Battery Replacement Research Articles

(Invited) 3D Printing for Tissue Scaffolds with Tunable Mechanical Durability and Sensing Capabilities

Pelvic organ prolapse (POP) is a prevalent condition among senior women, characterized by the descent or herniation of pelvic organs into the vaginal canal. It is a widespread issue, affecting a significant portion of the elderly female population, and yet, effective medical solutions for this condition remain elusive. This can be attributed to the complex nature of POP, where multiple factors, including tissue weakening and structural changes, contribute to its development. Despite the significant impact on women's quality of life, the absence of comprehensive treatment options underscores the urgency for innovative approaches to address this pressing health concern. In this context, 3D printing emerges as a valuable rapid prototyping tool, offering unique capabilities for designing and fabricating smart sensors and actuators. Its precision and versatility enable the creation of intricate devices that can be customized to specific medical applications. Moreover, 3D printing allows for the integration of sensors directly into biomedical devices, facilitating seamless monitoring and feedback mechanisms. Leveraging this technology, the research endeavors to harness the potential of 3D printing to develop advanced thermoelectric devices that can be seamlessly embedded within POP tissue scaffolds. These innovative devices will serve a dual purpose—detecting the health status of the tissue scaffolds and assessing their mechanical durability. In the realm of embedded medical devices, self-powered sensors hold particular significance due to their wireless powering capabilities and in-situ monitoring capabilities of human health. These sensors can operate without the need for external power sources or frequent battery replacements, making them ideal for long-term implantation within the human body. By tapping into the body's own energy sources or ambient environmental factors, self-powered sensors ensure continuous data collection and transmission, enhancing the efficiency and reliability of healthcare monitoring systems. Consequently, this talk aims to demonstrate the feasibility of 3D-printed thermoelectric devices as self-powered sensors, integrated into POP tissue scaffolds. These innovative devices will play a pivotal role in monitoring the tissue scaffold's condition, ensuring its mechanical integrity, and ultimately contributing to improved healthcare outcomes for women affected by POP.

Metaheuristic optimization‐based clustering with routing protocol in wireless sensor networks

SummaryIn recent years, the use of wireless sensor devices in several applications, for example, monitoring in dangerous geographical spaces and the Internet of Things, has dramatically increased. Though sensor nodes (SNs) have limited power, battery replacement is not feasible in most cases. Therefore, energy saving in wireless sensor networks (WSN) is the major concern in the design of effective transmission protocol. Clustering might lower energy usage and increase network lifetime. Routing protocol for WSN represents an engineering area that has gained considerable interest among researchers due to its rapid evolution and development. Among them, the clustering routing protocol corresponds to the most effective technique to manage the energy consumption of each SN. In this manuscript, we focus on the design of a new metaheuristic optimization‐based energy‐aware clustering with routing protocol for lifetime maximization (MOEACR‐LM) method in WSN. The purpose of the MOEACR‐LM method is to improve network efficiency via proper selection of cluster heads (CHs) and effective data transmission. Initially, a hunter–prey optimization (HPO) method‐based clustering technique is used for cluster construction and the CH selection process. Next, the clouded leopard optimization (CLO) model is used for the route selection process in WSN. The HPO and CLO models derive a fitness function involving multiple parameters for clustering and routing processes. A comprehensive experimental analysis is carried out to demonstrate the enhanced performance of the MOEACR‐LM technique. The overall comparison study pointed out the improved energy efficiency results of the MOEACR‐LM technique over other existing approaches.

Overview of Inorganic Electrolytes for All-Solid-State Sodium Batteries.

All-solid-state sodium batteries (AS3B) emerged as a strong contender in the global electrochemical energy storage market as a replacement for current lithium-ion batteries (LIB) owing to their high abundance, low cost, high safety, high energy density, and long calendar life. Inorganic electrolytes (IEs) are highly preferred over the conventional liquid and solid polymer electrolytes for sodium-ion batteries (SIBs) due to their high ionic conductivity (∼10-2-10-4 S cm-1), wide potential window (∼5 V), and overall better battery performances. This review discusses the bird's eye view of the recent progress in inorganic electrolytes such as Na-β"-alumina, NASICON, sulfides, antipervoskites, borohydride-type electrolytes, etc. for AS3Bs. Current state-of-the-art inorganic electrolytes in correlation with their ionic conduction mechanism present challenges and interfacial characteristics that have been critically reviewed in this review. The current challenges associated with the present battery configuration are overlooked, and also the chemical and electrochemical stabilities are emphasized. The substantial solution based on ongoing electrolyte development and promising modification strategies are also suggested.

The Future of Unmanned Aerial Vehicles (UAVs) Has No Batteries

Unmanned Aerial Vehicles (UAVs) hold immense potential across various fields, including precision agriculture, rescue missions, delivery services, weather monitoring, and many more. Despite this promise, the limited flight duration of current UAVs stands as a significant obstacle to their widespread adoption. Attempting to extend flight time by solar panel charging during midflight is not viable due to battery limitations and the eventual need for replacement. This article highlights exciting efforts towards the future of a UAV that survives in the air by harvesting solar energy and performing long-term missions without battery re-charging and/or replacement.

A model-based approach for formal verification and performance evaluation of energy harvesting architectures in IoT systems: A case study of a long-term healthcare application

Energy harvesting plays a significant role in the Internet of Things (IoT). Indeed, although numerous approaches exist to limit the system’s power consumption, the energy provided by the battery remains constrained, thereby limiting the system’s lifetime. Energy harvesting represents an interesting technique that allows a set of devices in an IoT architecture to operate for a potentially infinite time without the need for battery replacement or recharge. This work presents a formal modeling framework for the performance evaluation of energy harvesting architectures and strategies in IoT systems. We present a model-based approach using UPPAAL to model and analyze IoT device lifetimes and capture the energy-related behavior of nodes and various energy harvesters. Furthermore, the model is calibrated using measurements acquired from real-life IoT applications to demonstrate the effectiveness of the proposed model and its ability to investigate various energy-related aspects.

Total Cost of Ownership of Electric Car and Internal Combustion Engine Car with Performance Normalization

In the transportation sector, motorized vehicles are currently a contributor to pollution in Indonesia, producing CO2 emissions. In an effort to achieve environmentally friendly and low-carbon motorized vehicles, promoting electric cars to the public is an important strategy that is being pursued by countries throughout the world. The large target of converting ICE cars to electric cars requires a series of analyzes to accelerate efforts to convert the use of electric cars from various angles which will ultimately be successful as a promotional medium for potential electric car users. To carry out this analysis, there is one calculation method, namely calculating the total cost of ownership (TCO) of an electric car. This method has been studied in countries that are concerned with promoting the use of electric cars. In this research, we will present a calculation model for the TCO of electric cars and the TCO of conventional internal combustion engine (ICE) cars as a comparison. The cars analyzed in this research consist of several electric cars and ICE cars according to their respective performance levels, where for each electric car an analysis of several battery ownership schemes is also attempted, namely those owned by the car owner, rental batteries and those owned by the car owner. However, some components can be repaired. The calculation model will consist of capital and operational costs, namely the purchase price, resale value, government subsidies, retailer discounts, battery replacement costs, maintenance, insurance, vehicle tax and energy consumption costs. All data was obtained from literature studies and information from vehicle distributors in Indonesia. The TCO model is analyzed based on the average distance traveled, namely 20,000 km/year for 10 years of driving. The research results show that the TCO of BEVs with a rental battery scheme is cheaper than ICEs for cars with 93, 167 & 210 hp, namely 2,961; 6,227; & 5,633 (USD) and the TCO of ICE is cheaper than BEV with a rental battery scheme on cars with 40 & 95 hp, namely 796 & 5,793 USD. Several components that make up TCO costs will later be proposed to be considered for adjustment so that vehicle purchases can grow significantly. Knowledge from this research can be useful for consumers, product manufacturers, planners, and government policy makers.

Advancements in Energy Harvesting: Piezoelectric, Triboelectric, Pyroelectric, and Magnetoelectric Technologies for Self-powered Sensor Systems

In the era of Internet of Things (IoT) advancements, millions of sensors and electronic devices are interconnected through the internet. A reliable power source is required for their continuous operation. Self-powered sensor systems have emerged as promising solutions for various applications including wearable devices and environmental monitoring, benefiting both society and the environment. These systems can maintain themselves by eliminating the need for external power sources or frequent battery replacements and converting discarded energy sources around them into electrical energy. Among various energy harvesting technologies, piezoelectric, triboelectric, pyroelectric, and magnetoelectric mechanisms have garnered significant attention due to their ability to convert mechanical, frictional, thermal, and magnetic energy into electrical power, respectively. This review aims to provide a comprehensive overview of recent advances in these technologies for self-powered sensor systems, catering to a wide audience. It explores fundamental principles of their-energy harvesting mechanisms, highlighting their strengths, limitations, and potential applications. In addition, this review discusses challenges related to the development of nanogenerator-based self-powered sensor systems and presents new opportunities for their advancement.

Physical Side-Channel Attacks against Intermittent Devices

Intermittent (batteryless) devices operate solely using energy harvested from their environment. These devices turn on when they have energy and turn off during energy scarcity. Intermittent devices have recently become increasingly popular in smart buildings, manufacturing plants, and medical implantables as they eliminate the need for battery replacement and enable green computing. Despite their growing adoption in critical applications, the privacy implications of intermittent devices remain largely unexplored. In this paper, we introduce a novel remote side-channel attack. Our observation is that the network packet frequency of an intermittent device can be exploited to learn its turn-on/off patterns. From these patterns, we can infer the energy availability of a device, which reveals privacy-sensitive information about its operating environment, e.g., the presence or absence of individuals. To realize our attack, we develop a three-stage hierarchical inference framework that leverages the timestamped network packet sequence of intermittent devices. Our framework automatically extracts a set of temporal features from inter-packet-arrival timings. It then employs a series of models to uncover (1) whether a target intermittent device is present in the environment, (2) its energy harvester type (e.g., vibration or water flow), and (3) its energy availability conditions (e.g., high-vibration or no-vibration). To validate our attack effectiveness, we conduct experiments in two environments: a smart home and a miniature manufacturing plant equipped with three intermittent devices powered by solar energy, vibration, and temperature. By analyzing their energy availability patterns, we are able to infer user activities and presence in the smart home and the robot’s movement patterns in the manufacturing plant with an average accuracy of 85%. This sensitive information enables an adversary to launch domain-specific attacks, such as burglarizing a smart home when the user is asleep or timely tampering with plant sensors to cause maximum damage.

Machine learning deployment for energy monitoring of Internet of Things nodes in smart agriculture

SummaryLow‐Power Wide‐Area Network technologies, such as LoRa, are gaining popularity in the agricultural sector for field deployment. The crucial factors in these devices are their range and power efficiency. The energy consumption of a LoRa wireless sensor network is predominantly affected by transmission parameters like carrier frequency, bandwidth, transmit power, spreading factor, and coding rate. Incorrect chosen transmission parameters can lead to a reduction in the battery life of end nodes, requiring frequent battery replacements—a situation undesirable for field deployment. This study introduces a machine learning deployment in the form of a web application designed to monitor the energy consumption of end nodes in LoRa wireless sensor networks. The research initially employs 12 regression models, including Linear, Random Forest, K‐Nearest Neighbours, Decision Tree, Support Vector, Lasso, Ridge, AdaBoost, Gradient Boost, XGBoost, CatBoost, and LightGBM models. The findings of the study reveal that the LightGBM model surpasses other models in accurately predicting the energy consumption of Internet of Things (IoT) nodes, leading to its selection for the web application. This machine learning web application can be implemented in a programmable Long Range Wide Area Network (LoRaWAN) gateway to effectively monitor the energy consumption of IoT end nodes in the agricultural sector.

Low-Power Wireless Sensor Module for Machine Learning-Based Continuous Monitoring of Nuclear Power Plants.

This paper introduces the novel design and implementation of a low-power wireless monitoring system designed for nuclear power plants, aiming to enhance safety and operational efficiency. By utilizing advanced signal-processing techniques and energy-efficient technologies, the system supports real-time, continuous monitoring without the need for frequent battery replacements. This addresses the high costs and risks associated with traditional wired monitoring methods. The system focuses on acoustic and ultrasonic analysis, capturing sound using microphones and processing these signals through heterodyne frequency conversion for effective signal management, accommodating low-power consumption through down-conversion. Integrated with edge computing, the system processes data locally at the sensor level, optimizing response times to anomalies and reducing network load. Practical implementation shows significant reductions in maintenance overheads and environmental impact, thereby enhancing the reliability and safety of nuclear power plant operations. The study also sets the groundwork for future integration of sophisticated machine learning algorithms to advance predictive maintenance capabilities in nuclear energy management.

Numerical modeling of electrical parameters of LiFePO4 batteries

The object of research is the physical processes of electric energy storage in Li-ion batteries. The problem being solved in the work is related to the lack of reliable mathematical models of storage batteries, which leads to the appearance of undesirable effects or emergency situations when changing operating modes. In the course of the work, Li-ion battery models based on electrochemical theory and electrical circuits were considered. The six most common equivalent battery replacement schemes are presented. The advantages and disadvantages of the considered substitution schemes are given. The dual-polarization mathematical model was found to most accurately describe the performance of the battery at the end of the discharge and charge cycles compared to the first-order Thevenin model, the RC model, and the active resistance battery model. The physical processes in the storage battery during pulse discharge, which is the main part of electrical energy storage systems based on electrochemical technology, were studied. Mathematical modeling was carried out in the Matlab software package using the Simulink application program package. The dependence of the parameters of the equivalent lithium-ion battery replacement scheme according to the second-order Thevenin model on the ambient temperature and state of charge is considered. It was established that the value of EMF E depends more on the change in SOC than on temperature. In turn, the active resistance ROM shows a greater dependence on temperature than on the change in SOC. At high temperatures, the resistance value decreases. The parameters R1 and C1 characterizing the electrochemical polarization vary in the range from 10 to 75 % SOC. The parameters R2 and C2, which depend on the concentration polarization, vary in the intervals from 0 to 25 % SOC and 75 to 100 % SOC. The recommendations for choosing a Li-ion battery model developed in the work can be used in practice. The established dependencies will help to better design electrical energy storage systems based on electrochemical technology.

Advanced Energy Harvesters and Energy Storage for Powering Wearable and Implantable Medical Devices.

Wearable and implantable active medical devices (WIMDs) are transformative solutions for improving healthcare, offering continuous health monitoring, early disease detection, targeted treatments, personalized medicine, and connected health capabilities. Commercialized WIMDs use primary or rechargeable batteries to power their sensing, actuation, stimulation, and communication functions, and periodic battery replacements of implanted active medical devices pose major risks of surgical infections or inconvenience to users. Addressing the energy source challenge is critical for meeting the growing demand of the WIMD market that is reaching valuations in the tens of billions of dollars. This review critically assesses the recent advances in energy harvesting and storage technologies that can potentially eliminate the need for battery replacements. With a key focus on advanced materials that can enable energy harvesters to meet the energy needs of WIMDs, this review examines the crucial roles of advanced materials in improving the efficiencies of energy harvesters, wireless charging, and energy storage devices. This review concludes by highlighting the key challenges and opportunities in advanced materials necessary to achieve the vision of self-powered wearable and implantable active medical devices, eliminating the risks associated with surgical battery replacement and the inconvenience of frequent manual recharging.

Solution-processed MoS2 nanostructures and thermal oxidization on carbon fabric with enhanced thermopower factor for wearable thermoelectric application

Miniaturization of electronic devices has become the new phase of modern technology. Wearable Thermoelectric Generators (WTEG), being flexible, lightweight, self-sustained, and safe are the only replacement for conventional batteries for mobile applications. Molybdenum Disulfide (MoS2), a graphene akin compound having diverse transport behaviour and low thermal conductivity is one of the most preferable candidates for thermoelectric application. However, limited reports are available on MoS2 for Wearable thermoelectric applications. In this study, we have accomplished successful growth of MoS2 nanostructures on conductive carbon fabric via one step hydrothermal method and the effect of thermal oxidation of MoS2 has been studied at various annealing temperature. The structural and elemental analysis confirms the transformation of MoS2 into MoO3. Annealing creates an MoS2/MoO3 interface thus enhancing the carrier mobility resulting in enhancement of power factor (481 nW.m−1K−2) of the sample annealed at 200 °C (M1) by 1.29 times than that of pristine MoS2. The sample possessing high performance has been employed to fabricate a WTEG and its real time output has been measured. The design and operating conditions of the device is studied and optimized to obtain the output open circuit voltage as high as 6.4 mV. This paves a promising route to fabricate self- powered wearable biomedical devices.

An energy‐efficient method using PSO clustering and fuzzy routing to reduce energy consumption in IoT

SummaryIn “Internet of Things” (IoT) technology, all objects can connect to the Internet with a unique Internet address. The sensors have limited energy resources due to their use of batteries in supplying energy, and since battery replacement in these sensors is not usually feasible, the longevity of wireless sensor networks is limited. Therefore, reducing the energy consumption of the used sensors in IoT networks to increase the network lifetime is one of the crucial challenges and parameters in such networks. These networks are subject to a variety of errors. Errors in wireless communication channels, network dynamics due to the mobility of nodes, and the potential for mistakes in mobile nodes are among the causes of errors in these networks. Despite the above failures, the development of routing algorithms in IoT‐based networks has become a significant challenge for researchers, so it is essential to provide routing algorithms to increase network lifespan and reduce energy consumption. In this study, a two‐dimensional network‐based architecture for the Internet of Things is presented. The proposed algorithm first organizes the nodes into logical cells and selects nodes in each cell as the cluster head. The cluster head is selected based on the binary Particle Swarm Optimization algorithm. Then, each node sends its collected data to the cluster head. The cluster heads first aggregate the received data and then use a fuzzy method to send the data to the base station. The proposed algorithm uses the remaining energy level parameter, cluster density, and location of each cell using fuzzy system capabilities and the particle swarm algorithm to try to route data with less energy consumption and increase network lifespan. The proposed method is simulated in ns2 software, and for a more accurate evaluation, we compare it with LEACH, Ant‐C, PSO, EABC, and SOA‐CH algorithms. We see that the proposed method performs better than other methods in terms of energy consumption and network lifespan.

A simulation study of touch-free automatic alcohol-based handrub dispensers on hand hygiene disruption in healthcare settings

Automatic dispensers of alcohol-based handrub (ABHR) have been widely adopted in healthcare facilities to maintain hand hygiene (HH). A proper supply of energy and refill is crucial to ensure uninterrupted access to hand sanitizing and minimize workflow disruption and inefficiencies. Various energy design and refill replenishment technologies have emerged with promising potential to eliminate HH disruptions. However, there is a lack of quantitative studies assessing the design impact on hand hygiene performance in healthcare settings. In this paper, we employ data-driven discrete-event simulation (DES) to evaluate the long-term performance of various energy designs of automatic dispensers in healthcare facilities. We analyze 7 years of historical usage data from 4 US hospitals and identify the usage patterns, which serve as the input traffic for our simulation model. We then estimate the workflow disruption caused by different types of dispensers over a 6-year period, in terms of the number of missed HH opportunities, battery replacements, and duration of downtime. The simulation results suggest that the differences in performance are significant among dispenser types. In high usage, the number of missed HH opportunities caused by refill depletion ranges from 403.1 to 1232.4, and total downtime ranges from 0 to 96.3 h. Implementing proactive maintenance measures, such as service refill alerts, can greatly reduce the chances of ABHR depletion, resulting an 81.6 % decrease in HH disruptions for a single dispenser in high usage. Therefore, healthcare facilities should consider the variations in dispenser design, including the energy management system. They should also carefully study dispenser usage patterns to implement optimized policies and practices for ABHR refill maintenance to minimize overall missed HH opportunities.

Secondary cluster head based SEP in heterogeneous WSNs for IoT applications

AbstractA wireless sensor network (WSN) consists of specialized sensor nodes that perform sensing services for Internet of Things devices with limited battery power. As the replacement or recharging of batteries is not possible, energy consumption becomes the most important design issue in WSNs. The energy‐efficient routing protocol gets the most priority in such energy‐constrained networks. The utilization of clustering‐based routing protocols like stable election protocol (SEP) has gained much attention as the network's lifetime is significantly improved due to the clustering of sensor nodes. Moreover, the inclusion of a secondary CH (SCH) is beneficial when the member node with the most remaining energy performs data aggregation and reduces the energy burden of the CH. This article characterizes SEP and Prolong SEP (P‐SEP) protocols and then extends them by incorporating SCH, which ensures balanced energy consumption. The performance of the proposed Extended SEP and Extended P‐SEP protocols has been analysed regarding stability period, network lifetime, energy usage and throughput. The simulation results demonstrate that the proposed protocols outperform state‐of‐the‐art protocols like P‐SEP, Modified SEP (M‐SEP) and SEP. In particular, the stability period of Extended SEP and Extended P‐SEP has improved up to 42% and 89%, respectively, compared to M‐SEP.

Multi-objective optimization and scheduling research on electric bicycle charging and discharging in distribution networks

Abstract With the continuous development of urbanization, the rapid growth of electric bicycles has become a prominent trend. The number of battery-swapping stations for convenient battery replacement has also increased. The large-scale and unregulated connection of electric bicycles and battery-swapping stations to the power grid can significantly impact the distribution network, leading to localized overloads and affecting grid stability. Therefore, this paper proposes a multi-objective optimization scheduling strategy for electric bicycle charging and dis-charging based on both the transmission and distribution systems. Firstly, in the transmission system, the objective is to reduce the operating costs of generators and user charging costs. Sub-sequently, in the distribution system, the goal is to minimize network losses, considering system constraints and the spatial migration characteristics of electric bicycles. The study establishes a multi-objective optimization problem. Simulation analyses were conducted on the power system models based on a standard 10-machine transmission network and an IEEE 33-node distribution network to verify the effectiveness of the proposed multi-objective optimization scheduling strategy.

Hybrid energy storage system for electric motorcycles: Technical and economic analysis

This paper presents the multiple energy storage system usability for electric motorcycle focused on hybrid topology. This study focuses on evaluating the cost-effectiveness of a hybrid energy storage system (HESS) for e-motorcycles over a 10-year period by considering the number of battery replacements instead of solely the initial system price. The motorcycle studied in this manuscript, is a recreational electric motorcycle utilized as an experimental test bench in EV-SCHS laboratory at the OJEN company. The 10-year equivalent price of the energy storage system includes both the initial purchase cost and the cost of battery replacements over the system's lifespan. A battery life model is essential for estimating the frequency of replacements over the 10-year period. Incorporating ultracapacitors (UC) in the HESS enhances power specifications, leading to improved acceleration and regenerative braking capabilities. This reduces battery current and extends battery life, resulting in fewer required replacements and lower overall system costs over the 10-year period. The study findings demonstrate that optimally-sized HESS configurations offer favorable power specifications, with the 10-year equivalent price of HESS being $3466 lower than that of conventional energy storage systems, despite the initial price of HESS being $833 higher.

Aerial drone fleet deployment optimization with endogenous battery replacements for direct delivery of time-sensitive products

Aerial drones offer a distinct potential to reduce the delivery time and energy consumption for the delivery of time-sensitive and small products. However, there is still a need in the relevant industry to understand the performance of drone-based delivery under different business needs and drone operating conditions. We studied a drone deployment optimization problem for direct delivery of time-sensitive products with release dates to customers maintaining a specified time window. This paper presents a new mixed-integer programming model, new valid inequalities, a new greedy heuristic algorithm, and a Genetic algorithm to help business owners optimally schedule and route their drone fleet minimizing the required fleet size, the required number of additional batteries, and total energy consumption. A realistic feature of the optimization method is that instead of replacing the drone battery after each return to the depot, it keeps track of the remaining energy in the drone battery and decides on battery replacements accounting for the drone routing and the user-specified minimum required battery energy. Numerical results based on real data from drone flight tests and prepared food delivery industry provide insights into the effect of different practical drone operating parameters on the required fleet size, the required number of battery replacements, and energy consumption. Results demonstrate that the proposed heuristic algorithm substantially outperforms the accelerated CPLEX in runtime while sacrificing the solution quality by a small amount. Additionally, results show that using a mixed fleet of hexacopter and quadcopter drones reduces the total energy consumption by 48.52% compared to using a homogeneous fleet of only hexacopters.

The bail out option; using endocardial pacing and defibrillation leads epicardially

Abstract Introduction Although transvenous implantation of cardiac pacemakers and defibrillators is technically feasible and less invasive than surgical placement of epicardial leads, certain patients including pediatric patients and patients with complex congenital heart disease will require epicardial pacing and most of them will need pacing throughout their whole life. There are specific leads for epicardial pacing which were developed over time to the currently used bipolar steroid-eluting epicardial leads which may be rarely nationally unavailable. In addition, there are no available dedicated epicardial shock leads (1,2). There is no enough data on the techniques, feasibility, and efficacy of using endocardial leads, including shock leads for epicardial pacing/ defibrillation (3). Purpose We aim to describe our center’s experience in implanting endocardial leads for epicardial pacing or defibrillation and to assess its feasibility & durability. Methods Due to periods of national unavailability of epicardial permanent pacing leads and worldwide unavailability of dedicated epicardial shock leads, a limited cohort of patients requiring urgent epicardial pacing/ ICDs were offered pacing through epicardially implanted endocardial leads. The lead screw is advanced into myocardium, then a purse-string suture is placed around and tied to the lead for fixation. Ventricular pacing lead is fixed to LV apex, while atrial lead is fixed to RA free wall. Shock coils are fixed to LV and RA free walls (Fig. 1). We retrospectively revised our records for these patients & analyzed their clinical data, follow up programming parameters including sensing, pacing thresholds and impedances. Results In the last 8 years, surgeons implanted 9 endocardial leads epicardially. Two were pacing leads implanted for ventricular and atrial pacing in two different pediatric patients, and 7 were shock leads in patients with life threatening VTs despite medical treatment. The patients with ICDs were as follows: one was an adult patient with a Glenn shunt. The other 6 patients were pediatrics with low body weight; 4 of them had long QT syndrome and two had idiopathic VF. The mean follow-up periods were 38 months. Throughout the visits, the pacing thresholds, sensing functions, lead and shock impedances were stable in all patients (Table 1). Two of the patients with epicardial ICDs received appropriate shocks. None received inappropriate shocks. So far, only one patient required battery replacement. Conclusion In view of our findings, using endocardial leads as a bail out alternative to epicardial leads for pacing or defibrillation appears to be safe and effective. This finding is essentially important regarding the shock leads as there are no dedicated epicardial defibrillation leads. It should be noted that these results depend largely on the surgeon’s experience and on achieving acceptable pacing parameters and defibrillation thresholds intraoperatively.Table 1Figure 1