Elongation, flatness and compactness indices to characterise particle form

Decoding the nanoscale porosity in serpentinites from multidimensional electron microscopy and discrete element modelling

Serpentinites, widespread in Earth's lithosphere, exhibit inherent nanoporosity that may significantly impact their geochemical behaviour. This study provides a comprehensive investigation into the characteristics, scale dependence, and potential implications of nanoporosity in lizardite-dominated serpentinites. Through a combination of multidimensional imaging techniques and molecular-dynamics-based discrete element modelling, we reveal that serpentinites function as nanoporous media with pore sizes predominantly less than 100 nm. Crystallographic relationships between olivine, serpentine, and nanoporosity are explored, indicating a lack of significant correlations. Instead, stochastic growth and random packing of serpentine grains within mesh cores may result in interconnected porosity. The analysis of pore morphology suggests that the irregular pore shapes align with the crystal form of serpentine minerals. Furthermore, the nanoporosity within brucite-rich layers at the serpentine-olivine interface is attributed to delamination along weak van der Waals planes, while pore formation within larger brucite domains likely results from low-temperature alteration processes. The fractal nature of the pore size distribution and the potential interconnectivity of porosity across different scales further support the presence of a pervasive nanoporous network within serpentinites. Confinement within these nanopores may introduce unique emergent properties, potentially influencing fluid transport, mineral solubility, and chemical reactions. As such, these processes may have profound implications for the geochemical evolution of serpentinites.

Real-Time Modeling of Volume and Form Dependent Nanoparticle Fractionation in Tubular Centrifuges

A dynamic process model for the simulation of nanoparticle fractionation in tubular centrifuges is presented. Established state-of-the-art methods are further developed to incorporate multi-dimensional particle properties (traits). The separation outcome is quantified based on a discrete distribution of particle volume, elongation and flatness. The simulation algorithm solves a mass balance between interconnected compartments which represent the separation zone. Grade efficiencies are calculated by a short-cut model involving material functions and higher dimensional particle trait distributions. For the one dimensional classification of fumed silica nanoparticles, the numerical solution is validated experimentally. A creation and characterization of a virtual particle system provides an additional three dimensional input dataset. Following a three dimensional fractionation case study, the tubular centrifuge model underlines the fact that a precise fractionation according to particle form is extremely difficult. In light of this, the paper discusses particle elongation and flatness as impacting traits during fractionation in tubular centrifuges. Furthermore, communications on separation performance and outcome are possible and facilitated by the three dimensional visualization of grade efficiency data. Future research in nanoparticle characterization will further enhance the models use in real-time separation process simulation.

Form Factors as Potential Imaging Biomarkers to Differentiate Benign vs. Malignant Lung Lesions on CT Scans

Indeterminate lung nodules detected on CT scans are common findings in clinical practice. Their correct assessment is critical, as early diagnosis of malignancy is crucial to maximise the treatment outcome. In this work, we evaluated the role of form factors as imaging biomarkers to differentiate benign vs. malignant lung lesions on CT scans. We tested a total of three conventional imaging features, six form factors, and two shape features for significant differences between benign and malignant lung lesions on CT scans. The study population consisted of 192 lung nodules from two independent datasets, containing 109 (38 benign, 71 malignant) and 83 (42 benign, 41 malignant) lung lesions, respectively. The standard of reference was either histological evaluation or stability on radiological followup. The statistical significance was determined via the Mann-Whitney U nonparametric test, and the ability of the form factors to discriminate a benign vs. a malignant lesion was assessed through multivariate prediction models based on Support Vector Machines. The univariate analysis returned four form factors (Angelidakis compactness and flatness, Kong flatness, and maximum projection sphericity) that were significantly different between the benign and malignant group in both datasets. In particular, we found that the benign lesions were on average flatter than the malignant ones; conversely, the malignant ones were on average more compact (isotropic) than the benign ones. The multivariate prediction models showed that adding form factors to conventional imaging features improved the prediction accuracy by up to 14.5 pp. We conclude that form factors evaluated on lung nodules on CT scans can improve the differential diagnosis between benign and malignant lesions.