The power conversion efficiency of lead halide perovskite solar cells has been elevated to 25.2%. However, the toxicity of lead and the complex fabrication process of those cells considerably hinder the commercial application of such solar cells. Therefore, lead-free solar cells with comparable power conversion efficiency with a much lower environmental impact have recently attracted enormous attention in both academia and industry. This paper presents a theoretical study to assess the energy conversion capacity of lead-free perovskite solar cells with MASnI₃ perovskite as its absorber layer using solar cell capacitance simulator (SCAPS). In particular, the effects of materials of the perovskite solar cells’ electron transport layers (ETLs) and hole transport layers (HTLs) on their energy conversion performance are elaborated. Our results show that Cd_0.5Zn_0.5S and MASnBr₃ are the most suitable materials for ETL and HTL, respectively. It is also found from that the solar cell performance can be further enhanced through optimizing the thickness and defect density of its absorber layer. Moreover, the effects of defect densities in interface layers are investigated. In addition, the effects of ETL and HTL doping densities as well as influences of the back-contact work function and operating temperature of the tin-based perovskite solar cells are discussed. Finally, a glass substrate/FTO/Cd_0.5Zn_0.5S (ETL)/MASnI₃/MASnBr₃ (HTL)/back-contact solar cell with a power conversion efficiency of 23.86% is recommended for further optimization.

The rapid and sensitive detection of pesticide residue is essential for ensuring the food safety of consumers. However, there are many disadvantages for current approaches to detect pesticide residue, such as complex pre-treatment and low sensitivity. In this paper, we demonstrate a highly sensitive carbendazim detection method by using terahertz time-domain spectroscopy (THz-TDS) combined with metamaterial. The metamaterial composed of metal ohm ring arrays, is applied in detecting different concentrations of carbendazim. Resonant peaks of metamaterial move to a lower frequency as the concentration increases. The results illustrate that metamaterial can detect trace amounts of carbendazim, as small as 5 mg/L, which is about $10^4$ times enhancement compared to the squash method for THz-TDS detection. It also means that metamaterial combined with THz-TDS is a potential new approach for food quality and safety control.

To broaden the absorption spectrum of cells, enhance the cell stability, and avoid high costs, a novel perovskite solar cell (PSC) with the structure of fluorine-doped tin oxide (FTO)/ZnO/CsPbI₃/FAPbI₃/CuSCN/Au is designed using the solar cell capacitance simulator (SCAPS) software. The simulation results indicate that the CsPbI₃/FAPbI₃ heterojunction PSC has higher quantum efficiency (QE) characteristics than the single-junction CsPbI₃-based PSC, and it outputs a higher short-circuit current density (J_sc) and power conversion efficiency (PCE). In order to optimize the device performance, several critical device parameters, including the thickness and defect density of both the CsPbI₃ and FAPbI₃ layers, the work function of the contact electrodes, and the operating temperature are systematically investigated. Through the optimum analysis, the thicknesses of CsPbI₃ and FAPbI₃ are optimized to be 100 and 700 nm, respectively, so that the cell could absorb photons more sufficiently without an excessively high recombination rate, and the cell achieved the highest PCE. The defect densities of CsPbI₃ and FAPbI₃ are set to 10¹² cm⁻³ to effectively avoid the excessive carrier recombination centering on the cell to increase the carrier lifetime. Additionally, we found that when the work function of the metal back electrode is greater than 4.8 eV and FTO with a work function of 4.4 eV is selected as the front electrode, the excessively high Schottky barrier could be avoided and the collection of photogenerated carriers could be promoted. In addition, the operating temperature is proportional to the carrier recombination rate, and an excessively high temperature could inhibit V_oc. After implementing the optimized parameters, the cell performance of the studied solar cell was improved. Its PCE reaches 28.75%, which is higher than most of existing solar cells. Moreover, the open circuit voltage (V_oc), J_sc, and PCE are increased by 17%, 9.5%, and 25.1%, respectively. The results of this paper provide a methodology and approach for the construction of high-efficiency heterojunction PSCs.

Benefiting from the advantages of a high absorption coefficient, a long charge diffusion length, excellent carrier mobility, and a tunable bandgap, three-dimensional (3D) metal halide perovskites exhibit great potential for application in solar cells. However, 3D perovskite solar cells (PSCs) often suffer from poor long-term stability against moisture, heat, and light. To address this issue, reducing the dimension of perovskite and forming two-dimensional (2D) perovskites can be effective in slowing down the oxidation of the perovskite film and significantly improving device stability. In this study, 2D PSCs were designed with glass/FTO/TiO₂/Dion–Jacobson (DJ) perovskite/NiOx/Au structures, based on the solar cell simulation software SCAPS. The absorption layers employed in the study included PeDAMA₂Pb₃I₁₀, PeDAMA₃Pb₄I₁₃, PeDAMA₄Pb₅I₁₆, and PeDAMA₅Pb₆I₁₉. The influence of the conduction band offset (CBO) variation in the range of −0.5 to 0.5 eV on cell performance was explored through a numerical simulation. The simulation results indicate that the open-circuit voltage and fill factor continue to increase, whereas the short-circuit current density remains almost unchanged when the CBO increases from −0.5 eV to 0 eV. The devices exhibit better performance when the value of the CBO is positive and within a small range. For DJ PSCs, controlling the CBO within 0.1–0.4 eV is conducive to better cell performance.

Although perovskite solar cells have achieved excellent photoelectric conversion efficiencies, there are still some shortcomings, such as defects inside and at the interface as well as energy level dislocation, which may lead to non-radiative recombination and reduce stability. Therefore, in this study, a double electron transport layer (ETL) structure of FTO/TiO₂/ZnO/(FAPbI₃)_0.85(MAPbBr₃)_0.15/Spiro-OMeTAD is investigated and compared with single ETL structures of FTO/TiO₂/(FAPbI₃)_0.85(MAPbBr₃)_0.15/Spiro-OMeTAD and FTO/ZnO/(FAPbI₃)_0.85(MAPbBr₃)_0.15/Spiro-OMeTAD using the SCAPS-1D simulation software, with special attention paid to the defect density in the perovskite active layer, defect density at the interface between the ETL and the perovskite active layer, and temperature. Simulation results reveal that the proposed double ETL structure could effectively reduce the energy level dislocation and inhibit the non-radiative recombination. The increases in the defect density in the perovskite active layer, the defect density at the interface between the ETL and the perovskite active layer, and the temperature all facilitate carrier recombination. Compared with the single ETL structure, the double ETL structure has a higher tolerance for defect density and temperature. The simulation outcomes also confirm the possibility of preparing a stable perovskite solar cell.

Feature extraction is a key factor to detect pesticides using terahertz spectroscopy. Compared to traditional methods, deep learning is able to obtain better insights into complex data features at high levels of abstraction. However, reports about the application of deep learning in THz spectroscopy are rare. The main limitation of deep learning to analyse terahertz spectroscopy is insufficient learning samples. In this study, we proposed a WGAN-ResNet method, which combines two deep learning networks, the Wasserstein generative adversarial network (WGAN) and the residual neural network (ResNet), to detect carbendazim based on terahertz spectroscopy. The Wasserstein generative adversarial network and pretraining model technology were employed to solve the problem of insufficient learning samples for training the ResNet. The Wasserstein generative adversarial network was used for generating more new learning samples. At the same time, pretraining model technology was applied to reduce the training parameters, in order to avoid residual neural network overfitting. The results demonstrate that our proposed method achieves a 91.4% accuracy rate, which is better than those of support vector machine, k-nearest neighbor, naïve Bayes model and ensemble learning. In summary, our proposed method demonstrates the potential application of deep learning in pesticide residue detection, expanding the application of THz spectroscopy.