Results 241 to 250 of about 219,381 (279)

Dynamic Control of Synaptic Plasticity by Competing Ferroelectric and Trap‐Assisted Switching in IGZO Transistors with Al2O3/HfO2 Dielectrics

Advanced Functional Materials, EarlyView.
A frequency‐tunable ferroelectric synaptic transistor based on a buried‐gate InGaZnO channel and Al2O3/HfO2 dielectric stack exhibits linear and reversible weight updates using single‐polarity pulses. By switching between ferroelectric and trap‐assisted modes depending on input frequency, the device simplifies neuromorphic circuit design and achieves ...
Ojun Kwon, Dong Ho Lee, Seyoung Oh, Jongwon Yoon, Hyo‐Bae Kim, Ji‐Hoon Ahn, Woojin Park, Han Seul Kim, Byungjin Cho   +8 more
wiley   +1 more source

Helix-bundle and C-terminal GPCR domains differentially influence GRK-specific functions and β-arrestin-mediated regulation. [PDF]

Nat Commun
Matthees ESF, Haider RS, Klement L, Reichel M, Blum NK, Weitzel V, Trüpschuch T, Ziegler C, Drube J, Schulz S, Hoffmann C.   +10 more
europepmc   +1 more source

Confinement Driven Spin‐Texture Evolution in Directly Written Nanomagnets

Advanced Functional Materials, EarlyView.
Spin‐texture is directly written by triggering lattice ordering locally at the nanoscale. Geometric confinement drives the formation of spin‐textures such as Néel walls with perpendicular core spins, essential for technological applications. Abstract In low‐dimensional magnetic structures, magnetostatic energy minimization yields a uniform spin‐texture,
Md. Shadab Anwar, Iryna Zelenina, Paweł Sobieszczyk, Gregor Hlawacek, Kristian Tveitstøl, Kay Potzger, Jürgen Fassbender, Olav Hellwig, Jürgen Lindner, Michał Krupiński, Magnus Nord, Rantej Bali   +11 more
wiley   +1 more source

Current approaches for Usher syndrome disease models and developing therapies. [PDF]

Front Cell Dev Biol
Leith FK, Lye J, Delaney DS, McLenachan S, Chen FK, Atlas MD, Wong EYM.   +6 more
europepmc   +1 more source

Functional Materials for Environmental Energy Harvesting in Smart Agriculture via Triboelectric Nanogenerators

Advanced Functional Materials, EarlyView.
This review explores functional and responsive materials for triboelectric nanogenerators (TENGs) in sustainable smart agriculture. It examines how particulate contamination and dirt affect charge transfer and efficiency. Environmental challenges and strategies to enhance durability and responsiveness are outlined, including active functional layers ...
Rafael R. A. Silva, Giulio Fatti, Emanuel Carlos, Guilherme Ferreira, Sumita Goswami, Suman Nandy, Luiz H. C. Mattoso, Elvira Fortunato, Caio G. Otoni, Rodrigo Martins   +9 more
wiley   +1 more source

A mutual co-recognition mechanism ensures the proper assembly of heterotrimeric kinesin-2 for intraflagellar transport. [PDF]

Nat Commun
Ren J, Zhao L, Chen G, Ou G, Feng W.
europepmc   +1 more source

Harnessing Non‐Covalent Protein–Protein Interaction Domains for Production of Biocatalytic Materials Systems

Advanced Functional Materials, EarlyView.
Non‐covalent protein–protein interactions mediated by SH3, PDZ, or GBD domains enable the self‐assembly of stable and biocatalytically active hydrogel materials. These soft materials can be processed into monodisperse foams that, once dried, exhibit enhanced mechanical stability and activity and are easily integrated into microstructured flow ...
Julian S. Hertel, Maria Alessandra Martini, Marius Stoeckle, Annika J. Weber, Kersten S. Rabe, Christof M. Niemeyer   +5 more
wiley   +1 more source

A Multimodal Deep Learning Approach for White Matter Shape Prediction in Diffusion MRI Tractography. [PDF]

Hum Brain Mapp
Lo Y, Chen Y, Liu D, Zekelman L, Rushmore J, Rathi Y, Makris N, Golby AJ, Zhang F, Cai W, O'Donnell LJ.   +10 more
europepmc   +1 more source

Unleashing the Power of Machine Learning in Nanomedicine Formulation Development

Advanced Functional Materials, EarlyView.
A random forest machine learning model is able to make predictions on nanoparticle attributes of different nanomedicines (i.e. lipid nanoparticles, liposomes, or PLGA nanoparticles) based on microfluidic formulation parameters. Machine learning models are based on a database of nanoparticle formulations, and models are able to generate unique solutions
Thomas L. Moore, Cristiano Pesce, Antonietta Greco, Claudia Pisante, Greta Avancini, Valentina Di Francesco, Yosi Shamay, Paolo Decuzzi   +7 more
wiley   +1 more source

Polynomial Regression on Lie Groups and Application to SE(3). [PDF]

Entropy (Basel)
Aubray J, Nicol F.
europepmc   +1 more source