Numerical Analysis of Stable (FAPbI3)0.85(MAPbBr3)0.15-Based Perovskite Solar Cell with TiO2/ZnO Double Electron Layer.

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.

Terahertz wave modulation properties of graphene with different excitation laser power.

The terahertz wave modulation properties of graphene were investigated using an external 975 nm continuous wave laser with different power. Upon excitation laser, the transmission and modulation depth was measured using terahertz time-domain spectroscopy. The experimental results showed that the modulation depth of monolayer graphene and 3-layer graphene was 16% and 32% under the 1495 mW excitation power. Further, we analyzed the graphene modulation mechanism based on the Drude model and the thin-film approximation. Both theoretical analysis and calculation results showed that the terahertz wave could be modulated using graphene with different excitation laser power.

Analysis of reaction between vitamin B6 and bovine serum albumin based on a terahertz metamaterial sensor.

A four-peak terahertz metamaterial sensor was used to detect the reaction between different concentrations of vitamin B6 (VB6) and bovine serum albumin (BSA), which achieves a concentration range (0.015-0.125 mg/µl) of VB6 and a maximum binding concentration (0.05 mg/µl) of VB6 and 0.0875 mg/µl BSA. To understand the combination of VB6 and BSA, the reactants between VB (VB1, VB3, and VB5) with the same concentration (0.05 mg/µl) and a BSA solution with a concentration of 0.0875 mg/µl were carried on the surface of the sensor. Experimental results show that the reactants cause the four resonance peaks of the sensor to produce the coincident redshift, which is the same as the order of their binding coefficients determined by the fluorescence method. The experimental process indicates that it is feasible to use terahertz metamaterials to detect the reaction process of organic matter.

A Novel Method for Carbendazim High-Sensitivity Detection Based on the Combination of Metamaterial Sensor and Machine Learning.

Benzimidazole fungicide residue in food products poses a risk to consumer health. Due to its localized electric-field enhancement and high-quality factor value, the metamaterial sensor is appropriate for applications regarding food safety detection. However, the previous detection method based on the metamaterial sensor only considered the resonance dip shift. It neglected other information contained in the spectrum. In this study, we proposed a method for highly sensitive detection of benzimidazole fungicide using a combination of a metamaterial sensor and mean shift machine learning method. The unit cell of the metamaterial sensor contained a cut wire and two split-ring resonances. Mean shift, an unsupervised machine learning method, was employed to analyze the THz spectrum. The experiment results show that our proposed method could detect carbendazim concentrations as low as 0.5 mg/L. The detection sensitivity was enhanced 200 times compared to that achieved using the metamaterial sensor only. Our present work demonstrates a potential application of combining a metamaterial sensor and mean shift in benzimidazole fungicide residue detection.

Pesticide detection combining the Wasserstein generative adversarial network and the residual neural network based on terahertz spectroscopy.

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.

Reference-Driven Undersampled MR Image Reconstruction Using Wavelet Sparsity-Constrained Deep Image Prior.

Deep learning has shown potential in significantly improving performance for undersampled magnetic resonance (MR) image reconstruction. However, one challenge for the application of deep learning to clinical scenarios is the requirement of large, high-quality patient-based datasets for network training. In this paper, we propose a novel deep learning-based method for undersampled MR image reconstruction that does not require pre-training procedure and pre-training datasets. The proposed reference-driven method using wavelet sparsity-constrained deep image prior (RWS-DIP) is based on the DIP framework and thereby reduces the dependence on datasets. Moreover, RWS-DIP explores and introduces structure and sparsity priors into network learning to improve the efficiency of learning. By employing a high-resolution reference image as the network input, RWS-DIP incorporates structural information into network. RWS-DIP also uses the wavelet sparsity to further enrich the implicit regularization of traditional DIP by formulating the training of network parameters as a constrained optimization problem, which is solved using the alternating direction method of multipliers (ADMM) algorithm. Experiments on in vivo MR scans have demonstrated that the RWS-DIP method can reconstruct MR images more accurately and preserve features and textures from undersampled k-space measurements.

Intermolecular vibrational modes and H-bond interactions in crystalline urea investigated by terahertz spectroscopy and theoretical calculation.

The characteristic absorption spectra of crystalline urea in 0.6-1.8 THz region have been measured by terahertz time-domain spectroscopy at room temperature experimentally. Five broad absorption peaks were observed at 0.69, 1.08, 1.27, 1.47 and 1.64 THz respectively. Moreover, density functional theory (DFT) calculation has been performed for the isolated urea molecule, and there is no infrared intensity in the region below 1.8 THz. This means that single molecule calculations are failure to predict the experimental spectra of urea crystals. To simulate these spectra, calculations on a cluster of seven urea molecules using M06-2X and B3LYP-D3 are performed, and we found that M06-2X perform better. The observed THz vibrational modes are assigned to bending and torsional modes related to the intermolecular H-bond interactions with the help of potential energy distribution (PED) method. Using the reduced-density-gradient (RDG) analysis, the positions and types of intermolecular H-bond interactions in urea crystals are visualized. Therefore, we can confirm that terahertz spectroscopy can be used as an effective means to detect intermolecular H-bond interactions in molecular crystals.

Application of terahertz spectroscopy and theoretical calculation in dimethylurea isomers investigation.

The characteristic absorption spectra of two structural isomers of dimethylurea(DMU) in 0.6-1.8 THz region have been measured using terahertz time-domain spectroscopy (THZ-TDS) at room temperature. Significant differences have been found between their terahertz spectra and implied that the THZ-TDS is an effective means of identifying structural isomers. To simulate their spectra, calculations on single molecule and cluster of 1,1-DMU and 1,3-DMU were performed, and we found that the cluster calculations using DFT-D3 method are better to predict the experimental spectra. Using the normal mode as displacements in redundant internal coordinates and the GaussView program, most observed THz vibrational modes are assigned to bending and rocking modes related to the intermolecular hydrogen bonding interactions, and twisting mode of ethyl groups. The different spectral features of two isomers mainly arise from different intermolecular hydrogen bonds resulting from different atom arrangements in molecules and different molecule arrangements in crystals. Using the reduced-density-gradient (RDG) analysis, the positions and types of intermolecular hydrogen bonding interactions in 1,1-DMU and 1,3-DMU crystals are visualized. Therefore, we can confirm that THz-TDS can be used as an effective means for the recognition of structural isomers and detection of intermolecular hydrogen bonding interactions in these crystals.

Quantitative determination of Auramine O by terahertz spectroscopy with 2DCOS-PLSR model.

Residues of harmful dyes such as Auramine O (AO) in herb and food products threaten the health of people. So, fast and sensitive detection techniques of the residues are needed. As a powerful tool for substance detection, terahertz (THz) spectroscopy was used for the quantitative determination of AO by combining with an improved partial least-squares regression (PLSR) model in this paper. Absorbance of herbal samples with different concentrations was obtained by THz-TDS in the band between 0.2THz and 1.6THz. We applied two-dimensional correlation spectroscopy (2DCOS) to improve the PLSR model. This method highlighted the spectral differences of different concentrations, provided a clear criterion of the input interval selection, and improved the accuracy of detection result. The experimental result indicated that the combination of the THz spectroscopy and 2DCOS-PLSR is an excellent quantitative analysis method.

[Exploration Research of Treatment Effect Improvement of Transcutaneous Electrical Nerve Stimulation Using Parameter-changing Chaotic Signal].

This article presents a transcutaneous electric stimulator that is based on chaotic signal. Firstly, we in the study used the MATLAB platform in the PC to generate chaotic signal through the chaos equation, and then we transferred the signal out by data acquisition equipment of USB-6251 manufactured by NI Company. In order to obtain high-power signal for transcutaneous electric stimulator, we used the chip of LM3886 to amplify the signal. Finally, we used the power-amplified chaotic signal to stimulate the internal nerve of human through the electrodes fixed on the skin. We obtained different stimulation effects of transcutaneous electric stimulator by changing the parameters of chaotic model. The preliminary test showed that the randomness of chaotic signals improved the applicability of electrical stimulation and the rules of chaos ensured that the stimulation was comfort. The method reported in this paper provides a new way for the design of transcutaneous electric stimulator.