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Silicon carbide (SiC) is used in a variety of applications including photovoltaic due to its high stability. The wide bandgap of SiC limits these applications and necessitates bandgap tuning toward lower energy for efficient solar absorption. In this regard, the electronic properties of highly stable SiC polytype 6H‐SiC are studied by first‐principles calculations. By varying the Si composition of 6H‐SiC the electronic properties are tailored. The calculated results show that increasing the Si composition of 6H‐SiC from 50% to 66.66% reduces the bandgap from 2.98 to 1.54 eV. It is observed that this reduction in bandgap occurs due to the addition of extra Si s/p orbitals which close the conduction band and valance band. From the electronic conductivity calculations, it is found that with increasing Si composition of 6H‐SiC the conductivity is improved due to higher hole density which can be verified by the spectrum peaks shifted toward near‐infrared and visible region. [ABSTRACT FROM AUTHOR]

Silicon (Si) is a low‐cost, stable photovoltaic market contributor which is approaching its single‐junction theoretical efficiency limit. To further elevate the efficiency of crystalline Si (c‐Si) afterward, Si solar cells (SCs) need a proper top cell absorber in Si tandem SCs. Herein, the top cell absorber is studied by evaluating their current state of the art, the existing challenges, and possible future solutions which can enhance their efficiency. The progress of perovskite/Si tandem SC both for monolithic and four terminals is exceptional but still many challenges limit their commercialization. In view of these challenges, the relative's solutions are prescribed in detail to make possible their realization. III–V semiconductors being mature candidates still face a growth/fabrication problem, which makes them a costly option for III–V/Si tandem SCs. Effective growth strategies along with technical solutions are discussed in detail. Emerging chalcogenides are considered the future of tandem technology. Recent developments in the chalcogenides/Si tandem SC are highlighted and the possible options are presented. In the end, a short note is given on the near future tandem materials for Si tandem SCs. [ABSTRACT FROM AUTHOR]

Perovskite solar cells single-doped with Br or Cs ions have been proved to be an effective approach to improve their efficiency and stability. In our work, we took advantage of co-doping with Br and Cs. At our studied doping levels from CHNHI:PbI:CsBr = 1:1:0 ( x = 0) to 0.85:1:0.15 ( x = 0.15), CsBr doping does not introduce any detectable impurity, and the crystal grains grow larger with increasing CsBr doping level. Furthermore, when the CsBr doping level is less than x = 0.1, it can progressively enhance the optical absorption of the perovskite film, although the absorption begins to decrease when the doping level rises above x = 0.1. X-ray photoelectron spectroscopy measurements show that Br has successfully replaced I and bonds with Pb after CsBr doping. At the optimized doping level of x = 0.1, the incorporation of CsBr in the reaction system can improve the morphology of perovskite films and greatly enhance the efficiency from 9.8% for undoped sample to 13.6%, better than single Br or Cs doping. Our result shows that CsBr doping is an effective method to enhance the efficiency of perovskite solar cells. [ABSTRACT FROM AUTHOR]

The article provides information on simple designs to avoid high-voltage (HV) discharge in a silicon electron beam-heating cell. It is noted in the article that although compact electron beam evaporators (EBE) used for silicon evaporation are already commercially available, they are normally expensive and complex to maintain. To prevent HV discharge, much attention had been focused on the alumina support ring.

A combination of a semiconductor-based photosensitizer with molecular catalysts via covalent bonds is an effective way to utilize solar energy to reduce CO 2 into value-added chemicals with high efficiency and selectivity. In this study, 2,2'-bpy-5,5'-dialdehyde functioned as organic ligands and were embedded into the skeleton of g-CN through imine bonds via thermal copolymerization. The introduction of 2,2'-bpy can not only chelate with earth-abundant Co as single-site catalytic centers but also can optimize the properties of original g-CN such as the enlarged specific surface area and extended visible light absorption range. The CO evolution rate of g-CN-bpy-Co can reach up to 106.3 μmol g -1 h -1 with a selectivity of 97% over proton reduction, which was 82-fold than that of g-CN-Co. The different coordination environments and valence states of cobalt were also studied simultaneously and the results showed that Co(II) exhibited superior catalytic activity towards Co(III). Control experiments demonstrated that the covalent linkage between g-CN and Co-2,2'-bpy plays a vital role in photocatalytic activity and selectivity. Besides, the CO generation rate demonstrated linear growth upon visible light irradiation up to 72 h and preferable recyclability. This research provides a new facile way to fabricate low-priced photocatalysts with high activity and selectivity and bridge homogeneous and heterogeneous catalysis.

Imbalanced classification is widespread in the fields of medical diagnosis, biomedicine, smart city and Internet of Things. The imbalance of data distribution makes traditional classification methods more biased towards majority classes and ignores the importance of minority class. It makes the traditional classification methods ineffective in imbalanced classification. In this paper, a novel imbalance classification method based on deep learning and fuzzy support vector machine is proposed and named as DFSVM. DFSVM first uses a deep neural network to obtain an embedding representation of the data. This deep neural network is trained by using triplet loss to enhance similarities within classes and differences between classes. To alleviate the effects of imbalanced data distribution, oversampling is performed in the embedding space of the data. In this paper, we use an oversampling method based on feature and center distance, which can obtain more diverse new samples and prevent overfitting. To enhance the impact of minority class, we use a fuzzy support vector machine (FSVM) based on cost-sensitive learning as the final classifier. FSVM assigns a higher misclassification cost to minority class samples to improve the classification quality. Experiments were performed on multiple biological datasets and real-world datasets. The experimental results show that DFSVM has achieved promising classification performance., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Wang, An, Wei, Cui, Ma, Ma and Bao.)