Sharp Distribution Research Articles

Optimizing ACRT to reduce inclusion formation during the VGF growth of cadmium zinc telluride: II. Application to experiments

Two different accelerated crucible rotation technique (ACRT) rotation schedules are assessed, via theoretical computations and growth experiments, according to their ability to reduce tellurium-rich inclusions during the vertical gradient freeze (VGF) of cadmium zinc telluride (CZT). A vigorous rotation schedule based on classical ACRT guidelines produces a well-mixed melt that is expected to reduce tellurium inclusions compared to growth without crucible rotation. In contrast, a new ACRT schedule, derived from model-based optimization for interface stability, employs slower rotation with longer periods and is predicted to further reduce inclusions. Growth experiments corroborate these expectations. Namely, both ACRT schedules result in crystals with inclusion size and volume significantly decreased from levels found in material grown with no rotation, and material grown using the computationally optimized ACRT parameters exhibits a median inclusion size that is smaller and with a sharper distribution than in material grown via classical ACRT.

Synergic effect of selenium nanoparticles and lactic acid bacteria in reduction cadmium toxicity

Cadmium is a heavy metal, environmental pollutant and toxic for organisms. Lactic acid bacteria (LAB) and nanoparticles represent useful tools to remove heavy metals from different environments. The main goal of our study was to develop a novel experimental design for cadmium (Cd) disposal using L. casei or L. fermentum and exogenous selenium nanoparticles. The experimental design was developed in two steps. In the first step, bio-synthesis, morphological and chemical characterization of selenium nanoparticles (SeNPs) produced by L. casei was performed. In the next step, L. casei and L. fermentum were used to evidence their binding ability toward Cd, highlighting the role of SeNPs against Cd-induced toxicity. Homogenous, spherical shape SeNPs and sharp size distribution with maximum 200 nm were obtained via reduction route, using L. casei and Na2SeO3. Addition of Cd to LAB culture medium strongly inhibits both bacteria growth. SeNPs, in contrast to sodium selenite, clearly demonstrates the protective effect against Cd toxicity, as evidenced by dynamic growth curves of both bacteria. A better stimulation of endogenous SeNPs production was noticed for L. fermentum compared to L. casei. The efficiency of Cd bio-absorption by LAB was in the range 43.78% - 50.87%, the ratio of 1:2 LAB/SeNPs showing the best results for both bacteria. The synergic effect of biogenic SeNPs and LAB against Cd toxicity was demonstrated. The combination of FTIR (Fourier Transform Infrared), FT Raman (Fourier Transformed Raman) and ICP-OES (Inductively Coupled Plasma Optical Emission Spectroscopy) techniques provides specific qualitative and quantitative information on the behavior of probiotic species with respect to SeNPs synthesis and Cd disposal.

Carbon ion radiotherapy for prostate cancer with bladder invasion

BackgroundThe optimal management of clinical T4 (cT4) prostate cancer (PC) is still uncertain. At our institution, carbon ion radiotherapy (CIRT) for nonmetastatic PC, including tumors invading the bladder, has been performed since 2010. Since carbon ion beams provide a sharp dose distribution with minimal penumbra and have biological advantages over photon radiotherapy, CIRT may provide a therapeutic benefit for PC with bladder invasion. Hence, we evaluated CIRT for PC with bladder invasion in terms of the safety and efficacy.MethodsBetween March 2010 and December 2016, a total of 1337 patients with nonmetastatic PC received CIRT at a total dose of 57.6 Gy (RBE) in 16 fractions over 4 weeks. Among them, seven patients who had locally advanced PC with bladder invasion were identified. Long-term androgen-deprivation therapy (ADT) was also administered to these patients. Adverse events were graded according to the Common Terminology Criteria for Adverse Event version 5.0.ResultsAt the completion of our study, all the patients with cT4 PC were alive with a median follow-up period of 78 months. Grade 2 acute urinary disorders were observed in only one patient. Regarding late toxicities, only one patient developed grade 2 hematuria and urinary urgency. There was no grade 3 or worse toxicity, and gastrointestinal toxicity was not observed. Six (85.7%) patients had no recurrence or metastasis. One patient had biochemical and local failures 42 and 45 months after CIRT, respectively. However, the recurrent disease has been well controlled by salvage ADT.ConclusionsSeven patients with locally advanced PC invading the bladder treated with CIRT were evaluated. Our findings seem to suggest positive safety and efficacy profiles for CIRT.

Nanoparticles of γ-Sitoesterol and Ag on Clinoptilolite Zeolites

This work reports, the obtainment and stabilization of organic nanoparticles (γ-sitosterol) and Ag cations on the external area of Ca-clinoptilolite zeolite (Z17) are presented. The novelty of the present study lies in the formation and stabilization of nanoparticle organic molecules (γ-sitosterol) and inorganic cations (Ag) on the external area of Ca-clinoptilolite zeolite. The γ-sitosterol nanoparticles were obtained using Tournefortia hirsutissima and were later stabilized on the external area of Ca-clinoptilolite. The Ca-clinoptilolite-rich mineral as well as silver zeolitic minerals was characterized by using X-ray diffraction (XRD) and scanning electron microscopy (SEM); elementary composition of the zeolitic mineral was determined by microanalyses (EDS) and high-resolution adsorption (HRADS) using N2 adsorption as a probe molecule. The EDS results about the exchange with Ag indicate the following sequence: Z17Ag03 > Z17Ag-09 > Z17Ag-06 > Z17Ag-01. The XRD studies indicate that only in the sample Z17Ag-03 there is an average crystal size of nanoparticles formed of 45.387 nm as determined by Sherrer’s equation. SEM images of clinoptilolite showed the presence of crystals with irregular and poorly defined box shapes that are typical for this type of zeolites. The nanoporosity of natural zeolites was studied by N2 adsorption; external areas were estimated through the De Boer and BET equations. The specific surface area calculated by BET equation, A SB , presents the following sequence: γ-SZ17 > Z17 > Z17Ag09 > Z17Ag03 > Z17Ag01 > Z17Ag06, while for the values of the external area obtained by the t method, the sequence is A ST : γ-Z17 > Z17 > Z17Ag03 > z17Ag09 > Z17Ag06 > Z17Ag01. For its part, the pore size distributions tell us that the sample Z17 exhibits a sharp multimodal distribution with pore size maxima happening at 1.262, 1.933, 3.824, and 4.282 nm. Z17Ag01 exhibits a bimodal distribution with pore size maxima occurring at 5.45 and 5.533 nm. The sample Z17Ag06 exhibits a sharp bimodal distribution with pore size maxima happening at 3.666 and 7.021 nm, and the sample Z17Ag09 exhibits a sharp trimodal distribution with pore size maxima happening at 1.853, 4.275 and 5.967 nm. Antibacterial activities of the exchanged samples with Ag on the external surface area were measured as a function of exchange level against Gram-positive bacteria (P. aureoginosa, K. pneumonie, and A. baumannii) and Gram-negative bacteria (E. coli, S. saprophyticcus, S. aureus, and E. feacalis); the selectivity sequence of the clinoptilolite and antibacterial activity results are presented. Z17Ag09 clinoptilolite seemed to be a promising antibacterial material.

Remarks on the precise measurement of the X(3872) mass and its counting rate

The lineshapes of specific production experiments of the exotic state such as $X(3872)$ with $J^{PC}=1^{++}$ quantum numbers involving triangle singularities have been found to become highly sensitive to the binding energy of weakly bound states, thus offering in principle the opportunity of benchmark determinations. We critically analyze recent proposals to extract accurately and precisely the $X(3872)$ mass, which overlook an important physical effect by regarding their corresponding production lineshapes as a sharp mass distribution and, thus, neglecting the influence of initial nearby continuum states in the $1^{++}$ channel. The inclusion of these states implies an effective cancellation mechanism which operates at the current and finite experimental resolution of the detectors so that one cannot distinguish between the $1^{++}$ bound-state and nearby $D \bar D^*$ continuum states with the same quantum numbers. In particular, we show that the lineshape for resolutions above 1 MeV becomes rather insensitive to the binding energy unless high statistics is considered. The very existence of the observed bumps is a mere consequence of short distance correlated $\bar D D^*$ pairs, bound or unbound. The cancellation also provides a natural explanation for a recent study reporting missing but unknown decay channels in an absolute branching ratio global analysis of the $X(3872)$.

Postprocessing continental-scale, medium-range ensemble streamflow forecasts in South America using Ensemble Model Output Statistics and Ensemble Copula Coupling

Probabilistic hydrological forecasting and ensemble techniques have leveraged streamflow prediction at regional to continental scales up to several weeks in advance. However, ensembles that only account for meteorological forecast uncertainty are typically biased and subject to dispersion errors, thus limiting their use for rational decision-making and optimization systems. Statistical postprocessing is therefore necessary to convert ensemble forecasts into calibrated and sharp predictive distributions, and it should also account for dependencies between lead times to enable realistic forecast trajectories. This work provides a continental-scale assessment of the use of statistical postprocessing on medium-range, ensemble streamflow forecasts over South America (SA). These forecasts were produced through a large-scale hydrologic–hydrodynamic model forced with a global precipitation dataset and ECMWF reforecast data. The Ensemble Model Output Statistics (EMOS) technique was used to generate conditional predictive distributions in 488 locations at each forecast lead time, while the Ensemble Copula Coupling method with the transformation scheme (ECC-T) was applied to derive ensemble traces from EMOS distributions. Postprocessed streamflow forecasts were cross-validated for the period from 1996 to 2014 using a range of verification metrics. Results showed that the skill and reliability of EMOS forecasts substantially improve over the raw ensembles, and that EMOS leads to skillful predictions relative to discharge climatology and persistence forecasts up to 15 days in advance in most locations. Furthermore, EMOS results in predictive distributions that are generally sharper than the climatology. Limitations in depicting autocorrelations of forecast trajectories were observed in rivers for which the raw ensemble spread is very low and EMOS has to largely increase dispersion, especially at short lead times. The study’s findings suggest that combining a continental-scale hydrological model with EMOS and ECC-T methods can lead to skillful predictions and realistic ensemble traces in several locations in SA, even if in situ hydrometeorological observations are not available in real time.

Adaptive quantile control for stochastic system

Adaptive control has been successfully developed in deriving control law for stochastic systems with unknown parameters. The generation of reasonable control law depends on accurate parameter estimation. Recursive least square is widely used to estimate unknown parameters for stochastic systems; however, this approach only fits systems with Gaussian noises. In this paper, the adaptive quantile control is first proposed to cover the case where stochastic system noise follows sharp and thick tail distribution rather than Gaussian distribution. In the proposed approach, the system noise is modeled by the Asymmetric Laplace Distribution, and the unknown parameter is online estimated by our developed Bayesian quantile sum estimator, which combines recursive quantile estimations weighted by Bayesian posterior probabilities. With the real-time estimated parameter, the adaptive quantile control law is constructed based on the certainty equivalence principle. Our proposed estimator and controller are not computationally consuming and can be easily conducted in the Micro Controller Unit to fit practical applications. The comparison with some dominant controllers for the unknown stochastic system is conducted to verify the effectiveness of the adaptive quantile control.

Tailoring the metastable reversed austenite from metastable Mn-rich carbides

A two-steps tempering-partitioning process is designed for redistributing carbon and austenite stabilizer such as manganese in a cold rolled medium-manganese steel. After tempering at 450℃ for 0.5h and 1h, a large number of metastable Mn-rich M12C carbides and high density of NiAl-type nanoparticles were found, and the segregation of carbon at the dislocations/interfaces around NiAl precipitates was revealed by 3D atom probe tomography (APT). The competition between the M12C carbides and the dislocations/interfaces for carbon inhibits transformation of the metastable M12C carbides to more stable carbides. During the following partitioning at 630℃ for 0.5h, the metastable Mn-rich M12C dissolved and the fine reversed austenite nucleating on the metastable M12C carbides exhibits a sharp gradient Mn distribution. The tailored metastable austenite inherited from metastable M12C carbide shows strong enrichment of Mn and C (Mn: 18.1 at.%, C: 1.56 at.%), dispersed distribution (9.8 μm−2) and fine size (50-200 nm). With the nature of both hard particle and transitionable phase, the austenite couples Orowan strengthening and transformation induced plasticity (TRIP) assisted work hardening. Outstanding dynamic mechanical properties (yield strength: 1350 MPa, total elongation: 30%) were achieved due to this microstructure. Here we report a strategy for the design of ultrahigh-strength steels by making metastable austenite serve as a strengthening second phase in addition to the TRIP effect.

Continuous size fractionation of magnetic nanoparticles by using simulated moving bed chromatography

The size fractionation of magnetic nanoparticles is a technical problem, which until today can only be solved with great effort. Nevertheless, there is an important demand for nanoparticles with sharp size distributions, for example for medical technology or sensor technology. Using magnetic chromatography, we show a promising method for fractionation of magnetic nanoparticles with respect to their size and/or magnetic properties. This was achieved by passing magnetic nanoparticles through a packed bed of fine steel spheres with which they interact magnetically because single domain ferro-/ferrimagnetic nanoparticles show a spontaneous magnetization. Since the strength of this interaction is related to particle size, the principle is suitable for size fractionation. This concept was transferred into a continuous process in this work using a so-called simulated moving bed chromatography. Applying a suspension of magnetic nanoparticles within a size range from 20 to 120 nm, the process showed a separation sharpness of up to 0.52 with recovery rates of 100%. The continuous feed stream of magnetic nanoparticles could be fractionated with a space-time-yield of up to 5 mg/(L∙min). Due to the easy scalability of continuous chromatography, the process is a promising approach for the efficient fractionation of industrially relevant amounts of magnetic nanoparticles.

BDBB: A Novel Beta-Distribution-Based Biclustering Algorithm for Revealing Local Co-Methylation Patterns in Epi-Transcriptome Profiling Data.

N6-methyladenosine (m6A) has been shown to play crucial roles in RNA metabolism, physiology, and pathological processes. However, the specific regulatory mechanisms of most methylation sites remain uncharted due to the complexity of life processes. Biological experimental methods are costly to solve this problem, and computational methods are relatively lacking. The discovery of local co-methylation patterns (LCPs) of m6A epi-transcriptome data can benefit to solve the above problems. Based on this, we propose a novel biclustering algorithm based on the beta distribution (BDBB), which realizes the mining of LCPs of m6A epi-transcriptome data. BDBB employs the Gibbs sampling method to complete parameter estimation. In the process of modeling, LCPs are recognized as sharp beta distributions compared to the background distribution. Simulation study showed BDBB can extract all the three actual LCPs implanted in the background data and the overlap conditions between them with considerable accuracy (almost close to 100%). On MeRIP-Seq data of 69,446 methylation sites under 32 experimental conditions from 10 human cell lines, BDBB unveiled two LCPs, and Gene Ontology (GO) enrichment analysis showed that they were enriched in histone modification and embryo development, etc. important biological processes respectively. The GOE_Score scoring indicated that the biclustering results of BDBB in the m6A epi-transcriptome data are more biologically meaningful than the results of other biclustering algorithms.

Sharp minimax distribution estimation for current status censoring with or without missing

Nonparametric estimation of the cumulative distribution function and the probability density of a lifetime X modified by a current status censoring (CSC), including cases of right and left missing data, is a classical ill-posed problem with biased data. The biased nature of CSC data may preclude us from consistent estimation unless the biasing function is known or may be estimated, and its ill-posed nature slows down rates of convergence. Under a traditionally studied CSC, we observe a sample from $(Z,\Delta )$ where a continuous monitoring time $Z$ is independent of $X$, $\Delta :=I(X\leq Z)$ is the status, and the bias of observations is created by the density of $Z$ which is estimable. In presence of right or left missing, we observe corresponding samples from $(\Delta Z,\Delta )$ or $((1-\Delta )Z,\Delta )$; the data are again biased but now the density of $Z$ cannot be estimated from the data. As a result, to solve the estimation problem, either the density of $Z$ must be known (like in a controlled study) or an extra cross-sectional sampling of $Z$, which is typically simpler than an underlying CSC study, be conducted. The main aim of the paper is to develop for this biased and ill-posed problem the theory of efficient (sharp-minimax) estimation which is inspired by known results for the case of directly observed $X$. Among interesting aspects of the developed theory: (i) While sharp-minimax analysis of missing CSC may follow the classical Pinsker’s methodology, analysis of CSC requires a more complicated estimation procedure based on a special smoothing in both frequency and time domains; (ii) Efficient estimation requires solving an old-standing problem of approximating aperiodic Sobolev functions; (iii) If smoothness of the cdf of $X$ is known, then its rate-minimax estimation is possible even if the density of $Z$ is rougher. Real and simulated examples, as well as extensions of the core models to dependent $X$ and Z and case-control CSC, are presented.

A Two-Stage Network for Image Deblurring

Blind deblurring is a typical challenge in image processing, carried out to correct various complex types of distortions that occur in the real world. Although learning-based deblurring methods have substantially outperformed the traditional algorithms, they fail to make full use of image priors, leading to inconsistent data distributions between the restored images and sharp images. In this paper, we divide the deblurring process into two steps and propose a two-stage network. The first stage generates an initial deblurred image using a common convolutional network. The second stage converts the initial data distribution into a latent sharp image distribution to obtain sharp edges through a prior network. Moreover, we propose a relativistic training strategy to train the prior network, which aims to learn the latent sharp image priors. In addition, we use a coarse-to-fine multiscale framework that shares weights between the different scales. The experimental results show that compared with other state-of-the-art methods, our method achieves competitive performances on benchmark datasets.

Comparison of the BMA and EMOS statistical methods for probabilistic quantitative precipitation forecasting

AbstractThe main approach to probabilistic weather forecasting has been the use of ensemble forecasting. In ensemble forecasting, the probability information is generally derived by using several numerical model runs, with perturbation of the initial conditions, physical schemes or dynamic core of the numerical weather prediction (NWP) models. However, ensemble forecasting usually tends to be under‐dispersive. Statistical post‐processing has, therefore, become an essential component of any ensemble prediction system aiming to improve the quality of numerical weather forecasts as they seek to generate calibrated and sharp predictive distributions of future weather quantities. Different versions of the ensemble model output statistics (EMOS) and the Bayesian model averaging (BMA) post‐processing methods are used in the present paper to calibrate 24, 48 and 72 hr forecasts of 24 hr accumulative precipitation. The ensemble employs the weather and research forecasting (WRF) model with eight different configurations which were run over Iran for six months (September 2015–February 2016). The results reveal that the BMA and EMOS‐censored, shifted gamma (CSG) techniques are substantially successful at improving the raw WRF ensemble forecasts; however, each approach improves different aspects of the forecast quality. The BMA method is more accurate, skilful and reliable than the EMOS‐CSG method, but has poorer discrimination. Moreover, it has better resolution in predicting the probability of high‐precipitation events than the EMOS‐CSG method.

Classification of Pneumonia in Thoracic X-Ray images based on texture characteristics using the MLP (Multi-Layer Perceptron) method

One of the diseases that attack the lungs is pneumonia. This disease can attack someone with a weak immune system. Pneumonia is inflammation of the lungs that can be caused by pathogens, such as bacteria, viruses, and fungi. The purpose of this study was to classify fungal pneumonia, bacterial pneumonia, and lipoid pneumonia based on texture characteristics and the MLP method using machine learning WEKA. The method in this study has three stages including pre-processing, extraction of texture features consisting of Histogram and GLCM, and classification using the MLP (Multi Layer Perceptron) method. The results of the texture feature extraction showed that the three types of pneumonia were: lipoid pneumonia with brightness, sharp contrast random distribution on correlation characteristics, bacterial pneumonia with high brightness, high contrast random distribution on energy characteristics, and fungal pneumonia with brightness, sharp contrast, random distribution of homogeneity attributes. The third similarity of pneumonia is in the gray level that collects each other in the middle. The method used in this study resulted in the same accuracy, sensitivity, and specificity, namely 100%. The image classification in this study shows the success of the texture features and the MLP method in classifying pneumonia images accurately so that they can be used as additional tools that make it easier for medical experts. ©2020 JNSMR UIN Walisongo. All rights reserved.

Au\u2013Ag alloy nanoparticle-incorporated AgBr plasmonic photocatalyst

A solid-phase photochemical method produces Au–Ag alloy nanoparticles (NPs) with a sharp size distribution and varying composition in AgBr crystals (Au–Ag@AgBr). These features render Au–Ag@AgBr promising as a material for the plasmonic photocatalyst further to provide a possibility of elucidating the action mechanism due to the optical tunability. This study shows that the visible-light activity of Au–Ag@AgBr for degradation of model water pollutant is very sensitive to the alloy composition with a maximum at the mole percent of Au to all Ag in AgBr (y) = 0.012 mol%. Clear positive correlation is observed between the photocatalytic activity and the quality factor defined as the ratio of the peak energy to the full width at half maximum of the localized surface plasmon resonance band. This finding indicates that Au–Ag@AgBr works as a local electromagnetic field enhancement-type plasmonic photocatalyst in which the Au–Ag NPs mainly promotes the charge separation. This conclusion was further supported by the kinetic analysis of the light intensity-dependence of external quantum yield.

Area-covering postprocessing of ensemble precipitation forecasts using topographical and seasonal conditions

Probabilistic weather forecasts from ensemble systems require statistical postprocessing to yield calibrated and sharp predictive distributions. This paper presents an area-covering postprocessing method for ensemble precipitation predictions. We rely on the ensemble model output statistics (EMOS) approach, which generates probabilistic forecasts with a parametric distribution whose parameters depend on (statistics of) the ensemble prediction. A case study with daily precipitation predictions across Switzerland highlights that postprocessing at observation locations indeed improves high-resolution ensemble forecasts, with 4.5% CRPS reduction on average in the case of a lead time of 1 day. Our main aim is to achieve such an improvement without binding the model to stations, by leveraging topographical covariates. Specifically, regression coefficients are estimated by weighting the training data in relation to the topographical similarity between their station of origin and the prediction location. In our case study, this approach is found to reproduce the performance of the local model without using local historical data for calibration. We further identify that one key difficulty is that postprocessing often degrades the performance of the ensemble forecast during summer and early autumn. To mitigate, we additionally estimate on the training set whether postprocessing at a specific location is expected to improve the prediction. If not, the direct model output is used. This extension reduces the CRPS of the topographical model by up to another 1.7 % on average at the price of a slight degradation in calibration. In this case, the highest improvement is achieved for a lead time of 4 days.

Preparation of perfluorocarbon emulsions by premix membrane emulsification for Acoustic Droplet Vaporization (ADV) in biomedical applications.

Perfluorocarbon (PFC) droplets are used in acoustic droplet vaporization (ADV), a phenomenon where droplets vaporize into gas microbubbles under exposure to ultrasound. The size and the size distribution of a phase change contrast agent is an important factor in determining the ADV threshold and the biodistribution. Thus, high throughout manufacturing of uniform-sized droplets, required to maintain spatial control of the vaporization process, remains challenging. This work describes a parametric evaluation of a novel process using premix membrane emulsification (PME) to produce homogeneous PFC emulsions at high rate with moderate pressure using Shirasu Porous Glass (SPG) membranes. In this study, we investigated the effect of several process parameters on the resulting pressure and droplet size: membrane pore size, flow rate, and dispersed phase type. The functionality of the manufactured emulsions for ADV was also demonstrated. Vaporization of the PFC emulsions was obtained using an imaging ultrasound transducer at 7.813 MHz, and the ADV thresholds were determined. Here, the pressure threshold for ADV was determined to be 1.49 MPa for uniform-sized perfluorohexane (PFHex) droplets with a mean size of 1.51 μm and a sharp distribution (CV and span respectively of 20% and 0.6). Thus, a uniform-sized droplet showed a more homogeneous vaporization with a uniform response in the focal region of the transducer. Indeed, polydispersed droplets had a more diffuse response outside the focal region due to the presence of large droplets that vaporize at lower energies. The ADV threshold of uniform-sized PFC droplets was found to decrease with the droplet diameter and the bulk fluid temperature, and to increase with the boiling temperature of PFC and the presence of an oil layer surrounding the PFC core.

Fractional corner charges in a two-dimensional superlattice Bose-Hubbard model

We study a two dimensional super-lattice Bose-Hubbard model with alternating hoppings in the limit of strong on-site interactions. We evaluate the phase diagram of the model around half-filling using the density matrix renormalization group method and find two gapped phases separated by a gapless superfluid region. We demonstrate that the gapped states realize two distinct higher order symmetry protected topological phases, which are protected by a combination of charge conservation and $C_4$ lattice symmetry. The phases are distinguished in terms of a quantized fractional corner charge and a many-body topological invariant that is robust against arbitrary, symmetry preserving edge manipulations. We support our claims by numerically studying the full counting statistics of the corner charge, finding a sharp distribution peaked around the quantized values. These results are experimentally observable in ultracold atomic settings using state of the art quantum gas microscopy.

Hindcasting the 2017 dispersal of Sargassum algae in the Tropical North Atlantic

Since 2011, huge amounts of Sargassum algae are detected in the equatorial Atlantic, causing large strandings events on the coasts of the West Indies, Brazil and West Africa. The distribution of this stock shows strong annual and interannual variability, whose drivers are not settled yet. Here we use satellite Sargassum observations from MODIS and currents from an ocean reanalysis to simulate the passive transport of algae in 2017. Wind effect was necessary to fit the observed distribution. Simulations reasonably reproduce the satellite monthly distribution for up to seven months, confirming the prominent role of transport in the distribution cycle. Annual cycle appears as a zonal exchange between eastern (EAR) and western accumulation regions (WAR). EAR is well explained by advection alone, with sharp meridional distribution controlled by converging currents below the inter-tropical Convergence Zone. Instead, WAR is not explained by advection alone, suggesting local growth.

Selective removal of sulfur from 3-methyl thiophene under mild conditions over NiW/Al2O3-TiO2 modified by surfactants

Here we reported an experimental analysis of Al2O3-TiO2 binary mixed oxides used as supports of NiW hydrodesulfurization catalysts. The series of supports were prepared by soft template sol-gel synthesis assisted by four different surfactants (P-123(P), F-68(F), L-64(L) and CTAB(C)). The characterization results showed substantial differences in the formation of the mixed oxide, the surface, and the electronic. Noteworthy was the sharp unimodal pore-size distribution obtained in all cases. The surface composition measured by XPS confirmed surface enrichment with alumina, changing the activity of the NiW catalysts. The morphology of the active NiWS phase presented average slab length and stacking of about 2.3 nm and 2 slabs. Meanwhile, large quantities of curved slabs were observed. This fact confirmed that the WSx cluster interacts preferentially with the Al atoms instead with the Ti atoms. The highest activity was observed in the NiW/AlTi-P and NiW/AlTi-F sulfided catalysts. We noticed that the activity is related not only with the sulfidation degree, the Ni promotion, or the slabs length and staking but also with the slab shape and the interaction with the support. The results of this work confirm that these AlTi-x samples are suitable to be used as supports of hydrodesulfurization catalysts.