3,699 research outputs found
Universal Maximum Strength of Solid Metals and Alloys
No full textThis article is published as Johnson, Duane D., Prashant Singh, A. V. Smirnov, and Nicolas Argibay. "Universal Maximum Strength of Solid Metals and Alloys." Physical Review Letters 130, no. 16 (2023): 166101.
DOI: 10.1103/PhysRevLett.130.166101.
Copyright 2023 American Physical Society.
Posted with permission.
DOE Contract Number(s): AC02-07CH1135
Lightning Potential Index Using ICON Simulation at the km-scale over the Third Pole Region: ISS-LIS events and ICON-CLM simulated LPI
No full text<p>This dataset contains records of lightning events recorded by the International Space Station (ISS) Lightning Imaging Sensor (LIS) from October 2019 to September 2022 in the Third Pole region. Furthermore, the Icosahedral Nonhydrostatic Weather and Climate Model in Climate Limited-Area Mode (ICON-CLM) was utilized to simulate the hourly Lightning Potential Index (LPI) over the Third Pole region for the same duration. The aforementioned dataset was utilized in the creation of the research article titled "Modeling Lightning Activity in the Third Pole Region: Performance of a km-scale ICON-CLM Simulation" authored by Prashant Singh and Bodo Ahrens. The paper has been submitted to the journal Atmosphere. In CORDEX-FPS-CPTP contribution no. 17 (GUF), you can find more data from ICON-CLM, such as precipitation, CAPE, wind vectors, and more.</p>
Designing order–disorder transformation in high-entropy ferritic steels.
No full textOrder–disorder transformations hold an essential place in chemically complex high-entropy ferritic steels (HEFSs) due to their critical technological application. The chemical inhomogeneity arising from mixing of multi-principal elements of varying chemistry can drive property altering changes at the atomic scale, in particular short-range order. Using density-functional theory-based linear-response theory, we predict the effect of compositional tuning on the order–disorder transformation in ferritic steels—focusing on Cr–Ni–Al–Ti–Fe HEFSs. We show that Ti content in Cr–Ni–Al–Ti–Fe solid solutions can be tuned to modify short-range order that changes the order–disorder path from BCC-B2 (Ti atomic-fraction = 0) to BCC-B2-L21 (Ti atomic-fraction > 0) consistent with existing experiments. Our study suggests that tuning degree of SRO through compositional variation can be used as an effective means to optimize phase selection in technologically useful alloys.This article is published as Singh, Prashant, and Duane D. Johnson. "Designing order–disorder transformation in high-entropy ferritic steels." Journal of Materials Research 27 (2022): 136-144. DOI: 10.1557/s43578-021-00336-w. Copyright 2021 The Author(s). Attribution 4.0 International (CC BY 4.0). DOE Contract Number(s): AC02-07CH11358. Posted with permission
Large-Scale Production and Optical Properties of a High-Quality SnS2 Single Crystal Grown Using the Chemical Vapor Transportation Method
No full textThe scientific community believes that high-quality, bulk layered, semiconducting single crystals are crucial for producing two-dimensional (2D) nanosheets. This has a significant impact on current cutting-edge science in the development of next-generation electrical and optoelectronic devices. To meet this ever-increasing demand, efforts have been made to manufacture high-quality SnS2 single crystals utilizing low-cost CVT (chemical vapor transportation) technology, which allows for large-scale crystal production. Based on the chemical reaction that occurs throughout the CVT process, a viable mechanism for SnS2 growth is postulated in this paper. Optical, XRD with Le Bail fitting, TEM, and SEM are used to validate the quality, phase, gross structural/microstructural analyses, and morphology of SnS2 single crystals. Furthermore, Raman, TXRF, XPS, UV–Vis, and PL spectroscopy are used to corroborate the quality of the SnS2 single crystals, as well as the proposed energy level diagram for indirect transition in the bulk SnS2 single crystals. As a result, the suggested method provides a cost-effective method for growing high-quality SnS2 single crystals, which could lead to a new alternative resource for producing 2D SnS2 nanosheets, which are in great demand for designing next-generation optoelectronic and quantum devices
sj-docx-1-cnr-10.1177_10547738221085613 – Supplemental material for Severity, Risk Factors and Quality of Life of Patients associated with Chemotherapy-Induced Peripheral Neuropathy
No full textSupplemental material, sj-docx-1-cnr-10.1177_10547738221085613 for Severity, Risk Factors and Quality of Life of Patients associated with Chemotherapy-Induced Peripheral Neuropathy by Saumya P. Srivastava, Aditi Prashant Sinha, Kamlesh Kumari Sharma and Prabhat Singh Malik in Clinical Nursing Research</p
Effect of heterostructure engineering on electronic structure and transport properties of two-dimensional halide perovskites
No full textOrganic-inorganic halide perovskite solar cells have attracted much attention due to their low-cost fabrication, flexibility, and high-power conversion efficiency. More recent efforts show that the reduction from three- to two-dimensions (2D) of organic–inorganic halide perovskites promises an exciting opportunity to tune their electronic properties. Here, we explore the effect of reduced dimensionality and heterostructure engineering on the intrinsic material properties, such as energy stability, bandgap and transport properties of 2D hybrid organic–inorganic halide perovskites using first-principles density functional theory. We show that the energy stability of engineered perovskite heterostructures is significantly enhanced. The heterostructures with improved stability also show excellent transport properties similar to their bulk counterparts. These layered chemistries demonstrate the advantage of a broad range of tunable bandgaps and high-absorption coefficient in the visible spectrum. The proposed 2D heterostructured material holds potential for nano-optoelectronic devices as well as for effective photovoltaics.This is a manuscript of an article published as Singh, Rahul, Prashant Singh, and Ganesh Balasubramanian. "Effect of heterostructure engineering on electronic structure and transport properties of two-dimensional halide perovskites." Computational Materials Science 200 (2021): 110823.
DOI: 10.1016/j.commatsci.2021.110823.
Copyright 2021 Elsevier B.V.
DOE Contract Number(s): AC02-07CH11358; CMMI-1753770.
Posted with permission
Tuning bandgap and energy stability of Organic-Inorganic halide perovskites through surface engineering
No full textOrganohalide perovskite with a variety of surface structures and morphologies have shown promising potential owing to the choice of the type of heterostructure dependent stability. We systematically investigate and discuss the impact of 2-dimensional molybdenum-disulphide (MoS2), molybdenum-diselenide (MoSe2), tungsten-disulphide (WS2), tungsten-diselenide (WSe2), boron-nitiride (BN) and graphene monolayers on bandgap and energy stability of organic–inorganic halide perovskites. We found that MAPbI3 deposited on BN-ML shows room temperature stability (-25 meV ∼ 300 K) with an optimal bandgap of ∼ 1.68 eV. The calculated absorption coefficient also lies in the visible-light range with a maximum of 4.9 × 104 cm−1 achieved at 2.8 eV photon energy. On the basis of our calculations, we suggest that the encapsulation of an organic–inorganic halide perovskite monolayers by semiconducting monolayers potentially provides greater flexibility for tuning the energy stability and the bandgap.This is a manuscript of an article published as Singh, Rahul, Prashant Singh, and Ganesh Balasubramanian. "Tuning bandgap and energy stability of Organic-Inorganic halide perovskites through surface engineering." Computational Materials Science 213 (2022): 111649.
DOI: 10.1016/j.commatsci.2022.111649.
Copyright 2022 Elsevier B.V.
Posted with permission.
DOE Contract Number(s): AC02-07CH11358; CMMI-1404938
Rising burden of overweight and obesity among Indian adults: empirical insights for public health preparedness
No full textAbstract
With simultaneous efforts to address a huge burden of malnutrition, especially among children and younger women, India also encounters a mushrooming prevalence of overweight and obesity among the adult population. This study analysed data from two consecutive rounds of the National Family Health Survey (NFHS) conducted in 2005–06 and 2015–16, to present the burden of overweight and obesity among adult men and women in India. The findings highlight a rising burden of overweight and obesity, although the level and the extent of change over the study period varied across states. The district-wise analysis revealed geographical clusters of overweight and obesity. Further investigation suggests that overweight or obesity are not exclusive to urban areas, and economically well-off populations are more inclined to be overweight or obese. The trends and patterns of overweight and obesity in India argue for timely public health preparedness and interventions to avoid the rising incidence of non-communicable diseases in India
Consumer Behaviour towards Electricity–A Field Study
No full textAbstractStatistics focus on Generating Capacities, Transmission and Distribution. But consumer and his demand for power sourceare crucial for Electrical system and its sustenance. In fact, there is less or no study to predict consumer demand. Their behavi our is hardly understood through load forecasting. A Distribution feeder connects all consumers -poor to rich, rural to suburban then to urban, low demand agriculture to high demand industries etc. Present paper is intended to study domestic consumer covering all socioeconomic geographical sections. A Survey has been carried out to cover all parts of Goa state, India. A Pre-defined survey format, awareness to volunteers, door to door survey, hourly log of consumption etc form parts of it. This study has revealed and created inputs to Electrical Load Online Census, load dynamics like fields
Drug Synergy for Cancer using Computational Intelligence Techniques
No full textDoctor of Philosophy - CSECancer still remains a major health concern in the world. It is still a challenge for researchers to develop an effective cancer therapy in most types of cancers. Although cancer presents with a very high mortality rate, there are certain treatments available and the number is growing with new breakthroughs every day. A variety of cancer treatments exist, and the type of treatment a patient receives mainly depends upon the type of cancer they present with and its current stage. Various treatments of cancer exist such as radiation therapy, chemotherapy, drug synergy, hormonal therapy and surgery. Some patients need only one kind of treatment, but most of the patients will receive a combination of these. There are many side effects of aforementioned treatments including hair loss, vomiting, nausea, blood disorders, itchiness, constipation, diarrhea, vaginal dryness, enlarged and tender breast. Researchers are trying very hard to reduce the side effects caused by cancer treatment. Now days, more focus is being given by researchers towards targeted therapy such as drug synergism. It mainly targets the cancerous cells. Drug synergism is a branch which finds the optimal combination of two or more drugs on the basis of DDI (Drug Drug Interaction) to kill the cancerous cells in a more efficient manner as compared to their individual effects. However, there are huge number of drugs which leads to the combinatorial explosion problem. To handle the numerous amounts of drugs, machine learning (ML) plays a major role in this area. ML is categorized in supervised and unsupervised learning. In supervised learning, different problems can be categorized under classification and regression whereas in unsupervised learning, clustering problems are considered. Problem of drug synergy falls under the category of regression problems in which synergy score (considered as target) of different drugs is predicted from the given set of input parameters. ML techniques can be used to improve the results of such type of problems. There are some studies exist to predict the synergy score. But these studies have some limitations such as use of single model, a smaller number of input parameters, and lack of drug data to train the models. Because of such issues, the trained model may or may not produce a reliable and efficient prediction. Single model can be replaced with the ensemble models to predict synergy score. Ensemble learning is a process of combining more than one model or combination of one model with some optimization algorithm to solve a given computational intelligence problem. Generally, it is used to enhance the predictability as well as to improve the robustness of a model. The ensemble approach among different models is used because it is capable of boosting the weak learners. This approach has advantage that the ensemble model can adapt any diversity in the data more correctly as compared to single model . Neuro-fuzzy based ensembling technique is designed using biased-weighted aggregation(addition of more weights to model with higher prediction score). Four with the highest accuracy among nine ML models have been selected for developing ensemble-based machine learning model. On the other fold, ensembling of model with optimization algorithm gives the best result by finding the best combinations of parameters to be tuned for the given model. An effective ensemble based machine learning techniques are defined to predict drug synergy, such as SVM based differential evolution and evolutionary based neural network structure using Adaptive Lévy method to overcome the parameter tuning issue. It regulates the precision in prediction. It suggests that the ensemble approach is more efficient than the single model. Therefore, ensemble models have been proposed to predict the drug synergy score. To check the consistency of proposed ensemble model prediction, repeated k-fold cross validation has been performed. The existing machine learning techniques and this evolutionary based technique are tested on drug synergy score data. An extensive analysis shows the better performance of proposed method over existing techniques, in terms of following evaluation parameters such as accuracy, coefficient of correlation and root mean squared error
- …
