Infrared Physics & Technology最新文献

{"title":"CTAD-Net: Cloud detection in cloud-snow coexistence scenarios using a cascaded encoder based on ResCNN and vision transformer","authors":"Junchao Feng,&nbsp;Ming Zhao,&nbsp;Xining Yu,&nbsp;Jiali Cao,&nbsp;Yuelin Yang","doi":"10.1016/j.infrared.2025.106083","DOIUrl":"10.1016/j.infrared.2025.106083","url":null,"abstract":"<div><div>Cloud detection is a crucial preprocessing step in remote sensing image analysis. Despite numerous proposed methods, identifying clouds in mixed cloud/snow scenes remains challenging due to the high spectral similarity between snow/ice and clouds, which significantly interferes with detection performance. To address this, we propose a novel network architecture that integrates a Vision Transformer (ViT) with convolutional networks in order to leverage both global context and local features to enhance spatial and semantic feature extraction for cloud detection. We further improve the encoder’s multi-scale feature representation by incorporating Atrous Spatial Pyramid Pooling (ASPP). To mitigate the loss of low-level semantic information during upsampling, we design a Multi-scale Attention Aggregation Module (MAAM) for the decoder, which effectively fuses multi-branch features for superior image reconstruction. Experimental results on a high-resolution remote sensing dataset demonstrate that our approach outperforms state-of-the-art methods in detecting clouds within mixed cloud/snow regions, achieving a mIoU of 90.81 % and an F1-Score of 91.53 %.</div></div>","PeriodicalId":13549,"journal":{"name":"Infrared Physics & Technology","volume":"151 ","pages":"Article 106083"},"PeriodicalIF":3.4,"publicationDate":"2025-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144880148","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Data fusion of visible near-infrared and mid-infrared spectroscopy combined with feature selection and machine learning for rapid discrimination of fusarium head blight infection in wheat kernel and flour","authors":"Muhammad Baraa Almoujahed,&nbsp;Rebecca L. Whetton,&nbsp;Abdul M. Mouazen","doi":"10.1016/j.infrared.2025.106072","DOIUrl":"10.1016/j.infrared.2025.106072","url":null,"abstract":"<div><div>Fusarium head blight (FHB) is a significant crop fungal disease that downgrades the yield quality and affects food safety. There is a necessity for the development of fast and cost-effective detection approaches of FHB to meet the needs of the food industry, as the traditional methods are slow, costly, difficult, and expose chemicals to the environment. Visible near-infrared (vis-NIR) and mid-infrared (MIR) spectroscopy have been used as promising tools for the detection of FHB contamination and mycotoxins in cereal crops and foods. This study explores the potential of data fusion approaches of the vis-NIR (400–1650 nm) and MIR (4000 – 650 cm⁻¹) spectra for FHB detection in wheat kernels and flour of eight varieties of wheat. Spectra concatenation and feature selection methods were utilized as input data for two different machine learning models, namely, random forest (RF), and decision tree (DT). For the selection of the most informative wavebands from both sensors, genetic algorithm (GA), recursive feature elimination (RFE), and principal component analysis (PCA), were employed. Results showed that spectral concatenation data fusion has resulted in very high test accuracy for FHB detection in both kernel and flour, with all models reaching 100% classification accuracy, except the RF-kernel model, which achieved 96.6%. Among the three feature selection algorithms, GA was the best method for the selection of the most informative bands related to FHB, resulting in a correct classification accuracy of 100 %, for both RF and DT modelling tools. For the RFE feature selection method, a lower classification accuracy of 96.6 % was obtained with both RF and DT models in kernels. However, PCA resulted in the lowest accuracies, dropping down by 10.3 % to 17.3 %, compared to that of GA and RFE, respectively. Overall, the proposed data fusion methods allow the non-destructive, rapid, and accurate detection of FHB infection in wheat flour and kernels. This is particularly useful for the flour as it is not possible to visually estimate the infected from healthy samples.</div></div>","PeriodicalId":13549,"journal":{"name":"Infrared Physics & Technology","volume":"151 ","pages":"Article 106072"},"PeriodicalIF":3.4,"publicationDate":"2025-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144852422","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Synthesis of the infrared transparent and conductive DLC coating based on PLD","authors":"Yimin Lu,&nbsp;Manman Xu,&nbsp;Yaoyu Hu,&nbsp;Lin Xi,&nbsp;Chunlai Yang","doi":"10.1016/j.infrared.2025.106069","DOIUrl":"10.1016/j.infrared.2025.106069","url":null,"abstract":"<div><div>The diamond-like carbon (DLC) layer was deposited via pulsed laser deposition (PLD) to serve as an infrared anti-reflective and protective coating for the Si substrate, leveraging its low infrared absorption and high hardness. Concurrently, the Cu electrodes were grown with mask based on its excellent conductive property. A buffer-structure of SiC / gradient Ti-Cu-Ti layer / SiC layer was designed and prepared to address the severe mismatch interface between the Cu layer and Si substrate (or DLC layer), enhancing the adhesive strength of the transparent and conductive coating. The DLC coating with Cu skeleton exhibited an average transmission of 75.2 % in the wavelength range of 3–5 μm and a square resistance of 2.7 Ω/sq, showing a relatively high figure of merit. And this coating beard the vertical force of 2.74 N/cm from the scotch tape according to National Military Standard of ‘General Specifications for Optical films (GJB 2485–95)’. This work prioritized simultaneous MIR infrared transparency and conductivity along with protective function, whereas conventional materials like ITO and Ag grid optimized visible/near-IR performance or flexibility, therefore, a direct comparison with them was challenging due to the divergent design priorities.</div></div>","PeriodicalId":13549,"journal":{"name":"Infrared Physics & Technology","volume":"151 ","pages":"Article 106069"},"PeriodicalIF":3.4,"publicationDate":"2025-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144867260","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cantilever-amplified spindle bubble microcavity for high-sensitivity and robust fiber-optic strain sensing","authors":"Jianxin Wang ,&nbsp;Weiqiang Wang ,&nbsp;Jingwei Lv ,&nbsp;Famei Wang ,&nbsp;Wei Liu ,&nbsp;Zao Yi ,&nbsp;Qiang Liu ,&nbsp;Paul K. Chu ,&nbsp;Chao Liu","doi":"10.1016/j.infrared.2025.106071","DOIUrl":"10.1016/j.infrared.2025.106071","url":null,"abstract":"<div><div>Fiber-optic Fabry-Pérot interferometric (FPI) sensors based on bubble microcavities are fundamentally limited by the sensitivity-robustness trade-off. To overcome this, we propose a spindle-shaped bubble geometry with a cladding-protruding long axis, fabricated via an improved fiber micro-shaping technique using only a commercial fusion splicer. Through parametric optimization guided by experiments and finite element simulations, we demonstrate that the protruding axis acts as a cantilever amplifier, converting axial strain (short-axis direction) into amplified displacement at the long-axis free end, thereby enhancing cavity-length modulation efficiency by 86 %. The optimized structure achieves 49.65 pm/µε strain sensitivity at 1,550 nm while withstanding bending radii ≤ 2.5 cm—surpassing Fully-embedded bubble FPIs by 32.1 % in tensile resistance and 36.2 % in bending tolerance. This innovation bridges the gap between high sensitivity and mechanical robustness, making it ideal for flexible wearables or complex wiring scenarios.</div></div>","PeriodicalId":13549,"journal":{"name":"Infrared Physics & Technology","volume":"151 ","pages":"Article 106071"},"PeriodicalIF":3.4,"publicationDate":"2025-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144828640","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Lightweight Target Omni-Directional Enhancement Network for infrared small target detection","authors":"Yichuan Li,&nbsp;Feng He,&nbsp;Qiran Zhang,&nbsp;Wei Zhang","doi":"10.1016/j.infrared.2025.106058","DOIUrl":"10.1016/j.infrared.2025.106058","url":null,"abstract":"<div><div>Due to the limited number of pixels and weak features of small targets in infrared images, detecting such targets in complex backgrounds remains a highly challenging task. It is worthwhile to explore how prior knowledge can be used to compensate for the insufficient inherent information in the original images, thereby assisting deep learning methods in learning more effectively. Inspired by human visual perception, areas with greater local changes tend to attract more attention. In infrared images, while there is some grayscale gradient at the boundary between small targets and the background, background regions also exhibit grayscale variations.To address these issues and make better use of grayscale gradient information as prior knowledge, it is necessary to distinguish the gradients around small targets from those in complex background regions. Therefore, we propose a Target Omnidirectional Enhancement Network (TODENet). The network first uses a Target Enhancement Module to focus on the inherent prior knowledge of infrared images, amplifying the grayscale gradient at the boundary between small targets and the background, while suppressing gradient variations within the background. This approach reduces clutter interference from complex backgrounds and highlights small targets within the image. Building on this, we constructed an Inter-layer Feature Fusion Module based on transposed convolution, which effectively minimizes the loss of high-frequency information of small targets during upsampling. It also makes full use of the semantic information from deep feature maps and the spatial location information from shallow feature maps. Additionally, we developed a Dilated Convolution Module that adjusts the receptive field size to filter out background clutter and then extract fine features of small targets, addressing the problem of losing small target features in the deeper layers of network. Extensive experiments show that TODENet achieves state-of-the-art performance on the NUAA-SIRST, NUDT-SIRST, and IRSTD-1k datasets, with target-level detection rates (Pd) of 97.710%, 99.649%, and 94.218%, respectively. The source code of our work is available at <span><span>https://github.com/LYC-1021/TODE-Net</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":13549,"journal":{"name":"Infrared Physics & Technology","volume":"151 ","pages":"Article 106058"},"PeriodicalIF":3.4,"publicationDate":"2025-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144841336","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Pyroelectric characteristics of lead-free materials: A systematic review","authors":"Abhinav Sharma ,&nbsp;Sanjay Dhanka ,&nbsp;Ankur Kumar ,&nbsp;Jasvir Singh Kalsi ,&nbsp;Charanjiv Gupta ,&nbsp;Ajat Shatru Arora ,&nbsp;Surita Maini","doi":"10.1016/j.infrared.2025.106064","DOIUrl":"10.1016/j.infrared.2025.106064","url":null,"abstract":"<div><div>Pyroelectric materials, that generate electrical polarization in response to temperature fluctuations, are widely used in infrared detection, energy harvesting, and biomedical applications. However, environmental and health concerns associated with traditional lead-based materials, such as PZT and PMN-PT, have opened the new window of exploring sustainable lead-free alternatives. This systematic review comprehensively examines recent advancements in lead-free pyroelectric ceramics, focusing on their structural design, performance optimization, and key challenges. Promising materials, including bismuth sodium titanate (BNT), potassium sodium niobate (KNN), and sodium bismuth titanate (NBT)-based ceramics, exhibit competitive pyroelectric coefficients (up to 2720 µC/m²K) and thermal stability. Key strategies to enhance performance are; doping, phase boundary engineering, and porosity control, which significantly improve pyroelectric figures of merit (FOMs). For instance, La-doped BNT-BNN ceramics achieve a high pyroelectric coefficient (14.3 × 10⁻⁴C/m²K) with a depolarization temperature of 174 °C, while porous BaTiO₃-SnO₂ composites demonstrate a 47 % reduction in dielectric constant, enhancing detectivity. Despite these advancements, challenges such as high dielectric loss, thermal instability, and poling inefficiency also persist with these materials. These issues can be addressed through compositional tuning and microstructure optimization, that can enable lead-free materials to surpass conventional lead-based systems. This review provides a roadmap for lead-free pyroelectric technologies, emphasizing the balance between material properties as well as practical feasibility, further focussing on scalable synthesis, thermal endurance, and integration into functional devices to realize their full commercial potential.</div></div>","PeriodicalId":13549,"journal":{"name":"Infrared Physics & Technology","volume":"151 ","pages":"Article 106064"},"PeriodicalIF":3.4,"publicationDate":"2025-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144827031","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"IRTransUNet: Efficient transformer embedding UNet for infrared small target detection","authors":"Yangjun Pi ,&nbsp;Lingchuan Kong ,&nbsp;Bo Yang ,&nbsp;Rui Chang ,&nbsp;Huayan Pu ,&nbsp;Mingliang Zhou ,&nbsp;Jun Luo","doi":"10.1016/j.infrared.2025.106061","DOIUrl":"10.1016/j.infrared.2025.106061","url":null,"abstract":"<div><div>Infrared small target detection is of critical importance in the field of security. However, the inherent weak features and low signal-to-noise ratio of such targets make it particularly difficult to detect them effectively in cluttered and complex backgrounds. To address this issue, this paper proposes IRTransUNet, which integrates local and global information to more thoroughly exploit the differences between the target and the background, thereby achieving more effective discrimination. First, we design a robust feature extractor (RFE), a lightweight and efficient module that leverages a larger contextual receptive field to extract more discriminative fine-grained features. Next, we introduce the IRconvformer module, which focuses on capturing global dependencies and modeling the relationship between the target and background. Specifically, we enhance the target boundary features within tokens using atrous spatial embedding (ASE) and replace the self-attention mechanism with multi-slice linear attention (MSLA), allowing for more efficient global modeling and focused target feature extraction. Additionally, we incorporate a convolutional gated feedforward network (CGFN) to improve the feedforward network, adjusting the information flow between neighboring pixels, thus maintaining the model’s ability to perceive local features. Finally, extensive experiments on four widely used datasets demonstrate that IRTransUNet achieves state-of-the-art performance in infrared small target detection. The code will be publicly available at <span><span>https://github.com/LingchuanK/IRTransUnet</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":13549,"journal":{"name":"Infrared Physics & Technology","volume":"151 ","pages":"Article 106061"},"PeriodicalIF":3.4,"publicationDate":"2025-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144860787","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"High-precision identification of dihydrouracil, glycine anhydride, and piperazine using terahertz spectroscopy","authors":"Shan Tu ,&nbsp;Cheng Zhang ,&nbsp;Zhongzhou Song ,&nbsp;Wentao Zhang ,&nbsp;Tao Chen ,&nbsp;Yuanpeng Li ,&nbsp;Junhui Hu ,&nbsp;Heng Xiao ,&nbsp;Xianlan Tang ,&nbsp;Yanxin Li ,&nbsp;Qilin He ,&nbsp;Senhao Pang ,&nbsp;Jingkai Su","doi":"10.1016/j.infrared.2025.106066","DOIUrl":"10.1016/j.infrared.2025.106066","url":null,"abstract":"<div><div>Distinguishing structurally similar pharmaceutical compounds remains a significant challenge in drug quality assurance, especially when these compounds share overlapping physicochemical properties. This study focuses on three such compounds: dihydrouracil (DHU), glycine anhydride (GA), and piperazine (PIP). DHU and GA are structural isomers, while PIP features a distinct heterocyclic structure, thus providing a rigorous test of the method’s specificity. Traditional analytical techniques, such as high-performance liquid chromatography (HPLC) and nuclear magnetic resonance (NMR), are often limited in rapid on-site deployment due to the need for sample pretreatment and lengthy analysis times. Recent advancements in terahertz time-domain spectroscopy (THz-TDS) have enabled sub-microgram detection and rapid spectral acquisition, making it a promising tool for automated pharmaceutical authentication. In this work, we present a THz-TDS analytical pipeline that leverages machine learning algorithms to differentiate between DHU, GA, and PIP. By employing t-distributed stochastic neighbor embedding (t-SNE) and hierarchical density-based clustering (HDBSCAN) to analyze full-spectrum multivariate patterns, we achieve a clustering accuracy of 99.38 %. This methodology offers a rapid and non-invasive approach to pharmaceutical identification, with significant implications for counterfeit detection, quality assurance, and personalized medicine. It highlights the potential of terahertz (THz) spectroscopy as a transformative tool in modern analytical chemistry.</div></div>","PeriodicalId":13549,"journal":{"name":"Infrared Physics & Technology","volume":"151 ","pages":"Article 106066"},"PeriodicalIF":3.4,"publicationDate":"2025-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144841335","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Highly sensitive quantitative detection of carbendazim residue based on terahertz metamaterial enhancement and chemometrics","authors":"Jun Hu,&nbsp;Xiaodong Mao,&nbsp;Zhikai Huang,&nbsp;Shimin Yang","doi":"10.1016/j.infrared.2025.106045","DOIUrl":"10.1016/j.infrared.2025.106045","url":null,"abstract":"<div><h3>Objective</h3><div>Carbendazim is widely used as an effective fungicide in agriculture, but its residues on crops pose potential health risks to consumers. This study aims to develop a rapid, non-destructive, and highly sensitive method for detecting carbendazim residues.</div></div><div><h3>Methods</h3><div>Based on electromagnetic theory, this paper presented a terahertz metamaterial sensor incorporating a “cross” compound double-peak structure. Terahertz transmission spectra were collected from 21 different concentration gradients of carbendazim solutions. The spectral response showed a clear decreasing trend in transmission peak amplitude with increasing concentrations. By comparing the results of data preprocessing and feature extraction, the optimal model of terahertz metamaterial detection of carbendazim residue was established.</div></div><div><h3>Result</h3><div>The related coefficient of prediction set (R_P) and root mean square error of prediction set (RMSEP) of this model are 0.9825 and 0.2001, and the Limit of Detection (LOD) is 0.672 μg/mL.</div></div><div><h3>Conclusion</h3><div>The results demonstrate the feasibility of using terahertz metamaterial sensors combined with spectral analysis for high-sensitivity, non-destructive detection of carbendazim, offering a promising approach for food safety monitoring.</div></div>","PeriodicalId":13549,"journal":{"name":"Infrared Physics & Technology","volume":"151 ","pages":"Article 106045"},"PeriodicalIF":3.4,"publicationDate":"2025-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144826344","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"SV-CGAN: Infrared image generation based on CycleGAN","authors":"Xiaopeng Zhang ,&nbsp;Shuping Tao ,&nbsp;Qinping Feng ,&nbsp;Wei Dou ,&nbsp;Haocheng Du ,&nbsp;Miao Yu ,&nbsp;Xiaojuan Tai ,&nbsp;Mingyang Gao ,&nbsp;Han Liu","doi":"10.1016/j.infrared.2025.106051","DOIUrl":"10.1016/j.infrared.2025.106051","url":null,"abstract":"<div><div>Infrared image generation holds significant application value in fields such as night vision surveillance, military reconnaissance, autonomous driving, and disaster rescue. It overcomes the imaging limitations of visible light sensors under low-light conditions, harsh weather, or complex environments, providing critical data support for all-weather perception and decision-making. However, existing deep learning-based methods for generating infrared images still face challenges, including imprecise cross-modal feature alignment and modeling bias in thermal radiation distribution, which result in generated images with blurred details, artifact noise, and weak generalization across different spectral bands, severely limiting their practical applicability. This paper proposes the SV-CGAN model, which integrates semantic segmentation with CycleGAN and employs a generator combining U-Net and an improved Vision Transformer (ViT) at the bottleneck, along with an optimized multi-task loss function. This approach enables the generation of high-quality infrared images under conditions of unpaired data. Experimental results demonstrate that SV-CGAN achieves a peak signal-to-noise ratio (PSNR) of 30.8315 dB and a structural similarity index (SSIM) of 0.8934 on the Multispectral Pedestrian Dataset (MPD), outperforming CycleGAN (PSNR 26.5260 dB, SSIM 0.8257) by 16.2 % and 8.2 %, outperforming U-GAT-IT by 4.0 % and 2.7 %, respectively. Additionally, the Fréchet Inception Distance (FID) metric decreased by 33.1 % from 114.9876 to 76.9776. The model effectively achieves visible-to-infrared image translation under unpaired training conditions, producing images with higher realism and detail retention.</div></div>","PeriodicalId":13549,"journal":{"name":"Infrared Physics & Technology","volume":"151 ","pages":"Article 106051"},"PeriodicalIF":3.4,"publicationDate":"2025-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144867146","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}