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Scanning probe microscopy can be used to obtain topographical, mechanical, electrical, and electrochemical information on a wide range of materials in a variety of environments, including in situ and operando studies for rechargeable battery systems.
Scanning probe microscopy (SPM) is an important tool for performing measurements at the nanoscale in imaging bacteria or proteins in biology, as well as in the electronics industry. An essential element of SPM is a sharp, stable tip that possesses a small radius of curvature to enhance spatial resolution. Existing techniques for forming such tips are not ideal. High-aspect-ratio, monolithically integrated, as-grown carbon nanofibers (CNFs) have been formed that show promise for SPM applications by overcoming the limitations present in wet chemical and separate substrate etching processes.
Combinatorial materials libraries provide an efficient route for mapping composition–property relationships, but their broader impact depends on rapid, quantitative, and functionally relevant characterization. Scanning Probe Microscopy (SPM), including piezoresponse force microscopy (PFM), offers significant potential for quantitative, functionally relevant combi-library readouts. Here, we implement a fully automated SPM workflow for ferroelectric combinatorial libraries and benchmark Gaussian-process-based Bayesian optimization strategies for autonomous experiment planning. The workflow integrates automated probe motion, contact optimization, imaging, and dual amplitude resonance tracking-PFM spectroscopy, and uses scalarized spectroscopic observables to guide subsequent measurements. Stage motion, probe engagement, in-contact tuning, imaging, spectroscopy, and the choice of the next measurement location all proceed without human input. We demonstrate the approach on Sm-doped BiFeO 3 and Zn x Mg 1−x O libraries. By comparing vanilla Bayesian optimization with a measured-noise variant, we show that explicit treatment of local reproducibility can improve modeling of composition-dependent response when the measured variance is physically meaningful, but can also reduce robustness when variability is dominated by outliers or topographic artifacts. Furthermore, these results establish automated SPM as a bridge between combinatorial synthesis and quantitative functional characterization.
In this paper we present results on the stability and lateral resolution capability of carbon nanotube (CNT) scanning probes as applied to atomic force microscopy (AFM). Surface topography images of ultra-thin films (2-5 nm thickness) obtained with AFM are used to illustrate the lateral resolution capability of single-walled carbon nanotube probes. Images of metal films prepared by ion beam sputtering exhibit grain sizes ranging from greater than 10 nm to as small as approximately 2 nm for gold and iridium respectively. In addition, imaging stability and lifetime of multi-walled carbon nanotube scanning probes are studied on a relatively hard surface of silicon nitride (Si3N4). AFM images Of Si3N4 surface collected after more than 15 hrs of continuous scanning show no detectable degradation in lateral resolution. These results indicate the general feasibility of CNT tips and scanning probe microscopy for examining nanometer-scale surface features of deposited metals as well as non-conductive thin films. AFM coupled with CNT tips offers a simple and nondestructive technique for probing a variety of surfaces, and has immense potential as a surface characterization tool in integrated circuit manufacturing.
Microscopy is one of the primary sources of information on materials structure and functionality at the nanometer and atomic scales. The data generated through microscopy is often contained in well-structured datasets, enriched with extensive metadata and sample histories, although not always with the same level of detail or storage format. The broad incorporation of data management plans by major funding agencies ensures the preservation and accessibility of this data. However, deriving insights from these rich datasets remains challenging due to the lack of established code ecosystems, standardized benchmarks, and integration strategies. Correspondingly, the efficiency of data usage is very low, and time expenditures at the analysis stage are enormous. In addition to post-acquisition data analysis, the emergence of application programming interfaces by major microscope manufacturers now creates opportunities for real-time ML-based data analytics to enable automated decision making, and particularly ML-agent controlled real-time microscope operation. Despite these opportunities, there is a significant gap in integrating the ML community with the broader microscopy community, limiting the value that these methods bring to physics and materials discovery and materials optimization. Hackathons address these challenges by fostering collaboration between ML experts and microscopy professionals, encouraging the development of innovative solutions that leverage ML for microscopy and preparing the workforce of the future both for microscopy-intensive domains areas, instrument manufacturers, and ML scientists interested in real world applications for fundamental research, materials optimization, and manufacturing. The hackathon generated benchmark datasets and digital twins of microscopes that further contribute to the development of the field and establish data analysis ecosystems. All the codes can be found at GitHub(https://github.com/KalininGroup/Mic-hackathon-2024-codes-publication/tree/1.0.0.1) and Zenodo (https://zenodo.org/records/15579940).
Pd/SiC has been used as a hydrogen and a hydrocarbon gas sensor operated at high temperature. UHV (Ultra High Vacuum)-Scanning Tunneling Microscopy (STM), Atomic Force Microscopy (AFM) and X-ray Photoelectron Spectroscopy (XPS) techniques were applied to study the relationship between the morphology and chemical compositions for Pd ultra-thin films on SiC (less than 30 angstroms) at different annealing temperatures. Pd ultra-thin film on 6H-SiC was prepared by the RF sputtering method. The morphology from UHV-STM and AFM shows that the Pd thin film was well deposited on SiC substrate, and the Pd was partially aggregated to round shaped participates at an annealing temperature of 300 C. At 400 C, the amount of surface participates decreases, and some strap shape participates appear. From XPS, Pd2Si was formed on the surface after annealing at 300 C, and all Pd reacted with SiC to form Pd2Si after annealing at 400 C. The intensity of the XPS Pd peak decreases enormously at 400 C. The Pd film diffused into SiC, and the Schottky barrier height has almost no changes. The work shows the Pd sicilides/SiC have the same electronic properties with Pd/SiC, and explains why the Pd/SiC sensor still responds to hydrogen at high operating temperatures.
Controlling nanoscale tip‐induced material removal is crucial for achieving atomic‐level precision in tomographic sensing with atomic force microscopy (AFM). While advances have enabled volumetric probing of conductive features with nanometer accuracy in solid‐state devices, materials, and photovoltaics, limitations in spatial resolution and volumetric sensitivity persist. This work identifies and addresses in‐plane and vertical tip‐sample junction leakage as sources of parasitic contrast in tomographic AFM, hindering real‐space 3D reconstructions. Novel strategies are proposed to overcome these limitations. First, the contrast mechanisms analyzing nanosized conductive features are explored when confining current collection purely to in‐plane transport, thus allowing reconstruction with a reduction in the overestimation of the lateral dimensions. Furthermore, an adaptive tip‐sample biasing scheme is demonstrated for the mitigation of a class of artefacts induced by the high electric field inside the thin oxide when volumetrically reduced. This significantly enhances vertical sensitivity by approaching the intrinsic limits set by quantum tunneling processes, allowing detailed depth analysis in thin dielectrics. The effectiveness of these methods is showcased in tomographic reconstructions of conductive filaments in valence change memory, highlighting the potential for application in nanoelectronics devices and bulk materials and unlocking new limits for tomographic AFM.
This work introduces a knowledge-informed framework that integrates human expertise and prior interest into active learning-driven autonomous experimentation, which enhances the exploration efficiency through more targeted experimentation.
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Layered oxides exhibit high ionic mobility and chemical flexibility, attracting interest as cathode materials for lithium-ion batteries and the pairing of hydrogen production and carbon capture. Recently, layered oxides emerged as highly tunable semiconductors. For example, by introducing anti-Frenkel defects, the electronic hopping conductance in hexagonal manganites is increased locally by orders of magnitude. Here, local acceptor and donor doping in Er(Mn,Ti)O3 is demonstrated, facilitated by the controlled splitting of anti-Frenkel defects under applied d.c. voltage. By combining density functional theory calculations, scanning probe microscopy, atom probe tomography, and scanning transmission electron microscopy, it is shown that the oxygen defects can readily be moved through the layered crystal structure, leading to nano-sized interstitial-rich (p-type) and vacancy-rich (n-type) regions. The resulting pattern is comparable to dipolar npn-junctions and stable on the timescale of days. These findings reveal the possibility of temporarily functionalizing oxide semiconductors at the nanoscale, giving additional opportunities for the field of oxide electronics and the development of transient electronics in general.
Autonomous experiments (AEs) are transforming how scientific research is conducted by integrating artificial intelligence with automated experimental platforms. Current AEs primarily focus on the optimization of a predefined target; while accelerating this goal, such an approach limits the discovery of unexpected or unknown physical phenomena. Here, we introduce a novel framework, INS 2 ANE (Integrated Novelty Score−Strategic Autonomous Non-Smooth Exploration), to enhance the discovery of novel phenomena in autonomous microscopy experimentation. Our method integrates two key components: (1) a novelty scoring system that evaluates the uniqueness of experimental results and (2) a strategic sampling mechanism that promotes exploration of under-sampled regions even if they appear less promising by conventional criteria. We validate this approach on a preacquired data set with a known ground truth comprising of image−spectral pairs. We further implement the process on autonomous scanning probe microscopy experiments. INS 2 ANE significantly increases the diversity of explored phenomena in comparison to conventional optimization routines, enhancing the likelihood of discovering previously unobserved phenomena. These results demonstrate the potential for autonomous microscopy experiments to enhance the scientific discovery by navigating complex experimental spaces to uncover novel phenomena.
Time-resolved scanning probe microscopy methods, like time-resolved electrostatic force microscopy (trEFM), enable imaging of dynamic processes ranging from ion motion in batteries to electronic dynamics in microstructured thin film semiconductors for solar cells. Reconstructing the underlying physical dynamics from these techniques can be challenging due to the interplay of cantilever physics with the actual transient kinetics of interest in the resulting signal. Previously, quantitative trEFM used empirical calibration of the cantilever or feed-forward neural networks trained on simulated data to extract the physical dynamics of interest. Both these approaches are limited by interpreting the underlying signal as a single exponential function, which serves as an approximation but does not adequately reflect many realistic systems. Here, we present a multi-branched, multi-output convolutional neural network (CNN) that uses the trEFM signal in addition to the physical cantilever parameters as input. The trained CNN accurately extracts parameters describing both single-exponential and bi-exponential underlying functions, and more accurately reconstructs real experimental data in the presence of noise. This article demonstrates an application of physics-informed machine learning to complex signal processing tasks, enabling more efficient and accurate analysis of trEFM.
The parallel development of ultramicroelectrodes (UMEs) and groundbreaking scanning probe microscopy techniques in the late 1980s led to the development of the scanning electrochemical microscope. Scanning electrochemical microscopy (SECM) was born from the idea of using a tiny electrode to measure the local electrochemical behavior at operating electrodes. From its foundations, the technique displayed an inherent versatility in measuring sample properties beyond topography. It allowed experimenters to measure and map chemical reactions occurring at diverse interfaces, from inspecting the reversibility of redox mediators at metal electrodes, to detecting the hallmarks of cellular respiration on living plant leaves. Related but distinct electrochemical scanning probe techniques, such as electrochemical atomic force microscopy (EC-AFM), scanning ion conductance microscopy (SICM), and scanning electrochemical cell microscopy (SECCM) have developed in parallel. These techniques have demonstrated exquisite spatial resolution down to the nanoscale regime. However, it is the proposition of this review that SECM remains unmatched at revealing the chemical aspects of electrochemistry. Furthermore, it is our intention to review and demonstrate that the versatile architecture of SECM continues to evolve and address fundamental and emerging challenges in the fields of energy storage and conversion, chemical biology, materials science, and environmental chemistry, among others.
Dynamic spectroscopies in scanning probe microscopy (SPM) are critical for probing material properties, such as force interactions, mechanical properties, polarization switching, electrochemical reactions, and ionic dynamics. However, the practical implementation of these measurements is constrained by the need to balance imaging time and data quality. Signal to noise requirements favor long acquisition times and high frequencies to improve signal fidelity. However, these are limited on the low end by contact resonant frequency and photodiode sensitivity and on the high end by the time needed to acquire high-resolution spectra or the propensity for sample degradation under high field excitation over long times. The interdependence of key parameters such as instrument settings, acquisition times, and sampling rates makes manual tuning labor-intensive and highly dependent on user expertise, often yielding operator-dependent results. These limitations are prominent in techniques like dual amplitude resonance tracking in piezoresponse force microscopy that utilize multiple concurrent feedback loops for topography and resonance frequency tracking. Here, a reward-driven workflow is proposed that automates the tuning process, adapting experimental conditions in real time to optimize data quality. Furthermore, this approach significantly reduces the complexity and time required for manual adjustments and can be extended to other SPM spectroscopic methods, enhancing overall efficiency and reproducibility.
Domain-wall dynamics in ferroelectric materials are strongly position-dependent, since each polar interface is locked into a unique local microstructure. This necessitates spatially resolved studies of wall pinning using scanning-probe microscopy techniques. The pinning centers and pre-existing domain walls are usually sparse within the image plane, precluding the use of dense hyperspectral imaging modes and requiring time-consuming human experimentation. Here, a large-area epitaxial PbTiO 3 film on cubic KTaO 3 was investigated to quantify the electric-field-driven dynamics of the polar–strain domain structures using ML-controlled automated piezoresponse force microscopy. Analysis of 1500 switching events reveals that domain-wall displacement depends not only on field parameters but also on the local ferroelectric–ferroelastic configuration. For example, twin boundaries in polydomains regions, like a 1 – /c+ ∥ a 2 – /c – , stay pinned up to a certain level of bias magnitude and change only marginally as the bias increases from 20 to 30 V, whereas single-variant boundaries, like the a 2 + /c + ∥ a 2 – /c – stack, are already activated at 20 V. These statistics on the possible ferroelectric and ferroelastic wall orientations, together with the automated high-throughput AFM workflow, can be distilled into a predictive map that links domain configurations to pulse parameters. Here, this microstructure-specific rule set forms the foundation for the design of ferroelectric memories.
Polyimide binders are often used in electrodes made with silicon for lithium-ion batteries for their mechanical strength and adhesion, which help mitigate mechanical issues associated with large volumetric expansion. These binders can be electrochemically active, but it is difficult to characterize what physical and chemical changes occur due to a composite electrode with multiple components and processes at play. Here, in this work, we study electrodes consisting only of polyimide binder and conductive carbon, using scanning probe-based techniques—contact resonance, force volume, and scanning spreading resistance microscopy—along with cryo-scanning transmission electron microscopy, electron energy loss spectroscopy, and energy dispersive X-ray spectroscopy. We show that lithium becomes trapped in the binder during cycling and results in large initial capacity losses, the formation of dendrite-like features, column-like domains of significantly increased mechanical modulus, and a slight increase in electronic resistivity.
The continuous advancement of high-temperature superconductor (HTS) technologies has greatly accelerated the development and deployment of HTS applications. Among the critical tools supporting these advancements are reel-to-reel (R2R) critical current (I c ) measurement techniques, which are extensively used by both manufacturers and end users to characterize long-length REBCO conductors. These techniques play a vital role in quality assurance and quality control (QA/QC), ensuring the reliability and performance of HTS conductors and applications throughout the production cycle. We have developed a range of in-house devices for R2R measurements at the University of Houston and Princeton Plasma Physics Laboratory. These include one-dimensional (1D) scan using a magnetic circuit (MC) and two-dimensional (2D) magnetic field mapping systems based on scanning probe array microscope (SPAM) or scanning probe microscopy (SPM). Each technique offers distinct advantages: the MC system provides ultra-fast scanning speeds, ideal for rapid inspection in large-scale industrial production; the high-resolution SPM delivers detailed insights for conductor research and development; and the SPAM, with its simpler mechanical setup, can be upgraded for higher field and lower temperature conditions with a balanced 2D resolution. Here we compared the magnetization and detection capabilities of these techniques through experiments on rare-earth barium copper oxide (REBCO) coated conductor samples, with data analysis supported by numerical simulations. Based on our comprehensive comparative studies, we propose enhancements for each measurement system and provide guidelines for selecting the optimal technique combinations to meet specific application requirements.
Carbon nanotubes (CNTs) used as a probe for scanning probe microscopy has become one of the many potential usages of CNTs that is finding real applications in scientific research and industrial communities. It has been proposed that the unique mechanical buckling properties of the CNT would lessen the imaging force exerted on the sample and, thus, make CNT scanning probes ideal for imaging soft materials, including biological samples in liquid environments. The hydrophobic nature of the CNT graphitic sidewall is clearly chemically incompatible with the aqueous solution requirements in some biological imaging applications. In this paper, we present electron micrograph results demonstrating the instability of CNT scanning probes when submerged in aqueous solution. Moreover, we also introduce a novel approach to resolve this chemical incompatibility problem. By coating the CNT probe with ethylenediamine, thus rendering the CNT probe less hydrophobic, we demonstrate the liquid imaging capability of treated CNT probes. Experimental data for imaging in aqueous solutions are presented, which include an ultrathin Ir film and DNA molecules on a mica surface.