Results 221 to 230 of about 23,525 (295)

Advancing Lithium–Oxygen Batteries: Pioneering Cathode Catalyst Innovation and Artificial Intelligence‐Driven Design Paradigms

open access: yesAdvanced Materials, EarlyView.
This review summarizes the principles and challenges of nonaqueous lithium‐oxygen batteries and recent advances in cathode catalysts, including carbon‐based materials, metals, oxides, sulfides, nitrides, carbides, and redox mediators. It highlights emerging design strategies and artificial intelligence‐driven approaches, emphasizing data‐assisted ...
Yuqing Yao   +8 more
wiley   +1 more source

A two stage statistical framework for cold start spare part demand forecasting. [PDF]

open access: yesPLoS One
B SN   +5 more
europepmc   +1 more source

Orbital‐Hybridizable Nanoseed Interphase Enables One‐Minute Rechargeable, Energy‐Dense Anode‐Free Aqueous Zinc Batteries

open access: yesAdvanced Materials, EarlyView.
An orbital‐hybridizable nanoseed (OHNS) interphase induces strong orbital hybridization between Zn and carbon edges, accelerating Zn nucleation while suppressing 2D surface diffusion. This interfacial electronic regulation drives uniform, dense, and (002)‐oriented Zn growth, enabling stable and fast charging behavior in anode‐free aqueous Zn batteries.
Won‐Yeong Kim   +16 more
wiley   +1 more source

Weaving Intelligence: Thermally Drawn Multimaterial Fibers Toward AI‐Enabled Smart Textiles

open access: yesAdvanced Materials, EarlyView.
Thermally drawn multimaterial fibers are rapidly advancing as intelligent structural units for next‐generation smart textiles. Integrating multimaterial architectures with neuromorphic and spiking‐neural‐network principles enables fabrics that can sense, compute, and adapt autonomously.
Vuong Dinh Trung   +9 more
wiley   +1 more source

Deep Learning Inverse Design of Phase‐Change Reconfigurable Terahertz Metadevices for Multidimensional Secure Communication

open access: yesAdvanced Materials, EarlyView.
A deep learning inverse‐design framework is established to create versatile reconfigurable terahertz metadevices. By synergizing deep learning with phase‐change materials, this approach enables on‐demand customization of multidimensional electromagnetic responses.
Yisheng Dong   +11 more
wiley   +1 more source

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