Results 171 to 180 of about 487,705 (287)

Optimal Grazing Exclusion Duration to Enhance Soil Carbon Sequestration in Degraded Grasslands

open access: yesAdvanced Science, EarlyView.
Across China, grazing exclusion reaches the national mean soil organic carbon recovery benchmark sooner in high‐MAP regions (> 500 mm), but recovery is much slower where MAP < 300 mm. Scaling this strategy to 70% of China's degraded grasslands would sequester about 1.52 Pg of soil carbon over 10 years—roughly 17% of annual global fossil‐fuel emissions.
Bin Zhang   +9 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

ML Workflows for Screening Degradation‐Relevant Properties of Forever Chemicals

open access: yesAdvanced Science, EarlyView.
The environmental persistence of per‐ and polyfluoroalkyl substances (PFAS) necessitates efficient remediation strategies. This study presents physics‐informed machine learning workflows that accurately predict critical degradation properties, including bond dissociation energies and polarizability.
Pranoy Ray   +3 more
wiley   +1 more source

Pellet Printing for Soft Robotic Devices

open access: yesAdvanced Science, EarlyView.
Fused Granulate Fabrication (FGF) is established here as a reliable method for fabricating soft, airtight robotic devices. Through coordinated optimization of hardware, material selection, and process parameters, this approach enables high‐speed printing of thermoplastic elastomers with silicone‐like softness and modulus.
Yijia Wu   +6 more
wiley   +1 more source

Customizing Tactile Sensors via Machine Learning‐Driven Inverse Design

open access: yesAdvanced Science, EarlyView.
ABSTRACT Replicating the sophisticated sense of touch in artificial systems requires tactile sensors with precisely tailored properties. However, manually navigating the complex microstructure‐property relationship results in inefficient and suboptimal designs.
Baocheng Wang   +15 more
wiley   +1 more source

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