最新刊期
卷 41 ， 期 8 ， 2026
- 摘要：Blue-emitting quantum dots（QDs） serve as core luminescent materials for cadmium-free， environmentally benign display and healthy lighting technologies. However， their practical application is hindered by critical challenges， including inadequate color purity（full width at half-maximum， FWHM>30 nm）， low luminescence efficiency（photoluminescence quantum yield， PLQY<75%）， poor ambient air stability， and cumbersome synthetic protocols. To address these bottlenecks， this study proposes an innovative synergistic strategy integrating band engineering and surface passivation. Through the precise construction of a ZnSeTe/ZnSe/ZnSeS/ZnS gradient-composition core-shell structure coupled with halide-assisted growth， high-performance cadmium-free blue-emitting QDs were successfully synthesized. The four-layer gradient core-shell architecture enables a smooth transition of lattice constants from the core to the outer shell， thereby effectively suppressing interfacial mismatch defects and reducing non-radiative recombination losses. Meanwhile， halide ions （Cl-） selectively adsorb onto high-energy crystal facets during the crystal growth process， which not only regulates the growth kinetics to enhance particle size uniformity but also coordinates with surface unsaturated Zn2+ ions to passivate surface dangling bonds， further optimizing the photoluminescent properties of the QDs. The as-synthesized QDs exhibit an emission wavelength of 460 nm， a narrow FWHM as low as 18 nm， and a high PLQY of up to 92%. Notably， the PLQY retention rate remains above 80% even after 60 days of storage under ambient air， demonstrating excellent long-term stability. This synergistic strategy provides a universal and scalable design principle for the development of environmentally friendly， high-performance quantum dot materials for advanced optoelectronic applications.关键词：quantum dots;core-shell structure;halide regulation;blue light emission;high-end display;healthy lighting47|73|0更新时间：2026-09-16
Material Physics
- 摘要：Conventional methods cannot accurately determine the true pitch of cured twisted LCP films and often require sample destruction or prior thickness knowledge. To address this， we propose a method to simultaneously measure pitch and thickness of the LCP film， as well as helical twisting power（HTP） of its chiral dopant. First， we built a simple optical setup to obtain the experimental relative transmittance spectra， and generated theoretical curves by sweeping device parameters. Trend of variation and oscillation extent evaluation factors were introduced to retrieve the best-matching pitch-thickness combination via inversion and subsequently calculate the HTP. Twisted LCP films were prepared by doping the same LCP host with R5011（mass fraction 0.053 2%） and S5011（mass fraction 0.056 9%）. The retrieved pitches were 12.4 and 12.0 μm， thicknesses were 4.4 and 4.3 μm， and HTPs were 151.6 and 147.2 μm⁻¹， respectively. Simulated and experimental transmittance curves exhibited excellently consistency over the full inversion range. Polarimetry verification gave a discrepancy in the pitch of <0.2 μm. This method features a simple， non-destructive setup and enables rapid simultaneous inversion of pitch and thickness from a single spectrum， without prior thickness calibration. Thus， our method offers an effective tool for precise design and characterization of twisted LCP devices and accurate determination of HTP for the chiral dopants.关键词：liquid crystal;twist;pitch;polymer film43|63|0更新时间：2026-09-16
- 摘要：To address the issues of complex material systems and preparation processes in traditional liquid crystal（LC） smart windows， this study employs negative dielectric anisotropy nematic LC as the base matrix， doped with a cationic surfactant， to fabricate electrically controlled dimming smart window using ITO conductive glass without any alignment treatment. We investigated the transmittance variations of LC smart windows with different doping ratios under an alternating current（AC） electric field across the visible light spectrum. A comparative study on the dimming performance of three LC cell configurations—unaligned， parallel-aligned， and homeotropically aligned （vertical）， revealed that the unaligned LC cells outperform the other two alignment structures. The proposed LC smart window achieves reversible switching between transparent and hazy states via ion turbulence induced by the alternating electric field. Within the visible light spectrum， the optical transmittance decreases by approximately 60%. The rise and decay response times are 31.92 ms and 88.01 ms， respectively. Requiring no alignment treatment， this device features a simple structure and is facile fabrication， offering a novel solution for the industrial application of low-cost smart windows in buildings.关键词：electrically controlled smart window;negative nematic liquid crystal;surfactant;ion turbulence45|67|0更新时间：2026-09-16
Device Physics and Device Preparation
- 摘要：To address the light leakage issue at large polar angles of conventional 90°TN viewing angle controllers， this paper proposes a novel viewing angle controller（VAC） compensated with a wide‑viewing angle discotic-liquid-crystal phase retardation film. The viewing angle characteristics of the proposed structure are simulated based on the TechWiz LCD software. The proposed device consists of a TN cell and two layers of WVA compensation films， enabling electrically switchable shared mode and privacy mode. At 0 V （shared mode）， the structure delivers high transmittance across a wide viewing angle， satisfying the viewing requirements of both the driver and co-driver. When driven by voltages ranging from 2 V to 2.4 V （privacy mode）， it achieves one-way anti-peeping performance targeted at the driver. As the applied privacy voltage varies， the anti-peeping angle and optimal viewing angle change accordingly， supporting customized demands from different automotive manufacturers. The adoption of WVA compensation films significantly reduces transmittance at large polar angles and improves privacy performance. Moreover， compared with conventional TN structures compensated by negative-C films or positive-B films， the proposed structure exhibits lower driving voltage and thinner overall thickness. It presents promising application and popularization prospects for automotive cockpit display devices（CDD） and various unidirectional anti-peeping display products.关键词：TN structure;automotive display;one-way anti-peeping;WVA compensation film;light leakage suppression70|63|0更新时间：2026-09-16
- 摘要：Ambient light is a key factor leading to the degradation of the actual viewing experience of liquid crystal displays（LCDs）. To overcome the limitations of the traditional Contrast Ratio（CR） evaluation metric， this study introduces the Ambient Contrast Ratio（ACR）， aiming to more accurately assess display performance in real-world environments and to clarify its core influencing factors and optimization pathways. Under standard ambient light conditions， the ACR of six display panels utilizing different technologies was measured. By combining parameters such as native CR and reflectance（R）， the influence mechanisms of various factors on ACR were systematically analyzed. Furthermore， the enhancement effect and practical limitations of Local Dimming technology on ACR were investigated through testing on three display panels equipped with this feature. Experimental results demonstrate that under 100 lx ambient illumination， reflectance （R） is the dominant factor affecting ACR. The application of Local Dimming technology significantly reduces the black-level luminance （L0） to near zero， markedly diminishing the contribution of CR to ACR. Consequently， ACR becomes primarily determined by peak brightness （L255） and reflectance， indicating that improving ACR requires a focused effort on increasing peak brightness and reducing reflectance. ACR is a more comprehensive and scientific performance evaluation metric than CR. The key to enhancing display quality under ambient light lies in optimizing ACR， rather than solely pursuing a high CR value. Priority should be given to reducing screen reflectance to below 1.5%， coupled with the synergistic optimization of Local Dimming algorithms and an increase in peak brightness， to effectively enhance the user’s visual experience in real-world settings.关键词：ambient contrast ratio;liquid crystal display;display performance;reflectance;local dimming;peak brightness50|61|0更新时间：2026-09-16
- 摘要：Electrophoretic displays（EPDs） realize low-power and bistable reflective imaging by electrically controlling the spatial distribution of charged particles in microcapsules or microcups. The driving waveform determines the transition path from a historical state to a target grayscale or color state， and therefore strongly affects response time， ghosting， flicker， power consumption， DC balance， and reliability. In response to the insufficient cross-comparison， weak connection between evaluation indicators and engineering benchmarks， and limited discussion of color and intelligent driving challenges in existing reviews， this paper surveys representative studies published from 1998 to 2025 on driving waveforms， partial update， three-color and black-white-red e-paper， video-oriented refresh， and intelligent optimization. The literature is discussed along three lines： black-and-white EPD waveform design， three-color and black-white-red EPD waveform design， and intelligent waveform optimization. Major strategies， including reference grayscale， DC balance， multilevel voltage， high-frequency activation， damped oscillation， adaptive generation， convolutional neural networks， and dynamic programming， are compared in terms of mechanism， applicable scenarios， quantitative indicators， and engineering limitations. The analysis shows that EPD waveform design is moving from empirical rule-based tuning toward coordinated optimization combining physical constraints， experimental feedback， and algorithmic search. Temperature sensitivity， panel-size-dependent fringing fields， multi-threshold control of color particles， unified benchmarking， and controller-level deployment remain the key challenges.关键词：electrophoretic display;driving waveform;ghost suppression;three-color e-paper;intelligent optimization29|51|0更新时间：2026-09-16
- 摘要：To meet the low-temperature fabrication requirement of flexible electronics，MgZnO/ZTO TFT with aluminum zinc oxide（AZO） transparent electrodes were fabricated via a low-temperature annealing process at 300 ℃. The effects of AZO electrode thickness （110 nm， 165 nm， 220 nm， and 275 nm） on device performance were systematically investigated. The thin films were characterized by ultraviolet-visible spectrophotometry（UV-Vis）， atomic force microscopy（AFM）， scanning electron microscopy （SEM）， and X-ray photoelectron spectroscopy（XPS）.The results show that the device with an AZO electrode thickness of 165 nm exhibits the optimal electrical performance， achieving a field-effect mobility of 0.482 cm²·V-1·s-1， a threshold voltage of 6.362 V， a subthreshold swing of 3.418 V/dec， and an on/off current ratio up to the order of 107. Analysis reveals that the oxygen vacancy concentration in the thin film reaches an optimal level at the AZO thickness of 165 nm. The appropriate oxygen vacancy concentration provides sufficient carriers for the thin film to enhance electrical conductivity， which effectively guarantees the injection and transport efficiency of charge carriers.Furthermore， the positive bias stress（PBS） and negative bias stress（NBS） stability of the device were evaluated. Under a gate bias stress of ±20 V for 1 000 s， the threshold voltage shifts are only +1.99 V and -3.62 V， respectively， and the stability is slightly superior to that of conventional ITO-based devices. The final results demonstrate that the low-temperature annealed MgZnO/ZTO-TFT with a 165 nm-thick AZO electrode possesses favorable electrical properties and bias stability， providing a feasible strategy for the fabrication of low-temperature indium-free transparent TFT devices.关键词：AZO electrode;MgZnO/ZTO-TFT;low-temperature annealing;XPS;stability25|50|0更新时间：2026-09-16
Display Technology and Applications
- 摘要：To address the problems of small target scale， weak texture information， complex background interference， and obvious bounding-box localization deviation in wild environments， an improved YOLO11n-based algorithm for wild small object detection is proposed. First， an adaptive sub-pixel upsampling（ASU） module is introduced to enhance feature fusion for small objects. Through sub-pixel rearrangement and attention masks， ASU strengthens the representation of edge and texture details， thereby reducing the loss of fine-grained information during upsampling. Second， a multi-scale saliency enhancement module（MultiSEM） is introduced before the detection head. By using multi-scale dilated convolutions， the module extracts spatial features under different receptive fields， and dual-pooling channel attention is further used to enhance the response of small target regions while suppressing interference from complex backgrounds. Finally， a saliency-exponential Complete Intersection over Union loss function（SE-CIoU） is constructed for bounding-box regression. By introducing a center-distance exponential penalty and occlusion-aware shape constraints， SE-CIoU strengthens the position and shape constraints between predicted boxes and ground-truth boxes， thus improving the localization accuracy and stability of small object detection. Experimental results show that， on the self-built WDD dataset， the improved algorithm achieves a precision of 88.8%， a recall of 91.4%， and an mAP@0.5 of 94.0%， which are 0.6， 6.6， and 3.3 percentage points higher than those of the YOLO11n baseline network algorithm， respectively. Generalization experiments on the iWildCam2022-Small dataset show that the mAP@0.5 reaches 64.5%， which is 3.7 percentage points higher than that of YOLO11n. The results indicate that the proposed algorithm can effectively enhance detail representation， saliency response， and bounding-box localization ability for wild small objects while maintaining a lightweight detection framework. Therefore， the improved YOLO11n algorithm improves small object detection accuracy in complex wild scenes and shows certain generalization ability.关键词：wild small object;object detection;YOLO11n;subpixel upsampling;bounding box regression33|57|0更新时间：2026-09-16
- 摘要：To address the challenges in road disease detection， such as sample scarcity， high miss rates for weakly textured targets， and complex background interference， a road disease detection algorithm named SimCLR-YOLO is proposed to integrate unsupervised pre-training SimCLR with YOLOv8n. Firstly， the SimCLR backbone is replaced with YOLOv8n’s CSPDarknet to achieve structural alignment and seamless feature transfer， resolving the feature loss issue caused by network structure mismatch. Secondly， masked reconstruction is incorporated into contrastive learning， forcing the encoder to focus on the texture contours of cracks and potholes， thereby compensating for the under-fitting of contrastive learning to weakly textured features. Finally， a CBAM attention module is embedded to guide the model in suppressing background noise such as road texture and shadows during the pre-training phase， while enhancing feature responses in critical regions. Experimental results on the public RDD2022 dataset show that the SimCLR-YOLO algorithm achieves an mAP@0.5 of 89.4%， which is 1.7% higher than the baseline model， with a 1.1% improvement in mAP@0.5：0.95 and a 1.8% increase in F1-score. Validation under different annotation ratios demonstrates that the scheme incorporating pre-trained weights outperforms training from scratch across all data gradients. At a 50% annotation ratio， the SimCLR-YOLO algorithm still achieves an mAP@0.5 of 87%， approaching the baseline model under full annotation， significantly reducing the dependence on annotated data. Moreover， the improvements are only effective during pre-training， with no additional overhead in inference parameters （3.01M）， providing an effective technical solution for real-time road disease detection under sample-scarce conditions.关键词：road disease;YOLOv8n;SimCLR;unsupervised pre-training;low annotation dependence;pre-training25|53|0更新时间：2026-09-16
- 摘要：Surface defect detection of photovoltaic panels is of paramount importance for ensuring the power generation efficiency and operational safety of photovoltaic systems. To tackle challenges such as the difficulty in detecting small aerial targets in infrared images captured by drones， significant interference from outdoor thermal backgrounds， and the broad scale range of defect targets， this study introduces a photovoltaic infrared defect detection model that integrates multi-scale features with dynamic convolution. First， in the shallow pathway， spatial-to-depthwise convolution（SPD-Conv） is employed to substitute traditional downsampling methods， thereby preventing the loss of fine-grained features and edge information pertaining to small targets， including diode anomalies and hot spots. Second， a dynamic full-dimensional convolution（CSPOmniKernel） module is incorporated， which adaptively modulates the receptive field through a multi-branch mechanism， thereby enhancing low-contrast defect features while mitigating interference from complex thermal backgrounds. Finally， an efficient multi-scale feature fusion module（MFM） is devised， leveraging cross-level information interaction and adaptive aggregation to bolster the model’s robustness in perceiving targets of varying scales. On a photovoltaic infrared aerial dataset encompassing five typical defect types， the enhanced model attains an mAP@0.5 of 84.12%， marking a 5.16% improvement over the baseline RT-DETR-r18， and achieves an inference speed of 48 FPS， fulfilling the demands of industrial real-time inspection. The results demonstrate that this approach significantly elevates the accuracy and reliability of photovoltaic defect detection in intricate outdoor environments.关键词：photovoltaic defect detection;aerial infrared image;RT-DETR algorithm;dynamic convolution;Small target detection21|45|0更新时间：2026-09-16
- 摘要：Although salient object detection and camouflaged object detection exhibit opposite visual characteristics， both tasks impose stringent requirements on boundary segmentation accuracy. However， existing methods often suffer from limited cross-task generalization due to their reliance on task-specific feature preferences. To address this issue， we propose a universal segmentation model， SAM2-UFFM. The proposed framework adopts the Hiera backbone of SAM2 as the encoder， where the pretrained parameters are frozen and lightweight adapters are incorporated for parameter-efficient fine-tuning， thereby fully preserving the powerful feature representation capability of the foundation model. Furthermore， departing from the conventional U-Net architecture， we design an AttUNet decoder that employs full-scale skip connections to facilitate efficient multi-scale feature fusion and effectively alleviate information loss during feature propagation. In addition， a Feature Balance Attention module is introduced and integrated with a Feature Complementary Refinement module to dynamically balance global semantic information and local structural details. This design simultaneously enhances global perception for salient objects and promotes precise boundary delineation for camouflaged objects. Meanwhile， a deep supervision strategy is adopted to suppress over-segmentation and improve optimization stability. Experimental results on three public benchmark datasets demonstrate that the proposed method consistently outperforms state-of-the-art approaches. Specifically， on the COD and SOD benchmarks， the structural measure is improved by 1.02% and 1.83%， respectively， compared with the best existing methods， while the mean absolute error（MAE） is reduced to 0.035 and 0.018. Furthermore， on the steel surface defect segmentation task， the proposed model achieves a 2.77% improvement in IoU， further validating its superior cross-domain generalization capability.关键词：image segmentation;salient object;Camouflaged object;SAM2;attention mechanism21|43|0更新时间：2026-09-16
- 摘要：Vehicle re-identification aims to match the same vehicle across different camera views and is a critical task for intelligent transportation and public security surveillance. Aiming at the drawbacks of existing Vision Transformer-based vehicle re-identification methods， including insufficient mining of fine-grained local features， static side-information fusion strategies， and poor stability during multi-layer attention aggregation， this paper proposes a Vision Transformer vehicle re-identification network with adaptive side-information and residual hierarchical attention. Built upon the Vision Transformer backbone， the network embeds an Adaptive Side-information Integration module to dynamically modulate patch tokens and strengthen contextual cues， mitigating feature distribution discrepancies caused by varying cameras and viewing angles. Meanwhile， a Stable Residual Cross-layer Attention Aggregation module is devised， which embeds residual connections into multi-layer learnable attention fusion to stabilize multi-scale semantic aggregation and strengthen global feature guidance. In addition， a Multi-Head Hierarchical Adaptive Partitioning module is constructed to automatically extract local feature representations with a certain degree of spatial interpretability. Experimental results demonstrate that the proposed method reaches 83.8% mAP， 97.6% Rank-1 and 98.8% Rank-5 on the VeRi-776 dataset， and achieves Rank-1 accuracies of 80.6% and 78.6% on the Test-1600 and Test-2400 subsets of VehicleID respectively. The presented Vision Transformer model achieves remarkable accuracy in complicated vehicle re-identification scenarios， and possesses great practical application potential for large-scale intelligent transportation systems.关键词：vehicle re-identification;adaptive side-information;residual hierarchical attention;local feature learning16|43|0更新时间：2026-09-16
Image Processing
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