Random lasing in micron-sized individual supraparticles.

Self-assembled fluorescent particles have shown promise as a potential structure for random lasers. However, obtaining micron-sized random lasers made with fluorescent particles remains a challenge. Theoretically, achieving micron-sized random lasers could be possible by assembling supraparticles composed of colloidal particles. Despite extensive research on supraparticles, the generation of random lasers from this structure is rarely reported. In this study, we introduce a rapid and efficient method for producing supraparticles from fluorescent particles. The resulting supraparticles exhibit diameters ranging from 50 to 150 µm with particles well-connected and uniformly distributed throughout their structure. Under optical excitation, supraparticles with a diameter larger than 80 µm demonstrate lasing emission with a threshold of approximately 77 µJ·mm-2. Larger supraparticles exhibit a distinct redshift in lasing wavelength compared to the smaller ones. Specifically, the central peak lasing wavelength shows a shift of about 7.5 nm as the supraparticle diameter increases from 80 to 150 µm.

Machine-learning reinforcement for optimizing multilayered thin films: applications in designing broadband antireflection coatings.

The design and fabrication of nanoscale multilayered thin films play an essential role in regulating the operation efficiency of sensitive optical sensors and filters. In this paper, we introduce a packaged tool that employs flexible electromagnetic calculation software with machine learning in order to find the optimized double-band antireflection coatings in intervals of wavelength from 3 to 5 µm and 8 to 12 µm. Instead of computing or modeling an extremely enormous set of thin film structures, this tool enhanced with machine learning can swiftly predict the optical properties of a given structure with >99.7% accuracy and a substantial reduction in computation costs. Furthermore, the tool includes two learning methods that can infer a global optimal structure or suitable local optimal ones. Specifically, these well-trained models provide the highest accurate double-band average transmission coefficient combined with the lowest number of layers or the thinnest total thickness starting from a reference multilayered structure. Finally, the more sophisticated enhancement method, called the double deep Q-learning network, exhibited the best performance in finding optimal antireflective multilayered structures with the highest double-band average transmission coefficient of about 98.95%.

Protein-based microsphere biolasers fabricated by dehydration.

Biolasers made of biological materials have attracted considerable research attention due to their biocompatibility and biodegradability, and have the potential for biosensing and biointegration. However, the current fabrication methods of biolasers suffer from several limitations, such as complicated processing, time-consuming and environmentally unfriendly nature. In this study, a novel approach with green processes for fabricating solid-state microsphere biolasers has been demonstrated. By dehydration via a modified Microglassification™ technology, dye-doped bovine serum albumin (BSA) droplets could be quickly (less than 10 minutes) and easily changed into solid microspheres with diameters ranging from 10 µm to 150 µm. The size of the microspheres could be effectively controlled by changing either the concentration of the BSA solution or the diameter of the initial droplets. The fabricated microspheres could act as efficient microlasers under an optical pulse excitation. A lasing threshold of 7.8 µJ mm-2 and a quality (Q) factor of about 1700 to 3100 were obtained. The size dependence of lasing characteristics was investigated, and the results showed a good agreement with whispering gallery mode (WGM) theory. Our findings contribute an effective technique for the fabrication of high-Q factor microlasers that may be potential for applications in biological and chemical sensors.