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

Supervised and unsupervised machine learning algorithms are routinely applied 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.

Controllable hierarchical self-assembly: systematic study forming metallosupramolecular frameworks on the basis of helical beta-oligoamides.

Self-assembly is a key guiding principle for the design of complex nanostructures. Substituted beta oligoamides offer versatile building blocks that can have inherent folding characteristics, offering geometrically defined functionalities that can specifically bind and assemble with predefined morphological characteristics. In this work hierarchical self-assembly is implemented based on metal coordinating helical beta-oligoamides crosslinked with transition metals selected for their favourable coordination geometries, Fe2+, Cu2+, Ni2+, Co2+, Zn2+, and two metalates, MoO42-, and WO42-. The oligoamide Ac-ß3Aß3Vß3S-αHαHαH-ß3Aß3Vß3A (3H) was designed to allow crosslinking via three distinct faces of the helical unit, with a possibility of forming three dimensional framework structures. Atomic force microscopy (AFM) confirmed the formation of specific morphologies that differ characteristically with each metal. X-Ray photoelectron spectroscopy (XPS) results reveal that the metal centres can be reduced in the final structures, confirming strong chemical interaction. Time of flight secondary ion mass spectrometry (ToF-SIMS) confirmed the spatial distribution of metals within the self-assembled networks, also revealing molecular fragments that confirm coordination to histidine and carboxyl moieties. The metalates MoO42- and WO42- were also able to induce the formation of specific superstructure morphologies. It was observed that assembly with either of nickel, copper, and molybdate form thin films, while cobalt, zinc, and tungstate produced specific three dimensional networks of oligoamides. Iron was found to form both a thin film and a complex hierarchical assembly with the 3H simultaneously. The design of the 3H substituted beta oligoamide to readily form metallosupramolecular frameworks was demonstrated with a range of metals and metalates with a degree of control over layer thicknesses as a function of the metal/metalate. The results validate and broaden the metallosupramolecular framework concept and establish a platform technology for the design of functional thin layer materials.

Multi-Dimensional Machine Learning Analysis of Polyaniline Films Using Stitched Hyperspectral ToF-SIMS Data.

The self-organizing map with relational perspective mapping (SOM-RPM) is an unsupervised machine learning method that can be used to visualize and interpret high-dimensional hyperspectral data. We have previously used SOM-RPM for the analysis of time-of-flight secondary ion mass spectrometry (ToF-SIMS) hyperspectral images and three-dimensional (3D) depth profiles. This provides insightful visualization of features and trends of 3D depth profile data, using a slice-by-slice view, which can be useful for highlighting structural flaws including molecular characteristics and transport of contaminants to a buried interface and characterization of spectra. Here, we apply SOM-RPM to stitched ToF-SIMS data sets, whereby the stitched data are used to train the same model to provide a direct comparison in both 2D and 3D. We conduct an analysis of spin-coated polyaniline (PANI) films on indium tin oxide-coated glass slides that were subjected to heat treatment under atmospheric conditions to model PANI as a conformal aerospace industry coating. Replicates were shown to be precisely equivalent, both spatially and by composition, indicating a clear threshold for annealing of the film. Quantitative assessment was performed on the chemical breakdown trends accompanying annealing based on peak ratios, while spectral analysis alone shows only very subtle differences which are difficult to evaluate quantitatively. The SOM-RPM method considers data sets in their totality and highlights subtle differences between samples often simply differences in peak intensity ratios.

Large Area Ultrathin InN and Tin Doped InN Nanosheets Featuring 2D Electron Gases.

Indium nitride (InN) has been of significant interest for creating and studying two-dimensional electron gases (2DEG). Herein we demonstrate the formation of 2DEGs in ultrathin doped and undoped 2D InN nanosheets featuring high carrier mobilities at room temperature. The synthesis is carried out via a two-step liquid metal-based printing method followed by a microwave plasma-enhanced nitridation reaction. Ultrathin InN nanosheets with a thickness of ∼2 ± 0.2 nm were isolated over large areas with lateral dimensions exceeding centimeter scale. Room temperature Hall effect measurements reveal carrier mobilities of ∼216 and ∼148 cm2 V-1 s-1 for undoped and doped InN, respectively. Further analysis suggests the presence of defined quantized states in these ultrathin nitride nanosheets that can be attributed to a 2D electron gas forming due to strong out-of-plane confinement. Overall, the combination of electronic and plasmonic features in undoped and doped ultrathin 2D InN holds promise for creating advanced optoelectronic devices and functional 2D heterostructures.

Distinguishing Chemically Similar Polyamide Materials with ToF-SIMS Using Self-Organizing Maps and a Universal Data Matrix.

Time-of-flight secondary ion mass spectrometry (ToF-SIMS) is advancing rapidly, providing instruments with growing capabilities and resolution. The data sets generated by these instruments are likewise increasing dramatically in size and complexity. Paradoxically, methods for efficient analysis of these large, rich data sets have not improved at the same rate. Clearly, more effective computational methods for analysis of ToF-SIMS data are becoming essential. Several research groups are customizing standard multivariate analytical tools to decrease computational demands, provide user-friendly interfaces, and simplify identification of trends and features in large ToF-SIMS data sets. We previously applied mass segmented peak lists to data from PMMA, PTFE, PET, and LDPE. Self-organizing maps (SOMs), a type of artificial neural network (ANN), classified the polymers based on their molecular composition and primary ion probe type more effectively than simple PCA. The effectiveness of this approach led us to question whether it would be useful in distinguishing polymers that were very similar. How sensitive is the technique to changes in polymer chemical structure and composition? To address this question, we generated ToF-SIMS ion peak signatures for seven nylon polymers with similar chemistries and used our up-binning and SOM approach to classify and cluster the polymers. The widely used linear PCA method failed to separate the samples. Supervised and unsupervised training of SOMs using positive or negative ion mass spectra resulted in effective classification and separation of the seven nylon polymers. Our SOM classification method has proven to be tolerant of minor sample irregularities, sample-to-sample variations, and inherent data limitations including spectral resolution and noise. We have demonstrated the potential of machine learning methods to analyze ToF-SIMS data more effectively than traditional methods. Such methods are critically important for future complex data analysis and provide a pipeline for rapid classification and identification of features and similarities in large data sets.