Solar cells are devices that convert light energy into electricity. To drive greater adoption of solar cell technologies, higher cell efficiencies and reductions in manufacturing cost are necessary. Fluorogens containing rotor structures may be helpful in addressing some of these challenges due to their unique twisted structures and photophysics. In this review, we discuss the applications of rotor-containing molecules as dyes for luminescent down-shifting layers and luminescent solar concentrators, where their aggregation-induced emission properties and large Stokes shifts are highly desirable. We also discuss the applications of molecules containing rotors in third-generation solar cell technologies, namely dye-sensitized solar cells and organic photovoltaics, where the twisted 3-dimensional rotor structures are used primarily for aggregation control. Finally, we discuss perspectives on the future role of molecules containing rotor structures in solar cell technologies.

BACKGROUND: Artificial Intelligence(AI)-based solutions for Gleason grading hold promise for pathologists, while image quality inconsistency, continuous data integration needs, and limited generalizability hinder their adoption and scalability. METHODS: We present a comprehensive digital pathology workflow for AI-assisted Gleason grading. It incorporates A!MagQC (image quality control), A!HistoClouds (cloud-based annotation), Pathologist-AI Interaction (PAI) for continuous model improvement, Trained on Akoya-scanned images only, the model utilizes color augmentation and image appearance migration to address scanner variations. We evaluate it on Whole Slide Images (WSI) from another five scanners and conduct validations with pathologists to assess AI efficacy and PAI. RESULTS: Our model achieves an average F1 score of 0.80 on annotations and 0.71 Quadratic Weighted Kappa on WSIs for Akoya-scanned images. Applying our generalization solution increases the average F1 score for Gleason pattern detection from 0.73 to 0.88 on images from other scanners. The model accelerates Gleason scoring time by 43% while maintaining accuracy. Additionally, PAI improve annotation efficiency by 2.5 times and led to further improvements in model performance. CONCLUSIONS: This pipeline represents a notable advancement in AI-assisted Gleason grading for improved consistency, accuracy, and efficiency. Unlike previous methods limited by scanner specificity, our model achieves outstanding performance across diverse scanners. This improvement paves the way for its seamless integration into clinical workflows.