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Exploring critical pathways using robust strategies: Nanodiamond electrocatalysts for promoting boron removal via electrosorption. | LitMetric

Exploring critical pathways using robust strategies: Nanodiamond electrocatalysts for promoting boron removal via electrosorption.

Water Res

Department of Global Smart City, Sungkyunkwan University (SKKU), 2066, Seobu-ro, Jangan-gu, Suwon, Gyeonggi-do, 16419, Republic of Korea. Electronic address:

Published: December 2024

AI Article Synopsis

  • The study introduces a new electrosorption technology using nanodiamonds to effectively remove boron from wastewater, achieving an impressive boron adsorption capacity of 10.5 μmol/g.
  • It highlights the importance of different gas purging methods on the electrosorption process and characterizes the deterioration of electrodes through advanced spectroscopy techniques.
  • A machine learning model was developed to predict effluent properties and optimize the system, demonstrating the potential of ML in enhancing water treatment processes.

Article Abstract

This study presents the first instance of a crucial route for the efficient removal of boron from effluents using a strategically applied electrosorption technology using nanodiamonds annealed under argon (denoted as A-NDs). We demonstrate a significant enhancement in adsorption capacity for boron removal facilitated by a flow-through electrosorption cell, and outline the results of surface characterization and electrochemical activity tests of the fabricated nanodiamond (ND) anodes (e.g., Pristine ND and A-NDs annealed at 800 and 1200 ℃). To identify the role of DO in the electrosorption system, we compared the results obtained in the natural state (without gas purging) with those obtained with ambient air and N gas purging. In particular, the degree of electrode deterioration (change in the cathode carbon compositional ratio) during the charging process was characterized using X-ray photoelectron spectroscopy. Overall, our system exhibits a favorable boron removal capability (sorption capacity reached 10.5 μmol/g) and energy consumption of <3.4 kWh g-B. Finally, we developed a prediction model for effluent properties using time-series machine learning algorithms based on various electrosorption variables (e.g., DO, pH dynamics, charging/discharging modes and times, and voltage), Through post-process of constructed ML model, voltage showed significant predictive importance. Additionally, the necessity of sequential modeling was emphasized by SHAP analysis. The application of ML algorithms provided a novel approach for the system optimization of electrified water treatment.

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Source
http://dx.doi.org/10.1016/j.watres.2024.123080DOI Listing

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