Results 111 to 120 of about 29,154 (298)

Chiral Phase Change Nanomaterials

Advanced Functional Materials, EarlyView.
This work demonstrates reversible, non‐volatile phase transitions in chiral Ge2${\rm Ge}_2$Sb2${\rm Sb}_2$Te5${\rm Te}_5$ (GST) nanohelices for high‐speed optical modulation of chirality and dynamic control of the state of polarization (SOP). The chiral nanostructures are fabricated using a highly directional, wafer‐scale physical vapor deposition ...
Joshua A. Burrow, Md Shah Alam, Riad Yahiaoui, Evan M. Smith, Ryan Laing, Jules Gardener, Thomas A. Searles, Piyush J. Shah, Shivashankar Vangala, Joshua R. Hendrickson, Andrew M. Sarangan, Imad Agha   +11 more
wiley   +1 more source

Recent developments in temperature responsive shape memory polymers [PDF]

, 2009
Shape memory polymer is an important area of interest in the polymer science research because of its many applications in industry such as biomedical, smart textiles, actuations and so on.
Mondal, Subrata
core   +1 more source

Advances in Sustainable and Wearable Textile Based Soft Robotics

Advanced Functional Materials, EarlyView.
This Review examines advances in wearable textile‐based soft robotics, focusing on sustainable materials, integrated sensing, and scalable actuation. It discusses manufacturing and system integration across healthcare, assistive robotics, prosthetics, and human–machine interfaces, and highlights key challenges in circular design, including life‐cycle ...
Zahir Abbas, Marzia Dulal, Sophia K. Novoselov, Victoria K. Novoselov, Kostya S. Novoselov, Shaila Afroj, Nazmul Karim   +6 more
wiley   +1 more source

Redox- and Glucose-Induced Shape-Memory Polymers [PDF]

, 2013
A novel redox-induced shape-memory polymer (SMP) is prepared by crosslinking -cyclodextrin modified chitosan (-CD-CS) and ferrocene modified branched ethylene imine polymer (Fc-PEI).
Wang, Yi-Fu, Cao, Ya, Li, Bang-Jing, Zhang, Sheng, Dong, Zhen-Qiang, Li, Jian-Hu, Li, BJ (reprint author), Chinese Acad Sci, Key Lab Mt Ecol Restorat & Bioresource Utilizat, Chengdu Inst Biol, Chengdu 610041, Peoples R China, Yuan, Qi-Juan   +7 more
core  

3D Printed Shape Memory Polymers for Biomedical Applications [PDF]

, 2021
3D Printed Shape Memory Polymers for Biomedical Applications3D Printed Shape Memory Polymers for Biomedical ...
INVERARDI, NICOLETTA
core  

Advancing smart dry adhesives with shape memory polymers

International Journal of Smart and Nano Materials
Smart dry adhesives, a rapidly growing class of intelligent materials and structures, are engineered to provide strong, robust adhesion when needed while also allowing for controlled, easy detachment in response to specific stimuli.
Changhong Linghu, Tong Mu, Wei Zhao, Yangchengyi Liu, K. Jimmy Hsia, Jinsong Leng, Huajian Gao   +6 more
doaj   +1 more source

Shape‐Memory Effect in Polymers [PDF]

Macromolecular Chemistry and Physics, 2013
Lendlein, Andreas (Prof. Dr.), Sauter, Tilman (Dr.)   +1 more
openaire   +1 more source

Spatially‐Resolved Coherence of Organic Molecular Spins at Room‐Temperature

Advanced Functional Materials, EarlyView.
Molecular spin qubits are emerging as a promising quantum sensing technology. This paper studies the variation in coherence and sensitivity in both crystalline and thin‐film samples by combining microscopy and room temperature optically detected coherent control.
Adrian Mena, Nicholas P. Sloane, Max R. Bonengel, Cameron M. Gould, Scott A. Sulway, Dane R. McCamey   +5 more
wiley   +1 more source

Shape-Memory Polymers (SMP) [PDF]

, 2013
Tato bakalářská práce se věnuje polymerům s tvarovou pamětí. V první části jsou popsány mechanismy vyvolávající změnu tvarové paměti u polymerů a jejich kompozitů. Dále poukazuje na rozdíly mezi polymery s tvarovou pamětí a slitinami s tvarovou pamětí. V
Strouhalová, Jana
core  

Implantable Ionic Memristors Based on Natural Polymer Heterojunctions

Advanced Functional Materials, EarlyView.
We report an implantable natural polymer‐based ionic memristor composed of hyaluronic acid, chitosan, and PDMS. The device achieved 98.94% accuracy in MNIST classification while reducing training time by 36.8% compared with a conventional artificial neural network (ANN).
Dong‐yup Lee, Byung Ik Park, Gihyeon Yim, Hyunjae Yoo, Sangjun Ma, Jundong Park, Jeong‐Yun Sun   +6 more
wiley   +1 more source