Nb impurity-bound excitons as quantum emitters in monolayer WS.

Point defects in crystalline solids behave as optically addressable individual quantum systems when present in sufficiently low concentrations. In two-dimensional (2D) semiconductors, such quantum defects hold potential as versatile single photon sources. Here, we report the synthesis and optical properties of Nb-doped monolayer WS in the dilute limit where the average spacing between individual dopants exceeds the optical diffraction limit, allowing the emission spectrum to be studied at the single-dopant level.

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.

Enhanced Nitric Oxide Delivery Through Self-Assembling Nanoparticles for Eradicating Gram-Negative Bacteria.

In the current battle against antibiotic resistance, the resilience of Gram-negative bacteria against traditional antibiotics is due not only to their protective outer membranes but also to mechanisms like efflux pumps and enzymatic degradation of drugs, underscores the urgent need for innovative antimicrobial tactics. Herein, this study presents an innovative method involving the synthesis of three furoxan derivatives engineered to self-assemble into nitric oxide (NO) donor nanoparticles (FuNPs). These FuNPs, notably supplied together with polymyxin B (PMB), achieve markedly enhanced bactericidal efficacy against a wide spectrum of bacterial phenotypes at considerably lower NO concentrations (0.

High resolution imaging and analysis of extracellular vesicles using mass spectral imaging and machine learning.

Extracellular vesicles (EVs) are potentially useful biomarkers for disease detection and monitoring. Development of a label-free technique for imaging and distinguishing small volumes of EVs from different cell types and cell states would be of great value. Here, we have designed a method to explore the chemical changes in EVs associated with neuroinflammation using Time-of-Flight Secondary Ion Mass spectrometry (ToF-SIMS) and machine learning (ML).

Microglial activation induces nitric oxide signalling and alters protein S-nitrosylation patterns in extracellular vesicles.

Neuroinflammation is an underlying feature of neurodegenerative conditions, often appearing early in the aetiology of a disease. Microglial activation, a prominent initiator of neuroinflammation, can be induced through lipopolysaccharide (LPS) treatment resulting in expression of the inducible form of nitric oxide synthase (iNOS), which produces nitric oxide (NO). NO post-translationally modifies cysteine thiols through S-nitrosylation, which can alter function of the target protein.

Exploring the Performance of Linear and Nonlinear Models of Time-of-Flight Secondary Ion Mass Spectrometry Spectra.

Multivariate statistical tools and machine learning (ML) techniques can deconvolute hyperspectral data and control the disparity between the number of samples and features in materials science. Nevertheless, the importance of generating sufficient high-quality sample replicates in training data cannot be overlooked, as it fundamentally affects the performance of ML models. Here, we present a quantitative analysis of time-of-flight secondary ion mass spectrometry (ToF-SIMS) spectra of a simple microarray system of two food dyes using partial least-squares (PLS, linear) and random forest (RF, nonlinear) algorithms.

A Machine Learning-Driven Comparison of Ion Images Obtained by MALDI and MALDI-2 Mass Spectrometry Imaging.

Matrix-assisted laser desorption ionization mass spectrometry imaging (MALDI-MSI) enables label-free imaging of biomolecules in biological tissues. However, many species remain undetected due to their poor ionization efficiencies. MALDI-2 (laser-induced post-ionization) is the most widely used post-ionization method for improving analyte ionization efficiencies.

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.

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.

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, Fe, Cu, Ni, Co, Zn, and two metalates, MoO, and WO.

Interpretable Machine Learning Models for Phase Prediction in Polymerization-Induced Self-Assembly.

While polymerization-induced self-assembly (PISA) has become a preferred synthetic route toward amphiphilic block copolymer self-assemblies, predicting their phase behavior from experimental design is extremely challenging, requiring time and work-intensive creation of empirical phase diagrams whenever self-assemblies of novel monomer pairs are sought for specific applications. To alleviate this burden, we develop here the first framework for a data-driven methodology for the probabilistic modeling of PISA morphologies based on a selection and suitable adaption of statistical machine learning methods. As the complexity of PISA precludes generating large volumes of training data with simulations, we focus on interpretable low variance methods that can be interrogated for conformity with chemical intuition and that promise to work well with only 592 training data points which we curated from the PISA literature.

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.

Two-Dimensional and Three-Dimensional Time-of-Flight Secondary Ion Mass Spectrometry Image Feature Extraction Using a Spatially Aware Convolutional Autoencoder.

Feature extraction algorithms are an important class of unsupervised methods used to reduce data dimensionality. They have been applied extensively for time-of-flight secondary ion mass spectrometry (ToF-SIMS) imaging─commonly, matrix factorization (MF) techniques such as principal component analysis have been used. A limitation of MF is the assumption of linearity, which is generally not accurate for ToF-SIMS data.

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.

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.

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.

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.

Self-Organizing Map and Relational Perspective Mapping for the Accurate Visualization of High-Dimensional Hyperspectral Data.

We present an optimization of the toroidal self-organizing map (SOM) algorithm for the accurate visualization of hyperspectral data. This represents a significant advancement on our previous work, in which we demonstrated the use of toroidal SOMs for the visualization of time-of-flight secondary ion mass spectrometry (ToF-SIMS) imaging data. We have previously shown that the toroidal SOM can be used, unsupervised, to produce a multicolor similarity map of the analysis area, in which pixels with similar mass spectra are assigned a similar color.

ToF-SIMS and Machine Learning for Single-Pixel Molecular Discrimination of an Acrylate Polymer Microarray.

Combinatorial approaches to materials discovery offer promising potential for the rapid development of novel polymer systems. Polymer microarrays enable the high-throughput comparison of material physical and chemical properties-such as surface chemistry and properties like cell attachment or protein adsorption-in order to identify correlations that can progress materials development. A challenge for this approach is to accurately discriminate between highly similar polymer chemistries or identify heterogeneities within individual polymer spots.

Rapid evaluation of immobilized immunoglobulins using automated mass-segmented ToF-SIMS.

Surface interactions largely control how biomaterials interact with biology and how many other types of materials function in industrial applications. ToF-SIMS analysis is extremely useful for interrogating the surfaces of complex materials and shows great promise in analyzing biological samples. Previously, the authors demonstrated that segmentation (between 1 and 0.

Visualizing ToF-SIMS Hyperspectral Imaging Data Using Color-Tagged Toroidal Self-Organizing Maps.

Time-of-flight secondary ion mass spectrometry (ToF-SIMS) is a powerful surface characterization technique capable of producing high spatial resolution hyperspectral images, in which each pixel comprises an entire mass spectrum. Such images can provide insight into the chemical composition across a surface. However, issues arise due to the size and complexity of the data produced.

Engineering the Biointerface of Electrospun 3D Scaffolds with Functionalized Polymer Brushes for Enhanced Cell Binding.

Electrospun ultrafine fibers prepared using a blend of poly(lactide- co-glycolide) (PLGA) and bromine terminated poly(l-lactide) (PLA-Br), were surface modified using surface-initiated (SI) Cu(0) mediated polymerization. Copolymers based on N-acryloxysuccinimide (NAS) and a low fouling monomer (either N, N-dimethylacrylamide (DMA), N-(2-hydroxypropyl)acrylamide (HPA), or N-acryloylmorpholine (NAM)) were grafted from the fiber surface to impart surface functionality and to reduce nonspecific protein adsorption. Inclusion of the functional NAS monomer facilitated the conjugation of a nonbioactive cyclic RAD peptide and a bioactive cyclic RGD peptide, the latter expected to facilitate cell adhesion through its affinity for the αβ integrin receptor.

Optimisation of grafting of low fouling polymers from three-dimensional scaffolds via surface-initiated Cu(0) mediated polymerisation.

Electrospun fibres represent a realistic implantable scaffold containing most of the structural three-dimensional (3D) characteristics of the extracellular matrix. However, as a result of their often synthetic nature, surface energy and chemistry, these scaffolds may adsorb a layer of non-specific proteins which can evoke a foreign body response. The precise surface modification of the scaffolds is challenging due to the complex geometrical and structural organization of the fibre meshes, that may limit the efficacy and completeness of approaches used.

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.

Determining the limit of detection of surface bound antibody.

Determination of a limit of detection (LoD) for surface bound antibodies is crucial for the development and deployment of sensitive bioassays. The measurement of very low concentrations of surface bound antibodies is also important in the manufacturing of pharmaceutical products such as antibody-conjugated pharmaceuticals. Low concentrations are required to avoid an immune response from the target host.