Strong Coupling of Self-Trapped Excitons to Acoustic Phonons in Bismuth Perovskite Cs3Bi2I9\textrm{Cs}_{3}\textrm{Bi}_{2}\textrm{I}_{9}

To assess the potential optoelectronic applications of metal-halide perovskites, it is critical to have a detailed understanding of the nature, strength, and dynamics of the interactions between carriers and the polar lattices. Here, we report the electronic and structural dynamics of bismuth-based perovskite $\textrm{Cs}_{3}\textrm{Bi}_{2}\textrm{I}_{9}$ revealed by transient reflectivity and ultrafast electron diffraction. A cross-examination of these experimental results combined with theoretical analyses allows the identification of the major carrier-phonon coupling mechanism and the associated time scales. It is found that carriers photoinjected into $\textrm{Cs}_{3}\textrm{Bi}_{2}\textrm{I}_{9}$ form self-trapped excitons on an ultrafast time scale. However, they retain most of their energy and their coupling to Fr\"ohlich-type optical phonons is limited at early times. Instead, the long-lived excitons exert an electronic stress via deformation potential and develop a prominent, sustaining strain field as coherent acoustic phonons in 10 ps. From sub-ps to ns and beyond, a similar extent of the atomic displacements is found throughout the different stages of structural distortions, from limited local modulations to a coherent strain field to the Debye-Waller random atomic motions on longer times. The current results suggest the potential use of bismuth-based perovskites for applications other than photovoltaics to take advantage of carriers' stronger self-trapping and long lifetime.Comment: 21 pages, 4 figures for the main tex

Carbon-based adsorbents from naturally available Bermuda grasses : removal of TDS and arsenic ions

In the present study, we have reported the synthesis of nano porous carbon material (GC) by the thermal treatment of the commonly available Bermuda grasses, and metal oxides doped bio-compatible polymer chitosan-GC based porous cross-linked composites (CHGCCZ) as adsorbent materials for the removal of total dissolved solids (TDS) and efficient removal of arsenic (As(V)) ions from aqueous medium, respectively. The synthesized adsorbents have been characterized by FTIR, PXRD, FESEM, TGA, and the systematic investigations have shown that the incorporation of GCs into cross-linked matrix makes them porous, more resistant to degradation, and suitable adsorption matrix for the toxic As(V) removal. The presence of As(V) ions is quantified by Inductively Coupled Plasma Optical Emission Spectroscopy (ICP-OES) measurements. The amount of TDS and arsenic concentration was reduced to the minimum value of 103 ppm (average value∼119 ppm) from 414 ppm and 7.7 ppm from very high concentration of 10.15 ppm, respectively. The recyclability test has also been performed after regeneration of the CHGCCZ and the initial findings has been found to be promising. Therefore, we have systematically investigated the efficacy of TDS removal by GCs and As(V) adsorption properties of metal oxide doped cross-linked CHGCCZ composite from the aqueous system and demonstrated the regeneration process for CHGCCZ in our study

Photophysical study of P3HT/NDI based hybrid nanoparticles

Electron donor-acceptor based hybrid novel structure remains a frontier area of research to design optoelectronic, photovoltaic, and light harvesting devices. Here, we report the synthesis of interdyad and intradyad nanoparticles by using the electron donating polymer Poly-3-(hexylthiophine)) (P3HT) and the electron accepting molecule 1, 4, 5, 8 naphthalene tetracarboxylic diimide (NDI). The intradyad nanostructures are fabricated in situ by adding donor and acceptor molecules simultaneously whereas interdyad nanoparticles are fabricated by attaching the donor and acceptor nanoparticles electrostatically. The differential scanning calorimetry (DSC) confirms the segmental motion of the polymer chain and the uniform packing in intradyad nanostructures which is absent in the interdyad system. The photoluminescence quenching and the shortening of decay time of the excited state of the donor molecule were observed with increasing the concentration of acceptor molecule in the intradyad system which is attributed to the photoinduced electron transfer from donor to the acceptor molecule. However, in the case of the interdyad system, the change in photoluminescence quenching and the decay time is less significant due to different photophysical processes

Efficient and less-toxic indium-doped mapbi(3) perovskite solar cells prepared by metal alloying technique

WOS:000822057600001Perovskite materials with ABX(3) structure (A: organic, B: metal, and X: halides) have attracted tremendous attention due to their outstanding optoelectronic properties. Herein, a novel approach is developed using chemical vapor deposition (CVD), i.e., metal alloying of halide-perovskite domain via ion-transfer (MAHDI) for the growth of high-quality perovskite films, grown directly from a metal precursor. This technique easily enables us to replace the toxic Pb metal (B site) with other metals using alloying approach. Using the proposed approach, we fabricated stable and efficient Pb-In perovskite solar cells (PSCs) with a maximum power conversion efficiency (PCE) of 21.2%, which is more efficient than the pure Pb-based PSCs (19.23%). Our characterization results reveal that In-doping improves the crystallinity and photoluminescence (PL) of the perovskite film, resulting in higher photovoltaic properties in the device. To demonstrate the potential of our proposed method for other alloys, we also fabricated PSCs based on Pb-25%Sn alloy and obtained PCE of up to 15.2%. Overall, MAHDI technique opens up a new direction in the field of perovskite devices demonstrating great advantages such as lower price, higher performance, scalability, and fabrication flexibility

Predicting the state parameters of lithium ion batteries: The race between filter-based and data driven approaches

Lithium ion batteries (LIBs) have revolutionized the era of electrical energy storage by offering high energy density and longer life cycles in various applications such as electric vehicles, electronic gadgets, satellites and power grids. To achieve optimum and reliable performance throughout their life cycle, accurate monitoring of their state parameters such as state of charge (SOC), state of health (SOH), and remaining useful life (RUL) needs to be estimated precisely. Filter-based and data driven techniques estimate these parameters accurately even under dynamic battery operation. In this paper, first, we have given details about experimental techniques through which LIB state parameters are estimated, but due to poor nonlinearity handling capacity of these models, we showcase the potential of various filter-based and data driven techniques with a variety of features extracted from LIBs. Subsequently, we discuss the working and performance of various filter based and data driven algorithms utilised in predicting the state parameters of batteries such as SOC, SOH & RUL in detail. Additionally, a comparative table comprising features, predictive techniques and performance is made to highlight the effectiveness of each method. Finally, we propose a strategy to improve the estimation accuracy of LIBs. Overall, the paper provides a comprehensive review of various estimating lgorithms and their potential in predicting the state parameters of LIBs with an aim to develop an intelligent framework for required applications and highlights the challenges which are yet to be overcome

Recent progress of light intensity-modulated small perturbation techniques in perovskite solar cells

WOS:000731408300001The small perturbation frequency-resolved techniques have been powerful tools in unraveling the kinetic processes governing the operation of perovskite solar cells (PSCs). One such technique is electrochemical impedance spectroscopy (EIS). However, a thorough interpretation of the EIS response of PSCs is still lacking owing to the absence of a uniform electrical equivalent circuit. In this context, intensity-modulated photocurrent/photovoltage spectroscopy (IMPS/IMVS) can be the link between the optical and electrical responses of PSCs and complement the IS technique. In this review, the progress made in interpreting the IMPS/IMVS response of various types of PSCs is summarized and diverse prospects are discussed. First, the basic theory and models present in the literature are discussed. Next, the IMPS/IMVS response of mesoporous and planar PSCs based on various physical parameters is discussed. At last, proposed future prospects for the development of this field are discussed

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

BackgroundThe 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 findingsWe 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.ConclusionsOur 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