Results 221 to 230 of about 67,136 (339)

Optical Charge Trap Memory Based on Graphene/ZnO Heterostructures for Long‐Term Retention and Adaptive Learning

open access: yesAdvanced Electronic Materials, EarlyView.
A biocompatible graphene/ZnO optical charge trap memory (CTM) is reported with over 54 h retention, enabled by interfacial photodoping. Using transient absorption spectroscopy and electrical analysis, charge transfer quenching is elucidated and reveal that a large energy barrier at the interface is responsible for long‐term memory retention.
Seungmin Shin   +10 more
wiley   +1 more source

Dual-comb photoacoustic and photothermal spectroscopy: A comprehensive review. [PDF]

open access: yesPhotoacoustics
Zhang C   +5 more
europepmc   +1 more source

Liquid Metals in Radio Frequency Applications: A Review of Physics, Manufacturing, and Emerging Technologies

open access: yesAdvanced Electronic Materials, EarlyView.
This paper reviews the physics of liquid metals in RF devices, including the influence of mechanical strain on resonance as well as fabrication methods and strategies for designing tunable and strain‐tolerant inductors, capacitors, and antennas.
Md Saifur Rahman, William J. Scheideler
wiley   +1 more source

Fundamental Challenges, Physical Implementations, and Integration Strategies for Ising Machines in Large‐Scale Optimization Tasks

open access: yesAdvanced Electronic Materials, EarlyView.
Ising machines are emerging as specialized hardware solvers for computationally hard optimization problems. This review examines five major platforms—digital CMOS, analog CMOS, emerging devices, coherent optics, and quantum systems—highlighting physics‐rooted advantages and shared bottlenecks in scalability and connectivity.
Hyunjun Lee, Joon Pyo Kim, Sanghyeon Kim
wiley   +1 more source

Nonlinear optics in the LP_02 higher-order mode of a fiber

open access: gold, 2013
Y. Chen   +3 more
openalex   +1 more source

What to Make and How to Make It: Combining Machine Learning and Statistical Learning to Design New Materials

open access: yesAdvanced Intelligent Discovery, EarlyView.
Combining machine learning and probabilistic statistical learning is a powerful way to discover and design new materials. A variety of machine learning approaches can be used to identify promising candidates for target applications, and causal inference can help identify potential ways to make them a reality.
Jonathan Y. C. Ting, Amanda S. Barnard
wiley   +1 more source

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