Results 181 to 190 of about 49,371 (261)

Receptogenesis in a Vascularized Robotic Embodiment

open access: yesAdvanced Science, EarlyView.
Functional augmentation of situated robots via ex novo hardware generation extends physical adaptability. Drawing inspiration from open circulatory systems for mass and function redistribution, this study presents a vascularized robotic composite exhibiting receptogenesis ‐ the on‐demand construction of sensors ‐ from internal fluid reserves based on ...
Kadri‐Ann Pankratov   +8 more
wiley   +1 more source

Performance Estimation and Ex Vivo Validation of Untethered Magnetic Robots in Soft Tissue

open access: yesAdvanced Science, EarlyView.
This study presents an empirical model that quantitatively predicts the step‐out frequency of screw‐type untethered magnetic robots (UMRs) in soft viscoelastic environments, including ex vivo brain tissue. By integrating key parameters into a dimensionless framework, the model enables estimation of robot performance across a range of biological ...
Leendert‐Jan W. Ligtenberg   +19 more
wiley   +1 more source

Deciphering the Crystallinity‐Dependent Sensitivity of Charge Injection in n‐Channel Organic Transistors: Reliable Characterization and Optimized Performance

open access: yesAdvanced Science, EarlyView.
Precise characterization of n‐type organic semiconductors necessitates decoupling interfacial constraints from intrinsic charge transport. This work reveals that disordered polymers are particularly vulnerable to contact‐limited bottlenecks compared to their crystalline counterparts.
Walid Boukhili   +15 more
wiley   +1 more source

Computationally Evidence‐Grounded Sequence‐First Design of Peptide Binders

open access: yesAdvanced Science, EarlyView.
BOND‐PEP enables controllable, sequence‐first peptide binder design by grounding generation in binding evidence retrieved for each target. It uses topology‐conditioned message passing to integrate relevant peptide examples with the target protein sequence, forming a residue‐level representation that guides the generation of diverse, target‐specific ...
Wenze Ding
wiley   +1 more source

Learning Work Function via Implicit Reasoning on Electrostatic Potential Landscapes

open access: yesAdvanced Science, EarlyView.
StructPot‐CLR establishes a cross‐modal contrastive learning framework that aligns the crystal structures of 2D materials with plane‐averaged electrostatic potential landscapes for physically informed work‐function prediction. The model achieves an MAE of 0.265 eV and an R2 of 0.902 on the held‐out test set while accurately preserving key morphological
Haoyu Wan, Yue Wu, Tianhao Su, Deng Pan
wiley   +1 more source

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