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The recent implication of circular economy in Australia spurred the demand for waste material utilization for value-added product generations on a commercial scale. Therefore, this experimental study emphasized on agricultural waste biomass, rice husk (RH) as potential feedstock to produce valuable products. Rice husk biochar (RB) was obtained at temperature: 180 °C, pressure: 70 bar, reaction time: 20 min with water via hydrothermal carbonization (HTC), and the obtained biochar yield was 57.9%. Enhancement of zeta potential value from − 30.1 to − 10.6 mV in RB presented the higher suspension stability, and improvement of surface area and porosity in RB demonstrated the wastewater adsorption capacity. Along with that, an increase of crystallinity in RB, 60.5%, also indicates the enhancement of the catalytic performance of the material significantly more favorable to improve the adsorption efficiency of transitional compounds. In contrast, an increase of the atomic O/C ratio in RB, 0.51 delineated high breakdown of the cellulosic component, which is favorable for biofuel purpose. 13.98% SiO 2 reduction in RB confirmed ash content minimization and better quality of fuel properties. Therefore, the rice husk biochar through HTC can be considered a suitable material for further application to treat wastewater and generate bioenergy.

Electrochemical stability of energy storage devices is one of their major concerns. Polymeric binders are generally used to enhance the stability of the electrode, but the electrochemical performance of the device is compromised due to the poor conductivity of the binders. Herein, 3D binder-free electrode based on nickel oxide deposited on graphene (G-NiO) was fabricated by a simple two-step method. First, graphene was deposited on nickel foam via atmospheric pressure chemical vapour deposition followed by electrodeposition of NiO. The structural and morphological analyses of the fabricated G-NiO electrode were conducted through Raman spectroscopy, X-ray diffraction (XRD), field emission scanning electron microscopy (FESEM), and energy dispersive X-ray spectroscopy (EDS). XRD and Raman results confirmed the successful growth of high-quality graphene on nickel foam. FESEM images revealed the sheet and urchin-like morphology of the graphene and NiO, respectively. The electrochemical performance of the fabricated electrode was evaluated through cyclic voltammetry (CV), galvanostatic charge–discharge (GCD), and electrochemical impedance spectroscopy (EIS) in aqueous solution at room temperature. The G-NiO binder-free electrode exhibited a specific capacity of ≈ 243 C g −1 at 3 mV s −1 in a three-electrode cell. A two-electrode configuration of G-NiO//activated charcoal was fabricated to form a hybrid device (supercapattery) that operated in a stable potential window of 1.4 V. The energy density and power density of the asymmetric device measured at a current density of 0.2 A g −1 were estimated to be 47.3 W h kg −1 and 140 W kg −1 , respectively. Additionally, the fabricated supercapattery showed high cyclic stability with 98.7% retention of specific capacity after 5,000 cycles. Thus, the proposed fabrication technique is highly suitable for large scale production of highly stable and binder-free electrodes for electrochemical energy storage devices.

Mg–Al–Zn alloys are widely preferred in many applications by considering their excellent properties of high stiffness-to-weight ratio, lightweight, high strength-to-weight ratio, low density, castability, high-temperature mechanical properties, machinability, high corrosion resistance, and great damping. Improving the properties of such alloys is challenging due to their hexagonal crystal structure and other alloying limitations. This study aims to synthesize Mg–Al–Zn alloy by incorporating the alloying elements 8.3 wt% Al, 0.35 wt% Zn on pure magnesium (Control specimen). Then synthesize Mg–Al–Zn/BN/B 4 C hybrid composite by reinforcing B 4 C at three weight proportions (3 wt%, 6 wt%, 9 wt%) along with constant solid lubricant BN (3 wt%) through a stir casting process. The hybrid composite samples were characterized and compared with the performances of the control specimen. The results reveal that 9 wt% B 4 C reinforced samples outperformed through recording the improvement of tensile strength by 28.94%, compressive strength by 37.89%, yield strength by 74.63%, and hardness by 14.91% than the control specimen. Apart from this, it has reduced the corrosion area (37.81%) and noticed negligible changes in density (increased by 0.03%) and porosity (decreased by 0.01%) than the control specimen. The samples were characterized using SEM, XRD, and EDAX apparatus.

Toxic dyes in water bodies and bacterial pathogens pose serious global challenges to human health and the environment. Zinc oxide nanoparticles (ZnO NPs) demonstrate remarkable photocatalytic and antibacterial potency against reactive dyes and bacterial strains. In this work, PVP-ZnO NPs have been prepared via the co-precipitation method using polyvinylpyrrolidone (PVP) as a surfactant. The NPs’ microstructure and morphology were studied using X-ray diffraction (XRD), having a size of 22.13 nm. High-resolution transmission electron microscope (HR-TEM) and field emission scanning electron microscopy (FESEM) analysis showed spherical-shaped PVP-ZnO NPs with sizer ranging from 20 to 30 nm. Fourier Transform Infrared Spectroscopy (FT-IR) confirmed the hybrid nature of the NPs, and UV–Vis spectroscopy showed an absorption peak at 367 nm. The PVP-ZnO NPs exhibited high photocatalytic activity, achieving 88% and nearly 95% degradation of reactive red-141 azo dye with 10 mg and 20 mg catalyst dosages, respectively. The antibacterial properties of the NPs were demonstrated against Escherichia coli and Bacillus subtilis , with inhibition zones of 24 mm and 20 mm, respectively. These findings suggest that PVP-ZnO NPs can be effectively used for water treatment, targeting both dye and pathogenic contaminants.

A reliable and accurate diagnosis and identification system is required to prevent and manage tea leaf diseases. Tea leaf diseases are detected manually, increasing time and affecting yield quality and productivity. This study aims to present an artificial intelligence-based solution to the problem of tea leaf disease detection by training the fastest single-stage object detection model, YOLOv7, on the diseased tea leaf dataset collected from four prominent tea gardens in Bangladesh. 4000 digital images of five types of leaf diseases are collected from these tea gardens, generating a manually annotated, data-augmented leaf disease image dataset. This study incorporates data augmentation approaches to solve the issue of insufficient sample sizes. The detection and identification results for the YOLOv7 approach are validated by prominent statistical metrics like detection accuracy, precision, recall, mAP value, and F1-score, which resulted in 97.3%, 96.7%, 96.4%, 98.2%, and 0.965, respectively. Experimental results demonstrate that YOLOv7 for tea leaf diseases in natural scene images is superior to existing target detection and identification networks, including CNN, Deep CNN, DNN, AX-Retina Net, improved DCNN, YOLOv5, and Multi-objective image segmentation. Hence, this study is expected to minimize the workload of entomologists and aid in the rapid identification and detection of tea leaf diseases, thus minimizing economic losses.

Contamination of water resources by industrial effluents containing heavy metal ions and management of solid waste from agricultural and food industries is a serious issue. This study presents the valorization of waste walnut shells as an effective and environment-friendly biosorbent for sequestrating Cr(VI) from aqueous media. The native walnut shell powder (NWP) was chemically modified with alkali (AWP) and citric acid (CWP) to obtain modified biosorbents with abundant availability of pores as active centers, as confirmed by BET analysis. During batch adsorption studies, the process parameters for Cr(VI) adsorption were optimized at pH 2.0. The adsorption data were fitted to isotherm and kinetic models to compute various adsorption parameters. The adsorption pattern of Cr(VI) was well explained by the Langmuir model suggesting the adsorbate monolayer formation on the surface of the biosorbents. The maximum adsorption capacity, q m, for Cr(VI) was achieved for CWP (75.26 mg/g), followed by AWP (69.56 mg/g) and NWP (64.82 mg/g). Treatment with sodium hydroxide and citric acid improved the adsorption efficiency of the biosorbent by 4.5 and 8.2%, respectively. The endothermic and spontaneous adsorption was observed to trail the pseudo-second-order kinetics under optimized process parameters. Thus, the chemically modified walnut shell powder can be an eco-friendly adsorbent for Cr(VI) from aqueous solutions.

Brunei, similar to other nations, encounters difficulties in effectively managing solid waste, with 70% of the waste ending up in landfills, 2% through composting, and the remainder being disposed of through conventional methods. The current landfill site is anticipated to reach its maximum capacity in 2025. Energy recovery from waste is crucial for Brunei since it can improve waste management, mitigate environmental consequences, produce economic advantages, bolster energy security, and promote a circular economy. This study aims to identify the potential for energy recovery through landfill gas generated from solid waste disposal in Brunei Darussalam. The study finds that Brunei Darussalam can produce 129 thousand tonnes of CO 2 e/year landfill gas. Utilising gas to generate electricity of 367 GWh could save 1.6 million USD annually. In addition, it also identifies the strengths and weaknesses of the existing solid waste management in Brunei Darussalam. Furthermore, it formulates a waste management policy in Brunei Darussalam by identifying relevant stakeholders to overcome the weakness. Lastly, the framework for waste management is designed to consider short-, intermediate- and long-term goals and targets, with actions to be taken by respective stakeholders.

Two superior adsorbents, namely bentonite and graphene oxide (GO), were hybridised to study the removal of copper and nickel ions from synthetic and industrial wastewater. The as-synthesised GO, bentonite/GO and bentonite were characterised by Fourier transform infrared spectroscopy, scanning electron microscopy, energy-dispersive X-ray spectroscopy and N 2 adsorption-desorption analysis. The factors influencing the adsorption behaviours including contact time, initial solution pH, ionic strength, initial concentration of metal ions, temperature and adsorbent dosage were systematically investigated by batch equilibrium method. The adsorption equilibrium for copper and nickel onto bentonite was attained in 90 min while equilibrium was reached in 60 min on bentonite/GO. The adsorption of copper and nickel was pH-dependent in the range from pH 2 to pH 7 and from pH 2 to pH 8. Pseudo-first-order kinetic model excellently described the adsorption of copper and nickel onto bentonite and bentonite/GO. The equilibrium adsorption data was well described by the Langmuir isotherm model and the maximum adsorption capacity was 248.9 mg/g, 558.4 mg/g, 215.8 mg/g and 402.5 mg/g for bentonite-copper, bentonite/GO-copper, bentonite-nickel and bentonite/GO-nickel adsorption systems, respectively. The bentonite/GO composite exhibited a higher adsorption capacity of both cations from synthetic wastewater than pure bentonite owning to the synergistic effect between bentonite and GO. In all adsorption studies, copper was more efficiently removed than nickel due to its higher tendency to form bond with adsorbent surfaces. The adsorption of copper and nickel on bentonite/GO was mainly due to cation exchange, intermolecular and electrostatic interactions and physisorption dominated the adsorption processes. The practical application of bentonite/GO on adsorption of copper was investigated using real wastewater and its removal efficiency was beyond 98%. The excellent adsorption performances of composites for the copper and nickel removal from wastewater demonstrated its significant potential for pollution mitigations.

Due to the rapid growth in the heavy metal-based industries, their effluent and local dumping have created significant environmental issues. In the past, typically, removal of heavy metals was handled by reverse osmosis and ion exchange techniques, but these methods have many disadvantages. Therefore, extensive work into the development of improved techniques has increased, especially for heavy metal removal. Many countries are currently researching new materials and techniques based on nanotechnology for various applications that involve extracting heavy metals from different water sources such as wastewater, groundwater, drinking water and surface water. Nanotechnology provides the possibility of enhancing existing techniques to tackle problems more efficiently. The development in nanotechnology has led to the discovery of many new materials such as magnetic nanoparticles. These nanoparticles demonstrate excellent properties such as surface-volume ratio, higher surface area, low toxicity and easy separation. Besides, magnetic nanoparticles can be easily and efficiently recovered after adsorption compared with other typical adsorbents. This review mainly emphasises on the efficiency of heavy metal removal using magnetic nanoadsorbent from aqueous solution. In addition, an in-depth analysis of the synthesis, characterisation and modification approaches of magnetic nanoparticles is systematically presented. Furthermore, future opportunities and challenges of using magnetic particles as an adsorbent for the removal of heavy metals are also discussed.

Owing to the high toxicity of cerium toward living organisms, it is necessary to remove cerium from aqueous solutions. In this regard, the extraction of cerium (Ce (III)) from nitrate media by Cyanex 572 under different operating conditions was examined in this study. The effect of contact time, pH, extractant concentration, and nitrate ion concentration were investigated to characterize the extraction behavior of cerium and based on these outcomes, an extraction mechanism was suggested. The analysis of infrared spectra of Cyanex 572 before and after the extraction of cerium indicated that cerium extraction was performed via a cation-exchange mechanism. Then, the predictive models based on intelligent techniques [artificial neural network (ANN) and hybrid neural-genetic algorithm (GA-ANN)] were developed to predict the cerium extraction efficiency. The GA-ANN model provided better predictions that resulted higher R 2 and lower MSE compared to ANN model for predicting the extraction efficiency of cerium by Cyanex 572. The interactive effects of each process variable on cerium extraction were also investigated systematically. pH was the most influential parameter on cerium extraction, followed by extractant concentration, nitrate ion concentration and contact time. Finally, the separation of cerium from other rare earth elements like La (III), Nd (III), Pr (III), and Y (III) was conducted and observed that the present system provides a better separation of cerium from rare heavy earth than light rare earths.