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Covalent organic frameworks (COFs) are a class of crystalline porous organic polymers with polygonal porosity and highly ordered structures. The most prominent feature of the COFs is their excellent crystallinity and highly ordered modifiable one-dimensional pores. Since the first report of them in 2005, COFs with various structures were successfully synthesized and their applications in a wide range of fields including gas storage, pollution removal, catalysis, and optoelectronics explored. In the meantime, COFs also exhibited good performance in chemical and biological sensing, because their highly ordered modifiable pores allowed the selective adsorption of the analytes, and the interaction between the analytes and the COFs’ skeletons may lead to a detectable change in the optical or electrical properties of the COFs. In this review, we firstly demonstrate the basic principles of COFs-based chemical and biological sensing, then briefly summarize the applications of COFs in sensing some substances of practical value, including some gases, ions, organic compounds, and biomolecules. Finally, we discuss the trends and the challenges of COFs-based chemical and biological sensing.

The growing demand for enhanced computational power and energy efficiency has driven the development of optical Ising machines for solving combinatorial optimization problems. However, existing implementations face challenges in integration density and energy efficiency. Here, we propose a monolithically integrated four-spin Ising machine based on optoelectronic coupled oscillators. This system integrates a custom-designed Mach-Zehnder interferometer (MZI) symmetric matrix with a high-efficiency optical-electrical coupled (OEC) nonlinear unit. The OEC unit has an ultra-compact 0.01 mm² footprint and achieves a power efficiency of 4 mW per unit, ensuring scalability. The reconfigurable real-valued coupling matrix achieves a mean fidelity of 0.986. The spin evolution time is measured as 150 ns, with a 1.71 ns round-trip time confirmed through bandwidth measurements. The system successfully finds ground states for various four-spin Ising problems, demonstrating its effectiveness. This work represents a significant step toward monolithic integration of all-optical physical annealing systems, minimizing footprint, power consumption, and convergence time. Researchers demonstrate a monolithically integrated four-spin optical Ising machine using optoelectronic coupled oscillators. The system achieves high efficiency (4 mW per unit), fast spin evolution (150 ns), and reconfigurable coupling, advancing scalable optical computing.

Edge computing requires real-time processing of high-throughput analog signals, posing a major challenge to conventional electronics. Although integrated photonics offers low-latency processing, it struggles to directly handle raw analog data. Here, we present a photonic edge intelligence chip (PEIC) that fuses multiple analog modalities—images, spectra, and radio-frequency signals—into broad optical spectra for single-fiber input. After transmission onto the chip, these spectral inputs are processed by an arrayed waveguide grating (AWG) that performs both spectral sensing and energy-efficient convolution (29 fJ/OP). A subsequent nonlinear activation layer and a fully connected layer form an end-to-end optical neural network, achieving on-chip inference with a measured response time of 1.33 ns. We demonstrate both supervised and unsupervised learning on three tasks: drug spectral recognition, image classification, and radar target classification. Our work paves the way for on-chip solutions that unify analog signal acquisition and optical computation for edge intelligence. Edge devices require real-time processing of high-throughput analog signals. Here, authors present a photonic intelligence chip that fuses multiple analog signal types into optical spectra for ultra-fast, energy-efficient on-chip AI computation, enabling diverse edge intelligence applications.

Optical neural networks (ONNs) leverage the parallelism and low-energy consumption of photonic signal processing to overcome the limitations of traditional electronic computing. Optics inherently enables fan-in and fan-out without the Resistor-Capacitor (RC) and Inductor-Capacitor (LC) delays of electrical interconnects. However, for single-mode photonic integrated circuits, reciprocity constraints introduce unavoidable loss during beam combining, hindering large-scale on-chip photonic fan-in. To overcome this challenge, we provide a photonic lossless mode-division fan-in solution for the convolution accelerators. Using inverse design, we developed a compact multimode photonic convolution accelerator (0.42 mm 2 ) with ±15 nm fabrication tolerance and 35 nm optical bandwidth, enabling parallel computation across mode and wavelength dimensions. Experimental results in the C-band confirm a 6–7 bit convolution precision, leading to classification accuracies of 95.2% on MNIST and 87.9% on Fashion-MNIST. Moreover, the device offers a theoretical computational density of 125.14 TOPS/mm 2 , underscoring its potential for scalable and energy-efficient photonic computing accelerators. To solve on-chip beam combining losses in optical neural networks, the authors introduce a multimode photonic convolver via lossless mode division fan-in. With mode and wavelength multiplexing, it achieves 6–7 bit precision and a computational density of 125 TOPS/mm 2 .

Real-time, physically realistic rendering is a significant challenge in spatial computing systems due to the excessive computational intensity of ray tracing and the performance limitations of current electronic platform. Here, we propose and demonstrate the first photonic counterpart for ray tracing acceleration, capable of performing ray-box intersection tests in the optical domain. Leveraging the high bandwidth, high linearity, and superior efficiency of thin-film lithium niobate (TFLN), our photonic ray tracing core (PRTC) achieves significantly more rapid and energy-efficient computation compared to traditional electronic hardware. Furthermore, by exploiting the binary nature of ray-box intersection tests, we reduce the analog-to-digital converter (ADC) bit-width requirement to a single bit, effectively overcoming the primary bottleneck in analog computing accelerators—the power consumption dominated by ADCs. As a result, our PRTC achieves an energy efficiency of 326 femtojoules per operation (fJ/OP) and demonstrates a modulator bandwidth exceeding 100 GHz. This advancement achieves significant improvements in both speed and energy efficiency by orders of magnitude. Our work demonstrates the feasibility of using photonic chips for ray tracing, effectively circumventing the ADC bottleneck of optical computing systems, and paves the way for future innovations in high-performance, low-power spatial computing applications. Real-time ray tracing faces significant computational hurdles on electronic platforms. Here, authors present the first thin-film lithium niobate photonic circuit for ray tracing acceleration, enabling rapid, energy-efficient ray-box tests with a 1-bit ADC, achieving 326 fJ/OP and over 100 GHz bandwidth.

Computing with light is widely recognized as a promising paradigm for overcoming the energy and latency limitations of electronic computing. However, the energy consumption and latency in current optical computing hardware predominantly arise in the electrical domain rather than the optical domain, primarily due to frequent signal conversions between optical (analog) and electrical (digital) formats. Furthermore, as the operating frequency of optical computing surpasses the GHz range, the synchronization of parallel electrical signals and the management of optical delays become increasingly critical. These challenges exacerbate energy consumption and latency, particularly in recurrent optical operations. To address these limitations, we propose a novel asynchronous computing paradigm for on-chip optical recurrent accelerators based on wavelength encoding, effectively mitigating synchronization challenges. By leveraging the intrinsic causality of wavelength relay, our approach eliminates the need for rigorous temporal alignment. To demonstrate the flexibility and efficacy of this asynchronous paradigm, we present two advanced recurrent models—an optical hidden Markov model and an optical recurrent neural network—monolithically integrated for the first time. These models incorporate hundreds of linear and nonlinear computing units densely packed into a compact footprint of just 10 mm 2 . Experimental evaluations on various benchmark tasks underscore the superior energy efficiency and low latency of the proposed asynchronous optical accelerators. This innovation enables the efficient processing of large-scale parallel signals and positions optical processors as a pivotal technology for applications such as autonomous driving and intelligent robotics.

Purpose This study evaluated and compared the clinical support capabilities of ChatGPT 4o and ChatGPT 4o mini in diagnosing and treating lumbar disc herniation (LDH) with radiculopathy. Methods Twenty-one questions (across 5 categories) from NASS Clinical Guidelines were input into ChatGPT 4o and ChatGPT 4o mini. Five orthopedic surgeons assessed their responses using a 5-point Likert scale for accuracy and completeness, and a 7-point scale for reliability. Flesch Reading Ease scores were calculated to assess readability. Additionally, ChatGPT 4o analyzed lumbar images from 53 patients, comparing its recognizable agreement with orthopedic surgeons using Kappa values. Results Both models demonstrated strong clinical support capabilities with no significant differences in accuracy or reliability. However, ChatGPT 4o provided more comprehensive and consistent responses. The Flesch Reading Ease scores for both models indicated that their generated content was “very difficult to read,” potentially limiting patient accessibility. In evaluating lumbar disc herniation images, ChatGPT 4o achieved an overall accuracy of 0.81, with LDH recognition precision, recall, and F1 scores exceeding 0.80. The AUC was 0.80, and the Kappa value was 0.61, indicating moderate agreement between the model’s predictions and actual diagnoses, though with room for improvement. Conclusion While both models are effective, ChatGPT 4o offers more comprehensive clinical responses, making it more suitable for high-integrity medical tasks. However, the difficulty in reading AI-generated content and occasional use of misleading terms, such as “tumor,” indicate a need for further improvements to reduce patient anxiety.

Optical neural networks (ONNs) have gained significant attention due to their potential for high-speed and energy-efficient computation in artificial intelligence. The implementation of optical convolutions plays a vital role in ONNs, as they are fundamental operations within neural network architectures. However, state-of-the-art convolution architectures often suffer from redundant inputs, leading to substantial resource waste. Here, we demonstrate an integrated optical convolution architecture that leverages the inherent routing principles of arrayed waveguide grating (AWG) to execute the sliding of convolution kernel and summation of results. × multiply–accumulate (MAC) operations are facilitated by + units within a single clock cycle, thus eliminating the redundancy. In the experiment, we achieved 5 bit precision and 91.9 % accuracy in the handwritten digit recognition task confirming the reliability of our approach. Its redundancy-free architecture, low power consumption, high compute density (8.53 teraOP mm  s ) and scalability make it a valuable contribution to the field of optical neural networks, thereby paving the way for future advancements in high-performance computing and artificial intelligence applications.

The traditional detection method of transformer oil acid value has limitations, such as long detection period and toxicity of reagents; while, with the traditional spectral analysis, it is difficult to realize the efficient extraction of key features related to the acid value content. Early detection of rising acid levels is critical to prevent transformer insulation degradation, corrosion, and failure. Conversely, delayed detection accelerates aging and can cause costly repairs or unplanned outages. To address this need, this paper proposes a new method for predicting the acid value content of the transformer oil based on the infrared spectra in the transformer oil and a deep neural network (DNN). The infrared spectral data of the transformer oil is acquired by ALPHA II FT-IR spectrometer, the high frequency noise effect of the spectrum is reduced by wavelet packet decomposition (WPD), and the bootstrapping soft shrinkage (BOSS) algorithm is used to extract the spectra with the highest correlation with the acid value content. The BOSS algorithm is used to extract the feature parameters with the highest correlation with the acid value content in the spectrum, and the DNN prediction model is established to realize the fast prediction of the acid value content of the transformer oil. In comparison with the traditional infrared spectral preprocessing method and regression model, the proposed prediction model has a coefficient of determination (R2) of 97.12% and 95.99% for the prediction set and validation set, respectively, which is 4.96% higher than that of the traditional model. In addition, the accuracy is 5.45% higher than the traditional model, and the R2 of the proposed prediction model is 95.04% after complete external data validation, indicating that it has good accuracy. The results show that the infrared spectral analysis method combining WPD noise reduction, BOSS feature extraction, and DNN modeling can realize the rapid prediction of the acid value content of the transformer oil based on infrared spectroscopy technology, and the prediction model can be used to realize the analytical study of transformer oils. The model can be further applied to the monitoring field of the transformer oil characteristic parameter to realize the rapid monitoring of the transformer oil parameters based on a portable infrared spectrometer.

The terrain in hilly and mountainous regions is characterized by its discontinuity, constant undulations, and comr. environment, leading to significant vibrations and potential tipping over of operating machinery. Traditional crawler cha face challenges when encountering uneven terrain, with one side possibly being suspended and causing tipping. This st introduces a crawler chassis with a split structure to address this issue. The split design allows for crossing obstacles on side, enabling better adaptation to the dynamic terrain of hilly and mountainous areas, ultimately maximizing the chas performance. Through research, it was found that the split structure effectively prevents single-sided crawler suspens: allowing for a maximum longitudinal slope angle of 42.3°, transverse slope angle of 27.38°, and a maximum ravine widtl 445 mm. The prototype testing confirmed that the chassis can handle slopes of up to 41° on both sides and 30° on one s with a maximum ravine width of 430 mm. Considering that the typical cultivated land angle in hilly and mountainous regi ranges from 2° to 15°, the designed chassis is well-suited for driving operations in underground farmland within such areas.