Results 121 to 130 of about 9,264 (254)

Advancing Energy Materials by In Situ Atomic Scale Methods

Advanced Energy Materials, Volume 15, Issue 11, March 18, 2025.
Progress in in situ atomic scale methods leads to an improved understanding of new and advanced energy materials, where a local understanding of complex, inhomogeneous systems or interfaces down to the atomic scale and quantum level is required. Topics from photovoltaics, dissipation losses, phase transitions, and chemical energy conversion are ...
Christian Jooss, Michael Seibt, Martin Wenderoth, Oliver Bünermann, Ole Bunjes, Till Domröse, Christian Eckel, Francesca Falorsi, Christoph Flathmann, Monica Kolek Martinez de Azagra, Matthias Krüger, Jonas Lindner, Tobias Meyer, Claus Ropers, Ulrich Ross, Kai Rossnagel, Sreeju Sreekantan Nair Lalithambika, Simone Techert, Georg A. Traeger, Cynthia Volkert, R. Thomas Weitz, Alec M. Wodtke   +21 more
wiley   +1 more source

Optical Spin Angular Momentum: Properties, Topologies, Detection and Applications. [PDF]

Nanomaterials (Basel)
Liu S, Xie X, Shi P, Shen Y.
europepmc   +1 more source

Safety of Sodium‐Ion Batteries: Evaluation and Perspective from Component Materials to Cells, Modules, and Packs

Advanced Energy Materials, EarlyView.
This review provides a bottom‐up evaluation of sodium‐ion battery safety, linking material degradation mechanisms, cell engineering parameters, and module/pack assembly. It emphasizes that understanding intrinsic material stability and establishing coordinated engineering control across hierarchical levels are vital for preventing degradation coupling ...
Won‐Gwang Lim, Seoa Kim, Erik A. Wu, Darren H. S. Tan, Shawn Peng, Xiaolin Li   +5 more
wiley   +1 more source

Role of Wadsley Defects and Cation Disorder to Enhance MoNb12O33 Diffusion

Advanced Energy Materials, EarlyView.
Wadsley‐Roth niobates are high‐rate capable and durable anode materials for lithium‐ion batteries. Defect‐tailoring of MoNb12O33 is shown to substantially enhance lithium diffusion. Computational models were used to separate the effects of cation disorder and Wadsley defects to identify that both led to the occupation of fast diffusion sites at lower ...
CJ Sturgill, Manish Kumar, Nima Karimitari, Iva Milisavljevic, Coby S. Collins, Aaron Hegler, Hsin‐Yun Joy Chao, Santosh Kiran Balijepalli, Scott T. Misture, Christopher Sutton, Morgan Stefik   +10 more
wiley   +1 more source

Structure and Spectroscopic Characterisation of Phenanthroline‐Based Iodobismuthate(III) Complexes Utilised for Raw Acoustic Signal Classification

Advanced Intelligent Discovery, EarlyView.
Memristors based on trimethylsulfonium (phenanthroline)tetraiodobismuthate have been utilised as a nonlinear node in a delayed feedback reservoir. This system allowed an efficient classification of acoustic signals, namely differentiation of vocalisation of the brushtail possum (Trichosurus vulpecula).
Ewelina Cechosz, Gisya Abdi, Andrzej Sławek, Marlena Gryl, Konrad Szaciłowski   +4 more
wiley   +1 more source

Comparison of DeePMD, MTP, GAP, ACE and MACE Machine‐Learned Potentials for Radiation‐Damage Simulations: A User Perspective

Advanced Intelligent Discovery, EarlyView.
The authors evaluated six machine‐learned interatomic potentials for simulating threshold displacement energies and tritium diffusion in LiAlO2 essential for tritium production. Trained on the same density functional theory data and benchmarked against traditional models for accuracy, stability, displacement energies, and cost, Moment Tensor Potential ...
Ankit Roy, Ram Devanathan, Sarah Allec, Giridhar Nandipati, Andrew M. Casella, David J. Senor, Duane D. Johnson, Ganesh Balasubramanian, Ayoub Soulami   +8 more
wiley   +1 more source

Transverse optical torque from the magnetic spin angular momentum. [PDF]

Nanophotonics
Wen J, He F, Feng L, Lu W, Lin Z, Zheng H, Chen H.   +6 more
europepmc   +1 more source

Explaining the Origin of Negative Poisson's Ratio in Amorphous Networks With Machine Learning

Advanced Intelligent Discovery, EarlyView.
This review summarizes how machine learning (ML) breaks the “vicious cycle” in designing auxetic amorphous networks. By transitioning from traditional “black‐box” optimization to an interpretable “AI‐Physics” closed‐loop paradigm, ML is shown to not only discover highly optimized structures—such as all‐convex polygon networks—but also unveil hidden ...
Shengyu Lu, Xiangying Shen
wiley   +1 more source

Spin angular momentum-encoded single-photon emitters in a chiral nanoparticle-coupled WSe₂ monolayer. [PDF]

Sci Adv
Lee SJ, So JP, Kim RM, Kim KH, Rha HH, Na G, Han JH, Jeong KY, Nam KT, Park HG.   +9 more
europepmc   +1 more source

Harnessing Machine Learning to Understand and Design Disordered Solids

Advanced Intelligent Discovery, EarlyView.
This review maps the dynamic evolution of machine learning in disordered solids, from structural representations to generative modeling. It explores how deep learning and model explainability transform property prediction into profound physical insight.
Muchen Wang, Yue Fan
wiley   +1 more source