Intrinsic activity modulation and structural design of NiFe alloy catalysts for an efficient oxygen evolution reaction.

NiFe alloy catalysts have received increasing attention due to their low cost, easy availability, and excellent oxygen evolution reaction (OER) catalytic activity. Although it is considered that the co-existence of Ni and Fe is essential for the high catalytic activity, the identification of active sites and the mechanism of OER in NiFe alloy catalysts have been controversial for a long time. This review focuses on the catalytic centers of NiFe alloys and the related mechanism in the alkaline water oxidation process from the perspective of crystal structure/composition modulation and structural design. Briefly, amorphous structures, metastable phases, heteroatom doping and in situ formation of oxyhydroxides are encouraged to optimize the chemical configurations of active sites toward intrinsically boosted OER kinetics. Furthermore, the construction of dual-metal single atoms, specific nanostructures, carbon material supports and composite structures are introduced to increase the abundance of active sites and promote mass transportation. Finally, a perspective on the future development of NiFe alloy electrocatalysts is offered. The overall aim of this review is to shed light on the exploration of novel electrocatalysts in the field of energy.

Recursive Mahalanobis separability measure for gene subset selection.

Mahalanobis class separability measure provides an effective evaluation of the discriminative power of a feature subset, and is widely used in feature selection. However, this measure is computationally intensive or even prohibitive when it is applied to gene expression data. In this study, a recursive approach to Mahalanobis measure evaluation is proposed, with the goal of reducing computational overhead. Instead of evaluating Mahalanobis measure directly in high-dimensional space, the recursive approach evaluates the measure through successive evaluations in 2D space. Because of its recursive nature, this approach is extremely efficient when it is combined with a forward search procedure. In addition, it is noted that gene subsets selected by Mahalanobis measure tend to overfit training data and generalize unsatisfactorily on unseen test data, due to small sample size in gene expression problems. To alleviate the overfitting problem, a regularized recursive Mahalanobis measure is proposed in this study, and guidelines on determination of regularization parameters are provided. Experimental studies on five gene expression problems show that the regularized recursive Mahalanobis measure substantially outperforms the nonregularized Mahalanobis measures and the benchmark recursive feature elimination (RFE) algorithm in all five problems.