Co-cluster Structures Research Articles

Cobalt atom-cluster interactions synergistically enhance the activity of oxygen reduction reaction in seawater

Seawater electrocatalysis is highly desired for a variety of energy storage and conversion systems, such as water splitting and metal fuel cells that directly using seawater as electrolyte. However, the adsorption of chloride ions (Cl−) on active sites of cathodes would worsen the oxygen reduction reaction (ORR) activity and stability, thus lowering the battery performance. In this work, we firstly designed an electrocatalyst model, in which the Co atomic clusters were closely surrounded by satellite Co single atoms on N-doped carbon substrates, designated as Co-ACSAs. The theoretical calculation results suggested that the Co clusters possess stronger Cl− binding energy and acted as pre-adsorption group for Cl−, thus fully exposed Co single atoms as ORR active sites with stronger O2 adsorption energy to promote the oxygen reduction reaction process. Then, we developed an ultrafast high temperature shock strategy to synthesize the Co-ACSAs by controlling the N concentration in carbon substrates. Benefiting from the moderate interacting distance between Co clusters and satellite Co single atoms in Co-ACSAs, the electronic structure of Co clusters and Co single atoms was optimized through the modulation of the interconnected hexatomic ring. As a result, Co-ACSAs exhibit superior ORR activity and durability with on-set and half-wave potentials of 0.885 V and 0.782 V, respectively, and continuously catalyzing for 485 h in seawater electrolyte. The Co-ACSAs-based seawater battery exhibits a discharge voltage of 1.41 V at 5 mA cm−2 and realized stable energy supply for more than 390 h.

Double-Confined Ultrafine Cobalt Clusters for Efficient Peroxide Activation.

The construction of highly active catalysts presents great prospects, while it is a challenge for peroxide activation in advanced oxidation processes (AOPs). Herein, we facilely developed ultrafine Co clusters confined in mesoporous silica nanospheres containing N-doped carbon (NC) dots (termed as Co/NC@mSiO2) via a double-confinement strategy. Compared with the unconfined counterpart, Co/NC@mSiO2 exhibited unprecedented catalytic activity and durability for removal of various organic pollutants even in extremely acidic and alkaline environments (pH from 2 to 11) with very low Co ion leaching. Experiments and density functional theory (DFT) calculations proved that Co/NC@mSiO2 possessed strong peroxymonosulphate (PMS) adsorption and charge transfer capability, enabling the efficient O-O bond dissociation of PMS to HO• and SO4•- radicals. The strong interaction between Co clusters and mSiO2 containing NC dots contributed to excellent pollutant degradation performances by optimizing the electronic structures of Co clusters. This work represents a fundamental breakthrough in the design and understanding of the double-confined catalysts for peroxide activation.

Anion-induced electronic localization and polarized cobalt clusters for highly efficient water splitting.

It is a promising pathway to use anions to regulate electronic structures, reasonably design and construct highly efficient catalysts for water splitting. Herein, a N-regulated Co cluster catalyst confined in carbon nanotubes, N-Co NCNTs, was constructed successfully. Nitrogen anions played a crucial role in optimizing the electronic structures of Co clusters and enhancing localization of electrons, resulting in polarized cobalt clusters. The N-induced electronic localization and the resulting polarized Co clusters are responsible for the improvement of catalytic activity. N-Co NCNTs exhibited ultra-low overpotentials of 178 mV and 92 mV for the OER and HER to achieve 10 mA cm-2 in an alkaline electrolyte, respectively. Its long-term catalytic durability is mainly attributed to the obstacle to the surface oxidation of Co clusters caused by N-regulation. N-Co NCNTs maintained a stable current density for 160 h at 10 mA cm-2. DFT computations confirmed the decisive role played by nitrogen anions in regulating the electronic structure. This work provides a pathway for understanding and designing highly efficient anion-regulated catalysts.

Efficient Algebraic Multigrid Methods for Multilevel Overlapping Coclustering of User-Item Relationships

Various digital data sets that encode user-item relationships contain a multilevel overlapping cluster structure. The user-item relation can be encoded in a weighted bipartite graph and uncovering these overlapping coclusters of users and items at multiple levels in the bipartite graph can play an important role in analyzing user-item data in many applications. For example, for effective online marketing, such as placing online ads or deploying smart online marketing strategies, identifying co-occurring clusters of users and items can lead to accurately targeted advertisements and better marketing outcomes. In this paper, we propose fast algorithms inspired by algebraic multigrid methods for finding multilevel overlapping cocluster structures of feature matrices that encode user-item relations. Starting from the weighted bipartite graph structure of the feature matrix, the algorithms use agglomeration procedures to recursively coarsen the bipartite graphs that represent the relations between the coclusters on increasingly coarser levels. New fast coarsening routines are described that circumvent the bottleneck of all-to-all similarity computations by exploiting measures of direct connection strength between row and column variables in the feature matrix. Providing accurate coclusters at multiple levels in a manner that can scale to large data sets is a challenging task. In this paper, we propose heuristic algorithms that approximately and recursively minimize normalized cuts to obtain coclusters in the aggregated bipartite graphs on multiple levels of resolution. Whereas the main novelty and focus of the paper lies in algorithmic aspects of reducing computational complexity to obtain scalable methods specifically for large rectangular user-item matrices, the algorithmic variants also define several new models for determining multilevel coclusters that we justify intuitively by relating them to principles that underlie collaborative filtering methods for user-item relationships. Experimental results show that the proposed algorithms successfully uncover the multilevel overlapping cluster structure for artificial and real data sets. Summary of Contribution: This paper develops new and efficient computational methods for finding the multilevel overlapping cocluster structure of feature matrices that encode user-item relationships. We base our approach on the use of pairwise similarity measures between features, seeking clusters of points that are similar to each other and dissimilar from the points outside the cluster. We approximately solve the problem of finding optimal overlapping coclusters on multiple levels by employing a framework that is based on efficient multilevel methods that have been used previously to solve sparse linear systems and to cluster graphs. Our main contribution is that we extend these methods in efficient manners to find coclusters in the bipartite graphs that encode common and important user-item relationships or social network relations. The novel methods that we propose are inherently scalable to large problem sizes and are naturally able to uncover overlapping coclusters at multiple levels, whereas existing methods generally only find coclusters at the fine level. We illustrate the algorithm and its performance on some standard test problems from the literature and on a proof-of-concept real-world data set that relates LinkedIn users to their skills and expertise.

Visualization of Potential Technical Solutions by SOM and Co-Clustering and its Extension to Multi-View Situation

In order to support inspiration of potential technical solutions, this paper considers visualization of solving means varied in patent documents through SOM. Non-structured patent document data can be quantified through two different scheme: word level co-occurrence probability vectors and correlation coefficients of the generated co-occurrence probability vectors. Comparing the two SOMs derived with the above schemes is useful for supporting innovation acceleration through extraction of important pairs of related factors in new technology development. In this paper, co-cluster structures are utilized for emphasizing field-related solutions by constructing multiple SOMs after co-clustering. Document × keyword co-occurrence analysis achieves extraction of co-clusters consisting of mutually related pairs in particular fields. Additionally, this paper also considers an extension to a multi-view situation, where each patent is characterized by additional patent classification system of F-term by Japan Patent Office. Through multi-view co-clustering among documents × keywords × F-terms, theme field-related knowledge is demonstrated to be extracted.

Discovering Co-Cluster Structure from Relationships between Biased Objects

Discovering Co-Cluster Structure from Relationships between Biased Objects

Visual Co-Cluster Assessment with Intuitive Cluster Validation Through Cooccurrence-Sensitive Ordering

Co-cluster extraction is a basic approach for summarization of cooccurrence information. This paper proposes a visual assessment technique for co-cluster structure analysis through cooccurrence-sensitive ordering, which realizes the hybrid concept of the coVAT algorithm and distance-sensitive ordering in relational data clustering. Object-item cooccurrence information is first enlarged into an (object + item) × (object + item) cooccurrence data matrix, and then, cooccurrence-sensitive ordering is performed through spectral ordering of the enlarged matrix. Additionally, this paper also consider the intuitive validation of co-cluster structures considering cluster crossing curves, which was adopted in cluster validation with distance-sensitive ordering. The characteristic features of the proposed approach are demonstrated through several numerical experiments including application to social analysis of Japanese prefectural statistics.

Co-cluster Structure Visualization by Spectral Ordering and Its Characteristics

Cluster structure analysis is often utilized as the basic step in unsupervised classification. In this paper, a spectral ordering-based visual cluster assessment method for relational data analysis is modified so that it can be applied to co-cluster structure analysis in cooccurrence information data. Object-by-item cooccurrence information is first enlarged into an (object + item)-by-(object + item) relational data matrix, and then, co-cluster structure is visually assessed through simultaneous ordering of objects and items in the enlarged matrix. Additionally, due to the sparse nature of the enlarged matrix, the computational cost can be decreased with the eigen problem of a reduced matrix. The characteristic features of the proposed approach are demonstrated through several numerical experiments including social analysis of Japanese prefectural statistics.

FCM-Type Fuzzy Coclustering for Three-Mode Cooccurrence Data: 3FCCM and 3Fuzzy CoDoK

Cocluster structure analysis is a basic technique for revealing intrinsic structural information from cooccurrence data among objects and items, in which coclusters are composed of mutually familiar pairs of objects and items. In many real applications, it is also the case that we have not only cooccurrence information among objects and items but also intrinsic relation among items and other ingredients. For example, in food preference analysis, users’ preferences on foods should be found considering not only user-food cooccurrences but also the implicit relation among users and cooking ingredients. In this paper, two FCM-type fuzzy coclustering models, that is, FCCM and Fuzzy CoDoK, are extended for revealing intrinsic cocluster structures from three-mode cooccurrence data, where the aggregation degree of three elements in each cocluster is maximized through iterative updating of three types of fuzzy memberships for objects, items, and ingredients. The characteristic features of the proposed methods are demonstrated through a numerical experiment.

The Relevance Dependent Infinite Relational Model for Discovering Co-Cluster Structure from Relationships with Structured Noise

The Relevance Dependent Infinite Relational Model for Discovering Co-Cluster Structure from Relationships with Structured Noise

Exploiting Task-Feature Co-Clusters in Multi-Task Learning

In multi-task learning, multiple related tasks are considered simultaneously, with the goal to improve the generalization performance by utilizing the intrinsic sharing of information across tasks. This paper presents a multi-task learning approach by modeling the task-feature relationships. Specifically, instead of assuming that similar tasks have similar weights on all the features, we start with the motivation that the tasks should be related in terms of subsets of features, which implies a co-cluster structure. We design a novel regularization term to capture this task-feature co-cluster structure. A proximal algorithm is adopted to solve the optimization problem. Convincing experimental results demonstrate the effectiveness of the proposed algorithm and justify the idea of exploiting the task-feature relationships.

A Collaborative Framework for Privacy Preserving Fuzzy Co-Clustering of Vertically Distributed Cooccurrence Matrices

In many real world data analysis tasks, it is expected that we can get much more useful knowledge by utilizing multiple databases stored in different organizations, such as cooperation groups, state organs, and allied countries. However, in many such organizations, they often hesitate to publish their databases because of privacy and security issues although they believe the advantages of collaborative analysis. This paper proposes a novel collaborative framework for utilizing vertically partitioned cooccurrence matrices in fuzzy co-cluster structure estimation, in which cooccurrence information among objects and items is separately stored in several sites. In order to utilize such distributed data sets without fear of information leaks, a privacy preserving procedure is introduced to fuzzy clustering for categorical multivariate data (FCCM). Withholding each element of cooccurrence matrices, only object memberships are shared by multiple sites and their (implicit) joint co-cluster structures are revealed through an iterative clustering process. Several experimental results demonstrate that collaborative analysis can contribute to revealing global intrinsic co-cluster structures of separate matrices rather than individual site-wise analysis. The novel framework makes it possible for many private and public organizations to share common data structural knowledge without fear of information leaks.

Dual Exclusive Partition in Fuzzy CoDoK and SCAD-based Fuzzy Co-clustering

Fuzzy co-clustering is a basic technique for revealing intrinsic co-cluster structures from cooccurrence information among objects and items. In most of fuzzy co-clustering algorithms, objects and items are partitioned based on different constraints. Objects are forced to be exclusively partitioned like as Fuzzy c-Means (FCM), while item memberships often represent just the relative significance of items in each cluster, i.e., items can be shared by multiple clusters. In a previous work, exclusive partition of items were achieved by introducing a penalty term in Fuzzy Clustering for Categorical Multivariate data (FCCM), which is an FCM-type co-clustering with entropy regularization mechanism. In this paper, the applicability of dual exclusive partition of objects and items are discussed in the frameworks of Fuzzy CoDoK and SCAD-based fuzzy co-clustering.

Size effect on the frozen structures of Co clusters

This paper studies the freezing of Co clusters with diameters ranging from 2.52 to 4.62 nm by using molecular dynamics simulation with an embedded atom method. It was found that the frozen structures are closely related to cluster sizes. When the cluster size is small (≤ 2.94 nm), an icosahedron is formed. When the cluster size is large (≥ 3.79 nm), an hcp bulk-like structure is formed. If the cluster size is 3.37 nm, a decahedron is formed. It was also found that the frozen structure is strongly dependent on the energy of geometric shapes at given sizes. The size-dependent frozen structures of clusters provide a new method for fabricating novel materials with nano microstructures.

Phase transitions in Co nanoclusters grown by pulsed laser deposition

The crystalline structure of Co clusters embedded in an amorphous Al2O3 matrix was studied by transmission electron microscopy (TEM) and electron diffraction (TED). In the first stage of the growth a metastable structure (body-centred-cubic) is observed. A face-centred-cubic phase (fcc) is found when the size of the clusters increases ( diameter > 4 nm). The hexagonal-close-packed phase arises in the fcc phase by a succession of stacking faults at the largest sizes. The mechanisms of phase transformation have been determined by using high resolution electron microscopy (HREM). The chemical nature of the clusters, in particular the existence of Co-O bonds, was investigated by using electron energy loss spectroscopy (EELS).

Giant magnetoresistance and Co-cluster structure in phase-separated Co-Cu granular alloys.

We examine the effect of Co-cluster morphology on giant magnetoresistance (MR) in phase-separated Co-Cu films. The Co clusters were characterized through grazing incidence, anomalous, small-angle x-ray scattering. With thermal annealing the Co cluster diameter increases from 21 to \ensuremath{\sim}250 \AA{} with a concomitant drop from \ensuremath{\sim}35% to 1% in the 4.2 K MR. The MR scales approximately as the inverse cluster size. Comparison with theory indicates that interfacial spin-dependent electron scattering is the dominant scattering mechanism underlying giant MR for cluster diameters up to at least 250 \AA{}.