Effect of Graphene Interface on Potassiation in a Graphene- Selenium Heterostructure Cathode for Potassium-ion Batteries

Selenium (Se) cathodes are an exciting emerging high energy density storage system for Potassium ion batteries(KIB), where potassiation reactions are less understood. Here, we present an atomic-level investigation of KxSe cathode enclosed in hexagonal lattices of carbon(C) characteristic of multilayered graphene matrix and multiwalled carbon nanotubes (MW-CNTs). Microstructural changes directed by graphene substrate in KxSe cathode are contrasted with graphene-free cathode. Graphene's binding affinity for long-chain polyselenides (Se-Se-Se = -2.82 eV and Se-Se = -2.646 eV) and ability to induce reactivity between Se and K are investigated. Furthermore, intercalation voltage for graphene enclosed KxSe cathode reaction intermediates are calculated with K2Se as the final discharged product. Our results indicate a single-step reaction near a voltage of 1.55 V between K and Se cathode. Our findings suggest that operating at higher voltages (~2V) could result in the formation of reaction intermediates where intercalation/deintercalation of K could be a challenge, and therefore cause irreversible capacity losses in the battery. Primary issues are the high binding energy of long-chain polyselenides with graphene that discourage K storage and Se-Se bond dissociation at low K concentrations. A comparison with graphene-free cathode highlights the substantial changes a van der Waals (vdW) graphene interface can bring in atomic-structure and electrochemistry of the KxSe cathode.Comment: 7 Figures and 1 Tabl

Variation in interface strength of Silicon with surface engineered Ti3C2 MXenes

Current advancements in battery technologies require electrodes to combine high-performance active material such as Silicon (Si) with two-dimensional materials such as transition metal carbides (MXenes) for prolonged cycle stability and enhanced electrochemical performance. More so, it is the interface between these materials, which is the nexus for their applicatory success. Herein, the interface strength variations between amorphous Si and Ti3C2Tx MXene are determined as the MXene surface functional groups (Tx) are changed using first-principle calculations. Si is interfaced with three Ti3C2 MXene substrates having surface -OH, -OH and -O mixed, and -F functional groups. Density functional theory (DFT) results reveal that completely hydroxylated Ti3C2 has the highest interface strength of 0.563 J/m2 with amorphous Si. This interface strength value drops as the proportion of surface -O and -F groups increases. Additional analysis of electron redistribution and charge separation across the interface is provided for a complete understanding of underlying physiochemical factors affecting the surface chemistry and resultant interface strength values. The presented comprehensive analysis of the interface aims to aid in developing sophisticated MXene based electrodes by their targeted surface engineering.Comment: 21 pages with 4 figures and. 3 tabl

Prioritized Service Scheme with QoS Provisioning in a Cloud Computing System

A priority scheme is proposed in which the prioritized customers get guaranteed Quality of Service (QoS) by the cloud computing system in terms of lesser response time. The concept of selection probability is introduced according to which the cloud metascheduler chooses the next query for execution. The prioritized customers are categorized into different priority queues which are modeled as M/M/1/K/K queues and an analytical model is developed for the calculation of selection probabilities. Two algorithms are proposed for explaining the processing at the users’ end and at the cloud computing server’s end. The results obtained are validated using the numerical simulations

Infection status of Clinostomum complanatum (Rudolphi, 1819) metacercaria from Channa punctatus of Meerut District

Metacercaria Clinostomum complanatum is a digenetic trematode, which is mainly found in fresh water fishes. Present communication deals with the infection status of C. complanatum in C. punctatus of Meerut district, which is supported by the data spreading over one year. About 250 specimens of C. punctatus from different ponds of Meerut were studied through regular periodical collection in the year Jan 2010 to Dec 2010. Overall prevalence 35.6%, mean intensity 3.06 and abundance 1.09 were reported. The infection was maximum in winter and minimum in rainy season. Prevalence, intensity and abundance of the infestation were also found to be related to different length group and sex of the hosts, the medium sized fishes were more infected and the larger size fishes were less infected while the smaller size fishes showed moderate infection. Susceptibility of infection was not significantly different between male and female fish

Adaptive batching scheme for multicast near video-on-demand (nvod) system

Video-on-Demand is becoming most sought after multimedia applications. It is difficult to attain a true video-on-demand (TVOD) system, so near video-on-demand (NVOD) is catching the attention of people. In NVOD, requests are multicast in different streams. Important issue in this system is the choice of batching time. Traditionally the batching time is fixed depending on the number of requests. In this paper we have suggested an adaptive batching scheme (ABS) where batching time is adjusted according to the current arrival rate, which follows the hyper-exponential distribution pattern. A comparison is made between the fixed and adaptive batching schemes. Numerical illustrations are provided to show that adaptive batching policy is better than fixed batching policy for optimizing bandwidth requirements

Turbostratic Orientations, Water Confinement and Ductile-Brittle Fracture in Bi-layer Graphene

Bi-layer graphene (BLG) can be a cheaper and more stable alternative to graphene in several applications. With its mechanical strength being almost equivalent to graphene, BLG also brings advanced electronic and optical properties to the table. Furthermore, entrapment of water in graphene-based nano-channels and devices has been a recent point of interest for several applications ranging from energy to bio-physics. Therefore, it is crucial to study the over-all mechanical strength of such structures in order to prevent system failures in future applications. In the present work, Molecular Dynamics simulations have been used to study crack propagation in BLG with different orientations between the layers. There is a major thrust in analyzing how the angular orientation between the layers affect the horizontal and vertical crack propagation in individual layers of graphene. The study has been extended to BLG with confined water in interfaces. Over-all strength of graphene sheets when in contact with water content has been determined, and prominent regional conditions for crack initiation are pointed out. It was seen that in the presence of water content, graphene deviated from its characteristic brittle failure and exhibited the ductile fracture mechanism. Origin of cracks in graphenes was located at the region where the density of water dropped near the graphene surface, suggesting that the presence of hydroxyl groups decelerate the crack formation and propagation in straining graphenes.Comment: 24 pages, 10 Figure

Transferable and Robust Machine Learning Model for Predicting Stability of Si Anodes for Multivalent Cation Batteries

Data-driven methodology has become a key tool in computationally predicting material properties. Currently, these techniques are priced high due to computational requirements for generating sufficient training data for high-precision machine learning models. In this study, we present a Support Vector Regression (SVR)-based machine learning model to predict the stability of silicon (Si) - alkaline metal alloys, with a strong emphasis on the transferability of the model to new silicon alloys with different electronic configurations and structures. We elaborate on the role of the structural descriptor in imparting transferability to the model that is trained on limited data (~750 Si alloys) derived from the Material Project database. Three popular descriptors, namely X-Ray Diffraction (XRD), Sine Coulomb Matrix (SCM), and Orbital Field Matrix (OFM), are evaluated for representing Si alloys. The material structures are represented by descriptors in the SVR model, coupled with hyperparameter tuning techniques like Grid Search CV and Bayesian Optimization (BO), to find the best performing model for predicting total energy, formation energy and packing fraction of the Si alloy systems. The models are trained on Si alloys with lithium (Li), sodium (Na), potassium (K), magnesium (Mg), calcium (Ca), and aluminum (Al) metals, where Si-Na and Si-Al systems are used as test structures. Our results show that XRD, an experimentally derived characterization of structures, performs most reliably as a descriptor for total energy prediction of new Si alloys. The study demonstrates that by qualitatively selection of training data, using hyperparameter tuning methods, and employing appropriate structural descriptors, the data requirements for robust and accurate ML models can be reduced.Comment: 23 pages, 7 figure