Hydrogen generation from the hydrolysis of hydrazine-borane catalyzed by rhodium(0) nanoparticles supported on hydroxyapatite

Herein, we report the preparation and characterization of rhodium(0) nanoparticles supported on hydroxyapatite (Ca₁₀(OH)₂(PO₄)₆, HAP) and their catalytic use in the hydrolysis of hydrazine-borane, which attracts recent attention as promising hydrogen storage materials. Hydroxyapatite supported rhodium(0) nanoparticles were readily prepared by the hydrazine-borane reduction of rhodium(III)-exchanged hydroxyapatite in situ during the hydrolysis of hydrazine-borane at room temperature. Characterization of the resulting material by ICP–OES, TEM, SEM, EDX, XRD, XPS spectroscopies and N₂ adsorption–desorption technique, which shows the formation of rhodium(0) nanoparticles well dispersed on hydroxyapatite support. The catalytic performance of these new supported rhodium(0) nanoparticles in terms of activity, lifetime and reusability was tested in the hydrolysis of hydrazine-borane. They were found to be highly active, long-lived and reusable catalyst in this important catalytic reaction even at low temperatures and high initial [substrate]/[catalyst] conditions. This report also includes the detailed kinetic study of the hydrolysis of hydrazine-borane catalyzed by hydroxyapatite supported rhodium(0) nanoparticles depending on the catalyst concentration, substrate concentration, and temperature.     

Photophysics of Molecular‐Weight‐Induced Losses in Indacenodithienothiophene‐Based Solar Cells

The photovoltaic performance and optoelectronic properties of a donor–acceptor copolymer are reported based on indacenodithienothiophene (IDTT) and 2,3‐bis(3‐(octyloxy)phenyl)quinoxaline moieties (PIDTTQ) as a function of the number‐average molecular weight (M_n). Current–voltage measurements and photoinduced charge carrier extraction by linear increasing voltage (photo‐CELIV) reveal improved charge generation and charge transport properties in these high band gap systems with increasing M_n, while polymers with low molecular weight suffer from diminished charge carrier extraction because of low mobility–lifetime (μτ) product. By combining Fourier‐transform photocurrent spectroscopy (FTPS) with electroluminscence spectroscopy, it is demonstrate that increasing M_n reduces the nonradiative recombination losses. Solar cells based on PIDTTQ with M_n = 58 kD feature a power conversion efficiency of 6.0% and a charge carrier mobility of 2.1 × 10^?4 cm² V^?1 s^?1 when doctor bladed in air, without the need for thermal treatment. This study exhibits the strong correlations between polymer fractionation and its optoelectronics characteristics, which informs the polymer design rules toward highly efficient organic solar cells.     

An Organic Electrochemical Transistor Integrated Photodetector for High Quality Photoplethysmogram Signal Acquisition

The organic photodiode (OPD) is a promising building block for solution-processable, flexible, lightweight, and miniaturized photodetectors, ideal for wearable applications. Despite the advances in materials used in OPDs, their photocurrent and light responsivity are limited, and alternative methods are required to boost the signal response. Herein, a miniaturized organic electrochemical transistor (OECT) is integrated with an OPD module to unlock the potential of OPDs to acquire physiological signals. In this integrated photodetector (IPD) system, the light intensity regulates the OPD voltage output that modulates the OECT channel current. The high transconductance of the OECT provides efficient voltage-to-current conversion, enhancing the signal-to-noise ratio on the sensing site. A microscale, p-type enhancement-mode OECT with high g_m and fast switching speed performs better in this application than depletion-mode OECT of the same geometry. The IPD achieves a photocurrent and responsivity 318 and 140 times higher than the standalone OPD, respectively. It is shown that with the IPD, the amplitude of the photoplethysmogram signals detected by the OPD is enhanced by a factor of 2.9 × 10³, highlighting its potential as a wearable biosensor and to detect weak, often uncaptured, light-based signals from living systems.     

All Slot-Die Coated Non-Fullerene Organic Solar Cells with PCE 11%

Slot-die (SD) coating is used to fabricate fully solution processed organic solar cells (OSCs) based on a blend of high performance donor polymer (PTB7-Th) and a non-fullerene acceptor (IEICO-4F) for stable devices over extended periods of operation. The optimization of a sequential deposition process of transport and active layers, under ambient conditions, enable high efficiency slot-die coated solar cells with remarkable power conversion efficiencies (PCE) > 11.0% to bridge the gap between lab-to-fab. Fully slot-die coated inverted OSCs are demonstrated with efficiencies reaching 11% along with 1 cm² devices, proving the scalability and reproducibility of the proposed technique. Further, replacing the evaporated Ag electrode with solution processed Ag nanowire (AgNW) electrodes shows the highest light utilization efficiency of 5.26% for semi-transparent OSC with a PCE of 9.07% and average visible transmission of 58%.     

A Highly Conductive Titanium Oxynitride Electron-Selective Contact for Efficient Photovoltaic Devices

High-quality carrier-selective contacts with suitable electronic properties are a prerequisite for photovoltaic devices with high power conversion efficiency (PCE). In this work, an efficient electron-selective contact, titanium oxynitride (TiO_xN_y), is developed for crystalline silicon (c-Si) and organic photovoltaic devices. Atomic-layer-deposited TiO_xN_y is demonstrated to be highly conductive with a proper work function (4.3 eV) and a wide bandgap (3.4 eV). Thin TiO_xN_y films simultaneously provide a moderate surface passivation and enable a low contact resistivity on c-Si surfaces. By implementation of an optimal TiO_xN_y-based contact, a state-of-the-art PCE of 22.3% is achieved for a c-Si solar cell featuring a full-area dopant-free electron-selective contact. Simultaneously, conductive TiO_xN_y is proven to be an efficient electron-transport layer for organic photovoltaic (OPV) devices. A remarkably high PCE of 17.02% is achieved for an OPV device with an electron-transport TiO_xN_y layer, which is superior to conventional ZnO-based devices with a PCE of 16.10%. Atomic-layer-deposited TiO_xN_y ETL on a large area with a high uniformity may help accelerate the commercialization of emerging solar technologies.     

Combined neural network model to compute wavelet coefficients

Abstract: In recent years a novel model based on artificial neural networks technology has been introduced in the signal processing community for modelling the signals under study. The wavelet coefficients characterize the behaviour of the signal and computation of the wavelet coefficients is particularly important for recognition and diagnostic purposes. Therefore, we dealt with wavelet decomposition of time-varying biomedical signals. In the present study, we propose a new approach that takes advantage of combined neural network (CNN) models to compute the wavelet coefficients. The computation was provided and expressed by applying the CNNs to ophthalmic arterial and internal carotid arterial Doppler signals. The results were consistent with theoretical analysis and showed good promise for discrete wavelet transform of the time-varying biomedical signals. Since the proposed CNNs have high performance and require no complicated mathematical functions of the discrete wavelet transform, they were found to be effective for the computation of wavelet coefficients.     

An expert system for speaker identification using adaptive wavelet sure entropy

In this study, an expert speaker identification system is presented for speaker identification using Turkish speech signals. Here, a discrete wavelet adaptive network based fuzzy inference system (DWANFIS) model is used for this aim. This model consists of two layers: discrete wavelet and adaptive network based fuzzy inference system. The discrete wavelet layer is used for adaptive feature extraction in the time–frequency domain and is composed of discrete wavelet decomposition and discrete wavelet entropy. The performance of the used system is evaluated by using repeated speech signals. These test results show the effectiveness of the developed intelligent system presented in this paper. The rate of correct classification is about 90.55% for the sample speakers.     

Automatic hepatitis diagnosis system based on Linear Discriminant Analysis and Adaptive Network based on Fuzzy Inference System

In this paper, an automatic diagnosis system based on Linear Discriminant Analysis (LDA) and Adaptive Network based on Fuzzy Inference System (ANFIS) for hepatitis diseases is introduced. This automatic diagnosis system deals with the combination of feature extraction and classification. This automatic hepatitis diagnosis system has two stages, which feature extraction – reduction and classification stages. In the feature extraction – reduction stage, the hepatitis features were obtained from UCI Repository of Machine Learning Databases. Then, the number of these features was reduced to 8 from 19 by using Linear Discriminant Analysis (LDA). In the classification stage, these reduced features are given to inputs ANFIS classifier. The correct diagnosis performance of the LDA-ANFIS automatic diagnosis system for hepatitis disease is estimated by using classification accuracy, sensitivity and specificity analysis, respectively. The classification accuracy of this LDA-ANFIS automatic diagnosis system for the diagnosis of hepatitis disease was obtained in about 94.16%.     

An expert system based on fuzzy entropy for automatic threshold selection in image processing

In pattern recognition and image processing, the selection of appropriate threshold is a very significant issue. Especially, the selecting gray-level thresholds is a critical issue for many pattern recognition applications. Here, the maximum fuzzy entropy and fuzzy c-partition methods are used for the aim of the gray-level automatic threshold selection method. The fuzzy theory has been successfully applied to many areas, such as image processing, pattern recognition, computer vision, medicine, control, etc. The images have some fuzziness in nature. In this study, expert maximum fuzzy-Sure entropy (EMFSE) method for the maximum fuzzy entropy and fuzzy c-partition processes in automatic threshold selection is proposed. The experimental studies were conducted on many images by testing maximum fuzzy-Sure entropy against maximum fuzzy-Shannon entropy (MFSHE), maximum fuzzy-Havrada and Charvat entropy (MFHCE) methods for selecting optimum 2-level threshold value, respectively. The obtained experimental results show that the used MFSE method is superior to other MFSHE and MFHCE methods on selecting the 2-level threshold value automatically and effectively.