Structural Modules Research Articles

Enhancement of peroxymonosulfate activation with nickel foam-supported CuCo2O4 for tetracycline degradation: Performance and mechanism insights

The modulation of bimetallic oxide structures and development of efficient, easily recoverable catalysts are expected to effectively overcome the limitations associated with powdered catalysts in activating peroxymonosulfate (PMS). In this study, CuCo2O4 was successfully immobilized on the surface of nickel foam (NF) via an electrodeposition-calcination procedure, with highly efficient activation of PMS for tetracycline (TC) degradation (0.55 min−1). Besides acting as a support carrier and providing ample active sites, NF mediated electron transport, prevented the leaching of metal ions and enhanced the efficiency of recycling. Density functional theory (DFT) calculations and experimental tests illustrated that Cu/Co dual-sites can efficiently adsorb PMS, enabling simultaneous reduction and oxidation reactions. The dual-site synergy substantially decreased the adsorption barrier and increased the electron transfer rate. Especially, the Cu+/Cu2+ redox couple acted as an electron donor and facilitated rapid charge transfer, leading to the conversion of Co3+ to Co2+. Moreover, the CuCo2O4@NF + PMS system effectively eliminated TC by employing radical pathways (SO4•−, •OH) and nonradical processes (1O2, e−). Therefore, this study introduces a new approach to overcome the limitations of powdered bimetallic oxides, providing a promising solution for practical applications.

Internal Space Modulation of Yolk-Shell FeSe2@Carbon Anode with Peanut-Shaped Morphology Enabling Ultra-Stable and Fast Potassium-Ion Storage.

The poor cycling stability and rate performance of transition metal selenides (TMSs) are caused by their intrinsic low conductivity and poor structural stability, which hinders their application in potassium-ion batteries (PIBs). To address this issue, encapsulating TMSs within carbon nanoshells is considered a viable strategy. However, due to the lack and uncontrollability of internal void space, this structure cannot effectively mitigate the volume expansion induced by large K+, resulting in unsatisfactory electrochemical performance. Herein, peanut-shaped FeSe2@carbon yolk-shell capsules are prepared by modulation of the internal space. The active FeSe2 is encapsulated within a robust carbon shell and an optimal void space is retained between them. The outer carbon shell promotes electronic conductivity and avoids FeSe2 aggregation, while the internal void mitigates volume expansion and effectively ensures the structural integrity of the electrode. Consequently, the FeSe2@carbon anode demonstrates exceptional rate performance (242 mAh g-1 at 10 A g-1) and long cycling stability (350 mAh g-1 after 500 cycles at 1 A g-1). Furthermore, the effect of internal space modulation on electrochemical properties is elucidated. Meanwhile, ex situ characterizations elucidate the K+ storage mechanism. This work provides effective guidance for the design and the internal space modulation of advanced TMSs yolk-shell structures.

An Improved YOLOv8 OBB Model for Ship Detection through Stable Diffusion Data Augmentation.

Unmanned aerial vehicles (UAVs) with cameras offer extensive monitoring capabilities and exceptional maneuverability, making them ideal for real-time ship detection and effective ship management. However, ship detection by camera-equipped UAVs faces challenges when it comes to multi-viewpoints, multi-scales, environmental variability, and dataset scarcity. To overcome these challenges, we proposed a data augmentation method based on stable diffusion to generate new images for expanding the dataset. Additionally, we improve the YOLOv8n OBB model by incorporating the BiFPN structure and EMA module, enhancing its ability to detect multi-viewpoint and multi-scale ship instances. Through multiple comparative experiments, we evaluated the effectiveness of our proposed data augmentation method and the improved model. The results indicated that our proposed data augmentation method is effective for low-volume datasets with complex object features. The YOLOv8n-BiFPN-EMA OBB model we proposed performed well in detecting multi-viewpoint and multi-scale ship instances, achieving the mAP (@0.5) of 92.3%, the mAP (@0.5:0.95) of 77.5%, a reduction of 0.8 million in model parameters, and a detection speed that satisfies real-time ship detection requirements.

Research on the application of AHP-FAST-FBS in the design of home entrance disinfection devices in the post-pandemic era

With the outbreak and continued spread of the COVID-19 pandemic, people's demand for daily disinfection products has increased rapidly, and its innovative design has received widespread attention. In this context, this study aims to propose a design methodology for home entrance disinfection devices based on AHP-FAST-FBS. Firstly, the design requirements of the home entrance disinfection device were collected and analyzed through in-depth interviews and the KJ method, and a hierarchical model of design demand indicators was constructed. Secondly, the Analytical Hierarchy Process (AHP) was employed to quantify these design demand indicators, and core design demands for home entrance disinfection devices were identified by weight calculations. On this basis, the Functional Analysis System Technique (FAST) method was combined to rationally transform the design demands into product functional indicators, constructing a functional system model for the home entrance disinfection device through systematic decomposition and categorization. Lastly, based on the Function-Behavior-Structure (FBS) theoretical model, the mapping of each function of the product to its structure was realized, the product structure modules were determined, and the comprehensive design and output of the innovative design scheme for the home entrance disinfection device were completed. The results of this study indicate that the design methodology combining AHP-FAST-FBS can effectively improve the scientific rigor and effectiveness of the home entrance disinfection device design, thereby generating an ideal product design scheme. This study provides systematic theoretical guidance and practical reference for designers of subsequent related disinfection products and also offers a new path for improving social health and safety.

Biosynthesis of Halogenated Tryptophans for Protein Engineering Using Genetic Code Expansion.

Genetic Code Expansion technology offers significant potential in incorporating noncanonical amino acids into proteins at precise locations, allowing for the modulation of protein structures and functions. However, this technology is often limited by the need for costly and challenging-to-synthesize external noncanonical amino acid sources. In this study, we address this limitation by developing autonomous cells capable of biosynthesizing halogenated tryptophan derivatives and introducing them into proteins using Genetic Code Expansion technology. By utilizing inexpensive halide salts and different halogenases, we successfully achieve the selective biosynthesis of 6-chloro-tryptophan, 7-chloro-tryptophan, 6-bromo-tryptophan, and 7-bromo-tryptophan. These derivatives are introduced at specific positions with corresponding bioorthogonal aminoacyl-tRNA synthetase/tRNA pairs in response to the amber codon. Following optimization, we demonstrate the robust expression of proteins containing halogenated tryptophan residues in cells with the ability to biosynthesize these tryptophan derivatives. This study establishes a versatile platform for engineering proteins with various halogenated tryptophans.

Towards the scalable synthesis of two-dimensional heterostructures and superlattices beyond exfoliation and restacking.

Two-dimensional transition metal dichalcogenides, which feature atomically thin geometry and dangling-bond-free surfaces, have attracted intense interest for diverse technology applications, including ultra-miniaturized transistors towards the subnanometre scale. A straightforward exfoliation-and-restacking approach has been widely used for nearly arbitrary assembly of diverse two-dimensional (2D) heterostructures, superlattices and moiré superlattices, providing a versatile materials platform for fundamental investigations of exotic physical phenomena and proof-of-concept device demonstrations. While this approach has contributed importantly to the recent flourishing of 2D materials research, it is clearly unsuitable for practical technologies. Capturing the full potential of 2D transition metal dichalcogenides requires robust and scalable synthesis of these atomically thin materials and their heterostructures with designable spatial modulation of chemical compositions and electronic structures. The extreme aspect ratio, lack of intrinsic substrate and highly delicate nature of the atomically thin crystals present fundamental difficulties in material synthesis. Here we summarize the key challenges, highlight current advances and outline opportunities in the scalable synthesis of transition metal dichalcogenide-based heterostructures, superlattices and moiré superlattices.

Modulation of electronic structures and transport properties in 2D TM0.5Ga1.5O3 (TM = Al, Ga, In)

In this study, the band structures, density of states and transport properties of two-dimensional (2D) TM0.5Ga1.5O3 (TM=Al, Ga, In) are investigated by the first principle and the deformation potential theory. Both 2D Al0.5Ga1.5O3 and 2D In0.5Ga1.5O3 alloys tend to form under poor-oxygen conditions. Compared to bulk Ga2O3, the bandgap of 2D Ga2O3 is increased by 0.382 eV and its electron mobility is significantly increased from 143.352cm2·V−1·s−1 to 1486.638cm2·V−1·s−1. For 2D Al0.5Ga1.5O3, the bandgap and the electron effective mass are larger than those of 2D Ga2O3, but its electron mobility is reduced by >25%. In contrast, the bandgap of 2D In0.5Ga1.5O3 is narrower than that of 2D Ga2O3 while its electron mobility is increased by >52%, reaching 2262.901cm2·V−1·s−1. Therefore, 2D In0.5Ga1.5O3 has great potential to be used as a transport material for high-speed Ga2O3 electronic devices.

Modulation of dynamic DNA G-quadruplex structures in the hTERT promoter region by ligands.

Small molecules can inhibit cellular processes such as replication and transcription by binding to the promoter regions that are prone to form G-quadruplexes. However, since G-quadruplexes exist throughout the human genome, the G-quadruplex binders suffer from specificity issues. To tackle this problem, a G-quadruplex binder (Pyridostatin, or PDS) is conjugated with a ligand (Polyamide, or PA) that can specifically recognize DNA sequences flanking the G-quadruplex forming region. The binding mechanism of this hybrid ligand to the hTERT promoter region (hTERT 5-12) is then elucidated using optical tweezers. During mechanical unfolding processes, different intermediate structures of hTERT 5-12 in presence of PDS, PA, or PA-PDS conjugateare observed. These intermediate structures are consistent with two folding patterns of G-quadruplexes in the hTERT 5-12 fragment. While the duplex DNA binder PA facilitates the folding of a hairpin-G-quadruplex structure, the PDS assists the formation of two tandem G-quadruplexes. Both replication stop assay in vitro and dual luciferase assay in vivo established the effectiveness of the PA-PDS conjugate for hTERT 5-12 targeting. We expect such a ligand dependent folding dynamics will provide guidelines to the development of drugs that not only target hTERT expressions, but also other oncogenes via interactions with specific G-quadruplex structures formed in their promotor regions.

Wake flow behind a transversely rotating sphere confined in a pipe at low Reynolds numbers

Direct numerical simulations are performed to investigate the flow past a transversely rotating sphere confined in a pipe. Three sphere diameters of d=0.2D, 0.5D, and 0.8D (D is the pipe diameter) and sphere Reynolds numbers (based on the sphere cross-sectional mean velocity and d) of Res=250–500 are considered. The simulation results highlight the combined effects of the blockage ratio (BR=d/D) and the nondimensionalized rotation rate Ω in the range of 0.2–1.2 on the wake flow. For each BR, with increasing Res and Ω, the wake flow undergoes a transition of the flow pattern from steady symmetry and unsteady symmetry to unsteady asymmetry. For BR=0.2, the variation in flow behaviors with Ω is similar to those reported in the previous studies on a transversely rotating sphere subjected to a uniform flow. With increasing BR from 0.2 to 0.5, the unsteady asymmetric wake flow is characterized by distorted near-wake vortex rings and a series of staggered inward rolled-up vortex arcs on the double streamwise vortex threads, which results in a low-frequency modulation of the unsteady wake flow. These rolled-up vortex arcs are connected with downstream quasi-streamwise vortices and form hairpin-like vortices. Dynamic Mode Decomposition (DMD) analysis shows that the distorted near-wake vortex rings are associated with the antisymmetric DMD mode at the adjacent frequency around the symmetric primary DMD mode. With BR=0.8, there is a drastic increase in the sphere's force coefficients. A more complex wake flow behavior is found due to a strong interaction between the wake flow and the near-pipe-wall flow. At Ω=1.2, dominant asymmetric flow structures are characterized by impingement of the vortex filaments. The low-frequency modulation of these wake structures is also analyzed using the DMD analysis.

Low humidity hysteresis and fast response silicon based capacitive humidity sensor based on SiO2 supported GO film

Low humidity hysteresis and fast response are important parameters for evaluating sensor performances. However, it is difficult for a sensor to simultaneously obtain a low humidity hysteresis and fast response time. In this work, a low humidity hysteresis and fast response silicon based capacitive humidity sensor was fabricated based on a two-step deposition strategy using SiO2 and GO. The scanning electron microscope (SEM) was used to characterize the morphologies for the fabricated sensor, demonstrating the successful synthesis of sensitive films. The experimental results suggested that, compared with the pure GO and GO/SiO2 composite film sensors, the two-step deposition strategy fabricated layered SiO2 supported GO film humidity sensor exhibited the lowest humidity hysteresis and the fastest response speed. The humidity hysteresis and response/recovery time are 3.5 % and 3 s/2 s, respectively. The humidity sensing mechanism for the observed low humidity hysteresis and fast response was analyzed via morphologic structures and Young-Laplace equation. This work demonstrates that the morphological structures modulation is an important and effective method for improving sensor performance, providing a valuable reference for high performance sensor fabrication.

A fully automatic tool for development of population pharmacokinetic models.

Population pharmacokinetic (PK) models are widely used to inform drug development by pharmaceutical companies and facilitate drug evaluation by regulatory agencies. Developing a population PK model is a multi-step, challenging, and time-consuming process involving iterative manual model fitting and evaluation. A tool for fully automatic model development (AMD) of common population PK models is presented here. The AMD tool is implemented in Pharmpy, a versatile open-source library for pharmacometrics. It consists of different modules responsible for developing the different components of population PK models, including the structural model, the inter-individual variability (IIV) model, the inter-occasional variability (IOV) model, the residual unexplained variability (RUV) model, the covariate model, and the allometry model. The AMD tool was evaluated using 10 real PK datasets involving the structural, IIV, and RUV modules in three sequences. The different sequences yielded generally consistent structural models; however, there were variations in the results of the IIV and RUV models. The final models of the AMD tool showed lower Bayesian Information Criterion (BIC) values and similar visual predictive check plots compared with the available published models, indicating reasonable quality, in addition to reasonable run time. A similar conclusion was also drawn in a simulation study. The developed AMD tool serves as a promising tool for fast and fully automatic population PK model building with the potential to facilitate the use of modeling and simulation in drug development.

DNA polymerase swapping in Caudoviricetes bacteriophages

BackgroundViruses with double-stranded (ds) DNA genomes in the realm Duplodnaviria share a conserved structural gene module but show a broad range of variation in their repertoires of DNA replication proteins. Some of the duplodnaviruses encode (nearly) complete replication systems whereas others lack (almost) all genes required for replication, relying on the host replication machinery. DNA polymerases (DNAPs) comprise the centerpiece of the DNA replication apparatus. The replicative DNAPs are classified into 4 unrelated or distantly related families (A-D), with the protein structures and sequences within each family being, generally, highly conserved. More than half of the duplodnaviruses encode a DNAP of family A, B or C. We showed previously that multiple pairs of closely related viruses in the order Crassvirales encode DNAPs of different families.MethodsGroups of phages in which DNAP swapping likely occurred were identified as subtrees of a defined depth in a comprehensive evolutionary tree of tailed bacteriophages that included phages with DNAPs of different families. The DNAP swaps were validated by constrained tree analysis that was performed on phylogenetic tree of large terminase subunits, and the phage genomes encoding swapped DNAPs were aligned using Mauve. The structures of the discovered unusual DNAPs were predicted using AlphaFold2.ResultsWe identified four additional groups of tailed phages in the class Caudoviricetes in which the DNAPs apparently were swapped on multiple occasions, with replacements occurring both between families A and B, or A and C, or between distinct subfamilies within the same family. The DNAP swapping always occurs “in situ”, without changes in the organization of the surrounding genes. In several cases, the DNAP gene is the only region of substantial divergence between closely related phage genomes, whereas in others, the swap apparently involved neighboring genes encoding other proteins involved in phage genome replication. In addition, we identified two previously undetected, highly divergent groups of family A DNAPs that are encoded in some phage genomes along with the main DNAP implicated in genome replication.ConclusionsReplacement of the DNAP gene by one encoding a DNAP of a different family occurred on many independent occasions during the evolution of different families of tailed phages, in some cases, resulting in very closely related phages encoding unrelated DNAPs. DNAP swapping was likely driven by selection for avoidance of host antiphage mechanisms targeting the phage DNAP that remain to be identified, and/or by selection against replicon incompatibility.

Classification and segmentation of five photovoltaic types based on instance segmentation for generating more refined photovoltaic data

Photovoltaic types and spatial information are indispensable for power generation estimation, environmental impact assessment and photovoltaic policy formulation. However, previous studies have predominantly focused on extracting the spatial information of photovoltaics, overlooking the significance of photovoltaic types classification. Moreover, existing research on the classification and segmentation of some novel photovoltaic types is limited. With the widespread adoption of new photovoltaic technologies, the limitations of the data obtained from these studies have become increasingly apparent due to the lack of information on photovoltaic types. To tackle the challenge of the diversification and complexity of photovoltaics, we propose a photovoltaic classification and segmentation network (PV-CSN). This network can automatically classify and segment photovoltaics, providing segmentation results that include information about photovoltaic types. In addition, to enhance network performance, PV-CSN incorporates the receptive field enhancement segment module, BC-PAN-FPN structure, and efficient multi-scale attention module. The experimental results demonstrate the PV-CSN's capability to accurately classify and segment five types of photovoltaics: ground fixed-tilt photovoltaics, ground single-axis tracking photovoltaics, roof photovoltaics, floating water photovoltaics, and stationary water photovoltaics. The Mask-mAP and Box-mAP of this network reach 0.915 and 0.916 respectively, remaining competitive compared to the most advanced instance segmentation networks developed in recent years. These outcomes highlight the superiority of PV-CSN in improving the accuracy of photovoltaic classification and segmentation, as well as solving the identification of new photovoltaics, which provides a promising solution for photovoltaic classification and segmentation tasks.

Abnormal Detection of Commutator Surface Defects Based on YOLOv8

The YOLOv8 model has high detection efficiency and classification accuracy in detecting commutator surface defects, aimed at the problem of low working efficiency of a commutator, caused by commutator surface defects. First, the theoretical framework of Region-based Convolutional Neural Networks (R-CNN), spatial pyramid pooling (SPP)-net, Fast R-CNN, and Faster R-CNN is introduced, and the detection principle and process are described in detail. Secondly, the principle of the YOLOv8 network structure, head structure, neck structure, and C2f module are explained, and the loss function is described. The average precision of the proposed algorithm for detecting cracks and small points is more than 98%, and the frames per second (FPS) is 27. The detection results are mapped to the original image, and the visualization of the commutator surface defect detection is obtained, which has a higher robustness, accuracy, and real-time performance than the R-CNN, SPP-net, Fast R-CNN, and Faster R-CNN algorithms.

Learning facial structural dependency in 3D aligned space for face alignment

Facial structure's statistical characteristics offer pivotal prior information in facial landmark prediction, forming inter-dependencies among different landmarks. Such inter-dependencies ensure that predictions adhere to the shape distribution typical of natural faces. In challenging scenarios like occlusions or extreme facial poses, this structure becomes indispensable, which can help to predict elusive landmarks based on more discernible ones. While current deep learning methods do capture these landmark dependencies, it's often an implicit process heavily reliant on vast training datasets. We contest that such implicit modeling approaches fail to manage more challenging situations. In this paper, we propose a new method that harnesses the facial structure and explicitly explores inter-dependencies among facial landmarks in an end-to-end fashion. We propose a Structural Dependency Learning Module (SDLM). It uses 3D face information to map facial features into a canonical UV space, in which the facial structure is explicitly 3D semantically aligned. Besides, to explore the global relationships between facial landmarks, we take advantage of the self-attention mechanism in the image and UV spaces. We name the proposed method Facial Structure-based Face Alignment (FSFA). FSFA reinforces the landmark structure, especially under challenging conditions. Extensive experiments demonstrate that FSFA achieves state-of-the-art performance on the WFLW, 300W, AFLW, and COFW68 datasets.

Substituent Modulation of Structures, Luminescence, and TNP Sensing Abilities for Coordination Polymers

Substituent Modulation of Structures, Luminescence, and TNP Sensing Abilities for Coordination Polymers

Modulating electron structure of active sites in high-entropy metal sulfide nanoparticles with greatly improved electrocatalytic performance for oxygen evolution reaction

Excellent oxygen evolution reaction (OER) electrocatalysts hold paramount significance for related clean energy conversion technologies. High-entropy materials have garnered significant interests because of their novel structures and unprecedented potentials in application. Herein, we successfully prepared a high-entropy metal sulfide catalyst, specifically (FeCoNiMnMo)Sx, through a facile method. The resulting catalyst exhibited a phase similar to CoS2 and its electrocatalytic activity was rigorously evaluated as an OER catalyst in 1 M KOH. Endowed by the synergistic effect and the modulation of electronic structures among the various metals, the (FeCoNiMnMo)Sx catalyst demonstrated exceptional water oxidation performances. Notably, it required only 290 mV of overpotential to reach 10 mA cm−2. This remarkable performance underscores the effectiveness of high-entropy sulfides as electrocatalysts. The present work supplied a new successful example for exploring the vast potential of high-entropy materials in electrocatalysis and other related fields.

In vitro evolution driven by epistasis reveals alternative cholesterol-specific binding motifs of perfringolysin O

The crucial molecular factors that shape the interfaces of lipid-binding proteins with their target ligands and surfaces remain unknown due to the complex makeup of biological membranes. Cholesterol, the major modulator of bilayer structure in mammalian cell membranes, is recognized by various proteins, including the well-studied cholesterol-dependent cytolysins. Here, we use invitro evolution to investigate the molecular adaptations that preserve the cholesterol specificity of perfringolysin O, the prototypical cholesterol-dependent cytolysin from Clostridium perfringens. We identify variants with altered membrane-binding interfaces whose cholesterol-specific activity exceeds that of the wild-type perfringolysin O. These novel variants represent alternative evolutionary outcomes and have mutations at conserved positions that can only accumulate when epistatic constraints are alleviated. Our results improve the current understanding of the biochemical malleability of the surface of a lipid-binding protein.

Enhanced photocatalytic hydrogen evolution using dual templates of sulfur-doped carbon nitride

The band structure of semiconducting photocatalysts plays a crucial role in determining their charge carrier transport characteristics and photocatalytic activity. In this work, a simple method to modulate the band structures of carbon nitride by infusing dopants and porous materials is experimentally demonstrated. A combination of trithiocyanuric acid and ammonium chloride to calcinate in an air environment, sulfur dopants and a significant amount of porosity are infused into carbon nitride concurrently. The sulfur-doped and porous carbon nitride material shows remarkable performance in photocatalytic hydrogen production, achieving a maximum hydrogen evolution rate of 4516.84 μmol g−1 h−1. Furthermore, infusion of sulfur dopants and abundant porous into carbon nitride allows for substantial and continuous tuning of the conduction and valence band positions. The band structures modulation enhanced the optical absorption in visible light region, making it suitable for efficient solar energy conversion. The findings of this work are feasible to pave a foundation for future clean energy technology.

Whole-genome sequencing of Salmonella phage vB_SenS_ TUMS_E15 for bio-control in the food chain

The genome analysis of bacteriophages is crucial for their successful application in clinical and biocontrol settings. In this study, we isolated a new lytic phage, vB_SenS_TUMS-E15, from hospital sewage that was effective against Salmonella enteritidis, and analyzed its genomic features. The complete genome analysis revealed that E15 had circularly permuted double-stranded DNA of 43,048 base pairs (bp), with a G+C content of 49.7%. Sixty coding sequences (CDSs) were predicted in the genome, with 44 CDSs encoding known proteins in different modules, including packaging, structure, replication, metabolism, and lysis modules. No tRNA genes were found in the genome. Eight transcriptional promoter sequences and 37 rho-independent terminators were detected in the E15 genome. Phylogenetic analysis based on whole-genome sequences suggested that phage E15 should be classified as a member of the Jersyvirus genus in the subfamily Guernseyvirinae. Furthermore, no antibiotic-resistance genes, toxins, virulence factors, or lysogen-forming genes were observed in the genome. This suggests that E15 is a lytic phage, making it a promising candidate for clinical and biocontrol purposes.