Synthesis target structures for alkaline earth oxide clusters

Knowing the possible structures of individual clusters in nanostructured materials is an important first step in their design. With previous structure prediction data for BaO nanoclusters as a basis, data mining techniques were used to investigate candidate structures for magnesium oxide, calcium oxide and strontium oxide clusters. The lowest-energy structures and analysis of some of their structural properties are presented here. Clusters that are predicted to be ideal targets for synthesis, based on being both the only thermally accessible minimum for their size, and a size that is thermally accessible with respect to neighbouring sizes, include global minima for: sizes n = 9, 15, 16, 18 and 24 for (MgO)n; sizes n = 8, 9, 12, 16, 18 and 24 for (CaO) n ; the greatest number of sizes of (SrO) n clusters (n = 8, 9, 10, 12, 13, 15, 16, 18 and 24); and for (BaO) n sizes of n = 8, 10 and 16

Structure prediction of (BaO)n nanoclusters for n⩽24 using an evolutionary algorithm

Knowing the structure of nanoclusters is relevant to gaining insight into their properties for materials design. Computational studies predicting their structure should aim to reproduce experimental results. Here, barium oxide was chosen for its suitability for both computational structure prediction and experimental structure determination. An evolutionary algorithm implemented within the KLMC structure prediction package was employed to find the thermodynamically most stable structures of barium oxide nanoclusters (BaO)n with n=4-18and24. Evolutionary algorithm runs were performed to locate local minima on the potential energy landscape defined using interatomic potentials, the structures of which were then refined using density functional theory. BaO clusters show greater preference than MgO for adopting cuts from its bulk phase, thus more closely resemble clusters of KF. (BaO)4, (BaO)6, (BaO)8, (BaO)10 and (BaO)16 should be magic number clusters and each are at least 0.03 eV/BaO more stable than all other PBEsol local minima clusters found for the same size

The M33 Globular Cluster System with PAndAS Data: The Last Outer Halo Cluster?

We use CFHT/MegaCam data to search for outer halo star clusters in M33 as part of the Pan-Andromeda Archaeological Survey (PAndAS). This work extends previous studies out to a projected radius of 50 kpc and covers over 40 square degrees. We find only one new unambiguous star cluster in addition to the five previously known in the M33 outer halo (10 kpc <= r <= 50 kpc). Although we identify 2440 cluster candidates of various degrees of confidence from our objective image search procedure, almost all of these are likely background contaminants, mostly faint unresolved galaxies. We measure the luminosity, color and structural parameters of the new cluster in addition to the five previously-known outer halo clusters. At a projected radius of 22 kpc, the new cluster is slightly smaller, fainter and redder than all but one of the other outer halo clusters, and has g' ~ 19.9, (g'-i') ~ 0.6, concentration parameter c ~ 1.0, a core radius r_c ~ 3.5 pc, and a half-light radius r_h ~ 5.5 pc. For M33 to have so few outer halo clusters compared to M31 suggests either tidal stripping of M33's outer halo clusters by M31, or a very different, much calmer accretion history of M33.Comment: 37 pages, 9 figures. Accepted by the Astrophysical Journa

CALYPSO: a method for crystal structure prediction

We have developed a software package CALYPSO (Crystal structure AnaLYsis by Particle Swarm Optimization) to predict the energetically stable/metastable crystal structures of materials at given chemical compositions and external conditions (e.g., pressure). The CALYPSO method is based on several major techniques (e.g. particle-swarm optimization algorithm, symmetry constraints on structural generation, bond characterization matrix on elimination of similar structures, partial random structures per generation on enhancing structural diversity, and penalty function, etc) for global structural minimization from scratch. All of these techniques have been demonstrated to be critical to the prediction of global stable structure. We have implemented these techniques into the CALYPSO code. Testing of the code on many known and unknown systems shows high efficiency and high successful rate of this CALYPSO method [Wang et al., Phys. Rev. B 82 (2010) 094116][1]. In this paper, we focus on descriptions of the implementation of CALYPSO code and why it works.Comment: accepted in Computer Physics Communication

Modeling Excited States in TiO2 Nanoparticles: On the Accuracy of a TD-DFT Based Description.

We have investigated the suitability of Time-Dependent Density Functional Theory (TD-DFT) to describe vertical low-energy excitations in naked and hydrated titanium dioxide nanoparticles. Specifically, we compared TD-DFT results obtained using different exchange-correlation (XC) potentials with those calculated using Equation-of-Motion Coupled Cluster (EOM-CC) quantum chemistry methods. We demonstrate that TD-DFT calculations with commonly used XC potentials (e.g., B3LYP) and EOM-CC methods give qualitatively similar results for most TiO2 nanoparticles investigated. More importantly, however, we also show that, for a significant subset of structures, TD-DFT gives qualitatively different results depending upon the XC potential used and that only TD-CAM-B3LYP and TD-BHLYP calculations yield results that are consistent with those obtained using EOM-CC theory. Moreover, we demonstrate that the discrepancies for such structures originate from a particular combination of defects that give rise to charge-transfer excitations, which are poorly described by XC potentials that do not contain sufficient Hartree-Fock like exchange. Finally, we consider that such defects are readily healed in the presence of ubiquitously present water and that, as a result, the description of vertical low-energy excitations for hydrated TiO2 nanoparticles is nonproblematic

Augmented reality technology in image-guided therapy: State-of-the-art review.

Image-guided therapies have been on the rise in recent years as they can achieve higher accuracy and are less invasive than traditional methods. By combining augmented reality technology with image-guided therapy, more organs, and tissues can be observed by surgeons to improve surgical accuracy. In this review, 233 publications (dated from 2015 to 2020) on the design and application of augmented reality-based systems for image-guided therapy, including both research prototypes and commercial products, were considered for review. Based on their functions and applications. Sixteen studies were selected. The engineering specifications and applications were analyzed and summarized for each study. Finally, future directions and existing challenges in the field were summarized and discussed

Describing Excited State Relaxation and Localization in TiO2 Nanoparticles Using TD-DFT

We have investigated the description of excited state relaxation in naked and hydrated TiO2 nanoparticles using Time-Dependent Density Functional Theory (TD-DFT) with three common hybrid exchange-correlation (XC) potentials: B3LYP, CAM-B3LYP and BHLYP. Use of TD-CAM-B3LYP and TD-BHLYP yields qualitatively similar results for all structures, which are also consistent with predictions of coupled-cluster theory for small particles. TD-B3LYP, in contrast, is found to make rather different predictions; including apparent conical intersections for certain particles that are not observed with TD-CAM-B3LYP nor with TD-BHLYP. In line with our previous observations for vertical excitations, the issue with TD-B3LYP appears to be the inherent tendency of TD-B3LYP, and other XC potentials with no or a low percentage of Hartree–Fock like exchange, to spuriously stabilize the energy of charge-transfer (CT) states. Even in the case of hydrated particles, for which vertical excitations are generally well described with all XC potentials, the use of TD-B3LYP appears to result in CT problems during excited state relaxation for certain particles. We hypothesize that the spurious stabilization of CT states by TD-B3LYP even may drive the excited state optimizations to different excited state geometries from those obtained using TD-CAM-B3LYP or TD-BHLYP. Finally, focusing on the TD-CAM-B3LYP and TD-BHLYP results, excited state relaxation in small naked and hydrated TiO2 nanoparticles is predicted to be associated with a large Stokes’ shift

Carbon stable isotopes as a palaeoclimate proxy in vascular plant dominated peatlands

Carbon stable isotope (δ¹³C) records from vascular plant dominated peatlands have been used as a palaeoclimate proxy, but a better empirical understanding of fractionation processes in these ecosystems is required. Here, we test the potential of δ¹³C analysis of ombrotrophic restiad peatlands in New Zealand, dominated by the wire rush (Empodisma spp.), to provide a methodology for developing palaeoclimatic records. We took surface plant samples alongside measurements of water table depth and (micro)climate over spatial (six sites spanning > 10 latitude) and temporal (monthly measurements over 1 year) gradients and analysed the relationships between cellulose δ¹³C values and environmental parameters. We found strong, significant negative correlations between δ¹³C and temperature, photosynthetically active radiation and growing degree days above 0 C. No significant relationships were observed between δ¹³C and precipitation, relative humidity, soil moisture or water table depth, suggesting no growing season water limitation and a decoupling of the expected link between δ¹³C in vascular plants and hydrological variables. δ¹³C of Empodisma spp. roots may therefore provide a valuable temperature proxy in a climatically sensitive region, but further physiological and sub-fossil calibration studies are required to fully understand the observed signal

Assessing the cost of global biodiversity and conservation knowledge

Knowledge products comprise assessments of authoritative information supported by standards, governance, quality control, data, tools, and capacity building mechanisms. Considerable resources are dedicated to developing and maintaining knowledge products for biodiversity conservation, and they are widely used to inform policy and advise decision makers and practitioners. However, the financial cost of delivering this information is largely undocumented. We evaluated the costs and funding sources for developing and maintaining four global biodiversity and conservation knowledge products: The IUCN Red List of Threatened Species, the IUCN Red List of Ecosystems, Protected Planet, and the World Database of Key Biodiversity Areas. These are secondary data sets, built on primary data collected by extensive networks of expert contributors worldwide. We estimate that US$160 million (range: US$116-204 million), plus 293 person-years of volunteer time (range: 278-308 person-years) valued at US$14 million (range US$12-16 million), were invested in these four knowledge products between 1979 and 2013. More than half of this financing was provided through philanthropy, and nearly three-quarters was spent on personnel costs. The estimated annual cost of maintaining data and platforms for three of these knowledge products (excluding the IUCN Red List of Ecosystems for which annual costs were not possible to estimate for 2013 ) is US$6.5 million in total (range: US$6.2-6.7 million). We estimated that an additional US$114 million will be needed to reach pre-defined baselines of data coverage for all the four knowledge products, and that once achieved, annual maintenance costs will be approximately US$12 million. These costs are much lower than those to maintain many other, similarly important, global knowledge products. Ensuring that biodiversity and conservation knowledge products are sufficiently up to date, comprehensive and accurate is fundamental to inform decision-making for biodiversity conservation and sustainable development. Thus, the development and implementation of plans for sustainable long-term financing for them is critical

Accelerated discovery of two crystal structure types in a complex inorganic phase field

The discovery of new materials is hampered by the lack of efficient approaches to the exploration of both the large number of possible elemental compositions for such materials, and of the candidate structures at each composition1. For example, the discovery of inorganic extended solid structures has relied on knowledge of crystal chemistry coupled with time-consuming materials synthesis with systematically varied elemental ratios2,3. Computational methods have been developed to guide synthesis by predicting structures at specific compositions4,5,6 and predicting compositions for known crystal structures7,8, with notable successes9,10. However, the challenge of finding qualitatively new, experimentally realizable compounds, with crystal structures where the unit cell and the atom positions within it differ from known structures, remains for compositionally complex systems. Many valuable properties arise from substitution into known crystal structures, but materials discovery using this approach alone risks both missing best-in-class performance and attempting design with incomplete knowledge8,11. Here we report the experimental discovery of two structure types by computational identification of the region of a complex inorganic phase field that contains them. This is achieved by computing probe structures that capture the chemical and structural diversity of the system and whose energies can be ranked against combinations of currently known materials. Subsequent experimental exploration of the lowest-energy regions of the computed phase diagram affords two materials with previously unreported crystal structures featuring unusual structural motifs. This approach will accelerate the systematic discovery of new materials in complex compositional spaces by efficiently guiding synthesis and enhancing the predictive power of the computational tools through expansion of the knowledge base underpinning them