Doping and Switchable Photovoltaic Effect in Lead‐Free Perovskites Enabled by Metal Cation Transmutation

Creating defect tolerant lead‐free halide perovskites is the major challenge for development of high‐performance photovoltaics with nontoxic absorbers. Few compounds of Sn, Sb, or Bi possess ns² electronic configuration similar to lead, but their poor photovoltaic performances inspire us to evaluate other factors influencing defect tolerance properties. The effect of heavy metal cation (Bi) transmutation and ionic migration on the defects and carrier properties in a 2D layered perovskite (NH₄)₃(Sb_(1?x)Bi_x)₂I₉ system is investigated. It is shown, for the first time, the possibility of engineering the carriers in halide perovskites via metal cation transmutation to successfully form intrinsic p‐ and n‐type materials. It is also shown that this material possesses a direct–indirect bandgap enabling high absorption coefficient, extended carrier lifetimes >100 ns, and low trap densities similar to lead halide perovskites. This study also demonstrates the possibility of electrical poling to induce switchable photovoltaic effect without additional electron and hole transport layers.     

Efficient representation of turbulent flows using data-enriched finite elements

Efficient modal decomposition of high-dimensional turbulent flow data is an important first step for data reduction, analysis, and low-dimensional predictive modeling. The conventional modal decomposition techniques, such as proper orthogonal and dynamic mode decompositions, aim to represent the system response using spatially global basis vectors that span a broad spatial domain. A significant challenge facing approaches based on global domain decomposition is the rapid increase in both the amount of training data and the number of modes that must be retained for an accurate representation of convection dominated turbulent flows. An alternative generalized finite element (GFEM) based approach is explored for efficient representation of high-dimensional fluid flow data. Here, the standard finite element interpolation method is enriched with numerical functions that are learned from a small amount of high-fidelity training data over spatially localized subdomains. The GFEM approach is demonstrated on a 3D flow past a cylinder at Reynolds number of 100 000 and flows inside a 2D lid-driven cavity over a range of Reynolds numbers. Compared with a global proper orthogonal decomposition, the GFEM-based approach increases efficiency in reconstructing the datasets while also substantially reducing the amounts of training data.     

Automatic PV Grid Fault Detection System with IoT and LabVIEW as Data Logger

Fault detection of the photovoltaic (PV) grid is necessary to detect serious output power reduction to avoid PV modules’ damage. To identify the fault of the PV arrays, there is a necessity to implement an automatic system. In this IoT and LabVIEW-based automatic fault detection of 3 × 3 solar array, a PV system is proposed to control and monitor Internet connectivity remotely. Hardware component to automatically reconfigure the solar PV array from the series-parallel (SP) to the complete cross-linked array underneath partial shading conditions (PSC) is centered on the Atmega328 system to achieve maximum power. In the LabVIEW environment, an automated monitoring system is developed. The automatic monitoring system assesses the voltage drop losses present in the DC side of the PV generator and generates a decimal weighted value depending on the defective solar panels and transmits this value to the remote station through an RF modem, and provides an indicator of the faulty solar panel over the built-in Interface LabVIEW. The managing of this GUI indicator helps the monitoring system to generate a panel alert for damaged panels in the PV system. Node MCU in the receiver section enables transmission of the fault status of PV arrays via Internet connectivity. The IoT-based Blynk app is employed for visualizing the fault status of the 3 × 3 PV array. The dashboard of Blynk visualizes every array with the status.     

A multimodal liveness detection using statistical texture features and spatial analysis

Multimedia Tools and Applications - Biometric authentication can establish a person’s identity from their exclusive features. In general, biometric authentication can vulnerable to spoofing...     

Floating Point CGRA based Ultra-Low Power DSP Accelerator

Journal of Signal Processing Systems - Coarse Grained Reconfigurable Arrays (CGRAs) are emerging as energy efficient accelerators providing a high grade of flexibility in both academia and...     

Enhanced Binary Hexagonal Extrema Pattern (EBHXEP) Descriptor for Iris Liveness Detection

Wireless Personal Communications - Biometric traits are frequently used by security agencies for automatic recognition of a person. There are numerous biometric traits used for person...     

Trust-aware energy-efficient stable clustering approach using fuzzy type-2 Cuckoo search optimization algorithm for wireless sensor networks

Wireless Networks - With the advancement of communication and sensor technologies, it has become possible to develop low-cost circuitry to sense and transmit the state of surroundings. Wireless...     

Application of inline imaging for monitoring crystallization process in a continuous oscillatory baffled crystallizer

In this study, an in situ imaging system has been analysed to characterize the crystal size, the shape and the number of particles during a continuous crystallization process in a Continuous Oscillatory Baffled Crystallizer (COBC). Two image analysis approaches were examined for particle characterization in the suspension containing both small nuclei and larger grown crystals (nonspherical and irregular in shape). The pattern matching approach, in which the particles are approximated to be spherical, did result in an overestimation of the size. Alternatively, a segmentation‐based algorithm resulted in reliable crystal size and shape characteristics. The laser diffraction analysis in comparison to the image analysis overestimated the particle sizes due to the agglomeration of particles upon filtration and drying. The trend in the particle counts during the start of crystallization process, including nucleation, determined by the image analysis probe was comparable with the one measured by FBRM, highlighting the potential of in situ imaging for process monitoring. © 2018 American Institute of Chemical Engineers AIChE J, 64: 2450–2461, 2018     

Influence of normal contact force model on simulations of spherocylindrical particles

How the choice of elastic normal contact force model affects predictions from discrete element method simulations of spherocylindrical particles is investigated in this article. Three force models were investigated: (1) a Hertzian force model (HFM) which assumes a circular contact area; (2) a linear force model (LFM) with a constant stiffness; and (3) a modified HFM (MFM) that accounts for various contact areas and contact transitions. With the MFM, transitions between contact area types must be accounted for otherwise discontinuities in the contact force can occur. It is found that simple force models (HFM, LFM) can be substituted for more accurate force models if only force data and bulk properties are of interest. However, if more detailed contact information, such as contact area, contact overlap, contact duration, or collision frequency, are needed, for example, in population balance models and transient liquid bridge modeling, then a more accurate force model should be used. © 2018 American Institute of Chemical Engineers AIChE J, 64: 1986–2001, 2018     

Microfluidic platform for controlled synthesis of polymeric nanoparticles

A central challenge in the development of drug-encapsulated polymeric nanoparticles is the inability to control the mixing processes required for their synthesis resulting in variable nanoparticle physicochemical properties. Nanoparticles may be developed by mixing and nanoprecipitation of polymers and drugs dissolved in organic solvents with nonsolvents. We used rapid and tunable mixing through hydrodynamic flow focusing in microfluidic channels to control nanoprecipitation of poly(lactic- co-glycolic acid)- b-poly(ethylene glycol) diblock copolymers as a model polymeric biomaterial for drug delivery. We demonstrate that by varying (1) flow rates, (2) polymer composition, and (3) polymer concentration we can optimize the size, improve polydispersity, and control drug loading and release of the resulting nanoparticles. This work suggests that microfluidics may find applications for the development and optimization of polymeric nanoparticles in the newly emerging field of nanomedicine.