Search

ABSTRACT The complex molecular structures of electron donor (D)–acceptor (A) polymers provide a wealth of useful hints for producing high power conversion efficiency (PCE) as hole transport materials (HTMs) in perovskite solar cells (PVSCs). Given the recent improvements in PCE, various features are focused on altering the functionalities of HTMs. In this study, a pyrazine‐based acceptor is fused with two known donors benzodithiophene (BDT) and dithienobenzodithiophene (DTBDT) to synthesize two new D–A type polymers (NBD‐Pyz and NDT‐Pyz) to employ them as dopant‐free HTM in PVSCs. The insertion of pyrazine moiety downshifted the energy levels and enhanced coplanarity for both the HTMs. NBD‐Pyz can significantly lower the trap density and passivate the perovskite layer. More interestingly, the NBD‐Pyz HTM performs better than NDT‐Pyz, exhibiting higher hole mobility and better solubility in 2‐methyl anisole (2MA) and o‐xylene. Moreover, a 2MA/o‐xylene cosolvent‐processed dopant‐free polymeric NBD‐Pyz HTM‐based device achieved a champion PCE of 22.9%. Unlike NDT‐Pyz and Spiro‐OMeTAD‐based PVSCs, the unencapsulated NBD‐Pyz devices were more stable, retaining almost 90% of their initial efficiency after 1000 h. In addition, excellent thermal stability was demonstrated by the resulting PVSCs without encapsulation.

The junction temperature, one of the major parameters that strongly affect the performance of light-emitting diodes (LEDs), increases during operation because of the power dissipated as heat within an LED device. Therefore, LED devices with poor characteristics are expected to have higher junction temperatures for the same driving conditions. In this study, an observation contrary to this expectation is presented: a deep-ultraviolet LED device with superior electrical characteristics shows a higher junction temperature at the same input electrical power than a device with poor characteristics. A simple equivalent circuit comprising a diode, a series resistor, and shunt components is employed to elucidate this counter-intuitive observation by considering the possible heat sources inside the LED device. It is found that the junction temperature is mainly dominated by the power dissipated at the diode instead of the other possible heat sources including the Joule heating effect of the resistive components.

A new method was developed for classifying aerosol types involving a machine-learning approach to the use of satellite data. An Aerosol Robotic NETwork (AERONET)-based aerosol-type dataset was used as a target variable in a random forest (RF) model. The contributions of satellite input variables to the RF-based model were quantified to determine an optimal set of input variables. The new method, based on inputs of satellite variables, allows the classification of seven aerosol types: pure dust, dust-dominant mixed, pollution-dominant mixed aerosols, and pollution aerosols (strongly, moderately, weakly, and non-absorbing). The performance of the model was statistically evaluated using AERONET data excluded from the model training dataset. Model accuracy for classifying the seven aerosol types was 59%, improving to 72% for four types (pure dust, dust-dominant mixed, strongly absorbing, and non-absorbing). The performance of the model was evaluated against an earlier aerosol classification method based on the wavelength dependence of single-scattering albedo (SSA) and fine-mode-fraction values from AERONET. Typical wavelength dependences of SSA for individual aerosol types are consistent with those obtained for aerosol types by the new method. This study demonstrates that an RF-based model is capable of satellite aerosol classification with sensitivity to the contribution of non-spherical particles.

Drug-induced torsades de pointes (TdP), a life-threatening arrhythmia associated with prolongation of the QT interval, has been a significant reason for withdrawal of several medicines from the market. Prolongation of the QT interval is considered as the best biomarker for predicting the torsadogenic risk of a new chemical entity. Because of the difficulty assessing the risk for TdP during drug development, we evaluated the metabolic phenotype for predicting QT prolongation induced by sparfloxacin, and elucidated the metabolic pathway related to the QT prolongation. We performed electrocardiography analysis and liquid chromatography-mass spectroscopy-based metabolic profiling of plasma samples obtained from 15 guinea pigs after administration of sparfloxacin at doses of 33.3, 100, and 300 mg/kg. Principal component analysis and partial least squares modelling were conducted to select the metabolites that substantially contributed to the prediction of QT prolongation. QTc increased significantly with increasing dose (r = 0.93). From the PLS analysis, the key metabolites that showed the highest variable importance in the projection values (>1.5) were selected, identified, and used to determine the metabolic network. In particular, cytidine-5'-diphosphate (CDP), deoxycorticosterone, L-aspartic acid and stearic acid were found to be final metabolomic phenotypes for the prediction of QT prolongation. Metabolomic phenotypes for predicting drug-induced QT prolongation of sparfloxacin were developed and can be applied to cardiac toxicity screening of other drugs. In addition, this integrative pharmacometabolomic approach would serve as a good tool for predicting pharmacodynamic or toxicological effects caused by changes in dose.