Accelerating AFM Characterization via Deep-Learning-Based Image Super-Resolution

Precise Surface Profiling at the Nanoscale Enabled by Deep Learning

Surface topography, or height profile, is a critical property for various micro- and nanostructured materials and devices, as well as biological systems. At the nanoscale, atomic force microscopy (AFM) is the tool of choice for surface profiling due to its capability to noninvasively map the topography of almost all types of samples. However, this method suffers from one drawback: the convolution of the nanoprobe's shape in the height profile of the samples, which is especially severe for sharp protrusion features. Here, we report a deep learning (DL) approach to overcome this limit. Adopting an image-to-image translation methodology, we use data sets of tip-convoluted and deconvoluted image pairs to train an encoder-decoder based deep convolutional neural network. The trained network successfully removes the tip convolution from AFM topographic images of various nanocorrugated surfaces and recovers the true, precise 3D height profiles of these samples.

Prediction and Control in DNA Nanotechnology

DNA nanotechnology is a rapidly developing field that uses DNA as a building material for nanoscale structures. Key to the field's development has been the ability to accurately describe the behavior of DNA nanostructures using simulations and other modeling techniques. In this Review, we present various aspects of prediction and control in DNA nanotechnology, including the various scales of molecular simulation, statistical mechanics, kinetic modeling, continuum mechanics, and other prediction methods. We also address the current uses of artificial intelligence and machine learning in DNA nanotechnology. We discuss how experiments and modeling are synergistically combined to provide control over device behavior, allowing scientists to design molecular structures and dynamic devices with confidence that they will function as intended. Finally, we identify processes and scenarios where DNA nanotechnology lacks sufficient prediction ability and suggest possible solutions to these weak areas.

Towards smart scanning probe lithography: a framework accelerating nano-fabrication process with in-situ characterization via machine learning

Scanning probe lithography (SPL) is a promising technology to fabricate high-resolution, customized and cost-effective features at the nanoscale. However, the quality of nano-fabrication, particularly the critical dimension, is significantly influenced by various SPL fabrication techniques and their corresponding process parameters. Meanwhile, the identification and measurement of nano-fabrication features are very time-consuming and subjective. To tackle these challenges, we propose a novel framework for process parameter optimization and feature segmentation of SPL via machine learning (ML). Different from traditional SPL techniques that rely on manual labeling-based experimental methods, the proposed framework intelligently extracts reliable and global information for statistical analysis to fine-tune and optimize process parameters. Based on the proposed framework, we realized the processing of smaller critical dimensions through the optimization of process parameters, and performed direct-write nano-lithography on a large scale. Furthermore, data-driven feature extraction and analysis could potentially provide guidance for other characterization methods and fabrication quality optimization.

Super-resolution of magnetic systems using deep learning

We construct a deep neural network to enhance the resolution of spin structure images formed by spontaneous symmetry breaking in the magnetic systems. Through the deep neural network, an image is expanded to a super-resolution image and reduced to the original image size to be fitted with the input feed image. The network does not require ground truth images in the training process. Therefore, it can be applied when low-resolution images are provided as training datasets, while high-resolution images are not obtainable due to the intrinsic limitation of microscope techniques. To show the usefulness of the network, we train the network with two types of simulated magnetic structure images; one is from self-organized maze patterns made of chiral magnetic structures, and the other is from magnetic domains separated by walls that are topological defects of the system. The network successfully generates high-resolution images highly correlated with the exact solutions in both cases. To investigate the effectiveness and the differences between datasets, we study the network's noise tolerance and compare the networks' reliabilities. The network is applied with experimental data obtained by magneto-optical Kerr effect microscopy and spin-polarized low-energy electron microscopy.

Calcium-Differentiated Cellular Internalization of Allosteric Framework Nucleic Acids for Targeted Payload Delivery

Target delivery systems have extensively shown promising applications in cancer therapy, and many of them function smartly by responding to the cancer cell microenvironment. Here, we for the first time report Ca²⁺-differentiated cellular internalization of 2D/3D framework nucleic acids (FNAs), enabling the engineering of a conceptually new target delivery system using an allosteric FNA nanovehicle. The FNA vehicle is subject to a 2D-to-3D transformation on the cancer cell surface via G-quadruplexes responding to environmental K⁺ and thereby allows its cell entry to be more efficiently promoted by Ca²⁺. This design enables the FNA vehicle to target cancer cells and selectively deliver an antisense strand-containing cargo for live-cell mRNA imaging. It would open new avenues toward targeted drug delivery and find extensive applications in precise disease treatment.

Environment-Dependent Stability and Mechanical Properties of DNA Origami Six-Helix Bundles with Different Crossover Spacings

The internal design of DNA nanostructures defines how they behave in different environmental conditions, such as endonuclease-rich or low-Mg²⁺ solutions. Notably, the inter-helical crossovers that form the core of such DNA objects have a major impact on their mechanical properties and stability. Importantly, crossover design can be used to optimize DNA nanostructures for target applications, especially when developing them for biomedical environments. To elucidate this, two otherwise identical DNA origami designs are presented that have a different number of staple crossovers between neighboring helices, spaced at 42- and 21- basepair (bp) intervals, respectively. The behavior of these structures is then compared in various buffer conditions, as well as when they are exposed to enzymatic digestion by DNase I. The results show that an increased number of crossovers significantly improves the nuclease resistance of the DNA origami by making it less accessible to digestion enzymes but simultaneously lowers its stability under Mg²⁺ -free conditions by reducing the malleability of the structures. Therefore, these results represent an important step toward rational, application-specific DNA nanostructure design.

Rapid DNA origami nanostructure detection and classification using the YOLOv5 deep convolutional neural network

The intra-image identification of DNA structures is essential to rapid prototyping and quality control of self-assembled DNA origami scaffold systems. We postulate that the YOLO modern object detection platform commonly used for facial recognition can be applied to rapidly scour atomic force microscope (AFM) images for identifying correctly formed DNA nanostructures with high fidelity. To make this approach widely available, we use open-source software and provide a straightforward procedure for designing a tailored, intelligent identification platform which can easily be repurposed to fit arbitrary structural geometries beyond AFM images of DNA structures. Here, we describe methods to acquire and generate the necessary components to create this robust system. Beginning with DNA structure design, we detail AFM imaging, data point annotation, data augmentation, model training, and inference. To demonstrate the adaptability of this system, we assembled two distinct DNA origami architectures (triangles and breadboards) for detection in raw AFM images. Using the images acquired of each structure, we trained two separate single class object identification models unique to each architecture. By applying these models in sequence, we correctly identified 3470 structures from a total population of 3617 using images that sometimes included a third DNA origami structure as well as other impurities. Analysis was completed in under 20 s with results yielding an F1 score of 0.96 using our approach.