The development of multifunctional nanostructured catalysts with high electrochemical activity and stability is crucial for sustainable technologies. Herein, we report the synthesis of CTAB-capped NiCo₂O₄ (CNC) spinel nanostructures via a facile co-precipitation method, engineered to enhance surface activity and charge transport. The optical and structural properties of the nanocomposite were confirmed by UV-Vis and TEM analysis, and the functional group present in the composite was confirmed by FT-IR study. The cubic spinel phase of the CNC was confirmed by XRD analysis. The band gap value was determined to be 2.15 eV, which confirmed the semiconductor nature of the nanocomposite. The photocatalytic degradation efficiency was achieved up to approximately 97% against malachite green. Additionally, CNC demonstrated excellent electrocatalytic performance in non-enzymatic glucose detection, exhibiting high sensitivity and reproducibility across a broad concentration range. Hence, the CNC acted as a potent oxidant for photoelectrochemical reactions.

The impact of climate change on methane (CH₄) emissions from rice production systems in the Coimbatore region (Tamil Nadu, India) was studied by leveraging field experiments across two main treatments and four sub-treatments in a split-plot design. Utilizing the closed-chamber method for gas collection and gas chromatography analysis, this study identified significant differences in CH₄ emissions between conventional cultivation methods and the system of rice intensification (henceforth SRI). Over two growing seasons, conventional cultivation methods reported higher CH₄ emissions (range: from 36.9 to 59.3 kg CH₄ ha⁻¹ season⁻¹) compared with SRI (range: from 2.2 to 12.8 kg CH₄ ha⁻¹ season⁻¹). Experimental data were subsequently used to guide parametrization and validation of the DeNitrification–DeComposition (DNDC) model. The validation of the model showed good agreement between the measured and modeled data, as denoted by the statistical tests performed, which included CRM (0.09), D-index (0.99), RMSE (7.16), EF (0.96), and R² (0.92). The validated model was then used to develop future CH₄ emissions projections under various shared socio-economic pathways (henceforth SSPs) for the mid- (2021–2050) and late (2051–2080) century. The analysis revealed a potential increase in CH₄ emissions for the simulated scenarios, which was dependent on specific soil and irrigation management practices. Conventional cultivation produced the highest CH₄ emissions, but it was shown that they could be reduced if the current practice was replaced by minimal flooding or through irrigation with alternating wetting and drying cycles. Emissions were predicted to rise until SSP 370, with a marginal increase in SSP 585 thereafter. The findings of this work underscored an urgency to develop climate-smart location-specific mitigation strategies focused on simultaneously improving current water and nutrient management practices. The use of methanotrophs to reduce CH₄ production from rice systems should be considered in future work. This research also highlighted the critical interaction that exists between agricultural practices and climate change, and emphasized the need to implement adaptive crop management strategies that can sustain productivity and mitigate the environmental impacts of rice-based systems in southern India.

In this work, activated nanocarbon derived from lignocellulose-rich groundnut shells was synthesized by KOH impregnation to enhance surface area and porosity, resulting in Groundnut Shell Activated Nanocarbon (GSANC) and thoroughly characterized for its porosity, particle size, crystallinity, combustion profile, functional groups, and surface morphology. It exhibited a type I N2 adsorption-desorption isotherm with an H4 hysteresis loop, indicative of a mix of micro and mesopores, with a high specific surface area of 665.80 m² g-1 and an average pore diameter of 2.97 nm. This nanoparticulate activated carbon exhibited exceptional performance in methylene blue (MB) dye adsorption, achieving a maximum adsorption capacity (qmax) of 212.76 mg g-1. The adsorption mechanisms included electrostatic interactions, π-π stacking, and hydrogen bonding, contributing significantly to its efficacy as an adsorbent highlighting its potential for applications in wastewater treatment.

The application of artificial intelligence (AI) in soil health assessment presents significant advancements over conventional methods by enabling more efficient and precise measurements. This review examines and supports how AI monitors soil health and its significance for sustainable land management. AI technologies, including machine learning, remote sensing and big data analytics, enable researchers and practitioners to analyse diverse data sources, model soil-plant relations and predict soil health trends with greater accuracy. AI-integrated soil health monitoring enables tracking of key soil parameters, facilitating efficient nutrient management, soil erosion control and overall ecosystem sustainability. AI-driven precision agriculture helps stakeholders predict the long-term impacts of farming practices, optimize resource use, enhance crop yields and reduce environmental impacts. This review also demonstrates how updated highlights recent research, case studies and best practices that demonstrate how AI-based soil health monitoring contributes to agricultural sustainability, conservation and food security.

Ramsar Sites are designated wetlands of international importance under the Ramsar Convention, an intergovernmental treaty established in 1971. These sites play a crucial role in conserving biodiversity and providing ecosystem services. The Ramsar Convention encourages countries to designate and manage Ramsar Sites sustainably, aiming to maintain the ecological character of these wetlands. Wetlands are areas that include marsh, fen, peat land, or water, and it is well known that these wetlands are important ecosystems that have a major positive impact on productivity, biodiversity, and the well-being of residents. Though preserved, Ramsar Sites face numerous threats, including habitat loss, pollution, invasive species, and climate change, warranting management, conservation efforts, and international cooperation to safe guarding these valuable wetland ecosystems for future generations. Pollution from agriculture, industry, and urban runoff threatens water quality. Climate change exacerbates these issues, affecting wetland ecosystems and altering migration patterns. Due to the importance of Ramsar sites and to preserve for better livelihood, this paper explores the issues and challenges pertaining to the biodiversity of Ramsar sites in Tamil Nadu for better management through appropriate solutions.