In this research, the hafnium titanate oxide thin films, Ti_xHf_1–xO₂, with titanium contents of x = 0, 0.25, 0.9, and 1 were deposited on germanium substrates by atomic layer deposition (ALD) at 300 °C. The approximate deposition rates of 0.2 Å and 0.17 Å per cycle were obtained for titanium oxide and hafnium oxide, respectively. X-ray Photoelectron Spectroscopy (XPS) indicates the formation of GeO_x and germanate at the interface. X-ray diffraction (XRD) indicates that all the thin films remain amorphous for this deposition condition. The surface roughness was analyzed using an atomic force microscope (AFM) for each sample. The electrical characterization shows very low hysteresis between ramp up and ramp down of the Capacitance-Voltage (CV) and the curves are indicative of low trap densities. A relatively large leakage current is observed and the lowest leakage current among the four samples is about 1 mA/cm² at a bias of 0.5 V for a Ti_0.9Hf_0.1O₂ sample. The large leakage current is partially attributed to the deterioration of the interface between Ge and Ti_xHf_1–xO₂ caused by the oxidation source from HfO₂. Consideration of the energy band diagrams for the different materials systems also provides a possible explanation for the observed leakage current behavior.

The 4 at. % zirconium-doped zinc oxide (ZnO:Zr) films grown by atomic layer deposition (ALD) were annealed at various temperatures ranging from 350 to 950 °C. The structural, electrical, and optical properties of rapid thermal annealing (RTA) treated ZnO:Zr films have been evaluated to find out the stability limit. It was found that the grain size increased at 350 °C and decreased between 350 and 850 °C, while creeping up again at 850 °C. UV–vis characterization shows that the optical band gap shifts towards larger wavelengths. The Hall measurement shows that the resistivity almost keeps constant at low annealing temperatures, and increases rapidly after treatment at 750 °C due to the effect of both the carrier concentration and the Hall mobility. The best annealing temperature is found in the range of 350–550 °C. The ZnO:Zr film-coated glass substrates show good optical and electrical performance up to 550 °C during superstrate thin film solar cell deposition.

Aiming at the state estimation error caused by inaccurate battery model parameter estimation, a model-based state of charge (SOC) estimation method of lithium-ion battery is proposed. This method is derived from parameter identification using an adaptive genetic algorithm (AGA) and state estimation using fractional-order unscented Kalman filter (FOUKF). First, the fractional-order model is proposed to simulate the characteristics of lithium-ion batteries. Second, to tackle the problem of fixed values of probabilities of crossover and mutation in the genetic algorithm (GA) in model parameter identification, an AGA has been proposed. Then, the FOUKF method is used to assess battery SOC. For the data redundancy problem caused by the fractional-order algorithm, a time window is set to enhance the computational efficiency of the fractional-order operator. Finally, the experimental results show that the developed AGA-FOUKF algorithm can increase the correctness of SOC estimation.

Convolutional neural networks (CNN) can achieve accurate image classification, indicating the current best performance of deep learning algorithms. However, the complexity of spectral data limits the performance of many CNN models. Due to the potential redundancy and noise of the spectral data, the standard CNN model is usually unable to perform correct spectral classification. Furthermore, deeper CNN architectures also face some difficulties when other network layers are added, which hinders the network convergence and produces low classification accuracy. To alleviate these problems, we proposed a new CNN architecture specially designed for 2D spectral data. Firstly, we collected the reflectance spectra of five samples using a portable optical fiber spectrometer and converted them into 2D matrix data to adapt to the deep learning algorithms’ feature extraction. Secondly, the number of convolutional layers and pooling layers were adjusted according to the characteristics of the spectral data to enhance the feature extraction ability. Finally, the discard rate selection principle of the dropout layer was determined by visual analysis to improve the classification accuracy. Experimental results demonstrate our CNN system, which has advantages over the traditional AlexNet, Unet, and support vector machine (SVM)-based approaches in many aspects, such as easy implementation, short time, higher accuracy, and strong robustness.

Near-infrared (NIR) spectroscopy can improve the efficiency of soil property prediction, such as that of soil total nitrogen (TN) content. However, soil spectra are very sensitive to soil moisture content, which is a crucial factor affecting the accuracy of soil nutrient composition prediction. In response to this issue, the goal of this study is to identify the best model to correct the effect of soil moisture for predicting soil total nitrogen by near-infrared spectroscopy. The 107 collected soil samples were divided into six different water content (0%, 5%, 10%, 15%, 20%, and 25%) sample groups. Then, five correction methods, including direct standardization (DS), piecewise direct standardization (PDS), external parameter orthogonalization (EPO), spectral space transformation (SST), and slope/bias (S/B), were executed. Finally, partial least squares regression (PLSR) models were established to forecast TN content. The results showed that SST could minimize the influence of moisture. Furthermore, SST–PLSR had the best TN content prediction accuracy: $R_p^2$ (the coefficient of determination of the prediction set) in the range of 0.81–0.82, RMSEP (the root mean square error of the prediction set) in the range of 0.09–0.10 g/kg, and RPD (ratio of performance to deviation) in the range of 2.32–2.40. Therefore, the dry soil prediction model is competent for wet soil samples and could achieve preciseness in TN content prediction. The use of SST can effectively eliminate the influence of moisture and achieve high-precision TN prediction in wet soil samples. Additionally, the introduction of SST expands the application scope of soil nutrient prediction models and increases model robustness.

The PCHE, as an efficient heat exchanger, plays a crucial role in the storage and regasification of LNG. However, among the existing studies, those that integrate this field with deep learning are scarce. Moreover, research on explainability remains insufficient. To address these gaps, this study first constructs a dataset of heat transfer coefficients (h) through numerical simulations. Pearson correlation analysis is employed to screen out the most influential features. In terms of predictive modeling, the study compares five traditional machine learning models alongside deep learning models such as long short-term memory neural networks (LSTMs), gated recurrent units (GRUs), and Transformer. To further enhance prediction accuracy, three attention mechanisms—self-attention mechanism (SA), squeeze-and-excitation mechanism (SE), and local attention mechanism (LA)—are incorporated into the deep learning models. The experimental results demonstrate that the artificial neural network achieves the best performance among the traditional models, with a prediction accuracy for straight-path h reaching 0.891799 (R²). When comparing deep learning models augmented with attention mechanisms against the baseline models, both LSTM–SE in the linear flow channel and Transformer–LA in the hexagonal flow channel exhibit improved prediction accuracy. Notably, in predicting the heat transfer coefficient of the hexagonal channel, the determination coefficient (R²) of the Transformer–LA model reaches 0.9993, indicating excellent prediction performance. Additionally, this study introduces the SHAP interpretable analysis method to elucidate model predictions, revealing the contributions of different features to model outputs. For instance, in a straight flow channel, the hydraulic diameter (Dh) contributes most significantly to the model output, whereas in a hexagonal flow channel, wall temperature (Tinw) and heat flux (Qw) play more prominent roles. In conclusion, this study offers novel insights and methodologies for PCHE performance prediction by leveraging various machine learning and deep learning models enhanced with attention mechanisms and incorporating explainable analysis methods. These findings not only validate the efficacy of machine learning and deep learning in complex heat exchanger modeling but also provide critical theoretical support for engineering optimization.