Controllable and Scalable Fabrication of Superhydrophobic Hierarchical Structures for Water Energy Harvesting

Interfacial Polarization Strategy to Electroactive Poly(lactic acid) Nanofibers for Humidity-Resistant Respiratory Protection and Machine Learning-Assisted Monitoring

The advancement of intelligent and biodegradable respiratory protection equipment is pivotal in the realm of human health engineering. Despite significant progress, achieving a balance between efficient filtration and intelligent monitoring remains a great challenge, especially under conditions of high relative humidity (RH) and high airflow rate (AR). Herein, we proposed an interfacial stereocomplexation (ISC) strategy to facilitate intensive interfacial polarization for poly(lactic acid) (PLA) nanofibrous membranes, which were customized for machine learning-assisted respiratory diagnosis. Theoretical principles underlying the facilitated formation of the electroactive phase and aligned PLA chains were quantitatively depicted in the ISC-PLA nanofibers, contributing to the increased dielectric constant and surface potential (as high as 2.2 and 5.1 kV, respectively). Benefiting from the respiration-driven triboelectric mechanisms, the ISC-PLA demonstrated a high PM0.3 filtration efficiency of over 99% with an ultralow pressure drop (75 Pa), even in challenging circumstances (95 ± 5% RH, AR of 85 L/min). Furthermore, we implemented the ISC-PLA with multifunction respiratory monitoring (response time of 0.56 s and recovery time of 0.25 s) and wireless transmission technology, yielding a high recognition rate of 83% for personal breath states. This innovation has practical implications for health management and theoretical advancements in respiratory protection equipment.

Advances of Strategies to Increase the Surface Charge Density of Triboelectric Nanogenerators: A Review

Triboelectric nanogenerators (TENGs) have manifested a remarkable potential for harvesting environmental energy and have the prospects to be utilized for various uses, for instance, self-powered sensing devices, flexible wearables, and marine corrosion protection. However, the potential for further development of TENGs is restricted on account of their low output power that in turn is determined by their surface charge density. The current review majorly focuses on the selection and optimization of triboelectric materials. Subsequently, various methods capable of enhancing the surface charge density of TENGs, including environmental regulation, charge excitation, charge pumping, electrostatic breakdown, charge trapping, and liquid-solid structure are comprehensively reviewed. Lastly, the review is concluded by highlighting the existing challenges in enhancing the surface charge density of TENGs and exploring potential opportunities for future research endeavors in this area.

Self-Reinforced Piezoelectric Response of an Electroluminescent Film for the Dual-Channel Signal Monitoring of Damaged Areas

Organic piezoelectric nanogenerators (PENGs) show promise for monitoring damage in mechanical equipment. However, weak interfacial bonding between the reinforcing phase and the fluorinated material limits the feedback signal from the damaged area. In this study, we developed a PENG film capable of real-time identification of the damage location and extent. By incorporating core-shell barium titanate (BTO@PVDF-HFP) nanoparticles, we achieved enhanced piezoelectric characteristics, flexibility, and processability. The composite film exhibited an expanded output voltage range, reaching 41.8 V with an increase in frequency, load, and damage depth. Additionally, the film demonstrated self-powered electroluminescence (EL) during the wear process, thanks to its inherent ferroelectric properties and the presence of luminescent ZnS:Cu particles. Unlike conventional PENG electroluminescent devices, the PENG film exhibited luminescence at the damage location over a wide temperature range. Our findings offer a novel approach for realizing modular and miniaturized real-time damage mapping systems in the field of safety engineering.

High-Output Single-Electrode Droplet Triboelectric Nanogenerator Based on Asymmetrical Distribution Electrostatic Induction Enhancement

Droplet-based triboelectric nanogenerators (D-TENGs) have recently gained much attention due to their great potential in harvesting energy. However, the output performance of conventional single-electrode droplet-based TENGs is limited owing to low induced electrification efficiency. The asymmetric distribution of electric fields on both sides of the electrode edge enhances the electrostatic induction process and improves the output performance of D-TENG. Herein, an induced electrification-enhanced droplet-based triboelectric nanogenerator (IED-TENG) is developed to effectively enhance the output performance by simultaneously optimizing the electrode structure and the dynamics of the water droplet. One droplet falling from a height of 30 cm results in a -70 V output voltage and -6 µA short-circuit current, which is 70 times and 20 times the full-inductive-electrode mode, respectively. The working principle and the relationship between electric signal and droplet dynamics are analyzed in detail. Moreover, the peak output voltage can reach -110 V, and the peak current can get -140 µA by using the power generation of multiple water droplets. The present protocol provides an easy and reproducibility strategy in energy harvesting and sensing areas.

Modified Nonlinear Hysteresis Approach for a Tactile Sensor

Soft tactile sensors based on piezoresistive materials have large-area sensing applications. However, their accuracy is often affected by hysteresis which poses a significant challenge during operation. This paper introduces a novel approach that employs a backpropagation (BP) neural network to address the hysteresis nonlinearity in conductive fiber-based tactile sensors. To assess the effectiveness of the proposed method, four sensor units were designed. These sensor units underwent force sequences to collect corresponding output resistance. A backpropagation network was trained using these sequences, thereby correcting the resistance values. The training process exhibited excellent convergence, effectively adjusting the network's parameters to minimize the error between predicted and actual resistance values. As a result, the trained BP network accurately predicted the output resistances. Several validation experiments were conducted to highlight the primary contribution of this research. The proposed method reduced the maximum hysteresis error from 24.2% of the sensor's full-scale output to 13.5%. This improvement established the approach as a promising solution for enhancing the accuracy of soft tactile sensors based on piezoresistive materials. By effectively mitigating hysteresis nonlinearity, the capabilities of soft tactile sensors in various applications can be enhanced. These sensors become more reliable and more efficient tools for the measurement and control of force, particularly in the fields of soft robotics and wearable technology. Consequently, their widespread applications extend to robotics, medical devices, consumer electronics, and gaming. Though the complete elimination of hysteresis in tactile sensors may not be feasible, the proposed method effectively modifies the hysteresis nonlinearity, leading to improved sensor output accuracy.