Results 231 to 240 of about 248,172 (367)
The L(log L)e endpoint estimate for maximal singular integral operators
semanticscholar +1 more source
Powering the Future: A Cobalt‐Based Catalyst for Longer‐Lasting Zinc–Air Batteries
A novel N‐doped graphitic shell‐encapsulated Co catalyst reveals superior bifunctional ORR/OER activity in alkaline media, empowering outstanding liquid and quasi‐solid‐state ZAB activity. The system delivers long‐term durability, a peak power density of 127 mW cm−2 and successfully powers an LED and a mini fan.
Manami Banerjee +10 more
wiley +1 more source
Higher Equations of Motion for Boundary Liouville Conformal Field Theory from the Ward Identities. [PDF]
Cerclé B.
europepmc +1 more source
Regularity properties of singular integral operators [PDF]
Abdellah Youssfi
openalex +1 more source
A W/NbOx/Pt memristor demonstrates the coexistence of volatile, non‐volatile, and threshold switching characteristics. Volatile switching serves as a reservoir computing layer, providing dynamic short‐term processing. Non‐volatile switching, stabilized through ISPVA, improves reliable long‐term readout. Threshold switching operates as a leaky integrate
Ungbin Byun, Hyesung Na, Sungjun Kim
wiley +1 more source
The Double-Layer Potential for Spectral Constants Revisited. [PDF]
Schwenninger FL, de Vries J.
europepmc +1 more source
Spectrally Tunable 2D Material‐Based Infrared Photodetectors for Intelligent Optoelectronics
Intelligent optoelectronics through spectral engineering of 2D material‐based infrared photodetectors. Abstract The evolution of intelligent optoelectronic systems is driven by artificial intelligence (AI). However, their practical realization hinges on the ability to dynamically capture and process optical signals across a broad infrared (IR) spectrum.
Junheon Ha +18 more
wiley +1 more source
Dynamical study of different types of soliton solutions with bifurcation, chaos and sensitivity analysis to the non-linear coupled Schrödinger model. [PDF]
Nasir R +5 more
europepmc +1 more source
This review highlights how machine learning (ML) algorithms are employed to enhance sensor performance, focusing on gas and physical sensors such as haptic and strain devices. By addressing current bottlenecks and enabling simultaneous improvement of multiple metrics, these approaches pave the way toward next‐generation, real‐world sensor applications.
Kichul Lee +17 more
wiley +1 more source

