Soumitra Satapathi

Bio: Soumitra Satapathi is an academic researcher from Indian Institute of Technology Roorkee. The author has contributed to research in topics: Perovskite (structure) & Materials science. The author has an hindex of 15, co-authored 67 publications receiving 640 citations. Previous affiliations of Soumitra Satapathi include Indian Association for the Cultivation of Science & University of Massachusetts Lowell.

Recent progress in morphology optimization in perovskite solar cell

Abstract: Hybrid organic–inorganic halide perovskite based solar cell technology has passed through a phase of unprecedented growth in the efficiency scale from 3.8% to above 25% within a decade. This technology has drawn tremendous research interest because of facile solution processability, ease of large scale manufacturing and ultra-low cost production of perovskite based thin film solar cells. It has been observed that performances of perovskite-based solar cells are extremely dependent on the morphology and crystallinity of the perovskite layer. The high-quality perovskite films have made a significant impact on the fabrication of efficient and stable hybrid perovskite solar cells. It has also been observed that device lifetime depends on the perovskite morphology; devices with larger perovskite grains degrade slowly than those of the smaller ones. Various methods of perovskite growth such as sequential deposition, doctor blading, slot die coating and spray coating have been applied to achieve the most appropriate morphology necessary for highly efficient and stable solar cells. This review focuses on the recent progress in morphology optimizations by various processing condition such as annealing condition, additive effects, Lewis acid–base adduct approach, precursor solution aging and post-device ligand treatment emphasizing on grain sizes, film uniformity, defect passivation, ambient compatibility and device efficiency and stability. In this review, we also discussed recently developed bifacial stamping technique and deposition methods for large-area and roll-to-roll fabrication of highly efficient and stable perovskite solar cells.

Multimodal Fluorescent Polymer Sensor for Highly Sensitive Detection of Nitroaromatics.

Abstract: Detection of nitroaromatic explosives with high sensitivity and selectivity is extremely important for civilian and military safety. Here, we report the synthesis and multimodal sensing applications of an emissive alanine based dansyl tagged copolymer P(MMA-co-Dansyl-Ala-HEMA) (DCP), synthesized by RAFT copolymerization. The fluorescent co-polymer exhibited high sensitivity and selectivity towards conventional nitroaromatic explosives such as DNT, TNT and TNP in solution at lower range of µM level and also with saturated vapor of NACs. The quantum yield of the co-polymer was measured to be very high (Φf = 77%) which make it an ideal candidate for sensing in solution as well as in vapor phase. The fluorescence signal from DCP copolymer gets significantly quenched upon addition of aliquots of DNT, TNT, and TNP. The Stern-Volmer constant was calculated to be very high. The quenching mechanism was further established by fluorescence up-conversion, time-resolved fluorescence and steady state absorption spectroscopy. The energetics of sensing process was calculated by Density Functional Theory (DFT) studies. We also fabricate a thin film polymer sensor which was able to detect nitroaromatic vapors with high selectivity. This opens up the possibility of building a low-cost and light-weight nitroaromatic explosives sensor for field use.

Repurposing therapeutics for COVID-19: Rapid prediction of commercially available drugs through machine learning and docking.

Abstract: Background The outbreak of the novel coronavirus disease COVID-19, caused by the SARS-CoV-2 virus has spread rapidly around the globe during the past 3 months. As the virus infected cases and mortality rate of this disease is increasing exponentially, scientists and researchers all over the world are relentlessly working to understand this new virus along with possible treatment regimens by discovering active therapeutic agents and vaccines. So, there is an urgent requirement of new and effective medications that can treat the disease caused by SARS-CoV-2. Methods and findings We perform the study of drugs that are already available in the market and being used for other diseases to accelerate clinical recovery, in other words repurposing of existing drugs. The vast complexity in drug design and protocols regarding clinical trials often prohibit developing various new drug combinations for this epidemic disease in a limited time. Recently, remarkable improvements in computational power coupled with advancements in Machine Learning (ML) technology have been utilized to revolutionize the drug development process. Consequently, a detailed study using ML for the repurposing of therapeutic agents is urgently required. Here, we report the ML model based on the Naive Bayes algorithm, which has an accuracy of around 73% to predict the drugs that could be used for the treatment of COVID-19. Our study predicts around ten FDA approved commercial drugs that can be used for repurposing. Among all, we found that 3 of the drugs fulfils the criterions well among which the antiretroviral drug Amprenavir (DrugBank ID-DB00701) would probably be the most effective drug based on the selected criterions. Conclusions Our study can help clinical scientists in being more selective in identifying and testing the therapeutic agents for COVID-19 treatment. The ML based approach for drug discovery as reported here can be a futuristic smart drug designing strategy for community applications.

Machine learning

Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study

Abstract: Summary Background Since December, 2019, Wuhan, China, has experienced an outbreak of coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Epidemiological and clinical characteristics of patients with COVID-19 have been reported but risk factors for mortality and a detailed clinical course of illness, including viral shedding, have not been well described. Methods In this retrospective, multicentre cohort study, we included all adult inpatients (≥18 years old) with laboratory-confirmed COVID-19 from Jinyintan Hospital and Wuhan Pulmonary Hospital (Wuhan, China) who had been discharged or had died by Jan 31, 2020. Demographic, clinical, treatment, and laboratory data, including serial samples for viral RNA detection, were extracted from electronic medical records and compared between survivors and non-survivors. We used univariable and multivariable logistic regression methods to explore the risk factors associated with in-hospital death. Findings 191 patients (135 from Jinyintan Hospital and 56 from Wuhan Pulmonary Hospital) were included in this study, of whom 137 were discharged and 54 died in hospital. 91 (48%) patients had a comorbidity, with hypertension being the most common (58 [30%] patients), followed by diabetes (36 [19%] patients) and coronary heart disease (15 [8%] patients). Multivariable regression showed increasing odds of in-hospital death associated with older age (odds ratio 1·10, 95% CI 1·03–1·17, per year increase; p=0·0043), higher Sequential Organ Failure Assessment (SOFA) score (5·65, 2·61–12·23; p Interpretation The potential risk factors of older age, high SOFA score, and d-dimer greater than 1 μg/mL could help clinicians to identify patients with poor prognosis at an early stage. Prolonged viral shedding provides the rationale for a strategy of isolation of infected patients and optimal antiviral interventions in the future. Funding Chinese Academy of Medical Sciences Innovation Fund for Medical Sciences; National Science Grant for Distinguished Young Scholars; National Key Research and Development Program of China; The Beijing Science and Technology Project; and Major Projects of National Science and Technology on New Drug Creation and Development.

Principles Of Fluorescence Spectroscopy

Abstract: Thank you very much for downloading principles of fluorescence spectroscopy. As you may know, people have look hundreds times for their favorite novels like this principles of fluorescence spectroscopy, but end up in malicious downloads. Rather than reading a good book with a cup of tea in the afternoon, instead they cope with some harmful bugs inside their desktop computer. principles of fluorescence spectroscopy is available in our digital library an online access to it is set as public so you can download it instantly. Our digital library spans in multiple locations, allowing you to get the most less latency time to download any of our books like this one. Kindly say, the principles of fluorescence spectroscopy is universally compatible with any devices to read.

Introduction To Electrodynamics

Abstract: Thank you for downloading introduction to electrodynamics. Maybe you have knowledge that, people have look numerous times for their chosen books like this introduction to electrodynamics, but end up in infectious downloads. Rather than enjoying a good book with a cup of tea in the afternoon, instead they juggled with some malicious bugs inside their computer. introduction to electrodynamics is available in our book collection an online access to it is set as public so you can get it instantly. Our book servers spans in multiple countries, allowing you to get the most less latency time to download any of our books like this one. Merely said, the introduction to electrodynamics is universally compatible with any devices to read.