Emotion detection from handwriting and drawing samples using an attention-based transformer model.

Emotion detection (ED) involves the identification and understanding of an individual's emotional state through various cues such as facial expressions, voice tones, physiological changes, and behavioral patterns. In this context, behavioral analysis is employed to observe actions and behaviors for emotional interpretation. This work specifically employs behavioral metrics like drawing and handwriting to determine a person's emotional state, recognizing these actions as physical functions integrating motor and cognitive processes. The study proposes an attention-based transformer model as an innovative approach to identify emotions from handwriting and drawing samples, thereby advancing the capabilities of ED into the domains of fine motor skills and artistic expression. The initial data obtained provides a set of points that correspond to the handwriting or drawing strokes. Each stroke point is subsequently delivered to the attention-based transformer model, which embeds it into a high-dimensional vector space. The model builds a prediction about the emotional state of the person who generated the sample by integrating the most important components and patterns in the input sequence using self-attentional processes. The proposed approach possesses a distinct advantage in its enhanced capacity to capture long-range correlations compared to conventional recurrent neural networks (RNN). This characteristic makes it particularly well-suited for the precise identification of emotions from samples of handwriting and drawings, signifying a notable advancement in the field of emotion detection. The proposed method produced cutting-edge outcomes of 92.64% on the benchmark dataset known as EMOTHAW (Emotion Recognition via Handwriting and Drawing).

ChestCovidNet: An Effective DL-based Approach for COVID-19, Lung Opacity, and Pneumonia Detection Using Chest Radiographs Images.

Currently used lung disease screening tools are expensive in terms of money and time. Therefore, chest radiograph images (CRIs) are employed for prompt and accurate COVID-19 identification. Recently, many researchers have applied Deep learning (DL) based models to detect COVID-19 automatically. However, their model could have been more computationally expensive and less robust, i.e., its performance degrades when evaluated on other datasets. This study proposes a trustworthy, robust, and lightweight network (ChestCovidNet) that can detect COVID-19 by examining various CRIs datasets. The ChestCovidNet model has only 11 learned layers, eight convolutional (Conv) layers, and three fully connected (FC) layers. The framework employs both the Conv and group Conv layers, Leaky Relu activation function, shufflenet unit, Conv kernels of 3×3 and 1×1 to extract features at different scales, and two normalization procedures that are cross-channel normalization and batch normalization. We used 9013 CRIs for training whereas 3863 CRIs for testing the proposed ChestCovidNet approach. Furthermore, we compared the classification results of the proposed framework with hybrid methods in which we employed DL frameworks for feature extraction and support vector machines (SVM) for classification. The study's findings demonstrated that the embedded low-power ChestCovidNet model worked well and achieved a classification accuracy of 98.12% and recall, F1-score, and precision of 95.75%.

Advanced oxidation/reduction processes (AO/RPs) for wastewater treatment, current challenges, and future perspectives: a review.

Advanced oxidation/reduction processes (AO/RPs) are considered as effective water treatment technologies and thus could be used to solve the problem of water pollution. These technologies of wastewater treatment involve the production of highly reactive species such as •OH, H•, e-aq, SO4•-, and SO3•-. These radicals can attack the targeted contaminants present in aqueous media and result in their destruction. The efficiency of AO/RPs is highly affected by various operational parameters such as initial concentration of contaminant, solution pH, catalyst amount, intensity of light source, nature of oxidant and reductant used, and the presence of various ionic species in aquatic media. Among AO/RPs, the solar light-based AO/RPs are most widely used nowadays for contaminant removal from aqueous media because of their high environmental friendliness and cost effectiveness. By using these techniques, almost all types of pollutants can be easily removed from aquatic media within short intervals of time, and hence, the problem of water pollution can be solved effectively. This review focuses on various AO/RPs used for wastewater treatment. The effects of different operational parameters that affect the efficiency of these processes toward contaminant removal have been discussed. Besides, challenges and future recommendations are also briefly provided for the researchers in order to improve the efficiency of these processes.

Behind the Bait: Delving into PhishTank's hidden data.

Phishing constitutes a form of social engineering that aims to deceive individuals through email communication. Extensive prior research has underscored phishing as one of the most commonly employed attack vectors for infiltrating organizational networks. A prevalent method involves misleading the target by employing phishing URLs concealed through hyperlink strategies. PhishTank, a website employing the concept of crowd-sourcing, aggregates phishing URLs and subsequently verifies their authenticity. In the course of this study, we leveraged a Python script to extract data from the PhishTank website, amassing a comprehensive dataset comprising over 190,0000 phishing URLs. This dataset is a valuable resource that can be harnessed by both researchers and practitioners for enhancing phish- ing filters, fortifying firewalls, security education, and refining training and testing models, among other applications.

An effective approach for plant leaf diseases classification based on a novel DeepPlantNet deep learning model.

Introduction: Recently, plant disease detection and diagnosis procedures have become a primary agricultural concern. Early detection of plant diseases enables farmers to take preventative action, stopping the disease's transmission to other plant sections. Plant diseases are a severe hazard to food safety, but because the essential infrastructure is missing in various places around the globe, quick disease diagnosis is still difficult. The plant may experience a variety of attacks, from minor damage to total devastation, depending on how severe the infections are. Thus, early detection of plant diseases is necessary to optimize output to prevent such destruction. The physical examination of plant diseases produced low accuracy, required a lot of time, and could not accurately anticipate the plant disease. Creating an automated method capable of accurately classifying to deal with these issues is vital. Method: This research proposes an efficient, novel, and lightweight DeepPlantNet deep learning (DL)-based architecture for predicting and categorizing plant leaf diseases. The proposed DeepPlantNet model comprises 28 learned layers, i.e., 25 convolutional layers (ConV) and three fully connected (FC) layers. The framework employed Leaky RelU (LReLU), batch normalization (BN), fire modules, and a mix of 3×3 and 1×1 filters, making it a novel plant disease classification framework. The Proposed DeepPlantNet model can categorize plant disease images into many classifications. Results: The proposed approach categorizes the plant diseases into the following ten groups: Apple_Black_rot (ABR), Cherry_(including_sour)_Powdery_mildew (CPM), Grape_Leaf_blight_(Isariopsis_Leaf_Spot) (GLB), Peach_Bacterial_spot (PBS), Pepper_bell_Bacterial_spot (PBBS), Potato_Early_blight (PEB), Squash_Powdery_mildew (SPM), Strawberry_Leaf_scorch (SLS), bacterial tomato spot (TBS), and maize common rust (MCR). The proposed framework achieved an average accuracy of 98.49 and 99.85in the case of eight-class and three-class classification schemes, respectively. Discussion: The experimental findings demonstrated the DeepPlantNet model's superiority to the alternatives. The proposed technique can reduce financial and agricultural output losses by quickly and effectively assisting professionals and farmers in identifying plant leaf diseases.

Determination of lindane in surface water samples and its degradation by hydrogen peroxide and persulfate assisted TiO2-based photocatalysis.

Organochlorine pesticides (OCPs) have been used extensively as insecticides and herbicides. This study investigates the occurrence of lindane in surface water from the Peshawar valley (i.e., Peshawar, Charsadda, Nowshera, Mardan and Swabi districts of Khyber Pakhtunkhwa, Pakistan). Out of 75 samples tested (i.e., 15 samples from each district), 13 samples (including 2 from Peshawar, 3 from Charsadda, 4 from Nowshera, 1 from Mardan, and 3 from Swabi) are found to be contaminated with lindane. Overall, the detection frequency is 17.3%. The maximum concentration of lindane is detected in a water sample from Nowshera and found to be 2.60 µg L-1. Furthermore, the degradation of lindane in the water sample from Nowshera, containing the maximum concentration, is investigated by simulated solar-light/TiO2 (solar/TiO2), solar/H2O2/TiO2 and solar/persulfate/TiO2 photocatalysis. The degradation of lindane by solar/TiO2 photocatalysis is 25.77% after 10 h of irradiation. The efficiency of the solar/TiO2 process is significantly increased in the presence of 500 µM H2O2 and 500 µM persulfate (PS) (separately), represented by 93.85 and 100.00% lindane removal, respectively. The degradation efficiency of lindane is lower in natural water samples as compared to Milli-Q water, attributed to water matrix effect. Moreover, the identification of degradation products (DPs) shows that lindane follows similar degradation pathways in natural water samples as the one in Milli-Q water. The results show that the occurrence of lindane in surface waters of Peshawar valley is a matter of great concern for human beings and the environment. Interestingly, H2O2 and PS assisted solar/TiO2 photocatalysis is an effective method for the removal of lindane from natural water.

Telehealth for COVID-19: A Conceptual Framework.

The world has been going through the global crisis of the coronavirus (COVID-19). It is a challenging situation for every country to tackle its healthcare system. COVID-19 spreads through physical contact with COVID-positive patients and causes potential damage to the country's health and economy system. Therefore, to overcome the chance of spreading the disease, the only preventive measure is to maintain social distancing. In this vulnerable situation, virtual resources have been utilized in order to maintain social distance, i.e., the telehealth system has been proposed and developed to access healthcare services remotely and manage people's health conditions. The telehealth system could become a regular part of our healthcare system, and during any calamity or natural disaster, it could be used as an emergency response to deal with the catastrophe. For this purpose, we proposed a conceptual telehealth framework in response to COVID-19. We focused on identifying critical issues concerning the use of telehealth in healthcare setups. Furthermore, the factors influencing the implementation of the telehealth system have been explored in detail. The proposed telehealth system utilizes artificial intelligence and data science to regulate and maintain the system efficiently. Before implementing the telehealth system, it is required that prearrangements be made, such as appropriate funding measures, the skills to know technological usage, training sessions, and staff endorsement. The barriers and influencing factors provided in this article can be helpful for future developments in telehealth systems and for making fruitful progress in fighting pandemics like COVID-19. At the same time, the same approach can be used to save the lives of many frontline workers.

A Holistic Approach to Identify and Classify COVID-19 from Chest Radiographs, ECG, and CT-Scan Images Using ShuffleNet Convolutional Neural Network.

Early and precise COVID-19 identification and analysis are pivotal in reducing the spread of COVID-19. Medical imaging techniques, such as chest X-ray or chest radiographs, computed tomography (CT) scan, and electrocardiogram (ECG) trace images are the most widely known for early discovery and analysis of the coronavirus disease (COVID-19). Deep learning (DL) frameworks for identifying COVID-19 positive patients in the literature are limited to one data format, either ECG or chest radiograph images. Moreover, using several data types to recover abnormal patterns caused by COVID-19 could potentially provide more information and restrict the spread of the virus. This study presents an effective COVID-19 detection and classification approach using the Shufflenet CNN by employing three types of images, i.e., chest radiograph, CT-scan, and ECG-trace images. For this purpose, we performed extensive classification experiments with the proposed approach using each type of image. With the chest radiograph dataset, we performed three classification experiments at different levels of granularity, i.e., binary, three-class, and four-class classifications. In addition, we performed a binary classification experiment with the proposed approach by classifying CT-scan images into COVID-positive and normal. Finally, utilizing the ECG-trace images, we conducted three experiments at different levels of granularity, i.e., binary, three-class, and five-class classifications. We evaluated the proposed approach with the baseline COVID-19 Radiography Database, SARS-CoV-2 CT-scan, and ECG images dataset of cardiac and COVID-19 patients. The average accuracy of 99.98% for COVID-19 detection in the three-class classification scheme using chest radiographs, optimal accuracy of 100% for COVID-19 detection using CT scans, and average accuracy of 99.37% for five-class classification scheme using ECG trace images have proved the efficacy of our proposed method over the contemporary methods. The optimal accuracy of 100% for COVID-19 detection using CT scans and the accuracy gain of 1.54% (in the case of five-class classification using ECG trace images) from the previous approach, which utilized ECG images for the first time, has a major contribution to improving the COVID-19 prediction rate in early stages. Experimental findings demonstrate that the proposed framework outperforms contemporary models. For example, the proposed approach outperforms state-of-the-art DL approaches, such as Squeezenet, Alexnet, and Darknet19, by achieving the accuracy of 99.98 (proposed method), 98.29, 98.50, and 99.67, respectively.

A Robust End-to-End Deep Learning-Based Approach for Effective and Reliable BTD Using MR Images.

Detection of a brain tumor in the early stages is critical for clinical practice and survival rate. Brain tumors arise in multiple shapes, sizes, and features with various treatment options. Tumor detection manually is challenging, time-consuming, and prone to error. Magnetic resonance imaging (MRI) scans are mostly used for tumor detection due to their non-invasive properties and also avoid painful biopsy. MRI scanning of one patient's brain generates many 3D images from multiple directions, making the manual detection of tumors very difficult, error-prone, and time-consuming. Therefore, there is a considerable need for autonomous diagnostics tools to detect brain tumors accurately. In this research, we have presented a novel TumorResnet deep learning (DL) model for brain detection, i.e., binary classification. The TumorResNet model employs 20 convolution layers with a leaky ReLU (LReLU) activation function for feature map activation to compute the most distinctive deep features. Finally, three fully connected classification layers are used to classify brain tumors MRI into normal and tumorous. The performance of the proposed TumorResNet architecture is evaluated on a standard Kaggle brain tumor MRI dataset for brain tumor detection (BTD), which contains brain tumor and normal MR images. The proposed model achieved a good accuracy of 99.33% for BTD. These experimental results, including the cross-dataset setting, validate the superiority of the TumorResNet model over the contemporary frameworks. This study offers an automated BTD method that aids in the early diagnosis of brain cancers. This procedure has a substantial impact on improving treatment options and patient survival.

Stochastic Recognition of Human Physical Activities via Augmented Feature Descriptors and Random Forest Model.

Human physical activity recognition from inertial sensors is shown to be a successful approach for monitoring elderly individuals and children in indoor and outdoor environments. As a result, researchers have shown significant interest in developing state-of-the-art machine learning methods capable of utilizing inertial sensor data and providing key decision support in different scenarios. This paper analyzes data-driven techniques for recognizing human daily living activities. Therefore, to improve the recognition and classification of human physical activities (for example, walking, drinking, and running), we introduced a model that integrates data preprocessing methods (such as denoising) along with major domain features (such as time, frequency, wavelet, and time-frequency features). Following that, stochastic gradient descent (SGD) is used to improve the performance of the extracted features. The selected features are catered to the random forest classifier to detect and monitor human physical activities. Additionally, the proposed HPAR system was evaluated on five benchmark datasets, namely the IM-WSHA, PAMAP-2, UCI HAR, MobiAct, and MOTIONSENSE databases. The experimental results show that the HPAR system outperformed the present state-of-the-art methods with recognition rates of 90.18%, 91.25%, 91.83%, 90.46%, and 92.16% from the IM-WSHA, PAMAP-2, UCI HAR, MobiAct, and MOTIONSENSE datasets, respectively. The proposed HPAR model has potential applications in healthcare, gaming, smart homes, security, and surveillance.

EEG-Based Epileptic Seizure Detection via Machine/Deep Learning Approaches: A Systematic Review.

Epileptic seizure is one of the most chronic neurological diseases that instantaneously disrupts the lifestyle of affected individuals. Toward developing novel and efficient technology for epileptic seizure management, recent diagnostic approaches have focused on developing machine/deep learning model (ML/DL)-based electroencephalogram (EEG) methods. Importantly, EEG's noninvasiveness and ability to offer repeated patterns of epileptic-related electrophysiological information have motivated the development of varied ML/DL algorithms for epileptic seizure diagnosis in the recent years. However, EEG's low amplitude and nonstationary characteristics make it difficult for existing ML/DL models to achieve a consistent and satisfactory diagnosis outcome, especially in clinical settings, where environmental factors could hardly be avoided. Though several recent works have explored the use of EEG-based ML/DL methods and statistical feature for seizure diagnosis, it is unclear what the advantages and limitations of these works are, which might preclude the advancement of research and development in the field of epileptic seizure diagnosis and appropriate criteria for selecting ML/DL models and statistical feature extraction methods for EEG-based epileptic seizure diagnosis. Therefore, this paper attempts to bridge this research gap by conducting an extensive systematic review on the recent developments of EEG-based ML/DL technologies for epileptic seizure diagnosis. In the review, current development in seizure diagnosis, various statistical feature extraction methods, ML/DL models, their performances, limitations, and core challenges as applied in EEG-based epileptic seizure diagnosis were meticulously reviewed and compared. In addition, proper criteria for selecting appropriate and efficient feature extraction techniques and ML/DL models for epileptic seizure diagnosis were also discussed. Findings from this study will aid researchers in deciding the most efficient ML/DL models with optimal feature extraction methods to improve the performance of EEG-based epileptic seizure detection.

AFD-StackGAN: Automatic Mask Generation Network for Face De-Occlusion Using StackGAN.

To address the problem of automatically detecting and removing the mask without user interaction, we present a GAN-based automatic approach for face de-occlusion, called Automatic Mask Generation Network for Face De-occlusion Using Stacked Generative Adversarial Networks (AFD-StackGAN). In this approach, we decompose the problem into two primary stages (i.e., Stage-I Network and Stage-II Network) and employ a separate GAN in both stages. Stage-I Network (Binary Mask Generation Network) automatically creates a binary mask for the masked region in the input images (occluded images). Then, Stage-II Network (Face De-occlusion Network) removes the mask object and synthesizes the damaged region with fine details while retaining the restored face's appearance and structural consistency. Furthermore, we create a paired synthetic face-occluded dataset using the publicly available CelebA face images to train the proposed model. AFD-StackGAN is evaluated using real-world test images gathered from the Internet. Our extensive experimental results confirm the robustness and efficiency of the proposed model in removing complex mask objects from facial images compared to the previous image manipulation approaches. Additionally, we provide ablation studies for performance comparison between the user-defined mask and auto-defined mask and demonstrate the benefits of refiner networks in the generation process.

Enhanced solar light photocatalytic performance of Fe-ZnO in the presence of H2O2, S2O82-, and HSO5- for degradation of chlorpyrifos from agricultural wastes: Toxicities investigation.

This study reported Fe doped zinc oxide (Fe-ZnO) synthesis to degrade chlorpyrifos (CPY), a highly toxic organophosphate pesticide and important sources of agricultural wastes. Fourier transform infrared, X-ray diffraction, scanning electron microscope, and energy-dispersive X-ray spectroscopic analyses showed successful formation of the Fe-ZnO with highly crystalline and amorphous nature. Water collected from agricultural wastes were treated with Fe-ZnO and the results showed 67% degradation of CPY by Fe-ZnO versus 39% by ZnO at 140 min treatment time. Detail mechanism involving reactive oxygen species production from solar light activated Fe-ZnO and their role in degradation of CPY was assessed. Use of H2O2, peroxydisulfate (S2O82-) and peroxymonosulfate (HSO5-) with Fe-ZnO under solar irradiation promoted removal of CPY. The peroxides yielded hydroxyl (OH) and sulfate radical () under solar irradiation mediated by Fe-ZnO. Effects of several parameters including concentration of pollutant and oxidants, pH, co-existing ions, and presence of natural organic matter on CPY degradation were studied. Among peroxides, HSO5- revealed to provide better performance. The prepared Fe-ZnO showed high reusability and greater mineralization of CPY. The GC-MS analysis showed degradation of CPY resulted into several transformation products (TPs). Toxicity analysis of CPY as well as its TPs was performed and the formation of non-toxic acetate imply greater capability of the treatment technology.

A novel route for catalytic activation of peroxymonosulfate by oxygen vacancies improved bismuth-doped titania for the removal of recalcitrant organic contaminant.

In this work, bismuth-doped titania (BixTiO2) with improved oxygen vacancies was synthesized by sol-gel protocol as a novel peroxymonosulfate (PMS, HSO5-) activator. HSO5- and adsorbed oxygen molecules could efficiently be transformed into their respective radicals through defect ionization to attain charge balance after their trapping on oxygen vacancies of the catalyst. XRD study of BixTiO2 with 5 wt% Bi (5BiT) revealed anatase, crystalline nature, and successful doping of Bi into TiO2 crystal lattice. The particle size obtained from BET data and SEM observations was in good agreement. PL spectra showed the formation rates of •OH by 3BiT, 7BiT, 5BiTC, and 5BiT as 0.720, 1.200, 1.489, and 2.153 µmol/h, respectively. 5BiT catalyst with high surface area (216.87 m2 g-1) and high porosity (29.81%) was observed the excellent HSO5- activator. The catalytic performance of 0BiT, 3BiT, 5BiT, and 7BiT when coupled with 2 mM HSO5- for recalcitrant flumequine (FLU) removal under dark was 10, 27, 55, and 37%, respectively. Only 5.4% decrease in catalytic efficiency was observed at the end of seventh cyclic run. Radical scavenging studies indicate that SO4•- is the dominant species that caused 62.0% degradation. Moreover, strong interaction between Bi and TiO2 through Bi-O-Ti bonds prevents Bi leaching (0.081 mg L-1) as shown by AAS. The kinetics, degradation pathways, ecotoxicity, and catalytic mechanism for recalcitrant FLU were also elucidated. Cost-efficient, environment-friendly, and high mineralization recommends this design strategy; BixTiO2/HSO5- system is a promising advanced oxidation process for the aquatic environment remediation.

Degradation of highly chlorinated pesticide, lindane, in water using UV/persulfate: kinetics and mechanism, toxicity evaluation, and synergism by H2O2.

Sulfate radical-advanced oxidation processes (SR-AOPs) are emerging technologies for decomposing organic pollutants in water. This study investigated the efficiency of UV/persulfate (UV/S2O82-) process to degrade lindane in water, showing 93.2% lindane removal ([lindane]0 = 3.43 µM, [S2O82-]0 = 100 µM) at a UV fluence of 720 mJ/cm2. The lindane degradation followed first order kinetics and mechanistic studies suggested H-abstraction by SO4•- and Cl removal via C-Cl bond cleavage by UV-C light. Toxicity assessment using ECOSAR program showed toxicity gradually decreased and eventually no significant toxicity remained when all by-products vanished at high UV dose. Removal efficiency of lindane decreased from 93.2% to 38.4, 45.5, 56.0, 84.3 and 88.6%, by adding 1.0 mg/L humic acid or 1.0 mM CO32-, HCO3-, Cl- or SO42-, respectively. Coupling of H2O2 with UV/S2O82- showed a significant synergistic effect with 99.0% lindane removal at a UV fluence of 600 mJ/cm2, using [S2O82-]0 = [H2O2]0 = 50 µM while UV/H2O2 resulted in only 36.6% lindane removal ([lindane]0 = 3.43 µM, [H2O2]0 = 100 µM) at a UV fluence of 720 mJ/cm2. The results indicate that SR-AOP has potential for consideration as a remedial technology to treat persistent chlorinated pesticides such as lindane in contaminated water.

Nano-zerovalent manganese/biochar composite for the adsorptive and oxidative removal of Congo-red dye from aqueous solutions.

Congo-red (CR), a precursor of textile products and a contaminant of great concern, has contaminated aquatic environments. Here, we explored the synthesis of mesoporous nano-zerovalent manganese (nZVMn) and Phoenix dactylifera leaves biochar (PBC) composite for the removal of CR from water. The nZVMn/PBC adsorbed 117.647 mg/g of CR versus 25.316 mg/g by PBC at [CR]0 = 20 mg/L and [PBC]0 = [nZVMn/PBC]0 = 500 mg/L. Variation of [nZVMn/PBC]0, [CR]0 and pH influenced the adsorption of CR. Freundlich adsorption isotherm and pseudo-first-order kinetic models best fitted CR adsorption. The H2O2 coupling with nZVMn/PBC promoted removal of CR possibly due to the formation of hydroxyl radical (●OH) and caused 95 % removal of CR versus 77 % by nZVMn/PBC alone. The ●OH scavengers inhibited the removal of CR. The nZVMn/PBC showed a good reusability and efficient removal of CR up to the seventh cycle of treatment. Results reveal that nZVMn improved performance, thermal stability and reusability of biochar. Degradation products from ●OH-mediated degradation of CR were studied by ultraperformance liquid chromatography with mass spectrometric detector to establish degradation pathways. The ion-chromatographic analysis showed the formation of non-toxic inorganic acetate product, which suggests high potential of the newly fabricated adsorbent in the removal of CR.

Bismuth-Doped Nano Zerovalent Iron: A Novel Catalyst for Chloramphenicol Degradation and Hydrogen Production.

In this study, we showed that doping bismuth (Bi) at the surface of Fe0 (Bi/Fe0, bimetallic iron system)-synthesized by a simple borohydride reduction method-can considerably accelerate the reductive degradation of chloramphenicol (CHP). At a reaction time of 12 min, 62, 68, 74, 95, and 82% degradation of CHP was achieved with Fe0, Bi/Fe0-1 [1% (w/w) of Bi], Bi/Fe0-3 [3% (w/w) of Bi], Bi/Fe0-5 [5% (w/w) of Bi], and Bi/Fe0-8 [8% (w/w) of Bi], respectively. Further improvements in the degradation efficiency of CHP were observed by combining the peroxymonosulfate (HSO5 -) with Bi/Fe0-5 (i.e., 81% by Bi/Fe0-5 and 98% by the Bi/Fe0-5/HSO5 - system at 8 min of treatment). Interestingly, both Fe0 and Bi/Fe0-5 showed effective H2 production under dark conditions that reached 544 and 712 µM by Fe0 and Bi/Fe0-5, respectively, in 70 mL of aqueous solution containing 0.07 g (i.e., at 1 g L-1 concentration) of the catalyst at ambient temperature.

Ultrasound-assisted heterogeneous activation of persulfate and peroxymonosulfate by asphaltenes for the degradation of BTEX in water.

This study investigated - for the first time - the simultaneous degradation of benzene, toluene, ethylbenzene and o-xylene (BTEX) by persulfate (PS) and peroxymonosulfate (PMS) activated by asphaltenes (Asph) under ultrasound (US) irradiation. Advantageous properties such as high thermal stability, low production cost and extensive availability make asphaltenes as an appealing carbonaceous material for heterogeneous catalysis. The application of asphaltenes in PS/US increased the degradation of BTEXs from 31%, 34%, 35%, 32%-78%, 94%, 98% and 98%, while the removal of these compounds in PMS/US system was improved from 26%, 27%, 24%, 20%-76%, 91%, 97%, 97%, respectively. PS and PMS activation followed a typical sulfate-radical based advanced oxidation processes. In terms of activation of PS and PMS, the particles of asphaltenes intensified formation of reactive radicals by creating additional centers of cavitational events. Moreover, owing to π-π stacking interaction between asphaltenes and sp2-hybridized systems of BTEX, the contaminants undergo adsorption on the surface of asphaltenes and subsequent oxidation by formed radicals. The radical route of BTEX degradation in both PS/US/Asph and PMS/US/Asph systems was mainly contributed by sulfate (SO4•-) and hydroxyl radicals (HO•) and coexisting superoxide radical anions (O2•-) played a minor role.

Synergistic effects of bismuth coupling on the reactivity and reusability of zerovalent iron nanoparticles for the removal of cadmium from aqueous solution.

Removal of cadmium (Cd2+), a highly toxic heavy metal, from aqueous solutions was investigated using nano zerovalent iron (Fe0). Cadmium was efficiently removed by Fe0, although reactivity and reusability of Fe0 was significantly promoted by coupling with bismuth (Bi). At a reaction time of 20 min, 85% and 96% Cd2+ was removed by Fe0 and Bi/Fe0, respectively, at first cycle using [Cd2+]0 = 10 mg/L and [Fe0]0 = [Bi/Fe0]0 = 1.0 g/L. However, Cd2+ removal efficiency was reduced to 12% and 80% at sixth cycle by Fe0 and Bi/Fe0, respectively. The X-Ray diffraction and energy dispersive X-Ray spectroscopy analysis proved successful formation of Fe0 by the chemical reduction method and also confirmed coupling of Bi with Fe0 to form bimetallic Bi/Fe0. The oxidation of Fe0 and Bi/Fe0 yielded electron that played significant role in the conversion of toxic Cd2+ into non-toxic Cd0. The reactivity of electron with Cd2+ was calculated to be 4.3 × 109 M-1 s-1. The pH of solution showed pronounced effects on the reactivity of both Fe0 and Bi/Fe0. Removal of Cd2+ by both Fe0 and Bi/Fe0 followed pseudo-first-order kinetic model. The conversion of Cd2+ into non-toxic Cd0 proved Fe0 and Bi/Fe0 to be highly efficient and rewarding in detoxification of Cd2+ and other toxic metals in aqueous environments.

Oxidative removal of brilliant green by UV/S2O82‒, UV/HSO5‒ and UV/H2O2 processes in aqueous media: A comparative study.

The removal of brilliant green (BG), a toxic organic and cationic dye, has been examined by UV/S2O82- (PS), UV/HSO5- (PMS) and UV/H2O2 processes. BG showed insignificant direct photolysis at 254 nm (i.e., 8.6% after 30 min). However, enhanced BG degradation was observed in UV/PS, UV/PMS and UV/H2O2 systems as revealed from 63.1, 47.0 and 34.8% BG degradation, respectively, at 30 min of reaction time, using 0.05 mM BG and 1.0 mM oxidant initial concentration. The bimolecular rate constants of OH and SO4- with BG were determined to be 2.35 × 109 and 2.21 × 109 M-1 s-1, respectively. Electrical energy per order (EE/O) values for UV/PS, UV/PMS and UV/H2O2 processes were calculated to be 5.4, 6.8, and 7.8 KWh/m3/order, respectively. The addition of humic acid (HA) and inorganic anions inhibited the degradation of BG by UV/PS in the order of NO2- > HA > HCO3- > Cl-  > NO3- ≈ SO42-. The results of frontier electron densities (FEDs) showed that C-atom holding the three rings (C7), and C-atoms at para positions to N-alkyl groups of the two rings (C4 and C14) are the predominant sites for radical addition. Furthermore, nine degradation products (DPs) of BG were detected experimentally using LC/MS/MS.