In Situ Study of Hydrogen Permeable Electrodes for Electrolytic Ammonia Synthesis Using Near Ambient Pressure XPS

Hydrogen permeable electrodes can be utilized for electrolytic ammonia synthesis from dinitrogen, water, and renewable electricity under ambient conditions, providing a promising route toward sustainable ammonia. The understanding of the interactions of adsorbing N and permeating H at the catalytic interface is a critical step toward the optimization of this NH3 synthesis process. In this study, we conducted a unique in situ near ambient pressure X ray photoelectron spectroscopy experiment to investigate the solid gas interface of a Ni hydrogen permeable electrode under conditions relevant for ammonia synthesis. Here, we show that the formation of a Ni oxide surface layer blocks the chemisorption of gaseous dinitrogen. However, the Ni 2p and O 1s XPS spectra reveal that electrochemically driven permeating atomic hydrogen effectively reduces the Ni surface at ambient temperature, while H2 does not. Nitrogen gas chemisorbs on the generated metallic sites, followed by hydrogenation via permeating H, as adsorbed N and NH3 are found on the Ni surface. Our findings suggest that the first hydrogenation step to NH and the NH3 desorption might be limiting under the operating conditions. The study was then extended to Fe and Ru surfaces. The formation of surface oxide and nitride species on iron blocks the H permeation and prevents the reaction to advance; while on ruthenium, the stronger Ru N bond might favor the recombination of permeating hydrogen to H2 over the hydrogenation of adsorbed nitrogen. This work provides insightful results to aid the rational design of efficient electrolytic NH3 synthesis processes based on but not limited to hydrogen permeable electrode

Forecasting Daily Variability of the S and P 100 Stock Index using Historical, Realised and Implied Volatility Measurements

The increasing availability of financial market data at intraday frequencies has not only led to the development of improved volatility measurements but has also inspired research into their potential value as an information source for volatility forecasting. In this paper we explore the forecasting value of historical volatility (extracted from daily return series), of implied volatility (extracted from option pricing data) and of realised volatility (computed as the sum of squared high frequency returns within a day). First we consider unobserved components and long memory models for realised volatility which is regarded as an accurate estimator of volatility. The predictive abilities of realised volatility models are compared with those of stochastic volatility models and generalised autoregressive conditional heteroskedasticity models for daily return series. These historical volatility models are extended to include realised and implied volatility measures as explanatory variables for volatility. The main focus is on forecasting the daily variability of the Standard and Poor's 100 stock index series for which trading data (tick by tick) of almost seven years is analysed. The forecast assessment is based on the hypothesis of whether a forecast model is outperformed by alternative models. In particular, we will use superior predictive ability tests to investigate the relative forecast performances of some models. Since volatilities are not observed, realised volatility is taken as a proxy for actual volatility and is used for computing the forecast error. A stationary bootstrap procedure is required for computing the test statistic and its $p$-value. The empirical results show convincingly that realised volatility models produce far more accurate volatility forecasts compared to models based on daily returns. Long memory models seem to provide the most accurate forecasts