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Ionic conductive hydrogels are widely used in many applications such as electrochemical energy storage, flexible electronic devices, and catalyst transistors due to their excellent ionic conductivity as well as chemical stability. However, the fragile mechanical properties and the lack of shaping methods severely limit their further applications. Herein, an ionic conductive composite hydrogel with reinforced mechanical properties is demonstrated that can be rapidly 3D printed using digital light processing technology. By using both γ‐methacryloxypropyltrimethoxysilane moderately modified attapulgite rigid particle and polyvinyl alcohol (PVA) semicrystal dual‐network reinforcement, the mechanically robust and highly conductive N,N,N‐trimethylethanaminium‐chloride‐based hydrogels are obtained, demonstrating a 5 times higher tensile strength than the initial one due to the turning and orienting of the attapulgite as well as the robust PVA secondary network. Furthermore, encapsulation strategy is used to avoid the dehydration of hydrogel, and strain sensors that exceed the strain limit of the hydrogel are fabricated through structural design. This work provides a reference for attapulgite‐reinforced hydrogel in biosensing. [ABSTRACT FROM AUTHOR]

Reticular chemistry has been a cornerstone in the design of novel 2D materials. Despite numerous possibilities for topological arrangements, only a few with high symmetry can form stable networks. Here, starting from 2D carbons, four types of highly stable tessellations are discovered, which consist of chains of non‐hexagonal rings separated by hexagonal ribbons. A modified Read–Shockley model is established to perfectly describe the stability of these highly stable frameworks, which is based on the interaction between non‐hexagonal rings. Moreover, these four types of tessellations and the modified Read–Shockley model are found to be of general validity in designing highly stable 2D materials, which is verified by the calculations on polymorphs of boron nitride and molybdenum disulfide. Besides, among the studied 2D carbon allotropes, two semi‐metallic structures with highly anisotropic Dirac cones and one semimetal with a Dirac nodal line at the Fermi level are discovered, as protected by their D2h symmetry. Spin‐orbital coupling is further found to open small bandgaps for these three Dirac structures, making them nontrivial topological insulators. The in‐depth understanding of the stability of 2D crystals in this study provides a new way for rational design of 2D crystals that may show peculiar electronic structures. [ABSTRACT FROM AUTHOR]

The remote control ship is considered to be the most likely implementation of maritime autonomous surface ships (MASS) in the near-term future. With collaborative control from onboard controllers and operators ashore, ships may operate in three navigation control modes (NCMs), manual, autonomous, and remote control, based on different levels of control authority. The scientific selection of the appropriate NCM for MASS under multiple driving modes is crucial for ensuring ship navigation safety and holds significant importance for operators and regulatory authorities overseeing maritime traffic within specific areas. To aid in selecting the proper NCM, this study introduces a risk-based comparison method for determining optimal control modes in specific scenarios. Firstly, safety control paths and processes for MASS under different NCMs are constructed and analyzed using system-theoretic process analysis (STPA). By analyzing unsafe system control actions, key Risk Influencing Factors (RIFs) and their interrelationships are identified. Secondly, a Hidden Markov Model (HMM) process risk assessment model is developed to infer risk performance (hidden state) through measuring RIF states. Cloud modeling with expert judgments is utilized to parameterize the HMM while addressing inherent uncertainty. Lastly, the applicability of the proposed framework was verified through simulation case studies. Typical navigation scenarios of conventional ships in coastal waters were chosen, and real-time data collected by relevant sensors during navigation were used as simulation inputs. Results suggest that in the same scenario, process risks differ among the analyzed NCMs. Traffic complexity, traffic density, and current become the primary factors influencing navigation risks, and it is necessary to select the appropriate NCM based on their real-time changes. [ABSTRACT FROM AUTHOR]