The III-V compound semiconductor, which has the advantage of wide bandgap and high electron mobility, has attracted increasing interest in the optoelectronics and microelectronics field. The poor electronic properties of III-V semiconductor surfaces resulting from a high density of surface/interface states limit III-V device technology development. Various techniques have been applied to improve the surface and interface quality, which cover sulfur-passivation, plasmas-passivation, ultrathin film deposition, and so on. In this paper, recent research of the surface passivation on III-V semiconductors was reviewed and compared. It was shown that several passivation methods can lead to a perfectly clean surface, but only a few methods can be considered for actual device integration due to their effectiveness and simplicity.

This study aims to utilize hyperspectral imaging technology combined with machine learning methods for the authenticity identification and classification of Anoectochilus roxburghii and its counterfeit species. Hyperspectral data were collected from the front and back leaves of nine species of Goldthread and two counterfeit species (Bloodleaf and Spotted-leaf), followed by classification using a variety of machine learning models, including Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Random Forest (RF), Linear Discriminant Analysis (LDA), and Convolutional Neural Networks (CNN). The experimental results demonstrated that the SVM model achieved 100% classification accuracy for distinguishing Goldthread from its counterfeit species, effectively capturing the spectral differences between the front and back leaves. In contrast, traditional machine learning models showed varied performance, with SVM proving superior due to its ability to handle high-dimensional feature spaces. The introduction of a multi-view spectral fusion CNN model, which integrates spectral data from both the front and back leaves, further enhanced classification accuracy, achieving a perfect classification rate of 100%. This approach highlights the potential of hyperspectral imaging and machine learning in plant authenticity identification and provides a new perspective for the detection of counterfeit species.

In this study the effects of 1-Octadecanethiol (ODT, 1-CH₃ [CH₂]₁₇SH) passivation on GaAs (100) surface and GaAs/Al₂O₃ MOS capacitors are investigated. The results measured by X-ray photoelectric spectroscopy (XPS), Raman spectroscopy and scan electron microscopy (SEM) show that the ODT passivation can obviously suppress the formation of As-O bonds and Ga-O bonds on the GaAs surface and produce good surface morphology at the same time, and especially provide better protection against environmental degradation for at least 24 h. The passivation time is optimized by photoluminescence (PL), and the maximum enhancement of PL intensity was 116%. Finally, electrical property of a lower leakage current was measured using the metal-oxide-semiconductor capacitor (MOSCAP) method. The results confirm the effectiveness of ODT passivation on GaAs (100) surface.