Results 211 to 220 of about 251,636 (269)
Vacuum‐assisted capillary infiltration delivers perovskite precursors deep into gold nanohole plasmonic hotspots, enabling uniform in situ crystallization of bulk‐like CsPbBr3 within confined cavities. Verified by peel‐off and structural analyses, the hybrid platform shows simulated |E/E₀| ≈2.80 at 800 nm and ∼40‐fold two‐photon absorption‐enhanced ...
Jung‐Jae Do +8 more
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Resolving Heterogeneity of Targeted Lipid Nanoparticles Through Solution‐Based Biophysical Analyses
AF4‐UV‐DLS‐MALS‐SAXS resolves previously inaccessible targeted lipid nanoparticle (tLNP) subpopulations that differ in size, shape, and composition. Correlation of subpopulation‐resolved biophysical properties with in vivo RNA delivery reveals that targeted placental transfection is associated with distinct tLNP subpopulations rather than ensemble ...
Hannah C. Geisler +14 more
wiley +1 more source
Electrically Tunable Heliconical Smectic Superstructure in Polar Fluids
Strong dipole–dipole interactions give rise to an emergent polar smectic phase with spontaneous chiral symmetry breaking. This resulting polar heliconical smectic superstructure enables novel functionalities in polar fluids, such as color modulation driven by electric field strength and frequency, as well as second‐harmonic generation amplification ...
Hiroya Nishikawa +5 more
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Giant Anisotropy and High Second‐Order Nonlinearity of 3R‐MoS2 for Multifunctional Photonics
Graphical abstract highlights, 3R‐polytype of MoS2 (3R‐MoS2) as a multifunctional photonic platform enabled by giant optical anisotropy and optical nonlinearities, demonstrating subdiffractional waveguiding, giant second‐harmonic (SH) generation, and high‐performance reflective polarization control.
Georgy Ermolaev +17 more
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Optoelectronic Nanofluidic Neural Networks for Ionic Computing
An ion‐based optoelectronic nanofluidic memristor enables neuromorphic computing in aqueous environments. With tunable ionic memory and multimodal synaptic plasticity, it realizes densely connected ionic neural networks capable of image classification, motion prediction, logic computation, and real‐time in‐sensor computing, advancing fully connected ...
Yaxin Huang +10 more
wiley +1 more source
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Identification of nonlinear systems with nonlinear parameterization
2015 European Control Conference (ECC), 2015A contraction based identification scheme for systems with nonlinear parameterizations is developed in this paper. Given a system with a general parameterization, an identification model is proposed such that time evolution of estimation error is composed by two blocks of parameterization: one linear and one nonlinear function of linear ...
A. Flores-Perez +2 more
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Identification of nonlinear systems with hard nonlinearity
2018 5th International Conference on Control, Decision and Information Technologies (CoDIT), 2018The problem of identifying nonlinear systems is proposed in the presence of hard nonlinearity. Presently, the nonlinear system is structured by Wiener-Hammerstein systems consist of a series connection including a nonlinear element sandwiched with two linear subsystems.
Adil Brouri +2 more
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On the Identification of Nonlinear Systems
IFAC Proceedings Volumes, 1982Abstract For the identification of systems in which the nonlinear element is in the feedback path, a new technique based on the Volterra characterisation of nonlinear system, is presented. The method is shown to have distinct computational advantages. Simulation studies using the proposed method are given.
N.C. Jagan, D.C. Reddy
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Filtering by nonlinear systems
Chaos: An Interdisciplinary Journal of Nonlinear Science, 2008Synchronization of nonlinear systems forced by external signals is formalized as the response of a nonlinear filter. Sufficient conditions for a nonlinear system to behave as a filter are given. Some examples of generalized chaos synchronization are shown to actually be special cases of nonlinear filtering.
Campos Cantón, E. +2 more
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A nonlinear philosophy for nonlinear systems
Proceedings of the 39th IEEE Conference on Decision and Control (Cat. No.00CH37187), 2002A framework for system analysis and design is described based on nonlinear system models and nonperiodic signals generated by nonlinear systems. The proposed approach to analysis of nonlinear systems is based on an excitability index-a nonlinear counterpart of the magnitude frequency response of linear systems.
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