Developing a Cobalt Phosphide Catalyst with Combined Cobalt Defects and Phosphorus Vacancies to Boost Oxygen Evolution Reaction.

Defect engineering, by adjusting the surface charge and active sites of CoP catalysts, significantly enhances the efficiency of the oxygen evolution reaction (OER). We have developed a new Co1-xPv catalyst that has both cobalt defects and phosphorus vacancies, demonstrating excellent OER performance. Under both basic and acidic media, the catalyst incurs a modest overvoltage, with 238 mV and 249 mV needed, respectively, to attain a current density of 10 mA cm-2. In the practical test of alkaline electrocatalytic water splitting (EWS), the Co1-xPv || Pt/C EWS shows a low cell voltage of 1.51 V and superior performance compared to the noble metal-based EWS (RuO2 || Pt/C, 1.66 V). This catalyst's exceptional catalytic efficiency and longevity are mainly attributed to its tunable electronic structure. The presence of cobalt defects facilitates the transformation of Co2+ to Co3+, while phosphorus vacancies enhance the interaction with oxygen species (*OH, *O, *OOH), working in concert to improve the OER efficiency. This strategy offers a new approach to designing transition metal phosphide catalysts with coexisting metal defects and phosphorus vacancies, which is crucial for improving energy conversion efficiency and catalyst performance.

A class-specific mean vector-based weighted competitive and collaborative representation method for classification.

Collaborative representation-based classification (CRC), as a typical kind of linear representation-based classification, has attracted more attention due to the effective and efficient pattern classification performance. However, the existing class-specific representations are not competitively learned from collaborative representation for achieving more informative pattern discrimination among all the classes. With the purpose of enhancing the power of competitive and discriminant representations among all the classes for favorable classification, we propose a novel CRC method called the class-specific mean vector-based weighted competitive and collaborative representation (CMWCCR). The CMWCCR mainly contains three discriminative constraints including the competitive, mean vector and weighted constraints that fully employ the discrimination information in different ways. In the competitive constraint, the representations from any one class and the other classes are adapted for learning competitive representations among all the classes. In the newly designed mean vector constraint, the mean vectors of all the class-specific training samples with the corresponding class-specific representations are taken into account to further enhance the competitive representations. In the devised weighted constraint, the class-specific weights are constrained on the representation coefficients to make the similar classes have more representation contributions to strengthening the discrimination among all the class-specific representations. Thus, these three constraints in the unified CMWCCR model can complement each other for competitively learning the discriminative class-specific representations. To verify the CMWCCR classification performance, the extensive experiments are conducted on twenty-eight data sets in comparisons with the state-of-the-art representation-based classification methods. The experimental results show that the proposed CMWCCR is an effective and robust CRC method with satisfactory performance.

Weighted discriminative collaborative competitive representation for robust image classification.

Collaborative representation-based classification (CRC) is a famous representation-based classification method in pattern recognition. Recently, many variants of CRC have been designed for many classification tasks with the good classification performance. However, most of them ignore the inter-class pattern discrimination among the class-specific representations, which is very critical for strengthening the pattern discrimination of collaborative representation (CR). In this article, we propose a novel CR approach for image classification, called weighted discriminative collaborative competitive representation (WDCCR). The proposed WDCCR designs the discriminative and competitive collaborative representation among all the classes by fully considering the class information. On the one hand, we incorporate two discriminative constraints into the unified WDCCR model. Both constraints are the competitive class-specific representation residuals and the pairs of class-specific representations for each query sample. On the other hand, the constraint of the weighted categorical representation coefficients is introduced into the proposed model for further enhancing the power of discriminative and competitive representation. In the weighted constraint, we assume that the different classes of each query sample should have less contribution to the representation with the small representation coefficients, and then two types of weight factors are designed to constrain the representation coefficients. Furthermore, the robust WDCCR (R-WDCCR) is proposed with l1-norm representation fidelity for recognizing noisy images. Extensive experiments on six image data sets demonstrate the effective and robust superiorities of the proposed WDCCR and R-WDCCR over the related state-of-the-art representation-based classification methods.

Robust Nonnegative Patch Alignment for Dimensionality Reduction.

Dimensionality reduction is an important method to analyze high-dimensional data and has many applications in pattern recognition and computer vision. In this paper, we propose a robust nonnegative patch alignment for dimensionality reduction, which includes a reconstruction error term and a whole alignment term. We use correntropy-induced metric to measure the reconstruction error, in which the weight is learned adaptively for each entry. For the whole alignment, we propose locality-preserving robust nonnegative patch alignment (LP-RNA) and sparsity-preserviing robust nonnegative patch alignment (SP-RNA), which are unsupervised and supervised, respectively. In the LP-RNA, we propose a locally sparse graph to encode the local geometric structure of the manifold embedded in high-dimensional space. In particular, we select large p -nearest neighbors for each sample, then obtain the sparse representation with respect to these neighbors. The sparse representation is used to build a graph, which simultaneously enjoys locality, sparseness, and robustness. In the SP-RNA, we simultaneously use local geometric structure and discriminative information, in which the sparse reconstruction coefficient is used to characterize the local geometric structure and weighted distance is used to measure the separability of different classes. For the induced nonconvex objective function, we formulate it into a weighted nonnegative matrix factorization based on half-quadratic optimization. We propose a multiplicative update rule to solve this function and show that the objective function converges to a local optimum. Several experimental results on synthetic and real data sets demonstrate that the learned representation is more discriminative and robust than most existing dimensionality reduction methods.