The Indian Institute of Science (IISc) is a public, deemed, research university for higher education and research in science, engineering, design, and management. It is located in Bengaluru, in the Indian state of Karnataka. The institute was established in 1909 with active support from Jamsetji Tata and thus is also locally known as the "Tata Institute". It is ranked among the most prestigious academic institutions in India and has the highest citation per faculty among all the universities in the world. It was granted the deemed to be university status in 1958 and the Institute of Eminence status in 2018....
Droplets, which are ubiquitous in nature, are formed through intriguing processes, and one such route is air-assisted atomization or aerobreakup. This review focuses on secondary atomization, particularly the breakup of an individual droplet subjected to high-speed flows. This process involves complex interfacial dynamics with multiscale deformations, ranging from global flattening to local unstable waves. The deformations occur at progressively smaller scales while interacting with the surrounding gas phase, forming a nonlinear cascade. Each local undulation serves as a precursor to a self-similar evolution or subsecondary breakup process that ends with a ligament-mediated mechanism. In practical scenarios, droplets often encounter nonuniform, unsteady, impulsive, or compressible flows, like shock waves, which pose extreme conditions. The spatiotemporal scales of the nonuniformity or unsteadiness of the external flow must be comparable with the drop deformation scales at either global or local levels to influence aerobreakup that cascades across hierarchical deformation scales. The compressible effects at high Mach numbers are interestingly shown to suppress the tendency toward breakup.
The foremost environmental challenge the world is facing today is the pollution resulting from heavy metals (HMs). HMs in soil above permissible values can harm crops, disrupt the food chain, and pose serious health risks. Therefore, it's necessary to find a suitable remediation technique. This study aims to find the most efficient leaching agent for washing soil contaminated due to mining activity. Flame Atomic Absorption Spectroscopy (FASS) was used to determine the initial concentrations of HMs in the soil. The Cd, Ni, and Pb levels exceeded the WHO/FAO permissible limits by 510%, 81.68%, and 14.58%, respectively, highlighting severe contamination risks. Soil column leaching tests were conducted via four leaching agents i.e. 0.1 N EDTA, 0.1 N HCl, 0.1 N EDTA + 0.1 N HCl, and 0.1 N FeCl3, to assess the leaching efficiencies of the HMs (Fe, Mn, Zn, Cu, Cr, Cd, Ni & Pb) in the soil through experimental elution curves. The results indicate that 0.1 N FeCl3 was the most efficient leaching solution, with removal efficiencies ranging from 64.06 % to 97.35 %. The relative concentrations of heavy metals leached using four different solutions were compared using a one-way ANOVA to see if there were any statistically significant differences.
Bitumen-stabilized material (BSM) consists of aggregates, bitumen emulsion or foamed bitumen, cement, and water in specific proportions. It enhances the mechanical properties of unbound granular materials to serve as a stronger base course in the pavement structure. It is understood that the mechanical behavior of BSM evolves with time because of distinct constituent materials and their complex interactions. This study investigates the effects of bitumen emulsion content, cement, and curing duration on the volumetric and mechanical properties of BSM prepared with two different gradations as recommended by the Asphalt Academy (TG2) with nominal maximum aggregate size (NMAS) 26.5 and Austroads with NMAS 19.0 mm. Laboratory tests and statistical analysis examined the moisture loss, air voids, and indirect tensile strength (ITS) of BSM. Results indicate that combining cement and bitumen emulsion significantly improves ITS by reducing moisture loss and air voids. Being the coarser gradation, TG2 performed better with cement because of the enhanced hydration, whereas the relatively finer Austroads gradation resulted in higher ITS without cement because of better compaction and adhesion. Without cement, all the mixtures resulted in lower strength than the recommended minimum ITS of 225 kPa, highlighting cement's critical role in strength development. Further, it was established that the Michaelis-Menten (MM) reaction kinetic model effectively describes curing behavior, predicting the curing rate, theoretical maximum strength, and minimum curing time. These findings presented in this paper provide practical insights for optimizing BSM mix designs by balancing emulsion content, cement dosage, and aggregate gradation.
The microstructure evolution and mechanical properties of an ultra-low carbon (0.007 wt%) Fe-Cr-Ni-Ti-Mo precipitation-hardened martensitic stainless steel were investigated after sub-zero and aging treatments. Sub-zero treatment at -73 degrees C for 8 h produced dislocation-rich lath martensite with negligible retained austenite. Aging between 482 degrees C-621 degrees C resulted in the formation of Ni3Ti precipitates and reverted austenite. The volume fraction of reverted austenite (Vf gamma) increased with aging temperature due to Ni partitioning but decreased above 593 degrees C because the austenite became thermally unstable and retransformed to martensite during cooling. Fine Ni3Ti precipitates enhanced strength and hardness, while their coarsening and (Vf gamma) increased at higher temperatures improved ductility. The sub-zero treated (ST) specimen showed the highest impact toughness, while aging progressively improved toughness with aging temperature and time. Transformation-induced plasticity (TRIP) of reverted austenite contributed to enhanced strength, ductility, and impact toughness in aged specimens. The ST condition was strengthened mainly through solid-solution and dislocation mechanisms, while precipitation hardening was the predominant strengthening mechanism after aging.
Heterogeneous catalytic pathways for clean energy conversion involve thousands of elementary steps, but most quantum-mechanical models involve only a few dozen reactions. We combine extensive density functional theory (DFT) calculations, machine learning (ML) for activation barrier prediction, and human intelligence-inspired reaction enumeration and elementary reaction identification. This enables automated kinetic modeling of CO2 hydrogenation on copper, a key process to produce fuels and chemicals. We construct the largest dataset of 152 elementary CO2 reduction reactions and experimentally determine CO2 conversion, finding that even large networks with 100+ reactions are insufficient. In contrast, our approach reveals 9389 elementary reactions, reducing human bias in the reaction pathway. We unravel 40-fold higher CO2 conversion rates, following experimental trends of methanol and CO production. We establish the crucial role of intermolecular hydrogen transfer and hydrogenation by molecular hydrogen, a surprising ML-enabled discovery validated post-facto. The proposed strategy to comprehensively model complex catalytic mechanisms will significantly advance catalysis research and carbon conversion processes.
