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Snakes off the plane

Researchers discovered a simple strategy for snakes to stand upright without limbs, concentrating bending and muscle activity into a short boundary layer near their base. This approach reduces energy required while maintaining balance, offering design principles for soft robots and medical devices.

SAMSUNG T9 Portable SSD 2TB

SAMSUNG T9 Portable SSD 2TB transfers large imagery and model outputs quickly between field laptops, lab workstations, and secure archives.

Apple iPhone 17 Pro

Apple iPhone 17 Pro delivers top performance and advanced cameras for field documentation, data collection, and secure research communications.

Understanding the dynamic behavior of rubber materials

A team of researchers has developed a novel experimental system to simultaneously measure the mechanical properties and internal structure of rubber-like materials. The study found that strain within these materials is non-uniform, depending on the shape and size of composite particles.

A closer look at the dynamics of the p-Laplacian Allen–Cahn equation

A team of researchers from Korea investigated the dynamics of the p-Laplacian AC equation, finding that solutions maintain three criteria: phase separation, boundedness, and energy decay properties. They also identified an advantage of p-AC equation over classical Laplacian in adjusting interface sharpness.

Aranet4 Home CO2 Monitor

Aranet4 Home CO2 Monitor tracks ventilation quality in labs, classrooms, and conference rooms with long battery life and clear e-ink readouts.

Professor Li Faxin’s group develops world's first DMA for hard materials

The Li Faxin Research Group at Peking University has developed the world's first dynamic mechanical analyzer (DMA) suitable for hard materials. This instrument measures Young's modulus, shear modulus, and internal friction under variable temperature conditions, offering accurate and quick analysis of material properties.

Researchers use artificial neural networks to streamline materials testing

A team at NYU Tandon School of Engineering has designed an artificial neural network approach that can predict the elastic modulus of graphene-enhanced composites from just one sample, streamlining materials testing. This reduces the need for extensive experimentation, lowering costs and accelerating product development.