Cetrimonium bromide and potassium thiocyanate assisted post-vapor treatment approach to enhance power conversion efficiency and stability of FAPbI3 perovskite solar cells.

Formamidinium lead iodide (FAPbI3) is the most promising perovskite material for producing efficient perovskite solar cells (PSCs). Here, we develop a facile method to obtain an α-phase FAPbI3 layer with passivated grain boundaries and weakened non-radiative recombination. For this aim, during the FAPbI3 fabrication process, cetrimonium bromide + 5% potassium thiocyanate (CTABr + 5% KSCN) vapor post-treatment is introduced to remove non-perovskite phases in the FAPbI3 layer. Incorporation of CTA+ along with SCN- ions induces FAPbI3 crystallization and stitch grain boundaries, resulting in PSCs with lower defect losses. The vapor-assisted deposition increases the carriers' lifetime in the FAPbI3 and facilitates charge transport at the interfacial perovskite/hole transport layer via a band alignment phenomenon. The treated α-FAPbI3 layers bring an excellent PCE of 22.34%, higher than the 19.48% PCE recorded for control PSCs. Besides, the well-oriented FAPbI3 and its higher hydrophobic behavior originating from CTABr materials lead to improved stability in the treated PSCs.

Minimized Computations of Deep Learning Technique for Early Diagnosis of Diabetic Retinopathy Using IoT-Based Medical Devices.

Diabetes mellitus is the main cause of diabetic retinopathy, the most common cause of blindness worldwide. In order to slow down or prevent vision loss and degeneration, early detection and treatment are essential. For the purpose of detecting and classifying diabetic retinopathy on fundus retina images, numerous artificial intelligence-based algorithms have been put forth by the scientific community. Due to its real-time relevance to everyone's lives, smart healthcare is attracting a lot of interest. With the convergence of IoT, this attention has increased. The leading cause of blindness among persons in their working years is diabetic eye disease. Millions of people live in the most populous Asian nations, including China and India, and the number of diabetics among them is on the rise. To provide medical screening and diagnosis for this rising population of diabetes patients, skilled clinicians faced significant challenges. Our objective is to use deep learning techniques to automatically detect blind spots in eyes and determine how serious they may be. We suggest an enhanced convolutional neural network (ECNN) utilizing a genetic algorithm in this paper. The ECNN technique's accuracy results are compared to those of existing approaches like the K-nearest neighbor approach, convolutional neural network, and support vector machine with the genetic algorithm.