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Laboratory mice show poor translation to humans within a range of pre-clinical fields, which might be due to their specific pathogen free (SPF) status. SPF mice have CD8+ effector memory T cell levels more comparable to newborn than to adult humans or pet shop mice. However, reintroducing pathogens would re-introduce diseases, and, therefore, research facilities should look for safer alternatives. We inactivated mouse adenovirus type 1, minute virus of mice, mouse hepatitis virus, Sendai virus, Theiler’s encephalomyelitis virus (GD 7), and Mycoplasma pulmonis by ultraviolet irradiation, and mixed them with the adjuvant Addavax®. A subcutaneous injection twice with two weeks interval with 10 µg of each pathogen generated CD8+ effector memory T cell quantities significantly higher than untreated mice, and 82.3% of the pre-immunized mice were comparable with pet shop mice, while the overall quantities and percentages of T cells were un-affected. None of the pathogens were transferred from the pre-immunized mice to co-housed non-immunized mice. Pre-immunization with inactivated murine pathogens, therefore, may be used to safely create an immunological memory in mice.

Despite the widespread and increasing usage of Pd-catalyzed C–N cross couplings, finding good conditions for these reactions can be challenging. Practitioners mostly rely on few methodology studies or anecdotal experience. This is surprising, since the advent of data-driven experimentation and the large amount of knowledge in databases allow for data-driven insight. In this work, we address this by analyzing more than 62 000 Buchwald–Hartwig couplings gathered from CAS, Reaxys and the USPTO. Our meta-analysis of the reaction performance generates data-driven cheatsheets for reaction condition recommendation. It also provides an interactive tool to find rarer ligands with optimal performance regarding user-selected substrate properties. With this we give practitioners promising starting points. Furthermore, we study bias and diversity in the literature and summarize the current state of the reaction data, including its pitfalls. Hence, this work will also be useful for future data-driven developments such as the optimization of reaction conditions via machine learning., An analysis of the entire literature on Pd-catalyzed C–N couplings enables data-driven insight and provides recommendations for reaction conditions.

Significance Under typical conditions on Earth, ice exhibits two distinct polytypes: hexagonal and cubic. Yet, despite its importance to nature (e.g., the atmosphere) and technology (e.g., cryopreservation), the crystallization of liquid water to cubic ice has never been achieved in the laboratory. Here, we show how to design substrates to nucleate desired polytypes of ice, including the elusive cubic ice, and show that the process is controlled by the structure of the water in contact with the substrate. By unraveling the interplay of heterogeneous nucleation and polymorphism, we reveal a pathway to cubic ice vital to myriad phenomena and open ways to understand and control polymorphism and stacking disorder in materials in general., The freezing of water into ice is one of the most important processes in the physical sciences. However, it is still not understood at the molecular level. In particular, the crystallization of cubic ice (Ic)—rather than the traditional hexagonal polytype (Ih)—has become an increasingly debated topic. Although evidence for Ic is thought to date back almost 400 y, it is only in the last year that pure Ic has been made in the laboratory, and these processes involved high-pressure ice phases. Since this demonstrates that pure Ic can form, the question naturally arises if Ic can be made from liquid water. With this in mind, we have performed a high-throughput computational screening study involving molecular dynamics simulations of nucleation on over 1,100 model substrates. From these simulations, we find that 1) many different substrates can promote the formation of pristine Ic; 2) Ic can be selectively nucleated for even the mildest supercooling; 3) the water contact layer’s resemblance to a face of ice is the key factor determining the polytype selectivity and nucleation temperature, independent of which polytype is promoted; and 4) substrate lattice match to ice is not indicative of the polytype obtained. Through this study, we have deepened understanding of the interplay of heterogeneous nucleation and ice I polytypism and suggest routes to Ic. More broadly, the substrate design methodology presented here combined with the insight gained can be used to understand and control polymorphism and stacking disorder in materials in general.

Molecular adsorption on surfaces plays a central role in catalysis, corrosion, desalination, and many other processes of relevance to industry and the natural world. Few adsorption systems are more ubiquitous or of more widespread importance than those involving water and carbon, and for a molecular level understanding of such interfaces water monomer adsorption on graphene is a fundamental and representative system. This system is particularly interesting as it calls for an accurate treatment of electron correlation effects, as well as posing a practical challenge to experiments. Here, we employ many-body electronic structure methodologies that can be rigorously converged and thus provide faithful references for the molecule-surface interaction. In particular, we use diffusion Monte-Carlo (DMC), coupled cluster (CCSD(T)), as well as the random phase approximation (RPA) to calculate the strength of the interaction between water and an extended graphene surface. We establish excellent, sub-chemical, agreement between the complementary high-level methodologies, and an adsorption energy estimate in the most stable configuration of approximately -100\,meV is obtained. We also find that the adsorption energy is rather insensitive to the orientation of the water molecule on the surface, despite different binding motifs involving qualitatively different interfacial charge reorganisation. In producing the first demonstrably accurate adsorption energies for water on graphene this work also resolves discrepancies amongst previously reported values for this widely studied system. It also paves the way for more accurate and reliable studies of liquid water at carbon interfaces with cheaper computational methods, such as density functional theory and classical potentials.

Ice nucleation plays a significant role in a large number of natural and technological processes, but it is challenging to investigate experimentally because of the small time (ns) and short length scales (nm) involved. On the other hand, conventional molecular simulations struggle to cope with the relatively long timescale required for critical ice nuclei to form. One way to tackle this issue is to take advantage of free energy or path sampling techniques. Unfortunately, these are computationally costly. Seeded molecular dynamics is a much less demanding alternative that has been successfully applied already to study the homogeneous freezing of water. However, in the case of heterogeneous ice nucleation, nature's favourite route to form ice, an array of suitable interfaces between the ice seeds and the substrate of interest has to be built - and this is no trivial task. In this paper, we present a Heterogeneous SEEDing approach (HSEED) which harnesses a random structure search framework to tackle the ice-substrate challenge, thus enabling seeded molecular dynamics simulations of heterogeneous ice nucleation on crystalline surfaces. We validate the HSEED framework by investigating the nucleation of ice on: (i) model crystalline surfaces, using the coarse-grained mW model; and (ii) cholesterol crystals, employing the fully atomistic TIP4P/Ice water model. We show that the HSEED technique yields results in excellent agreement with both metadynamics and forward flux sampling simulations. Because of its computational efficiency, the HSEED method allows one to rapidly assess the ice nucleation ability of whole libraries of crystalline substrates - a long-awaited computational development in e.g. atmospheric science., Comment: 13 pages, 7 figures

What makes a material a good ice nucleating agent? Despite the importance of heterogeneous ice nucleation to a variety of fields, from cloud science to microbiology, major gaps in our understanding of this ubiquitous process still prevent us from answering this question. In this work, we have examined the ability of generic crystalline substrates to promote ice nucleation as a function of the hydrophobicity and the morphology of the surface. Nucleation rates have been obtained by brute-force molecular dynamics simulations of coarse-grained water on top of different surfaces of a model fcc crystal, varying the water-surface interaction and the surface lattice parameter. It turns out that the lattice mismatch of the surface with respect to ice, customarily regarded as the most important requirement for a good ice nucleating agent, is at most desirable but not a requirement. On the other hand, the balance between the morphology of the surface and its hydrophobicity can significantly alter the ice nucleation rate and can also lead to the formation of up to three different faces of ice on the same substrate. We have pinpointed three circumstances where heterogeneous ice nucleation can be promoted by the crystalline surface: (i) the formation of a water overlayer that acts as an in-plane template; (ii) the emergence of a contact layer buckled in an ice-like manner; and (iii) nucleation on compact surfaces with very high interaction strength. We hope that this extensive systematic study will foster future experimental work aimed at testing the physiochemical understanding presented herein., Main + SI

Freezing of water is arguably one of the most common phase transitions on Earth and almost always happens heterogeneously. Despite its importance, we lack a fundamental understanding of what makes substrates efficient ice nucleators. Here we address this by computing the ice nucleation (IN) ability of numerous model hydroxylated substrates with diverse surface hydroxyl (OH) group arrangements. Overall, for the substrates considered, we find that neither the symmetry of the OH patterns nor the similarity between a substrate and ice correlate well with the IN ability. Instead, we find that the OH density and the substrate-water interaction strength are useful descriptors of a material's IN ability. This insight allows the rationalization of ice nucleation ability across a wide range of materials, and can aid the search and design of novel potent ice nucleators in the future., Comment: main text + SI

The nucleation of crystals in liquids is one of nature’s most ubiquitous phenomena, playing an important role in areas such as climate change and the production of drugs. As the early stages of nucleation involve exceedingly small time and length scales, atomistic computer simulations can provide unique insights into the microscopic aspects of crystallization. In this review, we take stock of the numerous molecular dynamics simulations that, in the past few decades, have unraveled crucial aspects of crystal nucleation in liquids. We put into context the theoretical framework of classical nucleation theory and the state-of-the-art computational methods by reviewing simulations of such processes as ice nucleation and the crystallization of molecules in solutions. We shall see that molecular dynamics simulations have provided key insights into diverse nucleation scenarios, ranging from colloidal particles to natural gas hydrates, and that, as a result, the general applicability of classical nucleation theory has been repeatedly called into question. We have attempted to identify the most pressing open questions in the field. We believe that, by improving (i) existing interatomic potentials and (ii) currently available enhanced sampling methods, the community can move toward accurate investigations of realistic systems of practical interest, thus bringing simulations a step closer to experiments.

Non-bonded potentials are included in most force fields and therefore widely used in classical molecular dynamics simulations of materials and interfacial phenomena. It is commonplace to truncate these potentials for computational efficiency based on the assumption that errors are negligible for reasonable cutoffs or compensated for by adjusting other interaction parameters. Arising from a metadynamics study of the wetting transition of water on a solid substrate, we find that the influence of the cutoff is unexpectedly strong and can change the character of the wetting transition from continuous to first order by creating artificial metastable wetting states. Common cutoff corrections such as the use of a force switching function, a shifted potential, or a shifted force do not avoid this. Such a qualitative difference urges caution and suggests that using truncated non-bonded potentials can induce unphysical behavior that cannot be fully accounted for by adjusting other interaction parameters. Published by AIP Publishing. https://doi.org/10.1063/1.4997698\ud

The theoretical prediction that gray tin represents a strong topological insulator under strain [L. Fu and C.L. Kane, Phys. Rev. B 76, 045302 (2007)] is proven for biaxially strained $\ensuremath{\alpha}\text{\ensuremath{-}}\mathrm{Sn}$ layers with varying thickness by means of a generalized density functional theory with a nonlocal exchange-correlation potential that widely simulates quasiparticle bands and a tight-binding method including intra- and interatomic spin-orbit interaction. Hydrogen-passivated surfaces are modeled by symmetric slabs. In contrast to the conventional picture of a topological insulator, we find topological gapless surface states below the strain-induced bulk gap, in agreement with photoemission studies. The topological surfaces states do not emerge out of the lower ${\ensuremath{\Gamma}}_{8}^{+}$ states but are strongly influenced by the inverted bulk ${\ensuremath{\Gamma}}_{7}^{\ensuremath{-}}$ band, i.e., lie within the negative bulk $sp$ gap. We show that the position of the Dirac point and the dispersion of the surrounding bands depend on the thickness of the $\ensuremath{\alpha}\text{\ensuremath{-}}\mathrm{Sn}$ layer but are less influenced by strain. The resulting Fermi velocities agree well with available photoemission data.

The $\ensuremath{\alpha}$ phase of tin is a zero-gap semiconductor with an inverted band structure with respect to other group-IV elements like Ge. The ${\ensuremath{\Gamma}}_{6c}$ states lie energetically below the ${\ensuremath{\Gamma}}_{8v}$ levels. How these unique electronic properties transform in nanostructures with spatial confinement has not been studied. We apply density-functional theory within the local density approximation to investigate the energetic, structural, and electronic properties of bulk $\ensuremath{\alpha}$-Sn and its nanocrystals (NCs) up to a size of 363 Sn atoms. For NCs with larger diameters up to 14 nm the tight-binding method is applied for the electronic states. Spin-orbit coupling is taken into account. The clusters are modeled in such a way that the ${T}_{d}$ symmetry of the bulk system is conserved. Their surfaces are passivated with hydrogen. We show that the spatial confinement causes not only a decrease of the fundamental gap for increased NC size but also a topological transition where the ordering of $s$- and $p$-like highest-occupied molecular orbital and lowest-unoccupied molecular orbital states is interchanged. The influence of quasiparticle and excitonic effects on the lowest pair excitation energies is investigated within approximations based on the hybrid exchange-correlation functional by J. Heyd, G. E. Scuseria, and M. Ernzerhof [J. Chem. Phys. 118, 8207 (2003)] (HSE) and the $\ensuremath{\Delta}$SCF method.