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DOE OSTI2022

Mechanistic insights into sulfate and phosphate-mediated hexavalent chromium removal by tea polyphenols wrapped nano-zero-valent iron

Nano zero-valent iron via green synthesis (g-nZVI) has great potential in removing toxic hexavalent Cr(VI) from industrial wastewater. Sulfate and phosphate in wastewater can influence Cr(VI) removal by g-nZVI. Here, in this study, the Cr(VI) removal kinetics by different g-nZVI materials were investigated with the existence of sulfate and/or phosphate, and the corresponding mechanisms were first revealed using multiple characterizations, including X-ray absorption near-edge spectra (XANES) and X-ray photoelectron spectroscopy (XPS). The results showed that Cr(OH) 3 was the dominant species initially formed on the surface of g-nZVI particles before transforming to Cr 2 O 3 during the reaction of g-nZVI with Cr(VI). Sulfate in wastewater can promote the reduction from Cr(VI) to Cr(OH) 3 by g-nZVI, because sulfate triggers the release of Fe(II) and tea polyphenols (from tea extracts) from the g-nZVI surface due to the corrosion of Fe 0 core, which is in line with an obvious increase in pseudo-second-order rate constant (k 2 ) and subtle change in Cr(VI) removal capacity (q e ). However, phosphate impedes the g-nZVI corrosion and inhibits q e because of the inner-sphere complexation of phosphate onto g-nZVI decreasing the released Fe(II) for Cr 2 O 3 production. When sulfate and phosphate coexisted in contaminated water, the inhibition effect of phosphate in Cr(VI) removal by g-nZVI was stronger than the promotion of sulfate. Accordingly, q e value of g-nZVI declined from 93.4 mg g –1 to 77.5 mg g –1 , while k 2 remained constant as the molar ratio of phosphate/sulfate increased from 0.1 to 10 in water. This study provides new insights into applying g-nZVI in efficient Cr(VI) removal from contaminated water with enrichment of sulphates and phosphates.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

A multi-scale time-series dataset with benchmark for machine learning in decarbonized energy grids

The electric grid is a key enabling infrastructure for the ambitious transition towards carbon neutrality as we grapple with climate change. With deepening penetration of renewable resources, the reliable operation of the electric grid becomes increasingly challenging. In this paper, we present PSML, a first-of-its-kind open-access multi-scale time-series dataset, to aid in the development of data-driven machine learning (ML)-based approaches towards reliable operation of future electric grids. The dataset is synthesized from a joint transmission and distribution electric grid to capture the increasingly important interactions and uncertainties of the grid dynamics, containing power, voltage and current measurements over multiple spatio-temporal scales. Using PSML, we provide state-of-the-art ML benchmarks on three challenging use cases of critical importance to achieve: (i) early detection, accurate classification and localization of dynamic disturbances; (ii) robust hierarchical forecasting of load and renewable energy; and (iii) realistic synthetic generation of physical-law-constrained measurements. We envision that this dataset will provide use-inspired ML research in safety-critical systems, while simultaneously enabling ML researchers to contribute towards decarbonization of energy sectors.

54 ENVIRONMENTAL SCIENCES↗