Electronic structure of superconducting graphite intercalate compounds: The role of the interlayer state

Although not an intrinsic superconductor, it has been long--known that, when intercalated with certain dopants, graphite is capable of exhibiting superconductivity. Of the family of graphite--based materials which are known to superconduct, perhaps the most well--studied are the alkali metal--graphite intercalation compounds (GIC) and, of these, the most easily fabricated is the C${}_8$K system which exhibits a transition temperature $\bm{T_c\simeq 0.14}$K. By increasing the alkali metal concentration (through high pressure fabrication techniques), the transition temperature has been shown to increase to as much as $\bm 5$K in C${}_2$Na. Lately, in an important recent development, Weller \emph{et al.} have shown that, at ambient conditions, the intercalated compounds \cyb and \cca exhibit superconductivity with transition temperatures $\bm{T_c\simeq 6.5}$K and $\bm{11.5}$K respectively, in excess of that presently reported for other graphite--based compounds. We explore the architecture of the states near the Fermi level and identify characteristics of the electronic band structure generic to GICs. As expected, we find that charge transfer from the intercalant atoms to the graphene sheets results in the occupation of the $\bm\pi$--bands. Yet, remarkably, in all those -- and only those -- compounds that superconduct, we find that an interlayer state, which is well separated from the carbon sheets, also becomes occupied. We show that the energy of the interlayer band is controlled by a combination of its occupancy and the separation between the carbon layers.Comment: 4 Figures. Please see accompanying experimental manuscript "Superconductivity in the Intercalated Graphite Compounds C6Yb and C6Ca" by Weller et a

Imputation of Assay Bioactivity Data Using Deep Learning.

We describe a novel deep learning neural network method and its application to impute assay pIC50 values. Unlike conventional machine learning approaches, this method is trained on sparse bioactivity data as input, typical of that found in public and commercial databases, enabling it to learn directly from correlations between activities measured in different assays. In two case studies on public domain data sets we show that the neural network method outperforms traditional quantitative structure-activity relationship (QSAR) models and other leading approaches. Furthermore, by focusing on only the most confident predictions the accuracy is increased to R2 > 0.9 using our method, as compared to R2 = 0.44 when reporting all predictions

Defects and lithium migration in Li₂CuO₂

Li2CuO2 is an important candidate material as a cathode in lithium ion batteries. Atomistic simulation methods are used to investigate the defect processes, electronic structure and lithium migration mechanisms in Li2CuO2. Here we show that the lithium energy of migration via the vacancy mechanism is very low, at 0.11 eV. The high lithium Frenkel energy (1.88 eV/defect) prompted the consideration of defect engineering strategies in order to increase the concentration of lithium vacancies that act as vehicles for the vacancy mediated lithium self-diffusion in Li2CuO2. It is shown that aluminium doping will significantly reduce the energy required to form a lithium vacancy from 1.88 eV to 0.97 eV for every aluminium introduced, however, it will also increase the migration energy barrier of lithium in the vicinity of the aluminium dopant to 0.22 eV. Still, the introduction of aluminium is favourable compared to the lithium Frenkel process. Other trivalent dopants considered herein require significantly higher solution energies, whereas their impact on the migration energy barrier was more pronounced. When considering the electronic structure of defective Li2CuO2, the presence of aluminium dopants results in the introduction of electronic states into the energy band gap. Therefore, doping with aluminium is an effective doping strategy to increase the concentration of lithium vacancies, with a minimal impact on the kinetics

Li2SnO3 as a Cathode Material for Lithium-ion Batteries:Defects, Lithium Ion Diffusion and Dopants

Tin-based oxide Li2SnO3 has attracted considerable interest as a promising cathode material for potential use in rechargeable lithium batteries due to its high- capacity. Static atomistic scale simulations are employed to provide insights into the defect chemistry, doping behaviour and lithium diffusion paths in Li2SnO3. The most favourable intrinsic defect type is Li Frenkel (0.75 eV/defect). The formation of anti-site defect, in which Li and Sn ions exchange their positions is 0.78 eV/defect, very close to the Li Frenkel. The present calculations confirm the cation intermixing found experimentally in Li2SnO3. Long range lithium diffusion paths via vacancy mechanisms were examined and it is confirmed that the lowest activation energy migration path is along the c-axis plane with the overall activation energy of 0.61 eV. Subvalent doping by Al on the Sn site is energetically favourable and is proposed to be an efficient way to increase the Li content in Li2SnO3. The electronic structure calculations show that the introduction of Al will not introduce levels in the band gap

Efficacious calculation of Raman spectra in high pressure hydrogen

We present and evaluate an efficient method for simulating Raman spectra from molecular dynamics (MD) calculations {\it without} defining normal modes. We apply the method to high pressure hydrogen in the high-temperature "Phase IV": a plastic crystal in which the conventional picture of fixed phonon eigenmodes breaks down. Projecting trajectories onto in-phase molecular stretches is shown to be many orders of magnitude faster than polarisability calculations, allowing statistical averaging at high-temperature. The simulations are extended into metastable regimes and identify several regimes associated with symmetry-breaking on different timescales, which are shown to exhibit features in the Raman spectra at the current experimental limit of resolvability. In this paper we have concentrated on the methodology, a fuller description of the structure of Phase IV hydrogen is given in a previous paperComment: EHPRG conference 2013, High Pressure Research: Volume 34, Issue 2, 201

Digital reconstruction of the inner ear of Leptictidium auderiense (Leptictida, Mammalia) and North American leptictids reveals new insight into leptictidan locomotor agility

Leptictida are basal Paleocene to Oligocene eutherians from Europe and North America comprising species with highly specialized postcranial features including elongated hind limbs. Among them, the European Leptictidium was probably a bipedal runner or jumper. Because the semicircular canals of the inner ear are involved in detecting angular acceleration of the head, their morphometry can be used as a proxy to elucidate the agility in fossil mammals. Here we provide the first insight into inner ear anatomy and morphometry of Leptictida based on high-resolution computed tomography of a new specimen of Leptictidium auderiense from the middle Eocene Messel Pit (Germany) and specimens of the North American Leptictis and Palaeictops. The general morphology of the bony labyrinth reveals several plesiomorphic mammalian features, such as a secondary crus commune. Leptictidium is derived from the leptictidan groundplan in lacking the secondary bony lamina and having proportionally larger semicircular canals than the leptictids under study. Our estimations reveal that Leptictidium was a very agile animal with agility score values (4.6 and 5.5, respectively) comparable to Macroscelidea and extant bipedal saltatory placentals. Leptictis and Palaeictops have lower agility scores (3.4 to 4.1), which correspond to the more generalized types of locomotion (e.g., terrestrial, cursorial) of most extant mammals. In contrast, the angular velocity magnitude predicted from semicircular canal angles supports a conflicting pattern of agility among leptictidans, but the significance of these differences might be challenged when more is known about intraspecific variation and the pattern of semicircular canal angles in non-primate mammals

A roadmap of strain in doped anatase TiO2

Anatase titanium oxide is important for its high chemical stability and photocatalytic properties, however, the latter are plagued by its large band gap that limits its activity to only a small percentage of the solar spectrum. In that respect, straining the material can reduce its band gap increasing the photocatalytic activity of titanium oxide. We apply density functional theory with the introduction of the Hubbard + U model, to investigate the impact of stress on the electronic structure of anatase in conjunction with defect engineering by intrinsic defects (oxygen/titanium vacancies and interstitials), metallic dopants (iron, chromium) and non-metallic dopants (carbon, nitrogen). Here we show that both biaxial and uniaxial strain can reduce the band gap of undoped anatase with the use of biaxial strain being marginally more beneficial reducing the band gap up to 2.96 eV at a tensile stress of 8 GPa. Biaxial tensile stress in parallel with doping results in reduction of the band gap but also in the introduction of states deep inside the band gap mainly for interstitially doped anatase. Dopants in substitutional positions show reduced deep level traps. Chromium-doped anatase at a tensile stress of 8 GPa shows the most significant reduction of the band gap as the band gap reaches 2.4 eV

Some recommendations for developing multidimensional computerized adaptive tests for patient-reported outcomes

PURPOSE: Multidimensional item response theory and computerized adaptive testing (CAT) are increasingly used in mental health, quality of life (QoL), and patient-reported outcome measurement. Although multidimensional assessment techniques hold promises, they are more challenging in their application than unidimensional ones. The authors comment on minimal standards when developing multidimensional CATs. METHODS: Prompted by pioneering papers published in QLR, the authors reflect on existing guidance and discussions from different psychometric communities, including guidelines developed for unidimensional CATs in the PROMIS project. RESULTS: The commentary focuses on two key topics: (1) the design, evaluation, and calibration of multidimensional item banks and (2) how to study the efficiency and precision of a multidimensional item bank. The authors suggest that the development of a carefully designed and calibrated item bank encompasses a construction phase and a psychometric phase. With respect to efficiency and precision, item banks should be large enough to provide adequate precision over the full range of the latent constructs. Therefore CAT performance should be studied as a function of the latent constructs and with reference to relevant benchmarks. Solutions are also suggested for simulation studies using real data, which often result in too optimistic evaluations of an item bank's efficiency and precision. DISCUSSION: Multidimensional CAT applications are promising but complex statistical assessment tools which necessitate detailed theoretical frameworks and methodological scrutiny when testing their appropriateness for practical applications. The authors advise researchers to evaluate item banks with a broad set of methods, describe their choices in detail, and substantiate their approach for validation