In Situ Self-Assembled 1D/3D Mixed-Dimensional Perovskite Heterostructures for Efficient White Light Emission.

Mixed-dimensional perovskite (MDP) heterostructures are promising optoelectronic semiconductors. Yet, the current preparation methods involve complex experimental procedures and material compatibility constraints, limiting their widespread applications. Here, we present a one-step room temperature solution-based approach to synthesize a range of 1D C4N2H14PbBr4 and 3D APbBr3 (A = Cs+, MA+, FA+) self-assembled MDP heterostructures exhibiting high-efficiency white light-emitting properties. The ultra-broadband emission results from the synergy between the self-captured blue broadband emission from 1D perovskites and the green emission of 3D perovskites, covering the entire visible-light spectrum with a full width at half-maximum exceeding 170 nm and a remarkable photoluminescence quantum yield of 26%. This work establishes a novel prototype for the preparation of highly luminescent MDP heterostructures, offering insights for future research and industrialization in the realm of white light LEDs.

DNA Sequence Optimization Design of Arithmetic Optimization Algorithm Based on Billiard Hitting Strategy.

DNA computing is a very efficient way to calculate, but it relies on high-quality DNA sequences, but it is difficult to design high-quality DNA sequences. The sequence it is looking for must meet multiple conflicting constraints at the same time to meet the requirements of DNA calculation. Therefore, we propose an improved arithmetic optimization algorithm of billiard algorithm to optimize the DNA sequence. This paper contributes as follows. The introduction to the good point set initialization to obtain high-quality solutions improves the optimization efficiency. The billiard hitting strategy was used to change the position of the population to enhance the global search scope. The use of a stochastic lens opposites learning mechanism can increase the capacity of the algorithm to get rid of locally optimal. The harmonic search algorithm is introduced to clarify some unqualified secondary structures and improve the quality of the solution. 12 benchmark functions and six other algorithms are used for comparison and ablation experiments to ensure the effectiveness of the algorithms. Finally, the DNA sequences we designed are of higher quality compared to other advanced algorithms.

K-Means Segmentation of Underwater Image Based on Improved Manta Ray Algorithm.

Image segmentation plays an important role in daily life. The traditional K-means image segmentation has the shortcomings of randomness and is easy to fall into local optimum, which greatly reduces the quality of segmentation. To improve these phenomena, a K-means image segmentation method based on improved manta ray foraging optimization (IMRFO) is proposed. IMRFO uses Lévy flight to improve the flexibility of individual manta rays and then puts forward a random walk learning that prevents the algorithm from falling into the local optimal state. Finally, the learning idea of particle swarm optimization is introduced to enhance the convergence accuracy of the algorithm, which effectively improves the global and local optimization ability of the algorithm simultaneously. With the probability that K-means will fall into local optimum reducing, the optimized K-means hold stronger stability. In the 12 standard test functions, 7 basic algorithms and 4 variant algorithms are compared with IMRFO. The results of the optimization index and statistical test show that IMRFO has better optimization ability. Eight underwater images were selected for the experiment and compared with 11 algorithms. The results show that PSNR, SSIM, and FSIM of IMRFO in each image are better. Meanwhile, the optimized K-means image segmentation performance is better.