AEcroscopy: A Software-Hardware Framework Empowering Microscopy Toward Automated and Autonomous Experimentation

Microscopy has been pivotal in improving the understanding of structure-function relationships at the nanoscale and is by now ubiquitous in most characterization labs. However, traditional microscopy operations are still limited largely by a human-centric click-and-go paradigm utilizing vendor-provided software, which limits the scope, utility, efficiency, effectiveness, and at times reproducibility of microscopy experiments. Here, a coupled software-hardware platform is developed that consists of a software package termed AEcroscopy (short for Automated Experiments in Microscopy), along with a field-programmable-gate-array device with LabView-built customized acquisition scripts, which overcome these limitations and provide the necessary abstractions toward full automation of microscopy platforms. The platform works across multiple vendor devices on scanning probe microscopes and electron microscopes. It enables customized scan trajectories, processing functions that can be triggered locally or remotely on processing servers, user-defined excitation waveforms, standardization of data models, and completely seamless operation through simple Python commands to enable a plethora of microscopy experiments to be performed in a reproducible, automated manner. This platform can be readily coupled with existing machine-learning libraries and simulations, to provide automated decision-making and active theory-experiment optimization to turn microscopes from characterization tools to instruments capable of autonomous model refinement and physics discovery.

Explainability and human intervention in autonomous scanning probe microscopy

The broad adoption of machine learning (ML)-based autonomous experiments (AEs) in material characterization and synthesis requires strategies development for understanding and intervention in the experimental workflow. Here, we introduce and realize a post-experimental analysis strategy for deep kernel learning-based autonomous scanning probe microscopy. This approach yields real-time and post-experimental indicators for the progression of an active learning process interacting with an experimental system. We further illustrate how this approach can be applied to human-in-the-loop AEs, where human operators make high-level decisions at high latencies setting the policies for AEs, and the ML algorithm performs low-level, fast decisions. The proposed approach is universal and can be extended to other techniques and applications such as combinatorial library analysis.

Optimizing the accuracy of viscoelastic characterization with AFM force-distance experiments in the time and frequency domains

Atomic Force Microscopy (AFM) force-distance (FD) experiments have emerged as an attractive alternative to traditional micro-rheology measurement techniques owing to their versatility of use in materials of a wide range of mechanical properties. Here, we show that the range of time dependent behaviour which can reliably be resolved from the typical method of FD inversion (fitting constitutive FD relations to FD data) is inherently restricted by the experimental parameters: sampling frequency, experiment length, and strain rate. Specifically, we demonstrate that violating these restrictions can result in errors in the values of the parameters of the complex modulus. In the case of complex materials, such as cells, whose behaviour is not specifically understood a priori, the physical sensibility of these parameters cannot be assessed and may lead to falsely attributing a physical phenomenon to an artifact of the violation of these restrictions. We use arguments from information theory to understand the nature of these inconsistencies as well as devise limits on the range of mechanical parameters which can be reliably obtained from FD experiments. The results further demonstrate that the nature of these restrictions depends on the domain (time or frequency) used in the inversion process, with the time domain being far more restrictive than the frequency domain. Finally, we demonstrate how to use these restrictions to better design FD experiments to target specific timescales of a material's behaviour through our analysis of a polydimethylsiloxane (PDMS) polymer sample.

Automated Experiments of Local Non-Linear Behavior in Ferroelectric Materials

An automated experiment in multimodal imaging to probe structural, chemical, and functional behaviors in complex materials and elucidate the dominant physical mechanisms that control device function is developed and implemented. Here, the emergence of non-linear electromechanical responses in piezoresponse force microscopy (PFM) is explored. Non-linear responses in PFM can originate from multiple mechanisms, including intrinsic material responses often controlled by domain structure, surface topography that affects the mechanical phenomena at the tip-surface junction, and the presence of surface contaminants. Using an automated experiment to probe the origins of non-linear behavior in ferroelectric lead titanate (PTO) and ferroelectric Al_0.93 B_0.07 N films, it is found that PTO shows asymmetric nonlinear behavior across a/c domain walls and a broadened high nonlinear response region around c/c domain walls. In contrast, for Al_0.93 B_0.07 N, well-poled regions show high linear piezoelectric responses, when paired with low non-linear responses regions that are multidomain show low linear responses and high nonlinear responses. It is shown that formulating dissimilar exploration strategies in deep kernel learning as alternative hypotheses allows for establishing the preponderant physical mechanisms behind the non-linear behaviors, suggesting that automated experiments can potentially discern between competing physical mechanisms. This technique can also be extended to electron, probe, and chemical imaging.

Polymer Nanocomposite Dielectrics: Understanding the Matrix/Particle Interface

Polymer nanocomposite dielectrics possess exceptional electric properties that are absent in the pristine dielectric polymers. The matrix/particle interface in polymer nanocomposite dielectrics is suggested to play decisive roles on the bulk material performance. Herein, we present a critical overview of recent research advances and important insights in understanding the matrix/particle interfacial characteristics in polymer nanocomposite dielectrics. The primary experimental strategies and state-of-the-art characterization techniques for resolving the local property-structure correlation of the matrix/particle interface are dissected in depth, with a focus on the characterization capabilities of each strategy or technique that other approaches cannot compete with. Limitations to each of the experimental strategy are evaluated as well. In the last section of this Review, we summarize and compare the three experimental strategies from multiple aspects and point out their advantages and disadvantages, critical issues, and possible experimental schemes to be established. Finally, the authors' personal viewpoints regarding the challenges of the existing experimental strategies are presented, and potential directions for the interface study are proposed for future research.

A Robust Neural Network for Extracting Dynamics from Electrostatic Force Microscopy Data

Advances in scanning probe microscopy (SPM) methods such as time-resolved electrostatic force microscopy (trEFM) now permit the mapping of fast local dynamic processes with high resolution in both space and time, but such methods can be time-consuming to analyze and calibrate. Here, we design and train a regression neural network (NN) that accelerates and simplifies the extraction of local dynamics from SPM data directly in a cantilever-independent manner, allowing the network to process data taken with different cantilevers. We validate the NN's ability to recover local dynamics with a fidelity equal to or surpassing conventional, more time-consuming, calibrations using both simulated and real microscopy data. We apply this method to extract accurate photoinduced carrier dynamics on n = 1 butylammonium lead iodide, a halide perovskite semiconductor film that is of interest for applications in both solar photovoltaics and quantum light sources. Finally, we use SHapley Additive exPlanations to evaluate the robustness of the trained model, confirm its cantilever-independence, and explore which parts of the trEFM signal are important to the network.

Nanoscale Control of Polar Surface Phases in Layered van der Waals CuInP2S6

Antiferroelectric (AFE) materials, in which alternating dipole moments cancel out to a zero net macroscopic polarization, can be used for high-density energy storage and memory applications. The AFE phase can exist in bulk CuInP₂Se₆, CuBiP₂S₆, and a few other transition-metal thiophosphates below 200 K. The required low temperature poses challenges for practical applications. In this work, we report the coexistence of ferrielectric (FE) states and a stable surface phase that does not show piezoelectric response ("zero-response phase") in bulk CuInP₂S₆ at room temperature. Using piezoresponse force microscopy (PFM) tomographic imaging together with density functional theory, we find that direct and alternating voltages can locally and stably convert FE to zero-response phases and vice versa. While PFM loops show pinched hystereses reminiscent of antiferroelectricity, PFM tomography reveals that the zero-response areas form only on top of the FE phase in which the polarization vector is pointing up. Theoretical calculations suggest that the zero-response phase may correspond to AFE ordering where stacked CuInP₂S₆ layers have alternating polarization orientations thereby leading to a net-zero polarization. Consistent with experimental findings, theory predicts that the FE polarization pointing down is robust up to the top surface, whereas FE polarization pointing up energetically favors the formation of an AFE surface layer, whose thickness is likely to be sensitive to local strains. AFE order is likely to be more robust against detrimental size effects than polar order, therefore providing additional opportunities to create multifunctional heterostructures with 2D electronic materials.

Exploring Causal Physical Mechanisms via Non-Gaussian Linear Models and Deep Kernel Learning: Applications for Ferroelectric Domain Structures

Rapid emergence of multimodal imaging in scanning probe, electron, and optical microscopies has brought forth the challenge of understanding the information contained in these complex data sets, targeting the intrinsic correlations between different channels, and further exploring the underpinning causal physical mechanisms. Here, we develop such an analysis framework for Piezoresponse Force Microscopy. We argue that under certain conditions, we can bootstrap experimental observations with the prior knowledge of materials structure to get information on certain nonobserved properties, and demonstrate linear causal analysis for PFM observables. We further demonstrate that the strength of individual causal links between complex descriptors can be ascertained using the deep kernel learning (DKL) model. In this DKL analysis, we use the prior information on domain structure within the image to predict the physical properties. This analysis demonstrates the correlative relationships between morphology, piezoresponse, elastic property, etc., at nanoscale. The prediction of morphology and other physical parameters illustrates a mutual interaction between surface condition and physical properties in ferroelectric materials. This analysis is universal and can be extended to explore the correlative relationships of other multichannel data sets, and allow for high-fidelity reconstruction of underpinning functionalities and physical mechanisms.

Automated and Autonomous Experiments in Electron and Scanning Probe Microscopy

Machine learning and artificial intelligence (ML/AI) are rapidly becoming an indispensable part of physics research, with domain applications ranging from theory and materials prediction to high-throughput data analysis. In parallel, the recent successes in applying ML/AI methods for autonomous systems from robotics to self-driving cars to organic and inorganic synthesis are generating enthusiasm for the potential of these techniques to enable automated and autonomous experiments (AE) in imaging. Here, we aim to analyze the major pathways toward AE in imaging methods with sequential image formation mechanisms, focusing on scanning probe microscopy (SPM) and (scanning) transmission electron microscopy ((S)TEM). We argue that automated experiments should necessarily be discussed in a broader context of the general domain knowledge that both informs the experiment and is increased as the result of the experiment. As such, this analysis should explore the human and ML/AI roles prior to and during the experiment and consider the latencies, biases, and prior knowledge of the decision-making process. Similarly, such discussion should include the limitations of the existing imaging systems, including intrinsic latencies, non-idealities, and drifts comprising both correctable and stochastic components. We further pose that the role of the AE in microscopy is not the exclusion of human operators (as is the case for autonomous driving), but rather automation of routine operations such as microscope tuning, etc., prior to the experiment, and conversion of low latency decision making processes on the time scale spanning from image acquisition to human-level high-order experiment planning. Overall, we argue that ML/AI can dramatically alter the (S)TEM and SPM fields; however, this process is likely to be highly nontrivial and initiated by combined human-ML workflows and will bring challenges both from the microscope and ML/AI sides. At the same time, these methods will enable opportunities and paradigms for scientific discovery and nanostructure fabrication.

Lowering of Tc in Van Der Waals Layered Materials Under In-Plane Strain

The dependence of electromechanical behavior on strain in ferroelectric materials can be leveraged as parameter to tune ferroelectric properties such as the Curie temperature. For van der Waals materials, a unique opportunity arises because of wrinkling, bubbling, and Moiré phenomena accessible due to structural properties inherent to the van der Waals gap. Here, we use piezoresponse force microscopy and unsupervised machine learning methods to gain insight into the ferroelectric properties of layered CuInP₂S₆ where local areas are strained in-plane due to a partial delamination, resulting in a topographic bubble feature. We observe significant differences between strained and unstrained areas in piezoresponse images as well as voltage spectroscopy, during which strained areas show a sigmoid-shaped response usually associated with the response measured around the Curie temperature, indicating a lowering of the Curie temperature under tensile strain. These results suggest that strain engineering might be used to further increase the functionality of CuInP₂S₆ through locally modifying ferroelectric properties on the micro- and nanoscale.

Revealing Antiferroelectric Switching and Ferroelectric Wakeup in Hafnia by Advanced Piezoresponse Force Microscopy

Hafnium oxide (HfO₂)-based ferroelectrics offer remarkable promise for memory and logic devices in view of their compatibility with traditional silicon complementary metal oxide semiconductor (CMOS) technology, high switchable polarization, good endurance, and thickness scalability. These factors have led to a steep rise in the level of research on HfO₂ over the past number of years. While measurements on capacitors are promising for understanding macroscopic effects, many open questions regarding the emergence of ferroelectricity and electric field cycling behaviors remain. Continued progress requires information regarding the nanoscale ferroelectric behaviors on the bare surface (i.e., without encapsulation), which is notably absent. To overcome this barrier, we have applied complementary modes of piezoresponse force microscopy with the goal of directly and quantitatively sensing nanoscale ferroelectric behaviors in bare HfO₂ thin films. Our results on 8 nm Si-doped HfO₂ reveal nanoscale domains of local remnant polarization states exhibiting a weak piezoelectric coupling (d_eff) in the range 0.6-1.5 pm/V. While we observed localized enhancement of d_eff during progressive stressing of the bare HfO₂ thin film, we did not detect stable polarization switching which is a prerequisite of ferroelectric switching. This result could be explained using polarization switching spectroscopy which revealed antiferroelectric-like switching in the form of pinched hysteresis loops as well as increasing remnant response with repeated cycling. As such, our results offer a promising route for material scientists who want to explore the nanoscale origins of antiferroelectricity and ferroelectric wakeup in HfO₂.

Local Strain and Polarization Mapping in Ferrielectric Materials

CuInP₂S₆ (CIPS) is a van der Waals material that has attracted attention because of its unusual properties. Recently, a combination of density functional theory (DFT) calculations and piezoresponse force microscopy (PFM) showed that CIPS is a uniaxial quadruple-well ferrielectric featuring two polar phases and a total of four polarization states that can be controlled by external strain. Here, we combine DFT and PFM to investigate the stress-dependent piezoelectric properties of CIPS, which have so far remained unexplored. The two different polarization phases are predicted to differ in their mechanical properties and the stress sensitivity of their piezoelectric constants. This knowledge is applied to the interpretation of ferroelectric domain images, which enables investigation of local strain and stress distributions. The interplay of theory and experiment produces polarization maps and layer spacings which we compare to macroscopic X-ray measurements. We found that the sample contains only the low-polarization phase and that domains of one polarization orientation are strained, whereas domains of the opposite polarization direction are fully relaxed. The described nanoscale imaging methodology is applicable to any material for which the relationship between electromechanical and mechanical characteristics is known, providing insight on structural, mechanical, and electromechanical properties down to ∼10 nm length scales.

To switch or not to switch - a machine learning approach for ferroelectricity

With the advent of increasingly elaborate experimental techniques in physics, chemistry and materials sciences, measured data are becoming bigger and more complex. The observables are typically a function of several stimuli resulting in multidimensional data sets spanning a range of experimental parameters. As an example, a common approach to study ferroelectric switching is to observe effects of applied electric field, but switching can also be enacted by pressure and is influenced by strain fields, material composition, temperature, time, etc. Moreover, the parameters are usually interdependent, so that their decoupling toward univariate measurements or analysis may not be straightforward. On the other hand, both explicit and hidden parameters provide an opportunity to gain deeper insight into the measured properties, provided there exists a well-defined path to capture and analyze such data. Here, we introduce a new, two-dimensional approach to represent hysteretic response of a material system to applied electric field. Utilizing ferroelectric polarization as a model hysteretic property, we demonstrate how explicit consideration of electromechanical response to two rather than one control voltages enables significantly more transparent and robust interpretation of observed hysteresis, such as differentiating between charge trapping and ferroelectricity. Furthermore, we demonstrate how the new data representation readily fits into a variety of machine-learning methodologies, from unsupervised classification of the origins of hysteretic response via linear clustering algorithms to neural-network-based inference of the sample temperature based on the specific morphology of hysteresis.

Revealing ferroelectric switching character using deep recurrent neural networks

The ability to manipulate domains underpins function in applications of ferroelectrics. While there have been demonstrations of controlled nanoscale manipulation of domain structures to drive emergent properties, such approaches lack an internal feedback loop required for automatic manipulation. Here, using a deep sequence-to-sequence autoencoder we automate the extraction of latent features of nanoscale ferroelectric switching from piezoresponse force spectroscopy of tensile-strained PbZr_0.2Ti_0.8O₃ with a hierarchical domain structure. We identify characteristic behavior in the piezoresponse and cantilever resonance hysteresis loops, which allows for the classification and quantification of nanoscale-switching mechanisms. Specifically, we identify elastic hardening events which are associated with the nucleation and growth of charged domain walls. This work demonstrates the efficacy of unsupervised neural networks in learning features of a material’s physical response from nanoscale multichannel hyperspectral imagery and provides new capabilities in leveraging in operando spectroscopies that could enable the automated manipulation of nanoscale structures in materials.

The scale and dimensionality of imaging data means information is commonly overlooked. Here, using recurrent neural networks we understand temporal dependencies in hyperspectral imagery, enabling the observation of differences in ferroelectric switching mechanisms in PbZr_0.2Ti_0.8O₃ thin films due to formation of charged domain walls.

Toward Electrochemical Studies on the Nanometer and Atomic Scales: Progress, Challenges, and Opportunities

Electrochemical reactions and ionic transport underpin the operation of a broad range of devices and applications, from energy storage and conversion to information technologies, as well as biochemical processes, artificial muscles, and soft actuators. Understanding the mechanisms governing function of these applications requires probing local electrochemical phenomena on the relevant time and length scales. Here, we discuss the challenges and opportunities for extending electrochemical characterization probes to the nanometer and ultimately atomic scales, including challenges in down-scaling classical methods, the emergence of novel probes enabled by nanotechnology and based on emergent physics and chemistry of nanoscale systems, and the integration of local data into macroscopic models. Scanning probe microscopy (SPM) methods based on strain detection, potential detection, and hysteretic current measurements are discussed. We further compare SPM to electron beam probes and discuss the applicability of electron beam methods to probe local electrochemical behavior on the mesoscopic and atomic levels. Similar to a SPM tip, the electron beam can be used both for observing behavior and as an active electrode to induce reactions. We briefly discuss new challenges and opportunities for conducting fundamental scientific studies, matter patterning, and atomic manipulation arising in this context.

The existence and impact of persistent ferroelectric domains in MAPbI3

Methylammonium lead iodide (MAPbI₃) exhibits exceptional photovoltaic performance, but there remains substantial controversy over the existence and impact of ferroelectricity on the photovoltaic response. We confirm ferroelectricity in MAPbI₃ single crystals and demonstrate mediation of the electronic response by ferroelectric domain engineering. The ferroelectric response sharply declines above 57°C, consistent with the tetragonal-to-cubic phase transition. Concurrent band excitation piezoresponse force microscopy-contact Kelvin probe force microscopy shows that the measured response is not dominated by spurious electrostatic interactions. Large signal poling (>16 V/cm) orients the permanent polarization into large domains, which show stabilization over weeks. X-ray photoemission spectroscopy results indicate a shift of 400 meV in the binding energy of the iodine core level peaks upon poling, which is reflected in the carrier concentration results from scanning microwave impedance microscopy. The ability to control the ferroelectric response provides routes to increase device stability and photovoltaic performance through domain engineering.

Correlated Materials Characterization via Multimodal Chemical and Functional Imaging

Multimodal chemical imaging simultaneously offers high-resolution chemical and physical information with nanoscale and, in select cases, atomic resolution. By coupling modalities that collect physical and chemical information, we can address scientific problems in biological systems, battery and fuel cell research, catalysis, pharmaceuticals, photovoltaics, medicine, and many others. The combined systems enable the local correlation of material properties with chemical makeup, making fundamental questions of how chemistry and structure drive functionality approachable. In this Review, we present recent progress and offer a perspective for chemical imaging used to characterize a variety of samples by a number of platforms. Specifically, we present cases of infrared and Raman spectroscopies combined with scanning probe microscopy; optical microscopy and mass spectrometry; nonlinear optical microscopy; and, finally, ion, electron, and probe microscopies with mass spectrometry. We also discuss the challenges associated with the use of data originated by the combinatorial hardware, analysis, and machine learning as well as processing tools necessary for the interpretation of multidimensional data acquired from multimodal studies.

Decoupling Mesoscale Functional Response in PLZT across the Ferroelectric-Relaxor Phase Transition with Contact Kelvin Probe Force Microscopy and Machine Learning

Relaxor ferroelectrics exhibit a range of interesting material behavior, including high electromechanical response, polarization rotations, as well as temperature and electric field-driven phase transitions. The origin of this unusual functional behavior remains elusive due to limited knowledge on polarization dynamics at the nanoscale. Piezoresponse force microscopy and associated switching spectroscopy provide access to local electromechanical properties on the micro- and nanoscale, which can help to address some of these gaps in our knowledge. However, these techniques are inherently prone to artefacts caused by signal contributions emanating from electrostatic interactions between tip and sample. Understanding functional behavior of complex, disordered systems like relaxor materials with unknown electromechanical properties therefore requires a technique that allows distinguishing between electromechanical and electrostatic response. Here, contact Kelvin probe force microscopy (cKPFM) is used to gain insight into the evolution of local electromechanical and capacitive properties of a representative relaxor material lead lanthanum zirconate across the phase transition from a ferroelectric to relaxor state. The obtained multidimensional data set was processed using an unsupervised machine learning algorithm to detect variations in functional response across the probed area and temperature range. Further analysis showed the formation of two separate cKPFM response bands below 50 °C, providing evidence for polarization switching. At higher temperatures only one band is observed, indicating an electrostatic origin of the measured response. In addition, the junction potential difference, which was extracted from the cKPFM data, becomes independent of the temperature in the relaxor state. The combination of this multidimensional voltage spectroscopy technique and machine learning allows to identify the origin of the measured functional response and to decouple ferroelectric from electrostatic phenomena necessary to understand the functional behavior of complex, disordered systems like relaxor materials.

Exploring the Magnetoelectric Coupling at the Composite Interfaces of FE/FM/FE Heterostructures

Multiferroic materials have attracted considerable attention as possible candidates for a wide variety of future microelectronic and memory devices, although robust magnetoelectric (ME) coupling between electric and magnetic orders at room temperature still remains difficult to achieve. In order to obtain robust ME coupling at room temperature, we studied the Pb(Fe_0.5Nb_0.5)O₃/Ni_0.65Zn_0.35Fe₂O₄/Pb(Fe_0.5Nb_0.5)O₃ (PFN/NZFO/PFN) trilayer structure as a representative FE/FM/FE system. We report the ferroelectric, magnetic and ME properties of PFN/NZFO/PFN trilayer nanoscale heterostructure having dimensions 70/20/70 nm, at room temperature. The presence of only (00l) reflection of PFN and NZFO in the X-ray diffraction (XRD) patterns and electron diffraction patterns in Transmission Electron Microscopy (TEM) confirm the epitaxial growth of multilayer heterostructure. The distribution of the ferroelectric loop area in a wide area has been studied, suggesting that spatial variability of ferroelectric switching behavior is low, and film growth is of high quality. The ferroelectric and magnetic phase transitions of these heterostructures have been found at ~575 K and ~650 K, respectively which are well above room temperature. These nanostructures exhibit low loss tangent, large saturation polarization (P_s ~ 38 µC/cm²) and magnetization (M_s ~ 48 emu/cm³) with strong ME coupling at room temperature revealing them as potential candidates for nanoscale multifunctional and spintronics device applications.

Chemical nature of ferroelastic twin domains in CH3NH3PbI3 perovskite

The extraordinary optoelectronic performance of hybrid organic-inorganic perovskites has resulted in extensive efforts to unravel their properties. Recently, observations of ferroic twin domains in methylammonium lead triiodide drew significant attention as a possible explanation for the current-voltage hysteretic behaviour in these materials. However, the properties of the twin domains, their local chemistry and the chemical impact on optoelectronic performance remain unclear. Here, using multimodal chemical and functional imaging methods, we unveil the mechanical origin of the twin domain contrast observed with piezoresponse force microscopy in methylammonium lead triiodide. By combining experimental results with first principles simulations we reveal an inherent coupling between ferroelastic twin domains and chemical segregation. These results reveal an interplay of ferroic properties and chemical segregation on the optoelectronic performance of hybrid organic-inorganic perovskites, and offer an exploratory path to improving functional devices.

Probing of Local Multifield Coupling Phenomena of Advanced Materials by Scanning Probe Microscopy Techniques

The characterization of the local multifield coupling phenomenon (MCP) in various functional/structural materials by using scanning probe microscopy (SPM)-based techniques is comprehensively reviewed. Understanding MCP has great scientific and engineering significance in materials science and engineering, as in many practical applications, materials and devices are operated under a combination of multiple physical fields, such as electric, magnetic, optical, chemical and force fields, and working environments, such as different atmospheres, large temperature fluctuations, humidity, or acidic space. The materials' responses to the synergetic effects of the multifield (physical and environmental) determine the functionalities, performance, lifetime of the materials, and even the devices' manufacturing. SPM techniques are effective and powerful tools to characterize the local effects of MCP. Here, an introduction of the local MCP, the descriptions of several important SPM techniques, especially the electrical, mechanical, chemical, and optical related techniques, and the applications of SPM techniques to investigate the local phenomena and mechanisms in oxide materials, energy materials, biomaterials, and supramolecular materials are covered. Finally, an outlook of the MCP and SPM techniques in materials research is discussed.

Surface Chemistry Controls Anomalous Ferroelectric Behavior in Lithium Niobate

Polarization switching in ferroelectric materials underpins a multitude of applications ranging from nonvolatile memories to data storage to ferroelectric lithography. While traditionally considered to be a functionality of the material only, basic theoretical considerations suggest that switching is expected to be intrinsically linked to changes in the electrochemical state of the surface. Hence, the properties and dynamics of the screening charges can affect or control the switching dynamics. Despite being recognized for over 50 years, analysis of these phenomena remained largely speculative. Here, we explore polarization switching on the prototypical LiNbO₃ surface using the combination of contact mode Kelvin probe force microscopy and chemical imaging by time-of-flight mass-spectrometry and demonstrate pronounced chemical differences between the domains. These studies provide a consistent explanation to the anomalous polarization and surface charge behavior observed in LiNbO₃ and point to new opportunities in chemical control of polarization dynamics in thin films and crystals via control of surface chemistry, complementing traditional routes via bulk doping, and substrate-induced strain and tilt systems.

Towards nanoscale electrical measurements in liquid by advanced KPFM techniques: a review

Fundamental mechanisms of energy storage, corrosion, sensing, and multiple biological functionalities are directly coupled to electrical processes and ionic dynamics at solid-liquid interfaces. In many cases, these processes are spatially inhomogeneous taking place at grain boundaries, step edges, point defects, ion channels, etc and possess complex time and voltage dependent dynamics. This necessitates time-resolved and real-space probing of these phenomena. In this review, we discuss the applications of force-sensitive voltage modulated scanning probe microscopy (SPM) for probing electrical phenomena at solid-liquid interfaces. We first describe the working principles behind electrostatic and Kelvin probe force microscopies (EFM & KPFM) at the gas-solid interface, review the state of the art in advanced KPFM methods and developments to (i) overcome limitations of classical KPFM, (ii) expand the information accessible from KPFM, and (iii) extend KPFM operation to liquid environments. We briefly discuss the theoretical framework of electrical double layer (EDL) forces and dynamics, the implications and breakdown of classical EDL models for highly charged interfaces or under high ion concentrations, and describe recent modifications of the classical EDL theory relevant for understanding nanoscale electrical measurements at the solid-liquid interface. We further review the latest achievements in mapping surface charge, dielectric constants, and electrodynamic and electrochemical processes in liquids. Finally, we outline the key challenges and opportunities that exist in the field of nanoscale electrical measurements in liquid as well as providing a roadmap for the future development of liquid KPFM.

High-veracity functional imaging in scanning probe microscopy via Graph-Bootstrapping

The key objective of scanning probe microscopy (SPM) techniques is the optimal representation of the nanoscale surface structure and functionality inferred from the dynamics of the cantilever. This is particularly pertinent today, as the SPM community has seen a rapidly growing trend towards simultaneous capture of multiple imaging channels and complex modes of operation involving high-dimensional information-rich datasets, bringing forward the challenges of visualization and analysis, particularly for cases where the underlying dynamic model is poorly understood. To meet this challenge, we present a data-driven approach, Graph-Bootstrapping, based on low-dimensional manifold learning of the full SPM spectra and demonstrate its successes for high-veracity mechanical mapping on a mixed polymer thin film and resolving irregular hydration structure of calcite at atomic resolution. Using the proposed methodology, we can efficiently reveal and hierarchically represent salient material features with rich local details, further enabling denoising, classification, and high-resolution functional imaging.

Theory of Single-Impact Atomic Force Spectroscopy in liquids with material contrast

Scanning probe microscopy has enabled nanoscale mapping of mechanical properties in important technological materials, such as tissues, biomaterials, polymers, nanointerfaces of composite materials, to name only a few. To improve and widen the measurement of nanoscale mechanical properties, a number of methods have been proposed to overcome the widely used force-displacement mode, that is inherently slow and limited to a quasi-static regime, mainly using multiple sinusoidal excitations of the sample base or of the cantilever. Here, a different approach is put forward. It exploits the unique capabilities of the wavelet transform analysis to harness the information encoded in a short duration spectroscopy experiment. It is based on an impulsive excitation of the cantilever and a single impact of the tip with the sample. It performs well in highly damped environments, which are often seen as problematic in other standard dynamic methods. Our results are very promising in terms of viscoelastic property discrimination. Their potential is oriented (but not limited) to samples that demand imaging in liquid native environments and also to highly vulnerable samples whose compositional mapping cannot be obtained through standard tapping imaging techniques.

Exploring Anomalous Polarization Dynamics in Organometallic Halide Perovskites

Organometallic halide perovskites (OMHPs) have attracted broad attention as prospective materials for optoelectronic applications. Among the many anomalous properties of these materials, of special interest are the ferroelectric properties including both classical and relaxor-like components, as a potential origin of slow dynamics, field enhancement, and anomalous mobilities. Here, ferroelectric properties of the three representative OMHPs are explored, including FAPb_x Sn_1-x I₃ (x = 0, x = 0.85) and FA_0.85 MA_0.15 PbI₃ using band excitation piezoresponse force microscopy and contact mode Kelvin probe force microscopy, providing insight into long- and short-range dipole and charge dynamics in these materials and probing ferroelectric density of states. Furthermore, second-harmonic generation in thin films of OMHPs is observed, providing a direct information on the noncentrosymmetric polarization in such materials. Overall, the data provide strong evidence for the presence of ferroelectric domains in these systems; however, the domain dynamics is suppressed by fast ion dynamics. These materials hence present the limit of ferroelectric materials with spontaneous polarization dynamically screened by ionic and electronic carriers.

Machine learning-enabled identification of material phase transitions based on experimental data: Exploring collective dynamics in ferroelectric relaxors

Exploration of phase transitions and construction of associated phase diagrams are of fundamental importance for condensed matter physics and materials science alike, and remain the focus of extensive research for both theoretical and experimental studies. For the latter, comprehensive studies involving scattering, thermodynamics, and modeling are typically required. We present a new approach to data mining multiple realizations of collective dynamics, measured through piezoelectric relaxation studies, to identify the onset of a structural phase transition in nanometer-scale volumes, that is, the probed volume of an atomic force microscope tip. Machine learning is used to analyze the multidimensional data sets describing relaxation to voltage and thermal stimuli, producing the temperature-bias phase diagram for a relaxor crystal without the need to measure (or know) the order parameter. The suitability of the approach to determine the phase diagram is shown with simulations based on a two-dimensional Ising model. These results indicate that machine learning approaches can be used to determine phase transitions in ferroelectrics, providing a general, statistically significant, and robust approach toward determining the presence of critical regimes and phase boundaries.

In situ analytical techniques for battery interface analysis

Interface is a key to high performance and safe lithium-ion batteries or lithium batteries.

Chemical Changes in Layered Ferroelectric Semiconductors Induced by Helium Ion Beam

Multi-material systems interfaced with 2D materials, or entirely new 3D heterostructures can lead to the next generation multi-functional device architectures. Physical and chemical control at the nanoscale is also necessary tailor these materials as functional structures approach physical limit. 2D transition metal thiophosphates (TPS), with a general formulae Cu_1−xIn_1+x/3P₂S_6, have shown ferroelectric polarization behavior with a T_c above the room temperature, making them attractive candidates for designing both: chemical and physical properties. Our previous studies have demonstrated that ferroic order persists on the surface, and that spinoidal decomposition of ferroelectric and paraelectric phases occurs in non-stoichiometric Cu/In ratio formulations. Here, we discuss the chemical changes induced by helium ion irradiation. We explore the TPS compound library with varying Cu/In ratio, using Helium Ion Microscopy, Atomic Force Microscopy (AFM), and Time of Flight-Secondary Ion Mass Spectrometry (ToF-SIMS). We correlate physical nano- and micro- structures to the helium ion dose, as well as chemical signatures of copper, oxygen and sulfur. Our ToF-SIMS results show that He ion irradiation leads to oxygen penetration into the irradiated areas, and diffuses along the Cu-rich domains to the extent of the stopping distance of the helium ions.

Three-State Ferroelastic Switching and Large Electromechanical Responses in PbTiO3 Thin Films

Leveraging competition between energetically degenerate states to achieve large field-driven responses is a hallmark of functional materials, but routes to such competition are limited. Here, a new route to such effects involving domain-structure competition is demonstrated, which arises from strain-induced spontaneous partitioning of PbTiO₃ thin films into nearly energetically degenerate, hierarchical domain architectures of coexisting c/a and a₁ /a₂ domain structures. Using band-excitation piezoresponse force microscopy, this study manipulates and acoustically detects a facile interconversion of different ferroelastic variants via a two-step, three-state ferroelastic switching process (out-of-plane polarized c⁺ → in-plane polarized a → out-of-plane polarized c^- state), which is concomitant with large nonvolatile electromechanical strains (≈1.25%) and tunability of the local piezoresponse and elastic modulus (>23%). It is further demonstrated that deterministic, nonvolatile writing/erasure of large-area patterns of this electromechanical response is possible, thus showing a new pathway to improved function and properties.

Visualizing molecular distributions for biomaterials applications with mass spectrometry imaging: a review

Mass spectrometry imaging (MSI) is a rapidly emerging field that is continually finding applications in new and exciting areas. The ability of MSI to measure the spatial distribution of molecules at or near the surface of complex substrates makes it an ideal candidate for many applications, including those in the sphere of materials chemistry. Continual development and optimization of both ionization sources and analyzer technologies have resulted in a wide array of MSI tools available, both commercially available and custom-built, with each configuration possessing inherent strengths and limitations. Despite the unique potential of MSI over other chemical imaging methods, their potential and application to (bio)materials science remains in our view a largely underexplored avenue. This review will discuss these techniques enabling high parallel molecular detection, focusing on those with reported uses in (bio)materials chemistry applications and highlighted with select applications. Different technologies are presented in three main sections; secondary ion mass spectrometry (SIMS) imaging, matrix-assisted laser desorption ionization (MALDI) MSI, and emerging MSI technologies with potential for biomaterial analysis. The first two sections (SIMS and MALDI) discuss well-established methods that are continually evolving both in technological advancements and in experimental versatility. In the third section, relatively new and versatile technologies capable of performing measurements under ambient conditions will be introduced, with reported applications in materials chemistry or potential applications discussed. The aim of this review is to provide a concise resource for those interested in utilizing MSI for applications such as biomimetic materials, biological/synthetic material interfaces, polymer formulation and bulk property characterization, as well as the spatial and chemical distributions of nanoparticles, or any other molecular imaging application requiring broad chemical speciation.

Nanoscale Probing of Elastic-Electronic Response to Vacancy Motion in NiO Nanocrystals

Measuring the diffusion of ions and vacancies at nanometer length scales is crucial to understanding fundamental mechanisms driving technologies as diverse as batteries, fuel cells, and memristors; yet such measurements remain extremely challenging. Here, we employ a multimodal scanning probe microscopy (SPM) technique to explore the interplay between electronic, elastic, and ionic processes via first-order reversal curve I-V measurements in conjunction with electrochemical strain microscopy (ESM). The technique is employed to investigate the diffusion of oxygen vacancies in model epitaxial nickel oxide (NiO) nanocrystals with resistive switching characteristics. Results indicate that opening of the ESM hysteresis loop is strongly correlated with changes to the resonant frequency, hinting that elastic changes stem from the motion of oxygen (or cation) vacancies in the probed volume of the SPM tip. These changes are further correlated to the current measured on each nanostructure, which shows a hysteresis loop opening at larger (∼2.5 V) voltage windows, suggesting the threshold field for vacancy migration. This study highlights the utility of local multimodal SPM in determining functional and chemical changes in nanoscale volumes in nanostructured NiO, with potential use to explore a wide variety of materials including phase-change memories and memristive devices in combination with site-correlated chemical imaging tools.

Multifrequency AFM: from origins to convergence

Since the inception of the atomic force microscope (AFM) in 1986, influential papers have been presented by the community and tremendous advances have been reported. Being able to routinely image conductive and non-conductive surfaces in air, liquid and vacuum environments with nanoscale, and sometimes atomic, resolution, the AFM has long been perceived by many as the instrument to unlock the nanoscale. From exploiting a basic form of Hooke's law to interpret AFM data to interpreting a seeming zoo of maps in the more advanced multifrequency methods however, an inflection point has been reached. Here, we discuss this evolution, from the fundamental dilemmas that arose in the beginning, to the exploitation of computer sciences, from machine learning to big data, hoping to guide the newcomer and inspire the experimenter.

Big, Deep, and Smart Data in Scanning Probe Microscopy

Scanning probe microscopy (SPM) techniques have opened the door to nanoscience and nanotechnology by enabling imaging and manipulation of the structure and functionality of matter at nanometer and atomic scales. Here, we analyze the scientific discovery process in SPM by following the information flow from the tip-surface junction, to knowledge adoption by the wider scientific community. We further discuss the challenges and opportunities offered by merging SPM with advanced data mining, visual analytics, and knowledge discovery technologies.

Imaging via complete cantilever dynamic detection: general dynamic mode imaging and spectroscopy in scanning probe microscopy

We develop and implement a multifrequency spectroscopy and spectroscopic imaging mode, referred to as general dynamic mode (GDM), that captures the complete spatially- and stimulus dependent information on nonlinear cantilever dynamics in scanning probe microscopy (SPM). GDM acquires the cantilever response including harmonics and mode mixing products across the entire broadband cantilever spectrum as a function of excitation frequency. GDM spectra substitute the classical measurements in SPM, e.g. amplitude and phase in lock-in detection. Here, GDM is used to investigate the response of a purely capacitively driven cantilever. We use information theory techniques to mine the data and verify the findings with governing equations and classical lock-in based approaches. We explore the dependence of the cantilever dynamics on the tip-sample distance, AC and DC driving bias. This approach can be applied to investigate the dynamic behavior of other systems within and beyond dynamic SPM. GDM is expected to be useful for separating the contribution of different physical phenomena in the cantilever response and understanding the role of cantilever dynamics in dynamic AFM techniques.

Nanoforging Single Layer MoSe2 Through Defect Engineering with Focused Helium Ion Beams

Development of devices and structures based on the layered 2D materials critically hinges on the capability to induce, control, and tailor the electronic, transport, and optoelectronic properties via defect engineering, much like doping strategies have enabled semiconductor electronics and forging enabled introduction the of iron age. Here, we demonstrate the use of a scanning helium ion microscope (HIM) for tailoring the functionality of single layer MoSe2 locally, and decipher associated mechanisms at the atomic level. We demonstrate He(+) beam bombardment that locally creates vacancies, shifts the Fermi energy landscape and increases the Young's modulus of elasticity. Furthermore, we observe for the first time, an increase in the B-exciton photoluminescence signal from the nanoforged regions at the room temperature. The approach for precise defect engineering demonstrated here opens opportunities for creating functional 2D optoelectronic devices with a wide range of customizable properties that include operating in the visible region.

Unraveling the Mechanism of Nanoscale Mechanical Reinforcement in Glassy Polymer Nanocomposites

The mechanical reinforcement of polymer nanocomposites (PNCs) above the glass transition temperature, Tg, has been extensively studied. However, not much is known about the origin of this effect below Tg. In this Letter, we unravel the mechanism of PNC reinforcement within the glassy state by directly probing nanoscale mechanical properties with atomic force microscopy and macroscopic properties with Brillouin light scattering. Our results unambiguously show that the "glassy" Young's modulus in the interfacial polymer layer of PNCs is two-times higher than in the bulk polymer, which results in significant reinforcement below Tg. We ascribe this phenomenon to a high stretching of the chains within the interfacial layer. Since the interfacial chain packing is essentially temperature independent, these findings provide a new insight into the mechanical reinforcement of PNCs also above Tg.

Graphene engineering by neon ion beams

Achieving the ultimate limits of lithographic resolution and material performance necessitates engineering of matter with atomic, molecular, and mesoscale fidelity. With the advent of scanning helium ion microscopy, maskless He(+) and Ne(+) beam lithography of 2D materials, such as graphene-based nanoelectronics, is coming to the forefront as a tool for fabrication and surface manipulation. However, the effects of using a Ne focused-ion-beam on the fidelity of structures created out of 2D materials have yet to be explored. Here, we will discuss the use of energetic Ne ions in engineering graphene nanostructures and explore their mechanical, electromechanical and chemical properties using scanning probe microscopy (SPM). By using SPM-based techniques such as band excitation (BE) force modulation microscopy, Kelvin probe force microscopy (KPFM) and Raman spectroscopy, we are able to ascertain changes in the mechanical, electrical and optical properties of Ne(+) beam milled graphene nanostructures and surrounding regions. Additionally, we are able to link localized defects around the milled graphene to ion milling parameters such as dwell time and number of beam passes in order to characterize the induced changes in mechanical and electromechanical properties of the graphene surface.

Polarization Control via He-Ion Beam Induced Nanofabrication in Layered Ferroelectric Semiconductors

Rapid advances in nanoscience rely on continuous improvements of material manipulation at near-atomic scales. Currently, the workhorse of nanofabrication is resist-based lithography and its various derivatives. However, the use of local electron, ion, and physical probe methods is expanding, driven largely by the need for fabrication without the multistep preparation processes that can result in contamination from resists and solvents. Furthermore, probe-based methods extend beyond nanofabrication to nanomanipulation and to imaging which are all vital for a rapid transition to the prototyping and testing of devices. In this work we study helium ion interactions with the surface of bulk copper indium thiophosphate CuM(III)P2X6 (M = Cr, In; X= S, Se), a novel layered 2D material, with a Helium Ion Microscope (HIM). Using this technique, we are able to control ferrielectric domains and grow conical nanostructures with enhanced conductivity whose material volumes scale with the beam dosage. Compared to the copper indium thiophosphate (CITP) from which they grow, the nanostructures are oxygen rich, sulfur poor, and with virtually unchanged copper concentration as confirmed by energy-dispersive X-ray spectroscopy (EDX). Scanning electron microscopy (SEM) imaging contrast as well as scanning microwave microscopy (SMM) measurements suggest enhanced conductivity in the formed particles, whereas atomic force microscopy (AFM) measurements indicate that the produced structures have lower dissipation and are softer as compared to the CITP.

Atomic structures and oxygen dynamics of CeO2 grain boundaries

Material performance is significantly governed by grain boundaries (GBs), a typical crystal defects inside, which often exhibit unique properties due to the structural and chemical inhomogeneity. Here, it is reported direct atomic scale evidence that oxygen vacancies formed in the GBs can modify the local surface oxygen dynamics in CeO2, a key material for fuel cells. The atomic structures and oxygen vacancy concentrations in individual GBs are obtained by electron microscopy and theoretical calculations at atomic scale. Meanwhile, local GB oxygen reduction reactivity is measured by electrochemical strain microscopy. By combining these techniques, it is demonstrated that the GB electrochemical activities are affected by the oxygen vacancy concentrations, which is, on the other hand, determined by the local structural distortions at the GB core region. These results provide critical understanding of GB properties down to atomic scale, and new perspectives on the development strategies of high performance electrochemical devices for solid oxide fuel cells.

CuInP₂S₆ Room Temperature Layered Ferroelectric

We explore ferroelectric properties of cleaved 2-D flakes of copper indium thiophosphate, CuInP2S6 (CITP), and probe size effects along with limits of ferroelectric phase stability, by ambient and ultra high vacuum scanning probe microscopy. CITP belongs to the only material family known to display ferroelectric polarization in a van der Waals, layered crystal at room temperature and above. Our measurements directly reveal stable, ferroelectric polarization as evidenced by domain structures, switchable polarization, and hysteresis loops. We found that at room temperature the domain structure of flakes thicker than 100 nm is similar to the cleaved bulk surfaces, whereas below 50 nm polarization disappears. We ascribe this behavior to a well-known instability of polarization due to depolarization field. Furthermore, polarization switching at high bias is also associated with ionic mobility, as evidenced both by macroscopic measurements and by formation of surface damage under the tip at a bias of 4 V-likely due to copper reduction. Mobile Cu ions may therefore also contribute to internal screening mechanisms. The existence of stable polarization in a van-der-Waals crystal naturally points toward new strategies for ultimate scaling of polar materials, quasi-2D, and single-layer materials with advanced and nonlinear dielectric properties that are presently not found in any members of the growing "graphene family".

Big data and deep data in scanning and electron microscopies: deriving functionality from multidimensional data sets

The development of electron and scanning probe microscopies in the second half of the twentieth century has produced spectacular images of the internal structure and composition of matter with nanometer, molecular, and atomic resolution. Largely, this progress was enabled by computer-assisted methods of microscope operation, data acquisition, and analysis. Advances in imaging technology in the beginning of the twenty-first century have opened the proverbial floodgates on the availability of high-veracity information on structure and functionality. From the hardware perspective, high-resolution imaging methods now routinely resolve atomic positions with approximately picometer precision, allowing for quantitative measurements of individual bond lengths and angles. Similarly, functional imaging often leads to multidimensional data sets containing partial or full information on properties of interest, acquired as a function of multiple parameters (time, temperature, or other external stimuli). Here, we review several recent applications of the big and deep data analysis methods to visualize, compress, and translate this multidimensional structural and functional data into physically and chemically relevant information.

Co-registered Topographical, Band Excitation Nanomechanical, and Mass Spectral Imaging Using a Combined Atomic Force Microscopy/Mass Spectrometry Platform

The advancement of a hybrid atomic force microscopy/mass spectrometry imaging platform demonstrating the co-registered topographical, band excitation nanomechanical, and mass spectral imaging of a surface using a single instrument is reported. The mass spectrometry-based chemical imaging component of the system utilized nanothermal analysis probes for pyrolytic surface sampling followed by atmospheric pressure chemical ionization of the gas-phase species produced with subsequent mass analysis. The basic instrumental setup and operation are discussed, and the multimodal imaging capability and utility are demonstrated using a phase-separated polystyrene/poly(2-vinylpyridine) polymer blend thin film. The topography and band excitation images showed that the valley and plateau regions of the thin film surface were comprised primarily of one of the two polymers in the blend with the mass spectral chemical image used to definitively identify the polymers at the different locations. Data point pixel size for the topography (390 nm × 390 nm), band excitation (781 nm × 781 nm), and mass spectrometry (690 nm × 500 nm) images was comparable and submicrometer in all three cases, but the data voxel size for each of the three images was dramatically different. The topography image was uniquely a surface measurement, whereas the band excitation image included information from an estimated 20 nm deep into the sample and the mass spectral image from 110 to 140 nm in depth. Because of this dramatic sampling depth variance, some differences in the band excitation and mass spectrometry chemical images were observed and were interpreted to indicate the presence of a buried interface in the sample. The spatial resolution of the chemical image was estimated to be between 1.5 and 2.6 μm, based on the ability to distinguish surface features in that image that were also observed in the other images.

Probing local bias-induced transitions using photothermal excitation contact resonance atomic force microscopy and voltage spectroscopy

Nanomechanical properties are closely related to the states of matter, including chemical composition, crystal structure, mesoscopic domain configuration, etc. Investigation of these properties at the nanoscale requires not only static imaging methods, e.g., contact resonance atomic force microscopy (CR-AFM), but also spectroscopic methods capable of revealing their dependence on various external stimuli. Here we demonstrate the voltage spectroscopy of CR-AFM, which was realized by combining photothermal excitation (as opposed to the conventional piezoacoustic excitation method) with the band excitation technique. We applied this spectroscopy to explore local bias-induced phenomena ranging from purely physical to surface electromechanical and electrochemical processes. Our measurements show that the changes in the surface properties associated with these bias-induced transitions can be accurately assessed in a fast and dynamic manner, using resonance frequency as a signature. With many of the advantages offered by photothermal excitation, contact resonance voltage spectroscopy not only is expected to find applications in a broader field of nanoscience but also will provide a basis for future development of other nanoscale elastic spectroscopies.

Optimization of phase contrast in bimodal amplitude modulation AFM

Bimodal force microscopy has expanded the capabilities of atomic force microscopy (AFM) by providing high spatial resolution images, compositional contrast and quantitative mapping of material properties without compromising the data acquisition speed. In the first bimodal AFM configuration, an amplitude feedback loop keeps constant the amplitude of the first mode while the observables of the second mode have not feedback restrictions (bimodal AM). Here we study the conditions to enhance the compositional contrast in bimodal AM while imaging heterogeneous materials. The contrast has a maximum by decreasing the amplitude of the second mode. We demonstrate that the roles of the excited modes are asymmetric. The operational range of bimodal AM is maximized when the second mode is free to follow changes in the force. We also study the contrast in trimodal AFM by analyzing the kinetic energy ratios. The phase contrast improves by decreasing the energy of second mode relative to those of the first and third modes.