Infrared Physics & Technology最新文献_第2页

Adaptive water-mist infrared signature suppression for naval vessels via MPCM-constrained LSTM 基于mpcm约束的LSTM自适应水雾红外特征抑制

IF 3.4 3区物理与天体物理 Q2 INSTRUMENTS & INSTRUMENTATION

Infrared Physics & Technology

Pub Date : 2026-05-01 Epub Date: 2026-02-26 DOI: 10.1016/j.infrared.2026.106489

Chaoyi Dong , Yan Cao , Yuchen Feng , Chen Chen , Jun Yu , Ziyan Zhu , Feng Xiong

Infrared stealth is critical to the survivability and combat effectiveness of modern naval vessels, and fine water-mist spraying, as a mature and effective infrared-stealth technique, reduces ship detectability in the

8 \sim 12 μ m

infrared band through cooling and scattering. However, conventional water-mist systems lack responsiveness to dynamic environments, resulting in unstable stealth performance. To address this issue, an adaptive water-mist infrared-stealth optimisation approach integrating a multi-physics coupling model (MPCM) and a long short-term memory (LSTM) neural network is proposed in this study. First, environmental, navigational and device-level data are collected and fused to construct a unified input state; then, an MPCM is established to simulate the coupled physical processes of ship infrared radiation, temperature distribution and water-mist diffusion, thereby producing physics-constrained high-fidelity labels for training the control model; subsequently, an LSTM model is trained on historical and real-time feature windows to predict the optimal spraying parameters for the next time step; finally, background-difference-ratio-based thresholding is combined with virtual spray optimisation (VSO) to realise a dual closed-loop feedback mechanism. Experimental results indicate that, compared with non-adaptive baseline schemes, the proposed method reduces the peak infrared radiance by

18.3 %

, decreases the number of extreme hot spots by

57.1 %

, compresses the target–background temperature difference to

7.63 ° C

, and lowers the total water consumption over 13 h to

8, 075 m^{3}

. Moreover, the control system operates stably at 1 Hz with an end-to-end latency below 0.451 s, demonstrating that the method simultaneously achieves stronger suppression, reduced water consumption and real-time compliance, thereby providing a feasible route for the engineering deployment of shipborne infrared stealth.

红外隐身对现代海军舰艇的生存能力和战斗力至关重要，而细水雾喷涂作为成熟有效的红外隐身技术，通过冷却和散射降低了8 ~ 12μm红外波段的舰艇可探测性。然而，传统的水雾系统缺乏对动态环境的响应能力，导致隐身性能不稳定。为了解决这一问题，本研究提出了一种集成多物理场耦合模型（MPCM）和长短期记忆（LSTM）神经网络的自适应水雾红外隐身优化方法。首先，采集环境级、导航级和设备级数据并进行融合，构建统一的输入状态；然后，建立MPCM模型，模拟船舶红外辐射、温度分布和水雾扩散的耦合物理过程，生成物理约束的高保真标签，用于训练控制模型；然后，在历史和实时特征窗口上训练LSTM模型，预测下一时间步的最优喷涂参数；最后，将基于背景差比的阈值分割与虚拟喷雾优化（VSO）相结合，实现双闭环反馈机制。实验结果表明，与非自适应基线方案相比，该方法红外峰值辐射降低18.3%，极端热点减少57.1%，目标-背景温差压缩至7.63℃，13 h总耗水量降低至8075 m3。控制系统稳定运行在1hz，端到端时延低于0.451 s，表明该方法同时实现了更强的抑制、更低的耗水量和实时性，为舰载红外隐身的工程部署提供了一条可行的路径。

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引用次数: 0

Identification research based on polarization and Near-Infrared spectrum Dual-Mode image fusion with Multi-Stage overlapping 基于偏振与近红外光谱多阶段重叠双模图像融合的识别研究

IF 3.4 3区物理与天体物理 Q2 INSTRUMENTS & INSTRUMENTATION

Infrared Physics & Technology

Pub Date : 2026-05-01 Epub Date: 2026-02-21 DOI: 10.1016/j.infrared.2026.106485

Zhong Lv , Yong Tan , Xiaojun Yin , Shaopeng Ren , Feng Chen , Ye Zhang , Jianbo Wang , Xiaowei Sun , Haoyang Wu , Zhicheng Qin , Zhe Wang

Distinguishing camouflaged fabrics from similar backgrounds has long been a challenge for conventional imaging technologies. Polarimetric hyperspectral imaging offers a novel solution to this problem; however, the massive volume of raw images generated across multiple polarization states and spectral bands increases the complexity of target recognition. This paper proposes a multi-stage image fusion method that integrates existing techniques—Stokes vector analysis, the Normalized Difference Target Index (NDTI), Effective Guided Image Filtering (EGIF), and Retinex-Based Multiphase (RBMP)—into a novel sequential framework, combining dual-mode information from polarization and near-infrared spectra. The integration enhances the differentiation between targets and backgrounds through coordinated multi-stage feature extraction and fusion.Specifically, the method includes polarization image fusion based on the Stokes vector, background suppression using NDTI, detail enhancement through an improved EGIF algorithm, and brightness correction and target recognition via the RBMP algorithm. Validated through indoor bidirectional reflectance distribution function (BRDF) measurements and multi-angle outdoor experiments, the method effectively identifies camouflaged samples against vegetative backgrounds under various incident and imaging angles. Further testing on public datasets confirms its stability and applicability. This approach demonstrates robust performance for target recognition in grassland environments under multi-angle and varying illumination conditions. Further validation in diverse environments is needed before extending to broader scenarios.

从相似背景中区分伪装织物一直是传统成像技术面临的挑战。偏振高光谱成像为这一问题提供了一种新的解决方案；然而，在多个偏振状态和光谱波段产生的大量原始图像增加了目标识别的复杂性。本文提出了一种多阶段图像融合方法，该方法将现有的stokes矢量分析、归一化差分目标指数（NDTI）、有效制导图像滤波（EGIF）和基于RBMP的多相图像（RBMP）技术集成到一个新的序列框架中，结合偏振和近红外光谱的双模信息。通过协调多阶段特征提取和融合，增强了目标和背景的区分能力。具体包括基于Stokes矢量的偏振图像融合、基于NDTI的背景抑制、基于改进的EGIF算法的细节增强、基于RBMP算法的亮度校正和目标识别。通过室内双向反射分布函数（BRDF）测量和多角度室外实验验证，该方法在不同入射和成像角度下都能有效识别植物背景下的伪装样本。在公共数据集上的进一步测试证实了其稳定性和适用性。该方法对多角度、变光照条件下的草地目标识别具有较好的鲁棒性。在扩展到更广泛的场景之前，需要在不同的环境中进一步验证。

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引用次数: 0

Thermal imaging fault diagnosis of three-phase induction motors using neural networks 基于神经网络的三相异步电动机热成像故障诊断

IF 3.4 3区物理与天体物理 Q2 INSTRUMENTS & INSTRUMENTATION

Infrared Physics & Technology

Pub Date : 2026-05-01 Epub Date: 2026-02-26 DOI: 10.1016/j.infrared.2026.106490

Adam Glowacz

The article presents a technique for diagnosing faults in three-phase induction motors. It uses two thermal imaging cameras and a novel method called Differences of Color Thermal Images (DoCTI). Eight three-phase induction motors (TPIMs) were analyzed: four 550 W motors and four 500 W motors, under the following conditions: healthy, faulty squirrel cage ring, one broken bar, two broken bars, and three broken bars. Thermographic measurements were conducted with thermal camera vibrations ranging from 0 to 1.2 meters per second squared. A novel feature extraction method for color thermal images (DoCTI) was proposed. Three neural networks, NnetV04, NnetV05, and NnetV06, were presented. Convolutional neural networks were used to analyze the thermal images. High accuracy recognition of motor fault conditions was achieved. The computed results confirm the effectiveness of the proposed approach for the recognition of electrical faults of three-phase induction motors.

本文介绍了一种三相异步电动机故障诊断技术。它使用两台热成像仪和一种称为彩色热图像差异（DoCTI）的新方法。对8台三相感应电机（TPIMs）： 4台550 W和4台500 W，分别在鼠笼环健康、故障、1条断条、2条断条和3条断条的情况下进行了分析。热像仪的振动范围为0到1.2米/平方秒。提出了一种新的彩色热图像（DoCTI）特征提取方法。提出了NnetV04、NnetV05和NnetV06三个神经网络。采用卷积神经网络对热图像进行分析。实现了电机故障状态的高精度识别。计算结果验证了该方法对三相异步电动机电气故障识别的有效性。

引用次数: 0

Excitation of multi-channel cladding modes based on fan-shaped refractive index modulated long-period fiber gratings 扇形折射率调制长周期光纤光栅多通道包层模式的激发

IF 3.4 3区物理与天体物理 Q2 INSTRUMENTS & INSTRUMENTATION

Infrared Physics & Technology

Pub Date : 2026-05-01 Epub Date: 2026-03-02 DOI: 10.1016/j.infrared.2026.106494

Xinyi Zhao , Qingya Peng , Zuyao Liu , Fang Wang , Chengbo Mou , Yunqi Liu , Yufang Liu

We demonstrate a fan-shaped refractive-index-modulated long-period fiber grating (F-LPFG) in standard single-mode fiber, fabricated by CO₂-laser point-shaped exposure. The asymmetric, sector-shaped index perturbation enables the simultaneous coupling of the fundamental core mode to multiple cladding modes with different azimuthal orders (LP₁₄, LP₀₅, LP₁₅, and LP₀₆ mode) in a single grating. The F-LPFG was experimentally characterized for surrounding refractive index, torsion and temperature. The maximum refractive index (RI) sensitivity reaches 7,796.10 nm/RIU in the RI range of 1.445–1.457 RIU, torsional sensitivity is 0.1590 nm/(rad·m⁻¹) over −36 to + 36 rad/m, and temperature sensitivity is 71 pm/°C from 25°C to 110°C. By monitoring multiple resonances and forming a sensitivity matrix, we demonstrate simultaneous demodulation of RI, torsion and temperature. The F-LPFG combines simultaneous multi-parameter measurement capability and a simple structure, and thus offers a compact, scalable approach for multichannel and multi-parameter fiber sensing in demanding application scenarios.

我们展示了一个扇形折射率调制长周期光纤光栅（F-LPFG）在标准单模光纤，由二氧化碳激光点状曝光。不对称扇形折射率扰动使基本核心模式能够同时耦合到单个光栅中具有不同方位角阶的多个包层模式（LP14、LP05、LP15和LP06模式）。实验表征了F-LPFG的周围折射率、扭转率和温度。在1.445 ~ 1.457 RIU范围内，最大折射率灵敏度达到7796.10 nm/RIU，扭转灵敏度在- 36 ~ + 36 rad/m范围内为0.1590 nm/(rad·m−1)，温度灵敏度在25℃~ 110℃范围内为71 pm/℃。通过监测多个共振并形成灵敏度矩阵，我们演示了RI，扭转和温度的同时解调。F-LPFG结合了同时多参数测量能力和简单的结构，因此为要求苛刻的应用场景中的多通道和多参数光纤传感提供了一种紧凑、可扩展的方法。

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引用次数: 0

High-order Spatial-Frequency Interaction and Detail Compensation Network for infrared and visible image fusion 红外与可见光图像融合的高阶空频交互与细节补偿网络

IF 3.4 3区物理与天体物理 Q2 INSTRUMENTS & INSTRUMENTATION

Infrared Physics & Technology

Pub Date : 2026-05-01 Epub Date: 2026-02-03 DOI: 10.1016/j.infrared.2026.106431

Kanglin Jin, Mengtong Guo, Minghao Piao

Infrared and visible image fusion aims to generate a fused image that highlights salient targets while preserving fine textures. Existing deep learning-based methods predominantly rely on spatial-domain representations, which fail to fully capture the modality-specific frequency characteristics, leading to suboptimal texture preservation and detail enhancement. Since infrared and visible images exhibit distinct frequency distributions, relying solely on spatial-domain methods is insufficient for achieving high-quality fusion. To overcome this limitation, we propose a novel High-order Spatial-Frequency Interaction and Detail Compensation Network (HSFIDCNet), which jointly exploits spatial and frequency representations for more effective feature fusion. Specifically, the High-order Spatial-Frequency Interaction (HSFI) module enhances cross-domain feature integration, achieving a balanced fusion of global structures and local details, while the Detail Compensation (DC) module strengthens texture representation and highlights salient objects. Extensive experiments on three benchmark datasets (M³FD, LLVIP, and MSRS) against twelve state-of-the-art methods demonstrate that our approach consistently outperforms existing methods, producing fused images with higher contrast and richer textures. In particular, our method achieves the best performance across all three datasets in CC (0.5298, 0.7134, 0.6180),

Q^{A B / F}

(0.7102, 0.7326, 0.7025), MS-SSIM (0.9573, 0.9696, 0.9778), and

Q_{C V}

(478.6155, 267.7829, 203.6782), highlighting its robust and generalizable fusion capability. Code is available at https://github.com/sdat-max/HSFIDCNet.

红外和可见光图像融合的目的是产生融合图像，突出突出的目标，同时保持良好的纹理。现有的基于深度学习的方法主要依赖于空间域表示，不能完全捕获模态特定的频率特征，导致纹理保存和细节增强不理想。由于红外和可见光图像表现出不同的频率分布，仅依靠空间域方法不足以实现高质量的融合。为了克服这一限制，我们提出了一种新的高阶空间频率交互和细节补偿网络（HSFIDCNet），该网络联合利用空间和频率表示进行更有效的特征融合。其中，高阶空间-频率交互（HSFI）模块增强了跨域特征集成，实现了全局结构和局部细节的平衡融合；细节补偿（DC）模块增强了纹理表示，突出了突出目标。在三个基准数据集（M3FD， LLVIP和MSRS）上对12种最先进的方法进行了广泛的实验，结果表明我们的方法始终优于现有的方法，产生具有更高对比度和更丰富纹理的融合图像。该方法在CC（0.5298, 0.7134, 0.6180）、QAB/F（0.7102, 0.7326, 0.7025）、MS-SSIM（0.9573, 0.9696, 0.9778）和QCV（478.6155, 267.7829, 203.6782）三个数据集上均取得了最佳的融合性能，突出了其鲁棒性和泛化能力。代码可从https://github.com/sdat-max/HSFIDCNet获得。

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引用次数: 0

Drone detection network based on RGB-thermal imaging multimodal fusion 基于rgb -热成像多模态融合的无人机检测网络

IF 3.4 3区物理与天体物理 Q2 INSTRUMENTS & INSTRUMENTATION

Infrared Physics & Technology

Pub Date : 2026-05-01 Epub Date: 2026-01-30 DOI: 10.1016/j.infrared.2026.106426

Xingwei Yan , Kun Liu , Ji Li , Yan Zhang , Yaxiu Zhang , Chenchen Zhang

With the rapid proliferation of unmanned aerial vehicles, the issue of their security has gradually become a focal point of research. In infrared target detection tasks, due to the small target size, complex backgrounds, and low contrast, existing methods often rely solely on the internal features of a single modality, lacking the ability to interact with external information, which limits detection performance. To address this issue, this paper proposes a novel multi-modal image detection method, R2TNet, which can directly process misaligned RGB-T images, effectively avoiding the complexity of traditional manual registration. To achieve efficient modality alignment and fusion, this paper designs a supervised bottom-up multimodal alignment module, which adopts a coarse-to-fine layer-wise registration strategy. This effectively alleviates the modality misalignment issue in multimodal images, thereby achieving precise alignment between RGB and infrared features. On this basis, a semantic-guided module is further employed to optimize cross-modal feature fusion using high-level semantic information, significantly improving the accuracy and robustness of target detection. At the same time, a multi-scale gated dynamic fusion module is incorporated to realize fine-grained fusion of multimodal features, further enhancing the model’s adaptability in complex scenarios. Experimental results demonstrate that the proposed R2TNet significantly outperforms existing state-of-the-art bimodal detection methods across multiple evaluation metrics, including Em, Sm, Fm, and MAE, and exhibits stronger robustness and generalization capability in complex backgrounds and small target detection tasks. Moreover, comparative results with unimodal infrared detection methods further validate the advantages of the proposed method in cross-modal fusion detection.

随着无人飞行器的迅速普及，其安全问题逐渐成为研究的热点。在红外目标检测任务中，由于目标尺寸小、背景复杂、对比度低，现有方法往往只依赖于单一模态的内部特征，缺乏与外部信息交互的能力，限制了检测性能。针对这一问题，本文提出了一种新的多模态图像检测方法R2TNet，该方法可以直接处理不对齐的RGB-T图像，有效避免了传统人工配准的复杂性。为了实现高效的模态对齐和融合，设计了一种监督自底向上的多模态对齐模块，该模块采用从粗到细的分层配准策略。这有效地缓解了多模态图像中的模态不对准问题，从而实现了RGB与红外特征之间的精确对准。在此基础上，进一步采用语义引导模块利用高级语义信息优化跨模态特征融合，显著提高了目标检测的准确性和鲁棒性。同时，引入多尺度门控动态融合模块，实现多模态特征的细粒度融合，进一步增强了模型对复杂场景的适应性。实验结果表明，R2TNet在Em、Sm、Fm和MAE等多个评价指标上显著优于现有的双峰检测方法，在复杂背景和小目标检测任务中表现出更强的鲁棒性和泛化能力。与单峰红外检测方法的对比结果进一步验证了该方法在跨模态融合检测中的优势。

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引用次数: 0

Reconstruction-driven and class-balanced domain adaptation network for cross-scene hyperspectral image classification 跨场景高光谱图像分类的重构驱动类平衡域自适应网络

IF 3.4 3区物理与天体物理 Q2 INSTRUMENTS & INSTRUMENTATION

Infrared Physics & Technology

Pub Date : 2026-05-01 Epub Date: 2026-02-09 DOI: 10.1016/j.infrared.2026.106435

Chen Wang , Jun Li , Wenpeng Zhang , Quan Zhang , Zhen Liu

Cross-scene hyperspectral image (HSI) classification remains challenging due to domain shifts caused by variations in imaging conditions, atmospheric effects, and sensor characteristics. While unsupervised domain adaptation (UDA) presents a promising solution, conventional methods primarily focus on minimizing the distribution discrepancy between domains. This premature alignment strategy often neglects to first harness rich semantic information from complex HSI data, causing it to struggle with complex domain shifts and be prone to misaligning domain-specific noise with task-relevant features. This issue is further exacerbated by severe class imbalance, which biases the alignment process toward majority classes. This leads to the neglect of minority classes and an increased risk of biased alignment and minority-class degeneration. To address these challenges, a reconstruction-driven and class-balanced domain adaptation network (RBDA-Net) is proposed. Adopting a decoupled strategy, RBDA-Net first employs a self-supervised reconstruction task using a hyperspectral imaging masked autoencoder (HSI-MAE) to learn robust and domain-invariant structural representations, thus providing a noise-resilient feature foundation that mitigates negative transfer. Building upon this foundation, a class-balanced adversarial training (CBAT) module performs domain alignment while concurrently mitigating the impact of class imbalance. By integrating a bi-classifier adversarial framework with fast batch nuclear norm maximization (FBNM), RBDA-Net counteracts the imbalance-induced bias during alignment. This enhances prediction diversity and improves the discriminability of minority classes, critically requiring no prior knowledge of the target domain’s class distribution. Comprehensive experiments on three public cross-scene HSI datasets demonstrate that RBDA-Net significantly outperforms state-of-the-art UDA methods, validating its effectiveness in learning both discriminative and well-balanced representations for cross-domain HSI classification.

由于成像条件、大气效应和传感器特性的变化导致的域偏移，跨场景高光谱图像（HSI）分类仍然具有挑战性。虽然无监督域自适应（UDA）是一种很有前途的解决方案，但传统的方法主要侧重于最小化域之间的分布差异。这种过早的对齐策略通常忽略了首先利用复杂HSI数据中的丰富语义信息，导致它难以处理复杂的领域转移，并且容易将特定于领域的噪声与任务相关的特征错误地对齐。严重的类不平衡进一步加剧了这个问题，这使对齐过程偏向大多数类。这导致了对少数阶级的忽视，增加了有偏见的结盟和少数阶级退化的风险。为了解决这些问题，提出了重构驱动的类平衡域自适应网络（RBDA-Net）。采用解耦策略，RBDA-Net首先采用自监督重建任务，使用高光谱成像掩膜自编码器（HSI-MAE）来学习鲁棒和域不变的结构表示，从而提供抗噪声特征基础，减轻负迁移。在此基础上，类平衡对抗性训练（CBAT）模块执行领域对齐，同时减轻类不平衡的影响。通过将双分类器对抗框架与快速批核范数最大化（FBNM）相结合，RBDA-Net抵消了对齐过程中不平衡引起的偏差。这增强了预测的多样性，提高了少数类的可辨别性，关键是不需要对目标领域的类分布有先验知识。在三个公共跨场景HSI数据集上的综合实验表明，RBDA-Net显著优于最先进的UDA方法，验证了其在跨域HSI分类中学习判别和平衡表示的有效性。

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引用次数: 0

Beyond preprocessing and directional bias: Transformer models for robust and efficient cross-instrument NIR calibration in wheat flour analysis 超越预处理和方向偏差：在小麦粉分析中稳健和高效的跨仪器近红外校准变压器模型

IF 3.4 3区物理与天体物理 Q2 INSTRUMENTS & INSTRUMENTATION

Infrared Physics & Technology

Pub Date : 2026-05-01 Epub Date: 2026-02-06 DOI: 10.1016/j.infrared.2026.106448

Jing Liang, Hailong Feng, Yu Xue, Mingyue Huang, Bin Wang, Xiaoxuan Xu, Jing Xu

Cross-instrument variability remains a key barrier to the scalable application of near-infrared (NIR) spectroscopy in agri-food quality monitoring. This study introduces two Transformer-based calibration transfer models, Transpec and TPDS, designed to enhance spectral alignment across different instruments.

By combining global attention with localized spectral modeling, the proposed methods reduce reliance on extensive preprocessing and large paired transfer sets. Compared with classical techniques, Transpec and TPDS achieve higher predictive consistency across forward and backward transfers and demonstrate strong performance across multiple flour quality indicators. Their robustness and computational efficiency highlight their potential for real-time deployment in industrial multi-instrument environments. This work establishes a scalable framework for cross-device NIR modeling and contributes to the development of intelligent quality control systems in agricultural processing.

跨仪器可变性仍然是近红外（NIR）光谱在农业食品质量监测中可扩展应用的关键障碍。本研究介绍了两种基于变压器的校准传递模型，Transpec和TPDS，旨在增强不同仪器之间的光谱校准。通过将全局关注与局部光谱建模相结合，所提出的方法减少了对大量预处理和大型配对转移集的依赖。与传统技术相比，Transpec和TPDS在正向和反向转移上具有更高的预测一致性，并且在多个面粉质量指标上表现出较强的性能。它们的鲁棒性和计算效率突出了它们在工业多仪器环境中实时部署的潜力。这项工作为跨设备近红外建模建立了一个可扩展的框架，并有助于农业加工中智能质量控制系统的发展。

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引用次数: 0

Synergistic optimization of hardness and transmittance in a multilayer DLC/chalcogenide coating system for long-wave infrared As2Se3 windows 长波红外As2Se3窗口多层DLC/硫族化物涂层硬度和透光率协同优化

IF 3.4 3区物理与天体物理 Q2 INSTRUMENTS & INSTRUMENTATION

Infrared Physics & Technology

Pub Date : 2026-05-01 Epub Date: 2026-02-07 DOI: 10.1016/j.infrared.2026.106459

Keyi Li , Song Chen , Yimin Chen , Xiang Shen

To address the stringent requirements of long-wave infrared (LWIR) imaging systems, namely, exceptional environmental durability and high optical performance, we report the development of an advanced multifunctional anti-reflection (AR) and protective coating system on As₂Se₃ chalcogenide glass. Tailored for the 8–12 μm spectral band, the coating was fabricated via a hybrid deposition approach combining physical vapor deposition (PVD) with radio-frequency plasma-enhanced chemical vapor deposition (RF-PECVD). A key innovation lies in the design of a functionally graded transition layer based on compositionally compatible chalcogenide materials (e.g., Ge-As-Se), which simultaneously enhances interfacial adhesion and enables precise optical impedance matching through accurate thickness control. Systematic optimization of the diamond-like carbon (DLC) top layer revealed that deposition at 600 W RF power, 45 sccm C₄H₁₀ flow rate, and 10 Pa working pressure yields optimal mechanical properties, including a nano-hardness of 16 GPa and an elastic recovery parameter of 83%, a significant improvement over the bare substrate. The fully integrated coating achieves an average transmittance of 91% across the 8–12 μm range and demonstrates outstanding resilience, successfully passing a comprehensive suite of environmental stress tests (including thermal cycling, damp heat, solvent exposure, and abrasion). This work presents a robust, scalable, and technologically viable solution for high-performance AR and protective coatings on chalcogenide glasses, offering significant potential for next-generation infrared optical components in demanding environments.

为了满足长波红外（LWIR）成像系统的严格要求，即卓越的环境耐久性和高光学性能，我们在As2Se3硫系玻璃上开发了一种先进的多功能抗反射（AR）和保护涂层系统。针对8-12 μm波段，采用物理气相沉积（PVD）和射频等离子体增强化学气相沉积（RF-PECVD）相结合的混合沉积方法制备了该涂层。一个关键的创新在于基于成分相容的硫系材料（例如，Ge-As-Se）的功能梯度过渡层的设计，该过渡层同时增强了界面附着力，并通过精确的厚度控制实现了精确的光学阻抗匹配。对类金刚石（DLC）顶层的系统优化表明，在600 W射频功率、45 sccm C4H10流量和10 Pa工作压力下沉积，类金刚石（DLC）顶层的力学性能最佳，包括纳米硬度为16 GPa，弹性恢复参数为83%，比裸衬底有显著提高。完全集成的涂层在8-12 μm范围内实现了91%的平均透光率，并表现出出色的弹性，成功通过了一套全面的环境压力测试（包括热循环、湿热、溶剂暴露和磨损）。这项工作为硫系玻璃上的高性能增强现实和保护涂层提供了一个强大的、可扩展的、技术上可行的解决方案，为苛刻环境下的下一代红外光学元件提供了巨大的潜力。

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引用次数: 0

Enhanced near-infrared emission in Pr3+/ Cr3+ co-doped Ca3Sc2Ge3O12:Cr3+ phosphor for biometric illumination Pr3+/ Cr3+共掺杂Ca3Sc2Ge3O12:Cr3+荧光粉增强近红外发射，用于生物识别照明

IF 3.4 3区物理与天体物理 Q2 INSTRUMENTS & INSTRUMENTATION

Infrared Physics & Technology

Pub Date : 2026-05-01 Epub Date: 2026-03-06 DOI: 10.1016/j.infrared.2026.106495

Weiquan Zeng , Shuang Zhao , Changfu Xu , Haiyan Shi , Yan Yuan , Xiaohong Zhang , Zhaoqi Liu , Pengbo Lyu , Lizhong Sun

Near-infrared (NIR) Cr³⁺-doped phosphors are promising candidates for biometric illumination, yet their practical use is often restricted by limited luminescence efficiency. In this work, Ca₃Sc₂Ge₃O₁₂:Cr³⁺ (CSG:Cr³⁺) and Pr³⁺/Cr³⁺ co-doped Ca₃Sc₂Ge₃O₁₂ (CSG:Cr³⁺,Pr³⁺) phosphors were synthesized via a high-temperature solid-state reaction. Pr³⁺ ions acted as sensitizers and effectively enhanced the Cr³⁺ emission through energy transfer, enabling the co-doped sample to achieve an internal quantum efficiency of 56.8% and an external quantum efficiency of 7.72%. An NIR pc-LED device was further fabricated by combining the CSG:Cr³⁺,Pr³⁺ phosphor with a blue LED chip, which delivered stable photoelectrical conversion under a 100 mA driving current. The device was successfully used for iris and vein imaging, demonstrating its applicability for biometric acquisition.

近红外（NIR） Cr3+掺杂荧光粉是生物识别照明的理想选择，但其实际应用往往受到有限的发光效率的限制。通过高温固相反应合成了Ca3Sc2Ge3O12:Cr3+ (CSG:Cr3+)和Pr3+/Cr3+共掺杂的Ca3Sc2Ge3O12 （CSG:Cr3+,Pr3+）荧光粉。Pr3+离子作为敏化剂，通过能量转移有效增强了Cr3+的发射，共掺杂样品的内量子效率为56.8%，外量子效率为7.72%。将CSG:Cr3+，Pr3+荧光粉与蓝色LED芯片相结合，制备了近红外pc-LED器件，该器件在100ma驱动电流下实现了稳定的光电转换。该装置已成功用于虹膜和静脉成像，证明了其在生物特征采集方面的适用性。

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引用次数: 0