Industrial decarbonization: A revolution ahead

Clean Technologies and Environmental Policy -     

Charge Density Based Small Signal Modeling for InSb/AlInSb Asymmetric Double Gate Silicon Substrate HEMT for High Frequency Applications

Silicon - This paper proposes the Asymmetric Double Gate Silicon Substrate HEMT (ADG-Si-HEMT) to study the carrier concentration and intrinsic small signal parameters of the InSb/AlInSb silicon...     

Detection of Nontrivial Topology Driven by Charge Density Wave in a Semi-Dirac Metal

The presence of electron correlations in a system with topological order can lead to exotic ground states. Considering single crystals of LaAgSb₂ which has a square net crystal structure, one finds multiple charge density wave transitions (CDW) as the temperature is lowered. Large planar Hall (PHE) signals are found in the CDW phase, which are still finite in the high-temperature phase though they change sign. Optimizing the structure within first-principles calculations, one finds an unusual chiral metallic phase. This is because as the temperature is lowered, the separation between the Ag/Sb atoms on different layers decreases, leading to stronger repulsions between electrons associated with atoms on different layers. This leads to successive layers sliding with respect to each other, thereby stabilizing a chiral structure in which inversion symmetry is also broken. The large Berry curvature associated with the low-temperature structure explains the low-temperature PHE. At high temperature, the PHE arises from the changes induced in the anisotropic Dirac cone in presence of a magnetic field. This work represents a route toward detecting and understanding the mechanism in a correlation-driven topological transition through electron transport measurements, complemented by ab-initio electronic structure calculations.     

Optimization of the Two- and Three-Dimensional Characterization of Rare Earth-Traced Deoxidation Products

Tracing by means of the light rare earths (REs), particularly La and Ce, is a state-of-the-art method used to track deoxidation products during the steelmaking process. Traced heterogeneous multiphase inclusions are analyzed using scanning electron microscopy with energy-dispersive spectroscopy (SEM/EDS) to perform a 2D characterization. The sequential chemical extraction technique is implemented for a 3D investigation to determine traced particles’ actual sizes and shapes. The automated SEM/EDS measurement must be optimized since RE oxides appear brighter in the backscattered electron images due to their high atomic numbers. Therefore, two grayscales are implemented for the detection of RE-containing multiphase inclusions. Within this technique, individual RE-traced heterogeneous nonmetallic inclusions (NMIs) are counted as separate particles. Thus, the measured NMIs must be recombined, which is achieved using a self-developed MATLAB tool. The extracted particles are also analyzed by automated and manual SEM/EDS measurements to determine the 3D morphologies and sizes of traced NMIs.     

Celluloses microfibers (CMF)/poly (ethylene-co-vinyl acetate) (EVA) composites for food packaging applications: A study based on barrier and biodegradation behavior

CMF/EVA composites were prepared with different weight percentage of CMF. Swelling parameters, diffusion coefficient and oxygen transmission rate (OTR) of the composites were investigated to analyze the CMF–EVA interaction and application of these composites as food packaging materials. The experiments were carried out in milk, curd and orange juice at normal storage temperatures i.e., −4, 8, 15 and 25 °C. The composite showed an increased barrier property with CMF loading. This in turn also indicates good interfacial interaction between CMF and EVA which was further evaluated using FTIR spectra. SEM and optical microscopic photographs showed a uniform distribution of CMF in EVA. XRD and DSC results indicated increased crystallinity of composites with CMF loading. Biodegradability of composites were studied by inoculating the films with Aspergillus niger (A. niger) and showed increased biodegradation with CMF.     

Leveraging AI-enabled 6G-driven IoT for sustainable smart cities

Many scholastic researches have begun around the globe about the competitive technological interventions like 5G communication networks and its challenges. The incipient technology of 6G networks has emerged to facilitate ultrareliable and low-latency applications for sustainable smart cities which are infeasible with the existing 4G/5G standards. Therefore, the advanced technologies like machine learning (ML), block chain, and Internet of Things (IoT) utilizing 6G network are leveraged to develop cost-efficient mechanisms to address the issues of excess communication overhead in the present state of the art. Initially, the authors discussed the key vision of 6G communication technologies, its core technologies (such as visible light communication [VLC] and THz), and the existing issues with the existing network generations (such as 5G and 4G). A detailed analysis of benefits, challenges, and applications of blockchain-enabled IoT devices with application verticals like Smart city, smart factory plus, automation, and XR that form the key highlights for 6G wireless communication network is also presented. In addition, the key applications and latest research of artificial intelligence (AI) in 6G are discussed facilitating the dynamic spectrum allocation mechanism and mobile edge computing. Lastly, an in-depth study of the existing open issues and challenges in green 6G communication network technology, as well as review of solutions and potential research recommendations are also presented.     

Neural correlates of affective content: application to perceptual tagging of video

Neural Computing and Applications - Over the past years, a digital multimedia uprising has been experienced in every walk of life, due to which the un-annotated or unstructured multimedia content...     

Fabrication and testing of mixed matrix membranes of UiO-66-NH2 in cellulose acetate for CO2 separation from model biogas

Mixed Matrix Membranes (MMMs) of UiO-66-NH₂ nanoparticles dispersed in Cellulose Acetate (CA) were prepared with filler loading of 2–20 wt%. MMMs were tested for the upgradation of model biogas (60%–40%) mixture of CH₄/CO₂ at a feed pressure of 2 bar and 1.5 bar. Detailed characterization of MMMs was performed with Fourier transform infrared spectroscopy (FTIR), Thermo-gravimetric analysis (TGA), Differential scanning calorimetry (DSC), and Field emission scanning electron microscopy (FESEM) to investigate the physical and thermal properties. MMMs formed are defects-free, voids-free, and without polymer rigidification, indicating a better filler polymer interface. MMMs showed improved CO₂ permeability while retaining the CO₂/CH₄ selectivity. The 10 wt.% UiO-66-NH₂/CA MMM showed optimum gas separation performance with CO₂ permeability of 11 Barrer and CO₂/CH₄ selectivity of 10. The UiO-66-NH₂/CA MMMs performed better when compared to the pure CA membrane. The experimental permeability and selectivity data were compared with the predicted data using Maxwell, Lewis–Nielsen, Higuchi, and Bruggeman's model.