1,477 research outputs found

    Desalination of pigment industry wastewater by reverse osmosis using OPM-K membrane

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    Pigment production plants are among the most polluting industries due to their high-water consumption and complex releases. The current work investigates the removal efficiency of sodium chloride (NaCl), sodium acetate (C2H3NaO2), and acetic acid (CH3COOH), and also the permeate flux of a small-batch OPM-K membrane using reverse osmosis (RO) pilot plant at various concentrations and pressures. At 0.034 M and applied pressure of 30 bar, the results showed that the maximum sodium chloride removal and permeate flow were 93.4% and 8.3 × 10−6 m/s, respectively. When the feed concentration was increased to 0.17 M, the maximum removal efficiency and permeate flow were 88.5% and 4.7 × 10−6 m/s, respectively. In addition, acetic acid has a minimum removal efficiency of 76.2% at 0.062 M and 20 bar applied pressure, while sodium acetate has a minimum permeate flow of 2.8 × 10−6 at 0.061 M and 20 bar. To conclude, the results proved RO membrane's high removal efficiency and permeate flux at low salt concentrations. It should also be noted that RO would be more suitable for the retention of NaCl, C2H3NaO2, and CH3COOH, the three components with the highest concentration in wastewater discharged from pigment production plants

    Experimental capabilities for liquid jet samples at sub-MHz rates at the FXE Instrument at European XFEL

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    The Femtosecond X-ray Experiments (FXE) instrument at the European X-ray Free-Electron Laser (EuXFEL) provides an optimized platform for investigations of ultrafast physical, chemical and biological processes. It operates in the energy range 4.7–20 keV accommodating flexible and versatile environments for a wide range of samples using diverse ultrafast X-ray spectroscopic, scattering and diffraction techniques. FXE is particularly suitable for experiments taking advantage of the sub-MHz repetition rates provided by the EuXFEL. In this paper a dedicated setup for studies on ultrafast biological and chemical dynamics in solution phase at sub-MHz rates at FXE is presented. Particular emphasis on the different liquid jet sample delivery options and their performance is given. Our portfolio of high-speed jets compatible with sub-MHz experiments includes cylindrical jets, gas dynamic virtual nozzles and flat jets. The capability to perform multi-color X-ray emission spectroscopy (XES) experiments is illustrated by a set of measurements using the dispersive X-ray spectrometer in von Hamos geometry. Static XES data collected using a multi-crystal scanning Johann-type spectrometer are also presented. A few examples of experimental results on ultrafast time-resolved X-ray emission spectroscopy and wide-angle X-ray scattering at sub-MHz pulse repetition rates are given

    Integrated Electro-Ozonation and Fixed-Bed Column for the Simultaneous Removal of Emerging Contaminants and Heavy Metals from Aqueous Solutions

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    In the current study, an integrated physiochemical method was utilized to remove tonalide (TND) and dimethyl phthalate (DMP) (as emerging contaminants, ECs), and nickel (Ni) and lead (Pb) (as heavy metals), from synthetic wastewater. In the first step of the study, pH, current (mA/cm2), and voltage (V) were set to 7.0, 30, and 9, respectively; then the removal of TND, DMP, Ni, and Pb with an electro-ozonation reactor was optimized using response surface methodology (RSM). At the optimum reaction time (58.1 min), ozone dosage (9.4 mg L−1), initial concentration of ECs (0.98 mg L−1), and initial concentration of heavy metals (28.9 mg L−1), the percentages of TND, DMP, Ni, and Pb removal were 77.0%, 84.5%, 59.2%, and 58.2%, respectively. For the electro-ozonation reactor, the ozone consumption (OC) ranged from 1.1 kg to 3.9 kg (kg O3/kg Ecs), and the specific energy consumption (SEC) was 6.95 (kWh kg−1). After treatment with the optimum electro-ozonation parameters, the synthetic wastewater was transferred to a fixed-bed column, which was filled with a new composite adsorbent (named BBCEC), as the second step of the study. BBCEC improved the efficacy of the removal of TND, DMP, Ni, and Pb to more than 92%

    Treatment of As(III)-contaminated food waste using alkali treatment and its potential application for methylene blue removal from aqueous solutions

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    Conventional methods for managing food waste become ineffective when the waste is contaminated with toxic chemicals due to the risk of further contamination. To address this issue, this study proposes an alkali pre-treatment approach to decontaminate processed food waste containing As(III) and repurpose it as an adsorbent for treating wastewater. The study investigates the effects of different alkali treatment conditions, (concentration, treatment time, and temperature), on the decontamination of As(III) and its impact on the adsorption behavior of methylene blue. Experimental results demonstrate that treating the material with 0.8 M NaOH at 60 °C for 4 h effectively eliminates As(III), achieving a remarkable removal rate of 99.8 %. Characterization results revealed that the alkali-treated material exhibits desirable properties, including increased carboxylation, improved thermal stability, reduced crystallinity, and a significantly enlarged specific surface area (66.03 %) compared to the original contaminated food waste. The alkali-treated waste demonstrates good adsorption capacity towards methylene blue, with a maximum adsorption capacity of 534.6 mg/g achieved at pH 8.0, 40 g/L adsorbent dose, 10 h shaking time, and 20 °C. The Temkin (R2 = 0.978) and intraparticle diffusion models effectively describe the adsorption data. Notably, the adsorbent demonstrates excellent reusability, as it can be utilized for at least 4 cycles of adsorption-desorption without a significant reduction in methylene blue removal efficiency (only a 5.2 % decrease), highlighting its practical potential. The results strongly indicate that alkali pre-treatment represents a simple and efficient method for eliminating As contamination from food waste while concurrently producing an effective adsorbent for removing dyes from aqueous solutions

    Removal of GenX by APTES functionalized diepoxyoctane cross-linked chitosan beads

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    Perfluoro-2-propoxypropanoic acid ammonium salt, commonly known as GenX, is a persistent, bioaccumulative, and toxic synthetic organofluorine compound utilized in producing various products. It has become a concern due to its extensive presence in the aquatic environment and its resistance to conventional water treatment methods. This study achieved effective adsorption of GenX by employing chitosan (CS) modified adsorbent. Cross-linked and aminated CS beads were synthesized using 1,2:7,8-diepoxyoctane (DEO) and 3-aminopropyl triethoxysilane (APTES). The prepared CS-DEO-APTES adsorbent exhibited an adsorption capacity of 825.9 mg/g, which was 2.26 times higher than that of CS beads (364.6 mg/g), attributed to its higher content of amino groups. Additionally, the CS-DEO-APTES adsorbent demonstrated excellent stability under acidic conditions (optimal pH= 4) due to the cross-linking process. Kinetic data, following a pseudo-second-order rate and isothermal data, fitting well with the Langmuir model, indicated that the interactions between GenX and CS-DEO-APTES were chemisorptive, with a nearly uniform distribution of adsorption sites. GenX-saturated beads were successfully regenerated for at least 6 cycles using a 1 % w/v aqueous NaCl and methanol solution (30:70 % v/v). Density functional theory (DFT) calculations suggested that electrostatic interactions primarily influence the adsorption of GenX. These results highlight the effectiveness of the CS-DEO-APTES adsorbent as a viable option for removing GenX from aqueous solutions

    Prospects for Galactic transient sources detection with the Cherenkov Telescope Array

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    Several types of Galactic sources, like magnetars, microquasars, novae or pulsar wind nebulae flares, display transient emission in the X-ray band. Some of these sources have also shown emission at MeV-GeV energies. However, none of these Galactic transients have ever been detected in the very-high-energy (VHE; E>100 GeV) regime by any Imaging Air Cherenkov Telescope (IACT). The Galactic Transient task force is a part of the Transient Working group of the Cherenkov Telescope Array (CTA) Consortium. The task force investigates the prospects of detecting the VHE counterpart of such sources, as well as their study following Target of Opportunity (ToO) observations. In this contribution, we will show some of the results of exploring the capabilities of CTA to detect and observe Galactic transients; we assume different array configurations and observing strategies

    Reconstruction of stereoscopic CTA events using deep learning with CTLearn

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    The Cherenkov Telescope Array (CTA), conceived as an array of tens of imaging atmospheric Cherenkov telescopes (IACTs), is an international project for a next-generation ground-based gamma-ray observatory, aiming to improve on the sensitivity of current-generation instruments a factor of five to ten and provide energy coverage from 20 GeV to more than 300 TeV. Arrays of IACTs probe the very-high-energy gamma-ray sky. Their working principle consists of the simultaneous observation of air showers initiated by the interaction of very-high-energy gamma rays and cosmic rays with the atmosphere. Cherenkov photons induced by a given shower are focused onto the camera plane of the telescopes in the array, producing a multi-stereoscopic record of the event. This image contains the longitudinal development of the air shower, together with its spatial, temporal, and calorimetric information. The properties of the originating very-high-energy particle (type, energy, and incoming direction) can be inferred from those images by reconstructing the full event using machine learning techniques. In this contribution, we present a purely deep-learning driven, full-event reconstruction of simulated, stereoscopic IACT events using CTLearn. CTLearn is a package that includes modules for loading and manipulating IACT data and for running deep learning models, using pixel-wise camera data as input

    SCIROCCO+: Simulation Code of Interferometric-observations for ROtators and CirCumstellar Objects including Non-Radial Pulsations

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    This book is a collection of 19 articles which reflect the courses given at the Collège de France/Summer school “Reconstruction d'images − Applications astrophysiques“ held in Nice and Fréjus, France, from June 18 to 22, 2012. The articles presented in this volume address emerging concepts and methods that are useful in the complex process of improving our knowledge of the celestial objects, including Earth

    Modeling the diagnosis of coronary artery disease by discriminant analysis and logistic regression: a cross-sectional study

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    PURPOSE: Coronary artery disease (CAD) is one of the most significant cardiovascular diseases that requires accurate angiography to diagnose. Angiography is an invasive approach involving risks like death, heart attack, and stroke. An appropriate alternative for diagnosis of the disease is to use statistical or data mining methods. The purpose of the study was to predict CAD by using discriminant analysis and compared with the logistic regression. MATERIALS AND METHODS: This cross-sectional study included 758 cases admitted to Fatemeh Zahra Teaching Hospital (Sari, Iran) for examination and coronary angiography for evaluation of CAD in 2019. A logistics discriminant, Quadratic Discriminant Analysis (QDA) and Linear Discriminant Analysis (LDA) model and K-Nearest Neighbor (KNN) were fitted for prognosis of CAD with the help of clinical and laboratory information of patients. RESULTS: Out of the 758 examined cases, 250 (32.98) cases were non-CAD and 508 (67.22) were diagnosed with CAD disease. The results indicated that the indices of accuracy, sensitivity, specificity and area under the ROC curve (AUC) in the linear discriminant analysis (LDA) were 78.6, 81.3, 71.3, and 81.9, respectively. The results obtained by the quadratic discriminant analysis were respectively 64.6, 88.2, 47.9, and 81. The values of the metrics in K-nearest neighbor method were 74, 77.5, 63.7, and 82, respectively. Finally, the logistic regression reached 77, 87.6, 55.6, and 82, respectively for the evaluation metrics. CONCLUSIONS: The LDA method is superior to the Quadratic Discriminant Analysis (QDA), K-Nearest Neighbor (KNN) and Logistic Regression (LR) methods in differentiating CAD patients. Therefore, in addition to common non-invasive diagnostic methods, LDA technique is recommended as a predictive model with acceptable accuracy, sensitivity, and specificity for the diagnosis of CAD. However, given that the differences between the models are small, it is recommended to use each model to predict CAD disease. © 2022. The Author(s)