ABSTRACT
Clean water scarcity and energy shortage have become urgent global problems due to population growth and human industrial development. Low-grade waste heat (LGWH) is a widely available and ubiquitous byproduct of human activities worldwide, which can provide effective power to address the fresh water crisis without additional energy consumption and carbon emissions. In this regard, 3D superhydrophilic polyurethane/sodium alginate (PU/SA) foam and LGWH-driven interfacial water evaporation systems are developed, which can precipitate over 80 L m-2 h-1 steam generation from seawater and has favorable durability for purification of high salinity wastewater. The excellent water absorption ability, unobstructed water transport, and uniform thin water layer formed on 3D skeletons of PU/SA foam ensure the strong heat exchange between LGWH and fluidic water. As a result, the heat-localized PU/SA foam enables the efficient energy utilization and ultrafast water evaporation once LGWH is introduced into PU/SA foam as heat flow. In addition, the precipitated salt on PU/SA foam can be easily removed by mechanical compression, and almost no decrease in water evaporation rate after salt precipitation and removal for many times. Meanwhile, the collected clean water has high rejection of ions of 99.6%, which meets the World Health Organization (WHO) standard of drinking water. Above all, this LGWH-driven interfacial water evaporation system presents a promising and easily accessible solution for clean water production and water-salt separation without additional energy burden for the society.
ABSTRACT
Harvesting energy from spontaneous water flow within artificial nanochannels is a promising route to meet sustainable power requirements of the fast-growing human society. However, large-scale nanochannel integration and the multi-parameter coupling restrictive influence on electric generation are still big challenges for macroscale applications. In this regard, long-range (1 to 20 cm) ordered graphene oxide assembled framework with integrated 2D nanochannels have been fabricated by a rotational freeze-casting method. The structure can promote spontaneous absorption and directional transmission of water inside the channels to generate considerable electric energy. A transfer learning strategy is implemented to address the complicated multi-parameters coupling problem under limited experimental data, which provides highly accurate performance optimization and efficiently guides the design of 2D water flow enabled generators. A generator unit can produce ~2.9 V voltage or ~16.8 µA current in a controllable manner. High electric output of ~12 V or ~83 µA is realized by connecting several devices in series or parallel. Different water enabled electricity generation systems have been developed to directly power commercial electronics like LED arrays and display screens, demonstrating the material's potential for development of water enabled clean energy.
ABSTRACT
Simultaneous multimodal monitoring can greatly perceive intricately multiple stimuli, which is important for the understanding and development of a future human-machine fusion world. However, the integrated multisensor networks with cumbersome structure, huge power consumption, and complex preparation process have heavily restricted practical applications. Herein, a graphene oxide single-component multimodal sensor (GO-MS) is developed, which enables simultaneous monitoring of multiple environmental stimuli by a single unit with unique moist-electric self-power supply. This GO-MS can generate a sustainable moist-electric potential by spontaneously adsorbing water molecules in air, which has a characteristic response behavior when exposed to different stimuli. As a result, the simultaneous monitoring and decoupling of the changes of temperature, humidity, pressure, and light intensity are achieved by this single GO-MS with machine-learning (ML) assistance. Of practical importance, a moist-electric-powered human-machine interaction wristband based on GO-MS is constructed to monitor pulse signals, body temperature, and sweating in a multidimensional manner, as well as gestures and sign language commanding communication. This ML-empowered moist-electric GO-MS provides a new platform for the development of self-powered single-component multimodal sensors, showing great potential for applications in the fields of health detection, artificial electronic skin, and the Internet-of-Things.
Subject(s)
Graphite , Wearable Electronic Devices , Graphite/chemistry , Humans , Machine Learning , WaterABSTRACT
It is a challenge to spontaneously harvest multiple clean sources from the environment for upgraded energy-converting systems. The ubiquitous moisture and sunlight in nature are attractive for sustainable power generation especially. A high-performance light-coordinated "moist-electric generator" (LMEG) based on the rational combination of a polyelectrolyte and a phytochrome is herein developed. By spontaneous adsorption of gaseous water molecules and simultaneous exposure to sunlight, a piece of 1 cm2 composite film offers an open-circuit voltage of 0.92 V and a considerable short-circuit current density of up to 1.55 mA cm-2 . This record-high current density is about two orders of magnitude improvement over that of most conventional moisture-enabled systems, which is caused by moisture-induced charge separation accompanied with photoexcited carrier migration, as confirmed by a dynamic Monte Carlo device simulation. Flexible devices with customizable size are available for large-scale integration to effectively work under a wide range of relative humidity (about 20-100%), temperature (10-80 °C), and light intensity (30-200 mW cm-2 ). The wearable and portable LMEGs provide ample power supply in natural conditions for indoor and outdoor electricity-consuming systems. This work opens a novel avenue to develop sustainable power generation through collecting multiple types of natural energy by a single hybrid harvester.
