In this study, the effects of variations in the height ($h$) and bottom radius ($r$) of black silicon microstructures on their absorptance and photoelectric response efficiency were analyzed. By using the relation $\cot {\displaystyle \frac{\theta }{2}}={\displaystyle \frac{h}{r}}$ to combine the parameters, it was found that changes in morphology affected the absorptance of black silicon microstructures, with $h$ being directly proportional to the absorptance, while $r$ was inversely proportional. A positive correlation was observed between $\cot {\displaystyle \frac{\theta }{2}}$ and absorptance. However, the correlation between $\cot {\displaystyle \frac{\theta }{2}}$ and photoelectric response efficiency was not significant. Through Raman spectroscopy analysis of the samples, it was concluded that as the laser ablation energy density increased, more lattice defects were introduced, weakening the charge carrier transport efficiency. This study further elucidated the mechanism by which microstructural changes impacted the absorptance and energy density of black silicon, providing valuable insights for optimizing its energy density.

Raman spectroscopy is a type of inelastic scattering that provides rich information about a substance based on the coupling of the energy levels of their vibrational and rotational modes with an incident light. It has been applied extensively in many fields. As there is an increasing need for the remote detection of chemicals in planetary exploration and anti-terrorism, it is urgent to develop a compact, easily transportable, and fully automated remote Raman detection system for trace detection and identification of information, with high-level confidence about the target’s composition and conformation in real-time and for real field scenarios. Here, we present an unmanned vehicle-based remote Raman system, which includes a 266 nm air-cooling passive Q-switched nanosecond pulsed laser of high-repetition frequency, a gated ICMOS, and an unmanned vehicle. This system provides good spectral signals from remote distances ranging from 3 m to 10 m for simulating realistic scenarios, such as aluminum plate, woodblock, paperboard, black cloth, and leaves, and even for detected amounts as low as 0.1 mg. Furthermore, a convolutional neural network (CNN)-based algorithm is implemented and packaged into the recognition software to achieve faster and more accurate detection and identification. This prototype offers a proof-of-concept for an unmanned vehicle with accurate remote substance detection in real-time, which can be helpful for remote detection and identification of hazardous gas, explosives, their precursors, and so forth.

Raman spectroscopy is widely used for material detection due to its specificity, but its application to spectral recognition often faces limitations due to insufficient training data, unlike fields such as image recognition. Traditional machine learning or basic neural networks are commonly used, but they have limited ability to achieve high precision. We have proposed a novel approach that combines the Triplet network (TN) and K-nearest neighbor (KNN) techniques to address this issue. TN maps the Raman spectral sequences to a 128-dimensional Euclidean space to extract features, enabling the features in the new space to more accurately represent the similarities or differences between spectra, and then utilizes the KNN algorithm to perform classification tasks in this feature space. Our method exhibits superior performance in recognizing unknown Raman spectra with minimal training samples per class. We employed a handheld Raman spectrometer with an excitation wavelength of 785 nm to collect the Raman spectra of 36 samples, including 28 safe materials and eight hazardous materials. Using only one spectrum as a support set for each category, the hazardous samples were successfully distinguished from the safe samples with an accuracy of 99.6%. Additionally, our model offers adaptability without requiring exhaustive retraining when adding new prediction classes. In situations with high background fluorescence, the TN performs better in measuring the distance between spectra of the same class than traditional distance measurement methods.

Raman spectroscopy is a type of inelastic scattering that provides rich information about a sub-stance based on the coupling of the energy levels of their vibrational and rotational modes with incident light. It has been applied extensively in many fields. As there is an increasing need for remote detection of chemicals in planetary exploration and anti-terrorism, it is urgent to develop a compact and easily transportable fully automated remote Raman detection system for trace detection and identification of information with high-level confidence about the target’s compo-sition and conformation in real-time and for real field scenarios. Here, we present an unmanned vehicle-based remote Raman system, which includes a 266 nm air-cooling passive Q-switched nanosecond pulsed laser of high-repetition frequency, a gated ICMOS, and an unmanned vehicle. This system obtains good spectral signals from remote distances ranging from 3 m to 10 m for simulating realistic scenarios, such as aluminum plate, woodblock, paperboard, black cloth, and leaves, and even for detected amounts as low as 0.1 mg. Furthermore, a CNN-based algorithm is implemented and packaged into the recognition software to achieve fast and more accurate de-tection and identification. This prototype provides a proof-of-concept for an unmanned vehicle with accurate remote substance detection in real-time, which can be helpful for remote detection and identification of hazardous gas, explosives, their precursors, and so forth.

Raman spectroscopy is widely used for material identification, but mixture analysis remains challenging due to noise and fluorescence interference. To tackle this issue, we propose the Multiplet Network, an advanced deep-learning model specifically designed for identifying components in mixtures. This model employs a shared-weight residual network to map both mixture and candidate spectra into a unified feature space, where least-squares regression is utilized to predict the components. Our framework enhances feature extraction and component identification capabilities, outperforming traditional regression methods. Experimental evaluations on the RRUFF dataset showed that our model achieved superior accuracy, especially as the number of candidate spectra increased. Furthermore, it exhibited remarkable robustness against Gaussian noise and baseline variations, maintaining high accuracy under challenging conditions. To assess the real-world applicability, the model was tested on experimentally collected mixture spectra with significant noise and baseline shifts. The results confirmed that it effectively identified major components under complex spectral conditions. Additionally, the unique structure of the model’s feature extraction combined with least squares allowed it to handle varying sizes of spectral libraries, ensuring both flexibility and scalability. Overall, our approach provides a robust and adaptable solution for Raman mixture analysis, with strong potential for complex chemical and material identification in practical applications.

Spatially offset Raman spectroscopy (SORS) enables non-destructive subsurface analysis, but traditional methods are limited by the need to manually set parameters and lack full automation. This paper presents a deep neural network (DNN)-enhanced SORS method that simplifies spectrum reconstruction. The DNN model takes only two inputs (zero and non-zero offsets) to reconstruct the pure Raman spectra of deeper layers, eliminating the influence of surface layers. Experimental results demonstrate that the DNN achieves high reconstruction accuracy (RMSE0.96) for four powders, highlighting its potential for fully automated SORS data analysis.