过氧化氢
环境友好型
电化学
析氧
工艺工程
环境科学
化学
生化工程
纳米技术
材料科学
有机化学
电极
工程类
生态学
生物
物理化学
作者
Nishu Dhanda,Yogesh Kumar Panday,Sudesh Kumar
标识
DOI:10.1016/j.electacta.2024.143872
摘要
Hydrogen peroxide (H2O2) is an innovative and environmentally friendly oxidant that finds wide-ranging applications across multiple industries. In the past, H2O2 production predominantly relied on the anthraquinone method, which had drawbacks such as the generation of organic waste and the requirement for energy-intensive reactions. A cheap, efficient, and sustainable way of producing H2O2 may be achieved through the redox reaction between oxygen and water. On both small and large scales, the electrosynthesis of H2O2 is practical and affordable. In recent years, it has been thought that the energy-intensive anthraquinone process may be replaced by the electrochemical synthesis of H2O2 via the two-electron oxygen reduction reaction (ORR) route. To eliminate the organic pollutants found in drinking water and industrial effluent, highly effective hydrogen peroxide (H2O2) must be produced electrochemically using gas diffusion electrodes (GDEs). Compared to other carbonaceous cathodes, the GDEs as cathodic electrocatalysts demonstrate greater cost-effectiveness, lower energy consumption, and higher oxygen utilization efficiency for the formation of H2O2. A promising alternative for enabling the growth of sustainable economics in the W&W sector is microbial electrochemical systems (MESs) that create H2O2. To enhance the efficiency and predictability of H2O2 production in MESs, a machine-learning approach was adopted, incorporating a meta-learning methodology to forecast the generation rate of H2O2 in MES based on the seven input variables, comprising several design and operational parameters.
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