RESUMO
Excitonic devices operate based on excitons, which can be excited by photons as well as emitting photons and serve as a medium for photon-carrier conversion. Excitonic devices are expected to combine the advantages of both the high response rate of photonic devices and the high integration of electronic devices simultaneously. However, because of the neutral feature, exciton transport is generally achieved via diffusion rather than using electric fields, and the efficient control of exciton flux directionality has always been difficult. In this work, a precisely designed one-dimensional periodic nanostructure (1DPS) is used to introduce periodic strain field along with resonant mode to the WS2 monolayer, achieving exciton oriented diffusion with a 7.6-fold exciton diffusion coefficient enhancement relative to that of intrinsic, while enhancing the excitonic emission intensity by a factor of 10 and reducing exciton saturation threshold power by 2 orders of magnitude. Based on the analysis of the density functional theory (DFT) and the finite-element method (FEM), we attribute the anisotropy of exciton diffusion to exciton funneling induced by periodic potentials, which do not require excessive potential height difference for an efficient oriented diffusion. As a result of resonant emission, the exciton diffusion is dragged into the nonlinear regime owing to the high exciton density close to saturation, which improves the exciton diffusion coefficient and diffusion anisotropy more appreciably.
RESUMO
Background: Artificial intelligence (AI) technology has made breakthroughs in spinal cord neural injury and restoration in recent years. It has a positive impact on clinical treatment. This study explores AI research's progress and hotspots in spinal cord neural injury and restoration. It also analyzes research shortcomings related to this area and proposes potential solutions. Methods: We used CiteSpace 6.1.R6 and VOSviewer 1.6.19 to research WOS articles on AI research in spinal cord neural injury and restoration. Results: A total of 1,502 articles were screened, in which the United States dominated; Kadone, Hideki (13 articles, University of Tsukuba, JAPAN) was the author with the highest number of publications; ARCH PHYS MED REHAB (IF = 4.3) was the most cited journal, and topics included molecular biology, immunology, neurology, sports, among other related areas. Conclusion: We pinpointed three research hotspots for AI research in spinal cord neural injury and restoration: (1) intelligent robots and limb exoskeletons to assist rehabilitation training; (2) brain-computer interfaces; and (3) neuromodulation and noninvasive electrical stimulation. In addition, many new hotspots were discussed: (1) starting with image segmentation models based on convolutional neural networks; (2) the use of AI to fabricate polymeric biomaterials to provide the microenvironment required for neural stem cell-derived neural network tissues; (3) AI survival prediction tools, and transcription factor regulatory networks in the field of genetics were discussed. Although AI research in spinal cord neural injury and restoration has many benefits, the technology has several limitations (data and ethical issues). The data-gathering problem should be addressed in future research, which requires a significant sample of quality clinical data to build valid AI models. At the same time, research on genomics and other mechanisms in this field is fragile. In the future, machine learning techniques, such as AI survival prediction tools and transcription factor regulatory networks, can be utilized for studies related to the up-regulation of regeneration-related genes and the production of structural proteins for axonal growth.
