Multivariate analysis of ToF-SIMS data from multicomponent systems: the why, when, and how

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.

Development of Peptide Identification System for ToF-SIMS Spectra Using Supervised Machine Learning

Time-of-flight secondary ion mass spectrometry (ToF-SIMS) data interpretation for organic materials is complicated because of various fragment ions produced from each molecule and the overlapping of certain mass peaks from different molecules. Fragmentation mechanisms in SIMS are complex because different sputtering and ionization processes can simultaneously occur. Therefore, a prediction system that can identify materials in a sample is required. A novel prediction system for peptides based on ToF-SIMS and amino-acid-based teaching information (labels) for supervised machine learning was developed. To develop the prediction system for general organic materials, the annotation of materials is crucial to creating effective labels for supervised learning. Peptides are composed of 20 amino acid residues, which can be used as labels. We previously developed a peptide prediction system using Random Forest, a supervised machine-learning method. However, only the amino acids contained in the target peptide were predicted, and the amino acid sequence was unable to be assumed. In this study, the amino acid sequence of the test peptide was determined by adding the information on two adjacent amino acids to the labels. Once the prediction system learned the target peptide spectra, the peptides in the newly obtained ToF-SIMS spectra could be identified. The new prediction system also provides useful information for the identification of unknown peptides. The prediction results indicate that two adjacent permutations of amino acids are effective pieces of teaching information for expressing the amino acid sequence of a peptide.

Identifying Process Differences with ToF-SIMS: An MVA Decomposition Strategy

In time-of-flight secondary ion mass spectrometry (ToF-SIMS), multivariate analysis (MVA) methods such as principal component analysis (PCA) are routinely employed to differentiate spectra. However, additional insights can often be gained by comparing processes, where each process is characterized by its own start and end spectra, such as when identical samples undergo slightly different treatments or when slightly different samples receive the same treatment. This study proposes a strategy to compare such processes by decomposing the loading vectors associated with them, which highlights differences in the relative behavior of the peaks. This strategy identifies key information beyond what is captured by the loading vectors or the end spectra alone. While PCA is widely used, partial least-squares discriminant analysis (PLS-DA) serves as a supervised alternative and is the preferred method for deriving process-related loading vectors when classes are narrowly separated. The effectiveness of the decomposition strategy is demonstrated using artificial spectra and applied to a ToF-SIMS materials science case study on the photodegradation of N719 dye, a common dye in photovoltaics, on a mesoporous TiO2 anode. The study revealed that the photodegradation process varies over time, and the resulting fragments have been identified accordingly. The proposed methodology, applicable to both labeled (supervised) and unlabeled (unsupervised) spectral data, can be seamlessly integrated into most modern mass spectrometry data analysis workflows to automatically generate a list of peaks whose relative behavior varies between two processes, and is particularly effective in identifying subtle differences between highly similar physicochemical processes.

Self-Organizing Maps for Secondary Ion Mass Spectrometry

Secondary ion mass spectrometry (SIMS) is a powerful analytical technique for characterizing the molecular and elemental composition of surfaces. Individual mass spectra can provide information about the mean surface composition, while spatial mapping can elucidate the spatial distributions of molecular species in 2D and 3D with no prior labeling of molecular targets. The data sets produced by SIMS techniques are large and inherently complex, often containing subtle relationships between spatial and molecular features. Machine learning algorithms are well suited to exploring this complexity, making them ideal for data analysis, interpretation, and visualization of SIMS data sets. One such algorithm, the self-organizing map (SOM), is particularly well suited to clustering similar samples and reducing the dimensionality of hyperspectral data sets. Here, we present an introduction to the SOM, a concise mathematical description, and recent examples of its use in SIMS and other related mass spectrometry techniques. These examples demonstrate how SOMs may be used to interpret high volumes of individual mass spectra, imaging, or depth profiling data sets. This review will be useful for specialists in SIMS and other mass spectral techniques seeking to explore self-organizing maps for data analysis.

ToF-SIMS evaluation of PEG-related mass peaks and applications in PEG detection in cosmetic products

Polyethylene glycols (PEGs) are used in industrial, medical, health care, and personal care applications. The cycling and disposal of synthetic polymers like PEGs pose significant environmental concerns. Detecting and monitoring PEGs in the real world calls for immediate attention. This study unveils the efficacy of time-of-flight secondary ion mass spectrometry (ToF-SIMS) as a reliable approach for precise analysis and identification of reference PEGs and PEGs used in cosmetic products. By comparing SIMS spectra, we show remarkable sensitivity in pinpointing distinctive ion peaks inherent to various PEG compounds. Moreover, the employment of principal component analysis effectively discriminates compositions among different samples. Notably, the application of SIMS two-dimensional image analysis visually portrays the spatial distribution of various PEGs as reference materials. The same is observed in authentic cosmetic products. The application of ToF-SIMS underscores its potential in distinguishing PEGs within intricate environmental context. ToF-SIMS provides an effective solution to studying emerging environmental challenges, offering straightforward sample preparation and superior detection of synthetic organics in mass spectral analysis. These features show that SIMS can serve as a promising alternative for evaluation and assessment of PEGs in terms of the source, emission, and transport of anthropogenic organics.

Time-of-Flight Secondary Ion Mass Spectrometry Coupled with Unsupervised Methods for Advanced Saffron Authenticity Screening

Saffron, renowned for its aroma and flavor, is susceptible to adulteration due to its high value and demand. Current detection methods, including ISO standards, often fail to identify specific adulterants such as safflower or turmeric up to 20% (w/w). Therefore, the quest continues for robust screening methods using advanced techniques to tackle this persistent challenge of safeguarding saffron quality and authenticity. Advanced techniques such as time-of-flight secondary ion mass spectrometry (TOF-SIMS), with its molecular specificity and high sensitivity, offer promising solutions. Samples of pure saffron and saffron adulterated with safflower and turmeric at three inclusion levels (5%, 10%, and 20%) were analyzed without prior treatment. Spectral analysis revealed distinct signatures for pure saffron, safflower, and turmeric. Through principal component analysis (PCA), TOF-SIMS effectively discriminated between pure saffron and saffron adulterated with turmeric and safflower at different inclusion levels. The variation between the groups is attributed to the characteristic peaks of safflower and the amino group peaks and mineral peaks of saffron. Additionally, a study was conducted to demonstrate that semi-quantification of the level of safflower inclusion can be achieved from the normalized values of its characteristic peaks in the saffron matrix.

Carotenoids and Intestinal Harmony: Exploring the Link for Health

Carotenoids, prominent lipid-soluble phytochemicals in the human diet, are responsible for vibrant colours in nature and play crucial roles in human health. While they are extensively studied for their antioxidant properties and contributions to vitamin A synthesis, their interactions with the intestinal microbiota (IM) remain poorly understood. In this study, beta (β)-carotene, lutein, lycopene, a mixture of these three pigments, and the alga Osmundea pinnatifida were submitted to simulated gastrointestinal digestion (GID) and evaluated on human faecal samples. The results showed varying effects on IM metabolic dynamics, organic acid production, and microbial composition. Carotenoid exposure influenced glucose metabolism and induced the production of organic acids, notably succinic and acetic acids, compared with the control. Microbial composition analysis revealed shifts in phyla abundance, particularly increased Pseudomonadota. The α-diversity indices demonstrated higher diversity in β-carotene and the pigments' mixture samples, while the β-diversity analysis indicated significant dissimilarity between the control and the carotenoid sample groups. UPLC-qTOF MS analysis suggested dynamic changes in carotenoid compounds during simulated fermentation, with lutein exhibiting distinct mass ion fragmentation patterns. This comprehensive research enhances our understanding of carotenoid-IM interactions, shedding light on potential health implications and the need for tailored interventions for optimal outcomes.

High-Throughput Quantitative Analysis of Amino Acids in Freeze-Dried Drops Using Time-of-Flight Secondary Ion Mass Spectrometry

Time-of-flight secondary ion mass spectrometry (ToF-SIMS) has become a promising analytical tool for molecular profiling in biological applications. However, its ultrahigh vacuum environment and matrix effects hamper the absolute quantitation of solution samples. Herein, we present a rapid high-throughput platform for quantitative ToF-SIMS analysis of amino acids in matrix deposits formed from freeze-dried solution drops through ice sublimation on a parylene film microarray substrate. Droplets of the amino acid solutions, which were mixed with stable isotope-labeled phenylalanine (F*) of high concentration (10 mM), were loaded on wells of the microarray, then frozen and evaporated slowly below the freezing point, forming continuous solid-phase F* matrix deposits. The amino acids (≤500 μM), adequately well dispersed throughout the F* matrix deposits on each well, were quantitatively analyzed by ToF-SIMS in a rapid and high-throughput fashion. The lower limit of quantitation reached below 10 μM.

ToF-SIMS analysis of ultrathin films and their fragmentation patterns

Organic thin films are of great interest due to their intriguing interfacial and functional properties, especially for device applications such as thin-film transistors and sensors. As their thickness approaches single nanometer thickness, characterization and interpretation of the extracted data become increasingly complex. In this study, plasma polymerization is used to construct ultrathin films that range in thickness from 1 to 20 nm, and time-of-flight secondary ion mass spectrometry coupled with principal component analysis is used to investigate the effects of film thickness on the resulting spectra. We demonstrate that for these cross-linked plasma polymers, at these thicknesses, the observed trends are different from those obtained from thicker films with lower degrees of cross-linking: contributions from ambient carbon contamination start to dominate the mass spectrum; cluster-induced nonlinear enhancement in secondary ion yield is no longer observed; extent of fragmentation is higher due to confinement of the primary ion energy; and the size of the primary ion source also affects fragmentation (e.g., Bi1 versus Bi5). These differences illustrate that care must be taken in choosing the correct primary ion source as well as in interpreting the data.

Recent Developments and Application of Mass Spectrometry Imaging in N-Glycosylation Studies: An Overview

Among the most typical posttranslational modifications is glycosylation, which often involves the covalent binding of an oligosaccharide (glycan) to either an asparagine (N-linked) or a serine/threonine (O-linked) residue. Studies imply that the N-glycan portion of a glycoprotein could serve as a particular disease biomarker rather than the protein itself because N-linked glycans have been widely recognized to evolve with the advancement of tumors and other diseases. N-glycans found on protein asparagine sites have been especially significant. Since N-glycans play clearly defined functions in the folding of proteins, cellular transport, and transmission of signals, modifications to them have been linked to several illnesses. However, because these N-glycans' production is not template driven, they have a substantial morphological range, rendering it difficult to distinguish the species that are most relevant to biology and medicine using standard techniques. Mass spectrometry (MS) techniques have emerged as effective analytical tools for investigating the role of glycosylation in health and illness. This is due to developments in MS equipment, data collection, and sample handling techniques. By recording the spatial dimension of a glycan's distribution in situ, mass spectrometry imaging (MSI) builds atop existing methods while offering added knowledge concerning the structure and functionality of biomolecules. In this review article, we address the current development of glycan MSI, starting with the most used tissue imaging techniques and ionization sources before proceeding on to a discussion on applications and concluding with implications for clinical research.

Secondary Ion Mass Spectral Imaging of Metals and Alloys

Secondary Ion Mass Spectrometry (SIMS) is an outstanding technique for Mass Spectral Imaging (MSI) due to its notable advantages, including high sensitivity, selectivity, and high dynamic range. As a result, SIMS has been employed across many domains of science. In this review, we provide an in-depth overview of the fundamental principles underlying SIMS, followed by an account of the recent development of SIMS instruments. The review encompasses various applications of specific SIMS instruments, notably static SIMS with time-of-flight SIMS (ToF-SIMS) as a widely used platform and dynamic SIMS with Nano SIMS and large geometry SIMS as successful instruments. We particularly focus on SIMS utility in microanalysis and imaging of metals and alloys as materials of interest. Additionally, we discuss the challenges in big SIMS data analysis and give examples of machine leaning (ML) and Artificial Intelligence (AI) for effective MSI data analysis. Finally, we recommend the outlook of SIMS development. It is anticipated that in situ and operando SIMS has the potential to significantly enhance the investigation of metals and alloys by enabling real-time examinations of material surfaces and interfaces during dynamic transformations.

Identification of Lithium Compounds on Surfaces of Lithium Metal Anode with Machine-Learning-Assisted Analysis of ToF-SIMS Spectra

Detailed knowledge about contamination and passivation compounds on the surface of lithium metal anodes (LMAs) is essential to enable their use in all-solid-state batteries (ASSBs). Time-of-flight secondary ion mass spectrometry (ToF-SIMS), a highly surface-sensitive technique, can be used to reliably characterize the surface status of LMAs. However, as ToF-SIMS data are usually highly complex, manual data analysis can be difficult and time-consuming. In this study, machine learning techniques, especially logistic regression (LR), are used to identify the characteristic secondary ions of 5 different pure lithium compounds. Furthermore, these models are applied to the mixture and LMA samples to enable identification of their compositions based on the measured ToF-SIMS spectra. This machine-learning-based analysis approach shows good performance in identifying characteristic ions of the analyzed compounds that fit well with their chemical nature. Moreover, satisfying accuracy in identifying the compositions of unseen new samples is achieved. In addition, the scope and limitations of such a strategy in practical applications are discussed. This work presents a robust analytical method that can assist researchers in simplifying the analysis of the studied lithium compound samples, offering the potential for broader applications in other material systems.

Mass Spectral Imaging to Map Plant-Microbe Interactions

Plant-microbe interactions are of rising interest in plant sustainability, biomass production, plant biology, and systems biology. These interactions have been a challenge to detect until recent advancements in mass spectrometry imaging. Plants and microbes interact in four main regions within the plant, the rhizosphere, endosphere, phyllosphere, and spermosphere. This mini review covers the challenges within investigations of plant and microbe interactions. We highlight the importance of sample preparation and comparisons among time-of-flight secondary ion mass spectroscopy (ToF-SIMS), matrix-assisted laser desorption/ionization (MALDI), laser desorption ionization (LDI/LDPI), and desorption electrospray ionization (DESI) techniques used for the analysis of these interactions. Using mass spectral imaging (MSI) to study plants and microbes offers advantages in understanding microbe and host interactions at the molecular level with single-cell and community communication information. More research utilizing MSI has emerged in the past several years. We first introduce the principles of major MSI techniques that have been employed in the research of microorganisms. An overview of proper sample preparation methods is offered as a prerequisite for successful MSI analysis. Traditionally, dried or cryogenically prepared, frozen samples have been used; however, they do not provide a true representation of the bacterial biofilms compared to living cell analysis and chemical imaging. New developments such as microfluidic devices that can be used under a vacuum are highly desirable for the application of MSI techniques, such as ToF-SIMS, because they have a subcellular spatial resolution to map and image plant and microbe interactions, including the potential to elucidate metabolic pathways and cell-to-cell interactions. Promising results due to recent MSI advancements in the past five years are selected and highlighted. The latest developments utilizing machine learning are captured as an important outlook for maximal output using MSI to study microorganisms.

Back to the basics of time-of-flight secondary ion mass spectrometry data analysis of bio-related samples. II. Data processing and display

This is the second half of a two-part Tutorial on the basics of the time-of-flight secondary ion mass spectrometry (ToF-SIMS) analysis of bio-related samples. Part I of this Tutorial series covers planning for a ToF-SIMS experiment, preparing and shipping samples, and collecting ToF-SIMS data. This Tutorial aims at helping the ToF-SIMS user to process, display, and interpret ToF-SIMS data. ToF-SIMS provides detailed chemical information about surfaces but comes with a steep learning. The purpose of this Tutorial is to provide the reader with a solid foundation in the ToF-SIMS data analysis.

Review of Recent Advances in Gas-Assisted Focused Ion Beam Time-of-Flight Secondary Ion Mass Spectrometry (FIB-TOF-SIMS)

Time-of-flight secondary ion mass spectrometry (TOF-SIMS) is a powerful chemical characterization technique allowing for the distribution of all material components (including light and heavy elements and molecules) to be analyzed in 3D with nanoscale resolution. Furthermore, the sample's surface can be probed over a wide analytical area range (usually between 1 µm² and 10⁴ µm²) providing insights into local variations in sample composition, as well as giving a general overview of the sample's structure. Finally, as long as the sample's surface is flat and conductive, no additional sample preparation is needed prior to TOF-SIMS measurements. Despite many advantages, TOF-SIMS analysis can be challenging, especially in the case of weakly ionizing elements. Furthermore, mass interference, different component polarity of complex samples, and matrix effect are the main drawbacks of this technique. This implies a strong need for developing new methods, which could help improve TOF-SIMS signal quality and facilitate data interpretation. In this review, we primarily focus on gas-assisted TOF-SIMS, which has proven to have potential for overcoming most of the aforementioned difficulties. In particular, the recently proposed use of XeF₂ during sample bombardment with a Ga⁺ primary ion beam exhibits outstanding properties, which can lead to significant positive secondary ion yield enhancement, separation of mass interference, and inversion of secondary ion charge polarity from negative to positive. The implementation of the presented experimental protocols can be easily achieved by upgrading commonly used focused ion beam/scanning electron microscopes (FIB/SEM) with a high vacuum (HV)-compatible TOF-SIMS detector and a commercial gas injection system (GIS), making it an attractive solution for both academic centers and the industrial sectors.

Molecular identification of wines using in situ liquid SIMS and PCA analysis

Composition analysis in wine is gaining increasing attention because it can provide information about the wine quality, source, and nutrition. In this work, in situ liquid secondary ion mass spectrometry (SIMS) was applied to 14 representative wines, including six wines manufactured by a manufacturer in Washington State, United States, four Cabernet Sauvignon wines, and four Chardonnay wines from other different manufacturers and locations. In situ liquid SIMS has the unique advantage of simultaneously examining both organic and inorganic compositions from liquid samples. Principal component analysis (PCA) of SIMS spectra showed that red and white wines can be clearly differentiated according to their aromatic and oxygen-contained organic species. Furthermore, the identities of different wines, especially the same variety of wines, can be enforced with a combination of both organic and inorganic species. Meanwhile, in situ liquid SIMS is sample-friendly, so liquid samples can be directly analyzed without any prior sample dilution or separation. Taken together, we demonstrate the great potential of in situ liquid SIMS in applications related to the molecular investigation of various liquid samples in food science.

Probing the reaction mechanism between a laser welded polyamide thin film and titanium with XPS and ToF-SIMS

The biomedical industry uses more and more polymer/metal hybrid assemblies because of the ability to combine the advantages and lower the inconveniences of both materials. The key is to assemble them. Among the high variety of existing assembling techniques, laser welding appears as an excellent option. It is a quick process allowing a great design flexibility, high reproducibility without intermediate material needed to create the adhesion, which is advantageous for biomedical applications. The laser welding process creates strong adhesion between dissimilar materials, but the root cause for adhesion is still unclear. The analytical challenge is to gain an information at the molecular level from an interface that is deeply buried between the two materials. Such a study requires extremely surface sensitive analytical methods, such as ToF-SIMS or XPS in order to detect chemical bonds, but also a method to expose the interface to the X-ray or ion beam. In order to investigate the chemical bonding at the interface between polyamide-6.6 and titanium, mirror polished titanium surfaces were prepared, on which a thin polyamide-6.6 film was spin-coated. The samples were laser welded, and after dissolving the polymer thin film, XPS and ToF-SIMS measurement were performed. The ToF-SIMS data interpretation was assisted by a principal component analysis. This multivariate analysis is rather common for ToF-SIMS data but is more rarely used to solve adhesion problems. This allowed to show the nature of the chemical bond at the interface and to propose a reaction mechanism.

XPS and ToF-SIMS Characterization of New Biodegradable Poly(Peptide-Urethane-Urea) Block Copolymers

New, linear, segmented poly(peptide-urethane-urea) (PPUU) block copolymers are synthesized and their surface compositions are characterized with angle dependent X-ray photoelectron spectroscopy (ADXPS) and time-of-flight secondary ion mass spectrometry (ToF-SIMS). These new PPUU block copolymers contain three types of segments. The soft segment (SS) is poly(caprolactone diol) (PCL). The hard segment is lysine diisocyanate with a hydrazine chain extender. The oligopeptide segment (OPS) contains three types of amino acids (proline, hydroxyproline, and glycine). Incorporation of the OPS into the polyurethane backbone is done to provide a synthetic polymer material with controllable biodegradation properties. As biodegradation processes normally are initiated at the interface between the biomaterial and the living tissue, it is important to characterize the surface composition of biomaterials. ADXPS and ToF-SIMS results show that the surfaces of all four polymers are enriched with the PCL SS, the most hydrophobic component of the three polymer segments.

Raman Research on Bleomycin-Induced DNA Strand Breaks and Repair Processes in Living Cells

Even several thousands of DNA lesions are induced in one cell within one day. DNA damage may lead to mutations, formation of chromosomal aberrations, or cellular death. A particularly cytotoxic type of DNA damage is single- and double-strand breaks (SSBs and DSBs, respectively). In this work, we followed DNA conformational transitions induced by the disruption of DNA backbone. Conformational changes of chromatin in living cells were induced by a bleomycin (BLM), an anticancer drug, which generates SSBs and DSBs. Raman micro-spectroscopy enabled to observe chemical changes at the level of single cell and to collect hyperspectral images of molecular structure and composition with sub-micrometer resolution. We applied multivariate data analysis methods to extract key information from registered data, particularly to probe DNA conformational changes. Applied methodology enabled to track conformational transition from B-DNA to A-DNA upon cellular response to BLM treatment. Additionally, increased expression of proteins within the cell nucleus resulting from the activation of repair processes was demonstrated. The ongoing DNA repair process under the BLM action was also confirmed with confocal laser scanning fluorescent microscopy.

Molecular Formula Prediction for Chemical Filtering of 3D OrbiSIMS Datasets

Modern mass spectrometry techniques produce a wealth of spectral data, and although this is an advantage in terms of the richness of the information available, the volume and complexity of data can prevent a thorough interpretation to reach useful conclusions. Application of molecular formula prediction (MFP) to produce annotated lists of ions that have been filtered by their elemental composition and considering structural double bond equivalence are widely used on high resolving power mass spectrometry datasets. However, this has not been applied to secondary ion mass spectrometry data. Here, we apply this data interpretation approach to 3D OrbiSIMS datasets, testing it for a series of increasingly complex samples. In an organic on inorganic sample, we successfully annotated the organic contaminant overlayer separately from the substrate. In a more challenging purely organic human serum sample we filtered out both proteins and lipids based on elemental compositions, 226 different lipids were identified and validated using existing databases, and we assigned amino acid sequences of abundant serum proteins including albumin, fibronectin, and transferrin. Finally, we tested the approach on depth profile data from layered carbonaceous engine deposits and annotated previously unidentified lubricating oil species. Application of an unsupervised machine learning method on filtered ions after performing MFP from this sample uniquely separated depth profiles of species, which were not observed when performing the method on the entire dataset. Overall, the chemical filtering approach using MFP has great potential in enabling full interpretation of complex 3D OrbiSIMS datasets from a plethora of material types.

Applications of multivariate analysis and unsupervised machine learning to ToF-SIMS images of organic, bioorganic, and biological systems

Time-of-flight secondary ion mass spectrometry (ToF-SIMS) imaging offers a powerful, label-free method for exploring organic, bioorganic, and biological systems. The technique is capable of very high spatial resolution, while also producing an enormous amount of information about the chemical and molecular composition of a surface. However, this information is inherently complex, making interpretation and analysis of the vast amount of data produced by a single ToF-SIMS experiment a considerable challenge. Much research over the past few decades has focused on the application and development of multivariate analysis (MVA) and machine learning (ML) techniques that find meaningful patterns and relationships in these datasets. Here, we review the unsupervised algorithms—that is, algorithms that do not require ground truth labels—that have been applied to ToF-SIMS images, as well as other algorithms and approaches that have been used in the broader family of mass spectrometry imaging (MSI) techniques. We first give a nontechnical overview of several commonly used classes of unsupervised algorithms, such as matrix factorization, clustering, and nonlinear dimensionality reduction. We then review the application of unsupervised algorithms to various organic, bioorganic, and biological systems including cells and tissues, organic films, residues and coatings, and spatially structured systems such as polymer microarrays. We then cover several novel algorithms employed for other MSI techniques that have received little attention from ToF-SIMS imaging researchers. We conclude with a brief outline of potential future directions for the application of MVA and ML algorithms to ToF-SIMS images.

Discrimination between NSIP- and IPF-Derived Fibroblasts Based on Multi-Parameter Characterization of Their Growth, Morphology and Physic-Chemical Properties

Background: The aim of the research presented here was to find a set of parameters enabling discrimination between three types of fibroblasts, i.e., healthy ones and those derived from two disorders mimicking each other: idiopathic pulmonary fibrosis (IPF), and nonspecific interstitial pneumonia (NSIP). Methods: The morphology and growth of cells were traced using fluorescence microscopy and analyzed quantitatively using cell proliferation and substrate cytotoxicity indices. The viability of cells was recorded using MTS assays, and their stiffness was examined using atomic force microscopy (AFM) working in force spectroscopy (FS) mode. To enhance any possible difference in the examined parameters, experiments were performed with cells cultured on substrates of different elasticities. Moreover, the chemical composition of cells was determined using time-of-flight secondary ion mass spectrometry (ToF-SIMS), combined with sophisticated analytical tools, i.e., Multivariate Curve Resolution (MCR) and Principal Component Analysis (PCA). Results: The obtained results demonstrate that discrimination between cell lines derived from healthy and diseased patients is possible based on the analysis of the growth of cells, as well as their physical and chemical properties. In turn, the comparative analysis of the cellular response to altered stiffness of the substrates enables the identification of each cell line, including distinguishing between IPF- and NSIP-derived fibroblasts.

Denoising of Mass Spectrometry Images via Inverse Maximum Signal Factors Analysis

Improving signal-to-noise and, thereby, image contrast is one of the key challenges needed to expand the useful applications of mass spectrometry imaging (MSI). Both instrumental and data analysis approaches are of importance. Univariate denoising techniques have been used to improve contrast in MSI images with varying levels of success. Additionally, various multivariate analysis (MVA) methods have proven to be effective for improving image contrast. However, the distribution of important but low intensity ions can be obscured in the MVA analysis, leading to a loss of chemically specific information. In this work we propose inverse maximum signal factors (MSF) denoising as an alternative approach to both denoising and multivariate analysis for MSI imaging. This approach differs from the standard MVA techniques in that the output is denoised images for each original mass peak rather than the frequently difficult to interpret scores and loadings. Five tests have been developed to optimize and validate the resulting denoised images. The algorithm has been tested on a range of simulated data with different levels of noise, correlated noise, varying numbers of underlying components, and nonlinear effects. In the simulations, an excellent correlation between the true images and the denoised images was observed for peaks with an original signal-to-noise ratio as low as 0.1, as long as there was sufficient intensity in the sum of the selected peaks. The power of the approach was then demonstrated on two time-of-flight secondary ion mass spectrometry (ToF-SIMS) images that contained largely uncorrelated noise and a laser post-ionization matrix-assisted laser desorption/ionization mass spectrometry (MALDI-2-MS) image that contained strongly correlated noise. The improvements in signal-to-noise increased with decreasing intensity of the original peaks. A signal-to-noise improvement of as much as two orders of magnitude was achieved for very low intensity peaks. MSF denoising is a powerful addition to the suite of image processing techniques available for studying mass spectrometry images.

Properties of serum albumin in electrolyzed water

Introduction. Electrochemical activation of water controls the physicochemical parameters of aquatic food environment without any reagents. Electrolyzed water affects the properties of macronutrient solutions. The present research studied the effect of anodic and cathodic fractions of electrochemically activated water on protein molecules and their interaction patterns. Study objects and methods. The study featured bovine serum albumin and its properties in electrochemically activated water with nonstandard redox and acidity values. The aqueous solution of bovine serum albumin was studied by viscometry, UV spectrometry, time-of-flight secondary ion mass spectrometry, and electrophoresis. Results and discussion. By knowing the interaction patterns of electrochemically activated water and protein molecules, food producers can control the properties of biological raw materials. Bovine serum albumin was studied in metastable fractions of electrochemically activated water obtained in the anode or cathode chamber of an electrochemical reactor. Both fractions of electrochemically activated water appeared to modify the properties of bovine serum albumin. The oxidized fraction of electrochemically activated water (anolyte) converted the protein solution into a more homogeneous molecular composition. The solution of bovine serum albumin in the reduced fraction of electrochemically activated water (catholyte) had an abnormally negative redox potential (–800 mV). The aqueous solution of bovine serum albumin in catholyte retained its initial viscosity for a long time, and its level was lower than in the control sample. This effect was consistent with other physicochemical characteristics of the solution. Conclusion. The research revealed some patterns that make it possible to apply reagent-free viscosity regulation to protein media in the food industry.

Fundamental understanding of the synthesis of well-defined supported non-noble metal intermetallic compound nanoparticles

Fundamental insights into the synthesis of model-like, supported, non-noble metal intermetallic compound nanoparticle catalysts with phase pure bulk and bulk-like 1st-atomic-layer particle surface composition.

A bioinspired approach to fabricate fluorescent nanotubes with strong water adhesion by soft template electropolymerization and post-grafting

HYPOTHESIS

In this original work, we aim to control both the surface wetting and fluorescence properties of extremely ordered and porous conducting polymer nanotubes prepared by soft template electropolymerization and post-grafting. For reaching this aim, various substituents of different hydrophobicity and fluorescence were post-grafted and the post-grafting yields were evaluated by surface analyses. We show that the used polymer is already fluorescent before post-grafting while the post-grafting yield and as a consequence the surface hydrophobicity highly depend on the substituent.

EXPERIMENTS

Here, we have chosen to chemically grafting various fluorinated and aromatic substituents using a post-grafting in order to keep the same surface topography. Flat conducting polymer surfaces with similar properties have been also prepared for determining the surface energy with the Owens-Wendt equation and estimating the post-grafting yield by X-ray Photoemission Spectroscopy (XPS) and Time of Flight Secondary Emission Spectrometry (ToF-SIMS). For example, using fluorinated chains of various length (C₄F₉, C₆F₁₃ and C₈F₁₇), it is demonstrated that the surface hydrophobicity and oleophobicity do not increase with the fluorinated chain length due to the different post-grafting yields and because of the presence of nanoroughness after post-grafting.

FINDINGS

These surfaces have high apparent water contact angle up to 130.5° but also strong water adhesion, comparable to rose petal effect even if there are no nanotubes on petal surface. XPS and ToF-SIMS analyses provided a detailed characterisation of the surface chemistry with a qualitative classification of the grafted surfaces (F6 > F4 > F8). SEM analysis shows that grafting does not alter the surface morphology. Finally, fluorescence analyses show that the polymer surfaces before post-treatment are already nicely fluorescent. Although the main goal of this paper was and is to understand the role of surface chemistry in tailoring the wetting properties of these surfaces rather than provide specific application examples, we believe that the obtained results can help the development of specific nanostructured materials for potential applications in liquid transport, or in stimuli responsive antimicrobial surfaces.

Developments and Ongoing Challenges for Analysis of Surface-Bound Proteins

Proteins at surfaces and interfaces play important roles in the function and performance of materials in applications ranging from diagnostic assays to biomedical devices. To improve the performance of these materials, detailed molecular structure (conformation and orientation) along with the identity and concentrations of the surface-bound proteins on those materials must be determined. This article describes radiolabeling, surface plasmon resonance, quartz crystal microbalance with dissipation, X-ray photoelectron spectroscopy, secondary ion mass spectrometry, sum frequency generation spectroscopy, and computational techniques along with the information each technique provides for characterizing protein films. A multitechnique approach using both experimental and computation methods is required for these investigations. Although it is now possible to gain much insight into the structure of surface-bound proteins, it is still not possible to obtain the same level of structural detail about proteins on surfaces as can be obtained about proteins in crystals and solutions, especially for large, complex proteins. However, recent results have shown it is possible to obtain detailed structural information (e.g., backbone and side chain orientation) about small peptides (5-20 amino sequences) on surfaces. Current studies are extending these investigations to small proteins such as protein G B1 (∼6 kDa). Approaches for furthering the capabilities for characterizing the molecular structure of surface-bound proteins are proposed.

Time-of-flight secondary ion mass spectrometry fragment regularity in gallium-doped zinc oxide thin films

Time-of-flight secondary ion mass spectrometry fragment analysis remains a challenging task. The fragment appearance regularity (FAR) rule is particularly useful for two-element compounds such as ZnO. Ion fragments appearing in the form of ZnxOy obey the rule [Formula: see text] in the positive secondary ion spectrum and [Formula: see text] in the negative spectrum where the valence of Zn is + 2 and that of O is - 2. Fragment analysis in gallium-doped ZnO (GZO) films can give insights into the bonding of the elements in this important semiconductor. Fragment analysis of 1 and 7 wt% GZO films shows that only the negative ion fragments obey the FAR rule where ZnO‒, 66ZnO‒, 68ZnO‒ and ZnO2‒ ion fragments appear. In the positive polarity, subdued peaks from out-of-the-rule ZnO+, 66ZnO+ and 68ZnO+ ion fragments are observed. The Ga ion peaks are present in both the positive and negative spectra. The secondary ion spectra of undoped ZnO also shows consistency with the FAR rule. This implies that Ga doping even in amounts that exceed the ZnO lattice limit of solubility does not affect the compliance with the FAR rule.

Protein identification by 3D OrbiSIMS to facilitate in situ imaging and depth profiling

Label-free protein characterization at surfaces is commonly achieved using digestion and/or matrix application prior to mass spectrometry. We report the assignment of undigested proteins at surfaces in situ using secondary ion mass spectrometry (SIMS). Ballistic fragmentation of proteins induced by a gas cluster ion beam (GCIB) leads to peptide cleavage producing fragments for subsequent OrbitrapTM analysis. In this work we annotate 16 example proteins (up to 272 kDa) by de novo peptide sequencing and illustrate the advantages of this approach by characterizing a protein monolayer biochip and the depth distribution of proteins in human skin.

Analyzing 3D hyperspectral TOF-SIMS depth profile data using self-organizing map-relational perspective mapping

The advantages of applying multivariate analysis to mass spectrometry imaging (MSI) data have been thoroughly demonstrated in recent decades. The identification and visualization of complex relationships between pixels in a hyperspectral data set can provide unique insights into the underlying surface chemistry. It is now recognized that most MSI data contain nonlinear relationships, which has led to increased application of machine learning approaches. Previously, we exemplified the use of the self-organizing map (SOM), a type of artificial neural network, for analyzing time-of-flight secondary ion mass spectrometry (TOF-SIMS) hyperspectral images. Recently, we developed a novel methodology, SOM-relational perspective mapping (RPM), which incorporates the algorithm RPM to improve visualization of the SOM for 2D TOF-SIMS images. Here, we use SOM-RPM to characterize and interpret 3D TOF-SIMS depth profile data, voxel-by-voxel. An organic Irganox^™ multilayer standard sample was depth profiled using TOF-SIMS, and SOM-RPM was used to create 3D similarity maps of the depth-profiled sample, in which the mass spectral similarity of individual voxels is modeled with color similarity. We used this similarity map to segment the data into spatial features, demonstrating that the unsupervised method meaningfully differentiated between Irganox-3114 and Irganox-1010 nanometer-thin multilayer films. The method also identified unique clusters at the surface associated with environmental exposure and sample degradation. Key fragment ions characteristic of each cluster were identified, tying clusters to their underlying chemistries. SOM-RPM has the demonstrable ability to reduce vast data sets to simple 3D visualizations that can be used for clustering data and visualizing the complex relationships within.

Understanding mass spectrometry images: complexity to clarity with machine learning

The application of artificial intelligence and machine learning to hyperspectral mass spectrometry imaging (MSI) data has received considerable attention over recent years. Various methodologies have shown great promise in their ability to handle the complexity and size of MSI data sets. Advances in this area have been particularly appealing for MSI of biological samples, which typically produce highly complicated data with often subtle relationships between features. There are many different machine learning approaches that have been applied to MSI data over the past two decades. In this review, we focus on a subset of non-linear machine learning techniques that have mostly only been applied in the past 5 years. Specifically, we review the use of the self-organizing map (SOM), SOM with relational perspective mapping (SOM-RPM), t-distributed stochastic neighbor embedding (t-SNE) and uniform manifold approximation and projection (UMAP). While not their only functionality, we have grouped these techniques based on their ability to produce what we refer to as similarity maps. Similarity maps are color representations of hyperspectral data, in which spectral similarity between pixels-that is, their distance in high-dimensional space-is represented by relative color similarity. In discussing these techniques, we describe, briefly, their associated algorithms and functionalities, and also outline applications in MSI research with a strong focus on biological sample types. The aim of this review is therefore to introduce this relatively recent paradigm for visualizing and exploring hyperspectral MSI, while also providing a comparison between each technique discussed.

Multimodal Imaging Mass Spectrometry to Identify Markers of Pulmonary Arterial Hypertension in Human Lung Tissue Using MALDI-ToF, ToF-SIMS, and Hybrid SIMS

Pulmonary arterial hypertension (PAH) is a rare and deadly disease affecting roughly 15-60 people per million in Europe with a poorly understood pathology. There are currently no diagnostic tools for early detection nor does a curative treatment exist. The lipid composition of arteries in lung tissue samples from human PAH and control patients were investigated using matrix-assisted laser desorption ionization (MALDI) imaging mass spectrometry (IMS) combined with time-of-flight secondary ion mass spectrometry (TOF-SIMS) imaging. Using random forests as an IMS data analysis technique, it was possible to identify the ion at m/z 885.6 as a marker of PAH in human lung tissue. The m/z 885.6 ion intensity was shown to be significantly higher around diseased arteries and was confirmed to be a diacylglycerophosphoinositol PI(C18:0/C20:4) via MS/MS using a novel hybrid SIMS instrument. The discovery of a potential biomarker opens up new research avenues which may finally lead to a better understanding of the PAH pathology and highlights the vital role IMS can play in modern biomedical research.

Controlling orientation, conformation, and biorecognition of proteins on silane monolayers, conjugate polymers, and thermo-responsive polymer brushes: investigations using TOF-SIMS and principal component analysis

Control over orientation and conformation of surface-immobilized proteins, determining their biological activity, plays a critical role in biointerface engineering. Specific protein state can be achieved with adjusted surface preparation and immobilization conditions through different types of protein-surface and protein-protein interactions, as outlined in this work. Time-of-flight secondary ion mass spectroscopy, combining surface sensitivity with excellent chemical specificity enhanced by multivariate data analysis, is the most suited surface analysis method to provide information about protein state. This work highlights recent applications of the multivariate principal component analysis of TOF-SIMS spectra to trace orientation and conformation changes of various proteins (antibody, bovine serum albumin, and streptavidin) immobilized by adsorption, specific binding, and covalent attachment on different surfaces, including self-assembled monolayers on silicon, solution-deposited polythiophenes, and thermo-responsive polymer brushes. Multivariate TOF-SIMS results correlate well with AFM data and binding assays for antibody-antigen and streptavidin-biotin recognition. Additionally, several novel extensions of the multivariate TOF-SIMS method are discussed.

Graphical abstract

In Vivo Molecular Insights into Syntrophic Geobacter Aggregates

Direct interspecies electron transfer (DIET) has been considered as a novel and highly efficient strategy in both natural anaerobic environments and artificial microbial fuel cells. A syntrophic model consisting of Geobacter metallireducens and Geobacter sulfurreducens was studied in this work. We conducted in vivo molecular mapping of the outer surface of the syntrophic community as the interface of nutrients and energy exchange. System for Analysis at the Liquid Vacuum Interface combined with time-of-flight secondary ion mass spectrometry was employed to capture the molecular distribution of syntrophic Geobacter communities in the living and hydrated state. Principal component analysis with selected peaks revealed that syntrophic Geobacter aggregates were well differentiated from other control samples, including syntrophic planktonic cells, pure cultured planktonic cells, and single population biofilms. Our in vivo imaging indicated that a unique molecular surface was formed. Specifically, aromatic amino acids, phosphatidylethanolamine components, and large water clusters were identified as key components that favored the DIET of syntrophic Geobacter aggregates. Moreover, the molecular changes in depths of the Geobacter aggregates were captured using dynamic depth profiling. Our findings shed new light on the interface components supporting electron transfer in syntrophic communities based on in vivo molecular imaging.

General and adaptive synthesis protocol for high-quality organosilane self-assembled monolayers as tunable surface chemistry platforms for biochemical applications

The controlled modification of surface properties represents a pervasive requirement to be fulfilled when developing new technologies. In this paper, we propose an easy-to-implement protocol for the functionalization of glass with self-assembled monolayers (SAMs). The adaptivity of the synthesis route was demonstrated by the controlled anchoring of thiol, amino, glycidyloxy, and methacrylate groups onto the glass surface. The optimization of the synthetic pathway was mirrored by extremely smooth SAMs (approximately 150 pm roughness), layer thickness comparable to the theoretical molecule length, absence of silane islands along the surface, quasi-unitary degree of packing, and tailored wettability and charge. The functionalization kinetics of two model silanes, 3-mercapto- and 3-amino-propyltrimethoxysilane, was determined by cross-comparing x-ray photoelectron spectroscopy and time of flight secondary ion mass spectrometry data. Our SAMs with tailored physicochemical attributes will be implemented as supports for the crystallization of pharmaceuticals and biomolecules in upcoming studies. Here, the application to a small molecule drug model, namely aspirin, was discussed as a proof of concept.

Combining surface-sensitive microscopies for analysis of biological tissues after neural device implantation

In order to address the complexity of chemical analysis of biological systems, time-of-flight secondary ion mass spectrometry (ToF-SIMS), x-ray photoelectron spectroscopy (XPS), and x-ray photoemission electron microscopy (XPEEM) were used for combined surface imaging of a biological tissue formed around a surface neural device after implantation on a nonhuman primate brain. Results show patterns on biological tissue based on extracellular matrix (ECM) and phospholipid membrane (PM) molecular fragments, which were contrasted through principal component analysis of ToF-SIMS negative spectrum. This chemical differentiation may indicate severe inflammation on tissue with an early case of necrosis. Quantification of the elemental composition and the chemical bonding states on both ECM-rich and PM-rich features was possible through XPS analysis from survey and high-resolution spectra, respectively. Variable amounts of carbon (68%-80.5%), nitrogen (10%-2.4%), and oxygen (20.8%-16.5%) were detected on the surface of the biological tissue. Chlorine, phosphorous sodium, and sulfur were also identified in lower extends. Besides that, analysis of the C 1s high-resolution spectra for the same two regions (ECM and PM ones) showed that a compromise between C-C (41.8 at. %) and C-N/C-O (35.6 at. %) amounts may indicate a strong presence of amino acids and proteoglycans on the ECM fragment-rich region, while the great amount of C-C (70.1 at. %) on the PM fragment-rich region is attributed to the large chains of fatty acids connected to phospholipid molecules. The micrometer-scale imaging of these chemical states on tissue was accomplished through XPEEM analysis. The C-C presence was found uniformly distributed across the entire analyzed area, while C-N/C-O and C=O were in two distinct regions. The combination of ToF-SIMS, XPS, and XPEEM is shown here as a powerful, noninvasive approach to map out elemental and chemical properties of biological tissues, i.e., identification of chemically distinct regions, followed by quantification of the surface chemical composition in each distinct region.

Surface analysis tools for characterizing biological materials

Surfaces represent a unique state of matter that typically have significantly different compositions and structures from the bulk of a material. Since surfaces are the interface between a material and its environment, they play an important role in how a material interacts with its environment. Thus, it is essential to characterize, in as much detail as possible, the surface structure and composition of a material. However, this can be challenging since the surface region typically is only minute portion of the entire material, requiring specialized techniques to selectively probe the surface region. This tutorial will provide a brief review of several techniques used to characterize the surface and interface regions of biological materials. For each technique we provide a description of the key underlying physics and chemistry principles, the information provided, strengths and weaknesses, the types of samples that can be analyzed, and an example application. Given the surface analysis challenges for biological materials, typically there is never just one technique that can provide a complete surface characterization. Thus, a multi-technique approach to biological surface analysis is always required.

Unsupervised machine learning for exploratory data analysis in imaging mass spectrometry

Imaging mass spectrometry (IMS) is a rapidly advancing molecular imaging modality that can map the spatial distribution of molecules with high chemical specificity. IMS does not require prior tagging of molecular targets and is able to measure a large number of ions concurrently in a single experiment. While this makes it particularly suited for exploratory analysis, the large amount and high-dimensional nature of data generated by IMS techniques make automated computational analysis indispensable. Research into computational methods for IMS data has touched upon different aspects, including spectral preprocessing, data formats, dimensionality reduction, spatial registration, sample classification, differential analysis between IMS experiments, and data-driven fusion methods to extract patterns corroborated by both IMS and other imaging modalities. In this work, we review unsupervised machine learning methods for exploratory analysis of IMS data, with particular focus on (a) factorization, (b) clustering, and (c) manifold learning. To provide a view across the various IMS modalities, we have attempted to include examples from a range of approaches including matrix assisted laser desorption/ionization, desorption electrospray ionization, and secondary ion mass spectrometry-based IMS. This review aims to be an entry point for both (i) analytical chemists and mass spectrometry experts who want to explore computational techniques; and (ii) computer scientists and data mining specialists who want to enter the IMS field. © 2019 The Authors. Mass Spectrometry Reviews published by Wiley Periodicals, Inc. Mass SpecRev 00:1-47, 2019.

Characterizing protein G B1 orientation and its effect on immunoglobulin G antibody binding using XPS, ToF-SIMS, and quartz crystal microbalance with dissipation monitoring

Controlling how proteins are immobilized (e.g., controlling their orientation and conformation) is essential for developing and optimizing the performance of in vitro protein-binding devices, such as enzyme-linked immunosorbent assays. Characterizing the identity, orientation, etc., of proteins in complex mixtures of immobilized proteins requires a multitechnique approach. The focus of this work was to control and characterize the orientation of protein G B1, an immunoglobulin G (IgG) antibody-binding domain of protein G, on well-defined surfaces and to measure the effect of protein G B1 orientation on IgG antibody binding. The surface sensitivity of time-of-flight secondary ion mass spectrometry (ToF-SIMS) was used to distinguish between different proteins and their orientation on both flat and nanoparticle gold surfaces by monitoring intensity changes of characteristic amino acid mass fragments. Amino acids distributed asymmetrically were used to calculate peak intensity ratios from ToF-SIMS data to determine the orientation of protein G B1 cysteine mutants covalently attached to a maleimide surface. To study the effect of protein orientation on antibody binding, multilayer protein films on flat gold surfaces were formed by binding IgG to the immobilized protein G B1 films. Quartz crystal microbalance with dissipation monitoring and x-ray photoelectron spectroscopy analysis revealed that coverage and orientation affected the antibody-binding process. At high protein G B1 coverage, the cysteine mutant immobilized in an end-on orientation with the C-terminus exposed bound 443 ng/cm² of whole IgG (H + L) antibodies. In comparison, the high coverage cysteine mutant immobilized in an end-on orientation with the N-terminus exposed did not bind detectable amounts of whole IgG (H + L) antibodies.

Mass spectrometry as a tool to advance polymer science

In contrast to natural polymers, which have existed for billions of years, the first well-understood synthetic polymers date back to just over one century ago. Nevertheless, this relatively short period has seen vast progress in synthetic polymer chemistry, which can now afford diverse macromolecules with varying structural complexities. To keep pace with this synthetic progress, there have been commensurate developments in analytical chemistry, where mass spectrometry has emerged as the pre-eminent technique for polymer analysis. This Perspective describes present challenges associated with the mass-spectrometric analysis of synthetic polymers, in particular the desorption, ionization and structural interrogation of high-molar-mass macromolecules, as well as strategies to lower spectral complexity. We critically evaluate recent advances in technology in the context of these challenges and suggest how to push the field beyond its current limitations. In this context, the increasingly important role of high-resolution mass spectrometry is emphasized because of its unrivalled ability to describe unique species within polymer ensembles, rather than to report the average properties of the ensemble.

In situ molecular imaging of adsorbed protein films in water indicating hydrophobicity and hydrophilicity

In situ molecular imaging of protein films adsorbed on a solid surface in water was realized by using a vacuum compatible microfluidic interface and time-of-flight secondary ion mass spectrometry (ToF-SIMS). Amino acid fragments from such hydrated protein films are observed and identified in the positive ion mode and the results are in agreement with reported works on dry protein films. Moreover, water clusters from the hydrated protein films have been observed and identified in both the positive and negative ion mode for a series protein films. Thus, the detailed composition of amino acids and water molecules in the hydrated protein films can be characterized, and the protein water microstructures can be revealed by the distinct three-dimensional spatial distribution reconstructed from in situ liquid ToF-SIMS molecular imaging. Furthermore, spectral principal component analysis of amino acid fragment peaks and water cluster peaks provides unique insights into the water cluster distribution, hydrophilicity, and hydrophobicity of hydrated adsorbed protein films in water.

In-situ ToF-SIMS analyses of deuterium re-distribution in austenitic steel AISI 304L under mechanical load

Hydrocarbons fuel our economy. Furthermore, intermediate goods and consumer products are often hydrocarbon-based. Beside all the progress they made possible, hydrogen-containing substances can have severe detrimental effects on materials exposed to them. Hydrogen-assisted failure of iron alloys has been recognised more than a century ago. The present study aims to providing further insight into the degradation of the austenitic stainless steel AISI 304L (EN 1.4307) exposed to hydrogen. To this end, samples were electrochemically charged with the hydrogen isotope deuterium (²H, D) and analysed by scanning electron microscopy (SEM), electron back-scatter diffraction (EBSD) and time-of-flight secondary ion mass spectrometry (ToF-SIMS). It was found that deuterium caused a phase transformation from the original γ austenite into ε- and α'-martensite. Despite their low solubility for hydrogen, viz. deuterium, the newly formed phases showed high deuterium concentration which was attributed to the increased density of traps. Information about the behaviour of deuterium in the material subjected to external mechanical load was gathered. A four-point-bending device was developed for this purpose. This allowed to analyse in-situ pre-charged samples in the ToF-SIMS during the application of external mechanical load. The results indicate a movement of deuterium towards the regions of highest stress.

Correlative surface imaging reveals chemical signatures for bacterial hotspots on plant roots

A universal sample holder allows correlative imaging analysis of plant roots to reveal chemical signatures for bacterial hotspots.