Highly energy-efficient degradation of simulated dye wastewater by array type underwater bubble discharge plasma: Key factors identification and degradation pathways.

The extensive use of dyes presents a significant safety concern for the reuse of water resources, highlighting the critical need for a rapid and efficient degradation method for dye mixtures in wastewater. This study introduces a degradation system based on an array type underwater bubble discharge plasma specifically designed to treat a dye mixture wastewater containing methylene blue (MB) and methyl violet 2B (MV2B). Analysis of the optical and electrical characteristics reveals that the device experiences minimal temperature increase, with the highest intensity of the band associated with N2 in the emission spectra, and discharges occurring predominantly during the rising and falling edges of a pulse cycle. Experimental results demonstrate that the most effective degradation efficiencies (MB = 94.83%, MV2B = 93.48%) are achieved at an initial dye concentration of 50 mg/L, a power supply frequency of 6 kHz, an air flow rate of 2.5 SLM and an initial electrical conductivity of 50 µS/cm. The degradation of dyes is primarily attributed to the demethylation process of O3(aq) and other species. Toxicity analysis indicates that the plasma degradation process significantly reduces the toxicity of the intermediate products of dyes. This study not only presents a novel approach for treating high concentration mixed dye wastewater, but also provides valuable insights for future research in this area.

Deep learning-assisted diagnosis of benign and malignant parotid gland tumors based on automatic segmentation of ultrasound images: a multicenter retrospective study.

Objectives: To construct deep learning-assisted diagnosis models based on automatic segmentation of ultrasound images to facilitate radiologists in differentiating benign and malignant parotid tumors. Methods: A total of 582 patients histopathologically diagnosed with PGTs were retrospectively recruited from 4 centers, and their data were collected for analysis. The radiomics features of six deep learning models (ResNet18, Inception_v3 etc) were analyzed based on the ultrasound images that were obtained under the best automatic segmentation model (Deeplabv3, UNet++, and UNet). The performance of three physicians was compared when the optimal model was used and not. The Net Reclassification Index (NRI) and Integrated Discrimination Improvement (IDI) were utilized to evaluate the clinical benefit of the optimal model. Results: The Deeplabv3 model performed optimally in terms of automatic segmentation. The ResNet18 deep learning model had the best prediction performance, with an area under the receiver-operating characteristic curve of 0.808 (0.694-0.923), 0.809 (0.712-0.906), and 0.812 (0.680-0.944) in the internal test set and external test sets 1 and 2, respectively. Meanwhile, the optimal model-assisted clinical and overall benefits were markedly enhanced for two out of three radiologists (in internal validation set, NRI: 0.259 and 0.213 [p = 0.002 and 0.017], IDI: 0.284 and 0.201 [p = 0.005 and 0.043], respectively; in external test set 1, NRI: 0.183 and 0.161 [p = 0.019 and 0.008], IDI: 0.205 and 0.184 [p = 0.031 and 0.045], respectively; in external test set 2, NRI: 0.297 and 0.297 [p = 0.038 and 0.047], IDI: 0.332 and 0.294 [p = 0.031 and 0.041], respectively). Conclusions: The deep learning model constructed for automatic segmentation of ultrasound images can improve the diagnostic performance of radiologists for PGTs.

Growth of Hg0.7Cd0.3Te on Van Der Waals Mica Substrates via Molecular Beam Epitaxy.

In this paper, we present a study on the direct growth of Hg0.7Cd0.3Te thin films on layered transparent van der Waals mica (001) substrates through weak interface interaction through molecular beam epitaxy. The preferred orientation for growing Hg0.7Cd0.3Te on mica (001) substrates is found to be the (111) orientation due to a better lattice match between the Hg0.7Cd0.3Te layer and the underlying mica substrate. The influence of growth parameters (mainly temperature and Hg flux) on the material quality of epitaxial Hg0.7Cd0.3Te thin films is studied, and the optimal growth temperature and Hg flux are found to be approximately 190 °C and 4.5 × 10-4 Torr as evidenced by higher crystalline quality and better surface morphology. Hg0.7Cd0.3Te thin films (3.5 µm thick) grown under these optimal growth conditions present a full width at half maximum of 345.6 arc sec for the X-ray diffraction rocking curve and a root-mean-square surface roughness of 6 nm. However, a significant number of microtwin defects are observed using cross-sectional transmission electron microscopy, which leads to a relatively high etch pit density (mid-107 cm-2) in the Hg0.7Cd0.3Te thin films. These findings not only facilitate the growth of HgCdTe on mica substrates for fabricating curved IR sensors but also contribute to a better understanding of growth of traditional zinc-blende semiconductors on layered substrates.

Transcriptome Analysis Reveals Novel Inflammatory Signalings to Glaesserella parasuis Infection.

Glaesserella parasuis (GPS) can cause severe systemic inflammation in pigs, resulting in huge economic losses to the pig industry. At present, no effective method is available for the prevention and control of GPS infection. Molecular breeding for disease resistance is imminent, but disease-resistance genes have not been identified. To study the mechanism of systemic acute inflammation caused by GPS, we established three in vitro infection models (3D4/21 cells, PK15 cells, and PAVEC cells) according to its infection path. There was no significant difference in apoptosis among the three kinds of cells after 12 h of continuous GPS stimulation, while inflammatory factors were significantly upregulated. Subsequent transcriptome analysis revealed 1969, 1207, and 3564 differentially expressed genes (DEGs) in 3D4/21 cells, PK15 cells, and PAVEC cells, respectively, after GPS infection. Many of the DEGs were predicted to be associated with inflammatory responses (C3, CD44, etc.); cell proliferation, growth and apoptosis; gene expression; and protein phosphorylation. Key signaling pathways, including S100 family signaling, bacteria and virus recognition, and pathogen-induced cytokine storm signaling, were enriched based on Ingenuity Pathway Analysis (IPA). Furthermore, a total of three putative transmembrane receptors and two putative G-protein-coupled receptors, namely F3, ICAM1, PLAUR, ACKR3, and GPRC5A, were identified by IPA among the three types of cells. ACKR3 and GPRC5A play pivotal roles in bacterial adhesion, invasion, host immune response and inflammatory response through the S100 family signaling pathway. Our findings provide new insights into the pathological mechanisms underlying systemic inflammation caused by GPS infection in pigs, and they lay a foundation for further research on disease-resistance breeding to GPS.

Memristor based on carbon nanotube gelatin composite film as artificial optoelectronic synapse for image processing.

Photoelectric artificial synapses based on memristors is an effective method to realize neuromorphic computation. This study presents an optoelectronic responsive artificial synapse made of a composite material consisting of gelatin and carbon nanotubes. The memristor demonstrates characteristics of analog resistive switching, the ability to store multiple memory states, and impressive retention properties. It has the capability to induce an excitatory post-synaptic current by means of electrical pulses or pulsed light exposure. The excitatory post-synaptic current can be modulated by the number, amplitude and interval of electrical pulses, as well as the action time, interval and light intensity of optical pulses. The artificial synapse showcases the emulation of fundamental Hebbian learning protocols, including spike timing dependent plasticity and spike amplitude dependent plasticity. In addition, the charge transfer in the carbon nanotube gelatin composite optoelectronic memristor is investigated through first-principles calculations, shedding light on its operational mechanism. Experimental results show that these devices have the potential to be utilized for processing image information, resulting in a significant reduction of input data and training expenses when recognizing handwritten numbers. Overall, the optoelectronic synapse exhibits promising image processing prospects in the field of neuromorphic computing.

A bias voltage controlled electrode-molecule interface in single-molecule junctions.

Tuning the electrode-molecule interface stands at the heart of functional single-molecule devices. Herein, we report that the electrode-molecule interface of difluoro-substituted benzothiadiazole (FBTZ)-based single-molecule junctions can be modulated by the bias voltage. At low bias voltage (100 mV), the dative Au-N linkage is formed and at high bias voltage (600 mV), a covalent Au-C linkage is constructed. These junctions show distinct conductance. Interestingly, dominant charge carriers in Au-N- and Au-C-based junctions are different, as evidenced by dft calculations. These results provide a new strategy for regulating the electrode-molecule interface, which will advance the development of molecular electronics.

Causality of Genetically Determined Metabolites on Chronic Kidney Disease: A Two-Sample Mendelian Randomization Study In Silico.

Introduction: Chronic kidney disease (CKD) is associated with metabolic disorders. However, the evidence for the causality of circulating metabolites to promote or prevent CKD is still lacking. Methods: The two-sample Mendelian randomization (MR) analysis was conducted to evaluate the latent causal relationship between the genetically proxied 486 blood metabolites and CKD. Genome-wide association study (GWAS) data for exposures were derived from 7824 European GWAS on metabolite levels, which have been extensively utilized in the medical field to elucidate the mechanisms underlying disease onset and progression. The random inverse variance weighted (IVW) is the primary analysis for causality analysis while MR-Egger and weighted median as complementary analyses. For the further identification of metabolites, reverse MR and linkage disequilibrium score regression were performed for further evaluation. The drug target for N-acetylornithine was subsequently supplemented into the analysis, with MR and colocalization analysis being utilized. Key metabolic pathways were identified via MetaboAnalyst 4.0 (https://www.metaboanalyst.ca/) online website. Results: N-acetylornithine was identified as a reliable metabolite that increases the susceptibility to estimated glomerular filtration rate (eGFR) decrease (ß = 0.047; 95% confidence interval: -0.068 to -0.026; PIVW = 1.5E-5). The "glyoxylate and dicarboxylate metabolism" pathway showed significant relevance to CKD development (P = 6E-4), whereas the "glycine, serine, and threonine metabolism" pathway was also recognized as associated with CKD by general practitioners (P = 7E-4). Colocalization analysis revealed a robust genetic link between N-acetylornithine and both CKD and eGFR, with 85.1% and 99.4% colocalization rates, respectively. IVW-MR analysis substantiated these findings with a significant positive association for CKD (odds ratio = 1.43, P = 4.7E-5) and a negative correlation with eGFR (b = -0.04, P = 1.13E-31). Conclusions: MR was utilized to explore the potential causal links between 61 genetic serum metabolites and CKD. N-acetylornithine and NAT8 were further explored as a potential therapeutic target for CKD treatment.

Fundamental, mechanism and development of hydration lubrication: From bio-inspiration to artificial manufacturing.

Friction and lubrication are ubiquitous in all kinds of movements and play a vital role in the smooth operation of production machinery. Water is indispensable both in the lubrication systems of natural organisms and in hydration lubrication systems. There exists a high degree of similarity between these systems, which has driven the development of hydration lubrication from biomimetic to artificial manufacturing. In particular, significant advancements have been made in the understanding of the mechanisms of hydration lubrication over the past 30 years. This enhanced understanding has further stimulated the exploration of biomimetic inspiration from natural hydration lubrication systems, to develop novel artificial hydration lubrication systems that are cost-effective, easily transportable, and possess excellent capability. This review summarizes the recent experimental and theoretical advances in the understanding of hydration-lubrication processes. The entire paper is divided into three parts. Firstly, surface interactions relevant to hydration lubrication are discussed, encompassing topics such as hydrogen bonding, hydration layer, electric double layer force, hydration force, and Stribeck curve. The second part begins with an introduction to articular cartilage in biomaterial lubrication, discussing its compositional structure and lubrication mechanisms. Subsequently, three major categories of bio-inspired artificial manufacturing lubricating material systems are presented, including hydrogels, polymer brushes (e.g., neutral, positive, negative and zwitterionic brushes), hydration lubricant additives (e.g., nano-particles, polymers, ionic liquids), and their related lubrication mechanism is also described. Finally, the challenges and perspectives for hydration lubrication research and materials development are presented.

Suppressing the Conductance of Single-Molecule Junctions Fabricated by sp2 C-H Bond Metalation.

High-conducting single-molecule junctions have attracted a great deal of attention, but insulating single-molecule junctions, which are critical in molecular circuits, have been less investigated due to the long-standing challenges. Herein, the in situ formation of a Au-C linker via electrical-potential-mediated sp2 C-H bond metalation of polyfluoroarenes with the assistance of scanning tunneling microscope-based break junction technique is reported. This metalation process is bias-dependent and occurs with an electropositive electrode, and the formed junction is highly oriented. Surprisingly, these polyfluoroarenes exhibit unexpected low conductance even under short molecular lengths and are superior molecular insulators. Flicker noise analysis and DFT calculations confirm that the insulating properties of polyfluoroarenes are ascribed to their multiple fluorine substituents. Our results pave a way for constructing oriented asymmetric molecular junctions and provide an efficient strategy to suppress the single-molecule conductance, which will aid in the design of molecular insulators and advance the development of self-integrating functional molecular circuits.

In defense of local descriptor-based few-shot object detection.

State-of-the-art image object detection computational models require an intensive parameter fine-tuning stage (using deep convolution network, etc). with tens or hundreds of training examples. In contrast, human intelligence can robustly learn a new concept from just a few instances (i.e., few-shot detection). The distinctive perception mechanisms between these two families of systems enlighten us to revisit classical handcraft local descriptors (e.g., SIFT, HOG, etc.) as well as non-parametric visual models, which innately require no learning/training phase. Herein, we claim that the inferior performance of these local descriptors mainly results from a lack of global structure sense. To address this issue, we refine local descriptors with spatial contextual attention of neighbor affinities and then embed the local descriptors into discriminative subspace guided by Kernel-InfoNCE loss. Differing from conventional quantization of local descriptors in high-dimensional feature space or isometric dimension reduction, we actually seek a brain-inspired few-shot feature representation for the object manifold, which combines data-independent primitive representation and semantic context learning and thus helps with generalization. The obtained embeddings as pattern vectors/tensors permit us an accelerated but non-parametric visual similarity computation as the decision rule for final detection. Our approach to few-shot object detection is nearly learning-free, and experiments on remote sensing imageries (approximate 2-D affine space) confirm the efficacy of our model.

[Establishment of a Predictive Model for Chronic Cough after Pulmonary Resection].

BACKGROUND: Chronic cough after pulmonary resection is one of the most common complications, which seriously affects the quality of life of patients after surgery. Therefore, the aim of this study is to explore the risk factors of chronic cough after pulmonary resection and construct a prediction model. METHODS: The clinical data and postoperative cough of 499 patients who underwent pneumonectomy or pulmonary resection in The First Affiliated Hospital of University of Science and Technology of China from January 2021 to June 2023 were retrospectively analyzed. The patients were randomly divided into training set (n=348) and validation set (n=151) according to the principle of 7:3 randomization. According to whether the patients in the training set had chronic cough after surgery, they were divided into cough group and non-cough group. The Mandarin Chinese version of Leicester cough questionnare (LCQ-MC) was used to assess the severity of cough and its impact on patients' quality of life before and after surgery. The visual analog scale (VAS) and the self-designed numerical rating scale (NRS) were used to evaluate the postoperative chronic cough. Univariate and multivariate Logistic regression analysis were used to analyze the independent risk factors and construct a model. Receiver operator characteristic (ROC) curve was used to evaluate the discrimination of the model, and calibration curve was used to evaluate the consistency of the model. The clinical application value of the model was evaluated by decision curve analysis (DCA). RESULTS: Multivariate Logistic analysis screened out that preoperative forced expiratory volume in the first second/forced vital capacity (FEV1/FVC), surgical procedure, upper mediastinal lymph node dissection, subcarinal lymph node dissection, and postoperative closed thoracic drainage time were independent risk factors for postoperative chronic cough. Based on the results of multivariate analysis, a Nomogram prediction model was constructed. The area under the ROC curve was 0.954 (95%CI: 0.930-0.978), and the cut-off value corresponding to the maximum Youden index was 0.171, with a sensitivity of 94.7% and a specificity of 86.6%. With a Bootstrap sample of 1000 times, the predicted risk of chronic cough after pulmonary resection by the calibration curve was highly consistent with the actual risk. DCA showed that when the preprobability of the prediction model probability was between 0.1 and 0.9, patients showed a positive net benefit. CONCLUSIONS: Chronic cough after pulmonary resection seriously affects the quality of life of patients. The visual presentation form of the Nomogram is helpful to accurately predict chronic cough after pulmonary resection and provide support for clinical decision-making.

Polymer Coating with Balanced Coordination Strength and Ion Conductivity for Dendrite-Free Zinc Anode.

Decorating Zn anodes with functionalized polymers is considered as an effective strategy to inhibit dendrite growth. However, this normally brings extra interfacial resistance rendering slow reaction kinetics of Zn2+ . Herein, a poly(2-vinylpyridine) (P2VP) coating with modulated coordination strength and ion conductivity for dendrite-free Zn anode is reported. The P2VP coating favors a high electrolyte wettability and rapid Zn2+ migration speed (Zn2+ transfer number, tZn 2+ = 0.58). Electrostatic potential calculation shows that P2VP mildly coordinates with Zn2+ (adsorption energy = -0.94 eV), which promotes a preferential deposition of Zn along the (002) crystal plane. Notably, the use of partially (26%) quaternized P2VP (q-P2VP) further reduces the interfacial resistance to 126 Ω, leading to a high ion migration speed (tZn 2+ = 0.78) and a considerably low nucleation overpotential (18 mV). As a result of the synergistic effect of mild coordination and partial electrolysis, the overpotential of the q-P2VP-decorated Zn anode retains at a considerably low level (≈46 mV) over 1000 h at a high current density of 10 mA cm-2 . The assembled (NH4 )2 V6 O16 ·1.5H2 O || glass fiber || q-P2VP-Zn full cell reveals a lower average capacity decay rate of only 0.018% per cycle within 500 cycles at 1 A g-1 .

Association between MC1R gene and coat color segregation in Shanxia long black pig and Lulai black pig.

BACKGROUND: Coat color, as a distinct phenotypic characteristic of pigs, is often subject to preference and selection, such as in the breeding process of new breed. Shanxia long black pig was derived from an intercross between Berkshire boars and Licha black pig sows, and it was bred as a paternal strain with high-quality meat and black coat color. Although the coat color was black in the F1 generation of the intercross, it segregated in the subsequent generations. This study aims to decode the genetic basis of coat color segregation and develop a method to distinct black pigs from the spotted in Shanxia long black pig. RESULTS: Only a QTL was mapped at the proximal end of chromosome 6, and MC1R gene was picked out as functional candidate gene. A total of 11 polymorphic loci were identified in MC1R gene, and only the c.67_68insCC variant was co-segregating with coat color. This locus isn't recognized by any restriction endonuclease, so it can't be genotyped by PCR-RFLP. The c.370G > A polymorphic locus was also significantly associated with coat color, and has been in tightly linkage disequilibrium with the c.67_68insCC. Furthermore, it is recognized by BspHI. Therefore, a PCR-RFLP method was set up to genotype this locus. Besides the 175 sequenced individuals, another more 1,391 pigs were genotyped with PCR-RFLP, and all of pigs with GG (one band) were black. CONCLUSION: MC1R gene (c.67_68insCC) is the causative gene (mutation) for the coat color segregation, and the PCR-RFLP of c.370G > A could be used in the breeding program of Shanxia long black pig.

Comprehensive Simulation Framework for Space-Air-Ground Integrated Network Propagation Channel Research.

The space-air-ground integrated network (SAGIN) represents a pivotal component within the realm of next-generation mobile communication technologies, owing to its established reliability and adaptable coverage capabilities. Central to the advancement of SAGIN is propagation channel research due to its critical role in aiding network system design and resource deployment. Nevertheless, real-world propagation channel research faces challenges in data collection, deployment, and testing. Consequently, this paper designs a comprehensive simulation framework tailored to facilitate SAGIN propagation channel research. The framework integrates the open source QuaDRiGa platform and the self-developed satellite channel simulation platform to simulate communication channels across diverse scenarios, and also integrates data processing, intelligent identification, algorithm optimization modules in a modular way to process the simulated data. We also provide a case study of scenario identification, in which typical channel features are extracted based on channel impulse response (CIR) data, and recognition models based on different artificial intelligence algorithms are constructed and compared.

Li3Bi/Li2O layer with uniform built-in electric field distribution for dendrite free lithium metal batteries.

Lithium metal batteries have garnered significant attention as a promising energy storage technology, offering high energy density and potential applications across various industries. However, the formation of lithium dendrites during battery cycling poses a considerable challenge, leading to performance degradation and safety hazards. This study aims to address this issue by investigating the effectiveness of a protective layer on the lithium metal surface in inhibiting dendrite growth. The hypothesis is that continuous lithium consumption during battery cycling is a primary contributor to dendrite formation. To test this hypothesis, a protective layer of Li3Bi/Li2O was applied to the lithium foil through immersion in a BiN3O9 solution. Experimental techniques including kelvin probe force microscopy (KPFM) and density functional theory (DFT) calculations were employed to analyze the structural and electronic properties of the Li3Bi/Li2O layer. The findings demonstrate successful doping of Bi into the Li coating, forming Bi-Bi and Bi-O bonds. KPFM measurements reveal a higher work function of Li3Bi/Li2O, indicating its potential as an effective protective layer. DFT calculations further support this observation by revealing a greater adsorption energy of lithium on the Li3Bi/Li2O layer compared to the bulk material. Charge density analysis suggests that the adsorption of Li atoms onto the Li3Bi/Li2O layer induces a redistribution of charge, resulting in increased electron availability on the surface and preventing electrode-electrolyte contact. This study provides insights into the role of the Li3Bi/Li2O protective layer in inhibiting dendrite growth in lithium metal batteries. By mitigating dendrite formation, the protective layer holds promise for enhancing battery performance and longevity. These findings contribute to the development of strategies for improving the stability and reliability of lithium metal batteries, facilitating their wider adoption in energy storage applications.

Alcohol-Induced Strong Metal-Support Interactions in a Supported Copper/ZnO Catalyst.

Tuning the strong metal-support interaction (SMSI) in metal catalysts is a promising strategy to improve their catalytic performance. In this article, we systematically investigated the influences of different alcohol/water mixtures on the evolution of the interfacial structure of Cu/ZnO catalysts in the reduction stage. A series of in situ characterization and theoretical simulation studies were performed to elucidate the various mechanisms of alcohol induced SMSI. It was found that when methanol/water is added to H2 during the reduction pretreatment, more oxygen vacancies are formed on the ZnO support, which facilitates the dissociation of H2 O and the hydroxylation of ZnO species. Such promotion eventually favors the SMSI between Cu and ZnO and increases the catalytic activity for the methanol steam reforming reaction. In contrast, the addition of ethanol/water and 1-propanol/water during reduction leads to a physical blockage of the catalyst by alcohol molecules, poisoning the active Cu sites and limiting the migration of ZnO species.

A Defect Detection Model for Industrial Products Based on Attention and Knowledge Distillation.

Industrial quality detection is one of the important fields in machine vision. Big data analysis, the Internet of Things, edge computing, and other technologies are widely used in industrial quality detection. Studying an industrial detection algorithm that can be organically combined with the Internet of Things and edge computing is imminent. Deep learning methods in industrial quality detection have been widely proposed recently. However, due to the particularity of industrial scenarios, the existing deep learning-based general object detection methods have shortcomings in industrial applications. This study designs two isomorphic industrial detection models to solve these problems: T-model and S-model. Both proposed models combine swin-transformer with convolution in the backbone and design a residual fusion path. In the neck, this study designs a dual attention module to improve feature fusion. Second, this study presents a knowledge distiller based on the dual attention module to improve the detection accuracy of the lightweight S-model. According to the analysis of the experimental results on four public industrial defect detection datasets, the model in this study is more advantageous in industrial defect detection.

Covalent Organic Frameworks with Ionic Liquid-Moieties (ILCOFs): Structures, Synthesis, and CO2 Conversion.

CO2, an acidic gas, is usually emitted from the combustion of fossil fuels and leads to the formation of acid rain and greenhouse effects. CO2 can be used to produce kinds of value-added chemicals from a viewpoint based on carbon capture, utilization, and storage (CCUS). With the combination of unique structures and properties of ionic liquids (ILs) and covalent organic frameworks (COFs), covalent organic frameworks with ionic liquid-moieties (ILCOFs) have been developed as a kind of novel and efficient sorbent, catalyst, and electrolyte since 2016. In this critical review, we first focus on the structures and synthesis of different kinds of ILCOFs materials, including ILCOFs with IL moieties located on the main linkers, on the nodes, and on the side chains. We then discuss the ILCOFs for CO2 capture and conversion, including the reduction and cycloaddition of CO2. Finally, future directions and prospects for ILCOFs are outlined. This review is beneficial for academic researchers in obtaining an overall understanding of ILCOFs and their application of CO2 conversion. This work will open a door to develop novel ILCOFs materials for the capture, separation, and utilization of other typical acid, basic, or neutral gases such as SO2, H2S, NOx, NH3, and so on.

Design and Implementation of a Modular UUV Simulation Platform.

The complex and time-varying marine environment puts forward demanding requirements for the structural design and algorithm development of unmanned underwater vehicles (UUVs). It is inevitable to repeatedly evaluate the feasibility of autonomy schemes to enhance the intelligence and security of the UUV before putting it into use. Considering the high cost of the UUV hardware platform and the high risk of underwater experiments, this study aims to evaluate and optimize autonomy schemes in the manner of software-in-loop (SIL) simulation efficiently. Therefore, a self-feedback development framework is proposed and a multi-interface, programmable modular simulation platform for UUV based on a robotic operating system (ROS) is designed. The platform integrates the 3D marine environment, UUV models, sensor plugins, motion control plugins in a modular manner, and reserves programming interfaces for users to test various algorithms. Subsequently, we demonstrate the simulation details with cases, such as single UUV path planning, task scheduling, and multi-UUV formation control, and construct underwater experiments to confirm the feasibility of the simulation platform. Finally, the extensibility of the simulation platform and the related performance analysis are discussed.

Tuning Functionalized Ionic Liquids for CO2 Capture.

The increasing concentration of CO2 in the atmosphere is related to global climate change. Carbon capture, utilization, and storage (CCUS) is an important technology to reduce CO2 emissions and to deal with global climate change. The development of new materials and technologies for efficient CO2 capture has received increasing attention among global researchers. Ionic liquids (ILs), especially functionalized ILs, with such unique properties as almost no vapor pressure, thermal- and chemical-stability, non-flammability, and tunable properties, have been used in CCUS with great interest. This paper focuses on the development of functionalized ILs for CO2 capture in the past decade (2012~2022). Functionalized ILs, or task-specific ILs, are ILs with active sites on cations or/and anions. The main contents include three parts: cation-functionalized ILs, anion-functionalized ILs, and cation-anion dual-functionalized ILs for CO2 capture. In addition, classification, structures, and synthesis of functionalized ILs are also summarized. Finally, future directions, concerns, and prospects for functionalized ILs in CCUS are discussed. This review is beneficial for researchers to obtain an overall understanding of CO2-philic ILs. This work will open a door to develop novel IL-based solvents and materials for the capture and separation of other gases, such as SO2, H2S, NOx, NH3, and so on.