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Deep learning (DL) has become an indispensable tool in hyperspectral data analysis, automatically extracting valuable features from complex, high-dimensional datasets. Super-resolution reconstruction, an essential aspect of hyperspectral data, involves enhancing spatial resolution, particularly relevant to low-resolution hyperspectral data. Yet, the pursuit of super-resolution in hyperspectral analysis is fraught with challenges, including acquiring ground truth high-resolution data for training, generalization, and scalability. The pressing issue of extended spectral acquisition times, notably for high-resolution scans, is a significant roadblock in hyperspectral imaging. Super-resolution methods offer a promising solution by providing higher spatial resolution data to expedite data collection and yield more efficient outcomes. This paper delves into a practical application of these concepts using Raman imaging, where spectral acquisition times can be prohibitively long. In this context, DL-based super-resolution models demonstrate their efficacy by predicting and reconstructing high-resolution Raman data from low-resolution input, eliminating the need for resource-intensive high-resolution scans. While previous work often relied on substantial high-resolution datasets, this study showcases the ability to achieve similar outcomes even with limited data, presenting a more practical and cost-effective approach. The results offer a glimpse into the transformative potential of this technology to streamline hyperspectral imaging applications by saving valuable time and resources through the successful generation of high-resolution data from low-resolution inputs. [ABSTRACT FROM AUTHOR]

Polarization switching is a fundamental feature of ferroelectric materials, enabling a plethora of applications and captivating the attention of the scientific community for over half a century. Many previous studies considered ferroelectric switching as a purely physical process, whereas polarization is fully controlled by the superposition of electric fields. However, screening charge is required for thermodynamic stability of the single domain state that is of interest in many technological applications. The screening process has always been assumed to be fast; thus, the rate-limiting phenomena were believed to be domain nucleation and domain wall dynamics. In this manuscript, we demonstrate that polarization switching under an atomic force microscopy tip leads to reversible ionic motion in the top 3 nm of PbZr0.2Ti0.8O3 surface layer. This evidence points to a strong chemical component to a process believed to be purely physical and has major implications for understanding ferroelectric materials, making ferroelectric devices, and interpreting local ferroelectric switching.

The ability to control thin-film growth has led to advances in our understanding of fundamental physics as well as to the emergence of novel technologies. However, common thin-film growth techniques introduce a number of limitations related to the concentration of defects on film interfaces and surfaces that limit the scope of systems that can be produced and studied experimentally. Here, we developed an ion-beam based subtractive fabrication process that enables creation and modification of thin films with pre-defined thicknesses. To accomplish this we transformed a multimodal imaging platform that combines time-of-flight secondary ion mass spectrometry with atomic force microscopy to a unique fabrication tool that allows for precise sputtering of the nanometer-thin layers of material. To demonstrate fabrication of thin-films with in situ feedback and control on film thickness and functionality we systematically studied thickness dependence of ferroelectric switching of lead-zirconate-titanate, within a single epitaxial film. Our results demonstrate that through a subtractive film fabrication process we can control the piezoelectric response as a function of film thickness as well as improve on the overall piezoelectric response versus an untreated film.

Atomic force microscopy is widely used for nanoscale characterization of materials by scientists worldwide. The long-held belief of ambient AFM is that the tip is generally chemically inert but can be functionalized with respect to the studied sample. This implies that basic imaging and scanning procedures do not affect surface and bulk chemistry of the studied sample. However, an in-depth study of the confined chemical processes taking place at the tip-surface junction and the associated chemical changes to the material surface have been missing as of now. Here, we used a hybrid system that combines time-of-flight secondary ion mass spectrometry with an atomic force microscopy to investigate the chemical interactions that take place at the tip-surface junction. Investigations showed that even basic contact mode AFM scanning is able to modify the surface of the studied sample. In particular, we found that the silicone oils deposited from the AFM tip into the scanned regions and spread to distances exceeding 15 μm from the tip. These oils were determined to come from standard gel boxes used for the storage of the tips. The explored phenomena are important for interpreting and understanding results of AFM mechanical and electrical studies relying on the state of the tip-surface junction.

Domain formation and ferroelectric switching is fundamentally inseparable from polarization screening, which on free surfaces can be realized via band bending and ionic adsorption. In the latter case, polarization switching is intrinsically coupled to the surface electrochemical phenomena, and the electrochemical stage can control kinetics and induce long-range interactions. However, despite extensive evidence toward the critical role of surface electrochemistry, little is known about the nature of the associated processes. Here we combine SPM tip induce polarization switching and secondary ion mass spectrometry to explore the evolution of chemical state of ferroelectric during switching. Surprisingly, we find that even pristine surfaces contain ions (e.g., Cl-) that are not anticipated based on chemistry of the system and processing. In the ferroelectric switching regime, we find surprising changes in surface chemistry, including redistribution of base cations. At higher voltages in the electroforming regime significant surface deformation was observed and associated with a strong ion intermixing.

Imaging mass spectrometry (IMS) is a powerful analytical technique widely used in biology, chemistry, and materials science fields that continue to expand. IMS provides a qualitative compositional analysis and spatial mapping with high chemical specificity. The spatial mapping information can be 2D or 3D depending on the analysis technique employed. Due to the combination of complex mass spectra coupled with spatial information, large high-dimensional datasets (hyperspectral) are often produced. Therefore, the use of automated computational methods for an exploratory analysis is highly beneficial. The fast-paced development of artificial intelligence (AI) and machine learning (ML) tools has received significant attention in recent years. These tools, in principle, can enable the unification of data collection and analysis into a single pipeline to make sampling and analysis decisions on the go. There are various ML approaches that have been applied to IMS data over the last decade. In this review, we discuss recent examples of the common unsupervised (principal component analysis, non-negative matrix factorization, k-means clustering, uniform manifold approximation and projection), supervised (random forest, logistic regression, XGboost, support vector machine), and other methods applied to various IMS datasets in the past five years. The information from this review will be useful for specialists from both IMS and ML fields since it summarizes current and representative studies of computational ML-based exploratory methods for IMS. [ABSTRACT FROM AUTHOR]

Nanoscale hyperspectral techniques—such as electron energy loss spectroscopy (EELS)—are critical to understand the optical response in plasmonic nanostructures, but as systems become increasingly complex, the required sampling density and acquisition times become prohibitive for instrumental and specimen stability. As a result, there has been a recent push for new experimental methodologies that can provide comprehensive information about a complex system, while significantly reducing the duration of the experiment. Here, we present a pan-sharpening approach to hyperspectral EELS analysis, where we acquire two datasets from the same region (one with high spatial resolution and one with high spectral fidelity) and combine them to achieve a single dataset with the beneficial properties of both. This work outlines a straightforward, reproducible pathway to reduced experiment times and higher signal-to-noise ratios, while retaining the relevant physical parameters of the plasmonic response, and is generally applicable to a wide range of spectroscopy modalities. [ABSTRACT FROM AUTHOR]

A third of the energy from fuel combustion in passenger car gasoline engines is lost due to friction. Carefully designed engine lubricants can recover some of these losses by reducing friction and wear by forming a nanometer-scale chemico-physico tribofilm between surfaces. Accordingly, attention has focused on developing oil formulations that form low-friction tribofilms. However, analyses of resultant tribofilms are typically conducted after tribo-tests with conventional characterization tools and do not offer insights into tribofilm formation and evolution, precluding information critical to tuning tribofilm properties. In this work, we developed a unique multimodal methodology based on Atomic Force Microscopy (AFM) with local probe heating for in situ tribological studies that activates friction modifiers and simultaneously captures the evolution of friction and surface roughness, with nanometer resolution. As a platform to demonstrate the ability of this methodology to visualize dynamics of tribofilm formation in situ, we apply it to molybdenum-based friction modifiers to distinguish key factors in their functionality and correlate nanoscale AFM and Friction Force Microscopy data to bench tribo-tests used in the industry. To decode the formation mechanisms observed in situ and underlying chemistry of tribofilms, we performed ab initio Molecular Dynamics (AIMD) simulations at comparable conditions. AIMD simulations confirmed both nanoscale and bench tribo-test results and showed deviations in molecular organization in tribofilms that are formed from different molybdenum-based friction modifiers that corroborates with surface functionality. With this innovative methodology, we demonstrate proof-of-principle in situ formation of molybdenum-based tribofilms directly on steel surfaces that could be applied generally to studying tribofilm formation. [ABSTRACT FROM AUTHOR]