Antarctic Snow Failure Mechanics: Analysis, Simulations, and Applications.

Snow failure is the process by which the stability of snow or snow-covered slopes is destroyed, resulting in the collapse or release of snow. Heavy snowfall, low temperatures, and volatile weather typically cause consequences in Antarctica, which can occur at different scales, from small, localized collapses to massive avalanches, and result in significant risk to human activities and infrastructures. Understanding snow damage is critical to assessing potential hazards associated with snow-covered terrain and implementing effective risk mitigation strategies. This review discusses the theoretical models and numerical simulation methods commonly used in Antarctic snow failure research. We focus on the various theoretical models proposed in the literature, including the fiber bundle model (FBM), discrete element model (DEM), cellular automata (CA) model, and continuous cavity-expansion penetration (CCEP) model. In addition, we overview some methods to acquire the three-dimensional solid models and the related advantages and disadvantages. Then, we discuss some critical numerical techniques used to simulate the snow failure process, such as the finite element method (FEM) and three-dimensional (3D) material point method (MPM), highlighting their features in capturing the complex behavior of snow failure. Eventually, different case studies and the experimental validation of these models and simulation methods in the context of Antarctic snow failure are presented, as well as the application of snow failure research to facility construction. This review provides a comprehensive analysis of snow properties, essential numerical simulation methods, and related applications to enhance our understanding of Antarctic snow failure, which offer valuable resources for designing and managing potential infrastructure in Antarctica.

Skin-Contact Triboelectric Nanogenerator for Energy Harvesting and Motion Sensing: Principles, Challenges, and Perspectives.

Energy harvesting has become an increasingly important field of research as the demand for portable and wearable devices continues to grow. Skin-contact triboelectric nanogenerator (TENG) technology has emerged as a promising solution for energy harvesting and motion sensing. This review paper provides a detailed overview of skin-contact TENG technology, covering its principles, challenges, and perspectives. The introduction begins by defining skin-contact TENG and explaining the importance of energy harvesting and motion sensing. The principles of skin-contact TENG are explored, including the triboelectric effect and the materials used for energy harvesting. The working mechanism of skin-contact TENG is also discussed. This study then moves onto the applications of skin-contact TENG, focusing on energy harvesting for wearable devices and motion sensing for healthcare monitoring. Furthermore, the integration of skin-contact TENG technology with other technologies is discussed to highlight its versatility. The challenges in skin-contact TENG technology are then highlighted, which include sensitivity to environmental factors, such as humidity and temperature, biocompatibility and safety concerns, and durability and reliability issues. This section of the paper provides a comprehensive evaluation of the technological limitations that must be considered when designing skin-contact TENGs. In the Perspectives and Future Directions section, this review paper highlights various advancements in materials and design, as well as the potential for commercialization. Additionally, the potential impact of skin-contact TENG technology on the energy and healthcare industries is discussed.

Advancements in Triboelectric Nanogenerators (TENGs) for Intelligent Transportation Infrastructure: Enhancing Bridges, Highways, and Tunnels.

The development of triboelectric nanogenerators (TENGs) over time has resulted in considerable improvements to the efficiency, effectiveness, and sensitivity of self-powered sensing. Triboelectric nanogenerators have low restriction and high sensitivity while also having high efficiency. The vast majority of previous research has found that accidents on the road can be attributed to road conditions. For instance, extreme weather conditions, such as heavy winds or rain, can reduce the safety of the roads, while excessive temperatures might make it unpleasant to be behind the wheel. Air pollution also has a negative impact on visibility while driving. As a result, sensing road surroundings is the most important technical system that is used to evaluate a vehicle and make decisions. This paper discusses both monitoring driving behavior and self-powered sensors influenced by triboelectric nanogenerators (TENGs). It also considers energy harvesting and sustainability in smart road environments such as bridges, tunnels, and highways. Furthermore, the information gathered in this study can help readers enhance their knowledge concerning the advantages of employing these technologies for innovative uses of their powers.

Recent Progress of Triboelectric Nanogenerators for Biomedical Sensors: From Design to Application.

Triboelectric nanogenerators (TENG) have gained prominence in recent years, and their structural design is crucial for improvement of energy harvesting performance and sensing. Wearable biosensors can receive information about human health without the need for external charging, with energy instead provided by collection and storage modules that can be integrated into the biosensors. However, the failure to design suitable components for sensing remains a significant challenge associated with biomedical sensors. Therefore, design of TENG structures based on the human body is a considerable challenge, as biomedical sensors, such as implantable and wearable self-powered sensors, have recently advanced. Following a brief introduction of the fundamentals of triboelectric nanogenerators, we describe implantable and wearable self-powered sensors powered by triboelectric nanogenerators. Moreover, we examine the constraints limiting the practical uses of self-powered devices.

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.

A Numerical Study on 3D Printed Cementitious Composites Mixes Subjected to Axial Compression.

Aptly enabled by recent developments in additive manufacturing technology, the concept of functionally grading some cementitious composites to improve structural compression forms is warranted. In this work, existing concrete models available in Abaqus Finite Element (FE) packages are utilized to simulate the performance of some cementitious composites numerically and apply them to functional grading using the multi-layer approach. If yielding good agreement with the experimental results, two-layer and three-layer models case combinations are developed to study the role of layer position and volume. The optimal and sub-optimal performance of the multi-layer concrete configurations based on compressive strength and sustained strains are assessed. The results of the models suggest that layer volume and position influence the performance of multi-layer concrete. It is observed that when there exists a substantial difference in material strengths between the concrete mixes that make up the various layers of a functionally graded structure, the influence of position and of material volume are significant in a two-layer configuration. In contrast, in a three-layer configuration, layer position is of minimal effect, and volume has a significant effect only if two of the three layers are made from the same material. Thus, a multilayered design approach to compression structures can significantly improve strength and strain performance. Finally, application scenarios on some structural compression forms are shown, and their future trajectory is discussed.

Piezoelectric Sensing Techniques in Structural Health Monitoring: A State-of-the-Art Review.

Recently, there has been a growing interest in deploying smart materials as sensing components of structural health monitoring systems. In this arena, piezoelectric materials offer great promise for researchers to rapidly expand their many potential applications. The main goal of this study is to review the state-of-the-art piezoelectric-based sensing techniques that are currently used in the structural health monitoring area. These techniques range from piezoelectric electromechanical impedance and ultrasonic Lamb wave methods to a class of cutting-edge self-powered sensing systems. We present the principle of the piezoelectric effect and the underlying mechanisms used by the piezoelectric sensing methods to detect the structural response. Furthermore, the pros and cons of the current methodologies are discussed. In the end, we envision a role of the piezoelectric-based techniques in developing the next-generation self-monitoring and self-powering health monitoring systems.