Milutin Stanacevic

Triboelectric nanogenerators (TENGs) are attracting increasing attention as viable power sources for self-powered systems, due to advantages such as material and form-factor versatility, compatibility with low-frequency mechanical stimuli, and scalable, low-cost fabrication. Unlike conventional harvesters, TENGs exhibit two device-level characteristics that critically shape interface-circuit design: (i) a time-varying internal capacitance that induces a strongly dynamic source impedance, and (ii) a significantly high effective source impedance that yields very high open-circuit voltages at low currents. These characteristics directly affect impedance matching, rectification, voltage conversion, and maximum power point tracking (MPPT) strategies for maximum power transfer. While TENGs and piezoelectric energy harvesters (PEHs) share similar lumped electrical models, the time variance and voltage/current operating regime of TENGs fundamentally limit the portability of PEH-oriented power transfer methods. This paper provides two contributions. First, we introduce a figure-of-merit (FoM) that serves as an energy-extraction coefficient: the fraction of the ideal maximum power of the TENG device (under instantaneous impedance tracking) that appears at the rectifier input, typically the first stage of a power management unit (PMU). The FoM exposes losses arising from mismatch at the device-PMU boundary, thereby helping circuit designers localize dominant loss mechanisms (e.g., impedance mismatch, rectifier topology or suboptimal MPPT policies) and guiding device researchers to prioritize physical parameters (e.g., dielectric thickness, displacement, electrode area) with explicit awareness of interface constraints. Secondly, we conduct a thorough evaluation of the suitability of advanced PEH-derived methods, such as rectifiers (both passive and active configurations), DC–DC conversion, and MPPT, for application to TENGs. We derive theoretical upper bounds on extractable power for representative rectifier families under TENG-specific operating conditions, and we analyze technology-imposed voltage limits and their implications for architecture and control. We also survey recent TENG demonstrations together with their PMU interfaces and interpret reported performance through the proposed FoM. Overall, the analysis highlights that the unique characteristics of TENGs and technology limits for the voltages must be explicitly accounted for while developing interface circuits to realize maximum power extraction. This process can significantly benefit from coordinated device–circuit co-design via system-level metrics such as the proposed FoM.

This study addresses the critical need for self-powered, durable pressure sensors in total knee replacement (TKR) implants to enable the collection of postoperative information. The scope of this research encompasses the design, development, and testing of a triboelectric nanogenerator (TENG) integrated into an instrumented knee implant for energy harvesting and pressure sensing. The authors’ unique approach involves utilizing a porous silicone rubber as a dielectric material. This allows the TENG to withstand forces up to 2000 N and generate a maximum power output of $18\,\mu \text{W}$. Theoretical modeling combined with experimental validation provides deeper insight into the fundamental operating principles. We elucidate the TENG output performance under an MTS servo-hydraulic load frame compared with a VIVO joint simulator. Other important characteristics, such as load sensitivity and the influence of porosity, are also presented. The proposed TENG shows great potential as a pressure sensor in TKR applications, offering high sensitivity, stability, and low cost.

Embankments, levees, and dikes are earthen protection structures built to protect industrial, commercial, residential, and agricultural regions from flooding. The inadequate maintenance of such structures can lead to failures, resulting in significant property damage, loss of farmland, and loss of life. Thus, effective monitoring of earthen structures is essential for risk management. This paper presents the envisioned design of a Smart Earthen Embankment system, battery-less sensors, and associated networking technologies, as well as data analytics and machine learning methods for the predictive modeling of structural health based on the collected data. In particular, the aim of this paper is to describe the vision of the system design, substantiate its effectiveness, and investigate the physical foundations of its feasibility through experimental study. The paper establishes the conceptual feasibility of a system that is capable of supporting spatially continuous, autonomous, and multiparameter Structural Health Monitoring (SHM) of earthen flood barriers. Deployment of the envisioned system will aid in addressing some of the increased dangers caused by climate changes.

Although total knee replacements have an insignificant impact on patients’ mobility and quality of life, real-time performance monitoring remains a challenge. Monitoring the load over time can improve surgery outcomes and early detection of mechanical imbalances. Triboelectric nanogenerators (TENGs) present a promising approach as a self-powered sensor for load monitoring in TKR. A TENG was fabricated with dielectric layers consisting of Kapton tape and 3D-printed thermoplastic polyurethane (TPU) matrix incorporating CNT and BTO fillers, separated by an air gap and sandwiched between two copper electrodes. The sensor performance was optimized by varying the concentrations of BTO and CNT to study their effect on the energy-harvesting behavior. The test results demonstrate that the BTO/TPU composite that has 15% BTO achieved the maximum power output of 11.15 μW, corresponding to a power density of 7 mW/m², under a cyclic compressive load of 2100 N at a load resistance of 1200 MΩ, which was the highest power output among all the tested samples. Under a gait load profile, the same TENG sensor generated a power density of 0.8 mW/m² at 900 MΩ. By contrast, all tested CNT/TPU-based TENG produced lower output, where the maximum generated apparent power output was around 8 μW corresponding to a power density of 4.8 mW/m², confirming that using BTO fillers had a more significant impact on TENG performance compared with CNT fillers. Based on our earlier work, this power is sufficient to operate the ADC circuit. Furthermore, we investigated the durability and sensitivity of the 15% BTO/TPU samples, where it was tested under a compressive force of 1000 N for 15,000 cycles, confirming the potential of long-term use inside the TKR. The sensitivity analysis showed values of 37.4 mV/N for axial forces below 800 N and 5.0 mV/N for forces above 800 N. Moreover, dielectric characterization revealed that increasing the BTO concentration improves the dielectric constant while at the same time reducing the dielectric loss, with an optimal 15% BTO concentration exhibiting the most favorable dielectric properties. SEM images for BTO/TPU showed that the 10% and 15% BTO/TPU composites showed better morphological characteristics with lower fabrication defects compared with higher filler concentrations. Our BTO/TPU-based TENG sensor showed robust performance, long-term durability, and efficient energy conversion, supporting its potential for next-generation smart total knee replacements.

A power optimization strategy is described for triboelectric energy harvesting systems by optimizing the load capacitor size within a full wave rectifier (FWR). In AC harvesters such as triboelectric nanogenerators (TENGs) with an FWR, the average power delivered to the rectifier increases in each cycle, ultimately reaching a steady state determined by system parameters. Through cycle-level analysis of input voltage, current, and rectifier turn-on time during mechanical motion, an optimal load capacitance is identified that maximizes power delivery while minimizing transient time to reach this maximum power. This approach achieves peak power delivery via capacitor sizing alone, eliminating the need for additional circuitry. Experimental results using a vertical contact-separation triboelectric nanogenerator with internal capacitance varying from 24pF to 96pF demonstrate that the optimal rectifier capacitance of 390pF achieves maximum power delivery (900nW at 1.7Hz and 2.7μW at 5Hz) within the second cycle, while suboptimal capacitances either fail to reach peak power or delay it to the seventh cycle or later. Sensitivity analysis reveals that the method exhibits high robustness, with capacitances within ±30% of the optimal value can still maintain ≥ 90% of peak power, providing flexibility when implementing or selecting the capacitor size.

RF tag networks that utilize fully passive tags and backscatter communication offer a promising solution for ultralow-power and cost-effective Internet of Things (IoT) applications. Accurate estimation of tag-to-tag channel state information (CSI), particularly channel phase and path loss, is critical for enabling a wide range of such applications. However, the absence of active radio IQ demodulation and the limited computational resources in passive tags pose significant challenges for precise CSI estimation. To address these challenges, we propose a multi-phase modulator tag architecture and two optimized low-complexity channel estimation techniques: the Linear Least Squares (LLS) and the Three Reflection Loads (TRL) technique. Both techniques are designed to minimize estimation error while maintaining ultra-low computational cost, making them wellsuited for energy-constrained implementations. We validate the proposed techniques through measurements conducted in both indoor and outdoor environments with a discrete-component, battery-powered RF tag prototype. Experimental results show that the 90th percentile phase estimation error is 11. in outdoor settings and 25. in indoor settings under ambient RF excitation at 915 MHz. These phase errors correspond to ranging inaccuracies of approximately 10 mm and 23 mm, respectively, over tag-totag distances from 28 cm to 228 cm. These results highlight the robustness, energy efficiency, and scalability of the proposed channel estimation techniques for practical deployment in passive RF tag networks.

Transportation infrastructure experiences distress due to aging, overuse, and climate changes. To reduce maintenance costs and labor, researchers have developed various structural health monitoring systems. However, the existing systems are designed for short-term monitoring and do not quantify structural parameters. A long-term monitoring system that quantifies structural parameters is needed to improve the quality of monitoring. In this work, a novel Transportation Rf-bAsed Monitoring (TRAM) system is proposed. TRAM is a multi-parameter monitoring system that relies on embeddable backscatter-based, batteryless, and radio-frequency sensors. The system can monitor structural parameters with 3D spatial and temporal information. Laboratory experiments were conducted on a 1D scale to evaluate and examine the sensitivity and reliability of the monitored structural parameters, which are displacement and water content. In contrast to other existing methods, TRAM correlates phase change to the change in concerned parameters, enabling long-term monitoring.

Over the past three decades, we have seen significant advances in the field of wireless implantable medical devices (IMDs) that can interact with the nervous system. To further improve the stability, safety, and distribution of these interfaces, a new class of implantable devices is being developed: single-channel, sub-mm scale, and wireless microelectronic devices. In this research, we describe a new and simple technique for fabricating and assembling a sub-mm, wirelessly powered stimulating implant. The implant consists of an ASIC measuring 900 × 450 × 80 µm³, two PEDOT-coated microelectrodes, an SMD inductor, and a SU-8 coating. The microelectrodes and SMD are directly mounted onto the ASIC. The ultra-small device is powered using electromagnetic (EM) waves in the near-field using a two-coil inductive link and demonstrates a maximum achievable power transfer efficiency (PTE) of 0.17% in the air with a coil separation of 0.5 cm. In vivo experiments conducted on an anesthetized rat verified the efficiency of stimulation.

Backscattering tags transmit passively without an on-board active radio transmitter. Almost all present-day backscatter systems, however, rely on active radio receivers. This presents a significant scalability, power and cost challenge for backscatter systems. To overcome this barrier, recent research has empowered these passive tags with the ability to reliably receive backscatter signals from other tags. This forms the building block of passive networks wherein tags talk to each other without an active radio on either the transmit or receive side. For wider functionality, accurate localization of such tags is critical. All known backscatter tag localization techniques rely on active receivers for measuring and characterizing the received signal. As a result, they cannot be directly applied to passive tag-to-tag networks. This paper overcomes the gap by developing a localization technique for such passive networks based on a novel method for phase-based ranging in passive receivers. This method allows pairs of passive tags to collaboratively determine the inter-tag channel phase while effectively minimizing the effects of multipath and noise in the surrounding environment. Building on this, we develop a localization technique that benefits from large link diversity uniquely available in a passive tag-to-tag network. We evaluate the performance of our techniques with extensive micro-benchmarking experiments in an indoor environment using fabricated prototypes of tag hardware. We show that our phase-based ranging performs similar to active receivers, providing median 1D ranging error <1 cm and median localization error also <1 cm. Benefiting from the large-scale link diversity our localization technique outperforms several state-of-the-art techniques that use active receivers.