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. This microarray was generated by a high-throughput sample preparation and analysis workflow for fast and efficient acquisition of quality and reproducible spectra via ToF-SIMS. We drew insights from the bias-variance trade-off, investigated the performances of PLS and RF regression models as a function of training data size, and inferred the amount of data needed to construct accurate and reliable regression models. In addition, we found that the spectral concatenation of positive and negative ToF-SIMS spectra improved the model performances. This study provides an empirical basis for future design of high-throughput microarrays and multicomponent systems, for the purpose of analysis with ToF-SIMS and ML.

Polysaccharide-Targeting Lipid Nanoparticles to Kill Gram-Negative Bacteria.

The rapid increase and spread of Gram-negative bacteria resistant to many or all existing treatments threaten a return to the preantibiotic era. The presence of bacterial polysaccharides that impede the penetration of many antimicrobials and protect them from the innate immune system contributes to resistance and pathogenicity. No currently approved antibiotics target the polysaccharide regions of microbes. Here, describe monolaurin-based niosomes, the first lipid nanoparticles that can eliminate bacterial polysaccharides from hypervirulent Klebsiella pneumoniae, are described. Their combination with polymyxin B shows no cytotoxicity in vitro and is highly effective in combating K. pneumoniae infection in vivo. Comprehensive mechanistic studies have revealed that antimicrobial activity proceeds via a multimodal mechanism. Initially, lipid nanoparticles disrupt polysaccharides, then outer and inner membranes are destabilized and destroyed by polymyxin B, resulting in synergistic cell lysis. This novel lipidic nanoparticle system shows tremendous promise as a highly effective antimicrobial treatment targeting multidrug-resistant Gram-negative pathogens.

Streamlining the Generation of Advanced Polymer Materials Through the Marriage of Automation and Multiblock Copolymer Synthesis in Emulsion.

Synthetic polymers are of paramount importance in modern life - an incredibly wide range of polymeric materials possessing an impressive variety of properties have been developed to date. The recent emergence of artificial intelligence and automation presents a great opportunity to significantly speed up discovery and development of the next generation of advanced polymeric materials. We have focused on the high-throughput automated synthesis of multiblock copolymers that comprise three or more distinct polymer segments of different monomer composition bonded in linear sequence. The present work has exploited automation to prepare high molar mass multiblock copolymers (typically>100,000 g mol-1) using reversible addition-fragmentation chain transfer (RAFT) polymerization in aqueous emulsion. A variety of original multiblock copolymers have been synthesised via a Chemspeed robot, exemplified by a multiblock copolymer comprising thirteen constituent blocks. Moreover, libraries of copolymers of randomized monomer compositions (acrylates, acrylamides, methacrylates, and styrenes), block orders, and block lengths were also generated, thereby demonstrating the robustness of our synthetic approach. One multiblock copolymer contained all four monomer families listed in the pool, which is unprecedented in the literature. The present work demonstrates that automation has the power to render complex and laborious syntheses of such unprecedented materials not just possible, but facile and straightforward, thus representing the way forward to the next generation of complex macromolecular architectures.

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.

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

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

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. Recently, nonlinear autoencoders have been shown to outperform MF techniques for ToF-SIMS image feature extraction. However, another limitation of most feature extraction methods (including autoencoders) that is particularly important for hyperspectral data is that they do not consider spatial information. To address this limitation, we describe the application of the convolutional autoencoder (CNNAE) to hyperspectral ToF-SIMS imaging data. The CNNAE is an artificial neural network developed specifically for hyperspectral data that uses convolutional layers for image encoding, thereby explicitly incorporating pixel neighborhood information. We compared the performance of the CNNAE with other common feature extraction algorithms for two biological ToF-SIMS imaging data sets. We investigated the extracted features and used the dimensionality-reduced data to train additional ML algorithms. By converting two-dimensional convolutional layers to three-dimensional (3D), we also showed how the CNNAE can be extended to 3D ToF-SIMS images. In general, the CNNAE produced features with significantly higher contrast and autocorrelation than other techniques. Furthermore, histologically recognizable features in the data were more accurately represented. The extension of the CNNAE to 3D data also provided an important proof of principle for the analysis of more complex 3D data sets.

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. Time-of-flight secondary ion mass spectrometry (ToF-SIMS) offers unique potential in this regard, capable of describing the chemistry associated with the outermost layer of a sample with high spatial resolution and chemical sensitivity. However, this comes at the cost of generating large scale, complex hyperspectral imaging data sets. We have demonstrated previously that machine learning is a powerful tool for interpreting ToF-SIMS images, describing a method for color-tagging the output of a self-organizing map (SOM). This reduces the entire hyperspectral data set to a single reconstructed color similarity map, in which the spectral similarity between pixels is represented by color similarity in the map. Here, we apply the same methodology to a ToF-SIMS image of a printed polymer microarray for the first time. We report complete, single-pixel molecular discrimination of the 70 unique homopolymer spots on the array while also identifying intraspot heterogeneities thought to be related to intermixing of the polymer and the pHEMA coating. In this way, we show that the SOM can identify layers of similarity and clusters in the data, both with respect to polymer backbone structures and their individual side groups. Finally, we relate the output of the SOM analysis with fluorescence data from polymer-protein adsorption studies, highlighting how polymer performance can be visualized within the context of the global topology of the data set.

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. Here, we use an additional algorithm, relational perspective mapping (RPM), to produce more accurate visualizations of hyperspectral data. The SOM output is used as an input for the RPM algorithm, which is a nonlinear dimensionality reduction technique designed to produce a two-dimensional map of high-dimensional data. Using the topological information provided by the SOM, RPM provides complementary distance information. The result is a color scheme that more accurately reflects the local spectral distances between pixels in the data. We exemplify SOM-RPM using ToF-SIMS imaging data from a mouse tumor tissue section. The similarity maps produced are compared with those produced by two leading hyperspectral visualization techniques in the field of mass spectrometry imaging: t-distributed stochastic neighborhood embedding (t-SNE) and uniform manifold approximation and projection (UMAP). We evaluate the performance of each technique both qualitatively and quantitatively, investigating the correlations between distances in the models and distances in the data. SOM-RPM is demonstrably highly competitive with t-SNE and UMAP, according to our evaluations. Furthermore, the use of a neural network offers distinct advantages in data characterization, which we discuss. We also show how spectra extracted from regions of interest identified by SOM-RPM can be further analyzed using linear discriminant analysis for the validation and characterization of the surface chemistry.

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. Data are particularly complicated for biological samples, primarily due to overlapping spectra produced by similar components. The traditional approach of selecting individual ion peaks as representative of particular components is insufficient for such complex data sets. Multivariate analysis (MVA) can help to overcome this significant hurdle. We demonstrate that Kohonen self-organizing maps (SOMs) with a toroidal topology can be used to analyze a ToF-SIMS hyperspectral imaging data set and identify spectral similarities between pixels. We present a method for color-tagging the toroidal SOM output, which reduces the entire data set to a single RGB image in which similar pixels-based on their associated mass spectra-are assigned a similar color. This method was exemplified using a ToF-SIMS image of dried large multilamellar vesicles (LMVs), loaded with the antibiotic cefditoren pivoxil (CP). We successfully identified CP-loaded and empty LMVs without the need for any prior knowledge of the sample, despite their highly similar spectra. We also identified which specific ion peaks were most important in differentiating the two LMV populations. This approach is entirely unsupervised and requires minimal experimenter input. It was developed with the aim of providing a user-friendly yet sophisticated workflow for understanding complex biological samples using ToF-SIMS images.

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.

A QCM-D and SAXS Study of the Interaction of Functionalised Lyotropic Liquid Crystalline Lipid Nanoparticles with siRNA.

Biophysical studies were undertaken to investigate the binding and release of short interfering ribonucleic acid (siRNA) from lyotropic liquid crystalline lipid nanoparticles (LNPs) by using a quartz crystal microbalance (QCM). These carriers are based on phytantriol (Phy) and the cationic lipid DOTAP (1,2-dioleoyloxy-3-(trimethylammonium)propane). The nonlamellar phase LNPs were tethered to the surface of the QCM chip for analysis based on biotin-neutravidin binding, which enabled the controlled deposition of siRNA-LNP complexes with different lipid/siRNA charge ratios on a QCM-D crystal sensor. The binding and release of biomolecules such as siRNA from LNPs was demonstrated to be reliably characterised by this technique. Essential physicochemical parameters of the cationic LNP/siRNA lipoplexes-such as particle size, lyotropic phase behaviour, cytotoxicity, gene silencing and uptake efficiency-were also assessed. The SAXS data show that when the pH was lowered to 5.5 the structure of the lipoplexes did not change, thus indicating that the acidic conditions of the endosome were not a significant factor in the release of siRNA from the cationic lipidic carriers.

High-Throughput Production of Chromium(III) Complexes for Antibody Immobilization.

A robot-assisted high-throughput methodology was employed to produce chromium(III) complexes suitable for the surface modification of the commercially available PerkinElmer Optiplate96 well plate for use in enzyme-linked immunosorbent assays (ELISAs). The complexes were immobilized to the native functionality of the well plate and first screened using a horseradish-peroxidase-tagged (HRP) mouse antibody to quantify binding. The top "hits" were further assessed for their ability to present the antibody in a functional state using an ELISA. "Hits" from the second screen yielded four complexes capable of improving the signal intensity of the ELISA by greater than 500%. The metal/base ratio of these complexes was also investigated, and we isolated the most stable and reproducible candidate, [Cr(OH)6]3-, which was formed from chromium(III) perchlorate and pH adjusted with ethylenediamine. This chromium solution was employed in a clinically relevant setting for the detection of bovine TNFα producing up to a 200% increase in signal intensity.

UiO-66 MOF end-face-coated optical fiber in aqueous contaminant detection.

Optical quality metal organic framework (MOF) thin films were integrated, for the first time, to the best of our knowledge, with structured optical fiber substrates to develop MOF-fiber sensors. The MOF-fiber structure, UiO-66 (Zr-based MOF is well known for its water stability), is a thin film that acts as an effective analyte collector. This provided a Fabry-Perot sensor in which concentrations of up to 15 mM Rhodamine-B were detected via wavelength shifts in the interference spectrum.

High-Throughput Screening of Saturated Fatty Acid Influence on Nanostructure of Lyotropic Liquid Crystalline Lipid Nanoparticles.

Self-assembled lyotropic liquid crystalline lipid nanoparticles have been developed for a wide range of biomedical applications with an emerging focus for use as delivery vehicles for drugs, genes, and in vivo imaging agents. In this study, we report the generation of lipid nanoparticle libraries with information regarding mesophase and lattice parameter, which can aid the selection of formulation for a particular end-use application. In this study we elucidate the phase composition parameters that influence the internal structure of lipid nanoparticles produced from monoolein, monopalmitolein and phytantriol incorporating a variety of saturated fatty acids (FA) with different chain lengths at varying concentrations and temperatures. The material libraries were established using high throughput formulation and screening techniques, including synchrotron small-angle X-ray scattering. The results demonstrate the rich polymorphism of lipid nanoparticles with nonlamellar mesophases in the presence of saturated FAs. The inclusion of saturated FAs within the lipid nanoparticles promotes a gradual phase transition at all temperatures studied toward structures with higher negative surface curvatures (e.g., from inverse bicontinuous cubic phase to hexagonal phase and then emulsified microemulsion). The three partial phase diagrams produced are discussed in terms of the influence of FA chain length and concentration on nanoparticle internal mesophase structure and lattice parameters. The study also highlights a compositionally dependent coexistence of multiple mesophases, which may indicate the presence of multicompartment nanoparticles containing cubic/cubic and cubic/hexagonal mesophases.

ToF-SIMS and Principal Component Analysis Investigation of Denatured, Surface-Adsorbed Antibodies.

Antibody denaturation at solid-liquid interfaces plays an important role in the sensitivity of protein assays such as enzyme-linked immunosorbent assays (ELISAs). Surface immobilized antibodies must maintain their native state, with their antigen binding (Fab) region intact, to capture antigens from biological samples and permit disease detection. In this work, two identical sample sets were prepared with whole antibody IgG, F(ab')2 and Fc fragments, immobilized to either a silicon wafer or a diethylene glycol dimethyl ether plasma polymer surface. Analysis was conducted on one sample set at day 0, and the second sample set after 14 days in vacuum, with time-of-flight secondary ion mass spectrometry (ToF-SIMS) for molecular species representative of denaturation. A 1003 mass fragment peak list was compiled from ToF-SIMS data and compared to a 35 amino acid mass fragment peak list using principal component analysis. Several ToF-SIMS secondary ions, pertaining to disulfide and thiol species, were identified in the 14 day (presumably denatured) samples. A substrate and primary ion independent marker for denaturation (aging) was then produced using a ratio of mass peak intensities according to denaturation ratio: [I61.9534 + I62.9846 + I122.9547 + I84.9609 + I120.9461]/[I30.9979 + I42.9991 + I73.0660 + I147.0780]. The ratio successfully identifies denaturation on both the silicon and plasma polymer substrates and for spectra generated with Mn+, Bi+, and Bi3+ primary ions. We believe this ratio could be employed to as a marker of denaturation of antibodies on a plethora of substrates.

Surface Adsorbed Antibody Characterization Using ToF-SIMS with Principal Component Analysis and Artificial Neural Networks.

Artificial neural networks (ANNs) form a class of powerful multivariate analysis techniques, yet their routine use in the surface analysis community is limited. Principal component analysis (PCA) is more commonly employed to reduce the dimensionality of large data sets and highlight key characteristics. Herein, we discuss the strengths and weaknesses of PCA and ANNs as methods for investigation and interpretation of a complex multivariate sample set. Using time-of-flight secondary ion mass spectrometry (ToF-SIMS) we acquired spectra from an antibody and its proteolysis fragments with three primary-ion sources to obtain a panel of 72 spectra and a characteristic peak list of 775 fragment ions. We describe the use of ANNs as a means to interpret the ToF-SIMS spectral data, highlight the optimal neural network design and computational parameters, and discuss the technique limitations. Further, employing Bi3(+) as the primary-ion source, ANNs can accurately classify antibody fragments from the parent antibody based on ToF-SIMS spectra.

Lipid-PEG conjugates sterically stabilize and reduce the toxicity of phytantriol-based lyotropic liquid crystalline nanoparticles.

Lyotropic liquid crystalline nanoparticle dispersions are of interest as delivery vectors for biomedicine. Aqueous dispersions of liposomes, cubosomes, and hexosomes are commonly stabilized by nonionic amphiphilic block copolymers to prevent flocculation and phase separation. Pluronic stabilizers such as F127 are commonly used; however, there is increasing interest in using chemically reactive stabilizers for enhanced functionalization and specificity in therapeutic delivery applications. This study has explored the ability of 1,2-distearoyl-sn-glycero-3-phosphoethanolamine conjugated with poly(ethylene glycol) (DSPE-PEGMW) (2000 Da ≤ MW ≤ 5000 Da) to engineer and stabilize phytantriol-based lyotropic liquid crystalline dispersions. The poly(ethylene glycol) (PEG) moiety provides a tunable handle to the headgroup hydrophilicity/hydrophobicity to allow access to a range of nanoarchitectures in these systems. Specifically, it was observed that increasing PEG molecular weight promotes greater interfacial curvature of the dispersions, with liposomes (Lα) present at lower PEG molecular weight (MW 2000 Da), and a propensity for cubosomes (QII(P) or QII(D) phase) at MW 3400 Da or 5000 Da. In comparison to Pluronic F127-stabilized cubosomes, those made using DSPE-PEG3400 or DSPE-PEG5000 had enlarged internal water channels. The toxicity of these cubosomes was assessed in vitro using A549 and CHO cell lines, with cubosomes prepared using DSPE-PEG5000 having reduced cytotoxicity relative to their Pluronic F127-stabilized analogues.

Colloidally stabilized magnetic carbon nanotubes providing MRI contrast in mouse liver tumors.

The use of medical imaging contrast agents may lead to improved patient prognosis by potentially enabling an earlier detection of diseases and therefore an earlier initiation of treatments. In this study, we fabricated superparamagnetic iron oxide (SPIO) nanoparticles within the inner cavity of multiwalled carbon nanotubes (MWCNTs) for the first time; thereby ensuring high mechanical stability of the nanoparticles. A simple, but effective, self-assembled coating with RAFT diblock copolymers ensured the SPIO-MWCNTs have a high dispersion stability under physiological conditions. In vivo acute tolerance testing in mice showed a high tolerance dose up to 100 mg kg(-1). Most importantly, after administration of the material a 55% increase in tumor to liver contrast ratio was observed with in vivo MRI measurements compared to the preinjection image enhancing the detection of the tumor.

Protic ionic liquids (PILs) nanostructure and physicochemical properties: development of high-throughput methodology for PIL creation and property screens.

A high-throughput approach was developed in order to prepare and dry a series of protic ionic liquids (PILs) from 48 Brønsted acid-base combinations. Many combinations comprised an alkyl carboxylic acid paired with an alkyl amine. Visual screens were developed to identify which acid-base combinations formed PILs, and of those, which PILs were likely to have high surface tensions, low viscosities, and low melting points. The surface tension screen was validated through pendant drop surface tension measurements. Karl Fischer coulometric titration was used to obtain the water contents, and it was noted that there is a considerable difference in the drying rate throughout this series of PILs. It was observed that an octyl chain present on either the cation or anion was detrimental to the formation of a PIL with a low melting point, and instead increased the likelihood of a gel or solid forming. The nanostructure of the PILs was determined, using synchrotron small and wide angle X-ray scattering (SAXS/WAXS), to consist of polar and non-polar domains, with the alkyl chains on the cation and anion intercalating. The results indicate that both the alkyl chain on the cation and/or anion contribute to the correlation distance, for the intermediate range order, with the expectation that there is charge alternation of the ions in the polar region. The maximum correlation distance was observed when there was an alkyl chain present on only one ion. This correlation distance could be significantly reduced by varying the alkyl chain length present on the other ion, which was attributed to increased disorder and interdigitation of chains, and to toe-to-toe alignment of the chains. To the best of our knowledge this is the first PIL report into the effect of having an alkyl chain present on both the cation and the anion.

Nitroxide-loaded hexosomes provide MRI contrast in vivo.

The purpose of this work was to synthesize and screen, for their effectiveness to act as T1-enhancing magnetic resonance imaging (MRI) contrast agents, a small library of nitroxide lipids incorporated into cubic-phase lipid nanoparticles (cubosomes). The most effective nitroxide lipid was then formulated into lower-toxicity lipid nanoparticles (hexosomes), and effective MR contrast was observed in the aorta and spleen of live rats in vivo. This new class of lower-toxicity lipid nanoparticles allowed for higher relaxivities on the order of those of clinically used gadolinium complexes. The new hexosome formulation presented herein was significantly lower in toxicity and higher in relaxivity than cubosome formulations previously reported by us.