Infrared Physics & Technology最新文献

Microplastic detection has been acknowledged as challenging so far. Despite advancements in rapid detection methods for analyzing environmental microplastics, limited research has been conducted on detecting microplastics in food substrates. The objective of this study was to investigate the feasibility of utilizing Fourier near-infrared (FT-NIR) spectroscopy optimized characteristic model for quantitative detection of polystyrene (PS) microplastics in flour. A Fourier transform infrared spectrometer was employed to gather spectral information on flour with varying concentrations of PS. Four variable selection methods, namely iterative variable subset optimization (IVSO), bootstrapping soft shrinkage (BOSS), Interval variable iterative space shrinkage approach (IVISSA), and variable-dimensional particle swarm optimization movement window (VDPSO-CMW), were introduced to select features from the preprocessed near-infrared spectrum. Detection models based on partial least squares (PLS) were constructed with the aim of achieving quantitative detection of PS in flour, and comparisons were conducted to evaluate the detection performance of the four models. The VDPSO-CMW-PLS model demonstrates the highest level of generalization performance, according to the research findings. The coefficient of determination ($R_p^2$) is 0.9810, the root mean square error of prediction (RMSEP) is 0.0462%, and the relative percent deviation (RPD) is 7.3890. The research findings indicate that the constructed PLS detection model, utilizing FT-NIR spectral optimization characteristics, can rapidly and accurately detect PS in flour. This study presents a novel technical approach for the prompt quantitative identification of microplastics in food.

The fusion of infrared and visible images aims to synthesize a fused image that incorporates richer information by leveraging the distinct characteristics of each modality. However, the disparate quality of input images in terms of infrared and visible light significantly impacts fusion performance. To address this issue, we propose a novel deep adaptive fusion method called Adaptive FusionNet for Illumination-Robust Feature Extraction (AFIRE). This method involves the interactive processing of two input features and dynamically adjusts the fusion weights based on varying illumination conditions. Specifically, we introduce a novel interactive extraction structure during the feature extraction stage for both infrared and visible light, enabling the capture of more complementary information. Additionally, we design a Deep Adaptive Fusion module to assess the quality of input features and perform weighted fusion through a channel attention mechanism. Finally, a new loss function is formulated by incorporating the entropy and median of input images to guide the training of the fusion network. Extensive experiments demonstrate that AFIRE outperforms state-of-the-art methods in preserving pixel intensity distribution and texture details. Source code is available at: https://www.github.com/ISCLab-Bistu/AFIRE.

Mapping urbanization and built-up areas from remotely sensed data poses a formidable challenge, particularly when the spectral reflectance of built-up regions intersects with other land types. To address this, numerous spectral indices have been developed. This paper utilizes multiple indices: Normalized Difference Built-up Index (NDBI), Built-up Area Extraction Index (BAEI), Normalized Built-up Area Index (NBAI), New Built-up Index (NBI), Modified Built-up Index (MBI), Band Ratio for Built-up Area (BRBA), and Normalized Difference Vegetation Index (NDVI) to delineate built-up regions. An intersection approach between BAEI, NBAI, and NDVI refines the methodology, resulting in a final built-up map with 92.5 % accuracy and a 0.848 Kappa coefficient. Subsequently, a Deep Neural Network (DNN) model trained on this map achieves over 95 % accuracy in predicting built-up areas from Landsat 5 imagery, and the resultant built-up map achieved an overall accuracy of 92 % and a Kappa coefficient of 0.85. The proposed methodology demonstrates efficiency for time-series analysis and addresses misclassification in built-up areas. Moreover, the optimization of the DNN model proves effective when meticulous training and validation processes incorporate more precise sample datasets.

The long-wave infrared spectrum’s unique advantages in molecular fingerprinting and atmospheric transmission have led to its widespread application in detection, identification, medical diagnosis, and environmental monitoring. Consequently, there has been a strong focus on developing multispectral structure arrays with high transmission efficiency. In this study, we introduce a high-transmission-efficiency nanocoaxes field enhancement structures array based on complex nanogap engineering, exhibiting a transmission of 21.59 % within an open area of 3 % and accompanied by a 90-fold enhancement of the nanogap field. With the modulation of nanogaps, the absolute transmission can exceed 60 %. The experimental data and finite element simulation results of the spectrally tunable array confirm that the origin of resonance is attributed to the enhanced localized electromagnetic modes supported by nanogaps. Furthermore, we demonstrated the proposed nanocoaxes field-enhancement structures in practical applications such as molecular resonance absorption enhancement and material composition analysis. Our work not only provides a method for on-chip multispectral tuning in the long-wave infrared range but also contributes to further advancing the development of long-wave infrared nanophotonic structures.

In diagnostic applications based on tunable diode laser absorption spectroscopy, the measurement of target substances can be influenced by factors such as background thermal radiation in the combustion environment, extinction caused by solid or liquid particles, and other interfering absorptions. In this work, we developed a differential absorption diagnostic technique based on wavelength pairs, utilizing an interband cascade laser near 3.3 μm to simultaneously measure temperature and C₂H₄ concentration in hydrocarbon flames. Based on a detailed study of the C₂H₄ spectrum in this region and considering the optimal standard for spectral lines, two wavelength pairs were selected. The temperature is determined by the ratio of the absorption cross-sections of two wavelength pairs, and the C₂H₄ concentration is inferred based on the wavelength pair with higher differential absorption. In the initial stage, the system’s accuracy was verified in high-temperature static conditions (T = 300–800 K, P = 1 atm), and continuous time series measurements demonstrated the system’s stability. The limit of detection achieved by Allan-Werle variance analysis is 2.5 ppm at the optimal average time of 100 s. Subsequently, measurements were taken in a hydrocarbon flame. The obtained results indicate an average deviation of 1.021 % between the measured temperature in the flame and the reference value, with a standard deviation of 1.381 % for concentration measurement. All the measurements show that the system can be potentially applied to combustion diagnosis.