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Self-powered photodetectors (PDs) with distinguishable wavelength detection have great application potential in intelligent optoelectronics. However, slow response speed, low responsivity, and signal interference prevent its development and applications. Here, an asymmetric bipolar p-NiO/n-ZnO/p-Si heterojunction is designed as a wavelength-distinguishable PD by using two tandem p–n junctions to reduce the wavelength interference and effectively separate photo-generated carriers. Bidirectional transient photocurrents are achieved in the external circuit for ultraviolet and visible detection by smartly using the pyro-phototronic effect induced by the variations of spontaneous polarization and interface-induced polarization within the p–n–p junction. The transient peak-to-peak current are thus significantly improved by 2310% for 325 nm lasing and by 5950% for 442 nm lasing. This approach can be used to realize wavelength-distinguishable detection through flexibly selecting materials and using the pyro-phototronic effect. The designed PD might have potential applications in the fields of environmental detection, smart imaging, and intelligent optoelectronics. [ABSTRACT FROM AUTHOR]

The enhancement of spatial resolution has improved the spatial information conveyed by remote sensing images. Nevertheless, the impact of spectral-texture combined features on classification accuracy in deep learning remains understudied. This study develops five spectral texture features (Spe-GLCM, Spe-FRS, Spe-Gabor, Spe-CS-LMP, and Spe-FD) using GID15 and Vaihingen datasets. These features were compared with U-Net++, Deeplabv3+, and PSPNet models, incorporating fine-tuned model structures. The results indicate that integrating texture features enhances accuracy in both overall and sparsely sampled land cover classifications. This highlights the potential of deep learning combined with texture features to recognize land cover from high-resolution remote sensing imagery. [ABSTRACT FROM AUTHOR]

The Sanjiangyuan Nature Reserve of China (SNRC) is recognized as one of the most fragile and sensitive terrestrial ecosystems in China, posing challenges for obtaining reliable and complete Moderate Resolution Imaging Spectro Radiometer (MODIS) data for ecological environment quality (EEQ) monitoring due to adverse factors like clouds and snow. In this study, a complete high-quality framework for MODIS time-series data reconstruction was constructed utilizing the Google Earth Engine (GEE) cloud platform. The reconstructed images were used to compute the Remote Sensing based Ecological Index (RSEI) on a monthly scale in the SNRC from 2001 to 2020. The results were as follows: The EEQ of the study area exhibited a "first fluctuating decline, then significant improvement" trend, with the RSEI values increasing at a rate of 0.84%/a. The spatial pattern of the EEQ displayed significant spatial heterogeneity, characterized by a "low in the west and high in the east" distribution. The spatial distribution pattern of the RSEI exhibited significant clustering characteristics. From 2001 to 2020, the proportion of "high–high" clustering areas exceeded 35%, and the proportion of "low–low" clustering areas exceeded 30%. Poor ecological conditions are mainly associated with population agglomerations, cultivated land, unutilized land, and bare ground, while grasslands and forests have higher RSEI values. The result of the trend analysis revealed a significant trend in RSEI change, with 62.96% of the area significantly improved and 6.31% significantly degraded. The Hurst Index (HI) results indicated that the future trend of the RSEI is predominantly anti-persistence. The proportion of areas where the EEQ is expected to continue improving in the future is 33.74%, whereas 21.21% of the area is forecasted to transition from improvement to degradation. The results showed that the high-quality framework for MODIS time-series data reconstruction enables the effective continuous monitoring of EEQ over long periods and large areas, providing robust scientific support for long time-series data reconstruction research. [ABSTRACT FROM AUTHOR]

A single ZnO nanowire device with volatile resistive switching behavior has been prepared. Different from traditional resistive switching devices, such ZnO nanowire devices do not exhibit resistive switching behaviors under a single bias voltage, and appear resistive switching behavior under the combined action of light stimuli and bias voltage. Through the demonstration of the time-dependent hysteresis curve and atmosphere-dependent hysteresis loop of the resistive switching devices, it is believed that under the resistive switching process, ultraviolet illumination can increase the carrier concentration and modulate the barrier depletion structure, and external bias voltage can ionize the surface state. They work together to modulate the switching process of the devices. Such light stimuli and bias voltage dual-modulated resistive switching device enables optical control and may thus be considered for sensory applications or optically tunable memories. [ABSTRACT FROM AUTHOR]

The photoelectric synaptic devices with the bidirectional optical response and multilevel nonvolatile all‐optical pulse control are the basis for constructing the sensing‐memory‐computing hardware network. However, the current all‐optical‐pulse‐regulated photoelectric synaptic devices cannot realize the bidirectional and multistage nonvolatile regulation of photocurrent responsivity under the detection light excitation. Here, p‐Si/n‐ZnO heterojunction synaptic devices are constructed, and the bidirectional, nonvolatile control of the synaptic device under visible light excitation is realized with up to 60 states of photocurrent responsivity by ultraviolet (UV) and near‐infrared (NIR) irradiation. Based on the performance of the synaptic device, the artificial feedback network can be built to realize the classification and recognition of colored handwritten digits. The recognition accuracy for optical handwritten digits at 415, 530, and 970 nm is 78.1 ± 1.2%, 79.4 ± 1.9% and 79.0 ± 1.6%, respectively. Additionally, the 3 × 3 convolution kernel constructed by the synaptic device can realize image‐processing functions like mean filtering, Gaussian filtering, and edge enhancement. The development of sensing‐storage‐computing all‐optical control artificial vision hardware networks can be greatly aided by the new approach to bidirectional and multistage nonvolatile all‐optical pulse regulation of photoelectric synaptic devices that is proposed in this paper. [ABSTRACT FROM AUTHOR]