Results 281 to 290 of about 3,077,553 (314)

Covalent Adaptable Networks with Associative Siloxane Exchange Enabled by Amide‐Based Internal Catalysis: Designing for Reprocessability and Extrudability by Increasing the Cross‐Link Density

open access: yesAdvanced Functional Materials, EarlyView.
Internally catalyzed siloxane dynamic chemistry is demonstrated resulting from amides covalently linked through alkyl chains to siloxanes. The alkyl length in the siloxane‐containing monomer tunes the network cross‐link density. Siloxane exchange dynamics are faster with increasing cross‐link density, because associative exchange is second order in ...
Nathan S. Purwanto   +5 more
wiley   +1 more source

Bionic Energy-Efficient Inverse Kinematics Method Based on Neural Networks for the Legs of Hydraulic Legged Robots. [PDF]

open access: yesBiomimetics (Basel)
She J   +9 more
europepmc   +1 more source

Molecular‐Level Dual‐Ionophilic Passivation for High‐Areal‐Capacity Lithium Metal Anodes on Nanostructured Paper Electrodes

open access: yesAdvanced Functional Materials, EarlyView.
The effect of a molecular‐level dual‐ionophilic chitosan passivation layer is investigated on a nanostructured paper electrode featuring an interconnected network structure of highly oxygen‐functionalized single‐walled carbon nanotubes (C‐NPE). This molecular coating enabled a high Coulombic efficiency (>99.0%) and stable cycling over 350 cycles at an ...
Jisoo Kim   +11 more
wiley   +1 more source

Fluorophobic Effect Enables Selective Detection of PFAS in Water with Electrolyte‐Gated Organic Transistors

open access: yesAdvanced Functional Materials, EarlyView.
PerFluoroAlkyl Substances (PFAS) are responsible of major and persistent environmental pollution worldwide. This work demonstrates an ultra‐sensitive sensor for PFAS based on an organic transistor whose gate is functionalized with a binary self‐assembled monolayer containing a perfluorinated molecule.
Rian Zanotti   +8 more
wiley   +1 more source

Inverse Yogiisms

open access: yesNotices of the American Mathematical Society, 2016
openaire   +2 more sources

Artificial Intelligence‐Driven Development in Rechargeable Battery Materials: Progress, Challenges, and Future Perspectives

open access: yesAdvanced Functional Materials, EarlyView.
AI is transforming the research paradigm of battery materials and reshaping the entire landscape of battery technology. This comprehensive review summarizes the cutting‐edge applications of AI in the advancement of battery materials, underscores the critical challenges faced in harnessing the full potential of AI, and proposes strategic guidance for ...
Qingyun Hu   +5 more
wiley   +1 more source

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