Results 181 to 190 of about 32,255 (314)

Breaking Down Lignin: A Macromolecule's Path to the Nanoscale

open access: yesAdvanced Energy and Sustainability Research, EarlyView.
This section highlights lignin's critical role as a sustainable, multifunctional precursor for nanomaterial design. Its unique structure and abundance enable the creation of lignin‐based, lignin‐derived, and hybrid nanomaterials with tunable properties. Emphasis is placed on lignin's potential to drive innovation in nanotechnology, offering ecofriendly
Jelena Papan Djaniš   +3 more
wiley   +1 more source

Triboelectric Tactile Transducers for Neuromorphic Sensing and Synaptic Emulation: Materials, Architectures, and Interfaces

open access: yesAdvanced Energy and Sustainability Research, EarlyView.
Triboelectric nanogenerators are vital for sustainable energy in future technologies such as wearables, implants, AI, ML, sensors and medical systems. This review highlights improved TENG neuromorphic devices with higher energy output, better stability, reduced power demands, scalable designs and lower costs.
Ruthran Rameshkumar   +2 more
wiley   +1 more source

On the roles of function and selection in evolving systems. [PDF]

open access: yesProc Natl Acad Sci U S A, 2023
Wong ML   +8 more
europepmc   +1 more source

Copper Contact for Perovskite Solar Cells: Properties, Interfaces, and Scalable Integration

open access: yesAdvanced Energy and Sustainability Research, EarlyView.
Copper electrodes, as low‐cost, scalable contacts for perovskite solar cells, offer several advantages over precious metals such as Au and Ag, including performance, cost, deposition methods, and interfacial engineering. Copper (Cu) electrodes are increasingly considered practical, sustainable alternatives to noble‐metal contacts in perovskite solar ...
Shuwei Cao   +4 more
wiley   +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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