Search results for "parameters"

showing 10 items of 13273 documents

CCDC 1551402: Experimental Crystal Structure Determination

2017

Related Article: Rakesh Puttreddy, Ngong Kodiah Beyeh, Robin H. A. Ras, John F. Trant, Kari Rissanen|2017|CrystEngComm|19|4312|doi:10.1039/C7CE00975E

C-ethyl-2-bromoresorcinarene bis(pyridine N-oxide) acetone solvateSpace GroupCrystallographyCrystal SystemCrystal StructureCell ParametersExperimental 3D Coordinates
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MILES extended: Stellar population synthesis models from the optical to the infrared

2016

We present the first single-burst stellar population models which covers the optical and the infrared wavelength range between 3500 and 50000 Angstrom and which are exclusively based on empirical stellar spectra. To obtain these joint models, we combined the extended MILES models in the optical with our new infrared models that are based on the IRTF (Infrared Telescope Facility) library. The latter are available only for a limited range in terms of both age and metallicity. Our combined single-burst stellar population models were calculated for ages larger than 1 Gyr, for metallicities between [Fe/H] = -0.40 and 0.26, for initial mass functions of various types and slopes, and on the basis …

CAII TRIPLETStellar populationInfraredMetallicityINITIAL MASS FUNCTIONBROWN DWARFSInfrared telescopeFOS: Physical sciencesAstrophysicsAstrophysics::Cosmology and Extragalactic Astrophysics01 natural sciencesAstronomical spectroscopyinfrared: galaxiesATMOSPHERIC PARAMETERS0103 physical sciencesRange (statistics)Astrophysics::Solar and Stellar Astrophysics2.5 MU-MGIANT BRANCH STARS010303 astronomy & astrophysicsinfrared: starsEMPIRICAL CALIBRATIONAstrophysics::Galaxy AstrophysicsPhysics010308 nuclear & particles physicsNear-infrared spectroscopyHIGH-SPECTRAL-RESOLUTIONAstronomy and AstrophysicsEVOLUTIONARY SYNTHESISAstrophysics - Astrophysics of GalaxiesGalaxySpace and Planetary ScienceAstrophysics of Galaxies (astro-ph.GA)NEWTON-TELESCOPE LIBRARYgalaxies: stellar contentAstrophysics::Earth and Planetary Astrophysics
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Computer simulation studies of finite-size broadening of solid–liquid interfaces: from hard spheres to nickel

2009

Using Molecular Dynamics (MD) and Monte Carlo (MC) simulations interfacial properties of crystal-fluid interfaces are investigated for the hard sphere system and the one-component metallic system Ni (the latter modeled by a potential of the embedded atom type). Different local order parameters are considered to obtain order parameter profiles for systems where the crystal phase is in coexistence with the fluid phase, separated by interfaces with (100) orientation of the crystal. From these profiles, the mean-squared interfacial width w^2 is extracted as a function of system size. We rationalize the prediction of capillary wave theory that w^2 diverges logarithmically with the lateral size o…

Capillary waveMaterials scienceMonte Carlo methodFOS: Physical scienceschemistry.chemical_elementlocal order parametersPhysics::Fluid DynamicsCrystalMolecular dynamicsPhase (matter)Mesoscale and Nanoscale Physics (cond-mat.mes-hall)AtomGeneral Materials Sciencemelting transitionMonte Carlo simulationCondensed Matter - Materials ScienceCondensed Matter - Mesoscale and Nanoscale PhysicsCondensed matter physicscrystal growthMaterials Science (cond-mat.mtrl-sci)Hard spheresCondensed Matter Physicscapillary wave theoryNickelmolecular dynamics simulationchemistryinterfacial stiffnessJournal of Physics: Condensed Matter
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Odorant binding changes the electrical properties of olfactory receptors at the nanoscale

2021

Olfactory receptors (ORs) comprise the largest multigene family in the vertebrates. They belong to the class A (rhodopsin-like) family of G protein-coupled receptors (GPCRs), which are the most abundant membrane proteins, having widespread, significant roles in signal transduction in cells, and therefore, they are a major pharmacological target. Moreover, ORs displayed high selectivity and sensitivity towards odorant detection, a characteristic that raised the interest for developing biohybrid sensors based on ORs for the detection of volatile compounds. The transduction of odorant binding into cellular signaling by ORs is not well understood and knowing its mechanism would enable developin…

Cell signalingOlfactory receptorOdorant bindingChemistryolfactory receptorodorant bindingImpedance parameterslaw.invention[SDV.AEN] Life Sciences [q-bio]/Food and Nutritionmedicine.anatomical_structureopen-circuit voltagelawelectrochemical scanning tunneling microscopy (EC-STM)impedance[SDV.BBM] Life Sciences [q-bio]/Biochemistry Molecular BiologymedicineBiophysicsScanning tunneling microscope[SDV.BBM.BC]Life Sciences [q-bio]/Biochemistry Molecular Biology/Biochemistry [q-bio.BM]ReceptorTransduction (physiology)[SDV.AEN]Life Sciences [q-bio]/Food and NutritionElectrochemical potential
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Application of Genetic Algorithm on Parameter Optimization of Three Vehicle Crash Scenarios

2017

Abstract This paper focuses on the development of mathematical models for vehicle frontal crashes. The models under consideration are threefold: a vehicle into barrier, vehicle-occupant and vehicle to vehicle frontal crashes. The first model is represented as a simple spring-mass-damper and the second case consists of a double-spring-mass-damper system, whereby the front mass and the rear mass represent the vehicle chassis and the occupant, respectively. The third model consists of a collision of two vehicles represented by two masses moving in opposite directions. The springs and dampers in the models are nonlinear piecewise functions of displacements and velocities respectively. More spec…

ChassisComputer scienceModeling010103 numerical & computational mathematics02 engineering and technologyCollision01 natural sciencesCrash testfrontal crashvehicle-occupantDamperNonlinear system020303 mechanical engineering & transports0203 mechanical engineeringControl theoryControl and Systems EngineeringGenetic algorithmparameters estimationgenetic algorithm0101 mathematicsSimulationfrontal crash; genetic algorithm; Modeling; parameters estimation; vehicle-occupant; Control and Systems EngineeringMotor vehicle crash
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Primary- and secondary-octahedral distortion factors in bis(1,4-H2-1,2,4-triazolium) pentabromidoantimonate(III) –1,4-H2-1,2,4-triazolium bromide

2015

Abstract The analysis of octahedral distortion in the structure of inorganic–organic (C2H4N3)2[SbBr5]·(C2H4N3)Br (BTPTB) bromidoantimonate(III) determined at 295 and 85 K, supported by the Hirshfeld surface analysis and the data retrieved from the Cambridge Structural Database, is presented. The anionic substructure of BTPTB is built from distorted [SbBr6]3− octahedra that are connected by the cis corners forming polymeric one-dimensional [{SbBr5}n]2n− zig-zag chains running parallel to the a axis and isolated Br− ions. The organic substructure consists of the fully ordered 1,4-H2-1,2,4-triazolium cations. The oppositely charged substructures are linked by the system of N(C)–H⋯Br hydrogen b…

ChemistryHydrogen bondIonHybrid materials; Octahedral distortion; Hirshfeld surface analysis; Distortion parameters; Hydrogen bondingInorganic Chemistrychemistry.chemical_compoundCrystallographyOctahedronBromideDistortionOctahedral molecular geometryMaterials ChemistrySubstructurePhysical and Theoretical ChemistryHybrid materialPolyhedron
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Molecular Features Behind Formation of α or β Co-Crystalline and Nanoporous-Crystalline Phases of PPO

2022

Guest molecular features determining the formation of α and β phases of poly(2-6-dimethyl-1,4-phenylene) oxide (PPO) are explored by collecting literature data and adding many new film preparations, both by solution casting and by guest sorption in amorphous films. Independently of the considered preparation method, the α-form is favored by the hydrophobic and bulky guest molecules, while the hydrophilic and small guest molecules favor the β-form. Furthermore, molecular modeling studies indicate that the β-form inducer guests establish stronger dispersive interactions with the PPO units than the α-form inducer guests. Thus, the achievement of co-crystalline (and derived nanoporous crystalli…

Chemistrydispersive energy calculationsdispersive energy calculationDFT calculationguest molecular volumeGeneral ChemistryDFT calculationsQD1-999solubility parametersguest solubility in waterFrontiers in Chemistry
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CCDC 796466: Experimental Crystal Structure Determination

2014

Related Article: L. Koskinen, S. Jaaskelainen, E. Kalenius, P. Hirva and M. Haukka|2014|Cryst.Growth Des.|14|1989|doi:10.1021/cg500102c

Chloro-(3-methyl-13-benzothiazole-2(3H)-thione)-gold(i)Space GroupCrystallographyCrystal SystemCrystal StructureCell ParametersExperimental 3D Coordinates
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CCDC 724636: Experimental Crystal Structure Determination

2010

Related Article: Fei Cheng, J.M.Dyke, F.Ferrante, A.L.Hector, W.Levason, G.Reid, M.Webster, Wenjian Zhang|2010|Dalton Trans.|39|847|doi:10.1039/b911016j

Chloro-(NNN'N''N''-pentamethyldiethylenetriamine-NN'N'')-germanium(ii) trichlorogermanate(ii)Space GroupCrystallographyCrystal SystemCrystal StructureCell ParametersExperimental 3D Coordinates
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CCDC 249547: Experimental Crystal Structure Determination

2005

Related Article: M.Viciano, E.Mas-Marza, M.Poyatos, M.Sanau, R.H.Crabtree, E.Peris|2005|Angew.Chem.,Int.Ed.|44|444|doi:10.1002/anie.200461918

Chloro-(eta^4^-cyclo-15-octadiene)-hydrido-(bis(N-methyl-13-dihydridoimidazol-3-yl-2-ylidene)(ferrocenyl)methane)-iridium hexafluorophosphate monohydrateSpace GroupCrystallographyCrystal SystemCrystal StructureCell ParametersExperimental 3D Coordinates
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