The characterisation of dental enamel using transmission Kikuchi diffraction in the scanning electron microscope combined with dynamic template matching

The remarkable physical properties of dental enamel can be largely attributed to the structure of the hydroxyapatite (HAp) crystallites on the sub-micrometre scale. Characterising the HAp microstructure is challenging, due to the nanoscale of individual crystallites and practical challenges associated with HAp examination using electron microscopy techniques. Conventional methods for enamel characterisation include imaging using transmission electron microscopy (TEM) or specialised beamline techniques, such as polarisation-dependent imaging contrast (PIC). These provide useful information at the necessary spatial resolution but are not able to measure the full crystallographic orientation of the HAp crystallites. Here we demonstrate the effectiveness of enamel analyses using transmission Kikuchi diffraction (TKD) in the scanning electron microscope, coupled with newly-developed pattern matching methods. The pattern matching approach, using dynamic template matching coupled with subsequent orientation refinement, enables robust indexing of even poor-quality TKD patterns, resulting in significantly improved data quality compared to conventional diffraction pattern indexing methods. The potential of this method for the analysis of nanocrystalline enamel structures is demonstrated by the characterisation of a human enamel TEM sample and the subsequent comparison of the results to high resolution TEM imaging. The TKD - pattern matching approach measures the full HAp crystallographic orientation enabling a quantitative measurement of not just the c-axis orientations, but also the extent of any rotation of the crystal lattice about the c-axis, between and within grains. Results presented here show how this additional information highlights potentially significant aspects of the HAp crystallite structure, including intra-crystallite distortion and the presence of multiple high angle boundaries between adjacent crystallites with rotations about the c-axis. These and other observations enable a more rigorous understanding of the relationship between HAp structures and the physical properties of dental enamel.

Exploring water in oil emulsions simultaneously stabilized by solid hydrophobic silica nanospheres and hydrophilic soft PNIPAM microgel

A general drawback of microgels is that they do not stabilize water-in-oil (w/o) emulsions of non-polar oils. Simultaneous stabilization with solid hydrophobic nanoparticles and soft hydrophilic microgels overcomes this problem. For a fundamental understanding of this synergistic effect the use of well defined particle systems is crucial. Therefore, the present study investigates the stabilization of water droplets in a highly non-polar oil phase using temperature responsive, soft and hydrophilic PNIPAM microgel particles (MGs) and solid and hydrophobic silica nanospheres (SNs) simultaneously. The SNs are about 20 times smaller than the MGs. In a multiscale approach the resulting emulsions are studied from the nanoscale particle properties over microscale droplet sizes to macroscopic observations. The synergy of the particles allows the stabilization of water-in-oil (w/o) emulsions, which was not possible with MGs alone, and offers a larger internal interface than the stabilization with SNs alone. Furthermore, the incorporation of hydrophilic MGs into a hydrophobic particle layer accelerates the emulsions sedimentation speed. Nevertheless, the droplets are still sufficiently protected against coalescence even in the sediment and can be redispersed by gentle shaking. Based on droplet size measurements and cryo-SEM studies we elaborate a model, which explains the found phenomena.

The quantitative impact of fluid vs. solid interfaces on the catalytic performance of pickering emulsions

Pickering emulsions (PEs), i.e. particle stabilized emulsions, are used as reaction environments in biphasic catalysis for the hydroformylation of 1-dodecene into tridecanal using the catalyst rhodium (Rh)-sulfoxantphos (SX). The present study connects the knowledge about particle-catalyst interactions and PE structure with the reaction results. It quantifies the efficiency of the catalytic performance of the catalyst localized in the voids between the particles (liquid-liquid interface) and the catalyst adsorbed on the particle surface (liquid-solid interface) using a new numerical approach. First, it is ensured that the overall packing density and geometry at the droplet interface and the size of the water droplets of the resulting w/o PEs are predictable. Second, it is shown that approximately all particles assemble at the droplet surface after emulsion preparation and neither the packing parameter nor the droplet size change with the particle surface charge or size when the total particle cross section is kept constant. Third, studies on the influence of the catalyst on the emulsion structure reveal that irrespective of the particle charge the surface active and negatively charged catalyst Rh-SX reduces the PE droplet size significantly and decreases the particle packing parameter from s = 0.91 (hexagonal packing in 2D) to s = 0.69 (shattered structure). In this latter case, large voids of the free w/o interface form and become covered with the catalyst. With a deep knowledge about the PE structure the reaction efficiencies of the liquid-liquid vs. the solid-liquid interface are quantified. By excluding any other influence factors, it is shown that the activity of the catalyst is the same at the fluid and solid interface and the performance of the reaction is explained by the geometry of the system. After the reaction, the catalyst retention via membrane filtration is shown to be successfully achieved without damaging the emulsions. This enables the continuous recovery of the catalyst, i.e. the most expensive compound in PE-based catalytic reactions, being a crucial criterion for industrial applications.

A Murine Oral-Exposure Model for Nano- and Micro-Particulates: Demonstrating Human Relevance with Food-Grade Titanium Dioxide

Human exposure to persistent, nonbiological nanoparticles and microparticles via the oral route is continuous and large scale (10¹² -10¹³ particles per day per adult in Europe). Whether this matters or not is unknown but confirmed health risks with airborne particle exposure warns against complacency. Murine models of oral exposure will help to identify risk but, to date, lack validation or relevance to humans. This work addresses that gap. It reports i) on a murine diet, modified with differing concentrations of the common dietary particle, food grade titanium dioxide (fgTiO₂ ), an additive of polydisperse form that contains micro- and nano-particles, ii) that these diets deliver particles to basal cells of intestinal lymphoid follicles, exactly as is reported as a "normal occurrence" in humans, iii) that confocal reflectance microscopy is the method of analytical choice to determine this, and iv) that food intake, weight gain, and Peyer's patch immune cell profiles, up to 18 weeks of feeding, do not differ between fgTiO₂ -fed groups or controls. These findings afford a human-relevant and validated oral dosing protocol for fgTiO₂ risk assessment as well as provide a generalized platform for application to oral exposure studies with nano- and micro-particles.

Application of automated electron microscopy imaging and machine learning to characterise and quantify nanoparticle dispersion in aqueous media

For many nanoparticle applications it is important to understand dispersion in liquids. For nanomedicinal and nanotoxicological research this is complicated by the often complex nature of the biological dispersant and ultimately this leads to severe limitations in the analysis of the nanoparticle dispersion by light scattering techniques. Here we present an alternative analysis and associated workflow which utilises electron microscopy. The need to collect large, statistically relevant datasets by imaging vacuum dried, plunge frozen aliquots of suspension was accomplished by developing an automated STEM imaging protocol implemented in an SEM fitted with a transmission detector. Automated analysis of images of agglomerates was achieved by machine learning using two free open‐source software tools: CellProfiler and ilastik. The specific results and overall workflow described enable accurate nanoparticle agglomerate analysis of particles suspended in aqueous media containing other potential confounding components such as salts, vitamins and proteins.

Lay Description

In order to further advance studies in both nanomedicine and nanotoxicology, we need to continue to understand the dispersion of nanoparticles in biological fluids. These biological environments often contain a number of components such as salts, vitamins and proteins which can lead to difficulties when using traditional techniques to monitor dispersion. Here we present an alternative analysis which utilises electron microscopy. In order to use this approach statistically relevant large image datasets were collected from appropriately prepared samples of nanoparticle suspensions by implementing an automated imaging protocol. Automated analysis of these images was achieved through machine learning using two readily accessible freeware; CellProfiler and ilastik. The workflow presented enables accurate nanoparticle dispersion analysis of particles suspended in more complex biological media.