Results 111 to 120 of about 2,288,990 (250)

Inverse Design of Nanoparticulate Materials

Advanced Materials, EarlyView.
Inverse design shifts nanomaterial development from empirical trial‐and‐error to predictive model‐driven strategies. It can rely on knowledge‐based, data‐based, or hybrid process and property functions. This perspective article provides a practical framework for applying inverse design based on instructive examples. It discusses which modeling approach
Nabi Etienne Traoré, Annika Mauch, Michelle Berthold, Lukas Pflug, Wolfgang Peukert, Nicolas Vogel   +5 more
wiley   +1 more source

Tamper‐Responsive Physical Unclonable Functions via Graphene‐Interlayered Block Copolymer Random Structures

Advanced Materials, EarlyView.
Self‐invalidating multilayer physical unclonable functions (PUFs) integrate fingerprint‐like metal nanopattern bilayers from block copolymer templates with highly reactive reduced graphene oxide (rGO) interlayers. This tamper‐responsive architecture blocks all unauthorized physical and chemical replication attempts while autonomously deactivating ...
Gyu Hui Jo, Dae Hui Park, Jeong‐Ho Jang, Woo Seok Tack, Jin Man Kim, Hak Ki Yu, Hyeong Min Jin, Jang Hwan Kim   +7 more
wiley   +1 more source

Boundedness in a three-dimensional attraction-repulsion chemotaxis system with nonlinear diffusion and logistic source

Electronic Journal of Differential Equations, 2016
This article concerns the attraction-repulsion chemotaxis system with nonlinear diffusion and logistic source, $$\displaylines{ u_t=\nabla\cdot((u+1)^{m-1}\nabla u)-\nabla\cdot(\chi u\nabla v) +\nabla\cdot(\xi u\nabla w)+ru-\mu u^\eta, \cr x\in ...
Yilong Wang
doaj  

Memory-Induced Synchronization in a Time-Fractional Partly Diffusive Coupled Hindmarsh–Rose Network with Nonlinear Diffusion

Fractal and Fractional
We present and analyze a time-fractional, partly diffusive model of two electrically coupled Hindmarsh–Rose neurons in which only the membrane potentials undergo spatial transport, through a nonlinear Neumann m-Laplacian, while the temporal evolution is ...
Kavitha Velusamy, Sowmiya Ramasamy, Mallika Arjunan Mani, Seenith Sivasundaram   +3 more
doaj   +1 more source

Travelling wave solutions in a negative nonlinear diffusion-reaction model. [PDF]

J Math Biol, 2020
Li Y, Heijster PV, Marangell R, Simpson MJ.   +3 more
europepmc   +1 more source

Giant Anisotropic Spin Absorption in Chiral Hybrid Semiconductors

Advanced Materials, EarlyView.
Researchers uncover that chiral hybrid organic–inorganic semiconductors absorb spin differently depending on its polarization direction. This anisotropic spin absorption is primarily driven by the local symmetry breaking within the layered structure.
Cong Yang, Rui Sun, Mingwei Ge, Shehzad Khan, Subhrangsu Mukherjee, Matthew P. Hautzinger, Tianyou Lu, Harald Ade, Pius M. Theiler, Matthew C. Beard, Dali Sun, Mengxia Liu, Jun Liu   +12 more
wiley   +1 more source

Investigating effects from restricted diffusion in multi-component diffusion data [PDF]

, 2014
We have investigated model systems in which effects from non-Gaussian restricted diffusion could be separated from effects caused by multiple diffusion coefficients. We applied various models to analyze the experimental data.
Pavlin, Tina, Seland, John Georg
core   +1 more source

Networks for Nonlinear Diffusion Problems in Imaging. [PDF]

J Math Imaging Vis, 2020
Arridge S, Hauptmann A.
europepmc   +1 more source

Biomass DNA: An Emerging Building Block for Advanced Materials

Advanced Materials, EarlyView.
Biomass DNA is rapidly emerging from its traditional role as a genetic blueprint to serve as a versatile abundant, highly programmable polymeric building block. This review highlights the transformation of macroscopic bulk biomass DNA into next generation of functional materials, spanning from dynamic hydrogels, sustainable bioplastics, and high ...
Yuwei Hu, Itamar Willner, Li Niu
wiley   +1 more source

Modeling of nonlinear diffusion

, 2019
In this thesis, we study the nonlinear diffusion equation especially Porous Medium Equation (PME). u_t= \Delta(u^m) + f(u), Parameter m>1 in the case of slow diffusion, m=1 means linear model and $
Oyekan, Oluwadamilola Adeniyi
core