Characteristics of Chaotic Weak Reflection in Carbonate Fracture-Cavity Reservoirs: A Case Study of Ordovician Reservoirs in Block 10 of the Tahe Oilfield, Tarim Basin, China.

The identification of carbonate reservoirs is a critical task in oil and gas exploration. Chaotic weak reflection is an important type of reflection characteristic in carbonate reservoirs. Widespread distribution of chaotic weak reflection has been observed in the Ordovician Yijianfang Formation in the Tahe area. However, there is still a lack of systematic research on the distribution, characteristics, reservoir space, and reservoir types of chaotic weak reflection in the Tahe area. To address this issue, this study conducted a comprehensive analysis of multiscale data and found that chaotic weak reflections are distributed within 100 ms below the top interface of the Middle Ordovician. Seismic profiles exhibit a "string of pearls" with a "tail" or "pearl-like" widening feature. On well logs, all three porosity curves exhibit localized peaks, with increases in acoustic (AC) and compensated neutron (CNL) and decreases in density (DEN), while the deep and shallow lateral resistivity (RD, RS) curves show positive amplitude differences. In the FMI, they are represented by dark sinusoidal curves and dark small patches. Based on these response characteristics, two types of reservoir spaces are identified for chaotic weak reflections: high-angle vertical fractures and caves with heights less than 7 m, which can form independent reservoirs or fracture-cavity complexes. Three types of reservoirs are distinguished: fracture, cavity, and cavity-fracture types.

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.

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.