Results 171 to 180 of about 308,543 (305)

Phase Engineering of Nanomaterials (PEN): Evolution, Current Challenges, and Future Opportunities

open access: yesAdvanced Materials, EarlyView.
This review summarizes the synthesis, phase transition, advanced characterization spanning ex situ to in situ and operando techniques, and diverse applications of phase engineering of nanomaterials (PEN). It further outlines key challenges and future opportunities, such as phase stability, architecture control, and artificial intelligence (AI)‐driven ...
Ye Chen   +7 more
wiley   +1 more source

Targeted Modulation of d‐Band Center in MoS2 Interlayer With n‐Type Co/Fe Dopants Accelerating Sulfur Reaction Kinetics in Lithium‐Sulfur Batteries

open access: yesAdvanced Materials, EarlyView.
A targeted modulation of the MoS2 electronic structure is achieved via substitutional n‐type Co/Fe co‐doping. This strategy triggers S‐mediated d‐p hybridization, optimizing the binding affinity with polysulfides to establish a volcano‐shaped relationship between the d‐band center and catalytic activity.
Junhyuk Ji   +6 more
wiley   +1 more source

Is computer science in crisis? : research and analysis of enrollment and graduation trends in computer science

open access: yes, 2007
The last few years have seen an upsurge in research into problems facing computer science such as plummeting enrollments and outsourcing, and now there is a plethora of information about the causes and solutions for the issues facing computer science in ...
Peters-Cohen, Susan D.
core  

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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