Results 181 to 190 of about 32,255 (314)
Does a nonzero tunneling probability imply particle production in time-independent classical electromagnetic backgrounds? [PDF]
L. Sriramkumar, Τ. Padmanabhan
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Breaking Down Lignin: A Macromolecule's Path to the Nanoscale
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
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Biot-Savart law in the geometrical theory of dislocations. [PDF]
Kobayashi S, Tarumi R.
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Influence of classical electromagnetic effects on current-induced domain wall motion in a perpendicularly magnetized nanowire [PDF]
Takashi Komine, Tomosuke Aono, Ryo Ando
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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
Spin-Qubit Noise Spectroscopy of Magnetic Berezinskii-Kosterlitz-Thouless Physics. [PDF]
Potts M, Zhang S.
europepmc +1 more source
On the roles of function and selection in evolving systems. [PDF]
Wong ML +8 more
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Copper Contact for Perovskite Solar Cells: Properties, Interfaces, and Scalable Integration
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
Quantization of linear acoustic and elastic wave models in characterizations of isomorphism. [PDF]
Yang C.
europepmc +1 more source
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
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