Results 151 to 160 of about 6,039,832 (276)

Smart Exploration of Perovskite Photovoltaics: From AI Driven Discovery to Autonomous Laboratories

Advanced Energy Materials, EarlyView.
In this review, we summarize the fundamentals of AI in automated materials science, and review AI applications in perovskite solar cells. Then, we sum up recent progress in AI‐guided manufacturing optimization, and highlight AI‐driven high‐throughput and autonomous laboratories.
Wenning Chen, Zheyu Zhang, Kelvian T. Mularso, Bonghyun Jo, Hyun Suk Jung   +4 more
wiley   +1 more source

Medial prefrontal cortex encodes but is not required to generate goal-directed actions under threat. [PDF]

Elife
Sajid MS, Zhou J, Castro-Alamancos MA.
europepmc   +1 more source

Multiscale Perspectives on Perovskite Instability: From Atomistic Deformation to Device Degradation in Perovskite Solar Cells

Advanced Energy Materials, EarlyView.
Multiscale perspectives on how excess charges in soft lead halide perovskites induce operational instability in photovoltaic devices are presented. Localized carriers formed under thermodynamic non‐equilibrium states modify atomistic interactions and drive lattice distortions, accelerating device degradation.
Joo‐Hong Lee, Donghyeon Lee, Seoyoon Lee, Jin‐Wook Lee, Mansoo Choi, Min‐cheol Kim, Namyoung Ahn   +6 more
wiley   +1 more source

A 67-110 GHz Multi-Carrier Communication Front End Based on Broadband Diplexer and Filtering Waveguide-to-Microstrip Transitions. [PDF]

Micromachines (Basel)
Zhi Y, Zhong Y, Zhang B, Fan Y, Ou Z, Qiao J.   +5 more
europepmc   +1 more source

Machine Learning Interatomic Potentials for Energy Materials: Architectures, Training Strategies, and Applications

Advanced Energy Materials, EarlyView.
Machine learning interatomic potentials bridge quantum accuracy and computational efficiency for materials discovery. Architectures from Gaussian process regression to equivariant graph neural networks, training strategies including active learning and foundation models, and applications in solid‐state electrolytes, batteries, electrocatalysts ...
In Kee Park, Saeed Pourasad, Jinhong Mun, Arunkumar Chitteth Rajan, Tae Hyeon Park, Anand Rohit, Mohammad Zafari, Tan‐Lien Pham, Eunjae Jung, Bonseung Koo, Miran Ha, Gi Beom Sim, Hyun Gyu Park, Won Woong Choi, Kwanghyeon Jo, Soohaeng Yoo Willow, David ChangMo Yang, Chang Woo Myung, Geunsik Lee, Kwang S. Kim   +19 more
wiley   +1 more source

Breaking the phase noise-mode purity trade-off in long-loop OEOs via cascaded zero-dispersion recirculating loops. [PDF]

Light Sci Appl
Wang Z, Bernal S, Plant DV, Chen LR.
europepmc   +1 more source

MXene‐Derived Highly Oriented TiN Enables Ta3N5 Photoanodes for Transmission‐Mode Bias‐Free Solar Water Splitting

Advanced Energy Materials, EarlyView.
This work reports 2D MXene‐derived TiN thin films with (111)‐preferred orientation for transparent Ta3N5 photoanodes, demonstrating effective electron transfer behavior. All‐nitride electron‐collection architecture based on MXene‐derived TiN and n‐GaN resolves a long‐standing materials bottleneck between photocarrier transport and light transmission in
Jin Wook Yang, Hee Ryeong Kwon, Young Seon Yoon, Sung Yeon Lim, Tae Hyung Lee, Jiwoo Lee, Sung Hyuk Park, Sohyeon Park, Jungwon Park, Jin Young Kim, Ho Won Jang   +10 more
wiley   +1 more source

A simple demonstration of a privacy-preserving de-centralised genotype imputation workflow. [PDF]

PLoS One
Letaillandier A, Picard-Druet D, Ludwig TE, Marenne G, Herzig AF.   +4 more
europepmc   +1 more source

A Circular Crested Lamb Wave Resonator with Spurious Mode Suppression and Quality Factor Enhancement [PDF]

To date, nearly all the reported Lamb wave resonators (LWRs) are straight crested LWRs, which suffer from inherent spurious modes and low quality factors (Q). For the first time, this work demonstrates a circular crested LWR.
Ren, Hao, Lou, Liang, Lu, Xianzheng
core  

From Top to Bottom: Manufacturing Process‐Context Aware Resolution of Energy Device Electrodes Through a 3D Diffusion Generative Model

Advanced Energy Materials, EarlyView.
The application of a generative diffusion model, enhanced with a training data augmentation pipeline retaining the manufacturing process context of electrode microstructures, leads to improved fidelity of the through‐plane tortuosity factor in the AI generated samples.
Victor Ramirez‐Camacho, Siwar Ben Hadj Ali, Maxence Desnoyers, Francisco Fernandez, Nassima Benammar, Alejandro A. Franco   +5 more
wiley   +1 more source