Karunya Institute of Technology and Sciences, formerly Karunya University, is a private residential institute deemed to be university in Coimbatore, India. It was founded by D. G. S. Dhinakaran and his son Paul Dhinakaran.
The low thermal conductivity of phase change materials (<0.5 W∕m K) restricts heat spreading and limits thermal regulation. Fins can alleviate this by providing conductive pathways, but achieving enhanced thermal performance without adding significant BTMS mass remains a key design challenge. This study develops and optimizes a fin-enhanced PCM BTMS for a cylindrical 18650 cell using paraffin RT-44HC and evaluates fin topology effects under 5C discharge. Three conventional fin types (longitudinal, circular, and pin fins) are first compared, and two hybrid fin concepts are then proposed based on the observed heat-transfer mechanisms. A transient three-dimensional CFD model is established in ANSYS Fluent using the enthalpy-porosity method with buoyancy effects and is validated against experiments. Results show that fin geometry remains decisive even at equal mass. Relative to PCM-only cooling, mean temperature reduction reaches 8.07°C (longitudinal), 10.52°C (circular), and 11.49°C (pin fins), and the time to reach 45°C increases from 250 s to 575–900 s depending on fin type. Among hybrid designs, the circular-pin configuration yields the lowest temperatures and best uniformity, maintaining a maximum surface temperature difference below 1°C and promoting more uniform melting. Parametric optimization identifies an optimal pin diameter of 2 mm and an optimal number of pin fins at 204 based on thermal performance index results. At the thermal control point, the optimized hybrid design reduces thermal resistance by up to 2 K/W relative to PCM-only, lowers maximum temperature by up to 16.56°C, and extends operation within the optimal temperature limit by up to 1175 s.
The increasing environmental impacts of conventional plastic cutlery driven by persistent non-biodegradability, microplastic formation and fossil-fuel dependence have accelerated extensive research into biodegradable and edible alternatives. This review synthesises materials, processing technologies, functional properties and environmental performance of biodegradable cutlery derived from renewable and waste-based feedstocks. Key materials include starch, cellulose, plant proteins and biodegradable polyesters such as polylactic acid (PLA), polyhydroxyalkanoates (PHA), polybutylene adipate terephthalate (PBAT), lipids and agro-industrial by-products. Additives such as plasticizers, binders, cross-linkers, reinforcing agents, antioxidants and antimicrobials are compared based on their roles in enhancing mechanical strength, water resistance, thermal behaviour and biodegradability. Manufacturing techniques including injection moulding, compression moulding, extrusion and 3D printing are evaluated for their scalability and compatibility with biomaterials. Essential testing includes tensile strength, water absorption, thermal analysis and biodegradation under soil and composting conditions. Although notable progress is evident, challenges remain regarding production costs, consumer awareness and limited composting infrastructure. Agricultural waste valorization and enabling policies play a vital role in advancing a circular bioeconomy. By integrating recent developments, comparing material classes and identifying critical research gaps, this review provides strategic insights supporting future innovations and commercial adoption of sustainable cutlery alternatives.
Recent advances in energy storage devices have earned recognition for the rapid development of sustainable chemistry in the synthesis of metal oxides using plants and their components as reducing agents. In this study, Mentha piperita leaf extract was used as a reducing agent to synthesize zinc oxide (ZnO) for use in supercapacitors along with starch as stabilizing agent. Three powder samples, ZnO (Zn), ZnO reduced with Mentha piperita leaf extract (ZnM), and ZnO reduced with Mentha piperita leaf extract stabilized with starch (ZnMS) were synthesized and systematically characterized. Among the samples, ZnO reduced with Mentha. piperita leaf extract stabilized with starch (ZnMS) exhibited a larger crystallite size according to XRD analysis, and showed a rod-shaped morphology as observed in SEM and FESEM analyses. The presence of oxygen vacancies were confirmed by EDAX, a higher specific surface area from BET analysis, and a reduced optical band gap of 2.99 eV from optical studies. The electrochemical studies for the as prepared materials were made with three-electrode configuration. The electrode made using the powder sample ZnO reduced with Mentha piperita leaf extract stabilized with starch (ZnMS) coated on a graphite sheet exhibited a specific capacitance of 56 F/g at 1 A/g and retained 99
A narrowband Terahertz (THz) sensor is designed for biomedical sensing applications. The step impedance-based resonator (SIR) based square rings are used to design the top layer. Initially the analysis is carried out by considering the refractive index (n) value from 1 to 2 with step size 0.2. The peak resonant frequency (PRF) occurred at 2.1487THz with 99.9
Aerosol particle number size distribution (PNSD) measurements are fundamental for quantifying aerosol dynamics, as they govern processes such as new particle formation (NPF), coagulation, and the contribution of particles to cloud condensation nuclei (CCN), thereby influencing aerosol–cloud–climate interactions. However, in the Himalayan region of India, such measurements are particularly challenging due to complex terrain, strong vertical mixing, variable boundary layer dynamics, and limited observational coverage, further compounded by instrumental constraints and uncertainties in inversion techniques that can lead to incomplete or unrealistic datasets. Therefore, modeling approaches are essential for complementing observations and improving size-resolved aerosol characterization. In this study, observations were carried out at the high-altitude Himalayan Cloud Observatory (HCO; 30.34°N, 78.40°E, 1706 m AMSL) in Uttarakhand from 1 January to 31 December 2021. A feed-forward neural network (FFNN) model was developed using configurations with two hidden layers and 2–25 neurons, trained on 5-minute, hourly, and daily datasets to estimate particle number concentrations across different size bins using meteorological parameters. The optimal configuration (two layers with nine neurons) yielded moderate performance (R² = 0.22–0.45), with the highest accuracy at 27.4 nm and the lowest at 48.7 nm. The model demonstrates better performance for nucleation-mode particles, likely due to their relatively simple formation mechanisms associated with NPF, whereas Aitken- and accumulation-mode particles are influenced by multiple sources and complex growth processes. Diurnal analysis reveals a bimodal pattern in particle number concentrations, characterized by a short-lived morning peak and a more sustained evening peak. The model tends to overestimate concentrations from midnight to midday, while relatively improved performance is observed during the evening hours (16:00–21:00). This enhanced performance during the evening is likely associated with the prolonged increase in total particle concentrations, along with comparatively smoother variations in meteorological parameters, which together contribute to more stable and consistent model predictions. Seasonally, better model performance is observed during relatively stable winter and pre-monsoon periods, whereas increased fluctuations in meteorological parameters during the monsoon and post-monsoon periods introduce greater uncertainty. Stable wind speeds (1–2 m s⁻¹) from southeast to southwest directions further support improved predictions. Lower performance for finer particles is attributed to their dynamic behavior and higher removal rates in the atmosphere. Overall, further improvement in model performance requires incorporating longer-term and higher-resolution observational datasets, which would enhance training robustness and better capture the complex variability of aerosol processes in the region.
