Rotary kilns are widely used for various high temperature industrial applications, e.g., quicklime and cement production. Most of these operations rely on fossil fuels like coal; however, there is growing interest in increasing the share of biomass fuels. During the high-temperature combustion process, ash transport onto, and reactions with, refractory bricks can significantly influence refractory wear. Ash infiltration may impair refractory performance through chemical reactions and exacerbated cracking caused by structural changes in combination with thermal shock. Analysis of spent refractory materials provides valuable insights for understanding and predicting corrosion mechanisms. In the present study, three MgO-based spent refractory bricks were collected from different locations within the burn zone of a rotary lime kiln that was cofired with a mixture of coal, olive pomace (a potassium-rich biomass), and oil. Infiltrated ash and reaction products within the refractory bricks were sampled and characterized using SEM-EDX for elemental mapping and morphology analysis. EBSD analysis was employed to measure the grain size distributions. Micrographic images revealed that all three spent refractory bricks were more sintered and cracked on their hot sides compared to their middle sections and cold sides. Si-rich and K-rich ashes from the coal and biomass fuels, respectively, all infiltrated into the refractories as well as Ca-rich constituents from the limestone/quicklime. XRD analyses revealed the formation of phases such as Mg2SiO4, Ca12Al14O33, and Ca2SiO4. No potassium from the fuel ash was found on the hot side of the refractory bricks, but some were detected deeper within the middle section and cold side of the bricks. The combined use of analytical techniques enabled detailed mapping of ash-forming elements and identification of newly formed phases from the reactions between refractory bricks, ash, and quicklime. These findings provide critical insights into fuel-specific interactions and highlight potential risks for refractory degradation.

Plant extracts provide a rapid, cost-effective, and sustainable route for synthesizing metallic nanoparticles, and various extracts have been used to produce silver nanoparticles. The ethanolic extract of waste Kigelia africana flowers exhibited both reduction and stabilization effects. The formation of silver nanoparticles (Ka-AgNPs) was confirmed by visual color change and further validated using spectroscopic and microscopic techniques. Ultraviolet–Visible spectroscopy of the synthesized nanoparticles showed a characteristic absorption peak at 421.36 nm. Fourier Transform Infrared Spectroscopy (FT-IR) revealed absorption bands corresponding to phytoconstituents acting as capping agents. Field Emission Scanning Electron Microscopy (FESEM) provided insights into the morphology, while X-ray Diffraction (XRD) indicated an average crystalline size of 41.56 nm. Antioxidant activity, assessed via 2,2-Diphenyl-1-picrylhydrazyl (DPPH) and 2,2′-Azino-bis(3-ethylbenzothiazoline-6-sulfonic acid) (ABTS) radical scavenging assays, yielded IC₅₀ values of 27.88 and 17.18μg/mL, respectively. The nanoparticles also exhibited significant antimicrobial activity. Moreover, α-amylase and α-glucosidase inhibition studies demonstrated promising antidiabetic potential, with IC₅₀ values of 98.26μg/mL and 125.34μg/mL, respectively. Overall, this study highlights the multifunctionality of silver nanoparticles synthesized using waste K. africana flowers, underscoring their potential medicinal applications.

Biological and inorganic contaminants pose significant threats to ecosystems due to increasing environmental pollution. Conventional water treatment methods often struggle to remove these persistent pollutants, necessitating the need for advanced nanotechnology to enhance water and wastewater treatment. Nanotechnology offers promising solutions by improving water quality, promoting reuse and recycling, and enabling effective contaminant removal. This review article explores various nanoparticles, including organic and inorganic nanoparticles, silica nanoparticles, and nanocomposites, which have demonstrated remarkable efficiency in wastewater treatment. These nanoparticles act as potent disinfectants, efficient adsorbents, and effective catalysts for pollutant degradation. Their unique physicochemical properties at the nanoscale enable superior pollutant removal, including heavy metals, organic contaminants, and microbial pathogens. Despite significant advancements, challenges such as nanoparticle toxicity, cost-effectiveness, environmental impact, and large-scale applicability remain critical concerns. This review systematically examines different water pollutants and their consequences on living organisms, evaluates the suitability of nanoparticles for targeted contaminant removal, and highlights current challenges and future research directions in nano-enabled water treatment technologies.

To safeguard ecosystems amid rapid global changes, strategies must link ecological knowledge with advancements in technology. Traditional ecological models often encounter challenges due to the inherent complexity and unpredictability of ecosystems, limiting their ability to guide large-scale, long-term decisions effectively. Emerging technologies such as optimization algorithms, artificial intelligence, and big data analytics provide ways to address these issues by improving forecasting, monitoring, and management in evolving environments. The application of these technologies has broadened to essential areas like ecological restoration, management of invasive species, carbon capture, fisheries management, and wildfire readiness, enhancing effectiveness, accuracy, and scalability in conservation efforts. Beyond technical improvements, the integration of algorithms with ecosystem science highlights the importance of aligning data-driven strategies with socio-ecological realities, where careful consideration of trade-offs between biodiversity, economic gains, and resilience is essential. This review points out that algorithmic methods do not replace ecological expertise but rather expand its scope, enabling innovative avenues for adaptive, inclusive, and sustainable conservation practices. By embedding computational innovations within ecological and social contexts, it reveals pathways to more effective strategies that can address the urgent challenges of biodiversity conservation in the 21st century.

In the present scenario, medicinal plants play a crucial role in promoting a healthy lifestyle by protecting against numerous diseases. They also hold significant potential as a source of income, particularly for rural populations across the globe. Plants used for herbal medicine are known as medicinal plants, and each part of these plants may be utilized for medicinal purposes. Further, medicinal plants are beneficial in enhancing the human immune system. In this research, a new medicinal plant named as Central India Medicinal Plant Dataset (CIMPD) has been developed to support significant research in human health. The dataset contains 9130 leaf images (both healthy and unhealthy) from 23 medicinal plant species. These images were collected from various locations in central India. The entire work was carried out over a period of five months, which included plant selection, leaf collection, image capturing, and data organization into folders. This dataset provides comprehensive information, including the botanical name, common name, geographical origin, healthy and unhealthy leaf images, and medicinal uses of the plants. It serves as a valuable resource for research in machine learning, computer vision, and related domains. Additionally, it will enable the development and evaluation of methodologies for disease detection, plant identification, and other relevant applications.

Plant-based nutraceuticals are increasingly recognized for their bioactive compounds that promote health and assist in preventing chronic diseases. However, the rising demand has raised concerns about microbial safety, as contamination can occur at multiple stages of the production process-ranging from cultivation and harvesting to processing, storage, and distribution. Pathogens such as Escherichia coli, Salmonella, Listeria monocytogenes, and toxin-producing fungi pose risks to product quality, threaten consumer health, and contribute to antimicrobial resistance. This review provides a comprehensive overview of the sources and types of microbial contamination, associated health risks, and the shortcomings of conventional control methods. It highlights recent advancements in safety techniques, including cold plasma, ultraviolet light treatment, high hydrostatic pressure, nanocoatings, probiotic biocontrol, and AI-driven microbial monitoring. Additionally, the analysis addresses the role of regulatory frameworks, quality assurance practices, and consumer education as integral elements of a unified safety approach. It integrates technological progress, regulatory perspectives, and consumer behavior to offer a detailed guide for ensuring the microbial safety of plant-derived nutraceuticals, thereby fostering confidence in these products from production through to consumption.

Refractory liner bricks in the hot zone of rotary lime kilns can sustain wear and corrosion during contact with fuel ashes and quicklime (QL), a product composed mainly of CaO. The effects on a MgO-based refractory after exposure at 1400 degrees C for 96 h to olive pomace ash (OPA) and coal ash (CA), with and without QL, were investigated. Exposure of the refractory to only OPA caused slag intrusion with no ash deposits remaining on top, while CaMgSiO4 (monticellite) was also identified as a new phase. When exposed to only CA, the refractory exhibited dissolution into the molten slag and 0.5-2 mm cracks were found on the surface interfacing the ash. Mg2SiO4 (forsterite) and CaMgSiO4 were identified as new formed phases. Exposure of the refractory to OPA + QL and CA + QL caused less slag intrusion and substantial amounts of ash/QL deposit remained afterwards. No new phases were identified. The differences in interactions between the exposure materials and refractory were supported by thermochemical equilibrium analysis. Apparent Ca-Si-rich or Ca-rich melts were found in all the exposed samples, but potassium (K) was found to be depleted in all samples, including those involving OPA, which was rich in K. Furthermore, with the exception of exposure to only CA, the other exposures caused the cold crushing strength (CCS) of the refractory to increase compared to its original value. This was attributed to the sintering of the refractory microstructure. The CCS of the refractory decreased after exposure to only CA. The findings of this study enhance understanding of how CA and OPA impact MgO refractories in lime kilns, supporting initiatives aiming at reducing fossil fuel use. The results are encouraging and motivate further investigation.

In recent years, high-entropy alloys (HEAs) are attracting significant attention owing to their distinctive design adaptability and exceptional properties. Herein, machine learning methods namely extra tree (ET), K-nearest neighbors (KNN), random forest (RF), support vector regressor, and linear regression are utilized to predict the mechanical properties of MoNbTaTiVAlx refractory HEAs across varying compositions and temperatures. By doing so, the study aims to minimize the dependence on experimental testing. Among the models, ET, RF, and KNN exhibit superior predictive performance, achieving R2 values of 0.998 which closely align with experimental results. Additionally, a new stress-strain curve is generated for an aluminium composition of 0.4, with the ET, RF, and KNN models maintaining high predictive accuracy with R2 values of 0.985, 0.978, and 0.97, respectively. This innovative application of machine learning significantly reduces the need for exhaustive experimental testing, resulting in considerable savings in resources and accelerating advancements in HEA research and development.

Polysaccharides, as abundant and renewable biopolymers, have increasingly attracted attention for their potential uses in flexible electronics due to their sustainability, adaptability, and versatile functionality. Natural polymers, including cellulose, chitosan, alginate, starch, and hemicellulose, exhibit key characteristics such as biodegradability, biocompatibility, and tunable mechanical properties, making them attractive choices for advanced technological applications. Advances in chemical modification, blending, and nano structuring have led to improvements in conductivity, durability, and flexibility, broadening their use in areas such as wearable sensors, medical devices, energy storage solutions, and smart packaging. Recent research highlights strategies to overcome inherent challenges like low conductivity and sensitivity to environmental changes through innovative composite designs and hybrid systems. This review provides a comprehensive examination of synthesis methods, functionalization techniques, and application pathways for materials derived from polysaccharides within the flexible electronics domain. It also addresses challenges related to scalability, stability, and regulatory considerations. Ultimately, this review illustrates how systems based on polysaccharides can bridge sustainability with technological advancement, establishing them as crucial materials for the creation of eco-friendly, high-performance, and commercially viable flexible electronic solutions.

High entropy alloys (HEAs) have recently gained popularity due to their vast design possibilities and exceptional properties, offering a broad spectrum of mechanical and magnetic properties by combining various elements. This study employs machine learning models such as Extra Tree (ET), CatBoost (CB), Decision Tree (DT), K-Nearest Neighbors (KNN) and Support Vector Regressor (SVR) to predict how aluminium content influences the magnetic behavior of the homogenized CoCrFeNiAl_x HEAs at different temperatures. Both ET and CB models emerged as the most effective predictors, achieving high R² values of 0.992 and 0.989 respectively. The estimation of the saturation magnetization in close alignment with experimental data is very accurate using these models. A novel magnetization (M − H) curve was generated for a new composition at different temperatures, where ET and CB demonstrated robust R² values of 0.982 and 0.952, respectively. This method offers significant time, cost, and energy savings by minimizing the need for trialing with the extensive experimentation n HEA's vast composition space.