Explore the vast range of titles available.

The development of hydrogel‐based underwater electronics has gained significant attention due to their flexibility and portability compared to conventional rigid devices. However, common hydrogels face challenges such as swelling and poor underwater adhesion, limiting their practicality in water environments. Here, a water‐induced phase separation strategy to fabricate hydrogels with enhanced anti‐swelling properties and underwater adhesion is presented. By leveraging the contrasting affinity of different polymer chains to water, a phase‐separated structure with rich hydrophobic and dilute hydrophilic polymer phases is achieved. This dual‐phase structure, meticulously characterized from the macroscopic to the nanoscale, confers the hydrogel network with augmented retractive elastic forces and facilitates efficient water drainage at the gel‐substrate interface. As a result, the hydrogel exhibits remarkable swelling resistance and long‐lasting adhesion to diverse substrates. Additionally, the integration of carboxylic multiwalled carbon nanotubes into the hydrogel system preserves its anti‐swelling and adhesion properties while imparting superior conductivity. The conductive phase‐separated hydrogel exhibited great potential in diverse underwater applications, including sensing, communication, and energy harvesting. This study elucidates a facile strategy for designing anti‐swelling underwater adhesives by leveraging the ambient solvent effect, which is expected to offer some insights for the development of next‐generation adhesive soft materials tailored for aqueous environments.

Self‐healing soft electronic material composition is crucial to sustain the device long‐term durability. The fabrication of self‐healing soft electronics exposed to high moisture environment is a significant challenge that has yet to be fully achieved. This paper presents the novel concept of a water‐assisted room‐temperature autonomous self‐healing mechanism based on synergistically dynamic covalent Schiff‐based imine bonds with hydrogen bonds. The supramolecular water‐assisted self‐healing polymer (WASHP) films possess rapid self‐healing kinetic behavior and high stretchability due to a reversible dissociation–association process. In comparison with the pristine room‐temperature self‐healing polymer, the WASHP demonstrates favorable mechanical performance at room temperature and a short self‐healing time of 1 h; furthermore, it achieves a tensile strain of 9050%, self‐healing efficiency of 95%, and toughness of 144.2 MJ m−3. As a proof of concept, a versatile WASHP‐based light‐emitting touch‐responsive device (WASHP‐LETD) and perovskite quantum dot (PeQD)‐based white LED backlight are designed. The WASHP‐LETD has favorable mechanical deformation performance under pressure, bending, and strain, whereas the WASHP‐PeQDs exhibit outstanding long‐term stability even over a period exceeding one year in a boiling water environment. This paper provides a mechanically robust approach for producing eco‐friendly, economical, and waterproof e‐skin device components. This novel underwater self‐healing polymer, based on synergistically dynamic covalent Schiff‐based imine bonds with hydrogen bonds, is eco‐friendly, economical, waterproof, and resilient. It has outstanding performance in terms of stretchability (9050%), self‐healing efficiency (95%), self‐healing time (1 h at room temperature), and toughness (144.2 MJ m−3), giving it high potential for integration into underwater electronics.

Stretchable and self‐healing soft conductive materials are essential for soft electronics, robotics, wearables, and bioelectronics. However, achieving a single material that simultaneously offers high and stable conductivity, minimal resistance changes under extreme stretching, high‐resolution universal printability, autonomous self‐healing, and pressure‐sensitive adhesive properties for direct bonding of surface‐mountable components remains challenging. Here, a printable ink composed of liquid metal microparticles and carboxylic acid‐functionalized carbon nanotubes, blended into a bimodal supramolecular elastomer matrix is introduced. After photothermal activation, the material is capable of reorganizing conductive pathways and achieves a high conductivity (> 20000 S·cm−1 under strain), exceptional strain insensitivity (R/R0 < 3.95 up to 500%), and an elastic working range >700%. The reversible oxygen‐boron and hydrogen bonding enable both effective autonomous self‐healing and direct assembly of self‐healing hybrid electronic circuits and systems through self‐adhesiveness. To showcase the high performance and functionality, a highly stretchable, self‐healing, and waterproof 3 × 5 pixel display is fabricated. A printable, self‐healing, stretchable, and highly conductive ink with pressure‐activated adhesive properties is designed by incorporation of liquid metal microparticles and carboxyl‐functionalized carbon nanotubes into a bimodal supramolecular elastomer. After photothermal activation, it achieves conductivity of >20000 S·cm⁻¹, R/R₀ < 3.95 at < 500% strain, and >700% elastic working range. Demonstrated in a stretchable, self‐healing 3 × 5 pixel display with waterproofing.

In this paper, InSe nanosheets were synthesized by a ball milling method, and photoelectrochemical-type photodetectors (PEC PDs) based on the ball milling InSe (M-InSe) were fabricated using simulated seawater as the electrolyte. M-InSe nanosheets show good absorption in the visible region of 450–600 nm. The M-InSe PEC PDs display a good self-powered photoresponse under 525 nm irradiation, including a high responsivity of 0.8 mA/W, fast response time of 28/300 ms, and good stability. Furthermore, the InSe PEC PDs successfully demonstrated prototype application in wireless underwater optical communication and optical imaging. These results demonstrate that M-InSe holds good application prospects in underwater optoelectronic devices.

The term “Internet of Underwater Things (IoUT)” refers to a network of intelligent interconnected underwater devices designed to monitor various underwater activities. The IoUT allows for a network of autonomous underwater vehicles (AUVs) to communicate with each other, sense their surroundings, collect data, and transmit them to control centers on the surface at typical Internet speeds. These data serve as a valuable resource for various tasks, including conducting crash surveys, discovering shipwrecks, detecting early signs of tsunamis, monitoring animal health, obtaining real-time aquatic information, and conducting archaeological expeditions. This paper introduces an additional set of alternative simulation tools for underwater networks. We categorize these tools into open-source and licensed simulator options and recommend that students consider using open-source simulators for monitoring underwater networks. There has not been widespread deployment or extensive research on underwater 5G-based networks. However, simulation tools provide some general insights into the challenges and potential issues associated with evaluating such networks, based on the characteristics of underwater communication and 5G, by surveying 5G-based underwater networks and 5G key aspects addressed by the research community in underwater network systems. Through an extensive review of the literature, we discuss the architecture of both Internet of Underwater application-assisted AUVs and Internet of Underwater Things communications in the 5G-based system.

The underwater domain presents unique challenges and opportunities for scientific exploration, resource extraction, and environmental monitoring. Autonomous underwater vehicles (AUVs) rely on simultaneous localization and mapping (SLAM) for real-time navigation and mapping in these complex environments. However, traditional SLAM techniques face significant obstacles, including poor visibility, dynamic lighting conditions, sensor noise, and water-induced distortions, all of which degrade the accuracy and robustness of underwater navigation systems. Recent advances in deep learning (DL) have introduced powerful solutions to overcome these challenges. DL techniques enhance underwater SLAM by improving feature extraction, image denoising, distortion correction, and sensor fusion. This survey provides a comprehensive analysis of the latest developments in DL-enhanced SLAM for underwater applications, categorizing approaches based on their methodologies, sensor dependencies, and integration with deep learning models. We critically evaluate the benefits and limitations of existing techniques, highlighting key innovations and unresolved challenges. In addition, we introduce a novel classification framework for underwater SLAM based on its integration with underwater wireless sensor networks (UWSNs). UWSNs offer a collaborative framework that enhances localization, mapping, and real-time data sharing among AUVs by leveraging acoustic communication and distributed sensing. Our proposed taxonomy provides new insights into how communication-aware SLAM methodologies can improve navigation accuracy and operational efficiency in underwater environments. Furthermore, we discuss emerging research trends, including the use of transformer-based architectures, multi-modal sensor fusion, lightweight neural networks for real-time deployment, and self-supervised learning techniques. By identifying gaps in current research and outlining potential directions for future work, this survey serves as a valuable reference for researchers and engineers striving to develop robust and adaptive underwater SLAM solutions. Our findings aim to inspire further advancements in autonomous underwater exploration, supporting critical applications in marine science, deep-sea resource management, and environmental conservation.

The innovative concept of Internet of Underwater Things (IoUT) has a huge impact in different sectors including a small scientific laboratory, to a medium sized harbor, and to monitor vast undiscovered oceans. Internet of Underwater Things (IoUT) has become a powerful technology to support various applications such as collecting real-time aquatic information, naval military applications, maritime security, natural disaster prediction and control, archaeological expeditions, oil and gas exploration, shipwrecks discovery, water contamination, marine life observation and smart Ocean. IoUT is referred as smart intricately linked underwater objects to monitor these underwater operations. The IoUT framework incorporates several underwater communication technologies based on magnetic induction, optical signals, radio signals and acoustic waves. It is an emerging communication ecosystem which can reveal a new era of research, business and naval applications. It is a novel and vibrant paradigm for the Blue Economy sector bringing the ability to communicate autonomous underwater vehicles (AUVs), sensing, actuating and transferring this data to control centers using regular internet speeds through low cost technologies. It is anticipated to support future networking systems which can bring tremendous improvement in previous generations in terms of stable networking, high coverage, massive connectivity, low latency, high data rate and low power consumption. This study introduces the possible network framework of IoUT which is naturally heterogeneous and must be flexible enough to work under unpredicted ocean conditions. In this study, we examine channel models, routing protocols, networking topologies and simulation tools. Furthermore, we discussed recent advancements in IoUT in terms of smart devices, consumer electronics, communication and role of AUVs. In addition, edge computing, optical wireless communication (OWC), data analytics, blockchain, intelligent reflecting surfaces (IRS) and machine learning were viewed as promising techniques to support IoUT. We have dedicated a complete section to applications of IoUT. Finally, numerous open research challenges and future directions were presented. We believe this survey will be helpful to aggregate the research efforts and eliminate the technical uncertainties towards breakthrough novelties of IoUT.

Underwater Optical Wireless Communication (UOWC) is not a new idea, but it has recently attracted renewed interest since seawater presents a reduced absorption window for blue-green light. Due to its higher bandwidth, underwater optical wireless communications can support higher data rates at low latency levels compared to acoustic and RF counterparts. The paper is aimed at those who want to undertake studies on UOWC. It offers an overview on the current technologies and those potentially available soon. Particular attention has been given to offering a recent bibliography, especially on the use of single-photon receivers.

Underwater natural gas pipelines constitute critical infrastructure for energy transportation. Any damage or leakage in these pipelines poses serious security risks, directly threatening marine and lake ecosystems, and potentially causing operational issues and economic losses in the energy supply chain. However, current methods for detecting deterioration and regularly inspecting these submerged pipelines remain limited, as they rely heavily on divers, which is both costly and inefficient. Due to these challenges, the use of unmanned underwater vehicles (UUVs) becomes crucial in this field, offering a more effective and reliable solution for pipeline monitoring and maintenance. In this study, we conducted an underwater pipeline tracking and damage detection experiment using a remote-controlled unmanned underwater vehicle (UUV) with autonomous features. The primary objective of this research is to demonstrate that UUV systems provide a more cost-effective, efficient, and practical alternative to traditional, more expensive methods for inspecting submerged natural gas pipelines. The experimental method included vehicle (UUV) setup, pre-test calibration, pipeline tracking mechanism, 3D navigation control, damage detection, data processing, and analysis. During the tracking of the underwater pipeline, damages were identified, and their locations were determined. The navigation information of the underwater vehicle, including orientation in the x, y, and z axes (roll, pitch, yaw) from a gyroscope integrated with a magnetic compass, speed and position information in three axes from an accelerometer, and the distance to the water surface from a pressure sensor, was integrated into the vehicle. Pre-tests determined the necessary pulse width modulation values for the vehicle’s thrusters, enabling autonomous operation by providing these values as input to the thruster motors. In this study, 3D movement was achieved by activating the vehicle’s vertical thruster to maintain a specific depth and applying equal force to the right and left thrusters for forward movement, while differential force was used to induce deviation angles. In pool experiments, the unmanned underwater vehicle autonomously tracked the pipeline as intended, identifying damages on the pipeline using images captured by the vehicle’s camera. The images for damage assessment were processed using a convolutional neural network (CNN) algorithm, a deep learning method. The position of the damage relative to the vehicle was estimated from the pixel dimensions of the identified damage. The location of the damage relative to its starting point was obtained by combining these two positional pieces of information from the vehicle’s navigation system. The damages in the underwater pipeline were successfully detected using the CNN algorithm. The training accuracy and validation accuracy of the CNN algorithm in detecting underwater pipeline damages were 94.4% and 92.87%, respectively. The autonomous underwater vehicle also followed the designated underwater pipeline route with high precision. The experiments showed that the underwater vehicle followed the pipeline path with an error of 0.072 m on the x-axis and 0.037 m on the y-axis. Object recognition and the automation of the unmanned underwater vehicle were implemented in the Python environment.

The collection of delicate deep-sea specimens of biological interest with remotely operated vehicle (ROV) industrial grippers and tools is a long and expensive procedure. Industrial grippers were originally designed for heavy manipulation tasks, while sampling specimens requires dexterity and precision. We describe the grippers and tools commonly used in underwater sampling for scientific purposes, systematically review the state of the art of research in underwater gripping technologies, and identify design trends. We discuss the possibility of executing typical manipulations of sampling procedures with commonly used grippers and research prototypes. Our results indicate that commonly used grippers ensure that the basic actions either of gripping or caging are possible, and their functionality is extended by holding proper tools. Moreover, the approach of the research status seems to have changed its focus in recent years: from the demonstration of the validity of a specific technology (actuation, transmission, sensing) for marine applications, to the solution of specific needs of underwater manipulation. Finally, we summarize the environmental and operational requirements that should be considered in the design of an underwater gripper.