Battery Replacement Research Articles

Transport Properties of Hard Carbons

Sodium-ion batteries (SIB) have been heavily researched, as a potential replacement for Lithium ion batteries (LIB) as their components are more readily obtainable, lower cost and they have similar working principles to LIB. Hard Carbon (HC) is the current state-of-the-art anode for SIB material due to its relatively low cost, overall performance and availability. Great effort has been focused into improving and better understanding the overall mechanism of HC, which is still a subject of debate.To better understand the mechanism of, and the obstacles to, charge transfer during charge and discharge of the cell, Hard carbon has been studied at different temperatures. The transitions between different stable and unstable phases are observed which have not been taken into consideration as of now in the literature. Exploring the thermodynamics and kinetics of the cell-system during the charge and discharge at different temperature with different methods, such as Galvanostatic Intermittent Titration Technique (GITT) and Electrochemical impedance spectroscopy (EIS) provides much-needed insights to elucidate the limits and obstacles to ion transport, which can then be addressed. EIS is one a key tool which provides the exchange current density, a measure of the rates of electron transfer as the ions migrate between the electrolyte and the electrodes, indicating the impedance of the different components in the cell at different frequencies. This is combined with Raman to further investigate these phase changes at different temperature.The understanding gained of the limits and obstacles to charge transfer will be highly beneficial in designing better suited hard carbons to improve their overall cell performance in SIBs.

NiTiO3/Bi2O3/MoS2 double Z-type heterojunction catalysts realize dual-function applications of photocatalytic fuel cells and lactic acid sensing

Sensors in intelligent monitoring and personal healthcare generally face power supply problems, such as cycle charging, regular maintenance, and battery replacement, which greatly limit the applications of sensing devices. Self-powered sensing system with a visible-light-driven photo-fuel cell (PFC) exhibits the advantage of not requiring any external power source for the chemical sensing of analytes. In this work, an enzyme-free PFC self-powered sensing platform was established for sensing sweat lactate using photoelectric conversion technology. A NiTiO3/Bi2O3/MoS2 double Z-type heterojunction system was prepared by electrostatic interaction and self-assembly as the anode of the PFC. NiTiO3, Bi2O3 and MoS2 formed a double Z-type heterojunction system accelerated the electron transfer and improved the light absorption and photocatalytic efficiency, which in turn improved the perception of LA. In addition. lactic acid sensing experiments showed that the prepared non-enzymatic photoelectrochemical sensors had good detection performance. The assay results showed that the prepared biosensor has a wide detection range (30 μM-5 mM), low detection limit (6 μM), high selectivity(8μA μM−1 cm−2) and long-term stability. These assay results demonstrate its potential for applications in fields such as biomedicine and drug analysis.

Remaining useful life prediction for lithium-ion batteries with an improved grey particle filter model

Accurate prediction of remaining useful life is of great value for the maintenance and replacement of electric vehicles lithium-ion batteries. This paper aims to present a grey particle filter model for improving remaining useful life forecast accuracy. Firstly, a grey particle filter model with recursive least square parameter estimation is built, and the proposed model’s parameters are trained. Secondly, RUL is predicted by using the parameters and proposed model. Finally, NASA lithium-ion battery open data set was used for verification. The model was evaluated from two perspectives of RUL accuracy and mean absolute percentage error. Predictions are also made for lithium-ion batteries under conditions of elevated temperature. The findings demonstrate that the proposed model outperforms the other models.

Energy Consumption Reduction in Wireless Sensor Network-Based Water Pipeline Monitoring Systems via Energy Conservation Techniques

In wireless sensor network-based water pipeline monitoring (WWPM) systems, a vital requirement emerges: the achievement of low energy consumption. This primary goal arises from the fundamental necessity to ensure the sustained operability of sensor nodes over extended durations, all without the need for frequent battery replacement. Given that sensor nodes in such applications are typically battery-powered and often physically inaccessible, maximizing energy efficiency by minimizing unnecessary energy consumption is of vital importance. This paper presents an experimental study that investigates the impact of a hybrid technique, incorporating distributed computing, hierarchical sensing, and duty cycling, on the energy consumption of a sensor node in prolonging the lifespan of a WWPM system. A custom sensor node is designed using the ESP32 MCU and nRF24L01+ transceiver. Hierarchical sensing is implemented through the use of LSM9DS1 and ADXL344 accelerometers, distributed computing through the implementation of a distributed Kalman filter, and duty cycling through the implementation of interrupt-enabled sleep/wakeup functionality. The experimental results reveal that combining distributed computing, hierarchical sensing and duty cycling reduces energy consumption by a factor of eight compared to the lone implementation of distributed computing.

A sustainable battery scheduling and echelon utilization framework for electric bus network with photovoltaic charging infrastructure

Battery capacity degradation in battery electric buses (BEBs) poses a significant operational challenge for transit agencies. This study presents a sustainable battery scheduling and echelon utilization framework considering battery capacity fading and charging infrastructure integrated with solar photovoltaic (PV) and energy storage systems. The framework aims to minimize the sum of bus charging, battery replacement, and carbon emission costs during the BEB lifespan. We first present a power battery replacement problem for a single bus fleet, then extend it to a joint power battery replacement and fleet-depot matching problem. Finally, we propose a power battery replacement and fleet-depot problem by introducing solar PV and energy storage systems. Dynamic programming, Lagrange relaxation, and a two-step approach are adopted to solve the three problems. A case study involving six bus depots in Beijing demonstrates that optimal battery replacement schedules can significantly lower charging costs. Moreover, integrating solar PV and energy storage is shown to considerably reduce both charging costs and carbon emissions.

Open access dataset, code library and benchmarking deep learning approaches for state-of-health estimation of lithium-ion batteries

Great progress has been made in deep learning (DL) based state-of-health (SOH) estimation of lithium-ion batteries, which helps to provide recommendations for predictive maintenance and replacement of lithium-ion batteries. However, despite the abundance of articles, few open-source codes are publicly available. While there are several public datasets, they tend to be more oriented toward simulating laboratory environments rather than real-world usage scenarios. Moreover, they solely provide raw data without any corresponding preprocessing codes, resulting in inconsistencies in preprocessing methods across different papers. These reasons lead to unfair comparisons and ineffective improvements. In response to these problems, this paper publishes a large-scale lithium-ion battery run-to-failure dataset, consisting of 55 batteries, and provides a unified data preprocessing method. Besides, we comprehensively evaluate 5 well-known DL-based models to provide benchmark research. To be specific, first, the existing DL-based SOH estimation methods are reviewed in detail. Second, we provide a comprehensive evaluation of DL-based models on 2 large-scale datasets, including 100 batteries, with 3 input types and 3 normalization methods. Third, we make the complete evaluation codes and dataset publicly available for better comparison and model improvement. Fourth, we discuss future DL-based SOH estimation, including unsupervised learning, transfer learning, interpretability, and physics-informed machine learning. We emphasize the importance of open-source code, provide baseline estimation errors (error upper bounds), and discuss existing issues in this field. The code library is available at: https://github.com/wang-fujin/SOHbenchmark.

INDUSTRY 4.0: CHALLENGES IN ELECTRIC VEHICLES

Energy and environmental impacts are at the top of the list of major international challenges to be addressed at intervals in the ensuing few years. With transportation being one of the foremost waste management sectors, focus has been directed towards electric vehicles because of their considerably lower dependency on fossil fuels. Nonetheless, electrical quality is presently characterized by a restricted practice range, leading makers to work on new strategies that might increase variety and charging potency. Current analysis is especially centered on increasing battery performance and reducing charging time. This study concentrates on presenting a completely unique approach: supported commutation battery stations, wherever the vehicle’s batteries are replaced with a completely charged one (available at the station), whereas new standard batteries may be created on the spot. The prevailing service station networks may be used for the replacement of electrical automobile batteries. This could be addressing the issue of long battery charging times. Through the assimilation of Trade 4.0 Key Sanctioning Technologies, individual challenges are often addressed. This can be illustrated through a holistic framework whereby the necessities for the context-aware style of the automobile itself are given and a selected answer supporting the mechanical mounting of batteries is mentioned. Keywords: electric vehicle, battery replacement, Industry 4.0, car design.

Understanding the Impact of Environmental Conditions on Zero-Power Internet of Things: An Experimental Evaluation

The booming Internet of Things is transforming all industries, making them more productive and efficient. Unfortunately, with its widespread use, a series of challenges are posed by sustainable sensing, harsh working environments, laboursome battery replacement, and the requirement for an ultra-small form factor. The concept of zero-power Internet of Things has recently been proposed to overcome these challenges by integrating farfield wireless power transfer, ambient energy harvesting, and backscatter wireless communications into networked embedded systems. However, the development of far-field wireless power transfer is still in its infancy, and how environmental conditions affect its performance in the real world remains unclear. This article fills this gap and provides an extensive experimental study to quantify the impact of environmental conditions, namely, lineof- sight propagation, multipath interference, building materials, and non-air mediums on far-field wireless power transfer. Inspired by this study, we further shed light on some future research directions.

A total cost of ownership analysis of zero emission powertrain solutions for the heavy goods vehicle sector

Transport-related activities represented 34% of the total carbon emissions in the UK in 2022 and heavy-duty vehicles (HGVs) accounted for one-fifth of the road transport greenhouse gas (GHG) emissions. Currently, battery electric vehicles (BEVs) and hydrogen fuel cell electric vehicles (FCEVs) are considered as suitable replacements for diesel fleets. However, these technologies continue to face techno-economic barriers, creating uncertainty for fleet operators wanting to transition away from diesel-powered internal combustion engine vehicles (ICEVs). This paper assesses the performance and cost competitiveness of BEV and FCEV powertrain solutions in the hard-to-abate HGV sector. The study evaluates the impact of battery degradation and a carbon tax on the cost of owning the vehicles. An integrated total cost of ownership (TCO) model, which includes these factors for the first time, is developed to study a large retailer's HGV fleet operating in the UK. The modelling framework compares the capital expenditures (CAPEX) and operating expenses (OPEX) of alternative technologies against ICEVs. The TCO of BEVs and FCEVs are 11% to 33% and 37% to 78% higher than ICEVs; respectively. Despite these differences, by adopting a longer lifetime for the vehicle it can effectively narrow the cost gap. Alternatively, cost parity with ICEVs could be achieved if BEV battery cost reduces by 56% or if FCEV fuel cell cost reduces by 60%. Besides, the pivot point for hydrogen price is determined at £2.5 per kg. The findings suggest that BEV is closer to market as its TCO value is becoming competitive, whereas FCEV provides a more viable solution than BEV for long-haul applications due to shorter refuelling time and lower load capacity penalties. Furthermore, degradation of performance in lithium-ion batteries is found to have a minor impact on TCO if battery replacement is not required. However, critical component replacement and warranty can influence commercial viability. Given the high costs, we propose financial incentives and vehicle tax reforms to reduce costs of critical components that will encourage the roll-out of zero emission HGVs.

Impact of cobalt recycling on China's electrification process: Assessing the potential reduction in cobalt demand from battery recycling

China is actively promoting the transition from conventional internal combustion engine vehicles (ICEVs) to electric vehicles (EVs) to achieve its goal of carbon peaking and neutrality. However, this shift will result in a continuous increase in demand for lithium-ion batteries for EVs, leading to a significant rise in demand for cobalt, further exacerbating the risk of cobalt resource supply in China. The study considered three electrification levels, three battery technology routes, two battery capacities, two dynamic battery lifetimes, and two recycling efficiencies, resulting in 36 demand scenarios and 72 recycling scenarios while increasing the precision of the study to the inter-provincial level. The results indicate that China's cobalt demand will be huge, peaking around 2039, with annual demand ranging from 78.29 kt to 359.40 kt, and the current production capacity and reserves are completely inadequate to meet the growing cobalt demand. In some specific scenarios, secondary cobalt demand remains higher than or close to primary cobalt demand between 2035 and 2040, and secondary cobalt demand becomes a significant component affecting total cobalt demand. In terms of recycling, closed-loop recycling can cover 45.1%–59.3% of annual cobalt demand in 2039. With rapid recovery efficiency, cobalt recovery in multiple scenarios can already fully meet primary cobalt demand, and the recovery potential is enormous. Furthermore, adopting low-cobalt battery technology, which extends battery life and reduces battery replacement in EVs, can significantly reduce secondary cobalt demand. At the provincial level, cobalt demand and recycling in China are unevenly distributed, with provinces with high demand for cobalt showing a trend of shifting from inland areas in the northwest to central and eastern coastal areas. In contrast, the areas with the largest recycling volume remain in the north. Therefore, accelerating the promotion of low cobalt and long-life battery technology and improving recycling efficiency can greatly alleviate the cobalt supply risk in China's electrification process. Additionally, specific spatial layouts should be given attention to ensure the sound development of China's EV industry through national coordination so that the double carbon target can be accomplished on time or even ahead of schedule.

Hybrid backscatter communication for IoT devices in remote areas

The IoT devices placed in remote locations require a battery replacement very often, which is not a convenient option. Backscatter communication can resolve this problem, as backscatter communication is a data transmission in which an RF signal incident from the gateway is used for energy harvesting, and this energy will be employed for data transmission. In this paper, a hybrid contention-based TDMA scheme is proposed, which provides slots to devices by dividing them into groups, and then contention is employed in groups to acquire a slot; if a device is not able to transmit during harvest, then transmit (HTT) period, then it can transmit in variable sub frame and the devices which are not able to completely transmit during HTT period can reserve subframes. The proposed hybrid scheme is compared with the TDMA scheme for average transmission delay.The proposed scheme provides scalability. The difference between the average transmission delay of TDMA and the proposed hybrid scheme is from 6 to 20 s, depending on the number of devices added and when traffic is generated.

Swimmable Micro‐Battery for Targeted Power Delivery

AbstractThe boom of microelectronic devices and their diverse applications has heightened the demand for innovative and effective energy supply strategies. The longevity of contemporary microelectronic devices predominantly depends on direct human intervention power supply methods, encompassing battery replacement or recharging. However, these methods may falter in specific scenarios, such as when facing liquid conditions in vivo. Here, a concept of swimmable micro‐batteries (MBs) for targeted power delivery is presented. This is achieved through the design of a flexible polydimethylsiloxane/Fe3O4 soft‐magnetic substrate for actuation, polydimethylsiloxane/neodymium‐iron‐boron (NdFeB) hard‐magnetic tabs for precise targeting, and quasi‐solid‐state Zn//MnO2 battery for power generation. These swimmable MBs exhibit an impressive areal capacity of 102.3 µAh cm‐2, remarkable waterproofing, adjustable output voltage or capacity, rapid response, and accurate remote magnetic field manipulation, enabling targeted power supply available. This novel swimmable MB design broadens the function of MBs and may open a new avenue for their future development.

Optimizing Electric Vehicle Battery Performance: Comprehensive Study of Battery Heating Challenges and Sustainable Battery Swapping System

This paper conducts a comprehensive study on battery heating challenges in electric vehicles (EVs) and proposes a sustainable battery swapping system to optimize battery performance. With the increasing demand for EVs and the necessity for extended driving ranges, notable progress has been achieved in battery technologies. However, battery heating remains a crucial concern, particularly in cold weather conditions. This research thoroughly examines various factors contributing to battery heating, including battery size, chemistry, design, and ambient conditions. Diverse battery heating techniques, such as insulation and thermal management systems, are extensively analyzed. Underlining the significance of effective battery heating solutions, this study emphasizes the importance of maintaining an optimal battery temperature to enhance performance and prolong battery life. Additionally, the paper introduces a sustainable battery swapping system controlled by a Blynk app and Node MCU controller, facilitating seamless battery replacement and promoting the integration of renewable energy sources. This system ensures uninterrupted EV operation while providing users complete control and real-time monitoring capabilities via mobile devices, revolutionizing the EV charging paradigm.

Implantable magnetically-actuated capsule for on-demand delivery

Many implantable drug delivery systems (IDDS) have been developed for long-term, pulsatile drug release. However, they are often limited by bulky size, complex electronic components, unpredictable drug delivery, as well as the need for battery replacement and consequent replacement surgery. Here, we develop an implantable magnetically-actuated capsule (IMAC) and its portable magnetic actuator (MA) for on-demand and robust drug delivery in a tether-free and battery-free manner. IMAC utilizes the bistable mechanism of two magnetic balls inside IMAC to trigger drug delivery under a strong magnetic field (|Ba| > 90 mT), ensuring precise and reproducible drug delivery (9.9 ± 0.17 μg per actuation, maximum actuation number: 180) and excellent anti-magnetic capability (critical trigger field intensity: ∼90 mT). IMAC as a tetherless robot can navigate to and anchor at the lesion sites driven by a gradient magnetic field (∇ Bg = 3 T/m, |Bg| < 60 mT), and on-demand release drug actuated by a uniform magnetic field (|Ba| = ∼100 mT) within the gastrointestinal tract. During a 15-day insulin administration in vivo, the diabetic rats treated with IMAC exhibited highly similar pharmacokinetic and pharmacodynamic profiles to those administrated via subcutaneous injection, demonstrating its robust and on-demand drug release performance. Moreover, IMAC is biocompatible, batter-free, refillable, miniature (only Φ 6.3 × 12.3 mm3), and lightweight (just 0.8 g), making it an ideal alternative for precise implantable drug delivery and friendly patient-centered drug administration.

Powering Implanted Devices Wirelessly Using Spider-Web Coil

Implantable biomedical (IBM) systems and biomedical sensors can improve life quality, identify sickness, monitor biological signs, and replace the function of malfunctioning organs. However, these devices compel continuous battery power, which can be limited by the battery's capacity and lifetime, reducing the device's effectiveness. The wireless power transfer (WPT) technique, specifically magnetic resonator coupling (MRC), was utilized to address the limited battery capacity of IBMs. By using WPT–MRC, the device can obtain power wirelessly, thereby reducing the need for frequent battery replacements and increasing the device's potential. In this research, spider-web coil (S-WC) based MRC–WPT was conceived and carried out experimentally to enhance low-power IBM's rechargeable battery usage time. The presented S-WC–MRC–WPT design uses series–parallel (S–P) configuration to power the IBM. Both transmitter and receiver coils exhibit an operating oscillation frequency of 6.78 MHz. The paper reports on experiments performed in the laboratory to assess the performance of the proposed design in terms of output DC at three different resistive loads and transmission distances with alignment conditions among the receiver and the transmitter coils. Various transfer distances ranging from 10 to 100 mm were investigated to analyze the DC output current (Idc). Specifically, under a 30 V voltage source (VS) and a transfer distance of 20 mm, the DC output current was observed to be 330, 321, and 313 mA at resistive loads of 50, 100, and 150 Ω, respectively.

Initial clinical experience with new technology: ePatch extended Holter monitoring

Abstract Introduction Holter monitor electrocardiography is used to detect potential cardiac arrhythmias such as atrial fibrillation (AF). However, conventional ECG monitoring, data analysis and reporting methods can be labor-intensive and inefficient, leading to high upfront costs and long turnaround times. At the same time, traditional Holter devices can be cumbersome to wear, while inconvenient processes may reduce patient compliance and satisfaction – which impacts diagnostic yield. This new device (ePatch®) is a simple patch application, enrollment and activation with no battery charging needed and no cable attachments (Figure 1). Up to 14 days of continuous ECG recording. Wearable in shower, during exercise and while sleeping. Single-channel, up to 14 days. Two-channel, up to 5 days Purpose Our goal is to evaluate the quality and diagnostic efficacy of ePatch® in different clinical settings. Methods Prospective study of patients with ePatch® placed in our center requested for study of undocumented palpitations, syncope or cryptogenic stroke. Clinical variables, diagnostic accuracy, and time to diagnostic event are analyzed. Results We enrolled the first 57 patients, of whom 27 (50%) were women, with a mean age of 67.8 years (range 27 to 89). The most common indication was undocumented palpitations (35.1%) and cryptogenic stroke (35.1%), followed by syncope (29.6%). All the patients had correct compliance, tolerance and the ECG monitored was of good quality. A conclusive diagnosis: (symptomatic sustained tachyarrhythmia, atrial-ventricular block with bradyarrhythmia, significant pauses or AF) was obtained in 5 patients (8.7% of the total). The average event time since ePatch registration started was 2.4 days (range 1 to 5 days). Conclusions The ePatch® is a new extended Holter device offering a simple, complete and efficient ECG monitoring system. This new technology is safe and has numerous advantages: spend less time on logistics and device handling, prepare your patients efficiently, eliminate the need for additional consumables and constant battery replacement. The ECG monitor of our study is of 5 days, but it can be used for up to 14 days. It is designed to address the challenges of traditional Holter monitoring.

Remaining useful life prediction for lithium-ion batteries based on sliding window technique and Box-Cox transformation

Accurate remaining useful life (RUL) prediction technique matters in lithium-ion battery use, optimization, and replacement. This article presents an RUL prediction method combining the sliding window (SW) technique and Box-Cox transformation (BCT). This method achieves online RUL prediction with acceptable accuracy, is independent of offline training data, and only brings a low computational burden. The SW technique is employed for gathering a certain amount of capacity data, which is subsequently transformed using BCT-related techniques to construct a capacity degradation model. The identified model is then extrapolated to predict battery RUL, and the prediction uncertainties are calculated through Monte Carlo (MC) simulation. A simple implementation shows that this hybrid method outperforms the history-based polynomial and BCT methods in battery RUL prediction. Given the segmented capacity degradation trend, a constraint can be imposed on the model parameter for optimization purposes. Experimental results demonstrate that the optimized method obtains lower root-mean-square errors (RMSEs) of RUL predictions during the last 20 % of the battery lifetime than the original one, and the precise RULs are predicted with standard deviations mainly within [1, 10] cycles.

Enabling large-scale low-power LoRa data transmission via multiple mobile LoRa gateways

Low-Power Wide Area Networks (LPWANs) enable large-scale Internet of Things (IoT) applications. LoRa is a promising LPWAN technology, but LoRa nodes are battery-powered, so their lifetime depends on energy efficiency. In practice, LoRa nodes have short battery life because long transmission distances to gateways incur high energy costs. With expensive battery replacement, particularly in large networks, it is crucial to reduce LoRa nodes’ energy consumption. Existing low-power transmission techniques focus on static or single mobile gateways, consuming substantial remote node energy unfit for large-scale networks. This paper proposes integrating LoRa with mobility to minimize node energy use by shortening transmission distances. We design the first large-scale mobile LoRa system, MLoRaDrone, using multiple unmanned aerial vehicles (UAVs) equipped with LoRa gateways flying near nodes. First, we present a low-power mechanism for reliable node sensing and UAV communication. Next, we develop a LoRa transmission parameter assignment strategy encompassing a distributed ADMM-based optimal transmission policy and a 32-approximation channel allocation. Moreover, we propose a UAV trajectory scheduling scheme with a 4-approximation minimizing node energy and UAV flight paths. Evaluations on varied scales verify MLoRaDrone effectiveness for different node counts and UAV paths. Compared to baselines, MLoRaDrone reduces 3000-node energy consumption by up to 35.56× within 25 km2.

Recent advancements in piezoelectric energy harvesting for implantable medical devices

Biomedical implantable devices like deep brain stimulators, implantable cardioverter-defibrillators and cardiac pacemakers are essential for treating the human heart and brain-related diseases. In the past few decades, a considerable amount of research has focused on improving bio-implant technologies. Conventional bio implant devices consist of an external generator like a battery to power the system, which requires replacement after a particular time. Therefore, in recent years, self-powered implants with various energy harvesting techniques have been proposed to avoid frequent surgery for battery replacement and to miniaturise the implant systems. However, the research communities have yet to explore all the limitations and possibilities of improvement on such energy-scavenging technologies, especially when the application is in vivo. Several aspects of recent developments in energy harvesting technologies feasible for biomedical implantable devices are reported systematically. A detailed review of piezoelectric energy harvester mechanism and miniaturisation, electric output and power management and biocompatibility of an energy harvester for implantable medical devices in vitro and in vivo environments. Furthermore, the piezoelectric energy harvester’s durability, packaging material, connection and evaluation criteria are discussed.

A self-assembled implantable microtubular pacemaker for wireless cardiac electrotherapy.

The current cardiac pacemakers are battery dependent, and the pacing leads are prone to introduce valve damage and infection, plus a complete pacemaker retrieval is needed for battery replacement. Despite the reported wireless bioelectronics to pace the epicardium, open-chest surgery (thoracotomy) is required to implant the device, and the procedure is invasive, requiring prolonged wound healing and health care burden. We hereby demonstrate a fully biocompatible wireless microelectronics with a self-assembled design that can be rolled into a lightweight microtubular pacemaker for intravascular implantation and pacing. The radio frequency was used to transfer energy to the microtubular pacemaker for electrical stimulation. We show that this pacemaker provides effective pacing to restore cardiac contraction from a nonbeating heart and have the capacity to perform overdrive pacing to augment blood circulation in an anesthetized pig model. Thus, this microtubular pacemaker paves the way for the minimally invasive implantation of leadless and battery-free microelectronics.