Results 201 to 210 of about 436,473 (319)

Aboveground biomass in seven tropical forest patches of Western-Africa: comparison of manual inventory and terrestrial laser scanning. [PDF]

open access: yesAnn For Sci
Hepner S   +6 more
europepmc   +1 more source

Designing Strong, Tough, Fire‐Retardant and Self‐Healing Elastomers with Phosphorus/Nitrogen‐ and Biphenyl‐Containing Segments

open access: yesAdvanced Materials, EarlyView.
By designing a P/N‐ and π–π interacting biphenyl‐containing diol as hard segments but side groups, a strong, tough, fire‐extinguishing and self‐healing elastomer is developed, demonstrating a break strain of ∼2500%, a toughness of 379 MJ/m3 and a tensile strength of 46 MPa, as well as a healing efficiency of 95% (tensile strength) and 99% (break strain)
Yijiao Xue   +11 more
wiley   +1 more source

Leaf‐Inspired Eutectic Skin With Extreme Fatigue Resistance and Robust Wet Adhesion for Amphibious Epidermal Electronics

open access: yesAdvanced Materials, EarlyView.
Inspired by Acorus calamus leaves, a heterogeneous eutectic skin integrates an aligned fibrous network within a hydrophobic eutectogel matrix. This hierarchical architecture triggers strain‐induced crystallization to achieve exceptional mechanical toughness and durable wet adhesion.
Jiayu Hou   +16 more
wiley   +1 more source

Weaving Intelligence: Thermally Drawn Multimaterial Fibers Toward AI‐Enabled Smart Textiles

open access: yesAdvanced Materials, EarlyView.
Thermally drawn multimaterial fibers are rapidly advancing as intelligent structural units for next‐generation smart textiles. Integrating multimaterial architectures with neuromorphic and spiking‐neural‐network principles enables fabrics that can sense, compute, and adapt autonomously.
Vuong Dinh Trung   +9 more
wiley   +1 more source

Machine Learning Accelerated Computational Design of Bio‐Inspired Catalysts in the Nitrogen Reduction Reaction

open access: yesAdvanced Materials, EarlyView.
We introduce a computational workflow that combines quantum chemical calculations and machine learning techniques to predict the catalytic performance of a wide range of catalysts in the nitrogen reduction reaction (NRR). The analysis of the trained models provides insights into the complex structure–activity relationship in experimental catalytic ...
Leonardo Di Ciano   +5 more
wiley   +1 more source

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