Nanoenergy Advances

Managing the temperature of photovoltaic modules is essential for limiting efficiency losses under high solar loading. This work evaluates a minichannel-based cooling system using a ternary hybrid nanofluid subjected to periodic inlet-flow modulation. Its performance is benchmarked against natural convection, water cooling, and steady mono-, binary-, and ternary-nanofluid circulation. The numerical methodology integrates three-dimensional transient CFD, discrete ordinates radiation, a multiphase mixture formulation for nanoparticle–fluid slip, and a temperature-dependent single-diode model. Simulations were conducted at a mean Reynolds number of 50, with the pulsation amplitude varied from 0.3 to 0.8 and the Strouhal number from 1 to 10. Electrical quantities were calculated using cycle-averaged PV temperatures after periodic flow behaviour was established. The best-performing pulsating case, obtained at A = 0.5 and St = 5, limited the maximum PV temperature to approximately 314.7 K. This value was about 1.6 K below that achieved with steady ternary hybrid nanofluid flow and 5 K below that obtained with water. The corresponding maximum electrical power, electrical efficiency, and exergetic efficiency were approximately 4.0 W, 22.4%, and 23.8%, respectively. Periodic acceleration and deceleration enhanced coolant redistribution, repeatedly redeveloped the thermal boundary layer, and reduced downstream heat accumulation. The principal contribution is the integrated transient evaluation of pulsating ternary hybrid nanofluid cooling within a common CFD–radiation–electrical–exergy framework. Pulsation reduced the maximum PV temperature by approximately 1.6 K relative to steady ternary hybrid nanofluid cooling, confirming an additional but incremental benefit rather than a dominant improvement over steady liquid cooling.

Material selection plays a major role in determining the electrical performance of triboelectric nanogenerators (TENGs). Despite the exploration of several metal–organic frameworks (MOFs) for triboelectric energy harvesting, a large number of MOF materials remain unexplored as active triboelectric layers. Similarly, although TENGs have been widely investigated for powering low-power electronics and sensing systems, the utilization of their intrinsic high-voltage output for practical plasma and gas-ionization applications remains limited. In this work, a pristine MIL-101(Cr) MOF was explored as a new active triboelectric material for high-voltage energy harvesting applications. The triboelectric polarity of MIL-101(Cr) was systematically investigated using polarity-switching and surface potential studies. The relative triboelectric behavior of MIL-101(Cr) was evaluated against different counter materials, with the MIL-101(Cr)/silicone pair selected for further electrical characterization and high-voltage applications. The fabricated MIL-101(Cr)–silicone TENG delivered a high voltage of ~1.2 kV, a short-circuit current of ~110 μA, and a maximum power density of ~8.46 W/m². Finally, the generated high-voltage output was successfully utilized to power helium, neon, argon, and mercury discharge tubes through simple biomechanical hand tapping, directly demonstrating self-powered plasma excitation and gas ionization. The present work opens new opportunities for utilizing MOF-based TENGs in high-voltage pulsed-power, plasma-generation, and self-powered discharge-based applications.

Driven by the limitations and growing scarcity of conventional water-based cooling, this study explores nanofluids as a promising high-performance alternative due to their superior thermal properties. This study presents a computational fluid dynamics (CFD) investigation of Al₂O₃/water nanofluid-based cooling in a data center environment with hot server cabinets, simulated using the SIMPLE algorithms. The effects of the Richardson number (Ri), cabinet size (BR), and nanoparticle volume fraction (Φ) on the average Nusselt number were systematically analyzed using ANOVA, the full-factorial design, and response surface methodology. The model shows high accuracy (R² = 99.32%, adjusted R² = 98.40%). ANOVA identifies the Richardson number as the dominant factor (75%), followed by the cabinet size (25%), with the volume fraction having minimal influence. The results highlight the key role of buoyancy and geometry in optimizing efficient nanofluid-based data center cooling. The rapid growth of large-scale data centers, such as those supporting advanced platforms including ChatGPT, has driven significant cooling demands and heightened concerns about water consumption. To address the limitations and scarcity of conventional water-based cooling, nanofluids have emerged as a promising alternative due to their enhanced thermal properties. This work is original in its integration of CFD-based nanofluid cooling with advanced statistical optimization techniques specifically applied to a data center environment with hot server cabinets. Additionally, this study offers practically optimized operating conditions for sustainable data center cooling, addressing the real-world challenges of water scarcity and energy-efficient thermal management.