When surface plasmon resonance (SPR) occurs, the incident light is absorbed by the surface of the SPR structure, thus minimizing the intensity of the reflected light. Therefore, the SPR method is adopted in this paper to achieve perfect absorption of the absorbent. In this paper, we first propose a multi-frequency broadband absorber structure based on graphene SPR, which uses the continuous resonance of patterned graphene surface plasmon in the frequency spectrum to form a multi-frequency broadband absorption. In this simulation, a sandwich-stack structure was adopted, whereby the patterned graphene is situated on top of the SiO₂ layer and the metal layer. The broad-band absorption bands of the absorber were obtained as 4.14–4.38 THz, 5.78–6.36 THz, and 7.87–8.66 THz through the analog simulation of finite-difference time-domain method (FDTD) solutions. Then, based on the multi-layer resonant unit structure, through the superposition and combination of absorbing units responding to different frequency bands, the perfect absorption of ultra-wideband is achieved. The data results illustrate that the total absorption bandwidth of the absorber is 2.26 THz, and the relative absorption bandwidth B_w is equal to 28.93%. The electric field in X-Y direction of the absorber in the perfect absorption band is analyzed, respectively, and the dynamic tunability of the absorber is studied. Finally, we studied whether the absorbing structure still has efficient absorption characteristics for the two polarization modes when the incident angle is changed from 0° to 70°. The structure model proposed has potential value for application in terahertz photoelectric detection, filtering, and electromagnetic shielding.

Explainable artificial intelligence has been extensively studied recently. However, the research of interpretable methods in the agricultural field has not been systematically studied. We studied the interpretability of deep learning models in different agricultural classification tasks based on the fruit leaves dataset. The purpose is to explore whether the classification model is more inclined to extract the appearance characteristics of leaves or the texture characteristics of leaf lesions during the feature extraction process. The dataset was arranged into three experiments with different categories. In each experiment, the VGG, GoogLeNet, and ResNet models were used and the ResNet-attention model was applied with three interpretable methods. The results show that the ResNet model has the highest accuracy rate in the three experiments, which are 99.11%, 99.4%, and 99.89%, respectively. It is also found that the attention module could improve the feature extraction of the model, and clarify the focus of the model in different experiments when extracting features. These results will help agricultural practitioners better apply deep learning models to solve more practical problems.

Since the use of chemical fuels is permanently damaging the environment, the need for new energy sources is urgent for mankind. Given that solar energy is a clean and sustainable energy source, this study investigates and proposes a six-layer composite ultra-wideband high-efficiency solar absorber with an annular microstructure. It achieves this by using a combination of the properties of metamaterials and the quantum confinement effects of semiconductor materials. The substrate is W–Ti–Al₂O₃, and the microstructure is an annular InAs-square InAs film–Ti film combination. We used Lumerical Solutions’ FDTD solution program to simulate the absorber and calculate the model’s absorption, field distribution, and thermal radiation efficiency (when it is used as a thermal emitter), and further explored the physical mechanism of the model’s ultra-broadband absorption. Our model has an average absorption of 95.80% in the 283–3615 nm band, 95.66% in the 280–4000 nm band, and a weighted average absorption efficiency of 95.78% under AM1.5 illumination. Meanwhile, the reflectance of the model in the 5586–20,000 nm band is all higher than 80%, with an average reflectance of 94.52%, which has a good thermal infrared suppression performance. It is 95.42% under thermal radiation at 1000 K. It has outstanding performance when employed as a thermal emitter as well. Additionally, simulation results show that the absorber has good polarization and incidence angle insensitivity. The model may be applied to photodetection, thermophotovoltaics, bio-detection, imaging, thermal ion emission, and solar water evaporation for water purification.

Here, we document a D-type double open-loop channel floor plasmon resonance (SPR) photonic crystal fiber (PCF) for temperature sensing. The grooves are designed on the polished surfaces of the pinnacle and backside of the PCF and covered with a gold (Au) film, and stomata are distributed around the PCF core in a progressive, periodic arrangement. Two air holes between the Au membrane and the PCF core are designed to shape a leakage window, which no longer solely averts the outward diffusion of Y-polarized (Y-POL) core mode energy, but also sets off its coupling with the Au movie from the leakage window. This SPR-PCF sensor uses the temperature-sensitive property of Polydimethylsiloxane (PDMS) to reap the motive of temperature sensing. Our lookup effects point out that these SPR-PCF sensors have a temperature sensitivity of up to 3757 pm/°C when the temperature varies from 5 °C to 45 °C. In addition, the maximum refractive index sensitivity (RIS) of the SPR-PCF sensor is as excessive as 4847 nm/RIU. These proposed SPR-PCF temperature sensors have an easy nanostructure and proper sensing performance, which now not solely improve the overall sensing performance of small-diameter fiber optic temperature sensors, but also have vast application prospects in geo-logical exploration, biological monitoring, and meteorological prediction due to their remarkable RIS and exclusive nanostructure.

A passively mode-locked Yb fiber laser using PbSe colloidal quantum dots (CQDs) as saturable absorber (SA) is experimentally demonstrated. An all-fiber experimental scheme was designed to understand the SA property of PbSe CQDs. The non-saturable loss, modulation depth, and saturable intensity of SA measured were 23%, 7%, and 12 MW/cm², respectively. The PbSe CQDs were sandwiched in a fiber connector, which was further inserted into the Yb fiber laser for mode-locking. As the pump power up to 110 mW, the self-starting mode-locking pulses were observed. Under the pump power of 285 mW, a maximum average laser power with fundamental mode-locking operation was obtained to be 21.3 mW. In this situation, the pulse full width at half maximum (FWHM), pulse repetition rate, and spectral FWHM were measured to be 70 ps, 8.3 MHz, and 4.5 nm, respectively.

The development of the smartphone and computer vision technique provides customers with a convenient approach to identify tea species, as well as qualities. However, the prediction model may not behave robustly due to changes in illumination conditions. Fluorescence imaging can induce the fluorescence signal from typical components, and thus may improve the prediction accuracy. In this paper, a tea classification method based on fluorescence imaging and convolutional neural networks (CNN) is proposed. Ultra-violet (UV) LEDs with a central wavelength of 370 nm were utilized to induce the fluorescence of tea samples so that the fluorescence images could be captured. Five kinds of tea were included and pre-processed. Two CNN-based classification models, e.g., the VGG16 and ResNet-34, were utilized for model training. Images captured under the conventional fluorescent lamp were also tested for comparison. The results show that the accuracy of the classification model based on fluorescence images is better than those based on the white-light illumination images, and the performance of the VGG16 model is better than the ResNet-34 model in our case. The classification accuracy of fluorescence images reached 97.5%, which proves that the LED-induced fluorescence imaging technique is promising to use in our daily life.