Results 171 to 180 of about 1,000,248 (275)

Inherently Disordered Auxetic Metamaterials

open access: yesAdvanced Science, EarlyView.
Inherently disordered auxetic metamaterials based on random chiral Delaunay triangulations are designed and investigated using numerical simulations and experimental tests. These disordered frameworks exhibit orthotropic behavior and a large negative Poisson's ratio (ca.
Matteo Montanari   +3 more
wiley   +1 more source

Study of Free‐Space Optical Quantum Network: Review and Prospectives

open access: yesAdvanced Science, EarlyView.
Free from the constraints of fiber connections, free‐space quantum network enables longer and more flexible quantum network connections. This review summarizes and comparatively analyzes free‐space quantum network experiments based on ground stations, satellites, and mobile platforms.
Hua‐Ying Liu, Zhenda Xie, Shining Zhu
wiley   +1 more source

Leveraging Artificial Intelligence and Large Language Models for Cancer Immunotherapy

open access: yesAdvanced Science, EarlyView.
Cancer immunotherapy faces challenges in predicting treatment responses and understanding resistance mechanisms. Artificial intelligence (AI) and machine learning (ML) offer powerful solutions for cancer immunotherapy in patient stratification, biomarker discovery, treatment strategy optimization, and foundation model development.
Xinchao Wu   +4 more
wiley   +1 more source

Organic Crystal‐MXene Composites as Temperature‐Tolerant Strain Sensors

open access: yesAdvanced Science, EarlyView.
ABSTRACT Flexible organic crystals offer significant opportunities for organic electronics; however, their potential applications in fields like flexible sensors are at present limited by poor charge transport that result in failure under reversible conditions.
Xuesong Yang   +10 more
wiley   +1 more source

Physics‐Embedded Neural Network: A Novel Approach to Design Polymeric Materials

open access: yesAdvanced Science, EarlyView.
Traditional black‐box models for polymer mechanics rely solely on data and lack physical interpretability. This work presents a physics‐embedded neural network (PENN) that integrates constitutive equations into machine learning. The approach ensures reliable stress predictions, provides interpretable parameters, and enables performance‐driven, inverse ...
Siqi Zhan   +8 more
wiley   +1 more source

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