Multimodal Chemical Imaging Research Articles

Long-Term Tests and Advanced Post-Test Characterizations of the Oxygen Electrode in Solid Oxide Electrolysis Cells

Solid Oxide cells (SOCs) are promising high-temperature electrochemical energy-conversion devices, which enable a fossil-free sustainable development. Nevertheless, performance degradation is still a bottleneck for the large-scale commercialization. The most commonly employed material for the air electrode is La1-xSrxCoy-1FeyO3-δ. The chemical decomposition of this material is known to considerably impact the cell performance [1,2].The studied cells are composed of a La0.6Sr0.4Co0.2Fe0.8O3 -δ (LSCF) current collecting layer (CCL) and a LSCF-Ce0.8Gd0.2O2 -δ (LSCF-GDC) composite for the air electrode, GDC for the barrier layer, (Y2O3)0.08(ZrO2)0.92 (YSZ) for the electrolyte and Ni-YSZ for the functional layer and the support layer of the fuel electrode. The present study aims to investigate the degradation occurring in the air electrode of a complete cell aged at 750°C for 2000 h in electrolysis mode. The cell degradation is assessed by electrochemical measurements, such as polarization curves and electrochemical impedance spectroscopy (EIS), and post-mortem characterizations of the air electrode using high resolution multimodal chemical imaging based on synchrotron X-ray diffraction (µ-XRD) in Paul Scherrer Institut (PSI, microXAS), complemented by X-ray photoelectron spectroscopy (XPS).The electrochemical characterizations performed at the beginning of the test and after ageing showed a decrease in the cell performance. Notably, a significant evolution at the high frequency region was observed in the impedance spectrum. However, the medium frequency region of the EIS, mainly ascribed to the air electrode using a physically-based multiscale model [3], did not show any particular evolution. Post-mortem characterizations using synchrotron µ-XRD technique showed an inter-diffusion layer (IDL) in between the GDC and YSZ layers, for both pristine and aged samples (Fig. 1). In addition, a slight increase in the LSCF unit cell volume was observed after ageing. Nevertheless, XPS results did not highlight any significant evolution in the Sr segregation after operation. According to the impedance spectra and the post-mortem analyses, the ageing of the air electrode seems to have a limited impact on the cell performance loss in these operating conditions.

Long-Term Tests and Advanced Post-Test Characterizations of the Oxygen Electrode in Solid Oxide Electrolysis Cells

Solid oxide electrolysis cells (SOEC) are a promising energy conversion technology for the production of green hydrogen via steam electrolysis. However, performance loss remains a bottleneck for large-scale commercialization. Here, we investigate the different types of degradation occurring in a state-of-the-art cell composed of La0.6Sr0.4Co0.2Fe0.8O3-Ce0.8Gd0.2O2 (LSCF-GDC) composite for the oxygen electrode, GDC for the barrier layer, Y0.16Zr0.84O1.92 (8YSZ) for the electrolyte and Ni-YSZ for the fuel electrode. Electrochemical impedance spectra measured before and after operation at 750 °C for 2000 hours at -1 A cm-2 in SOEC mode revealed no significant evolution for the oxygen electrode contribution. To investigate potential degradation of this electrode, we performed multi-modal chemical imaging based on synchrotron X-ray diffraction (µ-XRD) with a resolution at the micrometer-scale. In addition, we monitored the evolution of the chemical composition and chemical environment before and after ageing using laboratory X-ray photoelectron spectroscopy (XPS). All these analyses revealed a slight evolution of the crystal lattice parameter in the oxygen electrode and the inter-diffusion layer. It was also found that Sr segregation accelerates with ageing. However, more severe ageing conditions and/or longer ageing durations are required in order to observe an effect on the performance.

Deep Learning on Multimodal Chemical and Whole Slide Imaging Data for Predicting Prostate Cancer Directly from Tissue Images.

Prostate cancer is one of the most common cancers globally and is the second most common cancer in the male population in the US. Here we develop a study based on correlating the hematoxylin and eosin (H&E)-stained biopsy data with MALDI mass-spectrometric imaging data of the corresponding tissue to determine the cancerous regions and their unique chemical signatures and variations of the predicted regions with original pathological annotations. We obtain features from high-resolution optical micrographs of whole slide H&E stained data through deep learning and spatially register them with mass spectrometry imaging (MSI) data to correlate the chemical signature with the tissue anatomy of the data. We then use the learned correlation to predict prostate cancer from observed H&E images using trained coregistered MSI data. This multimodal approach can predict cancerous regions with ∼80% accuracy, which indicates a correlation between optical H&E features and chemical information found in MSI. We show that such paired multimodal data can be used for training feature extraction networks on H&E data which bypasses the need to acquire expensive MSI data and eliminates the need for manual annotation saving valuable time. Two chemical biomarkers were also found to be predicting the ground truth cancerous regions. This study shows promise in generating improved patient treatment trajectories by predicting prostate cancer directly from readily available H&E-stained biopsy images aided by coregistered MSI data.

Effect of Micro-Patterned Mucin on Quinolone and Rhamnolipid Profiles of Mucoid Pseudomonas aeruginosa under Antibiotic Stress.

Pseudomonas aeruginosa (P. aeruginosa) is commonly implicated in hospital-acquired infections where its capacity to form biofilms on a variety of surfaces and the resulting enhanced antibiotic resistance seriously limit treatment choices. Because surface attachment sensitizes P. aeruginosa to quorum sensing (QS) and induces virulence through both chemical and mechanical cues, we investigate the effect of surface properties through spatially patterned mucin, combined with sub-inhibitory concentrations of tobramycin on QS and virulence factors in both mucoid and non-mucoid P. aeruginosa strains using multi-modal chemical imaging combining confocal Raman microscopy and matrix-assisted laser desorption/ionization-mass spectrometry. Samples comprise surface-adherent static biofilms at a solid-water interface, supernatant liquid, and pellicle biofilms at an air-water interface at various time points. Although the presence of a sub-inhibitory concentration of tobramycin in the supernatant retards growth and development of static biofilms independent of strain and surface mucin patterning, we observe clear differences in the behavior of mucoid and non-mucoid strains. Quinolone signals in a non-mucoid strain are induced earlier and are influenced by mucin surface patterning to a degree not exhibited in the mucoid strain. Additionally, phenazine virulence factors, such as pyocyanin, are observed in the pellicle biofilms of both mucoid and non-mucoid strains but are not detected in the static biofilms from either strain, highlighting the differences in stress response between pellicle and static biofilms. Differences between mucoid and non-mucoid strains are consistent with their strain-specific phenology, in which the mucoid strain develops highly protected biofilms.

Correlated Materials Characterization via Multimodal Chemical Imaging and Data Analytics

Correlated Materials Characterization via Multimodal Chemical Imaging and Data Analytics

Direct Multimodal Nanoscale Visualization of Early Phosphorus-Based Antiwear Tribofilm Formation.

Understanding the mechanism of antiwear (AW) tribofilm formation and how to tune surface chemistry to control functionality is essential for the development of the next generation of oil lubricants. In particular, understanding and optimizing early AW tribofilm formation can increase the energy efficiency of mechanical systems. However, the mechanism for how these films form is not well understood. The majority of prior work has focused on analyzing only end-of-test surfaces long after the film has formed. In this work, we develop an in situ multimodal chemical imaging methodology to directly visualize the early formation of AW films on steel surfaces. We investigate an oil formulation containing a phosphorus-based additive commonly used to protect surfaces from wear and fatigue processes in machine elements, such as gears, bearings, and sliding contacts. Using nanoscale multimodal chemical imaging on combined platforms of atomic force microscopy (AFM) coupled directly with in situ nano-infrared (nano-IR) spectroscopy, and further combined ex situ with time-of-flight secondary ion mass spectrometry (ToF-SIMS), we demonstrate a direct correlation between changes in friction and local surface chemistry. In these experiments, the AFM probe acts as a single asperity contact to generate the tribofilm as well as a tool to analyze it in situ as it is forming. To verify our in situ measurements, we compare these results to the ex situ ToF-SIMS of macroscale block-on-ring tribometer-formed samples. The understanding gained here on how AW films form and how film properties can be modified by tuning the chemistry of the additives will facilitate developing transmission fluids to meet increasing demands for vehicle performance and efficiency.

Structural amyloid plaque polymorphism is associated with distinct lipid accumulations revealed by trapped ion mobility mass spectrometry imaging.

Understanding of Alzheimer's disease (AD) pathophysiology requires molecular assessment of how key pathological factors, specifically amyloid β (Aβ) plaques, influence the surrounding microenvironment. Here, neuronal lipids have been implicated in Aβ plaque pathology, though the lipid microenvironment in direct proximity to Aβ plaques is still not fully resolved. A further challenge is the microenvironmental molecular heterogeneity, across structurally polymorphic Aβ features, such as diffuse, immature, and mature, fibrillary aggregates, whose resolution requires the integration of advanced, multimodal chemical imaging tools. Herein, we used matrix-assisted laser desorption/ionization trapped ion mobility spectrometry time-of-flight based mass spectrometry imaging (MALDI TIMS TOF MSI) in combination with hyperspectral confocal microscopy to probe the lipidomic microenvironment associated with structural polymorphism of Aβ plaques in transgenic Alzheimer's disease mice (tgAPPSWE ). Using on tissue and ex situ validation, TIMS MS/MS facilitated unambiguous identification of isobaric lipid species that showed plaque pathology-associated localizations. Integrated multivariate imaging data analysis revealed multiple, Aβ plaque-enriched lipid patterns for gangliosides (GM), phosphoinositols (PI), phosphoethanolamines (PE), and phosphatidic acids (PA). Conversely, sulfatides (ST), cardiolipins (CL), and polyunsaturated fatty acid (PUFA)-conjugated phosphoserines (PS), and PE were depleted at plaques. Hyperspectral amyloid imaging further delineated the unique distribution of PA and PE species to mature plaque core regions, while PI, LPI, GM2 and GM3lipids localized to immature Aβ aggregates present within the periphery of Aβ plaques. Finally, we followed AD pathology-associated lipid changes over time, identifying plaque- growth and maturation to be characterized by peripheral accumulation of PI (18:0/22:6). Together, these data demonstrate the potential of multimodal imaging approaches to overcome limitations associated with conventional advanced MS imaging applications. This allowed for the differentiation of both distinct lipid components in a complex micro-environment as well as their correlation to disease-relevant amyloid plaque polymorphs. Cover Image for this issue: https://doi.org/10.1111/jnc.15390.

Multimodal multisample spectroscopic imaging analysis of tumor tissues using multivariate curve resolution

Chemical imaging aims to characterize the molecular patterns present in a tissue or surface. High-resolution images with high content of molecular information represent a clear advantage in many research areas, such as in biomedicine. Specific molecular patterns, when related to pathological traits, could help understanding the molecular events related to the condition studied. Chemical composition of the different regions within the tissue may reveal interesting patterns that cannot be detected using conventional staining techniques. In this work, a new chemometrics approach is proposed for the simultaneous multimodal chemical imaging analysis of six tissue slices from two types of tumor tissues (EG and 1070) exhibiting different sensitivity to the docetaxel drug. Therefore, a deeper chemical imaging study of these tissues could reveal new chemical fingerprints related to the different susceptibility and response to this drug treatment. The spectral imaging techniques used in this study were MALDI-TOF mass spectrometry imaging (MSI) and FT-IR hyperspectral imaging. In addition, a RGB color picture of the hematoxylin/eosin staining was also included in the joint image analysis. The huge spectral data information generated by these multiple imaging techniques in the simultaneous analysis of the different tissue samples was organized in combined multiset and multimodal spectral image data structures which were then analyzed by multivariate curve resolution. Using this approach, the nature of the chemical constituents and their high-resolution spatial distribution were detangled showing what were the main chemical patterns present in specific areas of the tissues which distinguished the two types of tumors. In summary, in the present work, the application of multivariate curve resolution is confirmed to be a powerful and flexible data analysis tool able to extract chemical information from the simultaneous analysis of huge, fused image data sets from obtained by different type of spectroscopic imaging techniques having different spatial resolution powers.

Tracking the formation and degradation of fatty-acid-accumulated mitochondria using label-free chemical imaging

The mitochondrion is one of the key organelles for maintaining cellular homeostasis. External environmental stimuli and internal regulatory processes may alter the metabolism and functions of mitochondria. To understand these activities of mitochondria, it is critical to probe the key metabolic molecules inside these organelles. In this study, we used label-free chemical imaging modalities including coherent anti-Stokes Raman scattering and multiphoton-excited fluorescence to investigate the mitochondrial activities in living cancer cells. We found that hypothermia exposure tends to induce fatty-acid (FA) accumulation in some mitochondria of MIAPaCa-2 cells. Autofluorescence images show that the FA-accumulated mitochondria also have abnormal metabolism of nicotinamide adenine dinucleotide hydrogen, likely induced by the dysfunction of the electron transport chain. We also found that when the cells were re-warmed to physiological temperature after a period of hypothermia, the FA-accumulated mitochondria changed their structural features. To the best of our knowledge, this is the first time that FA accumulation in mitochondria was observed in live cells. Our research also demonstrates that multimodal label-free chemical imaging is an attractive tool to discover abnormal functions of mitochondria at the single-organelle level and can be used to quantify the dynamic changes of these organelles under perturbative conditions.

Biopolymer Patterning-Directed Secretion in Mucoid and Nonmucoid Strains of Pseudomonas aeruginosa Revealed by Multimodal Chemical Imaging.

Quinolone, pyocyanin, and rhamnolipid production were studied in Pseudomonas aeruginosa by spatially patterning mucin, a glycoprotein important to infection of lung epithelia. Mass spectrometric imaging and confocal Raman microscopy are combined to probe P. aeruginosa biofilms from mucoid and nonmucoid strains grown on lithographically defined patterns. Quinolone signatures from biofilms on patterned vs unpatterned and mucin vs mercaptoundecanoic acid (MUA) surfaces were compared. Microbial attachment is accompanied by secretion of 2-alkyl-4-quinolones as well as rhamnolipids from the mucoid and nonmucoid strains. Pyocyanin was also detected both in the biofilm and in the supernatant in the mucoid strain only. Significant differences in the spatiotemporal distributions of secreted factors are observed between strains and among different surface patterning conditions. The mucoid strain is sensitive to composition and patterning while the nonmucoid strain is not, and in promoting community development in the mucoid strain, nonpatterned surfaces are better than patterned, and mucin is better than MUA. Also, the mucoid strain secretes the virulence factor pyocyanin in a way that correlates with distress. A change in the relative abundance for two rhamnolipids is observed in the mucoid strain during exposure to mucin, whereas minimal variation is observed in the nonmucoid strain. Differences between mucoid and nonmucoid strains are consistent with their strain-specific phenology, in which the mucoid strain develops highly protected and withdrawn biofilms that achieve Pseudomonas quinolone signal production under limited conditions.

Multimodal chemical imaging of a single brain tissue section using ToF-SIMS, MALDI-ToF and immuno/histochemical staining.

Cluster ion beam ToF-SIMS and/or MALDI-ToF mass spectrometry imaging (using 1,5-DAN matrix via sublimation) of a single coronal rat brain tissue section followed by classical- or immuno- histochemical staining faclilated a new multimodal chemical imaging workflow allowing complementary correlation of the lipid molecular ion images with the immuno/histological features within cerebellum region of the same brain tisue section.

Multimodal Metabolic Imaging Reveals Pigment Reduction and Lipid Accumulation in Metastatic Melanoma.

Objective and Impact Statement. Molecular signatures are needed for early diagnosis and improved treatment of metastatic melanoma. By high-resolution multimodal chemical imaging of human melanoma samples, we identify a metabolic reprogramming from pigmentation to lipid droplet (LD) accumulation in metastatic melanoma. Introduction. Metabolic plasticity promotes cancer survival and metastasis, which promises to serve as a prognostic marker and/or therapeutic target. However, identifying metabolic alterations has been challenged by difficulties in mapping localized metabolites with high spatial resolution. Methods. We developed a multimodal stimulated Raman scattering and pump-probe imaging platform. By time-domain measurement and phasor analysis, our platform allows simultaneous mapping of lipids and pigments at a subcellular level. Furthermore, we identify the sources of these metabolic signatures by tracking deuterium metabolites at a subcellular level. By validation with mass spectrometry, a specific fatty acid desaturase pathway was identified. Results. We identified metabolic reprogramming from a pigment-containing phenotype in low-grade melanoma to an LD-rich phenotype in metastatic melanoma. The LDs contain high levels of cholesteryl ester and unsaturated fatty acids. Elevated fatty acid uptake, but not de novo lipogenesis, contributes to the LD-rich phenotype. Monounsaturated sapienate, mediated by FADS2, is identified as an essential fatty acid that promotes cancer migration. Blocking such metabolic signatures effectively suppresses the migration capacity both in vitro and in vivo. Conclusion. By multimodal spectroscopic imaging and lipidomic analysis, the current study reveals lipid accumulation, mediated by fatty acid uptake, as a metabolic signature that can be harnessed for early diagnosis and improved treatment of metastatic melanoma.

Nanoscale Mass Spectrometry Multimodal Imaging via Tip-Enhanced Photothermal Desorption.

Materials ranging from adhesives, pharmaceuticals, lubricants, and personal care products are traditionally studied using macroscopic characterization techniques. However, their functionality is in reality defined by details of chemical organization on often noncrystalline matter with characteristic length scales on the order of microns to nanometers. Additionally, these materials are traditionally difficult to analyze using standard vacuum-based approaches that provide nanoscale chemical characterization due to their volatile and beam-sensitive nature. Therefore, approaches that operate under ambient conditions need to be developed that allow probing of nanoscale chemical phenomena and correlated functionality. Here, we demonstrate a tool for probing and visualizing local chemical environments and correlating them to material structure and functionality using advanced multimodal chemical imaging on a combined atomic force microscopy (AFM) and mass spectrometry (MS) system using tip-enhanced photothermal desorption with atmospheric pressure chemical ionization (APCI). We demonstrate enhanced performance metrics of the technique for correlated imaging and point sampling and illustrate the applicability for the analysis of trace chemicals on a human hair, additives in adhesives on paper, and pharmaceuticals samples notoriously difficult to analyze in a vacuum environment. Overall, this approach of correlating local chemical environments to structure and functionality is key to advancing research in many fields ranging from biology, to medicine, to material science.

Rapid assessment of structural and compositional changes during early stages of zirconium alloy oxidation

A multimodal chemical imaging approach has been developed and applied to detail the dynamic, atomic-scale changes associated with oxidation of a zirconium alloy (Zircaloy-4). Scanning transmission electron microscopy, a gas-phase reactor chamber attached to an atom probe tomography instrument, and synchrotron-based X-ray absorption near-edge spectroscopy were employed to reveal morphology, composition, crystal, and electronic structure changes that occur during initial stages of oxidation at 300 °C. Oxidation was carried out in 10 mbar O2 gas for short exposure times of 1 and 5 min. A multilayered oxide film with a cubic ZrO adjacent to the oxide/metal interface, a nanoscopic transition region with a graded composition of ZrO2−x (where 0 < x < 1), and tetragonal ZrO2 in the outermost oxide were formed. Partitioning of the major alloying element (tin) to the oxide/metal interface and heterogeneously within the oxide accompanied the development of the layered oxide. Our work provides a rapid, high-throughput approach for detailed characterisation of initial stages of zirconium alloy oxidation at an accelerated time scale, with implications for several other alloy systems.

Multimodal MALDI Imaging Mass Spectrometry Reveals Spatially Correlated Lipid and Protein Changes in Mouse Heart with Acute Myocardial Infarction.

Acute myocardial infarction (MI) is a cardiovascular disease that remains a major cause of morbidity and mortality worldwide despite advances in its prevention and treatment. During acute myocardial ischemia, the lack of oxygen switches the cell metabolism to anaerobic respiration, with lactate accumulation, ATP depletion, Na+ and Ca2+ overload, and inhibition of myocardial contractile function, which drastically modifies the lipid, protein, and small metabolite profile in the myocardium. Imaging mass spectrometry (IMS) is a powerful technique to comprehensively elucidate the spatial distribution patterns of lipids, peptides, and proteins in biological tissue sections. In this work, we demonstrate an application of multimodal chemical imaging using matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS), which provided comprehensive molecular information in situ within the same mouse heart tissue sections with myocardial infarction. MALDI-IMS (at 30 μm per pixel) revealed infarct-associated spatial alterations of several lipid species of sphingolipids, glycerophospholipids, lysophospholipids, and cardiolipins along with the acyl carnitines. Further, we performed multimodal MALDI-IMS (IMS3) where dual polarity lipid imaging was combined with subsequent protein MALDI-IMS analysis (at 30 μm per pixel) within the same tissue sections, which revealed accumulations of core histone proteins H4, H2A, and H2B along with post-translational modification products, acetylated H4 and H2A, on the borders of the infarcted region. This methodology allowed us to interpret the lipid and protein molecular pathology of the very same infarcted region in a mouse model of myocardial infarction. Therefore, the presented data highlight the potential of multimodal MALDI imaging mass spectrometry of the same tissue sections as a powerful approach for simultaneous investigation of spatial infarct-associated lipid and protein changes of myocardial infarction.

High Resolution Multimodal Chemical Imaging Platform for Organics and Inorganics.

Chemical analysis at the nanoscale is critical to advance our understanding of materials and systems from medicine and biology to material science and computing. Macroscale-observed phenomena in these systems are in the large part driven by processes that take place at the nanoscale and are highly heterogeneous. Therefore, there is a clear need to develop a new technology that enables correlative imaging of material functionalities with nanoscale spatial and chemical resolutions that will enable us to untangle the structure-function relationship of functional materials. Therefore, here, we report on the analytical figures of merit of the newly developed correlative chemical imaging technique of helium ion microscopy coupled with secondary ion mass spectrometry (HIM-SIMS) that enables multimodal topographical/chemical imaging of organic and inorganic materials at the nanoscale. In HIM-SIMS, a focused ion beam acts as a sputtering and ionization source for chemical analysis along with simultaneous high-resolution surface imaging, providing an unprecedented level of spatial resolution for gathering chemical information on organic and inorganic materials. In this work, we demonstrate HIM-SIMS as a platform for a next-generation tool for an in situ material design and analysis capable of down to 8 nm spatial resolution chemical imaging, layered metal structure imaging in depth profiling, single graphene layer detection, and spectral analysis of metals, metal oxides, and polymers.

Non-conventional mechanism of ferroelectric fatigue via cation migration

The unique properties of ferroelectric materials enable a plethora of applications, which are hindered by the phenomenon known as ferroelectric fatigue that leads to the degradation of ferroelectric properties with polarization cycling. Multiple microscopic models explaining fatigue have been suggested; however, the chemical origins remain poorly understood. Here, we utilize multimodal chemical imaging that combines atomic force microscopy with time-of-flight secondary mass spectrometry to explore the chemical phenomena associated with fatigue in PbZr0.2Ti0.8O3 (PZT) thin films. Investigations reveal that the degradation of ferroelectric properties is correlated with a local chemical change and migration of electrode ions into the PZT structure. Density functional theory simulations support the experimental results and demonstrate stable doping of the thin surface PZT layer with copper ions, leading to a decrease in the spontaneous polarization. Overall, the performed research allows for the observation and understanding of the chemical phenomena associated with polarization cycling and their effects on ferroelectric functionality.

Spatiotemporal Dynamics of Molecular Messaging in Bacterial Co-Cultures Studied by Multimodal Chemical Imaging.

Microbial community behavior is coupled to a set of genetically-regulated chemical signals that correlate with cell density - the quorum sensing (QS) system - and there is growing appreciation that the QS-regulated behavior of bacteria is chemically, spatially, and temporally complex. In addition, while it has been known for some time that different species use different QS networks, we are beginning to appreciate that different strains of the same bacterial species also differ in their QS networks. Here we combine mass spectrometric imaging (MSI) and confocal Raman microscopy (CRM) approaches to investigate co-cultures involving different strains (FRD1 and PAO1C) of the same species (Pseudomonas aeruginosa) as well as those involving different species (P. aeruginosa and E. coli). Combining MSI and CRM makes it possible to supersede the limits imposed by individual imaging approaches and enables the spatial mapping of individual bacterial species and their microbial products within a mixed bacterial community growing in situ on surfaces. MSI is used to delineate the secretion of a specific rhamnolipid surfactant as well as alkyl quinolone (AQ) messengers between FRD1 and PAO1C strains of P. aeruginosa, showing that the spatial distribution and production rate of AQ messengers in PAO1C far outstrips that of FRD1. In the case of multiple species, CRM is used to show that the prolific secretion of AQs by the PAO1C strain of P. aeruginosa is used to mediate its interaction with co-cultured E. coli.

Multimodal Chemical Imaging for Linking Adhesion with Local Chemistry in Agrochemical Multicomponent Polymeric Coatings

Seed coatings improve germination and offer higher crop yields through a blend of active ingredients (such as insecticides and fungicides), polymers, waxes, fillers, and pigments. To reach their full potential, fundamental formulation challenges bridging structure and function need to be addressed. In some instances, during industrial-volume packing and transportation, coated seeds do not flow well through elevators, conveyers, and applicators, which may reduce yield and add cost. In this work, we illustrate a combinatorial chemical imaging approach to study seed coatings at the microscale to link chemical and physical properties responsible for low seed flowability. The local chemical composition was examined using time-of-flight secondary ion mass spectrometry (ToF-SIMS) and at comparable length scales, the local adhesive properties were examined using atomic force microscopy (AFM) force volume mapping. The link between the chemical and the adhesive properties was established by non-negative matrix factorization (NMF). The correlative multimodal imaging approach developed here utilizing AFM force volume mapping, ToF-SIMS chemical mapping, and data analytics offers a path for linking function with localized chemistry when investigating multicomponent soft material systems.

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.