Computational predictions of energy materials using density functional theory

In the search for new functional materials, quantum mechanics is an exciting starting point. The fundamental laws that govern the behaviour of electrons have the possibility, at the other end of the scale, to predict the performance of a material for a targeted application. In some cases, this is achievable using density functional theory (DFT). In this Review, we highlight DFT studies predicting energy-related materials that were subsequently confirmed experimentally. The attributes and limitations of DFT for the computational design of materials for lithium-ion batteries, hydrogen production and storage materials, superconductors, photovoltaics and thermoelectric materials are discussed. In the future, we expect that the accuracy of DFT-based methods will continue to improve and that growth in computing power will enable millions of materials to be virtually screened for specific applications. Thus, these examples represent a first glimpse of what may become a routine and integral step in materials discovery

propnet: A Knowledge Graph for Materials Science

Discovering the ideal material for a new application involves determining its numerous properties, such as electronic, mechanical, or thermodynamic, to match those of its desired application. The rise of high-throughput computation has meant that large databases of material properties are now accessible to scientists. However, these databases contain far more information than might appear at first glance, since many relationships exist in the materials science literature to derive, or at least approximate, additional properties. propnet is a new computational framework designed to help scientists to automatically calculate additional information from their datasets. It does this by constructing a network graph of relationships between different materials properties and traversing this graph. Initially, propnet contains a catalog of over 100 property relationships but the hope is for this to expand significantly in the future, and contributions from the community are welcomed

“A STUDY TO ASSESS THE IMPACT OF SOCIAL MEDIA USE ON LEARNING, SOCIAL INTERACTION AND SLEEP DURATION ON STUDENT’S LIFE IN SELECTED COLLEGES OF NADIAD CITY.”

discussions among its participant.” Social networking sites have changed how we engage with each other in person, how we obtain information, and how our social networks and friendships operate.(1) Additionally, the amount of time students spends daily utilizing social networks decreases the quality of their sleep and increases depression as well as impact on learning ability and social interaction using of social media impact on college students both : positively & negatively in learning, social interaction and sleep duration.(2) AIMS: The essence of this research work is to primarily study the influence of social media on the learning ability, sleeping pattern and social interaction of students. METHODOLOGY: A Quantitative research study was conducted. 100 college students from 1st and 2nd year were chosen for the study. A standardized questionnaire was used to examine their sociodemographic profile, structured 4-point Likert Scale to assess the impact of social media on learning, Social Interaction and Sleep duration. Descriptive and Inferential statistics were used to analyze the data. RESULT: In this study total 100 students are participated the result revealed than 48(48%) students have moderate impact of social media on learning, 50(50%) students have moderate impact of social media on social interaction, 55(55%) students have moderate impact of social media on sleep duration. CONCLUSION: The purpose of present study is to a Descriptive study to assess the effect of social media on learning, social interaction and sleep duration among students in selected colleges of Nadiad city. Based on the findings, social media occupies the most important part of the student’s life. It consumes most of their productive hours and results in anxiety, depression, loneliness, attention deficit, hyperactivity disorder and addiction. The students who addicted by dopamine spend excess time on social media sleeps only fewer hours in the night time. This makes them become stressed and sleepy in their daily classes which makes in the reduction of the concentration level of students. This study also reveals that student who do not have proper sleeping habit tend to be less interactive to lecture that result in poor academic performanc

Comparison of the tetrahedron method to smearing methods for the electronic density of states

The electronic density of states (DOS) highlights fundamental properties of materials that oftentimes dictate their properties, such as the band gap and Van Hove singularities. In this short note, we discuss how sharp features of the density of states can be obscured by smearing methods (such as the Gaussian and Fermi smearing methods) when calculating the DOS. While the common approach to reach a "converged" density of states of a material is to increase the discrete k-point mesh density, we show that the DOS calculated by smearing methods can appear to converge but not to the correct DOS. Employing the tetrahedron method for Brillouin zone integration resolves key features of the density of states far better than smearing methods

Optimizing CIGB-300 intralesional delivery in locally advanced cervical cancer

Background:We conducted a phase 1 trial in patients with locally advanced cervical cancer by injecting 0.5 ml of the CK2-antagonist CIGB-300 in two different sites on tumours to assess tumour uptake, safety, pharmacodynamic activity and identify the recommended dose.Methods:Fourteen patients were treated with intralesional injections containing 35 or 70 mg of CIGB-300 in three alternate cycles of three consecutive days each before standard chemoradiotherapy. Tumour uptake was determined using 99 Tc-radiolabelled peptide. In situ B23/nucleophosmin was determined by immunohistochemistry.Results:Maximum tumour uptake for CIGB-300 70-mg dose was significantly higher than the one observed for 35 mg: 16.1±8.9 vs 31.3±12.9 mg (P=0.01). Both, AUC 24h and biological half-life were also significantly higher using 70 mg of CIGB-300 (P<0.001). Unincorporated CIGB-300 diffused rapidly to blood and was mainly distributed towards kidneys, and marginally in liver, lungs, heart and spleen. There was no DLT and moderate allergic-like reactions were the most common systemic side effect with strong correlation between unincorporated CIGB-300 and histamine levels in blood. CIGB-300, 70 mg, downregulated B23/nucleophosmin (P=0.03) in tumour specimens.Conclusion:Intralesional injections of 70 mg CIGB-300 in two sites (0.5 ml per injection) and this treatment plan are recommended to be evaluated in phase 2 studies.Fil: Sarduy, M. R.. Medical-surgical Research Center; CubaFil: García, I.. Centro de Ingeniería Genética y Biotecnología; CubaFil: Coca, M. A.. Clinical Investigation Center; CubaFil: Perera, A.. Clinical Investigation Center; CubaFil: Torres, L. A.. Clinical Investigation Center; CubaFil: Valenzuela, C. M.. Centro de Ingeniería Genética y Biotecnología; CubaFil: Baladrón, I.. Centro de Ingeniería Genética y Biotecnología; CubaFil: Solares, M.. Hospital Materno Ramón González Coro; CubaFil: Reyes, V.. Center For Genetic Engineering And Biotechnology Havana; CubaFil: Hernández, I.. Isotope Center; CubaFil: Perera, Y.. Centro de Ingeniería Genética y Biotecnología; CubaFil: Martínez, Y. M.. Medical-surgical Research Center; CubaFil: Molina, L.. Medical-surgical Research Center; CubaFil: González, Y. M.. Medical-surgical Research Center; CubaFil: Ancízar, J. A.. Centro de Ingeniería Genética y Biotecnología; CubaFil: Prats, A.. Clinical Investigation Center; CubaFil: González, L.. Centro de Ingeniería Genética y Biotecnología; CubaFil: Casacó, C. A.. Clinical Investigation Center; CubaFil: Acevedo, B. E.. Centro de Ingeniería Genética y Biotecnología; CubaFil: López Saura, P. A.. Centro de Ingeniería Genética y Biotecnología; CubaFil: Alonso, Daniel Fernando. Universidad Nacional de Quilmes; ArgentinaFil: Gómez, R.. Elea Laboratories; ArgentinaFil: Perea Rodríguez, S. E.. Center For Genetic Engineering And Biotechnology Havana; Cuba. Centro de Ingeniería Genética y Biotecnología; Cub

Photo-stability study of a solution-processed small molecule solar cell system: correlation between molecular conformation and degradation

<p>Solution-processed organic small molecule solar cells (SMSCs) have achieved efficiency over 11%. However, very few studies have focused on their stability under illumination and the origin of the degradation during the so-called burn-in period. Here, we studied the burn-in period of a solution-processed SMSC using benzodithiophene terthiophene rhodamine:[6,6]-phenyl C₇₁ butyric acid methyl ester (BTR:PC₇₁BM) with increasing solvent vapour annealing time applied to the active layer, controlling the crystallisation of the BTR phase. We find that the burn-in behaviour is strongly correlated to the crystallinity of BTR. To look at the possible degradation mechanisms, we studied the fresh and photo-aged blend films with grazing incidence X-ray diffraction, UV–vis absorbance, Raman spectroscopy and photoluminescence (PL) spectroscopy. Although the crystallinity of BTR affects the performance drop during the burn-in period, the degradation is found not to originate from the crystallinity changes of the BTR phase, but correlates with changes in molecular conformation – rotation of the thiophene side chains, as resolved by Raman spectroscopy which could be correlated to slight photobleaching and changes in PL spectra.</p

FindFoci: a focus detection algorithm with automated parameter training that closely matches human assignments, reduces human inconsistencies and increases speed of analysis

Accurate and reproducible quantification of the accumulation of proteins into foci in cells is essential for data interpretation and for biological inferences. To improve reproducibility, much emphasis has been placed on the preparation of samples, but less attention has been given to reporting and standardizing the quantification of foci. The current standard to quantitate foci in open-source software is to manually determine a range of parameters based on the outcome of one or a few representative images and then apply the parameter combination to the analysis of a larger dataset. Here, we demonstrate the power and utility of using machine learning to train a new algorithm (FindFoci) to determine optimal parameters. FindFoci closely matches human assignments and allows rapid automated exploration of parameter space. Thus, individuals can train the algorithm to mirror their own assignments and then automate focus counting using the same parameters across a large number of images. Using the training algorithm to match human assignments of foci, we demonstrate that applying an optimal parameter combination from a single image is not broadly applicable to analysis of other images scored by the same experimenter or by other experimenters. Our analysis thus reveals wide variation in human assignment of foci and their quantification. To overcome this, we developed training on multiple images, which reduces the inconsistency of using a single or a few images to set parameters for focus detection. FindFoci is provided as an open-source plugin for ImageJ

Imaging in population science: cardiovascular magnetic resonance in 100,000 participants of UK Biobank - rationale, challenges and approaches

PMCID: PMC3668194SEP was directly funded by the National Institute for Health Research Cardiovascular Biomedical Research Unit at Barts. SN acknowledges support from the Oxford NIHR Biomedical Research Centre and from the Oxford British Heart Foundation Centre of Research Excellence. SP and PL are funded by a BHF Senior Clinical Research fellowship. RC is supported by a BHF Research Chair and acknowledges the support of the Oxford BHF Centre for Research Excellence and the MRC and Wellcome Trust. PMM gratefully acknowledges training fellowships supporting his laboratory from the Wellcome Trust, GlaxoSmithKline and the Medical Research Council

Data mining to identify risk factors associated with university students dropout

. This paper presents the identification of university students dropout patterns by means of data mining techniques. The database consists of a series of questionnaires and interviews to students from several universities in Colombia. The information was processed by the Weka software following the Knowledge Extraction Process methodology with the purpose of facilitating the interpretation of results and finding useful knowledge about the students. The partial results of data mining processing on the information about the generations of students of Industrial Engineering from 2016 to 2018 are analyzed and discussed, finding relationships between family, economic, and academic issues that indicate a probable desertion risk in students with common behaviors. These relationships provide enough and appropriate information for the decision-making process in the treatment of university dropout.Universidad Peruana de Ciencias Aplicadas, Universidad de la Costa, Universidad Libre Seccional Barranquilla, Corporación Universitaria Latinoamericana