Intrinsic Structural Features Research Articles

Upcycling of High‐Rate Ni‐Rich Cathodes through Intrinsic Structural Features

AbstractThe paradigm shift toward the closed‐loop recycling of spent lithium‐ion batteries necessitates the direct, efficient cathode recovery that goes beyond the traditional pyrometallurgy and hydrometallurgy techniques, meanwhile avoiding substantial energy consumption, tedious procedures, or chemical contamination. In this study, a straightforward, dual‐functional upcycling approach is presented for the spent nickel‐rich cathodes to boost their high‐rate performance. Specifically, the protocol rationally employs the Li vacancy within the degraded oxide to minimize the La diffusion barrier, expanding the lattice spacing of the layered structure; the Li+ conductive, conformal LiLaO2 encapsulation further suppresses the interfacial acid corrosion and structural deterioration into the rock‐salt phase. Transmission‐mode X‐ray diffraction tracks the reversible lattice breathing of the regenerated cathode in operando, suggesting the continuous, kinetically boosted solid‐solution process with all the microcracks repaired. The as‐assembled regenerated LiNi0.8Co0.1Mn0.1O2/Graphite pouch cell (1.4Ah) thus achieves 91.0% capacity retention for 500 cycles, the energy density of 277 Wh kg−1 as well as extreme power output of 1030 W kg−1 at the cell level. This upcycling strategy paves the way for value‐added utilization of the retired Ni‐rich cathodes in practical high‐rate battery prototypes.

Structural superlubricity at homogenous interface of penta-graphene

Two-dimensional (2D) van der Waals layered materials have been widely used as lubricant. Penta-graphene (PG), a 2D carbon allotrope exclusively composed of irregular carbon pentagons has recently been predicted to have superlubricating property. In the present study, by combining the molecular dynamics simulation and first-principles calculations, we investigated the frictional property of PG in both commensurate and incommensurate contacts. Our calculations show the ultra-low friction at the interface of relatively rotated bilayer PG with twist angles of more than 10° away from the commensurate configuration. Meanwhile, our calculations demonstrate the isotropy of the ultra-low friction at the interface of incommensurate contact, in contrast to the anisotropic of the commensurate contacting interface. Additionally, the evolution of friction force and the fluctuation of potential energy along sliding path correlate closely with the interface’s structure. The energetics and charge density explain the difference between the friction at the interfaces of the commensurate and incommensurate contacts. Not only that, we found the correlation between the intrinsic structural feature and interlayer binding energy. Importantly, our findings on the retainment of the ultra-low friction under work conditions indicates that the superlubricating state of PG has good practical adaptability.

Bulk transparent supramolecular glass enabled by host–guest molecular recognition

Supramolecular glass is a non-covalently cross-linked amorphous material that exhibits excellent optical properties and unique intrinsic structural features. Compared with artificial inorganic/organic glass, which has been extensively developed, supramolecular glass is still in the infancy stage, and itself is rarely recognized and studied thus far. Herein, we present the development of the host–guest molecular recognition motifs between methyl-β-cyclodextrin and para-hydroxybenzoic acid as the building blocks of supramolecular glass. Non-covalent polymerization resulting from the host–guest complexation and hydrogen bonding formation enables high transparency and bulk state to supramolecular glass. Various advantages, including recyclability, compatibility, and thermal processability, are associated with dynamic assembly pattern. Short-range order (host–guest complexation) and long-range disorder (three dimensional polymeric network) structures are identified simultaneously, thus demonstrating the typical structural characteristics of glass. This work provides a supramolecular strategy for constructing transparent materials from organic components.

Recent Advancement in Quantum Dot Modified Layered Double Hydroxide towards Photocatalytic, Electrocatalytic, and Photoelectrochemical Applications

AbstractLayered double hydroxides (LDHs) is a category of 2D materials that possess excellent physicochemical properties for enhancing photocatalytic (PC), electrocatalytic (EC), and photoelectrochemical (PEC) performances. However, pristine LDH encounters challenges like sluggish charge–carrier mobility, high rate of electron–hole recombination, low conductivity, and tendency to agglomerate, making them unsuitable for practical applications. Therefore, modifications such as composite preparations, co‐catalyst integration, semiconductor coupling, and ternary heterostructure engineering have been explored to disclose new possibilities for LDHs in PC, EC, and PEC applications. In the realm of semiconducting materials aimed at enhancing LDH productivity, quantum dots (QDs) i. e., 0D materials have proven to be effective due to their advantages, including abundant reserves, affordability, and environmental friendliness. This review explores the role of QDs as interlayer support, co‐catalyst, mediator, semiconductor, and sensitizer in QDs@LDH heterostructures to achieve superior photocatalytic activities. These QD‐infused heterostructures also deliver improved EC and PEC water–splitting performance coupled with long–term stabilities. Additionally, this review delves into characterization techniques, intrinsic structural features, and designing of the QD@LDH heterostructures. Future scopes and challenges in constructing and cutting–edge theoretical anticipations of QD@LDH are also discussed. This review may be a guiding light to a sustainable approach to outperform QD‐modified LDH for versatile catalysts.

Dynamic interdomain interactions in oligomeric and higher-order assemblies can modulate the intrinsic structural features of huntingtin exon 1

Dynamic interdomain interactions in oligomeric and higher-order assemblies can modulate the intrinsic structural features of huntingtin exon 1

Why Ceria Nanoparticles Obtained from Ce-MOFs Exhibit Higher Catalytic Efficient in Soot Combustion? Understanding the Role of Intrinsic Properties of a Cerium-Organic Framework to Produce CeO2

Metal-organic framework (MOF) derivatives, such as porous metal oxides with controlled morphology have received great attention for applications in various fields. In this paper, the experimental results show that porous CeO2 with high specific surface area (90.5 m2 g-1) and nanorod morphology can be obtained by calcining a Ce-MOF template at optimized temperature (300-500 °C). The formation mechanism of this porous structure as well as the influence of the calcination temperature are well explained by taking into account thermal behavior and intrinsic structural features of the Ce-MOF precursor. We employed the oxides formed as heterogeneous catalysts to reduce the soot originating from the incomplete combustion of diesel or diesel/biodiesel blends. The CeO2 materials exhibit outstanding catalytic activity, lowering the temperature of soot combustion from 610 to 370 °C. Compared with similar work, our catalyst exhibits enhanced soot oxidation activity, making it highly promising for diesel particulate filter applications. Such outstanding catalytic performance of the porous CeO2 nanorods benefits from their large specific surface area, and morphological and structural characteristics.

A General Synthesis of Nanostructured Conductive Metal-Organic Frameworks from Insulating MOF Precursors for Supercapacitors and Chemiresistive Sensors.

Two-dimensional conjugated metal-organic frameworks (2D c-MOFs) are emerging as a unique subclass of layer-stacked crystalline coordination polymers that simultaneously possess porous and conductive properties, and have broad application potential in energy and electronic devices. However, to make the best use of the intrinsic electronic properties and structural features of 2D c-MOFs, the controlled synthesis of hierarchically nanostructured 2D c-MOFs with high crystallinity and customized morphologies is essential, which remains a great challenge. Herein, we present a template strategy to synthesize a library of 2D c-MOFs with controlled morphologies and dimensions via insulating MOFs-to-c-MOFs transformations. The resultant hierarchically nanostructured 2D c-MOFs feature intrinsic electrical conductivity and higher surface areas than the reported bulk-type 2D c-MOFs, which are beneficial for improved access to active sites and enhanced mass transport. As proof-of-concept applications, the hierarchically nanostructured 2D c-MOFs exhibit a superior performance for electrical properties related applications (hollow Cu-BHT nanocubes-based supercapacitor and Cu-HHB nanoflowers-based chemiresistive gas sensor), achieving over 225 % and 250 % improvement in specific capacity and response intensity over the corresponding bulk type c-MOFs, respectively.

Polynuclear Rare-Earth Cluster-Directed Self-Assembly of Highly Porous Zeolite-like Metal-Organic Frameworks with Methane Storage Property.

Due to their intrinsic structural features, the design and synthesis of a new type of zeolite-like metal-organic frameworks (ZMOFs) is highly desirable but challenging. Herein, solvothermal reactions between an angular dicarboxylate linker and rare-earth (RE) ions afforded two RE-MOFs, namely, Tb-ZMOF-2 and Tb-ZMOF-3, respectively. Structural analyses reveal that Tb-ZMOF-2 encompasses a novel [446482] cage, while Tb-ZMOF-3 contains nonanuclear (i.e., D6R) and hexanuclear (i.e., D4R) RE clusters simultaneously, subsequently resulting in two new zeolitic topologies. Thanks to its high surface area and pore volume, Tb-ZMOF-2 demonstrates considerably high gravimetric and volumetric methane storage working capacities.

Cell-specific secretory granule sorting mechanisms: the role of MAGEL2 and retromer in hypothalamic regulated secretion.

Intracellular protein trafficking and sorting are extremely arduous in endocrine and neuroendocrine cells, which synthesize and secrete on-demand substantial quantities of proteins. To ensure that neuroendocrine secretion operates correctly, each step in the secretion pathways is tightly regulated and coordinated both spatially and temporally. At the trans-Golgi network (TGN), intrinsic structural features of proteins and several sorting mechanisms and distinct signals direct newly synthesized proteins into proper membrane vesicles that enter either constitutive or regulated secretion pathways. Furthermore, this anterograde transport is counterbalanced by retrograde transport, which not only maintains membrane homeostasis but also recycles various proteins that function in the sorting of secretory cargo, formation of transport intermediates, or retrieval of resident proteins of secretory organelles. The retromer complex recycles proteins from the endocytic pathway back to the plasma membrane or TGN and was recently identified as a critical player in regulated secretion in the hypothalamus. Furthermore, melanoma antigen protein L2 (MAGEL2) was discovered to act as a tissue-specific regulator of the retromer-dependent endosomal protein recycling pathway and, by doing so, ensures proper secretory granule formation and maturation. MAGEL2 is a mammalian-specific and maternally imprinted gene implicated in Prader-Willi and Schaaf-Yang neurodevelopmental syndromes. In this review, we will briefly discuss the current understanding of the regulated secretion pathway, encompassing anterograde and retrograde traffic. Although our understanding of the retrograde trafficking and sorting in regulated secretion is not yet complete, we will review recent insights into the molecular role of MAGEL2 in hypothalamic neuroendocrine secretion and how its dysregulation contributes to the symptoms of Prader-Willi and Schaaf-Yang patients. Given that the activation of many secreted proteins occurs after they enter secretory granules, modulation of the sorting efficiency in a tissue-specific manner may represent an evolutionary adaptation to environmental cues.

Promising M2CO2/MoX2 (M = Hf, Zr; X = S, Se, Te) Heterostructures for Multifunctional Solar Energy Applications.

Two-dimensional van der Waals (vdW) heterostructures are potential candidates for clean energy conversion materials to address the global energy crisis and environmental issues. In this work, we have comprehensively studied the geometrical, electronic, and optical properties of M2CO2/MoX2 (M = Hf, Zr; X = S, Se, Te) vdW heterostructures, as well as their applications in the fields of photocatalytic and photovoltaic using density functional theory calculations. The lattice dynamic and thermal stabilities of designed M2CO2/MoX2 heterostructures are confirmed. Interestingly, all the M2CO2/MoX2 heterostructures exhibit intrinsic type-II band structure features, which effectively inhibit the electron-hole pair recombination and enhance the photocatalytic performance. Furthermore, the internal built-in electric field and high anisotropic carrier mobility can separate the photo-generated carriers efficiently. It is noted that M2CO2/MoX2 heterostructures exhibit suitable band gaps in comparison to the M2CO2 and MoX2 monolayers, which enhance the optical-harvesting abilities in the visible and ultraviolet light zones. Zr2CO2/MoSe2 and Hf2CO2/MoSe2 heterostructures possess suitable band edge positions to provide the competent driving force for water splitting as photocatalysts. In addition, Hf2CO2/MoS2 and Zr2CO2/MoS2 heterostructures deliver a power conversion efficiency of 19.75% and 17.13% for solar cell applications, respectively. These results pave the way for exploring efficient MXenes/TMDCs vdW heterostructures as photocatalytic and photovoltaic materials.

Adsorptive removal of tetracycline and methylene blue from aqueous solution with a water resistance copper-based metal–organic framework

Copper-1,3,5-benzenetricarboxylic acid (Cu–BTC) is a research hotspot for metal–organic frameworks (MOFs) because of its remarkable intrinsic structural features.

Influence of uncoordinated N content on Ni[sbnd]N[sbnd]C electrocatalyst for CO2 reduction: Combining first principle with machine learning

Electrocatalysis of CO2 reduction reaction (CO2RR) has attracted elevated attentions since it is favorable to prompt the carbon cycle in a sustainable pathway. However, the applications of this technology is obstructed by the low activity and selectivity due to the inert of CO2 and favored hydrogen evolution reaction. Development of robust catalysts is the most feasible method to decrease the overpotential and suppress the competitive side reaction. Single-atom-catalysts (SACs) is one of the most promising catalysts with the satisfied activity. However, the clear and popular rules are still absence to explore the SACs with the desired properties due to the complicated items related with the overall performance. The suitable transition metals (TMs) are screened out based on the defective g-C3N4 (VN-C3N4) supporter by the first principle. Among 24 screened transition metals, the Ni-VN-C3N4 exhibits the better performance as compared to others. Later, the machine learning (ML) algorithm builds a bridge between catalytic activity and intrinsic structural and atomic features. Then, the same coordination environment is constructed on the graphene to explore the possibility to replace the VN-C3N4 with other carbon substrate. When the doped amount of uncoordinated N reaches 25 wt%, the catalytic activity of NiNC SAC based on the graphene is close to that based on the VN-C3N4. This work introduces a method to construct the SACs by regulating the N-doped amount on the supporter.

A Discussion on the Conceptual Design of Multifunctional Exoskeletons for Sustainable Regeneration of Buildings in Urban Areas

The design and erection of new buildings and structures are today driven by strict requirements in terms of sustainability and circular economy, but they represent only a minor part of the built heritage. Hence, it is clear that the maintenance and upgrading of existing buildings play a key role in the achievement of the objectives identified in many developed countries, so that the regeneration of buildings represents the core of many real-world projects. Regenerating existing buildings requires the achievement of modern reuse requirements, the modulation of structural and energy performance, but primarily the fulfilment of many objectives associated with extrinsic (urban planning) and intrinsic (architectural, structural and technological) features. In the present paper, a discussion on the role and potentialities of exoskeletons is reported with the objective of supporting decision makers in resolving the issues associated with the need to improve the overall performance of existing buildings in urban areas. In particular, relevant aspects associated with the conceptual design of multifunctional exoskeletons are reviewed in order to chart a rational course to perform multiscale and interdisciplinary design methodologies.

3D Real Scene Data Collection of Cultural Relics and Historical Sites Based on Digital Image Processing.

Traditional digital geometry mainly focuses on local geometric features such as curvature and normal of 3D models. Although the curvature can describe the geometric curvature of the model surface, these local geometric properties cannot describe the global functional structure and associated properties of the 3D model. The purpose of this paper was to study the accurate splicing of cultural relic fragments based on the intrinsic structural features of the 3D model, such as geometry, texture, and function, whose method is the key to realizing the virtual reconstruction of damaged cultural relics. In this paper, a method for analyzing the intrinsic structure of 3D point cloud data is proposed, the feature representation method of 3D discrete curves and surfaces is studied, and a method for identifying geometric features of 3D point clouds based on the similarity measure of the principal curvature is proposed, which realizes the effective extraction of geometric features of the 3D point cloud model of cultural relics. By calculating the visual curvature distribution of the model under multiscale constraints, the effective extraction of the structural primitives of cultural relics with rich surface noise is realized. The experimental results in this paper show that the initial matching time is 1.416 seconds, the final matching time is 1.555 seconds, the average number of iterations is 13, the average stitching error is 1.7233 mm, and the standard deviation is 1.0265 mm. The experimental data show that the algorithm proposed in this chapter has good convergence characteristics and effectively avoids the divergence phenomenon that is easy to occur in the existing stitching algorithms.

Design Concepts of Transition Metal Dichalcogenides for High-Performance Aqueous Zn-Ion Storage.

Aqueous Zn-ion batteries (AZIBs) are considered as promising large-scale energy storage devices due to their high safety and low cost. Transition metal dichalcogenides (TMDs) as the potential aqueous Zn-storage cathode materials are under the research spotlight because of their facile 2D ion-transport channels and weak electrostatic interactions with Zn2+ . In this concept article, we summarize the intrinsic structural features and aqueous Zn-storage mechanisms of the TMDs-based electrodes. More significantly, the latest design concepts of TMDs materials for high-performance AZIBs are discussed in detail from three aspects of interlayer expansion engineering, phase transition engineering, and structure defects engineering. Finally, the current challenges facing TMDs cathodes and possible remedies are outlined for future developments towards efficient, rapid, and stable aqueous Zn-ion storage.

Symbolic sequence representation with Markovian state optimization

Sequence representation, which is aimed at embedding sequentially symbolic data in a real space, is a foundational task in sequence pattern recognition. It is a difficult problem due to the challenges entailed in learning the intrinsic structural features within sequences in small sample size cases, in an unsupervised way. In this paper, we propose to represent each symbolic sequence by its transition probability distribution over discriminating topics, formalized by a set of optimized Hidden Markov Model (HMM) states shared by all sequences. An efficient method, called Markovian state clustering with hierarchical model selection, is proposed to optimize the Markovian states and to adaptively determine the number of topics. The proposed method is experimentally evaluated on human activity recognition and protein recognition, and results obtained demonstrate its effectiveness and efficiency.

Unveiling the Multiradical Character of the Biphenylene Network and Its Anisotropic Charge Transport.

Recent progress in the on-surface synthesis and characterization of nanomaterials is facilitating the realization of new carbon allotropes, such as nanoporous graphenes, graphynes, and 2D π-conjugated polymers. One of the latest examples is the biphenylene network (BPN), which was recently fabricated on gold and characterized with atomic precision. This gapless 2D organic material presents uncommon metallic conduction, which could help develop innovative carbon-based electronics. Here, using first principles calculations and quantum transport simulations, we provide new insights into some fundamental properties of BPN, which are key for its further technological exploitation. We predict that BPN hosts an unprecedented spin-polarized multiradical ground state, which has important implications for the chemical reactivity of the 2D material under practical use conditions. The associated electronic band gap is highly sensitive to perturbations, as seen in finite temperature (300 K) molecular dynamics simulations, but the multiradical character remains stable. Furthermore, BPN is found to host in-plane anisotropic (spin-polarized) electrical transport, rooted in its intrinsic structural features, which suggests potential device functionality of interest for both nanoelectronics and spintronics.

Spectrum Analysis Enabled Periodic Feature Reconstruction Based Automatic Defect Detection System for Electroluminescence Images of Photovoltaic Modules.

Electroluminescence (EL) imaging is a widely adopted method in quality assurance of the photovoltaic (PV) manufacturing industry. With the growing demand for high-quality PV products, automatic inspection methods based on machine vision have become an emerging area concern to replace manual inspectors. Therefore, this paper presents an automatic defect-inspection method for multi-cell monocrystalline PV modules with EL images. A processing routine is designed to extract the defect features of the PV module, eliminating the influence of the intrinsic structural features. Spectrum domain analysis is applied to effectively reconstruct an improved PV layout from a defective one by spectrum filtering in a certain direction. The reconstructed image is used to segment the PV module into cells and slices. Based on the segmentation, defect detection is carried out on individual cells or slices to detect cracks, breaks, and speckles. Robust performance has been achieved from experiments on many samples with varying illumination conditions and defect shapes/sizes, which shows the proposed method can efficiently distinguish intrinsic structural features from the defect features, enabling precise and speedy defect detections on multi-cell PV modules.

DS-TransUNet: Dual Swin Transformer U-Net for Medical Image Segmentation

Automatic medical image segmentation has made great progress owing to the powerful deep representation learning. Inspired by the success of self-attention mechanism in Transformer, considerable efforts are devoted to designing the robust variants of encoder-decoder architecture with Transformer. However, the patch division used in the existing Transformer-based models usually ignores the pixel-level intrinsic structural features inside each patch. In this paper, we propose a novel deep medical image segmentation framework called Dual Swin Transformer U-Net (DS-TransUNet), which aims to incorporate the hierarchical Swin Transformer into both encoder and decoder of the standard U-shaped architecture. Our DS-TransUNet benefits from the self-attention computation in Swin Transformer and the designed dual-scale encoding, which can effectively model the non-local dependencies and multi-scale contexts for enhancing the semantic segmentation quality of varying medical images. Unlike many prior Transformer-based solutions, the proposed DS-TransUNet adopts a well-established dual-scale encoding mechanism that utilizes dual-scale encoders based on Swin Transformer to extract the coarse and fine-grained feature representations of different semantic scales. Meanwhile, a well-designed Transformer Interactive Fusion (TIF) module is proposed to effectively perform the multi-scale information fusion through the self-attention mechanism. Furthermore, we introduce the Swin Transformer block into decoder to further explore the long-range contextual information during the up-sampling process. Extensive experiments across four typical tasks for medical image segmentation demonstrate the effectiveness of DS-TransUNet, and our approach significantly outperforms the state-of-the-art methods.

Powerful Direct C-H Amidation Polymerization Affords Single-Fluorophore-Based White-Light-Emitting Polysulfonamides by Fine-Tuning Hydrogen Bonds.

The development of white-light-emitting polymers has been actively pursued because of the importance of such polymers in various applications, such as lighting sources and displays. To generate white-light, numerous research efforts have focused on synthesizing multifluorophore-based random copolymers to effectively cover the entire visible region. However, due to their intrinsic synthetic and structural features, this strategy has limitations in securing color reproducibility and stability. Herein, we report the development of single-fluorophore-based white-light-emitting homopolymers with excellent color reproducibility. A powerful direct C-H amidation polymerization (DCAP) strategy enabled the synthesis of defect-free polysulfonamides that emit white-light via excited-state intramolecular proton-transfer (ESIPT). To gain structural insights for designing such polymers, we conducted detailed model studies by varying the electronic nature of substituents that allow facile tuning of the emission colors. Further analysis revealed precise control of the thermodynamics of the ESIPT process by fine-tuning the strength of the intramolecular hydrogen bond. By applying this design principle to polymerization, we successfully produced a series of well-defined polysulfonamides with single-fluorophore emitting white-light. The resulting polymers emitted consistent fluorescence, regardless of their molecular weights or phases (i.e., solution, powder, or thin film), guaranteeing excellent color reproducibility. With these advantages in hand, we also demonstrated practical use of our DCAP system by fabricating a white-light-emitting coated LED.