Water-splitting devices for hydrogen generation through electrolysis (hydrogen evolution reaction, HER) hold great promise for clean energy. However, their practical application relies on the development of inexpensive and efficient catalysts to replace precious platinum catalysts. We previously reported that HER can be largely enhanced through finely tuning the energy level of molybdenum sulfide (MoS) by hot electron injection from plasmonic gold nanoparticles. Under this inspiration, herein, we propose a strategy to improve the HER performance of MoS by engineering its energy level via direct transition-metal doping. We find that zinc-doped MoS (Zn-MoS) exhibits superior electrochemical activity toward HER as evidenced by the positively shifted onset potential to -0.13 V vs RHE. A turnover of 15.44 s at 300 mV overpotential is achieved, which by far exceeds the activity of MoS catalysts reported. The large enhancement can be attributed to the synergistic effect of electronic effect (energy level matching) and morphological effect (rich active sites) via thermodynamic and kinetic acceleration, respectively. This design opens up further opportunities for improving electrocatalysts by incorporating promoters, which broadens the understanding toward the optimization of electrocatalytic activity of these unique materials.

Hydrogen energy-based electrochemical energy conversion technologies offer the promise of enabling a transition of the global energy landscape from fossil fuels to renewable energy. Here, we present a comprehensive review of the fundamentals of electrocatalysis in alkaline media and applications in alkaline-based energy technologies, particularly alkaline fuel cells and water electrolyzers. Anion exchange (alkaline) membrane fuel cells (AEMFCs) enable the use of nonprecious electrocatalysts for the sluggish oxygen reduction reaction (ORR), relative to proton exchange membrane fuel cells (PEMFCs), which require Pt-based electrocatalysts. However, the hydrogen oxidation reaction (HOR) kinetics is significantly slower in alkaline media than in acidic media. Understanding these phenomena requires applying theoretical and experimental methods to unravel molecularlevel thermodynamics and kinetics of hydrogen and oxygen electrocatalysis and, particularly, the proton-coupled electron transfer (PCET) process that takes place in a proton-deficient alkaline media. Extensive electrochemical and spectroscopic studies, on single-crystal Pt and metal oxides, have contributed to the development of activity descriptors, as well as the identification of the nature of active sites, and the rate-determining steps of the HOR and ORR. Among these, the structure and reactivity of interfacial water serve as key potential and pH-dependent kinetic factors that are helping elucidate the origins of the HOR and ORR activity differences in acids and bases. Additionally, deliberately modulating and controlling catalyst−support interactions have provided valuable insights for enhancing catalyst accessibility and durability during operation. The design and synthesis of highly conductive and durable alkaline membranes/ionomers have enabled AEMFCs to reach initial performance metrics equal to or higher than those of PEMFCs. We emphasize the importance of using membrane electrode assemblies (MEAs) to integrate the often separately pursued/optimized electrocatalyst/support and membranes/ionomer components. Operando/in situ methods, at multiscales, and ab initio simulations provide a mechanistic understanding of electron, ion, and mass transport at catalyst/ionomer/membrane interfaces and the necessary guidance to achieve fuel cell operation in air over thousands of hours. We hope that this Review will serve as a roadmap for advancing the scientific understanding of the fundamental factors governing electrochemical energy conversion in alkaline media with the ultimate goal of achieving ultralow Pt or precious-metal-free highperformance and durable alkaline fuel cells and related technologies.

Electrocatalysis has been the cornerstone for enhancing energy efficiency, minimizing environmental impacts and carbon emissions, and enabling a more sustainable way of meeting global energy needs. Elucidating the structure and reaction mechanisms of electrocatalysts at electrode–electrolyte interfaces is fundamental for advancing renewable energy technologies, including fuel cells, water electrolyzers, CO2 reduction, and batteries, among others. One of the fundamental challenges in electrocatalysis is understanding how to activate and sustain electrocatalytic activity, under operating conditions, for extended time periods and with optimal activity and selectivity. Although traditional ex situ methods have provided a baseline understanding of heterogeneous (electro)­catalysts, they cannot provide real-time interfacial structural and compositional changes under reaction conditions, which calls for the use of in situ/operando methods. Herein, we provide a selective review of in situ and operando characterizations, in particular, the use of operando synchrotron-based X-ray techniques and in situ atomic-scale scanning transmission electron microscopy (STEM) in liquid/gas phases to advance our understanding of electrode–electrolyte interfaces at macro- and microscopic levels, which dictate the charge transfer kinetics and overall reaction mechanisms. The use of scanning electrochemical microscopy (SECM) enables direct probing of the local activity of electrocatalysts at the nanometer scale. In addition, differential electrochemical mass spectrometry (DEMS) and the electrochemical quartz crystal balance (EQCM) enable the simultaneous identification of multiple reaction intermediates and products for mechanistic studies of electrocatalyst selectivity and durability. We anticipate that continuous advances of in situ/operando techniques and probes will continue to make significant contributions to establishing structure/composition-reactivity correlations of electrocatalysts at unprecedented atomic-scale and molecular levels under realistic, real-time reaction conditions.

This article introduces a new optimization problem that involves searching for the spanning tree of minimum cost under a quadratic cost structure. This quadratic minimum spanning tree problem is proven to be NP‐hard. A technique for generating lower bounds for this problem is discussed and incorporated into branch‐and‐bound schemes for obtaining exact solutions. Two heuristic algorithms are also developed. Computational experience with both exact and heuristic algorithms is reported.

Data mining becomes a cutting-edge information technology tool in today's competitive business world. It helps the company discover previously unknown, valid, and actionable information from various and large databases for crucial business decisions. This paper provides a promising approach of data mining to classify the credit cardholders' behavior through multiple criteria linear programming. After reviewing the history of linear discriminant analyses, we will describe first a model for classifying two-group (e.g. bad or good) credit cardholder behaviors, and then a three-group (e.g. bad, normal, or good) credit model. Besides the discussion of the modeling structure, we will utilize the well-known commercial software package SAS to implement this technology by using a real-life credit card data warehouse. A number of potential business and financial applications will be finally summarized.