Results 131 to 140 of about 659 (220)

Multiferroic‐Centric Materials and Systems Engineering for Battery Applications: An Insight Into Mechanisms, Strategies, and Characterizations

open access: yesAdvanced Science, EarlyView.
Multiferroic order parameters – polarization, magnetization, and ferroelastic strain – are positioned as dynamic design variables for batteries. Their mechanistic roles, practical tuning through fabrication and external fields, and ferroic‐resolved characterization routes are unified into a closed‐loop framework, revealing how coupled ferroic responses
Jiaqi Su   +13 more
wiley   +1 more source

AI‐Physics‐Experiment Trinity for Integrated Protein Dynamics Modeling

open access: yesAdvanced Science, EarlyView.
This review unites experiments, physics‐based simulations, and AI as a synergistic triad for protein dynamics modeling. It highlights integrative strategies, resolves sampling and forcefield bottlenecks, and outlines challenges and future directions for accurate, interpretable conformational ensemble prediction.
Chen Shi   +4 more
wiley   +1 more source

FOS3D: A Fluorescence‐Enabled Toolkit for Characterizing a Three‐dimensional Osteosarcoma Model

open access: yesAdvanced Science, EarlyView.
FOS3D describes fluorescent (F) osteosarcoma (OS) cells in a tri‐dimensional (3D) model. The study comprises three phases: development, where biofabrication parameters are tuned to achieve cytocompatibility and tumor‐specific mechanical properties in cell‐laden gelatin methacryloyl constructs; validation, where whole‐well fluorescence reading is ...
William Humble   +9 more
wiley   +1 more source

Enumerating Finitary Processes. [PDF]

open access: yesEntropy (Basel)
Johnson BD   +3 more
europepmc   +1 more source

Circulating Amino Acid Network Remodeling Reveals Systemic Metabolic Reprogramming Predictive of Colorectal Cancer Recurrence and Metastasis

open access: yesAdvanced Science, EarlyView.
Blood‐based amino acid patterns measured by 19F NMR reveal hidden metabolic changes in colorectal cancer. By analyzing how these amino acids interact as a network, machine learning models identify patients at higher risk of recurrence and metastasis.
Ji‐Yeon Lee   +9 more
wiley   +1 more source

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