Intrinsic Structure Research Articles

Reinforcing Photogenerated Carrier Extraction of Environment-Friendly InP/ZnSeS Quantum Dots for High-Performing Photoelectrochemical Photodetection and Solar Energy Conversion.

Colloidal InP/ZnSeS-based quantum dots (QDs) are considered promising building blocks for light-emitting devices due to their environmental friendliness, high quantum yield (QY), and narrow emission. However, the intrinsic type-I band structure severely hinders potential photoelectrochemical (PEC) applications requiring efficient photoexcited carrier separation and transfer. In this study, the optoelectronic properties of InP/ZnSeS QDs are tailored by introducing Al dopants in the ZnSeS layer, which concurrently passivate the surface defects and act as shallow donor states for suppressed non-radiative recombination and improved charge extraction efficiency. Consequently, as-fabricated InP/ZnSeS:Al QDs-based PEC-type photodetector exhibited a high detectivity up to 1011 Jones and a remarkable responsivity of 0.66 AW-1 at 600nm even under self-powered condition (0V bias). In addition, as-prepared InP/ZnSeS:Al QDs-based photoanode can be alternatively used for PEC hydrogen generation, showing an H2 production rate of 73.7µmol cm-2h-1 under 1 sun illumination (AM 1.5G, 100mW cm-2). The results offer a prospective strategy for optimizing eco-friendly QDs for high-performance multifunctional light detection/conversion devices.

Modulating Pore Wall Chemistry Empowers Sonodynamic Activity of Two‐Dimensional Covalent Organic Framework Heterojunctions for Pro‐Oxidative Nanotherapy

AbstractCovalent organic frameworks (COFs) have garnered growing interest in the field of biomedicine; however, their application in sonodynamic therapy remains underexplored due to limited understanding of their intrinsic activity and structure–property relationships. Here, we present a pore wall chemistry modulation strategy for empowering sonodynamic activity to two‐dimensional (2D) COF heterojunctions through in situ growth of COFs on bismuth oxycarbonate nanosheets (B NSs). Compared to the negligible sonodynamic effects observed in the pristine B NSs, the 2D heterojunction with vinyl‐decorated COF pore walls demonstrates a 3.6‐fold enhancement in sonocatalytic singlet oxygen generation. This performance also significantly outperforms that of isoreticular COFs functionalized with methoxy or non‐substituted groups. Mechanistic studies reveal that the vinyl groups in the B@COF (BC) heterojunction facilitate the separation and transfer of charge carriers while also enhancing the adsorption of oxygen molecules. Furthermore, peroxymonosulfate (PMS) loading into the porous COFs boosts the therapeutic efficacy of antitumor nanotherapy via sonocatalytic dual oxidative species generation. These findings underscore the critical role of pore wall chemistry in modulating the sonocatalytic properties of COFs, and advance the development of COF‐based sonosensitizers for pro‐oxidative applications.

Fenton-Inactive Cd Enables Highly Selective O2-Derived Domino Reaction.

Advancing and deploying Fenton-inactive Cd that combines excellent catalytic activity, selectivity, and stability remains a serious challenge, predominantly owing to the difficulty in regulating the intrinsic electronic states and local geometric structures of such fully occupied d10s2 configuration. In this work, a combination of experiments and theoretical calculations reveals that the incorporation of boron (B) enables the tuning of the average oxidation state of Cd0 to Cdδ+, facilitating electron localization and implementing a different electrocatalytic preference compared to conventional d10-electron configurations. The resulting Cd(B) catalyst demonstrates high selectivity (>90% on average) in the O2-to-H2O2 conversion, negligible activity loss over 100h, and a superior H2O2 production rate (15.5mol∙gcat -1h-1 at -100mA). More unexpectedly, the in situ generated H2O2 exhibits a unique advantage over commercial products, selectively oxidizing cinnamaldehyde to benzaldehyde by modulating the practical current.

Tracking the topology of neural manifolds across populations

Neural manifolds summarize the intrinsic structure of the information encoded by a population of neurons. Advances in experimental techniques have made simultaneous recordings from multiple brain regions increasingly commonplace, raising the possibility of studying how these manifolds relate across populations. However, when the manifolds are nonlinear and possibly code for multiple unknown variables, it is challenging to extract robust and falsifiable information about their relationships. We introduce a framework, called the method of analogous cycles, for matching topological features of neural manifolds using only observed dissimilarity matrices within and between neural populations. We demonstrate via analysis of simulations and in vivo experimental data that this method can be used to correctly identify multiple shared circular coordinate systems across both stimuli and inferred neural manifolds. Conversely, the method rejects matching features that are not intrinsic to one of the systems. Further, as this method is deterministic and does not rely on dimensionality reduction or optimization methods, it is amenable to direct mathematical investigation and interpretation in terms of the underlying neural activity. We thus propose the method of analogous cycles as a suitable foundation for a theory of cross-population analysis via neural manifolds.

The VIRUS-dE Survey. I. Stars in Dwarf Elliptical Galaxies—3D Dynamics and Radially Resolved Stellar Initial Mass Functions

We analyze the stellar structure of a sample of dwarf ellipticals (dEs) inhabiting various environments within the Virgo cluster. Integral-field observations with a high spectral resolution allow us to robustly determine their low-velocity dispersions (∼25 km s−1) and higher-order kinematic moments out to the half-light radius. We find the dEs exhibit a diversity in ages, with the younger dEs being less enhanced than the older, suggesting a complex star formation history for those dEs that recently entered Virgo, while others have been quenched shortly after reionization. Orbit-superposition modeling allowed us to recover viewing angles, stellar mass-to-light ratios (with gradients), as well as the intrinsic orbit structure. We find that the angular momentum of the dEs is strongly suppressed compared to ordinary early-type galaxies and correlates with the environment. Flattened dEs are so because of a suppressed kinetic energy perpendicular to their equatorial plane. Combining population and dynamical modeling results, we find an age-dependent stellar initial mass function or, alternatively, evidence for a more extended star formation history for those galaxies that have had higher initial mass and/or inhabited lower-density environments. dEs appear to have a spatially homogeneous stellar structure, but the state they were “frozen” in as they stopped forming stars varies dramatically according to their initial conditions.

Dual-functional ratiometric fluorescence sensor based on silica spheres for highly selective detection of Hg2+ and Fe3+

Ratiometric fluorescence sensors, which validate the absence of ambient and excitation light intensity effects, have attracted significant attention. In this study, a novel nanohybrid dual-emission system was fabricated by combining nitrogen-doped carbon quantum dots (NCQDs) with 8-hydroxyquinoline (8-HQ). This system allows for the simultaneous detection of Fe3+ and Hg2+ ions by utilizing distinct signals. A ternary hollow structure nanosensor containing core-shell silica spheres modified by 8-HQ molecules and decorated with carbon quantum dots was fabricated by subsequent solvothermal synthesis. The designed nanohybrid emits dual-emission fluorescence peaks at 480 and 340nm, originating from the intrinsic structure of NCQDs and 8-hydroxyquinoline, respectively. When Hg2+ is present, only the fluorescence intensity at 480nm is noticeably reduced. However, in the presence of Fe3+ ions, the fluorescence intensity at 480nm remains mostly unchanged, but clear fluorescence quenching is detected at 340nm. There was a linear relationship between fluorescence intensity ratios at 340 and 480nm and the concentration of Hg2+ and Fe3+, with detection limits of 9.50nM and 0.96μM, respectively. Furthermore, it demonstrated satisfactory assay performance in quantifying the mercury content in amalgam alloy and the Fe3+ concentration in a water sample. The results of this study offer a new approach to designing multifunctional fluorescent sensors and a potential application for biomedicine and environmental monitoring.

Surface Corrosion‐Resistant and Multi‐Scenario MoNiP Electrode for Efficient Industrial‐Scale Seawater Splitting

AbstractThe construction of efficient and durable multifunctional electrodes for industrial‐scale hydrogen production presents a main challenge. Herein, molybdenum‐modulated phosphorus‐based catalytic electrodes (Mo‐NiP@NF) are prepared via mild electroless plating. Heteroatoms doping or heterostructures construction can reconfigure the intrinsic electronic structure of the pre‐catalyst and optimizes the key intermediates adsorption. Moreover, the (hypo/meta‐)phosphite anions (POxδ−) and molybdate ions (MoOxδ−) on the electrode surface of Mo‐NiP@NF afford resistance to chloride (Cl−) corrosion. Mo‐NiP@NF exhibits ultralow overpotentials of 278/550 and 282/590 mV at 1 A cm−2 during the hydrogen/oxygen evolution reaction (HER/OER) in alkaline simulated and real seawater, respectively, whereas catalytic overall seawater splitting (OWS) reach 1 A cm−2 at 1.96 and 1.97 Vcell. Remarkably, Mo‐NiP@NF maintains stable operation for 1500 h in OWS. The scalability of Mo‐NiP@NF allowing the assembly of proton exchange membrane (PEM) electrolyzer powered by photovoltaic energy, simulating a portable hydrogen‐oxygen respirator provides an oxygen/hydrogen flows of 160/320 mL min−1. Expanding further, the trace ruthenium‐loaded Mo‐NiP@NF catalyst sodium borohydride (NaBH4) hydrolysis achieving a hydrogen generation rate (HGR) of 11049.2 mL min−1 g−1. This work provides strategic innovations and optimization solutions for the economical and mild construction of multi‐scenario durable green energy conversion materials at industrial‐scale application.

Realization of Integrated Regional Ecological Management Based on Ecosystem Service Supply and Demand Flow Networks: An Example from a Dominant Mineral Resources Development Area

Understanding the flow processes and pattern optimization of ecosystem services (ESs) supply and demand is crucial for integrated regional ecological management. However, the understanding of the flow process of ESs at the 1 km grid scale is still limited, especially in areas dominated by mineral resource development. The landscape in these areas has undergone significant changes due to mining activities. It is urgent to construct a regional management model that integrates the flow of ecosystem services and mine restoration. This study developed a framework that links ecosystem service flows (ESFs) and ecological security patterns (ESP) based on multi-source ecological monitoring data, constructed an ES supply-demand flow network through the flow properties, and determined the sequence and optimization strategies for mine rehabilitation to achieve integrated regional management. The results show that, except for food production (FP), other services were in surplus overall, mostly in synergistic relationships, but the spatial distribution of their supply and demand was not coordinated. Surplus areas were located mainly in the eastern woodlands, and deficit areas were located in the northwestern production agglomeration centers, suggesting that areas of supply-demand imbalance can be mitigated through ecological integration. Among these, water yield (WY) had a small number of sources and sinks and is limited in area range. Habitat quality (HQ) sources and sinks had the largest area coverage and the highest number. The distribution of ESF corridors, influenced by factors such as the number of sources and sinks, flow characteristics, and spatial resistance, varied significantly. HQ exhibited a more uniform distribution range, while WY had a longer average length of flow path. Overlaying ecological and mining factors, we identified ecological strategic spots, important supply areas, beneficiary areas, and mine priority restoration areas to further optimize the overall layout and rationally allocate the intrinsic structure of the patches based on ES supply and demand.

A newton interpolation network for smoke semantic segmentation

Smoke has large variances of visual appearances that are very adverse to visual segmentation. Furthermore, its semi-transparency often produces highly complicated mixtures of smoke and backgrounds. These factors lead to great difficulties in labelling and segmenting smoke regions. To improve accuracy of smoke segmentation, we propose a Newton Interpolation Network (NINet) for visual smoke semantic segmentation. Unlike simply concatenating or point-wisely adding multi-scale encoded feature maps for information fusion or re-usage, we design a Newton Interpolation Module (NIM) to extract structured information by analyzing the feature values in the same position but from encoded feature maps with different scales. Interpolated features by our NIM contain long-range dependency and semantic structures across different levels, but traditional fusion of multi-scale feature maps cannot model intrinsic structures embedded in these maps. To obtain multi-scale structured information, we repeatedly use the proposed NIM at different levels of the decoding stages. In addition, we use more encoded feature maps to construct a higher order Newton interpolation polynomial for extracting higher order information. Extensive experiments validate that our method significantly outperforms existing state-of-the-art algorithms on virtual and real smoke datasets, and ablation experiments also validate the effectiveness of our NIMs.

Ultimate Rationality. Husserl on Critical Position-Taking (Stellungnahme) in the Theoretical and Axiological Spheres

AbstractAs a fervent rationalist, Husserl placed considerable emphasis on the delineation of the different levels of reason. Its highest form, he contends, is position-taking (Stellungnahme) understood as a critical stance towards a positional act P. Specifically, such a Stellungnahme is a three-step procedure: the subject, possibly motivated by a passive discordance, starts by questioning P (active doubt); she then seeks to validate P by returning to its originary fulfillment (active search for evidence); finally, she ratifies such a fulfillment in an act of acknowledgment (active decision), thereby making P a permanent personal conviction. In the literature, the role of position-taking in the formation of persons has been aptly emphasized, but insufficient attention has been paid to its intrinsic structure and concrete functioning; furthermore, the few existing studies on this topic are limited to the theoretical domain. The aim of this paper is to fill this twofold gap by providing a comprehensive account of critical Stellungnahme in both the intellectual and the axiological spheres, drawing especially on the manuscripts from the Studien zur Struktur des Bewusstseins. The first part examines Stellungnahme in the theoretical realm, underscoring its teleological orientation towards justification and the active role played by the subject in it. The second part shows how this procedure can be extended to rationalize value-experiences, by elucidating the nature of affective evidence and approval (Billigung). Finally, the third part demonstrates how a particular value, that of knowledge, can concretely pass the axiological position-taking and thus be recognized as an actual value.

Solvent-adaptive hydrogels with lamellar confinement cellular structure for programmable multimodal locomotion

Biological organisms can perform flexible and controllable multimodal motion under external stimuli owing to the hierarchical assembly of anisotropic structures across multiple length scales. However, artificial soft actuators exhibit the limited response speed, deformation programmability and movement capability especially in harsh environments because of insufficient anisotropic hierarchy and precision in structural design. Here, we report a programmed assembly directed confinement polymerization method for the fabrication of environmentally tolerant and fast responsive hydrogels with lamellar assembly-confined cellular structure interpenetrated with highly aligned nanopillars by the directional freezing-assisted polymerization in the predesigned anisotropic laminar scaffold. The obtained hydrogel exhibits ultrafast responsiveness and anisotropic deformation exposed to temperature/light/solvent stimulation, maintaining highly consistent responsive deformation capability in all-polarity solvents over 100 days of soaking. Moreover, the hydrogels implement photoactive programmable multi-gait locomotion whose amplitude and directionality are precisely regulated by the intrinsic structure, including controlled crawling and rotation in water and non-polar solvents, and 3D self-propulsion floating and swimming in polar solvents. Thus, this hydrogel with hierarchically ordered structure and dexterous locomotion may be suitable for flexible intelligent actuators serving in harsh solvent environments.

Dynamic multi-scale spatial-temporal graph convolutional network for traffic flow prediction

This paper proposes a dynamic multi-scale spatial-temporal graph convolutional network (DS-STGCN) for traffic flow prediction. The network aims to comprehensively extract global and local dependencies in dynamic spatial-temporal data by inputting traffic network flow data to construct node feature graphs, topology graphs, and time slot feature graphs, capturing the complexity and dynamics of traffic flow. DS-STGCN interprets feature information of the traffic network from both spatial and temporal dimensions through dynamic multi-scale graph convolutional blocks. In the spatial dimension, these blocks use constraints at different levels to balance fine-grained local features and extensive global features, revealing the intrinsic structure of traffic flow data. In the temporal dimension, these blocks jointly learn with temporal convolutional blocks to capture multi-frequency time patterns and handle long sequence data, effectively extracting potential dependencies of time series. Furthermore, DS-STGCN effectively models the changing spatial-temporal relationships in road network flow by constructing dynamically adaptive updated adjacency tensors, generating dynamic graph structures to address the challenge of changing spatial-temporal relationships in the transportation system. Experimental results show that our method significantly outperforms other competing methods on five real traffic datasets (PEMS03, PEMS04, PEMS07, PEMS08 and METR-LA).

Learnable Transform-Assisted Tensor Decomposition for Spatio-Irregular Multidimensional Data Recovery

Tensor decompositions have been successfully applied to multidimensional data recovery. However, classical tensor decompositions are not suitable for emerging spatio-irregular multidimensional data (i.e., spatio-irregular tensor), whose spatial domain is non-rectangular, e.g., spatial transcriptomics data from bioinformatics and semantic units from computer vision. By using preprocessing (e.g., zero-padding or element-wise 0-1 weighting), the spatio-irregular tensor can be converted to a spatio-regular tensor and then classical tensor decompositions can be applied, but this strategy inevitably introduces bias information, leading to artifacts. How to design a tensor-based method suitable for emerging spatio-irregular tensors is an imperative challenge. To address this challenge, we propose a learnable transform-assisted tensor singular value decomposition (LTA-TSVD) for spatio-irregular tensor recovery, which allows us to leverage the intrinsic structure behind the spatio-irregular tensor. Specifically, we design a learnable transform to project the original spatio-irregular tensor into its latent spatio-regular tensor, and then the latent low-rank structure is captured by classical TSVD on the resulting regular tensor. Empowered by LTA-TSVD, we develop spatio-irregular low-rank tensor completion (SIR-LRTC) and spatio-irregular tensor robust principal component analysis (SIR-TRPCA) models for the spatio-irregular tensor imputation and denoising respectively, and we design corresponding solving algorithms with theoretical convergence. Extensive experiments including the spatial transcriptomics data imputation and hyperspectral image denoising show SIR-LRTC and SIR-TRPCA are superior performance to competing approaches and benefit downstream applications.

Tensorized diversity and consistency with Laplacian manifold for multi-view clustering

The advantage of multi-view clustering lies in its ability to leverage the diversity and consistency among multiple views to better capture the intrinsic structure of the data. However, existing multi-view methods treat diversity and consistency as a set of opposing attributes, overlooking their inherent connections. Meanwhile, the complete information across multiple views is not fully utilized. To address these issues, this paper proposes the tensorized diversity and consistency with Laplacian manifold for multi-view clustering method (TDCLM). Specifically, starting from the self-expressive property of the original data, we obtain the diversity graphs and the consistency graph, and for the first time, we combined Laplacian manifold constraints to strengthen the relationship between diversity and consistency while jointly optimizing the diversity graphs and the consistency graph. Additionally, we innovatively combine the diversity graphs and the consistency graph into a tensor and subject it to the constraint of tensor nuclear norm. By doing so, we not only obtain the complete information between multiple views but also enable the mutual learning and mutual enhancement of the diversity graphs and the consistency graph. Finally, by adopting the augmented Lagrange multiplier method, we integrate the two steps into a comprehensive framework. The TDCLM shows a performance enhancement of up to 25.85%, with experimental results across diverse datasets demonstrating that the TDCLM algorithm surpasses the state-of-the-art algorithms. In other words, these experimental results validate the importance of obtaining complete information from multiple views and effectively leveraging the diversity and consistency inherent in this complete information. The code is publicly available at https://github.com/TongWuahpu/TDCLM.

Amorphous AlOCl Compounds Enabling Nanocrystalline LiCl with Abnormally High Ionic Conductivity.

LiCl is a promising solid electrolyte, providing it possesses high ionic conductivity. Numerous efforts have been made to enhance its ionic conductivity through aliovalent doping. However, aliovalent substitution changes the intrinsic structure of LiCl, compromising its cost-effectiveness and electrochemical stability. Here, we report nanocrystalline LiCl embedded in amorphous AlOCl compounds with a heterogeneous structure to enhance its ionic conductivity. Nanocrystallization enlarges the LiCl unit cell, while amorphization facilitates interfacial ion transport. As a result, the amorphous AlOCl-modified LiCl nanocrystal (AlOCl-nanoLiCl) demonstrates a high ionic conductivity of 1.02 mS cm-1, which is 5 orders of magnitude higher than that of LiCl. Additionally, it exhibits high oxidative stability, low cost ($19.87 US kg-1), and low Young's modulus (2-3 GPa). When AlOCl-nanoLiCl is coupled with Li-rich cathodes (Li1.17Mn0.55Ni0.24Co0.05O2, 4.8 V vs Li+/Li), all-solid-state batteries exhibit remarkable long-term cycling stability (>1000 cycles). This work presents a novel strategy to enhance the ionic conductivity of alkaline chlorides without compromising their intrinsic advantages.

Revealing Intrinsic Functionalization, Structure, and Photo-Thermal Oxidation in Hexagonal Antimonene.

Antimonene is one of the more exciting members of the post-graphene family with promising applications in optoelectronics, energy storage and conversion, catalysis, sensing or biomedicine. Efforts have been focused on developing a large-scale production route, and indeed, through a colloidal approach, high-quality few-layers antimonene (FLA) hexagons have been recently obtained. However, their oxidation behavior remains unexplored, as well as their interface, inner structure, and photothermal properties. Herein, it is revealed that the hexagons have an intrinsic surface functionalization with alkyl thiols that protects the core of the hexagonal flake against oxidation, and displayed inner defects related to the crystal formation during synthesis, as confirmed by cross-sectional scanning transmission electron microscopy energy dispersive X-ray spectroscopy (STEM-EDX) and temperature-dependent X-ray photoelectron spectroscopy (XPS) and selected area electron diffraction (SAED) analysis. A comprehensive study of temperature and laser power-dependent Raman spectroscopy on varying FLA hexagon thicknesses is carried out. Thinner flakes (<20nm) exhibited a blueshift and intensity decrease, contrasting with thicker ones resembling typical exfoliated flakes with a redshift. This work addresses a literature gap, providing insights into hexagonal FLA structure and characterization, and highlighting the influence of surface functional groups on oxidation behavior. Additionally, it emphasizes the potential of antimonene hexagons as building blocks for 2D heterostructures, including combinations with antimonene oxides and other 2D materials.

Promoting Surface Reconstruction in Spinel Oxides via Tetrahedral-Octahedral Phase Boundary Construction for Efficient Oxygen Evolution.

Understanding the origin of surface reconstruction is crucial for developing highly efficient lattice oxygen oxidation mechanism (LOM) based spinel oxides. Traditionally, the reconstruction has been achieved through electrochemical procedures, such as cyclic voltammetry (CV), linear sweep voltammetry (LSV). In this work, we found that the surface reconstruction in LOM-based CoFe0.25Al1.75O4 catalyst was an irreversible oxygen redox chemical reaction. And a lower oxygen vacancy formation energy (EO-V) could benefit the combination of the activated lattice oxygen atoms with adsorbed water molecular. Motivated by this finding, a strategy of phase boundary construction from Co tetrahedral to octahedral was employed to decrease EO-V in CoFe0.25Al1.75O4. The results showed that as the Co octahedral occupancy ratio rose to 64 %, a 3.5 nm-thick reconstructed layer formed on the catalyst surface with a 158 mV decrease in overpotential. Further experiments indicated that the coexistence of tetrahedral-octahedral (O-T) phase would result in lattice mismatch, promoting non-bonding oxygen states and lowering EO-V. Then more active lattice oxygen combined with H2O molecules to generate hydroxide ions (OH-), followed by soluble cation leaching, which enhanced the reconstruction process. This work provided new insights into the relationship between the intrinsic structure of pre-catalysts and surface reconstruction in LOM-based spinel electrocatalysts.

Fluorinated functional groups enhanced composite in-situ gel electrolytes for high voltage cathode of quasi solid-state lithium battery

Gel electrolyte is an ideal system for improving the cycling stability and safety of lithium metal battery. However, traditional gel system with acrylic ester system is still not suitable for high-voltage battery system limited by its intrinsic structure and unstable interface. The polymer structures with -CF3 modified acrylic ester are proposed to improve the stability of both structure and interface in high-voltage battery systems. The LiF-rich anode-electrolyte interface assisted by fluorine contained in-situ gel electrolyte ensures stable cycling of lithium anode. The quasi-solid state lithium battery assembled with the LiFePO4 can still achieve a capacity retention rate of 81.2 % after 900 cycles at a rate of 1 C. Furthermore, the uniform organic cathode-electrolyte interface constructed by crosslinked -CF3 modified polymer effectively inhibits the corrosion of HF on the cathode material. The interaction between the anion and solvent molecules prevents the continuous decomposition reaction of the electrolyte on the surface of the LiCoO2 cathode at high voltage, which guarantees LiCoO2 battery can still achieve a capacity retention rate of 82.1 % after 500 cycles at a voltage of 4.45V. Design of fluorine contained in-situ polymer gel electrolyte could promote the development of quasi solid-state battery in practical industrial application.

Large-scale multi-view spectral clustering based on two-stage well-distributed anchor selection

Spectral clustering has attracted much attention because of its good clustering effect, but its high computational cost makes it difficult to apply to large-scale multi-view clustering. In response to this issue, a simple and efficient large-scale multi-view spectral clustering algorithm is proposed, which is based on a Two-stage Well-distributed Anchor Selection strategy (TWAS). Firstly, the data set is divided into several disjoint sample blocks to get the global well-distributed anchor candidate. Then, the algorithm proceeds to select anchor points within each local candidate anchor set. This two-stage anchor selection strategy facilitates the identification of anchors with significant representativeness at a reduced computational expense, thereby adeptly capturing the intrinsic data structure. Secondly, the present study devises an adaptive near-neighbor graph learning approach to construct an anchor-based intra-view similarity matrix. Finally, the multiple views are fused to obtain a consistent inter-view similarity matrix, and the clustering results are obtained. Extensive experiments demonstrate the effectiveness, efficiency, and stability of the TWAS algorithm.

1-Azaazulene: A Ligand Moiety for Boron Difluoride (BF2) Complexes To Achieve Long-Wavelength Absorption.

1-Azaazulene was adopted as a new ligand moiety for boron difluoride (BF2) complexes, resulting in large red shifts of the absorption bands up to 103 nm compared to their isomeric quinoline-based complexes. Such change was attributed to the nonalternant nature of 1-azaazulene, as the complexes maintained the intrinsic 10π-peripheral electronic structure. 1a exhibited the most red-shifted absorption among reported BF2 complexes containing a phenolic backbone, and the absorption edge of 1b reached the red region.