Photothermal conversion—the process of transforming light into thermal energy—is pivotal for next-generation technologies such as solar energy harvesting,water purification,seawater desalination,and biomedical appli...Photothermal conversion—the process of transforming light into thermal energy—is pivotal for next-generation technologies such as solar energy harvesting,water purification,seawater desalination,and biomedical applications.Achieving high photothermal efficiency depends on materials with broad-spectrum light absorption,excellent heat conversion capabilities,and outstanding chemical stability[1–3].展开更多
Atmospheric turbulence degrades the performance of free-space optical(FSO)communication and remote sensing systems by introducing phase and intensity distortions.While a majority of research focuses on mitigating thes...Atmospheric turbulence degrades the performance of free-space optical(FSO)communication and remote sensing systems by introducing phase and intensity distortions.While a majority of research focuses on mitigating these effects to ensure robust signal transmission,an underexplored alternative is to leverage the transformation of structured light to characterize the turbulent medium itself.Here,we introduce a deep learning framework that fuses post-propagation intensity speckle patterns and orbital angular momentum(OAM)spectral data for atmospheric turbulence parameter inference.Our architecture,based on a modified InceptionNet backbone,is optimized to extract and integrate multi-scale features from these distinct optical modalities.This multimodal approach achieves validation accuracies exceeding 80%,substantially outperforming conventional single-modality baselines.The framework demonstrates high inference accuracy and enhanced training stability across a broad range of simulated turbulent conditions,quantified by varying Fried parameters(r_(0))and Reynolds numbers(Re).This work presents a scalable and data-efficient method for turbulence characterization,offering a pathway toward robust environmental sensing and the optimization of dynamic FSO systems.展开更多
In this article, the Capuli （Prunus serotina Ehrh. var. Capuli） cherry extract was used for the synthesis of silver nanoparticles （AgNPs） in the presence of white/visible solar and blue light-emitting diode （LED...In this article, the Capuli （Prunus serotina Ehrh. var. Capuli） cherry extract was used for the synthesis of silver nanoparticles （AgNPs） in the presence of white/visible solar and blue light-emitting diode （LED） light. For the characterization of the extract and the AgNPs, Fourier transform infrared spectroscopy and ultraviolet-visible spectroscopy were employed, along with hydrodynamic particle size analysis, trans- mission electron microscopy and X-ray diffraction. The Ag nanospheres obtained using white light were 40-100 nm in diameter and exhibited an absorption peak at λmax= 445 nm, whereas those obtained using blue LED light were 20-80 nm in diameter with an absorption peak at λmax= 425 nm. Thermal analysis revealed that the content of biomolecules surrounding the AgNPs was about 55-65%, and it was also found that blue LED light AgNPs （56.28%, 0.05 mM） had a higher antioxidant efficacy than the white solar light AgNPs （33.42%, 0.05 mM） against l,l-diphenyl-2-picrylhydrazyl. The results indicate that obtaining AgNPs using a blue LED light may prove to be a simple, cost-effective and easily reproducible method for creating future nanopharmaceuticals.展开更多
文摘Photothermal conversion—the process of transforming light into thermal energy—is pivotal for next-generation technologies such as solar energy harvesting,water purification,seawater desalination,and biomedical applications.Achieving high photothermal efficiency depends on materials with broad-spectrum light absorption,excellent heat conversion capabilities,and outstanding chemical stability[1–3].
基金111 Project(B17035)National Natural Science Foundation of China(U20B2059,62575227,62231021,61621005,62201613)+1 种基金Shanghai Aerospace Science and Technology Innovation Foundation(SAST-2022-069)Fundamental Research Funds for the Central Universities(ZYTS25121).
文摘Atmospheric turbulence degrades the performance of free-space optical(FSO)communication and remote sensing systems by introducing phase and intensity distortions.While a majority of research focuses on mitigating these effects to ensure robust signal transmission,an underexplored alternative is to leverage the transformation of structured light to characterize the turbulent medium itself.Here,we introduce a deep learning framework that fuses post-propagation intensity speckle patterns and orbital angular momentum(OAM)spectral data for atmospheric turbulence parameter inference.Our architecture,based on a modified InceptionNet backbone,is optimized to extract and integrate multi-scale features from these distinct optical modalities.This multimodal approach achieves validation accuracies exceeding 80%,substantially outperforming conventional single-modality baselines.The framework demonstrates high inference accuracy and enhanced training stability across a broad range of simulated turbulent conditions,quantified by varying Fried parameters(r_(0))and Reynolds numbers(Re).This work presents a scalable and data-efficient method for turbulence characterization,offering a pathway toward robust environmental sensing and the optimization of dynamic FSO systems.
文摘In this article, the Capuli （Prunus serotina Ehrh. var. Capuli） cherry extract was used for the synthesis of silver nanoparticles （AgNPs） in the presence of white/visible solar and blue light-emitting diode （LED） light. For the characterization of the extract and the AgNPs, Fourier transform infrared spectroscopy and ultraviolet-visible spectroscopy were employed, along with hydrodynamic particle size analysis, trans- mission electron microscopy and X-ray diffraction. The Ag nanospheres obtained using white light were 40-100 nm in diameter and exhibited an absorption peak at λmax= 445 nm, whereas those obtained using blue LED light were 20-80 nm in diameter with an absorption peak at λmax= 425 nm. Thermal analysis revealed that the content of biomolecules surrounding the AgNPs was about 55-65%, and it was also found that blue LED light AgNPs （56.28%, 0.05 mM） had a higher antioxidant efficacy than the white solar light AgNPs （33.42%, 0.05 mM） against l,l-diphenyl-2-picrylhydrazyl. The results indicate that obtaining AgNPs using a blue LED light may prove to be a simple, cost-effective and easily reproducible method for creating future nanopharmaceuticals.
