Secondary Ion Mass Spectrometry Imaging Research Articles

The Application of a Random Forest Classifier to ToF-SIMS Imaging Data.

Time-of-flight secondary ion mass spectrometry (ToF-SIMS) imaging is a potent analytical tool that provides spatially resolved chemical information on surfaces at the microscale. However, the hyperspectral nature of ToF-SIMS datasets can be challenging to analyze and interpret. Both supervised and unsupervised machine learning (ML) approaches are increasingly useful to help analyze ToF-SIMS data. Random Forest (RF) has emerged as a robust and powerful algorithm for processing mass spectrometry data. This machine learning approach offers several advantages, including accommodating nonlinear relationships, robustness to outliers in the data, managing the high-dimensional feature space, and mitigating the risk of overfitting. The application of RF to ToF-SIMS imaging facilitates the classification of complex chemical compositions and the identification of features contributing to these classifications. This tutorial aims to assist nonexperts in either machine learning or ToF-SIMS to apply Random Forest to complex ToF-SIMS datasets.

Getting to the root of the problem: Soil carbon and microbial responses to root inputs within a buried paleosol along an eroding hillslope in southwestern Nebraska, USA

Large quantities of soil carbon (C) can persist within paleosols for millennia due to burial and subsequent isolation from plant-derived inputs, atmospheric conditions, and microbial activity at the modern surface. Erosion exposes buried soils to modern root-derived C influx via root exudation and root turnover, thus stimulating microbial activity leading to SOC decomposition and accumulation through organo-mineral stabilization of modern C. With this study we aim to quantify how modern root-derived C inputs impact paleosol C decomposition and stabilization across varying degrees of isolation from modern surface conditions in southwestern Nebraska, USA, where hillslope erosion is bringing a buried Late-Pleistocene-early Holocene paleosol (the “Brady Soil”) closer to the modern surface. We collected Brady Soil samples from 0.2 m, 0.4 m, and 1.2 m below the modern surface and conducted two lab-based incubations. Soils were amended with either (1) a lab-synthesized mixture of low molecular weight compounds (12 atom% 13C), or (2) 13C enriched root residues (92 atom% 13C), in 30-day and 240-day incubation experiments, respectively. We determined microbial responses to synthetic root exudates and residues by partitioning the 13C label from Brady Soil C, including measurements of total, root, and primed C respiration, microbial biomass C (MBC), microbial C use efficiency (CUE). To assess the capacity of isolated paleosols to accrue modern plant C, we used Nano-scale Secondary Ion Mass Spectrometry imaging. We found that: (1) adding root-derived C inputs primed Brady Soil C across all depths, and was mediated by depth and composition of root additions; (2) root-derived C inputs stimulated microbial biomass C (MBC) growth similarly across depths, but the magnitude of CUE and MBC varied by chemistry of root-derived additions; (3) new particulate organic matter was incorporated into mineral-associated pools over time; (4) material from the added root residues was found in association with bacterial cells and fungal hyphae as well as with soil aggregate and mineral surfaces. Our study shows that paleosols defy expectations of C content and reactivity with depth, and changes in land cover and climate will expose buried paleosols to modern surface conditions, increasing respired C. This work highlights the importance of evaluating the role resurfacing buried soils through landscape change plays in C cycle feedbacks to the climate system.

Characteristics of phosphorus transformation from vivianite mediated by sulphide

The release of phosphorus (P) from anaerobic sediments becomes the dominant source of P loading with effective control of external P pollution. As a crucial component responsible for binding P, vivianite (Fe3(PO4)2·8H2O) in sediment inevitably impacts the P level and transformation. The release of P from vivianite mediated by sulphide was investigated using simulated overlying water-sediment and chemical reaction systems. The percentage of redox-stable P in the sediments increased with vivianite input in the overlying water-sediment system. Increasing P concentrations in both the overlying water and interstitial water occurred before day 10, accompanied by a decreasing percentage of redox-sensitive P in the sediments driven by sulphide. The continuous release of P from vivianite clarifies the influence of sulphide on promoting vivianite dissolution in a chemical reaction system with vivianite and sulphide solution. Additionally, Mössbauer spectrum and nanoscale secondary ion mass spectrometry (NanoSIMS) images based on the chemical reaction with 57Fe isotope labelling demonstrated the presence of initial vivianite, newly produced vivianite, and FeS2.

ToF-SIMS imaging reveals changes in tumor cell lipids during metastatic progression of melanoma.

Most melanomas progress from radial to vertical growth phase before spreading locoregionally and distally. Much is still unknown about the metabolic changes in the tumor cells and their microenvironment during this metastatic progression. We aimed to gain new insight into the molecular characteristics of melanoma in regard to spatial lipidomics to deliver new knowledge regarding tumor metastatic progression. We included 10 fresh tumor samples from 10 patients including two insitu melanomas, two invasive primary melanomas, and six metastatic melanomas (four in-transit metastases and two distant metastases). In addition, we analyzed four healthy skin controls from the same patients. Time-of-flight imaging secondary ion mass spectrometry (ToF-SIMS) enabled detailed spatial-lipidomics that could be directly correlated with conventional histopathological analysis of consecutive H&E-stained tissue sections. Significant differences in the lipid profiles were found in primary compared to metastatic melanomas, notably an increase in phosphatidylethanolamine lipids relative to phosphatidylinositol lipids and an increase in GM3 gangliosides in the metastatic samples. Furthermore, analysis of the data from in transit versus distant metastases samples highlighted that specific phospholipids, and a difference in the long versus shorter chain GM3 gangliosides, discriminated the metastatic routes. Further studies are warranted to verify these preliminary findings. Lipidomic changes could serve as a novel biomarker for tumor progression and even serve as a target for novel treatments. Furthermore, analyzing the lipid profiles could help to differentiate between primary and metastatic melanomas in challenging cases.

Incorporation of Intracellular NanoSIMS Tracers to Oligonucleotide Conjugates via Strain Promoted Sydnone-Alkyne Cycloaddition.

Extensive efforts have been dedicated to developing cell-specific targeting ligands that can be conjugated to therapeutic cargo, offering a promising yet still challenging strategy to deliver oligonucleotide therapeutics beyond the liver. Indeed, while the cargo and the ligand are crucial, the third component, the linker, is integral but is often overlooked. Here, we present strain-promoted sydnone-alkyne cycloaddition as a versatile linker chemistry for oligonucleotide synthesis, expanding the choices for bioconjugation of therapeutics while enabling subcellular detection of the linker and payload using nanoscale secondary ion mass spectrometry (NanoSIMS) imaging. This strategy was successfully applied to peptide and lipid ligands and profiled using the well characterized N-acetylgalactosamine (GalNAc) targeting ligand. The linker did not affect the expected activity of the conjugate and was detectable and distinguishable from the labeled cargo. Finally, this work not only offers a practical bioconjugation method but also enables the assessment of the linker's subcellular behavior, facilitating NanoSIMS imaging to monitor the three key components of therapeutic conjugates.

Lipid organization and turnover in the plasma membrane of human differentiating neural progenitor cells revealed by time-of-flight secondary ion mass spectrometry imaging

Membrane lipids have been known to influence multiple signalling and cellular processes. Dysregulation of lipids at the neuronal membrane is connected to a significant alteration of the brain function and morphology, leading to brain diseases and neurodegeneration. Understanding the lipid composition and turnover of neuronal membrane will provide a significant insight into the molecular events underlying the regulatory effects of these biomolecules in a neuronal system. In this study, we aimed to characterize the composition and turnover of the plasma membrane lipids in human neural progenitor cells (NPCs) at an early differentiation stage into midbrain neurons using ToF-SIMS imaging. Lipid composition of the native plasma membrane was explored, followed by an examination of the lipid turnover using different isotopically labelled lipid precursors, including 13C-choline, 13C-lauric acid, 15N-linoleic, and 13C-stearic. Our results showed that differentiating NPCs contain a high abundance of ceramides, glycerophosphoserines, neutral glycosphingolipids, diradylglycerols, and glycerophosphocholines at the plasma membrane. In addition, different precursors were found to incorporate into different membrane lipids which are specific for the short- or long-carbon chains, and the unsaturation or saturation stage of the precursors. The lipid structure of neuronal membrane reflects the differentiation status of NPCs, and it can be altered significantly using a particular lipid precursor. Our study illustrates a potential of ToF-SIMS imaging to study native plasma membrane lipids and elucidate complex cellular processes by providing molecular -rich information at a single cell level.

Multimodal mass spectrometry imaging identifies cell-type-specific metabolic and lipidomic variation in the mammalian liver

Spatial single-cell omics provides a readout of biochemical processes. It is challenging to capture the transient lipidome/metabolome from cells in a native tissue environment. We employed water gas cluster ion beam secondary ion mass spectrometry imaging ([H2O]n>28K-GCIB-SIMS) at ≤3μm resolution using a cryogenic imaging workflow. This allowed multiple biomolecular imaging modes on the near-native-state liver at single-cell resolution. Our workflow utilizes desorption electrospray ionization (DESI) to build a reference map of metabolic heterogeneity and zonation across liver functional units at tissue level. Cryogenic dual-SIMS integrated metabolomics, lipidomics, and proteomics in the same liver lobules at single-cell level, characterizing the cellular landscape and metabolic states in different cell types. Lipids and metabolites classified liver metabolic zones, cell types and subtypes, highlighting the power of spatial multi-omics at high spatial resolution for understanding celluar and biomolecular organizations in the mammalian liver.

Markedly Enhanced Analysis of Mass Spectrometry Images Using Weakly Supervised Machine Learning.

Supervised and unsupervised machine learning algorithms are routinelyapplied to time-of-flight secondary ion mass spectrometry (ToF-SIMS) imaging data and, more broadly, to mass spectrometry imaging (MSI). These algorithms have accelerated large-scale, single-pixel analysis, classification, and regression. However, there is relatively little research on methods suited for so-called weakly supervised problems, where ground-truth class labels exist at the image level, but not at the individual pixel level. Unsupervised learning methods are usually applied to these problems. However, these methods cannot make use of available labels. Here a novel method specifically designed for weakly supervised MSI data is presented. A dual-stream multiple instance learning (MIL) approach is adapted from computational pathology that reveals the spatial-spectral characteristics distinguishing different classes of MSI images. The method uses an information entropy-regularized attention mechanism to identify characteristic class pixels that are then used to extract characteristic mass spectra. This work provides a proof-of-concept exemplification using printed ink samples imaged by ToF-SIMS. A second application-oriented study is also presented, focusing on the analysis of a mixed powder sample type. Results demonstrate the potential of the MIL method for broader application in MSI, with implications for understanding subtle spatial-spectral characteristics in various applications and contexts.

Effect of optimal rapid thermal alloying of ohmic contacts to microwave pseudomorphic HEMTs

Gallium Arsenide (GaAs) pseudomorphic High Electron Mobility Transistors (pHEMTs) are currently used as active devices in manufacturing Monolithic Microwave Integrated Circuits (MMICs) for microwave and millimetre wave applications like Transmit-Receive (T/R) modules in Radars and communication. Low contact resistances (Rc) of pHEMT devices are necessary to obtain low access resistances to the device channel, for enhancement of transconductance and cut-off frequency. Devices formed by optimal and suboptimal conditions of rapid thermal alloying by infra-red optical radiation are investigated using cross-section transmission electron microscopy (XTEM), backside secondary ion mass spectrometry (backside SIMS) and thermal imaging techniques to understand the role of optimal alloy formation and its role in enhancement of device parameters.

Subcellular protein turnover in human neural progenitor cells revealed by correlative electron microscopy and nanoscale secondary ion mass spectrometry imaging.

Protein turnover is a critical process for accurate cellular function, in which damaged proteins in the cells are gradually replaced with newly synthesized ones. Many previous studies on cellular protein turnover have used stable isotopic labelling by amino acids in cell culture (SILAC), followed by proteomic bulk analysis. However, this approach does not take into account the heterogeneity observed at the single-cell and subcellular levels. To address this, we investigated the protein turnover of neural progenitor cells at the subcellular resolution, using correlative TEM and NanoSIMS imaging, relying on a pulse-chase analysis of isotopically-labelled protein precusors. Cellular protein turnover was found significantly heterogenous across individual organelles, which indicates a possible relation between protein turnover and subcellular activity. In addition, different isotopically-labelled amino acids provided different turnover patterns, in spite of all being protein precursors, suggesting that they undergo distinct protein synthesis and metabolic pathways at the subcellular level.

Towards Operando Secondary Ion Mass Spectrometry Imaging of Lithium Redistribution in Solid-State Lithium-Ion Batteries: Correlation of Structural, Chemical and Electrochemical Characteristics

Innovations in lithium-ion batteries rely crucially on the availability of advanced characterization techniques. High-resolution chemical imaging of low-Z elements e.g., lithium (Li) is often difficult in many conventional chemical analysis techniques such as Energy-Dispersive X-ray Spectroscopy. High-resolution Secondary Ion Mass Spectrometry (SIMS) imaging is a well-known technique for the analysis of all elements including isotopes. For this reason, SIMS imaging is used in numerous studies related to Li-ion battery research. While direct imaging of Li in post-mortem battery components is helpful to understand parts of the degradation mechanisms, a complete dynamic view of the evolution of the Li distribution at high resolution during operation (‘operando’) of batteries is required to fully understand the local interfacial processes, charge transport characteristics and the degradation mechanisms. A few reports presenting operando Time-of-Flight SIMS imaging of batteries have recently been published [1], but the lateral resolution demonstrated in these reports is not adequate to study local processes that occur at nanoscale.In order to demonstrate operando SIMS chemical imaging with sub-20 nm lateral resolution, we developed a novel operando methodology suitable for Focused Ion Beam (FIB)-SIMS imaging and analysis (see Fig. 1). An in-house designed magnetic-sector mass spectrometer [2] attached to a ThermoFisher SCIOS Ga+ FIB is used for SIMS chemical imaging. A special operando sample holder was designed to enable electrochemical cycling of batteries within the FIB-SIMS instrument. The micromanipulator inside the FIB (typically used for preparing thin lamellae for Transmission Electron Microscopy) is used to contact one of the battery electrodes through the operando sample holder and complete the electrical circuit. An external potentiostat is then connected to the instrument to drive the charging/discharging of batteries. The proof-of-concept experiments were performed using Li|Li7La3Zr2O12|Li symmetric half-cells. Galvanostatic cycling was performed in-situ inside the FIB-SIMS instrument until the sample failed. SIMS chemical mapping revealed a redistribution of Li during cycling. Lithium rich phases appeared during cycling which likely percolated through grain-boundaries and pores of the solid electrolyte causing a short-circuit failure. These results validate our methodology for operando analysis of Li-ion batteries with the possibility to obtain SIMS chemical images with sub-20 nm lateral resolution [3].This work was funded by the Luxembourg National Research Fund (FNR) through the grant INTER/MERA/20/13992061 (INTERBATT).[1] Y. Yamagishi, et al., J. Phys. Chem. Lett. 2021, 12, 19, 4623–4627[2] O. De Castro, et al., Analytical Chemistry, 2022, 94, 30, 10754–10763.[3] L. Cressa, et al., Analytical Chemistry, 2023, under review. Figure 1

Understanding Degradation Behavior of Direct-Ammonia Solid Oxide Fuel Cells Using Nanoscale Analyses

Direct-ammonia solid oxide fuel cells (SOFCs) offer a cost-effective and efficient avenue for accelerating the use of hydrogen as a major energy vector. The effect of directly feeding ammonia as a fuel into SOFCs and their associated degradation behavior, however, needs to be fully understood in order to design robust cells capable of operating with good performance and durability at the required high temperatures for extended periods of time. In this study, we performed detailed analyses using advanced techniques such as high-resolution secondary ion mass spectrometry (SIMS) imaging and microscopy methods to examine the microstructural evolution and factors affecting nickel nitriding behavior of model Ni metal surfaces as well as Ni/yttria-stabilized zirconia (YSZ) cermet anodes directly exposed to ammonia as a function of temperature. The results show preferential formation of nickel nitride and presence of OH- species on Ni metal surfaces directly exposed to ammonia, as well as grain boundaries in the bulk which were not directly exposed to ammonia, suggesting enhanced ionic diffusion along these regions. Nickel nitriding is also accompanied by volume expansion which is considered to result in additional mechanical stress. Ni/YSZ cermet anodes, on the other hand, exhibit a significantly higher amount of nickel nitride formed preferentially in pore regions at lower temperatures, suggesting the possibility of nickel nitride decomposition occurring at higher temperatures. The results further suggest a correlation between nickel nitride formation and microstructural features of Ni/YSZ cermet anodes which influence gas diffusion properties. Lastly, a model for the degradation behavior of Ni/YSZ cermet anodes on the basis these results will be proposed and discussed.

Symbiotic nutrient exchange enhances the long-term survival of cassiosomes, the autonomous stinging-cell structures of Cassiopea.

Medusae of the widely distributed upside-down jellyfish Cassiopea release autonomous, mobile stinging structures. These so-called cassiosomes play a role in predator defense and prey capture, and are major contributors to "contactless" stinging incidents in (sub-)tropical shallow waters. While the presence of endosymbiotic dinoflagellates in cassiosomes has previously been observed, their potential contribution to the metabolism and long-term survival of cassiosomes is unknown. Combining stable isotope labeling and correlative scanning electron microscopy and nanoscale secondary ion mass spectrometry imaging with a long-term in vitro experiment, our study reveals a mutualistic symbiosis based on nutritional exchanges in dinoflagellate-bearing cassiosomes. We show that organic carbon input from the dinoflagellates fuels the metabolism of the host tissue and enables anabolic nitrogen assimilation. This symbiotic nutrient exchange enhances the life span of cassiosomes for at least one month in vitro. Overall, our study demonstrates that cassiosomes, in analogy with Cassiopea medusae, are photosymbiotic holobionts. Cassiosomes, which are easily accessible under aquarium conditions, promise to be a powerful new miniaturized model system for in-depth ultrastructural and molecular investigation of cnidarian photosymbioses.IMPORTANCEThe upside-down jellyfish Cassiopea releases autonomous tissue structures, which are a major cause of contactless stinging incidents in (sub-) tropical coastal waters. These so-called cassiosomes frequently harbor algal symbionts, yet their role in cassiosome functioning and survival is unknown. Our results show that cassiosomes are metabolically active and supported by algal symbionts. Algal photosynthesis enhances the cassiosomes long-term survival in the light. This functional understanding of cassiosomes thereby contributes to explaining the prevalence of contactless stinging incidents and the ecological success of some Cassiopea species. Finally, we show that cassiosomes are miniaturized symbiotic holobionts that can be used to study host-microbe interactions in a simplified system.

Applying Multimodal Mass Spectrometry to Image Tumors Undergoing Ferroptosis Following In Vivo Treatment with a Ferroptosis Inducer.

Epithelial ovarian cancer (EOC) is the most common form of ovarian cancer. The poor prognosis generally associated with this disease has led to the search for improved therapies such as ferroptosis-inducing agents. Ferroptosis is a form of regulated cell death that is dependent on iron and is characterized by lipid peroxidation. Precise mapping of lipids and iron within tumors exposed to ferroptosis-inducing agents may provide insight into processes of ferroptosis in vivo and ultimately assist in the optimal deployment of ferroptosis inducers in cancer therapy. In this work, we present a method for combining matrix-assisted laser desorption/ionization (MALDI) mass spectrometry imaging (MSI) with secondary ion mass spectrometry (SIMS) to analyze changes in spatial lipidomics and metal composition, respectively, in ovarian tumors following exposure to a ferroptosis inducer. Tumors were obtained by injecting human ovarian cancer tumor-initiating cells into mice, followed by treatment with the ferroptosis inducer erastin. SIMS imaging detected iron accumulation in the tumor tissue, and sequential MALDI-MS imaging of the same tissue section displayed two chemically distinct regions of lipids. One region was associated with the iron-rich area detected with SIMS, and the other region encompassed the remainder of the tissue section. Bulk lipidomics confirmed the lipid assignments putatively assigned from the MALDI-MS data. Overall, we demonstrate the ability of multimodal MSI to identify the spatial locations of iron and lipids in the same tissue section and associate these regions with clinical pathology.

Mycorrhizal C/N ratio determines plant-derived carbon and nitrogen allocation to symbiosis

Carbon allocation of trees to ectomycorrhizas is thought to shape forest nutrient cycling, but the sink activities of different fungal taxa for host resources are unknown. Here, we investigate fungal taxon-specific differences in naturally composed ectomycorrhizal (EM) communities for plant-derived carbon and nitrogen. After aboveground dual labeling of young beech with 15N and 13C, ectomycorrhizas formed with different fungal taxa exhibit strong differences in label enrichment. Secondary Ion Mass Spectrometry (SIMS) imaging of nitrogen in cross sections of ectomycorrhizas demonstrates plant-derived 15N in both root and fungal structures. Isotope enrichment in ectomycorrhizas correlates with that in the corresponding ectomycorrhiza-attached lateral root, supporting fungal taxon-specific N and C fluxes in ectomycorrhizas. The enrichments with 13C and 15N in the symbiosis decrease with increasing C/N ratio of ectomycorrhizas, converging to zero at high C/N. The relative abundances of EM fungal species on roots are positively correlated with 13C enrichment, demonstrating higher fitness of stronger than of less C-demanding symbioses. Overall, our results support that differences among the C/N ratios in ectomycorrhizas formed with different fungal species regulate the supply of the symbioses with host-derived carbon and provide insights on functional traits of ectomycorrhizas, which are important for major ecosystem processes.

Leveraging ToF-SIMS imaging to investigate tenofovir disoproxil fumarate degradation at excipient interfaces in oral compressed tablets

Time-of-flight secondary ion mass spectrometry (ToF-SIMS) imaging has been used to study the hydrolysis of tenofovir disoproxil fumarate (TDF) to tenofovir monosoproxil (TM) within an oral compressed tablet. The ToF-SIMS images displayed a heterogenous distribution of the matrix components. Evaluation of the TM distribution revealed that it was primarily co-localized with areas of higher excipient concentration pointing toward excipient driven degradation. To support these observations, a compatibility study of TDF with each tablet component was performed via liquid chromatography. The ToF-SIMS imaging and compatibility study indicated that the excipient, Avicel® PH-102, was the primary driver of TM formation in the tablet. The hydrolysis degradation mechanism within the tablet is further rationalized through discussion of chemical and physical properties of the matrix components. The sum of this work demonstrates a new analytical workflow for probing and understanding matrix driven degradation in oral compressed tablets utilizing ToF-SIMS imaging.

Comparison of Tiling Artifact Removal Methods in Secondary Ion Mass Spectrometry Images.

Time-of-flight secondary ion mass spectrometry (ToF-SIMS) imaging is used across many fields for the atomic and molecular characterization of surfaces, with both high sensitivity and high spatial resolution. When large analysis areas are required, standard ToF-SIMS instruments allow for the acquisition of adjoining tiles, which are acquired by rastering the primary ion beam. For such large area scans, tiling artifacts are a ubiquitous challenge, manifesting as intensity gradients across each tile and/or sudden changes in intensity between tiles. Such artifacts are thought to be related to a combination of sample charging, local detector sensitivity issues, and misalignment of the primary ion gun, among other instrumental factors. In this work, we investigated six different computational tiling artifact removal methods: tensor decomposition, multiplicative linear correction, linear discriminant analysis, seamless stitching, simple averaging, and simple interpolating. To ensure robustness in the study, we applied these methods to three hyperspectral ToF-SIMS data sets and one OrbiTrapSIMS data set. Our study includes a carefully designed statistical analysis and a quantitative survey that subjectively assessed the quality of the various methods employed. Our results demonstrate that while certain methods are useful and preferred more often, no one particular approach can be considered universally acceptable and that the effectiveness of the artifact removal method is strongly dependent on the particulars of the data set analyzed. As examples, the multiplicative linear correction and seamless stitching methods tended to score more highly on the subjective survey; however, for some data sets, this led to the introduction of new artifacts. In contrast, simple averaging and interpolation methods scored subjectively poorly on the biological data set, but more highly on the microarray data sets. We discuss and explore these findings in depth and present general recommendations given our findings to conclude the work.

High-resolution analysis of bound Ca2+ in neurons and synapses.

Calcium (Ca2+) is a well-known second messenger in all cells, and is especially relevant for neuronal activity. Neuronal Ca2+ is found in different forms, with a minority being freely soluble in the cell and more than 99% being bound to proteins. Free Ca2+ has received much attention over the last few decades, but protein-bound Ca2+ has been difficult to analyze. Here, we introduce correlative fluorescence and nanoscale secondary ion mass spectrometry imaging as a tool to describe bound Ca2+ As expected, bound Ca2+ is ubiquitous. It does not correlate to free Ca2+ dynamics at the whole-neuron level, but does correlate significantly to the intensity of markers for GABAergic pre-synapse and glutamatergic post-synapses. In contrast, a negative correlation to pre-synaptic activity was observed, with lower levels of bound Ca2+ observed in the more active synapses. We conclude that bound Ca2+ may regulate neuronal activity and should receive more attention in the future.

Scratch‐Resistant Hydrophobic Coating with Supramolecular‐Polymer Co‐Assembly

AbstractSupramolecular assembly for superhydrophobic coatings is known for its efficiency and efficacy. However, the mechanical fragility of the coatings limits their use as coating materials. Herein, the combination of (±)‐N,N'‐(trans‐cyclohexane‐1,2‐diyl)‐bis(perfluorooctanamide) CF7, a cyclohexyl diamide‐based low molecular weight gelator, with acrylate polymers for the generation of semi‐transparent omniphobic coatings with significantly enhanced scratch proofness is presented. CF7 has shown the ability to self‐assemble in common solvents into highly entangled fibrous networks with extreme water repellency. The incorporation of covalent polymers, specifically poly(methyl methacrylate) (PMMA) and poly(trifluoroethyl methacrylate) (PTFEMA), helps to fixate the supramolecular CF7 fibers without interfering with the self‐assembled structures. The resulting coatings, namely CF7/PMMA and CF7/PTFEMA, show significantly improved mechanical resistance as well as optical transparency while maintaining excellent water and oil repellency. Furthermore, the homogeneity of the coating in bulk is confirmed by depth profiling of the 3D distribution of the components using time‐of‐flight secondary ion mass spectrometry imaging, which turns out to be an essential technique in order to characterize such materials.

Effect of data preprocessing and machine learning hyperparameters on mass spectrometry imaging models

The self-organizing map (SOM) is a nonlinear machine learning algorithm that is particularly well suited for visualizing and analyzing high-dimensional, hyperspectral time-of-flight secondary ion mass spectrometry (ToF-SIMS) imaging data. Previously, we compared the capabilities of the SOM with more traditional linear techniques using ToF-SIMS imaging data. Although SOMs perform well with minimal data preprocessing and negligible hyperparameter optimization, it is important to understand how different data preprocessing methods and hyperparameter settings influence the performance of SOMs. While these investigations have been reported outside of the ToF-SIMS field, no such study has been reported for hyperspectral MSI data. To address this, we used two labeled ToF-SIMS imaging datasets, one of which was a polymer microarray dataset, while the other was semisynthetic hyperspectral data. The latter was generated using a novel algorithm that we describe here. A grid-search was used to evaluate which data preprocessing methods and SOM hyperparameters had the largest impact on the performance of the SOM. This was assessed using multiple linear regression, whereby performance metrics were regressed onto each variable defining the preprocessing-hyperparameter space. We found that preprocessing was generally more important than hyperparameter selection. We also found statistically significant interactions between several parameters studied, suggesting a complex interplay between preprocessing and hyperparameter selection. Importantly, we identified interesting trends, both dataset specific and dataset agnostic, which we describe and discuss in detail.