Pollution due to pharmaceutical contaminants has emerged as a significant ecological issue, affecting water quality, marine ecosystems, and humans across the globe. Due to the increasing concern over pharmaceutical pollutants, effective and sustainable treatment methods are of critical need. This study investigates the solar-assisted photocatalytic degradation of tetracycline hydrochloride (TCT), a persistent pharmaceutical contaminant in aqueous systems, using magnetite nanoparticles as immobilized catalyst. The catalyst was synthesized using a precipitation method. Key operational variables influencing degradation efficiency were systematically analyzed. The degradation followed pseudo-first-order kinetics with approximately 48% TCT removal within 30 minutes and up to 99% within 120 minutes under sunlight exposure. The introduction of a Fenton reagent notably enhanced the photocatalytic performance. The synthesized catalyst was characterized using various techniques. The process also contributed to a reduction in chemical oxygen demand (COD), and by using liquid chromatography–mass spectrometry (LC-MS), the intermediates during the degradation were identified. The catalyst reusability tests indicated minimal loss in activity over successive cycles. These findings highlight the viability of integrating solar energy with immobilized magnetite nanocatalysts for sustainable wastewater treatment, offering an effective method for removing antibiotic residues and promoting water reuse. Future research should focus on exploring new magnetite composites and the potential for integrating this technology with real-world applications.

The production of methanol via the gasification of waste polyethylene offers a promising approach to plastic waste management and chemical production. This study utilizes Aspen Plus to simulate the gasification of waste polyethylene and subsequent methanol production. The simulation involves modeling the gasification reactor, the syngas treatment, and the methanol synthesis reactor. The effects of gasifier temperature, the steam-to-feed-mass ratio, and reactor pressure on methanol yield and purity are investigated. The results show that the Aspen Plus simulation accurately predicts the methanol production process, providing valuable insights into process optimization and design. The simulation results indicate that a gasifier temperature of 1000°C, a steam-to-feed-mass ratio of 0.5, and a reactor pressure of 50 bar yield a methanol purity of 99%. This study also examines the economic viability of the process, including the costs of the feedstock, energy, and equipment. The results demonstrate that the production cost of methanol via the gasification of waste polyethylene is competitive with that of the traditional methods. This study provides a comprehensive framework for simulating and optimizing methanol production from waste polyethylene, offering a valuable tool for industry and research applications. This study's findings have significant implications for the development of sustainable waste management practices and the production of clean energy. Future research will focus on scaling up the process.

The purpose of this research is to enhance the overall efficiency of hydrogen production by optimizing the OER via the chiral modification of a catalyst. Hydrogen production through electrochemical water splitting is a promising solution to the energy crisis and may help control harmful emissions from the burning of hydrocarbon-based fuels. The oxygen evolution reaction is a slow and energy-consuming step. The Chiral-Induced Spin Selectivity (CISS) effect can be used to improve the efficiency of the OER by spin-filtering the anodic current. CoBTC MOFs modified with L-glutamine and DL-glutamine (CoBTC-G and CoBTC-DL) were characterized by XRD, Raman, FTIR, elemental analysis, SEM, and TEM and it was confirmed that the MOFs were successfully modified with an amino acid. Pristine and modified MOFs were used as a catalyst to modify the electrode for the OER. The overall performance of the chiral-modified MOF was excellent compared to the racemic mixture, as confirmed by LSV, CVs and chronoamperometry tests. The maximum current density achieved by CoBTC-G is 74.17mA cm^-2 at 1.99 (V vs RHE), while 69.34 mA cm-² and 50.05 mA cm^-2 were achieved by pristine CoBTC and CoBTC-D (modified with DL-glutamine), respectively. CoBTC, CoBTC-G and CoBTC-DL achieved a current density of 10mA cm^-2 at over potential of 384mV, 392mV and 424mV, respectively. The tafel slope values calculated for CoBTC, CoBTC-G and CoBTC-DL are 189, 170 and 234mV/sec, respectively, which shows that the reaction rate of CoBTC-G is faster than that of the other two materials. Electrochemical impedance spectroscopy was performed and the results presented in Nyquist plots show a smaller semicircle at higher frequencies (indicating lower Rct) for CoBTC-G, suggesting better conductivity and faster electron transfer. The purpose of this research is to enhance the overall efficiency of hydrogen production by optimizing the OER via the chiral modification of a catalyst.

This study explores the valorisation of agro-industrial waste, specifically cassava peel (Manihot esculenta), as a reinforcing phase in polymeric biocomposites with potential application as biodegradable bags. Through a multidisciplinary approach integrating materials engineering and principles of sustainable development, the mechanical, physical, and chemical behaviour of biocomposites formulated with thermoplastic matrices and natural fibres is analysed.

The cassava peel residue was characterised in terms of particle size, moisture content, and chemical composition. Specimens with varying proportions of natural reinforcement were fabricated and subjected to a series of tests to evaluate their tensile strength, water absorption capacity, surface morphology, and chemical functionality using Fourier-transform infrared spectroscopy (FTIR). The FTIR analysis confirmed the presence of functional groups relevant to bonding within the composite matrix.

The preliminary results suggest that the incorporation of this agro-industrial residue contributes to the enhancement of mechanical integrity and durability of the material while reducing its overall environmental footprint. Furthermore, its application in the development of biodegradable bags provides an innovative pathway to mitigate plastic pollution.

This research promotes circular economy practices by providing a feasible strategy for the recovery and reuse of waste materials in both rural and industrial contexts. It fosters cleaner production processes, supports the transition to bio-based resources, and contributes to the development of sustainable materials for packaging and low-impact construction systems.

Urban transportation necessitates the development of efficient and sustainable propulsion technologies capable of adapting to future infrastructure. This study presents a hybrid electric–pneumatic propulsion system for lightweight urban vehicles that addresses range limitations and environmental concerns through intelligent energy management.

The system utilizes a 10-15 kW brushless DC motor and a 7-10 kWh lithium-ion battery (72 V/96 V) as the primary drive to achieve a range of 50-70 km. The pneumatic subsystem employs an aluminum composite tank (80-120 L) pressurized at 200-300 bar to provide auxiliary power during emergencies (when the battery is below 15%) and when ascending steep inclines (>8%).

A pivotal innovation is the dual-mode energy management system, which dynamically allocates power between the electric and pneumatic subsystems based on the battery's state of charge, air pressure, the vehicle load, and terrain. This configuration facilitates seamless transitions, thereby optimizing the energy distribution and system efficiency.

The vehicle's carbon fiber chassis contributes to its lightweight design, with a weight of less than 500 kilograms, thereby enhancing its acceleration performance. Preliminary calculations indicate that the vehicle will demonstrate robust competitiveness, with maximum speeds ranging from 70 to 90 kilometers per hour. An environmental assessment indicates that the vehicle emits no local pollutants and generates 15-20 g CO₂/km well-to-wheel emissions with a coal grid. This results in a reduction of less than 5 g CO₂/km when utilizing renewable energy sources.

In contrast to unsuccessful hybrid air systems (such as the Peugeot Hybrid Air and Tata/MDI AirPod), which demonstrate limited efficiency gains, this approach utilizes pneumatic assistance in a targeted manner, exclusively during periods of high-torque demand. This approach has been demonstrated to achieve energy savings while extending battery life. The system employs existing electric vehicle (EV) charging infrastructure in conjunction with standard air compression facilities, thereby ensuring its feasibility for implementation in urban micro-mobility contexts.

Aromatic hydrocarbons such as benzene, toluene, and xylene (BTX) are persistent pollutants in refinery wastewater and are known for their high toxicity, carcinogenicity, and adverse effects on human health and aquatic ecosystems. Prolonged exposure to BTX, even at low concentrations, has been linked to respiratory disorders, neurological impairment, and increased cancer risk, especially in communities located near petroleum refining facilities. This study investigates the application of a deep eutectic solvent (DES), a class of green solvents composed of hydrogen bond donors and acceptors, for the efficient extraction of BTX from refinery waste. Unlike conventional organic solvents, DESs are biodegradable, non-volatile, and tunable, making them safer for both the environment and human health. Using a TBAB–decanoic acid-based DES system, various compositions were synthesized, and only the one that remained stable was evaluated for BTX extraction efficiency through liquid–liquid extraction experiments. Key performance indicators included solvent mass fraction, stirring speed, time and temperature, and the reusability of the DES systems. Results revealed that DES exhibited high affinity for BTX compounds, with extraction efficiencies exceeding 80% for benzene and toluene under optimized conditions. The findings demonstrate the potential of DES not only as effective extractants but also as health-conscious alternatives in industrial wastewater treatment. This work highlights the relevance of green chemistry innovations in mitigating environmental and public health risks, aligning with global efforts toward cleaner water and safer communities.

This research uses a modified cellulose nanocrystal composite, a green and biodegradable adsorbent, to remove cobalt (II) using a column study. Fourier transform infrared spectroscopy, scanning electron microscopy, and thermogravimetric analysis were used to characterise the adsorbent. The fixed-bed column was used to remove cadmium (II) at room temperature with process factors, such as pH (4−8), bed height (3–9 cm), flow rate (3–7 mL/min), and concentration (10−20 mg/L). According to the findings, the cobalt (II) breakthrough occurred quicker for a lower bed height, greater flow rate, and higher cadmium(II) concentration. The Yoon–Nelson model is the most appropriate kinetic model. Deep learning models, such as the adaptive neuro-fuzzy inference model with two algorithms (Backpropagation and Least Squares Estimation), were effectively used to model the effectiveness of cadmium (II) removal in aqueous solution using modified cellulose nanocrystals. To compare the model's predicted results with experimental data, statistical approaches were used, including Marquardt's percentage standard deviation (MPSD), the coefficient of determination (R²), and Mean Square Error (MSE). The ANFIS model used to predict cadmium (II) adsorption using modified cellulose nanocrystals had a strong correlation value of 0.997 for the Least Squares Estimation (LSE) and 0.999 for the Gradient Descent (Backpropagation), indicating how effectively the trained model predicted the cadmium(II) adsorption process.

The growing deployment of renewable energy systems has raised concerns about the end-of-life management of their components, particularly photovoltaic (PV) panels and lithium-ion batteries. These two waste streams contain valuable materials such as high-purity silicon and lithium, which are critical for energy storage technologies. This study explores a circular approach by recovering silicon from used solar cells and lithium from spent batteries to fabricate lithium–silicon (Li–Si) batteries, known for their high theoretical capacity and potential to replace conventional graphite-based lithium-ion batteries.

The methodology involves the extraction and purification of silicon from photovoltaic cells through a multi-step process, including chemical etching, thermal treatment, and acid leaching. Simultaneously, lithium is recovered from lithium-ion battery cathodes using hydrometallurgical techniques. The purified silicon is then used as an anode material in a prototype lithium–silicon battery. Characterization techniques such as SEM, XRD, and FTIR are employed to analyze the morphology and purity of the recovered silicon.

Preliminary results show the promising purity levels and structural integrity of the extracted silicon, which meets the basic requirements for battery-grade materials. The study demonstrates the feasibility of transforming waste into high-value components while addressing environmental concerns and supply chain risks.

This project contributes to the development of sustainable, closed-loop energy systems by integrating waste management and battery innovation, supporting both circular economy principles and climate change mitigation strategies.

Access to clean and reliable water sources is becoming increasingly difficult in many parts of the world. Industrial activity, growing populations, and climate-related disruptions are putting significant stress on available water supplies. These pressures have encouraged the development and use of more advanced technologies for treating both drinking water and wastewater. In recent years, a number of promising solutions have gained attention, including technologies such as membrane bioreactors, reverse osmosis systems, advanced oxidation methods, electrocoagulation setups, granular activated carbon filters, zero liquid discharge approaches, and bioelectrochemical treatments. While each of these technologies has demonstrated success in specific applications, they differ considerably in terms of operating conditions, efficiency, and resource demands. Because of the variety and complexity involved, identifying the most appropriate method for a specific application presents a substantial challenge. Decision makers often have to balance technical performance with environmental impact, cost, and other practical concerns. In light of this, methods that allow for the structured comparison of multiple alternatives—particularly multi-criteria decision-making (MCDM) approaches—can offer valuable guidance. This study proposes to examine and compare several of the most recognized sustainable water treatment technologies using a decision-making framework. Rather than relying on just one metric, the evaluation will consider a range of factors that are relevant in real-world settings, including both qualitative and quantitative elements. The outcomes of this analysis are intended to support more informed and context-sensitive decisions in water treatment planning, especially in environments where sustainability is an increasingly critical concern.

Biodiesel is a type of renewable, biodegradable, non-toxic, and eco-friendly fuel which has been considered one of the best alternative resources to fossil fuels. Nowadays, one of the most promising substitute sources of biodiesel is considered to be microalgae. It offers several advantages over traditional crops, such as a high lipid content, rapid growth rate, greater photosynthetic efficiency, bio-remediation potential, and versatile growing conditions. This research focuses on the integration of ionic liquids into the process of biodiesel production from microalgae, which holds the promise of overcoming some of the limitations associated with the conventional methods.

This work focuses on the production of biodiesel from Nannochloropsis sp. microalgae. This study compares the effectiveness of conventional methods such as Soxhlet extraction and the Bligh and Dyer method for lipid extraction. The Bligh and Dyer method exhibited a higher lipid yield compared with that from Soxhlet extraction. Sonication parameters (sample volume, time, and power rate) were optimized to maximize the lipid yield. It further investigates the use of ionic liquids for lipid extraction from microalgae. Two ionic liquids, 1-(4 sulphonic acid) butyl-3-Methyl imidazolium hydrogen sulphate and 1-(4 sulphonic acid) butyl pyridinium hydrogen sulphate, were synthesized and characterized using FTIR, NMR, and TGA. As compared to the conventional methods, extraction using ionic liquids showed greater lipid efficiency. Biodiesel was produced through the transesterification of algal oil. FTIR was used to analyze the presence of lipids and esters in biodiesel. The produced biodiesel, with significant properties, meets the terms of the ASTM specifications (pH, density, acid value, free fatty acid, iodine value, and calorific value), indicating its suitability as biofuel.