Results 251 to 260 of about 1,420,126 (325)
Structure–Transport–Ion Retention Coupling for Enhanced Nonvolatile Artificial Synapses
Nitrogen incorporation into the conjugated backbone of donor–acceptor polymers enables efficient charge transfer and deep ion embedding in organic electrochemical synaptic transistors (OESTs). This molecular‐level design enhances non‐volatile synaptic properties, providing a new strategy for developing high‐performance and reliable neuromorphic devices.
Donghwa Lee +5 more
wiley +1 more source
Personality Traits and E-learning Course Satisfaction: A Study of Health Science Students. [PDF]
Ashouri A +3 more
europepmc +1 more source
Fibrous benzenetrispeptide (BTP) hydrogels, fabricated via strain‐promoted azide‐alkyne cycloaddition (SPAAC) crosslinking, form robust, bioinert networks. These hydrogels can support 3D cell culture, where cell viability and colony growth depend on the fiber content.
Ceren C. Pihlamagi +5 more
wiley +1 more source
Predicting Atomic Charges in MOFs by Topological Charge Equilibration
An atomic charge prediction method is presented that is able to accurately reproduce ab‐initio‐derived reference charges for a large number of metal–organic frameworks. Based on a topological charge equilibration scheme, static charges that fulfill overall neutrality are quickly generated.
Babak Farhadi Jahromi +2 more
wiley +1 more source
The perception of dental students and educators about e-learning during COVID - 19 pandemic. [PDF]
Fouda SM +7 more
europepmc +1 more source
Anion‐excessive gel‐based organic synaptic transistors (AEG‐OSTs) that can maintain electrical neutrality are developed to enhance synaptic plasticity and multistate retention. Key improvement is attributed to the maintenance of electrical neutrality in the electrolyte even after electrochemical doping, which reduces the Coulombic force acting on ...
Yousang Won +3 more
wiley +1 more source
Medical staff's knowledge of delirium by occupation and the effectiveness of an on-demand e-learning. [PDF]
Yamagata H +4 more
europepmc +1 more source
PREdicting LNP In Vivo Efficacy (PRELIVE) framework enables the prediction of lipid nanoparticle (LNPs) organ‐specific delivery through dual modeling approaches. Composition‐based models using formulation parameters and protein corona‐based models using biological fingerprints both achieve high predictive accuracy across multiple organs.
Belal I. Hanafy +3 more
wiley +1 more source

