Electronic structure of solid coronene: differences and commonalities to picene

We have obtained the first-principles electronic structure of solid coronene, which has been recently discovered to exhibit superconductivity with potassium doping. Since coronene, along with picene, the first aromatic superconductor, now provide a class of superconductors as solids of aromatic compounds, here we compare the two cases in examining the electronic structures. In the undoped coronene crystal, where the molecules are arranged in a herringbone structure with two molecules in a unit cell, the conduction band above an insulating gap is found to comprise four bands, which basically originate from the lowest two unoccupied molecular orbitals (doubly-degenerate, reflecting the high symmetry of the molecular shape) in an isolated molecule but the bands are entangled as in solid picene. The Fermi surface for a candidate of the structure of K$_x$coronene with $x=3$, for which superconductivity is found, comprises multiple sheets, as in doped picene but exhibiting a larger anisotropy with different topology.Comment: 5 pages, to be published in Phys. Rev.

High throughput methodology for synthesis, screening, and optimization of solid state Lithium ion electrolytes

A study of the lithium ion conductor Li3xLa2/3–xTiO3 solid solution and the surrounding composition space was carried out using a high throughput physical vapor deposition system. An optimum total ionic conductivity value of 5.45 × 10–4 S cm–1 was obtained for the composition Li0.17La0.29Ti0.54 (Li3xLa2/3–xTiO3x = 0.11). This optimum value was calculated using an artificial neural network model based on the empirical data. Due to the large scale of the data set produced and the complexity of synthesis, informatics tools were required to analyze the data. Partition analysis was carried out to determine the synthetic parameters of importance and their threshold values. Multivariate curve resolution and principal component analysis were applied to the diffraction data set. This analysis enabled the construction of phase distribution diagrams, illustrating both the phases obtained and the compositional zones in which they occur. The synthetic technique presented has significant advantages over other thin film and bulk methodologies, in terms of both the compositional range covered and the nature of the materials produce

Predicting animal behaviour using deep learning: GPS data alone accurately predict diving in seabirds

1.:To prevent further global declines in biodiversity, identifying and understanding key habitats is crucial for successful conservation strategies. For example, globally, seabird populations are under threat and animal movement data can identify key at‐sea areas and provide valuable information on the state of marine ecosystems. To date, in order to locate these areas, studies have used global positioning system (GPS) to record position and are sometimes combined with time–depth recorder (TDR) devices to identify diving activity associated with foraging, a crucial aspect of at‐sea behaviour. However, the use of additional devices such as TDRs can be expensive, logistically difficult and may adversely affect the animal. Alternatively, behaviours may be resolved from measurements derived from the movement data alone. However, this behavioural analysis frequently lacks validation data for locations predicted as foraging (or other behaviours). 2.: Here, we address these issues using a combined GPS and TDR dataset from 108 individuals by training deep learning models to predict diving in European shags, common guillemots and razorbills. We validate our predictions using withheld data, producing quantitative assessment of predictive accuracy. The variables used to train these models are those recorded solely by the GPS device: variation in longitude and latitude, altitude and coverage ratio (proportion of possible fixes acquired within a set window of time). 3.: Different combinations of these variables were used to explore the qualities of different models, with the optimum models for all species predicting non‐diving and diving behaviour correctly over 94% and 80% of the time, respectively. We also demonstrate the superior predictive ability of these supervised deep learning models over other commonly used behavioural prediction methods such as hidden Markov models. 4.: Mapping these predictions provides useful insights into the foraging activity of a range of seabird species, highlighting important at sea locations. These models have the potential to be used to analyse historic GPS datasets and further our understanding of how environmental changes have affected these seabirds over time

Fruit scent and observer colour vision shape food-selection strategies in wild capuchin monkeys

The senses play critical roles in helping animals evaluate foods, including fruits that can change both in colour and scent during ripening to attract frugivores. Although numerous studies have assessed the impact of colour on fruit selection, comparatively little is known about fruit scent and how olfactory and visual data are integrated during foraging. We combine 25 months of behavioural data on 75 wild, white-faced capuchins (Cebus imitator) with measurements of fruit colours and scents from 18 dietary plant species. We show that frequency of fruit-directed olfactory behaviour is positively correlated with increases in the volume of fruit odours produced during ripening. Monkeys with red-green colour blindness sniffed fruits more often, indicating that increased reliance on olfaction is a behavioural strategy that mitigates decreased capacity to detect red-green colour contrast. These results demonstrate a complex interaction among fruit traits, sensory capacities and foraging strategies, which help explain variation in primate behaviour.https://www.nature.com/articles/s41467-019-10250-9Published versio