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Haoshi Gao

Bio: Haoshi Gao is an academic researcher from University of Macau. The author has contributed to research in topics: Single crystal & Pharmaceutical formulation. The author has an hindex of 4, co-authored 8 publications receiving 48 citations.

Papers
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Journal ArticleDOI
TL;DR: In this paper, temperature-dependent inverse magnetic susceptibility coincides with a piecewise linear function with five regimes, with which they fit a Curie-Weiss law and calculate the frustration factor $f, which indicates a formation of magnetic polarons between 300 and 540 K and a very strong magnetic frustration.
Abstract: Magnetization measurements and time-of-flight neutron powder-diffraction studies on the high-temperature (300--980 K) magnetism and crystal structure (321--1200 K) of a pulverized ${\mathrm{YCrO}}_{3}$ single crystal have been performed. Temperature-dependent inverse magnetic susceptibility coincides with a piecewise linear function with five regimes, with which we fit a Curie-Weiss law and calculate the frustration factor $f$. The fit results indicate a formation of magnetic polarons between 300 and 540 K and a very strong magnetic frustration. By including one factor $\ensuremath{\eta}$ that represents the degree of spin interactions into the Brillouin function, we can fit well the applied-magnetic-field dependence of magnetization. No structural phase transition was observed from 321 to 1200 K. The average thermal expansions of lattice configurations ($a$, $b$, $c$, and V) obey well the $\mathrm{Gr}\stackrel{\ifmmode \ddot{}\else \"{}\fi{}}{\mathtt{u}}\mathrm{neisen}$ approximations with an anomaly appearing around 900 K, implying an isosymmetric structural phase transition, and display an anisotropic character along the crystallographic $a$, $b$, and $c$ axes with the incompressibility ${K}_{0}^{a}g{K}_{0}^{c}g{K}_{0}^{b}$. It is interesting to find that at 321 K, the local distortion size $\mathrm{\ensuremath{\Delta}}$(O2) $\ensuremath{\approx}1.96\mathrm{\ensuremath{\Delta}}$(O1) $\ensuremath{\approx}4.32\mathrm{\ensuremath{\Delta}}$(Y) $\ensuremath{\approx}293.89\mathrm{\ensuremath{\Delta}}$(Cr). Based on the refined Y-O and Cr-O bond lengths, we deduce the local distortion environments and modes of Y, Cr, O1, and O2 ions. Especially, the Y and O2 ions display obvious atomic displacement and charge subduction, which may shed light on the dielectric property of the ${\mathrm{YCrO}}_{3}$ compound. Additionally, by comparing Kramers ${\mathrm{Mn}}^{3+}$ with non-Kramers ${\mathrm{Cr}}^{3+}$ ions, it is noted that being a Kramers or non-Kramers ion can strongly affect the local distortion size, whereas, it may not be able to change the detailed distortion mode.

24 citations

Journal ArticleDOI
TL;DR: This research combined machine learning, central composite design, molecular modeling and experimental approaches for rational SEDDS formulation design, which revealed the diffusion behavior in water and the role of cosurfactants.

22 citations

Journal ArticleDOI
TL;DR: To validate the prediction modeling, berberine (BBR) was used as the model drug to form the complex with phospholipid, and molecular dynamics simulation was used to investigate the molecular mechanism for self-aggregation of BBR in solution and BBR-phospholIPid complex complexation.

21 citations

Journal ArticleDOI
Qianqian Zhao1, Haoshi Gao1, Yan Su1, Tianhe Huang1, Jiahong Lu1, Hua Yu1, Defang Ouyang1 
TL;DR: In this article, the molecular interactions between poorly water-soluble drug Ketoprofen (KTP) and six commonly used CDs were investigated by combined experimental and modeling methods, and it was shown that the combination of experimental and modelling methods could clearly reveal the molecular mechanism of drug-CD complexes.

14 citations

Journal ArticleDOI
29 Jun 2020
TL;DR: In this article, single-crystal growths of the SrTb2O4 compound by a supernecking technique with a laser-floating-zone furnace were reported.
Abstract: We report on single-crystal growths of the SrTb2O4 compound by a super-necking technique with a laser-floating-zone furnace and study the stoichiometry, growth mode, and structural and magnetic pro...

14 citations


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03 Dec 2003
TL;DR: In this paper, the density functional theory of the ground state magnetic properties of rare earths and actinides is presented, as well as the properties of binary rare-earth 3d-transition-metal intermetallic compounds.
Abstract: Preface. Contents of volumes 1-6. 1. Magnetism in ultrathin transition metal films (U. Gradmann). 2. Energy band theory of metallic magnetism in the elements (V.L. Moruzzi, P.M. Marcus). 3. Density functional theory of the ground state magnetic properties of rare earths and actinides (M.S.S. Brooks, B. Johansson). 4. Diluted magnetic semiconductors (J. Kossut, W. Dobrowolski). 5. Magnetic properties of binary rare-earth 3d-transition-metal intermetallic compounds (J.J.M. Franse, R.J. Radwanski). 6. Neutron scattering on heavy fermion and valence fluctuation 4f-systems (M. Loewenhaupt, K.H. Fischer). Author index. Subject index. Materials index.

488 citations

Journal Article
TL;DR: Unlike in spiral magnetoelectrics where antisymmetric exchange coupling is active, the symmetry breaking in Ca3(Co,Mn)2O6 occurs through exchange striction associated with symmetric superexchange.
Abstract: We report discovery of collinear-magnetism-driven ferroelectricity in the Ising chain magnet Ca3Co2-xMn(x)O6 (x approximately 0.96). Neutron diffraction shows that Co2+ and Mn4+ ions alternating along the chains exhibit an up-up-down-down ( upward arrow upward arrow downward arrow downward arrow) magnetic order. The ferroelectricity results from the inversion symmetry breaking in the upward arrow upward arrow downward arrow downward arrow spin chain with an alternating charge order. Unlike in spiral magnetoelectrics where antisymmetric exchange coupling is active, the symmetry breaking in Ca3(Co,Mn)2O6 occurs through exchange striction associated with symmetric superexchange.

271 citations

Journal Article
TL;DR: In this article, the presence of dynamic frustration may explain the spin-glass behavior in a well-ordered system without static frustration, and the authors suggest that dynamic frustration can lead to magnetic spins that point in random directions.
Abstract: Disorder and geometric frustration usually lead to magnetic spins that point in random directions, as in a spin glass. So how can spin-glass behaviour emerge in a well-ordered system without static frustration? The presence of ‘dynamic frustration’ may explain the situation.

47 citations

Journal ArticleDOI
TL;DR: In this paper, the authors introduce the basic concepts of ML-directed workflows and discuss how these tools can be used to aid in the development of various types of drug formulations.

47 citations

Journal ArticleDOI
TL;DR: The machine learning methods commonly used in pharmaceutical sciences are discussed, with a specific emphasis on artificial neural networks due to their capability to model the nonlinear relationships that are commonly encountered in pharmaceutical research.
Abstract: Artificial intelligence (AI) and machine learning, in particular, have gained significant interest in many fields, including pharmaceutical sciences. The enormous growth of data from several sources, the recent advances in various analytical tools, and the continuous developments in machine learning algorithms have resulted in a rapid increase in new machine learning applications in different areas of pharmaceutical sciences. This review summarizes the past, present, and potential future impacts of machine learning technologies on different areas of pharmaceutical sciences, including drug design and discovery, preformulation, and formulation. The machine learning methods commonly used in pharmaceutical sciences are discussed, with a specific emphasis on artificial neural networks due to their capability to model the nonlinear relationships that are commonly encountered in pharmaceutical research. AI and machine learning technologies in common day-to-day pharma needs as well as industrial and regulatory insights are reviewed. Beyond traditional potentials of implementing digital technologies using machine learning in the development of more efficient, fast, and economical solutions in pharmaceutical sciences are also discussed.

46 citations