Transition of Carbon Nanotube Sheets from Hydrophobicity to Hydrophilicity by Facile Electrochemical Wetting.

The present study delves into the transformative effects of electrochemical oxidation on the hydrophobic-to-hydrophilic transition of carbon nanotube (CNT) sheets. The paper elucidates the inherent advantages of CNT sheets, such as high electrical conductivity and mechanical strength, and contrasts them with the limitations posed by their hydrophobic nature. A comprehensive investigation is conducted to demonstrate the efficacy of electrochemical oxidation treatment in modifying the surface properties of CNT sheets, thereby making them hydrophilic.

Electrochemically Oxidized Carbon Nanotube Sheets for High-Performance and Flexible-Film Supercapacitors.

The development of flexible, high-performance supercapacitors has been a focal point in energy storage research. While carbon nanotube (CNT) sheets offer promising mechanical and electrical properties, their low electrical double-layer capacitance significantly limits their practicability. Herein, we introduce a novel approach to address this challenge via the electrochemical oxidation treatment of CNT sheets stacked on a polyethylene terephthalate substrate.

Enhanced Hydro-Actuation and Capacitance of Electrochemically Inner-Bundle-Activated Carbon Nanotube Yarns.

Recently, several attempts have been made to activate or functionalize macroscopic carbon nanotube (CNT) yarns to enhance their innate abilities. However, a more homogeneous and holistic activation approach that reflects the individual nanotubes constituting the yarns is crucial. Herein, a facile strategy is reported to maximize the intrinsic properties of CNTs assembled in yarns through an electrochemical inner-bundle activation (EIBA) process.

Highly Elastically Deformable Coiled CNT/Polymer Fibers for Wearable Strain Sensors and Stretchable Supercapacitors.

Stretchable yarn/fiber electronics with conductive features are optimal components for different wearable devices. This paper presents the construction of coil structure-based carbon nanotube (CNT)/polymer fibers with adjustable piezoresistivity. The composite unit fiber is prepared by wrapping a conductive carbon CNT sheath onto an elastic spandex core.

Wearable and Washable MnO-Zn Battery Packaged by Vacuum Sealing.

Batteries are used in all types of electronic devices from conventional to advanced devices. Currently, batteries are evolving in the direction of extremely personalized yarn- or textile-structured textronic systems. However, the absence of a protective layer on such batteries is a critical limitation to their practical use.

High-Performance Biscrolled Ni-Fe Yarn Battery with Outer Buffer Layer.

The increasing demand for portable and wearable electronics has promoted the development of safe and flexible yarn-based batteries with outstanding electrochemical properties. However, achieving superior energy storage performance with a high active material (AM) load and long cycle life with this device format remains a challenge. In this study, a stable and rechargeable high-performance aqueous Ni-Fe yarn battery was constructed via biscrolling to embed AMs within helical carbon nanotube (CNT) yarn corridors.

Integrated Mechano-Electrochemical Harvesting Fiber and Thermally Responsive Artificial Muscle for Self-Powered Temperature-Strain Dual-Parameter Sensor.

Significant progress in healthcare fields around the world has inspired us to develop a wearable strain−temperature sensor that can monitor biomedical signals in daily life. This novel self-powered temperature−strain dual-parameter sensor comprises a mechano-electrochemical harvester (MEH) and a thermally responsive artificial muscle (TAM). The MEHTAM system generates electricity from strain and thermal fluctuations.

Real-Time Traffic Risk Detection Model Using Smart Mobile Device.

Automatically recognizing dangerous situations for a vehicle and quickly sharing this information with nearby vehicles is the most essential technology for road safety. In this paper, we propose a real-time deceleration pattern-based traffic risk detection system using smart mobile devices. Our system detects a dangerous situation through machine learning on the deceleration patterns of a driver by considering the vehicle's headway distance.