Anisotropic Amorphization and Phase Transition in Na2 Ti3 O7 Anode Caused by Electron Beam Irradiation.

Na2 Ti3 O7 is considered one of the most promising anode materials for sodium ion batteries due to its superior safety, environmental friendliness, and low manufacturing cost. However, its structural stability and reaction mechanism still have not been fully explored. As the electron beam irradiation introduces a similar impact on the Na2 Ti3 O7 anode as the extraction of Na+ ions during the battery discharge process, the microstructure evolution of the materials is investigated by advanced electron microscopy techniques at the atomic scale. Anisotropic amorphization is successfully observed. Through the integrated differential phase contrast-scanning transmission electron microscopy technique and density functional theory calculation, a phase transition pathway involving a new phase, Na2 Ti24 O49 , is proposed with the reduction of Na atoms. Additionally, it is found that the amorphization is dominated by the surface energy and electron dose rate. These findings will deepen the understanding of structural stability and deintercalation mechanism of the Na2 Ti3 O7 anode, providing new insight into exploring the failure mechanism of electrode materials.

Metal Organic Frameworks as Polysulfide Reaction Modulators for Lithium Sulfur Batteries: Advances and Perspectives.

Currently, lithium sulfur (Li-S) battery with high theoretical energy density has attracted great research interest. However, the diffusion and loss process of intermediate lithium polysulfide during charge-discharge hindered the application of the Li-S battery in modern life. To overcome this issue, metal organic frameworks (MOFs) and their composites have been regarded as effective additions to restrain the LiPS diffusion process for Li-S battery. Benefiting from the unique structure with rich active sites to adsorb LiPS and accelerate the LiPS redox, the Li-S batteries with MOFs modified exhibit superior electrochemical performance. Considering the rapid development of MOFs in Li-S battery, this review summarizes the recent studies of MOFs and their composites as the sulfur host materials, functional interlayer, separator coating layer, and separator/solid electrolyte for Li-S batteries in detail. In addition, the promising design strategies of functional MOF materials are proposed to improve the electrochemical performance of Li-S battery.

Positioning of Unmanned Underwater Vehicle Based on Autonomous Tracking Buoy.

This paper presents a novel method for the dynamic positioning of an unmanned underwater vehicle (UUV) with unknown trajectories based on an autonomous tracking buoy (PUVV-ATB) that indirectly positions the UUV using ultra-short baseline measurements. The method employs a spatial location geometric model and divides the positioning process into four steps, including data preprocessing to detect geometric errors and apply mean filtering, direction capture, position tracking, and position synchronization. To achieve these steps, a new adaptive tracking control algorithm is proposed that does not require trajectory prediction and is applied to the last three steps. The algorithm is deployed to the buoy for tracking simulation and sea trial experiments, and the results are compared with those of a model predictive control algorithm. The autonomous tracking buoy based on the adaptive tracking control algorithm runs more stably and can better complete the precise tracking task for the UUV with a positioning error of less than 10 cm. This method breaks the premise of trajectory prediction based on traditional tracking control algorithms, providing a new direction for further research on UUV localization. Furthermore, the conclusion of this paper has important reference value for other research and application fields related to UUV.

A Positioning and Navigation Method Combining Multimotion Features Dead Reckoning with Acoustic Localization.

Accurate location information can offer huge commercial and social value and has become a key research topic. Acoustic-based positioning has high positioning accuracy, although some anomalies that affect the positioning performance arise. Inertia-assisted positioning has excellent autonomous characteristics, but its localization errors accumulate over time. To address these issues, we propose a novel positioning navigation system that integrates acoustic estimation and dead reckoning with a novel step-length model. First, the features that include acceleration peak-to-valley amplitude difference, walk frequency, variance of acceleration, mean acceleration, peak median, and valley median are extracted from the collected motion data. The previous three steps and the maximum and minimum values of the acceleration measurement at the current step are extracted to predict step length. Then, the LASSO regularization spatial constraint under the extracted features optimizes and solves for the accurate step length. The acoustic estimation is determined by a hybrid CHAN-Taylor algorithm. Finally, the location is determined using an extended Kalman filter (EKF) merged with the improved pedestrian dead reckoning (PDR) estimation and acoustic estimation. We conducted some comparative experiments in two different scenarios using two heterogeneous devices. The experimental results show that the proposed fusion positioning navigation method achieves 8~56.28 cm localization accuracy. The proposed method can significantly migrate the cumulative error of PDR and high-robustness localization under different experimental conditions.

Deep Neural Network-Based Fusion Localization Using Smartphones.

Indoor location-based services (LBS) have tremendous practical and social value in intelligent life due to the pervasiveness of smartphones. The magnetic field-based localization method has been an interesting research hotspot because of its temporal stability, ubiquitousness, infrastructure-free nature, and good compatibility with smartphones. However, utilizing discrete magnetic signals may result in ambiguous localization features caused by random noise and similar magnetic signals in complex symmetric and large-scale indoor environments. To address this issue, we propose a deep neural network-based fusion indoor localization system that integrates magnetic and pedestrian dead reckoning (PDR). In this system, we first propose a ResNet-GRU-LSTM neural network model to achieve magnetic localization more accurately. Afterward, we put forward a multifeatured-driven step length estimation. A hierarchy GRU (H-GRU) neural network model is proposed, and a multidimensional dataset using acceleration and a gyroscope is constructed to extract more valid characteristics. Finally, more reliable and accurate pedestrian localization can be achieved under the particle filter framework. Experiments were conducted at two trial sites with two pedestrians and four smartphones. Results demonstrate that the proposed system achieves better accuracy and robustness than other traditional localization algorithms. Moreover, the proposed system exhibits good generality and practicality in real-time localization with low cost and low computational complexity.

Activated carbon as an insoluble electron shuttle to enhance the anaerobic ammonium oxidation coupled with Fe(III) reduction process.

Anaerobic ammonium oxidation coupled with Fe(III) reduction (Feammox) is an autotrophic biological nitrogen removal (BNR) technique in treating low-C/N wastewater. However, the nitrogen removal rate of Feammox is limited by the extracellular electron transfer. In this study, wood activated carbon (AC) was chosen as electron shuttle to enhance the start-up of the Feammox process. Within an operational period of 150 days, the NH4+-N removal efficiency reached 97.9-99.5% with a volumetric loading rate (VLR) of 0.04-0.06 kg N m-3 d-1. Batch experiments indicated that compared with Fe2O3-AQDS and Fe2O3 groups, Fe2O3-AC group showed higher catalytic performance and TN removal efficiency reached 85.7%. Quinone (CO) and phenolic (-OH) chemical groups of AC were equipped with electron transfer capacity (76.51 ± 9.27 µmol e- g-1). Moreover, Fe(II)/Fe(III) species and the secondary iron minerals were found in our system. Microbial analysis showed that Proteobacteria and Acidobacteriota, which observed with relatively high abundance, were played an important role in the integrated Feammox system. This study demonstrates the significant influence of AC on Feammox process and provides an enhanced biological nitrogen removal strategy for practice engineering application.

Description and genome analysis of Luteimonas viscosa sp. nov., a novel bacterium isolated from soil of a sunflower field.

Strain XBU10T was isolated from a soil sample of a sunflower plot in Inner Mongolia, China. The isolate was a Gram-stain-negative, aerobic, non-motile, rod-shaped bacterium, and its colonies were bright yellow in colour. Phylogenetic analysis based on 16S rRNA gene sequences indicated that strain XBU10T belonged to the genus Luteimonas of the family Lysobacteraceae and was most closely related to Luteimonas panaciterrae Gsoil 068T (97.8%), Luteimonas marina FR1330T (97.6%), Luteimonas aquatica RIB1-20T (97.4%) and Luteimonas huabeiensis HB2T (97.2%). Growth occurred at 4-40 °C (optimum, 28-30 °C), with 0-5.0% (w/v) NaCl (optimum, 0.5%) and at pH 6.0-10.0 (optimum, pH 7.0 - 8.0). The chemotaxonomic characteristics of strain XBU10T, which had Q-8 as its predominant quinone and iso-C17:1 ω9c, iso-C15:0, iso-C17:0 and iso-C16:0 as its major fatty acids, were consistent with classification in the genus Luteimonas. The polar lipid profile of strain XBU10T comprised phosphatidylglycerol, diphosphatidylglycerol, phosphatidylethanolamine, one unidentified phospholipid, two unidentified aminophospholipids and three unidentified polar lipids. The genome of strain XBU10T was 4.17 Mbp with a G + C content of 69.9%. Its genome sequence showed genes encoding alkaline phosphatase and catalase. Protein-coding genes related to carbohydrate-active enzymes were also observed. Average nucleotide identity (ANI) values between XBU10T and other species of the genus Luteimonas were found to be low (ANIm < 88.0%, ANIb < 85.0% and OrthoANIu < 85.0%). Furthermore, digital DNA-DNA hybridization (dDDH) and average amino acid identity (AAI) values between strain XBU10T and the closely related species ranged from 20.3 to 28.9% and from 64.2 to 82.3%, respectively. Based on the results of our phylogenetic, phenotypic, genotypic and chemotaxonomic analyses, it is concluded that strain XBU10T represents a novel species within the genus Luteimonas, for which the name Luteimonas viscosa sp. nov. is proposed. The type strain is XBU10T (= CGMCC 1.12158T = KCTC 23878T).

A Low-Cost and Efficient Indoor Fusion Localization Method.

Accurate indoor location information has considerable social and economic value in applications, such as pedestrian heatmapping and indoor navigation. Ultrasonic-based approaches have received significant attention mainly since they have advantages in terms of positioning with temporal correlation. However, it is a great challenge to gain accurate indoor localization due to complex indoor environments such as non-uniform indoor facilities. To address this problem, we propose a fusion localization method in the indoor environment that integrates the localization information of inertial sensors and acoustic signals. Meanwhile, the threshold scheme is used to eliminate outliers during the positioning process. In this paper, the estimated location is fused by the adaptive distance weight for the time difference of arrival (TDOA) estimation and improved pedestrian dead reckoning (PDR) estimation. Three experimental scenes have been developed. The experimental results demonstrate that the proposed method has higher localization accuracy in determining the pedestrian location than the state-of-the-art methods. It resolves the problem of outliers in indoor acoustic signal localization and cumulative errors in inertial sensors. The proposed method achieves better performance in the trade-off between localization accuracy and low cost.

Strain effects in core-shell PtCo nanoparticles: a comparison of experimental observations and computational modelling.

Strain in Pt nanoalloys induced by the secondary metal has long been suggested as a major contributor to the modification of catalytic properties. Here, we investigate strain in PtCo nanoparticles using a combination of computational modelling and microscopy experiments. We have used a combination of molecular dynamics (MD) and large-scale density functional theory (DFT) for our models, alongside experimental work using annular dark field scanning transmission electron microscopy (ADF-STEM). We have performed extensive validation of the interatomic potential against DFT using a Pt568Co18 nanoparticle. Modelling gives access to 3 dimensional structures that can be compared to the 2D ADF-STEM images, which we use to build an understanding of nanoparticle structure and composition. Strain has been measured for PtCo and pure Pt nanoparticles, with MD annealed models compared to ADF-STEM images. Our analysis was performed on a layer by layer basis, where distinct trends between the Pt and PtCo alloy nanoparticles are observed. To our knowledge, we show for the first time a way in which detailed atomistic simulations can be used to augment and help interpret the results of ADF-STEM strain mapping experiments, which will enhance their use in characterisation towards the development of improved catalysts.

A new algorithm for context-based biomedical diagram similarity estimation.

MOTIVATION: Diagrams embedded in the biomedical literature convey rich contents, which often concisely and intuitively highlight key thesis of a research article. Despite their vital importance and informative clues for biomedical literature navigation and retrieval; currently, we miss an effective computational method for automatically understanding and accessing these valuable resources. PROPOSED METHOD: To address the aforementioned gap, we propose a novel context-based algorithm for estimating the similarity between a pair of biomedical diagrams. The main difference of the proposed algorithm with respect to the existing methods lies in the new algorithm's incorporation of the semantic context associated with diagrams in their source documents into the diagram similarity estimation process. In addition, the new approach also performs a series of advanced image processing and text mining operations to comprehensively extract the semantic content graphically encoded inside diagram images. RESULTS: The new algorithm can be deployed as a reusable component providing a fundamental function for building many advanced, semantic-aware applications on biomedical diagram processing. As a case study, in our experiments, we demonstrate the advantage of the new algorithm for diagram retrieval. A set of biomedical diagram search and ranking experiments were conducted, where the performance of the new method was compared with that of five peer methods. The comparison results demonstrate the performance superiority of the new algorithm with all peer methods with statistical significance.

Planomicrobium soli sp. nov., isolated from soil.

A Gram-staining-positive bacterium, designated strain XN13(T), was isolated from a soil sample collected from ALaShan National Geological Park in Inner Mongolia Autonomous Region, China and subjected to a taxonomic study using a polyphasic approach. Strain XN13(T) was found to have a range of chemical and morphological properties consistent with its classification in the genus Planomicrobium. Phylogenetic analyses based on 16S rRNA gene sequences revealed that strain XN13(T) was related to members of the genus Planomicrobium. The closest phylogenetic relatives were Planomicrobium okeanokoites NBRC 12536(T), Planomicrobium koreense JG07(T), Planomicrobium mcmeekinii S23F2(T) and Planomicrobium flavidum ISL-41(T) with 98.2%, 97.8%, 97.8% and 97.7% 16S rRNA gene sequence similarity, respectively. The major cellular fatty acids were anteiso-C(15 : 0), C(16 : 1)ω7c alcohol, iso-C(14 : 0) and C(16 : 1)ω11c. The predominant menaquinones were MK-8 and MK-7. The DNA G+C content was 40.3 mol%. The DNA-DNA relatedness values between strain XN13(T) and Planomicrobium okeanokoites KCTC 3672(T), Planomicrobium koreense KCTC 3684(T), P. mcmeekinii CGMCC 1.2724(T), Planomicrobium flavidum KCTC 13261(T), Planomicrobium chinense CGMCC 1.3454(T) and Planomicrobium glaciei CGMCC 1.6846(T) were 36%, 30%, 34%, 29%, 30% and 31%, respectively. The organism is different from recognized species of the genus Planomicrobium in several phenotypic characteristics. On the basis of phenotypic and genotypic properties, strain XN13(T) represents a novel species of the genus Planomicrobium, for which the name Planomicrobium soli sp. nov. is proposed. The type strain is XN13(T) ( = CGMCC 1.12259(T) = KCTC 33047(T)).

Understanding and Mitigating Lattice Collapse Degradation in Layered Oxide Materials for Sodium-Ion Battery Anode.

Na2Ti3O7 has attracted significant attention due to its ecofriendliness and cost-effectiveness for sodium-ion batteries. However, their limited cycling stability hampers their practical applications. Herein, we elucidate a mechanism of structural degradation caused by the heterogeneous phase transition in the Na2Ti3O7 anode using aberration-corrected (scanning) transmission electron microscopy (S)TEM and in situ TEM. It is found that the unevenly distributed phase transition results in the accumulation of strain, which promotes the growth of microcracks and eventually leads to structural decomposition and electrochemical failure. Motivated by this degradation mechanism, nanowires were proposed, and the structural stability is thus improved with the lattice strain effectively released. These findings deepen our understanding of ion transport and degradation mechanisms in intercalated layered electrode materials while emphasizing the significance of the material structure engineered for improving electrode performance.

Cluster-Aware Grid Layout.

Grid visualizations are widely used in many applications to visually explain a set of data and their proximity relationships. However, existing layout methods face difficulties when dealing with the inherent cluster structures within the data. To address this issue, we propose a cluster-aware grid layout method that aims to better preserve cluster structures by simultaneously considering proximity, compactness, and convexity in the optimization process. Our method utilizes a hybrid optimization strategy that consists of two phases. The global phase aims to balance proximity and compactness within each cluster, while the local phase ensures the convexity of cluster shapes. We evaluate the proposed grid layout method through a series of quantitative experiments and two use cases, demonstrating its effectiveness in preserving cluster structures and facilitating analysis tasks.

Massilia lurida sp. nov., isolated from soil.

A bacterial isolate, designated strain D5(T), was isolated from a soil sample collected from the Inner Mongolia Autonomous Region, China, and subjected to a taxonomic investigation using a polyphasic approach. Strain D5(T) was aerobic, Gram-stain-negative, rod-shaped and motile. Strain D5(T) fell within the evolutionary radius of the genus Massilia in the phylogenetic tree based on 16S rRNA gene sequences and was most closely related to Massilia plicata 76(T) with 97.3% 16S rRNA gene sequence similarity. The predominant quinone of strain D5(T) was Q-8. The major polar lipids were phosphatidylethanolamine, phosphatidylglycerol and diphosphatidylglycerol. The major fatty acids were summed feature 3 (comprising C16:1ω7c and/or iso-C15:0 2-OH) and C16:0. These chemotaxonomic data supported the affiliation of strain D5(T) to the genus Massilia. The genomic DNA G+C content was 65.9 mol%. Mean DNA-DNA relatedness values between strain D5(T) and the phylogenetically most closely related species of the genus Massilia, Massilia plicata KCTC 12344(T) and Massilia dura KCTC 12342(T), were 26 and 21%, respectively. Strain D5(T) could be differentiated from recognized species of the genus Massilia by several phenotypic characteristics. It is clear from the data presented that strain D5(T) represents a novel species of the genus Massilia, for which the name Massilia lurida sp. nov. is proposed. The type strain is D5(T) (=CGMCC 1.10822(T)=KCTC 23880(T)).

Searching by parts: Towards fine-grained image retrieval respecting species correlation.

Most of the existing works on fine-grained image categorization and retrieval focus on finding similar images from the same species and often give little importance to inter-species similarities. However, these similarities may carry species correlations such as the same ancestors or similar habits, which are helpful in taxonomy and understanding biological traits. In this paper, we devise a new fine-grained retrieval task that searches for similar instances from different species based on body parts. To this end, we propose a two-step strategy. In the first step, we search for visually similar parts to a query image using a deep convolutional neural network (CNN). To improve the quality of the retrieved candidates, structural cues are introduced into the CNN using a novel part-pooling layer, in which the receptive field of each part is adjusted automatically. In the second step, we re-rank the retrieved candidates to improve the species diversity. We achieve this by formulating a novel ranking function that balances between the similarity of the candidates to the queried parts, while decreasing the similarity to the query species. We provide experiments on the benchmark CUB200 dataset and Columbia Dogs dataset, and demonstrate clear benefits of our schemes.

Intrabasal Plane Defect Formation in NiFe Layered Double Hydroxides Enabling Efficient Electrochemical Water Oxidation.

Defect engineering has proven to be one of the most effective approaches for the design of high-performance electrocatalysts. Current methods to create defects typically follow a top-down strategy, cutting down the pristine materials into fragmented pieces with surface defects yet also heavily destroying the framework of materials that imposes restrictions on the further improvements in catalytic activity. Herein, we describe a bottom-up strategy to prepare free-standing NiFe layered double hydroxide (LDH) nanoplatelets with abundant internal defects by controlling their growth behavior in acidic conditions. Our best-performing nanoplatelets exhibited the lowest overpotential of 241 mV and the lowest Tafel slope of 43 mV/dec for the oxygen evolution reaction (OER) process, superior to the pristine LDHs and other reference cation-defective LDHs obtained by traditional etching methods. Using both material characterization and density functional theory (DFT) simulation has enabled us to develop relationships between the structure and electrochemical properties of these catalysts, suggesting that the enhanced electrocatalytic activity of nanoplatelets mainly results from their defect-abundant structure and stable layered framework with enhanced exposure of the (001) surface.

Autotrophic Fe-Driven Biological Nitrogen Removal Technologies for Sustainable Wastewater Treatment.

Fe-driven biological nitrogen removal (FeBNR) has become one of the main technologies in water pollution remediation due to its economy, safety and mild reaction conditions. This paper systematically summarizes abiotic and biotic reactions in the Fe and N cycles, including nitrate/nitrite-dependent anaerobic Fe(II) oxidation (NDAFO) and anaerobic ammonium oxidation coupled with Fe(III) reduction (Feammox). The biodiversity of iron-oxidizing microorganisms for nitrate/nitrite reduction and iron-reducing microorganisms for ammonium oxidation are reviewed. The effects of environmental factors, e.g., pH, redox potential, Fe species, extracellular electron shuttles and natural organic matter, on the FeBNR reaction rate are analyzed. Current application advances in natural and artificial wastewater treatment are introduced with some typical experimental and application cases. Autotrophic FeBNR can treat low-C/N wastewater and greatly benefit the sustainable development of environmentally friendly biotechnologies for advanced nitrogen control.

High-precision atomic-scale strain mapping of nanoparticles from STEM images.

Strain is a crucial factor that influences the physicochemical properties of nanoparticles. Being able to precisely measure strain is important in understanding the intrinsic mechanism of the enhanced performance of nanoparticles. Techniques that have been developed for strain analysis using scanning transmission electron microscopy (STEM) images can be categorized into diffraction-based method and imaging-based method. Here, using image simulation techniques, it is found that the measured two-dimensional (2D) displacements from annular dark field (ADF) STEM images of a nanoparticle are a good approximation to a projection of the actual three-dimensional (3D) displacements. A methodology for deformation analysis is presented which is based on the detection of atomic columns from atomic-resolution STEM images in real space. Elastic deformation parameters such as strain are usually defined on the basis of a continuum of deformation. The appropriateness of various deformation parameters for atomic-scale investigation on STEM images is explored and a method for determining these is presented. We found that the local lattice parameter and principal strain components are the most physically meaningful parameters to express the materials distortion behaviour. Apart from the local lattice parameter, the other deformation parameters such as normal strains, shear strains and displacements, heavily rely on the choice of reference lattice. It is also found that different reference grids add a series of uniform offsets to these strain variations. Finally, this approach is applied to a PtCo3 bimetallic nanoparticle to quantify its deformation behaviour.

Bisulfite activated permanganate for oxidative water decontamination.

Recently, bisulfite-activated permanganate (MnO4-; Mn(VII)) process has attracted considerable attention as a novel class of advanced oxidation technology for destruction of organic contaminants in water. However, disputes over the underlying activation mechanism as well as reactive species generated in the Mn(VII)/bisulfite system remain for a long period due to the fairly complex chemistry involved in this system. This article aims to present a critical review on scientific development of the Mn(VII)/bisulfite system, with particular focus on the generation and contribution of various reactive intermediates. Both reactive manganese species (RMnS) (i.e., soluble Mn(III), Mn(V), and Mn(VI)) and radical species (primarily SO4•-) are identified as the oxidizing components responsible for enhanced degradation of organic contaminants by the Mn(VII)/bisulfite system. Bisulfite plays a dual role of being an activating agent for reactive intermediates generation and acting as a complexing agent to stabilize RMnS. Solution chemistry (e.g., the [Mn(VII)]/[bisulfite] molar ratio, solution pH, the type of contaminants, ligands, and water matrix components) greatly impacts the generation and consumption of RMnS and radicals, thus influencing the degradation kinetics and pathways of organics. Particularly, dissolved oxygen (DO) is a vital factor for driving the oxidation of organics since the absence of DO can block the generation of SO4•- and meantime causes the consumption of RMnS by excess SO3•- as a strong reductant. Interestingly, ferrate (FeO42-, Fe(VI)) and hexavalent chromium (CrO42-/HCrO4-, Cr(VI)) that are high-valent metal oxyanions analogous to Mn(VII) can be activated by bisulfite via a similar pathway (i.e. both high-valent metal-oxo intermediates and reactive radicals are involved). Furthermore, key knowledge gaps are identified and future research needs are proposed to address the potential challenges encountered in practical application of the Mn(VII)/bisulfite oxidation technology.

A Two-Phase Learning-Based Swarm Optimizer for Large-Scale Optimization.

In this article, a simple yet effective method, called a two-phase learning-based swarm optimizer (TPLSO), is proposed for large-scale optimization. Inspired by the cooperative learning behavior in human society, mass learning and elite learning are involved in TPLSO. In the mass learning phase, TPLSO randomly selects three particles to form a study group and then adopts a competitive mechanism to update the members of the study group. Then, we sort all of the particles in the swarm and pick out the elite particles that have better fitness values. In the elite learning phase, the elite particles learn from each other to further search for more promising areas. The theoretical analysis of TPLSO exploration and exploitation abilities is performed and compared with several popular particle swarm optimizers. Comparative experiments on two widely used large-scale benchmark datasets demonstrate that the proposed TPLSO achieves better performance on diverse large-scale problems than several state-of-the-art algorithms.