Results 131 to 140 of about 108,893 (185)

Milling-Assisted Loading of Drugs into Mesoporous Silica Carriers: A Green and Simple Method for Obtaining Tunable Customized Drug Delivery. [PDF]

Pharmaceutics, 2023
Moutamenni B, Tabary N, Mussi A, Dhainaut J, Ciotonea C, Fadel A, Paccou L, Dacquin JP, Guinet Y, Hédoux A.   +9 more
europepmc   +1 more source

Dynamics from picoseconds to nanoseconds of trehalose in aqueous solutions as seen by quasielastic neutron scattering [PDF]

, 2004
Ingo Köper, Marie‐Claire Bellissent‐Funel, W. Petry   +2 more
openalex   +1 more source

Dynamical Behavior of Disordered Regions in Disease-Related Proteins Revealed by Quasielastic Neutron Scattering. [PDF]

Medicina (Kaunas), 2022
Fujiwara S.
europepmc   +1 more source

Tau lepton polarization in quasielastic neutrino-nucleon scattering [PDF]

, 2004
Konstantin S. Kuzmin, V. Lyubushkin, V. A. Naumov   +2 more
openalex   +1 more source

Coulomb corrections for quasielastic scattering: eikonal approximation [PDF]

, 2004
Andreas Aste, Kai Hencken, Jürg Jourdan, I. Sick, D. Trautmann   +4 more
openalex   +1 more source

Final state interaction effects in neutrino-nucleus quasielastic scattering [PDF]

, 2004
C. Maieron, M. C. Martínez, J. A. Caballero, J. M. Udı́as   +3 more
openalex   +1 more source

Coincident onset of charge order and pseudogap in a homogeneous high-temperature superconductor. [PDF]

Nat Commun
Betto D, Nakata S, Pisani F, Liu Y, Hameed S, Knauft M, Lin CT, Sant R, Kummer K, Yakhou F, Brookes NB, Le Tacon M, Keimer B, Minola M.   +13 more
europepmc   +1 more source

Resonant inelastic x-ray scattering data for Ruddlesden-Popper and reduced Ruddlesden-Popper nickelates. [PDF]

Sci Data, 2023
Fabbris G, Meyers D, Shen Y, Bisogni V, Zhang J, Mitchell JF, Norman MR, Johnston S, Feng J, Chiuzbăian GS, Nicolaou A, Jaouen N, Dean MPM.   +12 more
europepmc   +1 more source

Advanced Characterization of Solid-State Battery Materials Using Neutron Scattering Techniques. [PDF]

Materials (Basel)
Novak E, Daemen L, Jalarvo N.
europepmc   +1 more source

Machine-learning-assisted material discovery of oxygen-rich highly porous carbon active materials for aqueous supercapacitors. [PDF]

Nat Commun, 2023
Wang T, Pan R, Martins ML, Cui J, Huang Z, Thapaliya BP, Do-Thanh CL, Zhou M, Fan J, Yang Z, Chi M, Kobayashi T, Wu J, Mamontov E, Dai S.   +14 more
europepmc   +1 more source