A Rolling-Bead Triboelectric Nanogenerator for Harvesting Omnidirectional Wind-Induced Energy toward Shelter Forests Monitoring.

Shelter forests (or shelter-belts), while crucial for climate regulation, lack monitoring systems, e.g., Internet of Things (IoT) devices, but their abundant wind energy can potentially power these devices using the trees as mounting points. To harness wind energy, an omnidirectional fluid-induced vibration triboelectric nanogenerator (OFIV-TENG) has been developed. The device is installed on shelter forest trees to harvest wind energy from all directions, employing a fluid-induced vibration (FIV) mechanism (fluid-responding structure) that can capture and use wind energy, ranging from low wind speeds (vortex vibration) to high wind speeds (galloping). The rolling-bead triboelectric nanogenerator (TENG) can efficiently harvest energy while minimizing wear and tear. Additionally, the usage of double electrodes results in an effective surface charge density of 21.4 µC m-2 , which is the highest among all reported rolling-bead TENGs. The collected energy is utilized for temperature and humidity monitoring, providing feedback on the effect of climate regulation in shelter forests, alarming forest fires, and wireless wind speed warning. In general, this work provides a promising and rational strategy, using natural resources like trees as the supporting structures, and shows broad application prospects in efficient energy collection, wind speed warning, and environmentally friendliness.

Conformable Multifunctional Space Fabric by Metal 3D Printing for Collision Hazard Protection and Self-Powered Monitoring.

The monitoring of space debris assumes paramount significance to ensure the sustainability and security of space activities as well as underground bases in outer space. However, designing a wide range monitoring system with easy fabrication, low power, and high precision remains an urgent challenge under the scarcity of materials and extreme environment conditions of outer space. Here, we designed a one-piece, robust, but flexible, and repairable 3D metal-printed triboelectric nanogenerator (FR-TENG) by incorporating the advantages of standardization and customization of outer space 3D metal printing. Inspired by the structure of hexagonal and pangolin scales, a curved structure is ingeniously applied in the design of 3D printed metal to adapt different curved surfaces while maintaining superior compressive strength, providing excellent flexibility and shape adaptability. Benefiting from the unique structural design, the FR-TENG has a minimum length of 1 cm with a weight of only 3.5 g and the minimum weight resolution detected of 9.6 g, with a response time of 20 ms. Furthermore, a multichannel self-powered collision monitoring system has been developed to monitor minor collisions, providing warnings to determine potential impacts on the space station and bases surfaces. The system may contribute to ensuring the successful completion of space missions and providing a safer space environment for the exploration of extraterrestrial life and the establishment of underground protective bases.

Predicting in-stream water quality constituents at the watershed scale using machine learning.

Predicting in-stream water quality is necessary to support the decision-making process of protecting healthy waterbodies and restoring impaired ones. Data-driven modeling is an efficient technique that can be used to support such efforts. Our objective was to determine if in-stream concentrations of contaminants, nutrients-total phosphorus (TP) and total nitrogen (TN) -total suspended solids (TSS), dissolved oxygen (DO), and fecal coliform bacteria (FC) can be predicted satisfactorily using machine learning (ML) algorithms based on publicly available datasets. To achieve this objective, we evaluated four modeling scenarios, differing in terms of the required inputs (i.e., publicly available datasets (e.g., land-use/land cover)), antecedent conditions, and additional in-stream water quality observations (e.g., pH and turbidity). We implemented five ML algorithms-Support Vector Machines, Random Forest (RF), eXtreme Gradient Boost (XGB), ensemble RF-XGB, and Artificial Neural Network (ANN) -and demonstrated our modeling framework in an inland stream-Bullfrog Creek, located near Tampa, Florida. The results showed that, while including additional water quality drivers improved overall model performance for all target constituents, TP, TN, DO, and TSS could still be predicted satisfactorily using only publicly available datasets (Nash-Sutcliffe efficiency [NSE] > 0.75 and percent bias [PBIAS] < 10%), whereas FC could not (NSE < 0.49 and PBIAS >25%). Additionally, antecedent conditions slightly improved predictions and reduced the predictive uncertainty, particularly when paired with other water quality observations (6.9% increase in NSE for FC, and 2.7% for TP, TN, DO, and TSS). Also, comparable model performances of all water quality constituents in wet and dry seasons suggest minimal season-dependence of the predictions (<4% difference in NSE and < 10% difference in PBIAS). Our developed modeling framework is generic and can serve as a complementary tool for monitoring and predicting in-stream water quality constituents.

Self-Powered High-Voltage Recharging System for Removing Noxious Tobacco Smoke by Biomimetic Hairy-Contact Triboelectric Nanogenerator.

The most common size range of particulate matter (PM) in tobacco smoke is 1.0 to 5.0 microns; however, a high number of the most harmful PM is as small as 0.5 micron that is a serious threat to human health, and it is difficult to remove. There is an urgent need to develop a new purification technology for high-efficiency removing tobacco smoke with easily construction and low cost. Here, a method of self-powered high-voltage recharging system is demonstrated by designing biomimetic hairy-contact triboelectric nanogenerator (BHC-TENG) for long-lasting adsorption with a wide range from PM 0.5 to PM 10. The open-circuit voltage of BHC-TENGs reaches 8.42 KV, which can continuously charge injection to the melt-blown fabric, whose surface potential is able to maintain nearly 260 V level and create a uniform electrostatic adsorption field on the surface. This high-voltage recharging system reduces the concentration of PMs to World Health Organization (WHO) standards, maintaining the purification efficiency of PM 0.5- PM 10 persistently over 90%.

Irregular Wind Energy Harvesting by a Turbine Vent Triboelectric Nanogenerator and Its Application in a Self-Powered On-Site Industrial Monitoring System.

Wind is a regenerative and sustainable green energy, but it is intermittent; especially, harvesting irregular wind energy is a great challenge for existing technologies. This study demonstrates a turbine vent triboelectric nanogenerator (TV-TENG), which can be utilized as both an irregular wind harvester and a self-powered environmental sensing system on the rooftops of buildings. At a wind speed of nearly 7 m/s, the TV-TENG delivers an open-circuit voltage of up to 178.2 V, a short-circuit current of 38.2 µA, and a corresponding peak power of 2.71 mW under an external load of 5 MΩ, which can be used to directly light up 120 green light-emitting diodes. Furthermore, a self-powered on-site industrial monitoring system has been developed, which can be improve the easiness and simpleness of the industry environment for temperature monitoring and safety warning. Increasing the fluidity of air inside and outside the device is a key factor in fabricating an efficient TV-TENG; it is a novel approach for harvesting irregular wind energy and is sensitive, reliable, waterproof, and easy to use. This work greatly expands the applicability of TENGs as energy harvesters for irregular wind and also as self-powered sensing systems for ambient detection.

Spatio-temporal trajectory estimation based on incomplete Wi-Fi probe data in urban rail transit network.

This study presents a methodology for estimating passenger's spatio-temporal trajectory with personalization and timeliness by using incomplete Wi-Fi probe data in urban rail transit network. Unlike the automatic fare collection data that only records passenger's entries and exits, the Wi-Fi probe data can capture more detailed passenger movements, such as riding a train or waiting on a platform. However, the estimation of spatio-temporal trajectories remains as a challenging task because a few unfavorable situations could result into deficient data. To address this problem, we first describe the Wi-Fi probe data and summarize their common defects. Then, the n-gram method is developed to infer missing spatio-temporal location information. Next, an estimation algorithm is designed to generate feasible spatio-temporal trajectories for each individual passenger by integrating multiple data sources, i.e., urban rail transit network topology, Wi-Fi probe data, train schedules, etc. This proposed method is tested on both simulated data in blind experiments and real-world data from a complex urban rail transit network. The results of case study show that 93% of passengers' unique physical routes can be estimated. Then, for 80% of passengers, the number of feasible spatio-temporal trajectories can be reduced to one or two. Potential applications of the trajectory estimation approach are also identified.