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Unraveling local dynamic charge processes is vital for progress in diverse fields, from microelectronics to energy storage. This relies on the ability to map charge carrier motion across multiple length- and timescales and understanding how these processes interact with the inherent material heterogeneities. Towards addressing this challenge, we introduce high-speed sparse scanning Kelvin probe force microscopy, which combines sparse scanning and image reconstruction. This approach is shown to enable sub-second imaging (>3 frames per second) of nanoscale charge dynamics, representing several orders of magnitude improvement over traditional Kelvin probe force microscopy imaging rates. Bridging this improved spatiotemporal resolution with macroscale device measurements, we successfully visualize electrochemically mediated diffusion of mobile surface ions on a LaAlO 3 /SrTiO 3 planar device. Such processes are known to impact band-alignment and charge-transfer dynamics at these heterointerfaces. Furthermore, we monitor the diffusion of oxygen vacancies at the single grain level in polycrystalline TiO 2 . Through temperature-dependent measurements, we identify a charge diffusion activation energy of 0.18 eV, in good agreement with previously reported values and confirmed by DFT calculations. Together, these findings highlight the effectiveness and versatility of our method in understanding ionic charge carrier motion in microelectronics or nanoscale material systems. Dynamic mapping of charge motion across multiple length- and timescales is essential for understanding a variety of phenomena. Here, the authors introduce sparse scanning KPFM, which enables fast nanoscale charge mapping at 3 frames per second to track ion migration.

Microscopy is an essential tool in scientific research, enabling the visualization of structures at micro- and nanoscale resolutions. However, the field of microscopy often encounters limitations in field-of-view (FOV), restricting the amount of sample that can be imaged in a single capture. To overcome this limitation, image stitching techniques have been developed to seamlessly merge multiple overlapping images into a single, high-resolution composite. The images collected from microscope need to be optimally stitched before accurate physical information can be extracted from post analysis. However, the existing stitching tools either struggle to stitch images together when the microscopy images are feature sparse or cannot address all the transformations of images when performing image stitching. To address these issues, we propose a bi-channel aided feature-based image stitching method and demonstrate its use on Atomic Force Microscopy (AFM) generated Pantoea sp. YR343 biofilm and PTO thin film sample images as experimental data. The topographical channel image of AFM data captures the morphological details of the sample, and a stitched topographical image is desired for researchers. We utilize the amplitude and phase channels of AFM data to maximize the matching features and to estimate the position of the original topographical images and show that the proposed bi-channel aided stitching method outperforms the traditional direct stitching approach in AFM topographical image stitching task. Here, we demonstrated the application on AFM, but similar approaches could be employed of optical microscopy with brightfield and fluorescence channels. We believe this proposed workflow can serve as a valuable augmentation strategy for microscopy image stitching tasks and will benefit the experimentalist to avoid erroneous analysis and discovery due to incorrect stitching.

With the advent of large language models (LLMs), in both the open source and proprietary domains, attention is turning to how to exploit such artificial intelligence (AI) systems in assisting complex scientific tasks, such as material synthesis, characterization, analysis and discovery. Here, we explore the utility of LLMs, particularly ChatGPT4, in combination with application program interfaces (APIs) in tasks of experimental design, programming workflows, and data analysis in scanning probe microscopy, using both in-house developed APIs and APIs given by a commercial vendor for instrument control. We find that the LLM can be especially useful in converting ideations of experimental workflows to executable code on microscope APIs. Beyond code generation, we find that the GPT4 is capable of analyzing microscopy images in a generic sense. At the same time, we find that GPT4 suffers from an inability to extend beyond basic analyses for more in-depth technical experimental design. We argue that an LLM specifically fine-tuned for individual scientific domains can potentially be a better language interface for converting scientific ideations from human experts to executable workflows. Such a synergy between human expertise and LLM efficiency in experimentation can open new doors for accelerating scientific research, enabling effective experimental protocols sharing in the scientific community.

Atomic force microscopy (AFM) is a widely used tool for nanoscale characterization across materials science, energy research, and biology. However, its adoption in high-throughput materials discovery and statistically driven studies remains limited by a strong dependence on expert operator input and by the scarcity of annotated experimental AFM datasets needed to enable data-driven automation. Here, we introduce SimuScan, a synthetic-data–driven framework that enables reliable AFM feature identification, segmentation, and targeted imaging without requiring large manually labeled experimental datasets. SimuScan generates tunable, high-fidelity synthetic AFM images of defined morphologies while incorporating realistic experimental artifacts, including tip–sample convolution, noise, flattening distortions, and surface debris. These datasets are shown to support scalable, label-free training of modern deep learning models for AFM analysis. When integrated into data-driven AFM workflows, SimuScan-trained models can locate and analyze nanoscale structures across large datasets and guide targeted follow-up imaging. We validate this approach on nanostructured surfaces, DNA assemblies, and bacterial cells, demonstrating robust generalization across diverse sample types with minimal operator intervention. More broadly, this work establishes a general strategy for generating explicitly conditioned, task-relevant synthetic data to improve the reliability of downstream models in autonomous microscopy. This study introduces SimuScan, a synthetic data-driven deep learning framework that enables autonomous atomic force microscopy without manual annotation, accelerating AI-based nanoscale characterization.

Hierarchical assemblies of ferroelectric nanodomains, so-called super-domains, can exhibit exotic morphologies that lead to distinct behaviours. Controlling these super-domains reliably is critical for realizing states with desired functional properties. Here we reveal the super-switching mechanism by using a biased atomic force microscopy tip, that is, the switching of the in-plane super-domains, of a model ferroelectric Pb 0.6 Sr 0.4 TiO 3 . We demonstrate that the writing process is dominated by a super-domain nucleation and stabilization process. A complex scanning-probe trajectory enables on-demand formation of intricate centre-divergent, centre-convergent and flux-closure polar structures. Correlative piezoresponse force microscopy and optical spectroscopy confirm the topological nature and tunability of the emergent structures. The precise and versatile nanolithography in a ferroic material and the stability of the generated structures, also validated by phase-field modelling, suggests potential for reliable multi-state nanodevice architectures and, thereby, an alternative route for the creation of tunable topological structures for applications in neuromorphic circuits. A biased atomic force microscopy tip can write complex in-plane polar topologies in a model ferroelectric Pb 0.6 Sr 0.4 TiO 3 by means of a smart scan path design. Hence, on-demand generation, reading and erasing of tunable topologies is possible.

Biofilms are complex microbial communities critical in medical, industrial, and environmental contexts. Understanding their assembly, structure, genetic regulation, interspecies interactions, and environmental responses is key to developing effective control and mitigation strategies. While atomic force microscopy (AFM) offers critically important high-resolution insights on structural and functional properties at the cellular and even sub-cellular level, its limited scan range and labor-intensive nature restricts the ability to link these smaller scale features to the functional macroscale organization of the films. We begin to address this limitation by introducing an automated large area AFM approach capable of capturing high-resolution images over millimeter-scale areas, aided by machine learning for seamless image stitching, cell detection, and classification. Large area AFM is shown to provide a very detailed view of spatial heterogeneity and cellular morphology during the early stages of biofilm formation which were previously obscured. Using this approach, we examined the organization of Pantoea sp. YR343 on PFOTS-treated glass surfaces. Our findings reveal a preferred cellular orientation among surface-attached cells, forming a distinctive honeycomb pattern. Detailed mapping of flagella interactions suggests that flagellar coordination plays a role in biofilm assembly beyond initial attachment. Additionally, we use large-area AFM to characterize surface modifications on silicon substrates, observing a significant reduction in bacterial density. This highlights the potential of this method for studying surface modifications to better understand and control bacterial adhesion and biofilm formation.

High strength and ductility are highly desired in fiber‐reinforced composites, yet achieving both simultaneously remains elusive. A hierarchical architecture is developed utilizing high aspect ratio chemically transformable thermoplastic nanofibers that form covalent bonding with the matrix to toughen the fiber‐matrix interphase. The nanoscale fibers are electrospun on the micrometer‐scale reinforcing carbon fiber, creating a physically intertwined, randomly oriented scaffold. Unlike conventional covalent bonding of matrix molecules with reinforcing fibers, here, the nanofiber scaffold is utilized ‒ interacting non‐covalently with core fiber but bridging covalently with polymer matrix ‒ to create a high volume fraction of immobilized matrix or interphase around core reinforcing elements. This mechanism enables efficient fiber‐matrix stress transfer and enhances composite toughness. Molecular dynamics simulation reveals enhancement of the fiber‐matrix adhesion facilitated by nanofiber‐aided hierarchical bonding with the matrix. The elastic modulus contours of interphase regions obtained from atomic force microscopy clearly indicate the formation of stiffer interphase. These nanoengineered composites exhibit a ≈60% and ≈100% improved in‐plane shear strength and toughness, respectively. This approach opens a new avenue for manufacturing toughened high‐performance composites. High strength and ductility are highly desired in fiber‐reinforced composites, yet achieving both simultaneously remains elusive. A hierarchical architecture is developed utilizing high aspect ratio chemically transformable thermoplastic nanofibers that form covalent bonding with the matrix to toughen the fiber‐matrix interphase. This approach is practical yet straightforward and can potentially produce composites with superior fiber‐matrix interfacial properties.

While hexagonal boron nitride (hBN) has been widely used as a buffer or encapsulation layer for emerging electronic devices, interest in utilizing it for large‐area chemical barrier coating has somewhat faded. A chemical vapor deposition process is reported here for the conformal growth of hBN on large surfaces of various alloys and steels, regardless of their complex shapes. In contrast to the previously reported very limited protection by hBN against corrosion and oxidation, protection of steels against 10% HCl and oxidation resistance at 850 °C in air is demonstrated. Furthermore, an order of magnitude reduction in the friction coefficient of the hBN coated steels is shown. The growth mechanism is revealed in experiments on thin metal films, where the tunable growth of single‐crystal hBN with a selected number of layers is demonstrated. The key distinction of the process is the use of N2 gas, which gets activated exclusively on the catalyst's surface and eliminates adverse gas‐phase reactions. This rate‐limiting step allowed independent control of activated nitrogen along with boron coming from a solid source (like elemental boron). Using abundant and benign precursors, this approach can be readily adopted for large‐scale hBN synthesis in applications where cost, production volume, and process safety are essential. A novel hexagonal boron nitride (hBN) synthesis method is demonstrated for two very different application classes: (i) single‐crystal electronic grade hBN with a controllable number of layers for emerging 2D devices and (ii) protection of industrial alloys with extremely large‐scale usage such as low‐carbon and stainless steels, cupronickels and Inconels. The results suggest that steels protected by hBN can be used in very different industries.

Mapping the dielectric properties of cells with nanoscale spatial resolution can be an important tool in nanomedicine and nanotoxicity analysis, which can complement structural and mechanical nanoscale measurements. Recently we have shown that dielectric constant maps can be obtained on dried fixed cells in air environment by means of scanning dielectric force volume microscopy. Here, we demonstrate that such measurements can also be performed in the much more challenging case of fixed cells in liquid environment. Performing the measurements in liquid media contributes to preserve better the structure of the fixed cells, while also enabling accessing the local dielectric properties under fully hydrated conditions. The results shown in this work pave the way to address the nanoscale dielectric imaging of living cells, for which still further developments are required, as discussed here.