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Abstract The field of nucleic acid self‐assembly has advanced significantly, enabling the creation of multi‐dimensional nanostructures with precise sizes and shapes. These nanostructures hold great potential for various applications, including biocatalysis, smart materials, molecular diagnosis, and therapeutics. Here, dynamic light scattering (DLS) and nanoparticle tracking analysis (NTA) are employed to investigate DNA origami nanostructures, focusing on size distribution and particle concentration. Compared to DLS, NTA provided higher resolution in size measurement with a smaller full‐width at half‐maximum (FWHM), making it particularly suitable for characterizing DNA nanostructure. To enhance sensitivity, a fluorescent NTA method is developed by incorporating an intercalation dye to amplify the fluorescence signals of DNA origami. This method is validated by analyzing various DNA origami structures, ranging from 1 and 2D flexible structures to 3D compact shapes, and evaluating structural assembly yields. Additionally, NTA is used to analyze dynamic DNA nanocages that undergo conformational switches among linear, square, and pyramid shapes in response to the addition of trigger strands. Quantitative size distribution data is crucial not only for production quality control but also for providing mechanistic insights into the various applications of DNA nanomaterials. 

Abstract DNA tiles serve as the fundamental building blocks for DNA self-assembled nanostructures such as DNA arrays, origami, and designer crystals. Introducing additional binding arms to DNA crossover tiles holds the promise of unlocking diverse nano-assemblies and potential applications. Here, we present one-, two-, and three-layer T-shaped crossover tiles, by integrating T junction with antiparallel crossover tiles. These tiles carry over the orthogonal binding directions from T junction and retain the rigidity from antiparallel crossover tiles, enabling the assembly of various 2D tessellations. To demonstrate the versatility of the design rules, we create 2-state reconfigurable nanorings from both single-stranded tiles and single-unit assemblies. Moreover, four sets of 4-state reconfiguration systems are constructed, showing effective transformations between ladders and/or rings with pore sizes spanning ~20 nm to ~168 nm. These DNA tiles enrich the design tools in nucleic acid nanotechnology, offering exciting opportunities for the creation of artificial dynamic DNA nanopores. 

We created 29 parallel double-crossover DNA motifs varying in hybridization pathways, domain lengths, and crossover locations, producing diverse assemblies. 

Heuristic algorithms can generalize the design process of stiff and round capsule-like nanostructures made from DNA. 

High-resolution albedo has the advantage of a higher spatial scale from tens to hundreds of meters, which can fill the gaps of albedo applications from the global scale to the regional scale and can solve problems related to land use change and ecosystems. The Sentinel-2 satellite provides high-resolution observations in the visible-to-NIR bands, giving possibilities to generate a high-resolution surface albedo at 10 m. This study attempted to evaluate the performance of the four data-driven machine learning algorithms (i.e., random forest (RF), artificial neural network (ANN), k-nearest neighbor (KNN), and XGBoost (XGBT)) for the generation of a Sentinel-2 albedo over flat and rugged terrain. First, we used the RossThick-LiSparseR model and the 3D discrete anisotropic radiative transfer (DART) model to build the narrowband surface reflectance and broadband surface albedo, which acted as the training and testing datasets over flat and rugged terrain. Second, we used the training and testing datasets to drive the four machine learning models, and evaluated the performance of these machine learning models for the generation of Sentinel-2 albedo. Finally, we used the four machine learning models to generate a Sentinel-2 albedo and compared them with in situ albedos to show the models’ application potentials. The results show that these machine learning models have great performance in estimating Sentinel-2 albedos at a 10 m spatial scale. The comparison with in situ albedos shows that the random forest model outperformed the others in estimating a high-resolution surface albedo based on Sentinel-2 datasets over the flat and rugged terrain, with an RMSE smaller than 0.0308 and R2 larger than 0.9472. 

DNA nanotechnology has been proven to be a powerful platform to assist the development of imaging probes for biomedical research. The attractive features of DNA nanostructures, such as nanometer precision, controllable size, programmable functions, and biocompatibility, have enabled researchers to design and customize DNA nanoprobes for bioimaging applications. However, DNA probes with low molecular weights (e.g., 10–100 nt) generally suffer from low stability in physiological buffer environments. To improve the stability of DNA nanoprobes in such environments, DNA nanostructures can be designed with relatively larger sizes and defined shapes. In addition, the established modification methods for DNA nanostructures are also essential in enhancing their properties and performances in a physiological environment. In this review, we begin with a brief recap of the development of DNA nanostructures including DNA tiles, DNA origami, and multifunctional DNA nanostructures with modifications. Then we highlight the recent advances of DNA nanostructures for bioimaging, emphasizing the latest developments in probe modifications and DNA-PAINT imaging. Multiple imaging modules for intracellular biomolecular imaging and cell membrane biomarkers recognition are also summarized. In the end, we discuss the advantages and challenges of applying DNA nanostructures in bioimaging research and speculate on its future developments.