Satoka Aoyagi

ABSTRACT Time‐of‐flight secondary ion mass spectrometry (ToF‐SIMS) is a powerful tool for imaging molecules in biological tissues owing to its high spatial resolution and sensitivity. Effective detection of key molecules in a sample is crucial for detailed evaluation of complex samples such as tissues. In this study, a target is a biomolecule, allantoin (C₄H₆N₄O₃), and the allantoin [M + H]⁺ and [M‐H]⁻ are adequately detected using ToF‐SIMS with a Bi cluster ion beam from an allantoin control sample. However, the detection of ions related to allantoin permeated in human skin tissue is not straightforward because there are interfering mass peaks in the ToF‐SISM spectra of control skin samples that make allantoin detection challenging, and the allantoin fragment ions have the same chemical structure as the fragment ion from other biomolecules in the tissue. In order to sufficiently detect the allantoin‐related ions, we focused on ion beams with a higher ionization yield, such as gas cluster ion beams (GCIBs). As a result, a ToF‐SIMS with water GCIB was significantly more effective in detecting the allantoin [M + H]⁺ and [M‐H]⁻ in the skin samples in both positive and negative ion spectra. The results revealed that the water GCIB approach is better suited for studying biological samples, as it effectively distinguishes the mass peaks of allantoin related ions using ToF‐SIMS.

The interpretation of time-of-flight secondary ion mass spectrometry (ToF-SIMS) data is often complicated because ToF-SIMS has a high sensitivity for detecting extremely low amounts of molecules and generally produces numerous types of fragment ions from each molecule. Although machine learning techniques have been applied to such complex ToF-SIMS data interpretation to classify the components in a sample, identifying unknown molecules is often difficult, even after classification or segmentation of complex datasets. We developed a new secondary ion mass spectrometry (SIMS) identification system based on full ToF-SIMS spectra by applying a supervised machine learning method, random forest (RF), with effective teaching information to express common organic molecules. We automatically extracted chemical structures for unknown material identification from string-converted molecules using a simplified molecular-input line-entry system. The ToF-SIMS spectra of 32 organic molecules, including peptides, polymers, and biomolecules such as cellulose, were used as a training dataset, and these molecules were correctly predicted using the SIMS identification system. The importance of RF indicated that mass peaks representing these structures were detected in the ToF-SIMS spectra and that the materials were identified based on the essential chemical structures of a target molecule. Moreover, the ToF-SIMS spectra of Styrofoam-like Ocean plastic samples were correctly identified as polystyrene by the system. This study demonstrates the potential of our SIMS identification system to accurately identify unknown organic molecules from full ToF-SIMS spectra, offering a robust approach for expanding molecular identification in complex samples.

Mass spectrometry imaging (MSI) is essential for visualizing drug distribution, metabolites, and significant biomolecules in pharmacokinetic studies. This study mainly focuses on imipramine, a tricyclic antidepressant that affects endogenous metabolite concentrations. The aim was to use atmospheric pressure matrix-assisted laser desorption/ionization (AP-MALDI)-MSI combined with different dimensionality reduction methods to examine the distribution and impact of imipramine on endogenous metabolites in the brains of treated wild-type mice. Brain sections from both control and imipramine-treated mice underwent AP-MALDI-MSI. Dimensionality reduction methods, including principal component analysis, multivariate curve resolution, and sparse autoencoder (SAE), were employed to extract valuable information from the MSI data. Only the SAE method identified phosphorylcholine (ChoP) as a potential marker distinguishing between the control and treated mice brains. Additionally, a significant decrease in ChoP accumulation was observed in the cerebellum, hypothalamus, thalamus, midbrain, caudate putamen, and striatum ventral regions of the treated mice brains. The application of dimensionality reduction methods, particularly the SAE method, to the AP-MALDI-MSI data is a novel approach for peak selection in AP-MALDI-MSI data analysis. This study revealed a significant decrease in ChoP in imipramine-treated mice brains.

Machine learning is a useful tool when extracting hidden information from complex measurement data obtained via surface analysis, as in secondary ion mass spectrometry. Flexible learning methods often require significant effort to adjust parameters, as these parameters may have a significant effect on results. However, machine learning methods enable the extraction of new information that cannot be found by manual analysis. This paper presents some examples of complex data analyses using conventional multivariate analysis methods based on linear combinations (principal component analysis and multivariate curve resolution), an unsupervised learning method based on artificial neural networks (sparse autoencoder), and a supervised learning method based on decision trees (random forest). To obtain reproducible and useful results from machine learning applications to surface analysis data, the preparation of data sets—including the selection of variables and the raw data conversion process—is crucial. Moreover, sufficient information representing analytical purposes, such as the chemical structures of unknown samples, material types, and physical or chemical properties of particular materials, must be contained in the data set for supervised learning.

<title>Abstract</title>The dynamics of hydrogen in metals with mixed grain structure is not well understood at a microscopic scale. One of the biggest issues facing the hydrogen economy is “hydrogen embrittlement” of metal induced by hydrogen entering and diffusing into the material. Hydrogen diffusion in metallic materials is difficult to grasp owing to the non-uniform compositions and structures of metal. Here a time-resolved “operando hydrogen microscope” was used to interpret local diffusion behaviour of hydrogen in the microstructure of a stainless steel with austenite and martensite structures. The martensite/austenite ratios differed in each local region of the sample. The path of hydrogen permeation was inferred from the time evolution of hydrogen permeation in several regions. We proposed a model of hydrogen diffusion in a dual-structure material and verified the validity of the model by simulations that took into account the transfer of hydrogen at the interfaces.

Time-of-flight secondary ion mass spectrometry (ToF-SIMS) is a powerful tool for determining surface information of complex systems such as polymers and biological materials. However, the interpretation of ToF-SIMS rawdata is often difficult. Especially the identification of all spectra of complex samples including large molecules is difficult, since the ToF-SIMS spectrum data of polymer samples includes many fragment ion peaks originating in polymers and other contamination peaks. Multivariate analysis has become effective methods for the interpretation of ToF-SIMS data. Some of multivariate analysis methods such as principal component analysis and multivariate curve resolution are useful for simplifying ToF-SIMS data consisting of many components to that explained by a smaller number of components. In this study, the ToF-SIMS data of four layers of three polymers was analyzed using these analysis methods. The information acquired by using each method was compared in terms of the spatial distribution of the polymers and identification. Moreover, in order to investigate the influence of surface contamination, the ToF-SIMS data before and after Ar cluster ion beam sputtering was compared. As a result, materials in the sample of multiple components, including unknown contaminants, were distinguished.