Search

Among the existing metal-oxide gas sensors, cobalt oxide has the flexibility to revise the morphology through Cr-dopant to enhance sensing properties. Sensitive-surface of the chromium-doped cobalt oxide has proven its effective sensing nature to hydrogen sulfide gas. Interestingly, chromium-dopant increases the surface area, leading to particle size reduce and produces the more active sites for gas molecules. Also, the dopant creates impurity phases on the material which extends the sites for more reaction. To confirms these characteristics, the photoluminescence spectra showed intense peak that mimics the faster transport of electron to accelerate the sensing reaction. According to sensing measurement, the doped sensor is showing three-fold increase of response to 10 ppm gas and also, it detects the 2 ppm efficiently. The doped sensor warrants the stable response to gas due to higher reproducibility. Notably, the doped sensor detects the 1 ppm of gas at 120 s and recovery itself around 200 s. The doped sensor imparts response at room-temperature, affirming sensitive-surface. The doped sensor has shown the capable under humidity environment through response. [Display omitted] • The Cr-dopant increases the charge transfer between the material during the reaction. • The dopant increases the actives sites through their secondary phase formation. • The doped Co3O4 sensor shows rich metallic surface of higher conductivity due to the exposure of H 2 S gas. [ABSTRACT FROM AUTHOR]

In-depth research has been conducted in recent years on novel, environmentally acceptable adsorbent materials in response to the growing concern over dye contamination in industrial effluents. Due to exceptional adsorption efficiency, cost-effectiveness, and environmental friendliness, montmorillonite-based adsorbents have stood out as one of the viable possibilities. The complicated topic of dye removal using adsorbents based on montmorillonite (MMT) is thoroughly examined in this investigation. To provide the groundwork for its numerous applications in dye adsorption, the review begins by presenting a fundamental overview of the structure, characteristics, properties, and modification methods of montmorillonite. The process-affecting variables, such as pH, temperature, starting dye concentration, and contact duration, are all examined in detail, as well as the mechanisms behind the adsorption and artificial intelligence (AI) techniques employed. Emphasizing the connection between scientific innovation and sustainability, their ability to reduce the environmental effects while adhering to regulatory criteria is highlighted. [Display omitted] [ABSTRACT FROM AUTHOR]

The present study focused on the biosorption of Aspergillus terreus dead biomass for the removal of silver nanoparticles (AgNPs) from aqueous solution. For the better removal of AgNPs, pH, temperature, incubation time, initial biomass concentration and agitation speed were optimized and AgNPs treated as well as control biosorbents were characterized using Fourier transform infrared (FTIR) spectroscopy and field emission scanning electron microscopy (FESEM) coupled with energy dispersive X-ray spectroscopy (EDX) analyses. In our study, the optimum conditions of pH, temperature, incubation time, initial biomass concentration and agitation speed for the maximum removal of AgNPs was found to be 4, 30 °C, 200 min, 1 mg/mL and 125 rpm, respectively. Interestingly, in our study, Langmuir isotherm model was best correlated with the highest regression coefficient (R2) of 0.992 and the maximum biosorption was found to be 740.63 μg/mL. FTIR results showed that carboxyl, carbonyl and secondary amine groups were involved in the biosorption of AgNPs. FESEM images of AgNPs treated biosorbent depicted rough surface and the presence of AgNPs on the surface of biosorbent was clearly observed. EDX spectrum showed Ag peak, which confirmed the successful uptake of AgNPs by A. terreus dead biomass. Thus, our study shows that A. terreus could be used as a cost-effective eco-friendly biosorbent for AgNPs removal from aqueous solution. Therefore, exploiting the same fungus for both synthesis and removal of AgNPs could be a novel approach in the context of mycoremediation practices. [Display omitted] • Biosorption ability of A. terreus dead biomass was investigated for AgNPs removal. • The biosorption conditions were optimized for maximum removal of AgNPs. • Langmuir model showed the maximum biosorption of 740.63 μg/mL. • FTIR and FESEM coupled with EDX studies confirmed the successful uptake of AgNPs. • A. terreus could be used as the cost-effective biosorbent for AgNPs removal. [ABSTRACT FROM AUTHOR]

A unique technique was used to synthesize reduced graphene oxide (rGO) from graphene oxide (GOX) using Ziziphus mauritiana plant leaf extract as both a reducing agent as well stabilizing agent. We examined the green, efficient, and cost-effective method for producing reduced graphene oxide nanoparticles by utilizing selected plant. This study utilized a range of analytical techniques, such as Ultraviolet Visible Spectroscopy (UV–Vis), Fourier transform infrared Spectroscopy (FTIR), X-Ray diffraction analysis (XRD), Zeta potential measurement, and Scanning Electron Microscope (SEM) in order to confirm the successful green production of reduced graphene oxide nanoplatlets. Our work reveals that reduced graphene oxide generally exhibits a layered structure characterized by smooth surfaces, transparency, and wavy patterns. The antibacterial activity was determined by analyzing the values of minimal inhibitory concentration (MIC) with agar diffusion method. The synthesized graphene material showed more effectiveness against E.coli and S.aureus. The increased concentration correspondingly led to an increase in bacterial growth inhibition. The highest zone of inhibition (ZOI) observed was 9 mm for E.coli and 10 mm for S. aureus at a concentration of 100 μg/ml. The efficacy of reduced graphene oxide (rGO) in combating cancer was evaluated using MCF-7 human breast cancer cells. The IC 50 of the synthesized rGO against the chosen cancer cells was found to be 12.3 ± 0.8 μg/ml, which is comparable to the standard drug. The results suggest that rGO has significant potential as an anticancer agent for selected human bacterial pathogen as well human cancer cells. • Ziziphus mauritiana methanol extracts contain secondary metabolites that capable to reduce graphene oxide. • Green synthesis process is a sustainable and cost-effective method for large-scale graphene production. • Study show efficient oxygen removal capacity, comparable to chemical procedures. • Plant leaf extracts serve as capping agents, stabilizing produced graphene nanoparticles. • Graphene nanoplatlets effectively suppress breast cancer cell proliferation by inducing apoptotic activity. • Synthesized rGO demonstrates notable bacteriostatic properties and effectively suppresses MCF-7 cell proliferation. [ABSTRACT FROM AUTHOR]

• Enhanced Stacking Ensemble (ESE) Model: Novel two-layer framework for early anxiety prediction. • Unique Approach: Combines diverse classifiers, and introduces statistical-based ensemble pruning. • Promising Advancement: The innovative approach holds the potential for precise and timely anxiety level classification. Early anxiety prediction is essential because it facilitates the delivery of timely and appropriate medical services. Despite the recent development of a variety of severity scores and machine-learning algorithms for early anxiety prediction, such prediction is still difficult regarding accuracy and a few other performance indicators. The enhanced stacking ensemble strategy is suggested in this research to forecast and classify the level of anxiety. This framework involves two layers where the first layer experiments with very popular and diverse machine learning classifiers in the literature; the second layer consists of a selective fusion of top-performing base learners who have performed well among the pool of base classifiers. The selective fusion of top learners (ensemble pruning) involved in this framework is purely based on a statistical procedure and the subsequent fusion of decisions is been done using logistic regression as a meta -classifier. This proposal gives a new dimension to handle the concept of ensemble pruning using a statistical approach where effective anxiety level classification is done. To the best of the authors' knowledge, such research has not been reported earlier in the literature. Thus, the proposed statistical approach-based pruned ensemble named Enhanced Stacking Ensemble (ESE) Model lifts the overall ensemble's predictive performance. After analyzing the results, it is noticed that our proposed ESE model performed better than most of the conventional methods (bagging, boosting, and voting) based on an accuracy of 94.11 percent, precision of 97 percent, recall of 91 percent, and F1 score of 94 percent. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) connects daily use devices to the Internet, such as home appliances, health care equipment, sensors, and industrial devices. Concurrently, numerous cyber-attacks target those objects and their backbone IoT networks consecutively. Therefore, several researchers have adopted Machine Learning (ML) and Deep Learning (DL) algorithms to develop efficient Intrusion Detection Systems (IDSs). However, the restricted resources of IoT devices hinder integrating those systems with those tiny devices. Hence, designing lightweight IDSs gets more interest from researchers to build efficient detection models to discard attacks in IoT networks. To give a holistic insight into this research domain, this paper presents a Systematic Literature Review (SLR) to review and analyse the recent ML and DL techniques to lighten the IDS models for detecting attacks in IoT devices. In addition, the literature studies were retrieved from six scientific databases Google Scholar, Science Direct, IEEE Xplore®, Scopus, Web of Science, and Springer. From 4,703 identified records, 57 studies were adopted based on predesigned research questions and inclusion/exclusion criteria. The study's findings illustrate the most recently used ML and DL mechanisms and feature engineering techniques to lighten the proposed IDS models. It also shows the most attacks detected, datasets used, tools and network simulators employed, and evaluation metrics and parameters. Furthermore, it suggests the research challenges and future direction after discussing the limitations of the currently proposed techniques. This study shows that most selected studies are journal articles published in IEEE Xplore®. Furthermore, the most used feature engineering techniques are filter-based, as they deliver better performance and lightness than the developed models. Most studies use correlation algorithms as a feature selection technique. Finally, the most discussed attack in the selected studies is the DoS attack. [ABSTRACT FROM AUTHOR]