Unraveling the mechanisms behind sodium persulphate-induced changes in petroleum-contaminated aquifers' biogeochemical parameters and microbial communities.

Sodium persulphate (PS) is a highly effective oxidising agent widely used in groundwater remediation and wastewater treatment. Although numerous studies have examined the impact of PS with respect to the removal efficiency of organic pollutants, the residual effects of PS exposure on the biogeochemical parameters and microbial ecosystems of contaminated aquifers are not well understood. This study investigates the effects of exposure to different concentrations of PS on the biogeochemical parameters of petroleum-contaminated aquifers using microcosm batch experiments. The results demonstrate that PS exposure increases the oxidation-reduction potential (ORP) and electrical conductivity (EC), while decreasing total organic carbon (TOC), dehydrogenase (DE), and polyphenol oxidase (PO) in the aquifer. Three-dimensional excitation-emission matrix (3D-EEM) analysis indicates PS is effective at reducing fulvic acid-like and humic acid-like substances and promoting microbial metabolic activity. In addition, PS exposure reduces the abundance of bacterial community species and the diversity index of evolutionary distance, with a more pronounced effect at high PS concentrations (31.25 mmol/L). Long-term (90 d) PS exposure results in an increase in the abundance of microorganisms with environmental resistance, organic matter degradation, and the ability to promote functional genes related to biological processes such as basal metabolism, transmission of genetic information, and cell motility of microorganisms. Structural equation modeling (SEM) further confirms that ORP and TOC are important drivers of change in the abundance of dominant phyla and functional genes. These results suggest exposure to different concentrations of PS has both direct and indirect effects on the dominant phyla and functional genes by influencing the geochemical parameters and enzymatic activity of the aquifer. This study provides a valuable reference for the application of PS in ecological engineering.

Silicon-Based Metastructure Optical Scattering Multiply-Accumulate Computation Chip.

Optical neural networks (ONN) have become the most promising solution to replacing electronic neural networks, which have the advantages of large bandwidth, low energy consumption, strong parallel processing ability, and super high speed. Silicon-based micro-nano integrated photonic platforms have demonstrated good compatibility with complementary metal oxide semiconductor (CMOS) processing. Therefore, without completely changing the existing silicon-based fabrication technology, optoelectronic hybrid devices or all-optical devices of better performance can be achieved on such platforms. To meet the requirements of smaller size and higher integration for silicon photonic computing, the topology of a four-channel coarse wavelength division multiplexer (CWDM) and an optical scattering unit (OSU) are inversely designed and optimized by Lumerical software. Due to the random optical power splitting ratio and incoherency, the intensities of different input signals from CWDM can be weighted and summed directly by the subsequent OSU to accomplish arbitrary multiply-accumulate (MAC) operations, therefore supplying the core foundation for scattering ONN architecture.