最新刊期
卷 41 ， 期 3 ， 2026
- 摘要：Liquid crystals are anisotropic soft materials that combine fluidity with long-range orientational order， exhibiting pronounced optical， electrical， and mechanical anisotropy， which underpins their widespread use in displays and optical modulation. Furthermore， their unique ordered states also make them ideal model systems for studying symmetry breaking and continuous media order. However， such order is not perfect； defects readily form during self-assembly or under external perturbation. Advances in soft-matter physics and topological field theory have shifted the perception of defects from mere structural imperfections to programmable functional units. In this review， we summarize the origins， classification， and formation mechanisms of defects in liquid crystals， with a focus on their controllable construction via boundary conditions and their emerging applications. First， we outline fundamental defect types， including disclinations and dislocations， and describe their topological characteristics. This is followed by an analysis of the relationship between orientational order and defect formation. We then discuss strategies for regulating defects from three interfacial perspectives： microstructured solid boundaries， spontaneous anchoring at free interfaces， and field-responsive soft fluid interfaces， with an emphasis on spatial and configurational control. Finally， we highlight prospective applications in topological photonics， tunable lasers， microfluidic manipulation， and colloidal assembly.关键词：liquid crystals;Topological defects;manipulation;alignment;interface575|514|0更新时间：2026-09-20
- 摘要：All-inorganic CsPbBr3 perovskite quantum dots （QDs） have demonstrated immense potential for next-generation display technologies owing to their exceptional optical properties. However， surface defects and ion migration induce environmental instability， thereby limiting their practical applications. Although polymer encapsulation can enhance stability， luminescence degradation and poor dispersion remain critical challenges for high-performance composites. In this work， an in-situ slow polycondensation strategy is proposed to construct a polyphosphazene （PZS） coating on CsPbBr3 QDs. The mild polymerization process enables uniform dispersion of QDs within the polymer matrix， while the abundant heteroatoms （N， P， and S） in the PZS effectively passivate surface Pb2+ defects through Lewis base coordination and suppress ion migration. As a result， the average photoluminescence （PL） lifetime is significantly prolonged from 7.50 ns for the pristine QDs to 20.92 ns for the composite， and the composites exhibit excellent stability under water， light， and thermal conditions. After continuous ultraviolet irradiation for 10 h， the PL intensity retention of the composites is improved by 34% compared to that of the pristine QDs. A prototype color-conversion device based on the composite exhibits a narrow-band emission centered at approximately 520 nm with CIE coordinates of （0.155 6， 0.702 9）. This study provides a facile strategy for developing highly stable perovskite-based composites and establishes a solid foundation for their application in high-performance display technologies.关键词：CsPbBr3 perovskite quantum dots;polyphosphazene;surface encapsulation;stability;color-conversion devices334|203|0更新时间：2026-09-20
Material Physics
- 摘要：To address the mismatch between the high-temperature curing required by conventional vertical alignment layers and the limited thermal tolerance of flexible transparent conductive substrates in flexible reverse-mode liquid-crystal （LC） dimming films， a low-temperature vertical alignment process and substrate compatibility screening were investigated. On glass substrates， a low-temperature vertical alignment agent was cured at 120~140 ℃ with a 5 ℃ step to evaluate the effects of curing temperature on LC pretilt angle and its uniformity， together with the variations in transmittance and haze before/after curing. A rubbing treatment was further introduced at the optimal curing temperature to improve alignment quality. The electro-optical response of the aligned LC cell was verified by voltage-transmittance （V-T） characterization， and stability was assessed via room-temperature storage and voltage cycling. Finally， under the same processing conditions， five commercial flexible transparent conductive substrates were compared in terms of sheet-resistance thermal stability and the changes in transmittance/haze after alignment-layer curing， aiming to identify compatible substrates. As the curing temperature increased from 120 ℃ to 140 ℃， the mean pretilt angle decreased from 0.69° to 0.38° and the standard deviation decreased from 0.30° to 0.04°. With rubbing at 140 ℃， the pretilt angle was further reduced to 0.19°， accompanied by a decrease in haze. The LC cell exhibited a clear threshold behavior in the V-T curve （Vth≈8.69 V； Von≈14.06 V）. After 30-day storage at room temperature， the pretilt change was ~0.02°， and electro-optical parameters remained stable after voltage cycling. Among the tested substrates， the f3-type PET/ITO showed the most stable sheet resistance after thermal treatment at 140 ℃ and maintained a high transmittance （~90%） with limited haze variation after curing， outperforming the other candidates. A low-temperature vertical alignment processing window suitable for flexible LC dimming films was established， and a compatible flexible transparent conductive substrate was identified under unified conditions， providing practical guidance for process transfer and substrate selection of flexible reverse-mode LC dimming films.关键词：reverse-mode liquid crystal dimming device;flexible transparent conductive films;low-temperature vertical alignment;pretilt angle307|495|0更新时间：2026-09-20
- 摘要：In recent years， with the development of tendency toward thinner smartphones and higher reliability （RA） requirements， the proportion of OLED panels exhibiting rainbow phenomenon around the punch-hole area has increased. In response to this phenomenon， we established qualitative and quantitative analyses methods to identify the reasons of this phenomenon and conducted a comprehensive， mutually corroborative set of multi-dimensional analytical testing methods. Furthermore， with optimization of the TFE film-stack composition， the risk of dark spots could be minimized after RA tests， which is associated with rainbow-mura formation in OLED punch-hole displays. Through the cleavage of panel module， rainbow-mura around the punch-hole area was exactly located. Cross-sectional TEM， TOF-SIMS and XPS were then used to elucidate the formation mechanism. The data show that the SiON layer in CVD1 can be gradually converted to SiO under high temperature and high humidity conditions； TEM and TOF-SIMS results reveal that nitrogen content in SiON is below 2% after RA tests， while XPS peaks indicate that the SiON on the CVD1-2 surface has been almost completely transformed into SiO. By increasing the refractive index of the SiON layer from 1.72 to 1.76 in CVD1-1 of the TFE， the oxidation depth of the SiON film was reduced by 70%. The above-mentioned establishment of analytical method not only clarifies that the rainbow-mura phenomenon around the punch-hole is caused by the formation of SiO due to film oxidation， but also quantifies the influences of the punch-hole regions under long-term RA test. This strategic approach provides a viable solution path for OLED’s future deployments in automotive， IT signage， and other applications with more stringent reliability requirements.关键词：rainbow phenomenon;punch-hole in OLED displays;thin-film encapsulation;silicon oxynitride;silicon oxide518|1935|0更新时间：2026-09-20
Device Physics and Device Preparation
- 摘要：To solve the problems of low greyscale nonlinearity， greyscale inversion and low precision in the digital driving of OLED-on-silicon microdisplay， a dual-mode fusion scanning （DMFS） algorithm is proposed in this paper. The sub-frames are divided into high bit subframe （HBSF） and low bit subframe （LBSF）， in which HBSF adopts progressive luminescence （PE） mode to ensure luminous efficiency， while LBSF adopts synchronous luminescence （SE） mode to achieve fine brightness regulation and linearity correction. Experiments on a 2 560×2 560×3@90 Hz full-color microdisplay show that DMFS significantly improves greyscale linearity， reduces greyscale inversion points of 13 872， and reduces the root mean square of luminance error of DMFS-4 and DMFS-6 by 96.63% and 99.31% compared with the 25-subfield method. Furthermore， DMFS can achieve a minimum brightness accuracy of 0.076 cd/m² and expand the effective mapping coverage range of gamma to 253 greyscale. The DMFS algorithm achieves significant advancements in display linearity and greyscale precision， effectively enhancing image detail performance and immersive visual experience， providing a feasible solution for high-precision digital driving in near-eye display devices.关键词：OLED-on-silicon microdisplay;dual-mode fusion scanning;greyscale linearity;Low greyscale accuracy316|606|0更新时间：2026-09-20
Display Technology
- 摘要：To tackle degraded vehicle detection performance caused by hardware constraints， multi-scale objects， and occlusions in autonomous driving， this paper proposes RT-DETR-light， a lightweight detection algorithm. First， we design a CG Block to enhance the backbone network， forming the lightweight feature extractor CGResNet， which balances speed and accuracy. A bidirectional feature pyramid network BiFPN is then introduced for feature fusion to improve precision via bidirectional information flow. Furthermore， an enhanced loss function， EPGIoU， is proposed to improve localization accuracy for small and occluded vehicles by stabilizing gradient optimization via multi-constraint collaboration. Experiments on the UA-DETRAC dataset show a mAP@0.5 of 75.0% and a precision of 74.5%. Compared to the baseline， it reduces parameters and computation by 26.4% and 18.0%， respectively， while improving detection speed by 1.4 percentage points. Cross-dataset evaluation on BDD100K-Sub confirms its strong generalization ability. The proposed algorithm offers superior accuracy， lightweight design， and inference speed， providing an effective solution for real-time vehicle detection and edge device deployment.关键词：deep learning;RT-DETR algorithm;lightweight;vehicle object detection557|830|0更新时间：2026-09-20
- 摘要：To address the low detection accuracy caused by the small size of traffic signs in autonomous driving scenarios and their susceptibility to environmental interference， as well as the limited computing capability and power budget of onboard platforms that make complex models difficult to deploy， an improved lightweight detector named YOLO-DyMiF （Dynamic Mixer and Feature Fusion） is proposed. The proposed model， based on YOLOv10n， introduces two major improvements. Firstly， an Efficient Dynamic Mixer Structure （EDMS） based on Adaptive Efficient Convolution （AEConv） is designed and embedded into the C3k2 module to get a new module named C3k2_EDMS， which replaces the C2f module in YOLOv10n. This design effectively reduces the parameter scale while preserving the feature representation capability of the backbone network. Secondly， a dynamic feature fusion neck network is developed with the Hierarchical Multi-scale Spatial Enhancement （HMSE） module. Through cross-layer interactions and adaptive weighted fusion， the neck enhances multi-scale feature representation， improving the detection accuracy of small traffic signs while keeping detection performance on medium and large objects. Experimental results on the TT100K dataset show that， in comparison with the state-of-the-art Mamba-YOLOt， YOLO-DyMiF improves mAP50 by 1.0%， reduces the number of parameters by 58.3%， and decreases computational cost by 42.3%. The proposed model significantly reduces the computational cost while ensuring high detection accuracy， which provides reliable technical support for traffic sign detection in autonomous driving applications.关键词：object detection;traffic signs;autonomous driving;multi-scale objects;edge computing502|400|0更新时间：2026-09-20
- 摘要：With the development of high-resolution remote sensing earth observation technology， the rich texture details in images enhance information content but also introduce complex background noise caused by lighting， shadows， and seasonal phenological differences. To address the issues of pseudo-change misdetection caused by complex background noise and the loss of small target details caused by traditional upsampling in high-resolution remote sensing change detection， a network based on feature decoupling and iterative refinement（DIR-Net） is proposed. First， the pre-trained FastSAM is utilized as a visual prior encoder to extract multi-scale robust features. Next， a Feature Subspace Decoupling Module is designed to explicitly decompose bi-temporal features into a shared semantic subspace and a differential feature subspace through orthogonal projection and cross-recalibration strategies， suppressing environmental noise from the source. Finally， an Iterative Residual Refinement Module is proposed. By introducing a coordinate attention mechanism， the decoding process is modeled as a coarse-to-fine residual regression problem， gradually recovering the edge details of small targets in the resolution-maintained feature space. Experimental results on three public datasets， LEVIR-CD， WHU-CD， and SYSU-CD， demonstrate that the F1 scores of DIR-Net reached 91.33%， 93.31%， and 86.29%， respectively. Compared with mainstream ChangeFormer and BIT algorithms， the F1 score improved by an average of approximately 5.0%. The proposed method significantly reduces the false alarm rate of pseudo-changes while maintaining a very high recall rate. This method effectively resolves the challenges of feature coupling and detail loss， demonstrating stronger robustness and higher boundary localization accuracy in complex scenes.关键词：remote sensing image change detection;feature decoupling;iterative residual refinement;deep learning;DIR-Net;high-resolution image;Small target detection288|405|0更新时间：2026-09-20
- 摘要：Methane （CH4） is the second largest anthropogenic greenhouse gas after carbon dioxide. Rapid and accurate identification of methane emission sources is of great significance for climate change monitoring. With the deepening concern over climate change issues， the application of hyperspectral remote sensing technology in methane plume monitoring has gradually become a research hotspot. However， existing studies mostly focus on large-scale methane emission events， while paying insufficient attention to dense and weak methane plumes. Weak methane plumes often fail to present complete plume shapes and have blurred boundary features in hyperspectral images， and are more susceptible to interference from multiple factors such as the atmosphere， surface， and instrument response. To address the problems of blurred boundaries and susceptibility to background noise in weak methane plumes， this paper proposes a deep learning model called LightMethaneNet （LM-Net）. LM-Net improves the existing deep learning models for methane plume detection by introducing a dynamic Gaussian module （DGMM） to enhance the spatial features of the plume and a hybrid attention mechanism to refinie features and suppressing background noise. This solves the problem that existing deep learning-based segmentation methods have difficulty balancing accuracy and efficiency when dealing with such gas plumes. The proposed method was trained and evaluated on the public dataset STARCOP， achieving 53% accuracy and 82.7% recall rate for the detection of weak plumes. Compared with classic segmentation models， the maximum improvements of LM-Net in mIoU， F1 score， and precision were 32.34%， 38.01%， and 34.57%， respectively. This method effectively improves the detection accuracy of weak methane plumes， enabling better identification of weak plumes and their suspected emission areas， and provides a feasible technical solution for high-precision methane plume remote sensing detection under complex backgrounds.关键词：methane;deep learning;weak plume detection;Hybrid attention mechanism;dynamic Gaussian mixture model356|454|0更新时间：2026-09-20
- 摘要：To overcome the impact of sky background noise on the measurement accuracy of atmospheric coherence length， this paper applies a Hartmann wavefront sensor， combined with a Gaussian background modeling algorithm to suppress and remove strong background noise， achieving the measurement of atmospheric coherence length under low signal-to-noise ratio conditions.The method proposed in this paper first enhances the calculation accuracy of the sub-aperture spot centroids of the Hartmann wavefront sensor through the Gaussian background modeling algorithm， thereby improving the accuracy of wavefront reconstruction and the calculation results of Zernike coefficients. Furthermore， based on the wavefront information， the wavefront phase variance is calculated， and according to the principle of the wavefront phase variance method， the variance of Zernike coefficients is solved， ultimately obtaining the atmospheric coherence length.Simulation results show that introducing the Gaussian background modeling algorithm in the centroid calculation stage can effectively suppress the influence of skylight background noise and improve the measurement accuracy of the atmospheric coherence length. After introducing the Gaussian background modeling algorithm， following the Zernike coefficient calculation and atmospheric coherence length measurement process， the total relative error of the atmospheric coherence length is about 2.8%.This method can achieve measurements of atmospheric coherence length under strong background noise conditions， and can be applied to various areas such as performance evaluation of daytime laser communication systems and daytime seeing evaluation of ground-based large-aperture telescopes， extending the application scope of Hartmann sensors and wavefront phase variance methods in atmospheric coherence length measurements.关键词：gaussian background modeling;atmospheric coherence length;adaptive optics;wavefront phase variance method314|299|0更新时间：2026-09-20
- 摘要：To address the limitations of existing video super-resolution methods in complex motion scenes-including inaccurate frame-to-frame alignment， insufficient utilization of temporal information， and high computational complexity of traditional attention mechanisms， this paper proposes an optical flow-guided cross-attention video super-resolution network （OFCA-Transformer）. First， a lightweight multi-scale optical flow estimation module is designed to generate multi-granularity motion information. Second， we innovatively introduce a flow-guided cross-attention mechanism. By establishing local attention windows centered on flow-predicted positions， we achieve an explicit fusion of geometric priors with implicit content awareness. This approach significantly enhances alignment accuracy while substantially reducing computational complexity. Additionally， we construct a hierarchical feature aggregation module to enable more efficient spatio-temporal feature fusion within the Transformer architecture. Our method was evaluated against other approaches on three public datasets at magnification factors of ×2， ×3， and ×4. The results demonstrate that OFCA-Transformer achieves PSNR values only 0.16 dB lower than the state-of-the-art methods across multiple datasets， while reducing model parameters by 82.8%， effectively improving computational efficiency. Furthermore， the proposed method exhibits more precise detail recovery and better temporal consistency in complex motion scenes， objectively achieving superior quantitative metrics across all magnification factors.关键词：video super-resolution;Tranformer;optical flow estimation;cross-attention;feature fusion310|880|0更新时间：2026-09-20
- 摘要：To address the issues of color distortion and severe loss of details in underwater images， an underwater image enhancement method based on the rime optimization algorithm （RIME） and multi-scale information fusion is proposed. Firstly， to tackle the problem of color distortion， an innovative adaptive gamma correction red channel compensation framework is introduced， which integrates the Dark Channel Prior （DCP） algorithm and is optimized using RIME， ensuring natural color restoration for underwater images. Secondly， the Contrast Limited Adaptive Histogram Equalization （CLAHE） method is applied to enhance image contrast. Finally， the enhanced results from the two channels are processed through a four-weight Gaussian-Laplacian pyramid and fused at multiple scales， ultimately producing images of superior visual quality. Experimental results show that the average values of UIQM， UCIQE， and SSIM achieved by this algorithm reach 4.598， 0.633， and 0.671， respectively. Additionally， comparative experiments conducted on the UIEB， RUIE， and EUVP datasets demonstrate that the proposed method significantly outperforms other comparison algorithms， confirming its superior effectiveness in improving the quality of underwater images.关键词：dark channel prior;gamma correction;rime optimization algorithm;multi-scale fusion363|655|0更新时间：2026-09-20
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Image Processing
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