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The western Pacific seamount area is abundant in both biological and mineral resources, making it a crucial location for international investigation of regional seabed resources. An essential stage in comprehending and advancing seamounts is gaining knowledge about the distribution characteristics and laws governing the seabed substrate. Deep-sea geological sampling is challenging because of the intricate nature of the deep-sea environment, resulting in increased difficulty in identifying and evaluating substrates. This study addresses the aforementioned issues by utilizing in-situ video footage obtained from the “Jiaolong” manned deep submersible and shipborne deep-water multibeam data. This data is used as a foundation for constructing a Western Pacific seamount areas substrate classification point set. Additionally, the paper introduces the mRMR-XGBoost substrate classification model. Substrate categorization in deep sea and mountainous regions has been successfully accomplished, yielding a classification accuracy of 92.5%. The classification experiments and box sampling results demonstrate that the mRMR-XGBoost substrate classification model proposed in this paper can efficiently use acoustic and optical data to accurately divide the substrate types in seamount areas, with better classification accuracy, when compared with commonly used machine learning models. It has a significant application value and the best classification effect on the two types of substrates: nodules and gravel substrates.

Deep-sea sediment disturbance may occur when collecting polymetallic nodules, resulting in the creation of plumes that could have a negative impact on the ecological environment. This study aims to investigate the potential solution of using polyaluminum chloride (PAC) in the water jet. The effects of PAC are examined through a self-designed simulation system for deep-sea polymetallic nodule collection and sediment samples from a potential deep-sea mining area. The experimental results showed that the optimal PAC dose was found to be 0.75 g/L. Compared with the test conditions without the addition of PAC, the presence of PAC leads to a reduction in volume, lower characteristic turbidity, smaller diffusion velocity, and shorter settling time of the plume. This indicates that PAC inhibits the entire development process of the plume. The addition of PAC leads to the flocculation of mm-sized particles, resulting in the formation of cm-sized flocs. The flocculation of particles decreases the rate of erosion on the seabed by around 30%. This reduction in erosion helps to decrease the formation of plumes. Additionally, when the size of suspended particles increases, it reduces the scale at which they diffuse. Furthermore, the settling velocity of flocs (around 10−2 m/s) is much higher that of compared to sediment particles (around 10−5 m/s), which effectively reduces the amount of time the plume remains in suspension.

To address the issue of harsh marine background noise impacting the monitoring signal of fiber-optic hydrophones, we propose a low-noise fiber Bragg grating (FBG) hydroacoustic monitoring system with a reference sensor based on genetic algorithm backpropagation (GA-BP). Through theoretical analysis, we deduce the noise suppression steps of the GA-BP algorithm based on the reference sensor and construct train and test sets based on the data from the reference sensor and monitoring sensor at different times, optimizing the GA-BP algorithm to find the best fitting results and thereby obtaining the low-noise monitoring signal. Experimental results from the anechoic tank show that the proposed method can suppress background noise interference on effective signals and that the suppression effect improves as the background noise increases. The sound pressure sensitivity ranges from −173.76 dB to −171.33 dB at frequencies of 8 kHz to 12 kHz, with a response flatness of less than 2.43 dB. The noise suppression effect is obvious under the condition of poor signal-to-noise ratio (SNR), which can reach more than 18.3 dB. The advantages of the proposed algorithm in array signal processing are further demonstrated by the directivity experiment, which proves that the algorithm has a great potential for engineering applications in harsh marine environment.

Detecting deep-sea megabenthic organisms is of foremost importance for seabed resource surveys, typical habitat protection, and biodiversity surveys. However, the complexity of the deep-sea environment, uneven illumination, and small biological targets that are easily obscured all increase target detection difficulty significantly. To address these, this paper proposes a deep-sea megabenthic detection algorithm, DS-YOLO, based on YOLOv5s. To improve the detection ability of the model for deep-sea megabenthic organisms, the space-to-depth module and the spatial pyramid pooling cross stage partial channel module are introduced in the Backbone layer to enlarge the receptive field and enhance the retention of small-scale features. Then, the space-to-depth and normalization-based attention modules and the Add and Concat functions of the bidirectional feature pyramid network are introduced in the Neck layer to increase the multiscale fusion ability of the model and highlight the insignificant features. Finally, the two branches of the decoupling header output the category and location of the target, which causes the model to utilize the feature information to the maximum extent. Experiments showed that DS-YOLO improved mAP0.5 from 89.6% to 92.4% and mAP0.5:0.95 from 65.7% to 72.3% compared to the original YOLOv5s on the homemade dataset and outperformed other algorithms in the YOLO series. DS-YOLO reaches 84.7 FPS for deployment on mobile platforms. In addition, the combined DS-YOLO and DeepSORT algorithm can be used to calculate the abundance and community structure of deep-sea megabenthos. The model outperforms general target detection models for deep-sea megabenthos detection and is suitable for use in complex deep-sea environments.

To address the issue of harsh marine background noise impacting the monitoring signal of fiber-optic hydrophones, we propose a low-noise fiber Bragg grating (FBG) hydroacoustic monitoring system with a reference sensor based on genetic algorithm backpropagation (GA-BP). Through theoretical analysis, we deduce the noise suppression steps of the GA-BP algorithm based on the reference sensor and construct train and test sets based on the data from the reference sensor and monitoring sensor at different times, optimizing the GA-BP algorithm to find the best fitting results and thereby obtaining the low-noise monitoring signal. Experimental results from the anechoic tank show that the proposed method can suppress background noise interference on effective signals and that the suppression effect improves as the background noise increases. The sound pressure sensitivity ranges from −173.76 dB to −171.33 dB at frequencies of 8 kHz to 12 kHz, with a response flatness of less than 2.43 dB. The noise suppression effect is obvious under the condition of poor signal-to-noise ratio (SNR), which can reach more than 18.3 dB. The advantages of the proposed algorithm in array signal processing are further demonstrated by the directivity experiment, which proves that the algorithm has a great potential for engineering applications in harsh marine environment. [ABSTRACT FROM AUTHOR]

Ionic hydrogels with designable properties that can detect a broad spectrum of stimuli and have the potential for the preparation of flexible electronic devices are available. However, the incorporation of structurally ameliorated zwitterionic monomers can further enhance the performance of ionic hydrogels and provide them with more functions. In this study, we synthesized a new class of zwitterionic monomers (CNPU) with hydroxyurethane chromophores using a straightforward method and subsequently fabricated a double-network ionic hydrogel, containing as-synthesized zwitterionic monomers and common gelation monomers. The luminescent monomer CNPU provides the hydrogels with fluorescence properties, while the double network structure imparts robust mechanical properties. Furthermore, the abundant hydrogen bond and electrostatic interaction give excellent fatigue resistance and anti-freezing properties (−28.70 °C) to the hydrogel. Ultimately, this hydrogel, characterized by high ionic conductivity (81.08 mS cm−1) and temperature-sensitive sensing, was utilized to develop temperature sensors with remarkable anti-strain interference capabilities (GF = 0.16) and versatile applications including fluorescence positioning and information storage. Given its numerous advantages, this double-network ionic hydrogel exhibits tremendous potential for practical application in flexible sensors. [ABSTRACT FROM AUTHOR]

The document is a contents list of the Journal of Materials Chemistry C, published by The Royal Society of Chemistry in 2024. It features reviews, papers, and communications on various topics related to materials for optical, magnetic, and electronic devices. The first paper explores the correlation between conducting channels in self-assembled monolayer tunnel junctions, while the second paper is a correction for a previous article on saturated-blue-emitting iridium complexes for organic light-emitting devices. The journal aims to connect the world with the chemical sciences and provides a list of articles and authors featured in this issue. [Extracted from the article]