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Abstract The unique nature of constituent chemical elements gives rise to fundamental differences in materials. Assessing materials based on their phase fields, defined as sets of constituent elements, before specific differences emerge due to composition and structure can reduce combinatorial complexity and accelerate screening, exploiting the distinction from composition-level approaches. Discrimination and evaluation of novelty of materials classes align with the experimental challenge of identifying new areas of chemistry. To address this, we present PhaseSelect, an end-to-end machine learning model that combines representation, classification, regression and novelty ranking of phase fields. PhaseSelect leverages elemental characteristics derived from computational and experimental materials data and employs attention mechanisms to reflect the individual element contributions when evaluating functional performance of phase fields. We demonstrate this approach for high-temperature superconductivity, high-temperature magnetism, and targeted bandgap energy applications, showcasing its versatility and potential for accelerating materials exploration.

Abstract The application of machine learning models to predict material properties is determined by the availability of high-quality data. We present an expert-curated dataset of lithium ion conductors and associated lithium ion conductivities measured by a.c. impedance spectroscopy. This dataset has 820 entries collected from 214 sources; entries contain a chemical composition, an expert-assigned structural label, and ionic conductivity at a specific temperature (from 5 to 873 °C). There are 403 unique chemical compositions with an associated ionic conductivity near room temperature (15–35 °C). The materials contained in this dataset are placed in the context of compounds reported in the Inorganic Crystal Structure Database with unsupervised machine learning and the Element Movers Distance. This dataset is used to train a CrabNet-based classifier to estimate whether a chemical composition has high or low ionic conductivity. This classifier is a practical tool to aid experimentalists in prioritizing candidates for further investigation as lithium ion conductors.

Epitaxial heterostructures composed of complex correlated metal oxides, grown along specific crystallographic orientations, offer a route to investigating emergent phenomena such as topological states and spin liquids through geometrical lattice engineering. A2Ru2O7 pyrochlore ruthenates, in particular, exhibit a metal–insulator transition with varying A cation, whose mechanism is not fully understood. We report on the epitaxial growth, and structural and electrical properties of metallic pyrochlore bismuth ruthenate heterostructures, grown along both the [001] and [111] directions. Ordered pyrochlore thin films were obtained with a highly oriented texture along the [001] and [111] crystallographic directions. Density functional theory calculations of the electronic band structure and density of states indicated that Bi2Ru2O7 is semimetallic and that hybridization of the Ru 4d and Bi 6p orbitals via the anion network at the Fermi energy was responsible for the metallicity. Electrical conductivity measurements confirmed that the compound is weakly metallic, in agreement with the reported conductivity for the stoichiometric bulk compound. The carrier concentration and mobility of the electrons compared favorably with previous reports on bulk material and indicate strong electron–electron interactions. The measured and computed optical conductivities were found to share coincident spectral features and confirm the electronic correlation. Comparison of the electrical and optical properties of the two distinct orientations indicates differences that cannot be attributed to differences in crystalline quality or dislocations and may indicate anisotropy in the electronic structure of Bi2Ru2O7. This study will enable access to the kagome lattice arising naturally in the 111 planes of the pyrochlore B cation sublattice, which may be used to uncover emergent topological properties.

Machine learning has the potential to significantly speed-up the discovery of new materials in synthetic materials chemistry. Here the authors combine unsupervised machine learning and crystal structure prediction to predict a novel quaternary lithium solid electrolyte that is then synthesized.

Abstract Background Safe and rational development of nanomaterials for clinical translation requires the assessment of potential biocompatibility. Autophagy, a critical homeostatic pathway intrinsically linked to cellular health and inflammation, has been shown to be affected by nanomaterials. It is, therefore, important to be able to assess possible interactions of nanomaterials with autophagic processes. Results CEM (T cell), Raji (B lymphocyte), and THP-1 (human monocyte) cell lines were subject to treatment with rapamycin and chloroquine, known to affect the autophagic process, in order to evaluate cell line-specific responses. Flow cytometric quantification of a fluorescent autophagic vacuole stain showed that maximum observable effects (105%, 446%, and 149% of negative controls) were achieved at different exposure durations (8, 6, and 24 h for CEM, Raji, and THP-1, respectively). THP-1 was subsequently utilised as a model to assess the autophagic impact of a small library of nanomaterials. Association was observed between hydrodynamic size and autophagic impact (r2 = 0.11, p = 0.004). An ELISA for p62 confirmed the greatest impact by 10 nm silver nanoparticles, abolishing p62, with 50 nm silica and 180 nm polystyrene also lowering p62 to a significant degree (50%, 74%, and 55%, respectively, p

Porous materials acting as molecular sieves for propylene/propane separation are important for the petrochemical industry. Here the authors show an example of how specific guest-host interactions can result in structural changes in the porous host and shut down diffusion of one of the two similar guest molecules.

Metal-organic frameworks have shown promise as nanoreactors, facilitating the synthesis of molecules that are otherwise difficult to isolate. Here, the authors design a framework featuring unobstructed adenine linkers to which thymine molecules can base-pair, allowing for thymine dimerization in the pores upon UV irradiation.

Abstract Background Cell-based regenerative medicine therapies are now frequently tested in clinical trials. In many conditions, cell therapies are administered systemically, but there is little understanding of their fate, and adverse events are often under-reported. Currently, it is only possible to assess safety and fate of cell therapies in preclinical studies, specifically by monitoring animals longitudinally using multi-modal imaging approaches. Here, using a suite of in vivo imaging modalities to explore the fate of a range of human and murine cells, we investigate how route of administration, cell type and host immune status affect the fate of administered cells. Methods We applied a unique imaging platform combining bioluminescence, optoacoustic and magnetic resonance imaging modalities to assess the safety of different human and murine cell types by following their biodistribution and persistence in mice following administration into the venous or arterial system. Results Longitudinal imaging analyses (i) suggested that the intra-arterial route may be more hazardous than intravenous administration for certain cell types, (ii) revealed that the potential of a mouse mesenchymal stem/stromal cell (MSC) line to form tumours depended on administration route and mouse strain and (iii) indicated that clinically tested human umbilical cord (hUC)-derived MSCs can transiently and unexpectedly proliferate when administered intravenously to mice. Conclusions In order to perform an adequate safety assessment of potential cell-based therapies, a thorough understanding of cell biodistribution and fate post administration is required. The non-invasive imaging platform used here can expose not only the general organ distribution of these therapies, but also a detailed view of their presence within different organs and, importantly, tumourigenic potential. Our observation that the hUC-MSCs but not the human bone marrow (hBM)-derived MSCs persisted for a period in some animals suggests that therapies with these cells should proceed with caution.

Irreversible bond formation in covalent organic frameworks leads to amorphous materials. Here, the authors show that crystalline and ultra-stable covalent amide frameworks can be accessed by devitrification of amorphous polyamide networks.

Investigation of the potential for nanomaterials to generate immunogenic effects is a key aspect of a robust preclinical evaluation. In combination with physicochemical characterization, such assessments also provide context for how material attributes influence biological outcomes. Furthermore, appropriate models for these assessments allow accurate in vitro to in vivo extrapolation, which is vital for the mechanistic understanding of nanomaterial action. Here we have assessed the immunogenic impact of a small panel of commercially available and in-house prepared nanomaterials on primary human peripheral blood mononuclear cells (PBMCs). A diethylaminoethyl-dextran (DEAE-dex) functionalized superparamagnetic iron oxide nanoparticle (SPION) generated detectable quantities of tumor necrosis factor α (TNFα), interleukin-1β (IL-1β), and IL-10, the only tested material to do so. The human leukemia monocytic cell line THP-1 was used to assess the potential for the nanomaterial panel to affect cellular oxidation-reduction (REDOX) via measurement of reactive oxygen species and reduced glutathione. Negatively charged sulfonate-functionalized polystyrene nanoparticles demonstrated a size-related trend for the inhibition of caspase-1, which was not observed for amine-functionalized polystyrene of similar sizes. Silica nanoparticles (310 nm) resulted in a 93% increase in proliferation compared to the untreated control (p < 0.01). No other nanomaterial treatments resulted in significant change from that of unstimulated PBMCs. Responses to the nanomaterials in the assays described demonstrate the utility of primary cells as ex vivo models for nanomaterial biological impact.