Birla Institute of Technology and Science

About: Birla Institute of Technology and Science is a education organization based out in Pilāni, Rajasthan, India. It is known for research contribution in the topics: Computer science & Population. The organization has 8897 authors who have published 13947 publications receiving 170008 citations.

A perspective on diverse adsorbent materials to recover precious palladium and the way forward

Abstract: The removal and recovery of precious noble metals is noteworthy in a variety of applications. The need to recover these precious metals is associated with their high cost and other environmental impacts. The Nobel Prize conferred to Suzuki, Heck and Negishi in 2010 has underlined the remarkable significance of palladium as a catalyst in several important transformations. Palladium is one such platinum group noble metal that finds diverse applications in the automobile industry, electronics, jewelry, pharmaceutics, catalysis, etc. Therefore, the recovery of palladium has acquired importance. Methods such as liquid–liquid extraction and adsorption using biopolymers, polymeric resins, carbonaceous materials and silica based materials are discussed based on their removal efficiency, adsorption capacities, regeneration and other parameters. The review looks at a perspective based on the applicability of certain important adsorbents employed in recent years pertaining to the removal of palladium from aqueous solution, spent catalysts and industrial wastes.

Interfacial Coordination Nanosheet Based on Nonconjugated Three-Arm Terpyridine: A Highly Color-Efficient Electrochromic Material to Converge Fast Switching with Long Optical Memory.

Abstract: An electrochromic (EC) hyperbranched coordination nanosheet (CONASH) comprising a three-arm terpyridine (3tpy)-based ligand and Fe(II) ion has been synthesized by interfacial complexation at the liquid-liquid interface. The film can be easily deposited on the desired substrate such as indium tin oxide (ITO) glass. Characterization of CONASH deposited on ITO by microscopic methods reveals the homogeneous nanosheet film with an ∼350 nm thickness after 48 h of reaction. The fabricated solid-state EC device (ECD) undergoes a reversible redox reaction (Fe2+ → Fe3+) in the potential range of +3 to -2 V in ECDs accompanied with a distinct color change from intense pink to colorless for several switching cycles with a coloration time of 1.15 s and a bleaching time of 2.49 s along with a high coloration efficiency of 470.16 cm2 C-1. Besides, the nonconjugated 3tpy ligand restricts the easy electron redox conduction inside the EC film to enhance the EC memory in open-circuit condition as it shows 50% retention of its colorless state until 25 min. The long EC memory compared to other metallo-supramolecular polymers having a conjugated ligand suggests the potentiality of the 3tpy-Fe CONASH film to be used as a power-efficient EC material for modern display device applications.

AgriSegNet: Deep Aerial Semantic Segmentation Framework for IoT-Assisted Precision Agriculture

Abstract: Aerial inspection of agricultural regions can provide crucial information to safeguard from numerous obstacles to efficient farming. Farmland anomalies such as standing water, weed clusters, hamper the farming practices, which causes improper use of farm area and disrupts agricultural planning. Monitoring of farmland and crops through Internet-of-Things (IoT)-enabled smart systems has potential to increase the efficiency of modern farming techniques. Unmanned Aerial Vehicle (UAV)-based remote sensing is a powerful technique to acquire farmland images on a large scale. Visual data analytics for automatic pattern recognition from the collected data is useful for developing Artificial intelligence (AI)-assisted farming models, which holds great promise in improving the farming outputs by capturing the crop patterns, farmland anomalies and providing predictive solutions to the inherent challenges faced by farmers. In this work, we propose a deep learning framework AgriSegNet for automatic detection of farmland anomalies using multiscale attention semantic segmentation of UAV acquired images. The proposed model is useful for monitoring of farmland and crops to increase the efficiency of precision farming techniques.