Robust Half-Metallic Character and Large Oxygen Magnetism in a Perovskite Cuprate

The new perovskite cuprate material Sr$_{8}$CaRe$_{3}$Cu$_{4}$O$_{24}$, which behaves ferrimagnetically and shows an unusually high Curie temperature ($T_c \sim$ 440 K), is found from density-functional theory calculation to display several surprising properties after hole doping or chemical substitution: (1) Half metal (HM) is realized by replacing Re with W or Mo while $T_c$ remains high; (2) hole-doped Sr$_{8}$CaRe$_{3}$Cu$_{4}$O$_{24}$ is also HM with high $T_c$. Moreover, we find that the O atoms will carry a large magnetic moment after hole doping, which is in sharp contrast with the generally accepted concept that magnetism in solid requires partially filled shells of $d$ or $f$ electrons in cations. The material Sr$_8$CaRe$_3$Cu$_4$O$_{24}$ is therefore expected to provide a very useful platform for material design and development.Comment: 5 pages and 4 figure

BaFe2Se2O as an Iron-Based Mott Insulator with Antiferromagnetic Order

A new compound with a quasi-two-dimensional array of FeSe3O tetrahedra and an orthorombic structure, namely BaFe2Se2O, has been successfully fabricated. Experimental results show that this compound is an insulator and has an antiferromagnetic (AF) transition at 240 K. Band structure calculation reveals the narrowing of Fe 3d bands near the Fermi energy, which leads to the localization of magnetism and the Mott insulating behavior. The large distances between the Fe atoms perhaps are responsible for the characters. Linear response calculation further indicates a strong in-plane AF exchange $J$, this can account for the enhanced magnetic susceptibility (which has a maximum at about 450 K) above the Neel temperature.Comment: submitted to PRL on 2 May 2012, resubmitted to PRB on 31 May 2012, and accepted by PRB on 5 July 201

Distinct behaviors of suppression to superconductivity in LaRu3Si2LaRu_3Si_2 induced by Fe and Co dopants

In the superconductor LaRu$_3$Si$_2$ with the Kagome lattice of Ru, we have successfully doped the Ru with Fe and Co atoms. Contrasting behaviors of suppression to superconductivity is discovered between the Fe and the Co dopants: Fe-impurities can suppress the superconductivity completely at a doping level of only 3%, while the superconductivity is suppressed slowly with the Co dopants. A systematic magnetization measurements indicate that the doped Fe impurities lead to spin-polarized electrons yielding magnetic moments with the magnitude of 1.6 $\mu_B$\ per Fe, while the electrons given by the Co dopants have the same density of states for spin-up and spin-down leading to much weaker magnetic moments. It is the strong local magnetic moments given by the Fe-dopants that suppress the superconductivity. The band structure calculation further supports this conclusion.Comment: 6 pages, 7 figure

Orbital order and ferrimagnetic properties of the new compound Sr8CaRe3Cu4O24Sr_8 Ca Re_3 Cu_4 O_{24}

By means of the LSDA+U method and the Green function method, we investigate the electronic and magnetic properties of the new material of Sr$_8$CaRe$_3$Cu$_4$O$_{24}$. Our LSDA+U calculation shows that this system is an insulator with a net magnetic moment of 1.01 $\mu_{\rm B}$/f.u., which is in good agreement with the experiment. Magnetic moments are mainly located at Cu atoms, and the magnetic moments of neighboring Cu sites align anti-parallel. It is the non-magnetic Re atoms that induce an orbital order of $d$ electrons of Cu atoms, which is responsible for the strong exchange interaction and the high magnetic transition temperature. Based on the LSDA+U results, we introduce an effective model for the spin degrees of freedom, and investigate the finite-temperature properties by the Green function method. The obtained results are consistent with the experimental results, indicating that the spin-alternating Heisenberg model is suitable for this compound.Comment: 8 pages and 5 figur

Development potential of nanoenabled agriculture projected using machine learning

The controllability and targeting of nanoparticles (NPs) offer solutions for precise and sustainable agriculture. However, the development potential of nanoenabled agriculture remains unknown. Here, we build an NP-plant database containing 1,174 datasets and predict (R2 higher than 0.8 for 13 random forest models) the response and uptake/transport of various NPs by plants using a machine learning approach. Multiway feature importance analysis quantitatively shows that plant responses are driven by the total NP exposure dose and duration and plant age at exposure, as well as the NP size and zeta potential. Feature interaction and covariance analysis further improve the interpretability of the model and reveal hidden interaction factors (e.g., NP size and zeta potential). Integration of the model, laboratory, and field data suggests that Fe2O3 NP application may inhibit bean growth in Europe due to low night temperatures. In contrast, the risks of oxidative stress are low in Africa because of high night temperatures. According to the prediction, Africa is a suitable area for nanoenabled agriculture. The regional differences and temperature changes make nanoenabled agriculture complicated. In the future, the temperature increase may reduce the oxidative stress in African bean and European maize induced by NPs. This study projects the development potential of nanoenabled agriculture using machine learning, although many more field studies are needed to address the differences at the country and continental scales