Multifunctional Nanogenerator-Integrated Metamaterial Concrete Systems for Smart Civil Infrastructure.

Creating multifunctional concrete materials with advanced functionalities and mechanical tunability is a critical step toward reimagining the traditional civil infrastructure systems. Here, the concept of nanogenerator-integrated mechanical metamaterial concrete is presented to design lightweight and mechanically tunable concrete systems with energy harvesting and sensing functionalities. The proposed metamaterial concrete systems are created via integrating the mechanical metamaterial and nano-energy-harvesting paradigms. These advanced materials are composed of reinforcement auxetic polymer lattices with snap-through buckling behavior fully embedded inside a conductive cement matrix. We rationally design their composite structures to induce contact-electrification between the layers under mechanical excitations/triggering. The conductive cement enhanced with graphite powder serves as the electrode in the proposed systems, while providing the desired mechanical performance. Experimental studies are conducted to investigate the mechanical and electrical properties of the designed prototypes. The metamaterial concrete systems are tuned to achieve up to 15% compressibility under cycling loading. The power output of the nanogenerator-integrated metamaterial concrete prototypes reaches 330 µW. Furthermore, the self-powered sensing functionality of the nanogenerator concrete systems for distributed health monitoring of large-scale concrete structures is demonstrated. The metamaterial concrete paradigm can possibly enable the design of smart civil infrastructure systems with a broad range of advanced functionalities.

Magnetic capsulate triboelectric nanogenerators.

Triboelectric nanogenerators have received significant research attention in recent years. Structural design plays a critical role in improving the energy harvesting performance of triboelectric nanogenerators. Here, we develop the magnetic capsulate triboelectric nanogenerators (MC-TENG) for energy harvesting under undesirable mechanical excitations. The capsulate TENG are designed to be driven by an oscillation-triggered magnetic force in a holding frame to generate electrical power due to the principle of the freestanding triboelectrification. Experimental and numerical studies are conducted to investigate the electrical performance of MC-TENG under cyclic loading in three energy harvesting modes. The results indicate that the energy harvesting performance of the MC-TENG is significantly affected by the structure of the capsulate TENG. The copper MC-TENG systems are found to be the most effective design that generates the maximum mode of the voltage range is 4 V in the closed-circuit with the resistance of 10 GΩ. The proposed MC-TENG concept provides an effective method to harvest electrical energy from low-frequency and low-amplitude oscillations such as ocean wave.

Studying the Feasibility of Postoperative Monitoring of Spinal Fusion Progress Using a Self-Powered Fowler-Nordheim Sensor-Data-Logger.

OBJECTIVE: This study investigates the feasibility of using a new self-powered sensing and data logging system for postoperative monitoring of spinal fusion progress. The proposed diagnostic technology directly couples a piezoelectric transducer signal into a Fowler-Nordheim (FN) quantum tunneling-based synchronized dynamical system to record the mechanical usage of spinal fixation devices. The operation of the proposed implantable FN sensor-data-logger is completely self-powered by harvesting the energy from the micro-motion of the spine during the course of fusion. Bench-top testing is performed using corpectomy models to evaluate the performance of the proposed monitoring system. In order to simulate the spinal fusion process, different materials with gradually increasing elastic modulus are used to fill the intervertebral space gap. Besides, finite element models are developed to analyze the strains induced on the spinal rods during the applied cyclic loading. Data measured from the benchtop experiment is processed using an FN sensor-data-logger model to obtain time-evolution curves representing each spinal fusion state. This feasibility study shows that the obtained curves are viable tools to differentiate between conditions of osseous union and assess the effective fusion period.

Multifunctional Triboelectric Nanogenerator-enabled Structural Elements for Next Generation Civil Infrastructure Monitoring Systems.

There is a critical shortage in research needed to explore a new class of multifunctional structural components that respond to their environment, empower themselves and self-monitor their condition. Here, we propose the novel concept of triboelectric nanogenerator-enabled structural elements (TENG-SEs) to build the foundation for the next generation civil infrastructure systems with intrinsic sensing and energy harvesting functionalities. In order to validate the proposed concept, we develop proof-of-concept multifunctional composite rebars with built-in triboelectric nanogenerator mechanisms. The developed prototypes function as structural reinforcements, nanogenerators and distributed sensing mediums under external mechanical vibrations. Experiential and theoretical studies are performed to verify the electrical and mechanical performance of the developed self-powering and self-sensing composite structural components. We demonstrate the capability of the embedded structural elements to detect damage patterns in concrete beams at multiscale. Finally, we discuss how this new class of TENG-SEs could revolutionize the large-scale distributed monitoring practices in civil infrastructure and construction fields.

Multifunctional Meta-Tribomaterial Nanogenerators for Energy Harvesting and Active Sensing.

Discovering novel multifunctional metamaterials with energy harvesting and sensing functionalities is likely to be the next technological evolution of the metamaterial science. Here, we introduce a novel concept called self-aware composite mechanical metamaterial (SCMM) that can transform mechanical metamaterials into nanogenerators and active sensing mediums. In pursuit of this goal, we examine new paradigms where finely tailored and seamlessly integrated self-recovering snapping microstructures composed of topologically different triboelectric materials can form self-powering and self-sensing meta-tribomaterial systems. We explore various deformation mechanisms required to induce contact electrification between these snapping microstructures under periodic deformations. The multifunctional meta-tribomaterial systems created under the SCMM concept will act as triboelectric nanogenerators capable of generating electrical signals in response to the applied mechanical excitations. The generated electrical signal can be used for active sensing of the applied force and can be stored for empowering sensors and embedded electronics. We conduct theoretical and experimental studies to understand the mechanical and electrical behavior of the multifunctional SCMM systems. The broad application of the proposed SCMM concept for designing artificial materials with novel properties and functionalities is highlighted via prototyping self-powering and self-sensing blood vessel stents and shock absorbers.

A Novel Data Reduction Approach for Structural Health Monitoring Systems.

The massive amount of data generated by structural health monitoring (SHM) systems usually affects the system's capacity for data transmission and analysis. This paper proposes a novel concept based on the probability theory for data reduction in SHM systems. The beauty salient feature of the proposed method is that it alleviates the burden of collecting and analysis of the entire strain data via a relative damage approach. In this methodology, the rate of variation of strain distributions is related to the rate of damage. In order to verify the accuracy of the approach, experimental and numerical studies were conducted on a thin steel plate subjected to cyclic in-plane tension loading. Circular holes with various sizes were made on the plate to define damage states. Rather than measuring the entire strain response, the cumulative durations of strain events at different predefined strain levels were obtained for each damage scenario. Then, the distribution of the calculated cumulative times was used to detect the damage progression. The results show that the presented technique can efficiently detect the damage progression. The damage detection accuracy can be improved by increasing the predefined strain levels. The proposed concept can lead to over 2500% reduction in data storage requirement, which can be particularly important for data generation and data handling in on-line SHM systems.