–1.5. The fast magnetosonic Mach numbers derived from modelling the CME and the coronal magnetic field around the type II source were found to lie in the range 1.4–1.6.

Context. LOFAR offers the unique capability of observing pulsars across the 10-240 MHz frequency range with a fractional bandwidth of roughly 50%. This spectral range is well suited for studying the frequency evolution of pulse profile morphology caused by both intrinsic and extrinsic effects such as changing emission altitude in the pulsar magnetosphere or scatter broadening by the interstellar medium, respectively. Aims. The magnitude of most of these effects increases rapidly towards low frequencies. LOFAR can thus address a number of open questions about the nature of radio pulsar emission and its propagation through the interstellar medium. Methods. We present the average pulse profiles of 100 pulsars observed in the two LOFAR frequency bands: high band (120-167 MHz, 100 profiles) and low band (15-62 MHz, 26 profiles). We compare them with Westerbork Synthesis Radio Telescope (WSRT) and Lovell Telescope observations at higher frequencies (350 and 1400 MHz) to study the profile evolution. The profiles were aligned in absolute phase by folding with a new set of timing solutions from the Lovell Telescope, which we present along with precise dispersion measures obtained with LOFAR. Results. We find that the profile evolution with decreasing radio frequency does not follow a specific trend; depending on the geometry of the pulsar, new components can enter into or be hidden from view. Nonetheless, in general our observations confirm the widening of pulsar profiles at low frequencies, as expected from radius-to-frequency mapping or birefringence theories.

Context. The Sun is an active source of radio emission which is often associated with energetic phenomena such as solar flares and coronal mass ejections (CMEs). At low radio frequencies (<100 MHz), the Sun has not been imaged extensively because of the instrumental limitations of previous radio telescopes.

For the successful development and application of novel lubricant additives, a full understanding of their tribological behaviour at the nanoscale is required, but this can be difficult to obtain experimentally. In this study, nonequilibrium molecular dynamics simulations are used to examine the friction and wear reduction mechanisms of promising carbon nanoparticle friction modifier additives. Specifically, the friction and wear behaviour of carbon nanodiamonds (CNDs) and carbon nano-onions (CNOs) confined between α-iron slabs is probed at a range of coverages, pressures, and sliding velocities. At high coverage and low pressure, the nanoparticles do not indent into the α-iron slabs during sliding, leading to zero wear and a low friction coefficient. At low coverage and high pressure, the nanoparticles indent into, and plough through the slabs during sliding, leading to atomic-scale wear and a much higher friction coefficient. This contribution to the friction coefficient is well predicted by an expression developed for macroscopic indentation by Bowden and Tabor. Even at the highest pressures and lowest coverages simulated, both nanoparticles were able to maintain separation of the opposing slabs and reduce friction by approximately 75 % compared to when no nanoparticle was present, which agrees well with experimental observations. CNO nanoparticles yielded a lower indentation (wear) depth and lower friction coefficients at equal coverage and pressure with respect to CND, making them more attractive friction modifier additives. Potential changes in behaviour on harder and softer surfaces are also discussed, together with the implications that these results have in terms of the application of the studied nanoparticles as lubricants additives.

The predictive simulation of molecular liquids requires potential energy surface (PES) models that are not only accurate but also computationally efficient enough to handle the large systems and long time scales required for reliable prediction of macroscopic properties. We present a new approach to the systematic approximation of the first-principles PES of molecular liquids using the GAP (Gaussian Approximation Potential) framework. The approach allows us to create potentials at several different levels of accuracy in reproducing the true PES and thus to determine the level of quantum chemistry that is necessary to accurately predict macroscopic properties. We test the approach by building a series of many-body potentials for liquid methane (CH4), which is difficult to model from first principles because its behavior is dominated by weak dispersion interactions with a significant many-body component. The increasing accuracy of the potentials in predicting the bulk density correlates with their fidelity to the true PES, whereas the trend with the empirical potentials tested is surprisingly the opposite. We conclude that an accurate, consistent prediction of its bulk density across wide ranges of temperature and pressure requires not only many-body dispersion but also quantum nuclear effects to be modeled accurately.

We have synthesized g-C3N4 decorated over dendritic fibrous nanosilica (DFNS). The generation of C–N–Si interfaces by coating each fiber of DFNS with g-C3N4 not only provided high surface area but also affected the optical and electronic properties of the composite. The catalyst synthesis reproducibility issue of g-C3N4 was resolved using a vacuum-sealed quartz tube. The extended light absorption in the visible region, enhanced lifetime of photogenerated charge carriers due to the formation of interfaces between silica and g-C3N4 (confirmed by solid-state NMR), and increased surface area result in the improved photocatalytic activity of DFNS/g-C3N4 for hydrogen generation and CO2 conversion.

The paper discusses numerical prediction of erosion wear trends in centrifugal pump casing pumping dilute slurries. The casing geometry is considered two-dimensional. Discrete Phase Model (DPM) in FLUENT 6.1® is utilized to obtain dilute slurry flow field through the pump casing employing two-way coupling. Standard k — ε model is used for turbulence. Effect of several operational parameters viz. pump flow rate, pump speed (RPM), particle diameter and various geometry conditions viz. tongue curvature, slope of the discharge pipe and casing width is studied. Qualitative trends of erosion wear is described for these operational and geometric parameters with an idea to lower the wear rates and to make the wear pattern along the casing wall as uniform as possible. For example, with increase in pump flow rate, wear rates tends to even out whereas with increased casing width, wear rates are found to decrease.

Abstract Digital rock technology and pore-scale physics have become increasingly relevant topics in a wide range of porous media with important applications in subsurface engineering. This technology relies heavily on images of pore space and pore-level fluid distribution determined by X-ray microcomputed tomography (micro-CT). Digital images of pore space (or pore-scale fluid distribution) are typically obtained as gray-level images that first need to be processed and segmented to obtain the binary images that uniquely represent rock and pore (including fluid phases). This processing step is not trivial. Rock complexity, image quality, noise, and other artifacts prohibit the use of a standard processing workflow. Instead, an array of strategies of increasing sophistication has been developed. Typical processing pipelines consist of filtering, segmentation, and postprocessing steps. For each step, various choices and different options exist. This makes selection and validation of an optimum processing pipeline difficult. Using Darcy-scale quantities as a benchmark is not a good option because of rock heterogeneity and different scales of observation. Here, we present a conceptual workflow where noisy images are derived from a ground truth by systematically including typical image artifacts and noise. Artifacts and noise are not simply added to the images. Instead, tomographic forward projection and reconstruction steps are used to incorporate the artifacts in a physically correct way. A proof of concept of this workflow is demonstrated by comparing seven different image-segmentation pipelines ranging from absolute thresholding to a machine-learning approach (Trainable Weka Segmentation). The Trainable Weka Segmentation showed the best performance of the tested methods.

The influence of product shape selectivity on the bifunctional conversion of n-C7 by zeolite catalysts is investigated. Three different zeolite catalysts with different pore sizes (MFI-type, MEL-type, and BEA-type zeolites) have been investigated experimentally. For all three catalysts, n-C7 is isomerized to monobranched isomers which are further isomerized into dibranched isomers, and these dibranched molecules are converted into cracking products. More dibranched isomers and less cracking products are produced by BEA-type zeolite compared to MFI-type and MEL-type zeolites and clear differences are observed in the distribution of dibranched isomers produced by different catalysts. Molecular simulation is used to compute the adsorption isotherms and free energy barriers for diffusion of dibranched isomers in MFI-type, MEL-type, and BEA-type zeolites. Combining simulation results and experimental observations, it is shown that product shape selectivity can explain the distribution of dibranched molecules while transition state shape selectivity fails to do so. For the medium-pore zeolites (MFI-type and MEL-type zeolites), free energy barriers for diffusion of dibranched molecules are significant. For MFI-type and MEL-type zeolites, the dibranched molecule that has to overcome lower diffusion barrier is produced with a higher yield and the distribution of dimethylpentane molecules is determined by their diffusion rate. It is shown that there is almost no free energy barrier for the diffusion of any of these molecules in BEA-type zeolite. As BEA-type zeolite imposes no free energy barrier for diffusion of any of dibranched isomers, the distribution of dibranched isomers is very close to the equilibrium distribution in the gas phase. Due to the limited mobility of dimethylpentanes within the pores of MFI-type and MEL-type zeolites, most of the dimethylpentane molecules are trapped inside the zeolite and undergo consecutive cracking. Dimethylpentane molecules diffuse sufficiently fast in the large pores of BEA-type zeolite and transfer to the gas phase, before consecutive reaction converts these molecules into cracking products. Moreover, the effect of the MFI-type crystal size on the production of dibranched isomers is investigated. The yield of dibranched isomers reduces by increasing the size of the crystal and larger part of dibranched isomers are cracked as the crystal size of MFI-type is increased.