Enhanced Antifouling Capability of In Situ-Grown Hydrophilic-Hydrophobic Nanodomains on Membrane Surface in the Ultralow Pressurized Ultrafiltration Process.

Although hydrophilic modification of the membrane surface is widely adopted, polymeric membranes still suffer from irreversible fouling caused by hydrophilic components in surface water. Here, an ultrathin hydrogel layer (40 nm) with hydrophilic-hydrophobic textures was in situ grown onto the polysulfone ultrafiltration membrane surface using an organic-radical-initiated interfacial polymerization technique. The interfacial polymerization of hydrophilic and hydrophobic monomers ensured the molecular-scale distribution of hydrophilic and hydrophobic nanodomains on the membrane surface. These nanodomains, with their molecular lengths, facilitated dynamic repulsion interactions between the uniformly textured surface and foulant components with different degrees of hydrophilicity. Chemical force characterization confirmed that the adhesion force between the hydrophilic-hydrophobic textured membrane surface and foulants (dodecane, bovine serum albumin, and humic acid) was greatly reduced. Dynamic filtration experiments showed that a hydrophilic-hydrophobic textured membrane always possessed the largest water flux and the best antifouling performance. Furthermore, the foulant coverage ratio on the membrane surface was first evaluated by measuring changes in surface streaming potentials, which demonstrated a 69% reduction in the amount of foulant adhering to the hydrophilic-hydrophobic textured membrane surface. Therefore, the construction of hydrophilic-hydrophobic nanodomains on the membrane surface provides a promising strategy for alleviating membrane fouling caused by both hydrophobic and hydrophilic components during ultralow pressurized ultrafiltration processes.

Enhanced Water Permeability and Antifouling Property of Coffee-Ring-Textured Polyamide Membranes by In Situ Incorporation of a Zwitterionic Metal-Organic Framework.

Modulation of the polyamide structure is critically important for the reverse-osmosis performance of thin-film composite (TFC) membranes in the field of water reuse and desalination. Herein, zwitterionic nanoparticles of zeolitic imidazolate framework-8 (PZ@ZIF-8) were fabricated and incorporated into the polyamide active layer through the interfacial polymerization method. A hydrophilic, zwitterionic coffee-ring structure was formed on the surface of polyamide thin-film nanocomposite (TFN) membranes due to the adjusted diffusion rate of m-phenylenediamine (MPD) from the aqueous phase into the organic phase during the interfacial polymerization process. Surface characterization demonstrated that the coffee-ring structure increased the amounts of water transport channels on the membrane surface and the intrinsic pores of PZ@ZIF-8 maintained the salt rejection. Antifouling and bactericidal activities of TFN membranes were enhanced remarkably owing to the bacterial-"defending" and bacterial-"attacking" behaviors of hydrophilic and zwitterionic groups from PZ@ZIF-8 nanoparticles. This work would provide a promising method for the application of MOFs to enhance the bio-/organic-fouling resistance of TFN membranes with high water permeation and salt rejection.

Ultramicropore Engineering by Dehydration to Enable Molecular Sieving of H2 by Calcium Trimesate.

The high energy footprint of commodity gas purification and increasing demand for gases require new approaches to gas separation. Kinetic separation of gas mixtures through molecular sieving can enable separation by molecular size or shape exclusion. Physisorbents must exhibit the right pore diameter to enable separation, but the 0.3-0.4 nm range relevant to small gas molecules is hard to control. Herein, dehydration of the ultramicroporous metal-organic framework Ca-trimesate, Ca(HBTC)⋅H2 O (H3 BTC=trimesic acid), bnn-1-Ca-H2 O, affords a narrow pore variant, Ca(HBTC), bnn-1-Ca. Whereas bnn-1-Ca-H2 O (pore diameter 0.34 nm) exhibits ultra-high CO2 /N2 , CO2 /CH4 , and C2 H2 /C2 H4 binary selectivity, bnn-1-Ca (pore diameter 0.31 nm) offers ideal selectivity for H2 /CO2 and H2 /N2 under cryogenic conditions. Ca-trimesate, the first physisorbent to exhibit H2 sieving under cryogenic conditions, could be a prototype for a general approach to exert precise control over pore diameter in physisorbents.

Probing Calcium-Based Metal-Organic Frameworks via Natural Abundance 43 Ca Solid-State NMR Spectroscopy.

Calcium-based metal-organic frameworks (MOFs) are of high importance due to their low cost and bio-compatible metal centers. Understanding the local environment of calcium in these materials is critical for unraveling the origins of specific MOF properties. 43 Ca solid-state NMR spectroscopy is one of the very few techniques that can directly characterize calcium metal centers, however, the 43 Ca nucleus is a very challenging target for solid-state NMR spectroscopy due to its extremely low natural abundance and resonant frequency. In this work, natural abundance 43 Ca solid-state NMR spectroscopy, at a high magnetic field of 21.1 T, has been employed to characterize several calcium-based MOFs. We demonstrate that 43 Ca NMR spectra and quantum chemical calculations can probe the local structure of calcium metal centers within MOFs, investigate the presence of guests, and monitor phase changes.

Contrastive Multiview Attribute Graph Clustering With Adaptive Encoders.

Multiview attribute graph clustering aims to cluster nodes into disjoint categories by taking advantage of the multiview topological structures and the node attribute values. However, the existing works fail to explicitly discover the inherent relationships in multiview topological graph matrices while considering different properties between the graphs. Besides, they cannot well handle the sparse structure of some graphs in the learning procedure of graph embeddings. Therefore, in this article, we propose a novel contrastive multiview attribute graph clustering (CMAGC) with adaptive encoders method. Within this framework, the adaptive encoders concerning different properties of distinct topological graphs are chosen to integrate multiview attribute graph information by checking whether there exists high-order neighbor information or not. Meanwhile, the number of layers of the GCN encoders is selected according to the prior knowledge related to the characteristics of different topological graphs. In particular, the feature-level and cluster-level contrastive learning are conducted on the multiview soft assignment representations, where the union of the first-order neighbors from the corresponding graph pairs is regarded as the positive pairs for data augmentation and the sparse neighbor information problem in some graphs can be well dealt with. To the best of our knowledge, it is the first time to explicitly deal with the inherent relationships from the interview and intraview perspectives. Extensive experiments are conducted on several datasets to verify the superiority of the proposed CMAGC method compared with the state-of-the-art methods.

Dual Information Enhanced Multiview Attributed Graph Clustering.

Multiview attributed graph clustering is an important approach to partition multiview data based on the attribute characteristics and adjacent matrices from different views. Some attempts have been made in using graph neural network (GNN), which have achieved promising clustering performance. Despite this, few of them pay attention to the inherent specific information embedded in multiple views. Meanwhile, they are incapable of recovering the latent high-level representation from the low-level ones, greatly limiting the downstream clustering performance. To fill these gaps, a novel dual information enhanced multiview attributed graph clustering (DIAGC) method is proposed in this article. Specifically, the proposed method introduces the specific information reconstruction (SIR) module to disentangle the explorations of the consensus and specific information from multiple views, which enables graph convolutional network (GCN) to capture the more essential low-level representations. Besides, the contrastive learning (CL) module maximizes the agreement between the latent high-level representation and low-level ones and enables the high-level representation to satisfy the desired clustering structure with the help of the self-supervised clustering (SC) module. Extensive experiments on several real-world benchmarks demonstrate the effectiveness of the proposed DIAGC method compared with the state-of-the-art baselines.

Inhibition of biofouling by in-situ grown zwitterionic hydrogel nanolayer on membrane surface in ultralow-pressurized ultrafiltration process.

Ultralow-pressurized ultrafiltration membrane process with low energy consumption is promising in surface water purification. However, membrane fouling and low selectivity are significant barriers for the wide application of this process. Herein, an ultrathin zwitterionic hydrogel nanolayer was in-situ grown on polysulfone ultrafiltration membrane surface through interfacially-initiated free radical polymerization. The hydrogel-modified membrane possessed improved biological fouling resistance during the dynamic filtration process (bovine serum albumin, Escherichia coli and Staphylococcus aureus), comparing with commercial polysulfone membrane. The enhanced biofouling resistance ability of zwitterionic hydrogel nanolayer was derived from the foulant repulsion of hydration shell and the bactericidal effect of quaternary ammonium, according to the results of foulant-membrane interaction energy analyses and antibacterial performances. In surface water treatment, the zwitterionic hydrogel layer inhibited biofouling and resulted in the formation of a loose and thin biofilm. In addition, the hydrogel-modified membrane possessed 22% improvement in dissolved organic carbon (DOC) removal and 134% increasement in stable water flux, compared to commercial polysulfone membrane. The in-situ grown zwitterionic hydrogel nanolayer on membrane surface offers a prospectively alternative for biofouling control in ultralow-pressurized membrane process.

MIGP: Metapath Integrated Graph Prompt Neural Network.

Graph neural networks (GNNs) leveraging metapaths have garnered extensive utilization. Nevertheless, the escalating parameters and data corpus within graph pre-training models incur mounting training costs. Consequently, GNN models encounter hurdles including diminished generalization capacity and compromised performance amidst small sample datasets. Drawing inspiration from the efficacy demonstrated by self-supervised learning methodologies in natural language processing, we embark on an exploration. We endeavor to imbue graph data with augmentable, learnable prompt vectors targeting node representation enhancement to foster superior adaptability to downstream tasks. This paper proposes a novel approach, the Metapath Integrated Graph Prompt Neural Network (MIGP), which leverages learnable prompt vectors to enhance node representations within a pretrained model framework. By leveraging learnable prompt vectors, MIGP aims to address the limitations posed by mall sample datasets and improve GNNs' model generalization. In the pretraining stage, we split symmetric metapaths in heterogeneous graphs into short metapaths and explicitly propagate information along the metapaths to update node representations. In the prompt-tuning stage, the parameters of the pretrained model are fixed, a set of independent basis vectors is introduced, and an attention mechanism is employed to generate task-specific learnable prompt vectors for each node. Another notable contribution of our work is the introduction of three patent datasets, which is a pioneering application in related fields. We will make these three patent datasets publicly available to facilitate further research on large-scale patent data analysis. Through comprehensive experiments conducted on three patent datasets and three other public datasets, i.e., ACM, IMDB, and DBLP, we demonstrate the superior performance of the MIGP model in enhancing model applicability and performance across a variety of downstream datasets. The source code and datasets are available in the website.1.

Poly[diaqua[µ4-4-(isonicotinamido)phthalato]nickel(II)] displaying an sra topology.

In the title compound, [Ni(C14H8N2O5)(H2O)2]n, the Ni(II) cation is six-coordinate with a slightly distorted octahedral coordination geometry and the 4-(isonicotinamido)phthalate ligand links the Ni(II) centres into a three-dimensional structure with sra topology. The structure is also stabilized by N-H···O hydrogen bonding between the uncoordinated amide groups of the ligand and extensive O-H···O hydrogen bonding between the two coordinated water molecules. The magnetic and thermal stability properties of the title compound are also discussed.

(4,6-Dimethyl-2-sulfanylidenepyrimidin-1-ium) trichlorido(thiourea-κS)zinc(II).

The title compound, (C6H9N2S)[ZnCl3{SC(NH2)2}], exists as a zincate where the zinc(II) centre is coordinated by three chloride ligands and a thiourea ligand to form the anion. The organic cation adopts the protonated 4,6-dimethyl-2-sulfanylidenepyrimidin-1-ium (L) form of 4,6-dimethylpyrimidine-2(1H)-thione. Two short N-H···Cl hydrogen bonds involving the pyrimidine H atoms and the [ZnCl3L](-) anion form a crystallographically centrosymmetric dimeric unit consisting of two anions and two cations. The packing structure is completed by longer-range hydrogen bonds donated by the thiourea NH2 groups to chloride ligand hydrogen-bond acceptors.

Poly[[tetra-aqua-tetra-kis-[µ3-5-(pyridine-4-carboxamido)-isophthalato]nickel(II)diterbium(III)] tetra-hydrate].

In the title compound, {[NiTb2(C14H8N2O5)4(H2O)4]·4H2O} n , the Tb(III) ion is coordinated by one water mol-ecule and seven O atoms from four 5-(pyridine-4-carboxamido)-isophthalate (L) ligands in a distorted square-anti-prismatic arrangement, while the Ni(II) ion, lying on an inversion center, is six-coordinated in an octa-hedral geometry by two pyridine N atoms, two carboxyl-ate O atoms and two water mol-ecules. One L ligand bridges two Tb(III) ions and one Ni(II) ion through two carboxyl-ate groups and one pyridine N atom. The other L ligand bridges two Tb(III) ions and one Ni(II) ion through two carboxyl-ate groups, while the uncoordinating pyridine N atom is hydrogen bonded to an adjacent coordinating water mol-ecule. Extensive O-H⋯O, N-H⋯O and O-H⋯N hydrogen bonds play an important role in stabilizing the crystal structure.

Poly[[(µ2-4,4'-bipyridine)(µ3-5-isonicotinamidoisophthalato)cobalt(II)] trihydrate].

In the title compound, {[Co(C(14)H(8)N(2)O(5))(C(10)H(8)N(2))]·3H(2)O}(n), the Co(II) cation is five-coordinated with a slightly distorted trigonal-bipyramidal geometry, and the 5-isonicotinamidoisophthalate ligands link Co(II) atoms into a layered structure. These two-dimensional arrays are further pillared by rod-like 4,4'-bipyridine ligands to give a three-dimensional framework with pcu (primitive cubic) topology. The magnetic and adsorption properties of the title compound are also discussed.

A Tensor Approach for Uncoupled Multiview Clustering.

Multiview clustering plays an important part in unsupervised learning. Although the existing methods have shown promising clustering performances, most of them assume that the data is completely coupled between different views, which is unfortunately not always ensured in real-world applications. The clustering performance of these methods drops dramatically when handling the uncoupled data. The main reason is that: 1) cross-view correlation of uncoupled data is unclear, which limits the existing multiview clustering methods to explore the complementary information between views and 2) features from different views are uncoupled with each other, which may mislead the multiview clustering methods to partition data into wrong clusters. To address these limitations, we propose a tensor approach for uncoupled multiview clustering (T-UMC) in this article. Instead of pairwise correlation, T-UMC chooses a most reliable view by view-specific silhouette coefficient (VSSC) at first, and then couples the self-representation matrix of each view with it by pairwise cross-view coupling learning. After that, by integrating recoupled self-representation matrices into a third-order tensor, the high-order correlations of all views are explored with tensor singular value decomposition (t-SVD)-based tensor nuclear norm (TNN). And the view-specific local structures of each individual view are also preserved with the local structure learning scheme with manifold learning. Besides, the physical meaning of view-specific coupling matrix is also discussed in this article. Extensive experiments on six commonly used benchmark datasets have demonstrated the superiority of the proposed method compared with the state-of-the-art multiview clustering methods.

Multiview Subspace Clustering With Grouping Effect.

Multiview subspace clustering (MVSC) is a recently emerging technique that aims to discover the underlying subspace in multiview data and thereby cluster the data based on the learned subspace. Though quite a few MVSC methods have been proposed in recent years, most of them cannot explicitly preserve the locality in the learned subspaces and also neglect the subspacewise grouping effect, which restricts their ability of multiview subspace learning. To address this, in this article, we propose a novel MVSC with grouping effect (MvSCGE) approach. Particularly, our approach simultaneously learns the multiple subspace representations for multiple views with smooth regularization, and then exploits the subspacewise grouping effect in these learned subspaces by means of a unified optimization framework. Meanwhile, the proposed approach is able to ensure the cross-view consistency and learn a consistent cluster indicator matrix for the final clustering results. Extensive experiments on several benchmark datasets have been conducted to validate the superiority of the proposed approach.

Refining Graph Structure for Incomplete Multi-View Clustering.

As a challenging problem, incomplete multi-view clustering (MVC) has drawn much attention in recent years. Most of the existing methods contain the feature recovering step inevitably to obtain the clustering result of incomplete multi-view datasets. The extra target of recovering the missing feature in the original data space or common subspace is difficult for unsupervised clustering tasks and could accumulate mistakes during the optimization. Moreover, the biased error is not taken into consideration in the previous graph-based methods. The biased error represents the unexpected change of incomplete graph structure, such as the increase in the intra-class relation density and the missing local graph structure of boundary instances. It would mislead those graph-based methods and degrade their final performance. In order to overcome these drawbacks, we propose a new graph-based method named Graph Structure Refining for Incomplete MVC (GSRIMC). GSRIMC avoids recovering feature steps and just fully explores the existing subgraphs of each view to produce superior clustering results. To handle the biased error, the biased error separation is the core step of GSRIMC. In detail, GSRIMC first extracts basic information from the precomputed subgraph of each view and then separates refined graph structure from biased error with the help of tensor nuclear norm. Besides, cross-view graph learning is proposed to capture the missing local graph structure and complete the refined graph structure based on the complementary principle. Extensive experiments show that our method achieves better performance than other state-of-the-art baselines.

Incomplete Data Meets Uncoupled Case: A Challenging Task of Multiview Clustering.

Incomplete multiview clustering (IMC) methods have achieved remarkable progress by exploring the complementary information and consensus representation of incomplete multiview data. However, to our best knowledge, none of the existing methods attempts to handle the uncoupled and incomplete data simultaneously, which affects their generalization ability in real-world scenarios. For uncoupled incomplete data, the unclear and partial cross-view correlation introduces the difficulty to explore the complementary information between views, which results in the unpromising clustering performance for the existing multiview clustering methods. Besides, the presence of hyperparameters limits their applications. To fill these gaps, a novel uncoupled IMC (UIMC) method is proposed in this article. Specifically, UIMC develops a joint framework for feature inferring and recoupling. The high-order correlations of all views are explored by performing a tensor singular value decomposition (t-SVD)-based tensor nuclear norm (TNN) on recoupled and inferred self-representation matrices. Moreover, all hyperparameters of the UIMC method are updated in an exploratory manner. Extensive experiments on six widely used real-world datasets have confirmed the superiority of the proposed method in handling the uncoupled incomplete multiview data compared with the state-of-the-art methods.

Reversible single-crystal-to-single-crystal transformation and highly selective adsorption property of three-dimensional cobalt(II) frameworks.

A three-dimensional (3D) coordination polymer, [Co(3)(L)(2)(BTEC)(H(2)O)(2)]·2H(2)O [1, HL = 3,5-di(imidazol-1-yl)benzoic acid, H(4)BTEC = 1,2,4,5-benzenetetracarboxylic acid], with tfz-d topology has been hydrothermally synthesized. The framework of 1 has high thermal stability and exhibits single-crystal-to-single-crystal (SCSC) transformations upon removing and rebinding the noncoordinated and coordinated water molecules. X-ray crystallographic analyses revealed that the coordination geometry of Co(II) changes from octahedral to square pyramid upon dehydration, accompanying the appearance of one-dimensional (1D) open channels with dimensions of 2.0 × 2.8 Å. The dehydrated form [Co(3)(L)(2)(BTEC)] (2) exhibits highly selective adsorption of water molecules over N(2), CH(3)OH, and CH(3)CH(2)OH, which could be used as sensors for water molecules. Furthermore, the magnetic properties of 1 and 2 were investigated, showing the existence of ferromagnetic interaction between the Co(II) atoms within the trinuclear subunit.

Poly[[tetra-aqua-tetra-kis-[µ(3)-5-(pyridine-4-carboxamido)-isophthalato]-cobalt(II)-diholmium(III)] tetra-hydrate].

In the centrosymmetric polymeric title compound, {[CoHo(2)(C(14)H(8)N(2)O(5))(4)(H(2)O)(4)]·4H(2)O}(n), the Ho(III) ion is coordinated by one water mol-ecule and four 5-(pyridine-4-carboxamido)-isophthalate (L) ligands in a distorted square-anti-prismatic arrangement. The Co(II) ion, located on an inversion center, is coordinated by two pyridine N atoms, two carboxyl-ate O atoms and two water mol-ecules in a distorted octa-hedral geometry. One L ligand bridges two Ho ions and one Co ion through two carboxyl-ate groups and one pyridine N atom. The other L ligand bridges two Ho ions and one Co ion through two carboxyl-ate groups, while the uncoordinated pyridine N atom accepts a hydrogen bond from an adjacent coordinated water mol-ecule. Extensive O-H⋯O, N-H⋯O and O-H⋯N hydrogen bonding is present in the crystal.

Poly[[tetra-aqua-tetra-kis-[µ(3)-5-(pyridine-4-carboxamido)-isophthalato]cobalt(II)dierbium(III)] tetra-hydrate].

In the centrosymmetric polymeric title compound, {[CoEr(2)(C(14)H(8)N(2)O(5))(4)(H(2)O)(4)]·4H(2)O}(n), the Er(III) cation has a coordination number of eight and is surrounded by seven carboxyl-ate O atoms from four 5-(pyridine-4-carboxamido)-isophthalate (L) ligands and one water mol-ecule, forming a distorted square-anti-prismatic arrangement. The Co(II) cation is located on an inversion center and is coordinated by two pyridine N atoms, two carboxyl-ate O atoms and two water mol-ecules in a distorted octa-hedral geometry. The asymmetric unit contains two anionic L ligands. One bridges two Er(III) cations and one Co(II) cation through two carboxyl-ate groups and one pyridine N atom, while the other bridges two Er(III) cations and one Co(II) cation through two carboxyl-ate groups. Extensive O-H⋯O, O-H⋯N and N-H⋯O hydrogen-bonding inter-actions are present in the crystal, involving all uncoordinated water mol-ecules and the uncoordinated pyridine N atom of one of the ligands bonded to an adjacent coordinated water mol-ecule. The title compound is isotypic with the gadolinium analogue.

Poly[[tetra-aquatetrakis-[µ(3)-5-(pyridine-4-carboxamido)-isophthalato]cobalt(II)digadolinium(III)] tetra-hydrate].

In the centrosymmetric polymeric title compound, {[CoGd(2)(C(14)H(8)N(2)O(5))(4)(H(2)O)(4)]·4H(2)O}(n), the Gd(III) cation is coordinated by one water mol-ecule and four pyridine-4-carboxamido-isophthalate (L) anions in a distorted square-anti-prismatic arrangement, while the Co(II) cation, located on an inversion center, is coordinated by two pyridyl-N atoms, two carboxyl-ate-O atoms and two water mol-ecules in a distorted octa-hedral geometry. The asymmetric unit contains two anionic L ligands: one bridges two Gd cations and one Co cation through two carboxyl groups and one pyridine-N atom; the other bridges two Gd cations and one Co cation through two carboxyl groups and the uncoordinated pyridine-N atom is hydrogen-bonded to the adjacent coordinated water mol-ecule. Extensive O-H⋯O and N-H⋯O hydrogen bonds are present in the crystal structure.