Exploring critical pathways using robust strategies: Nanodiamond electrocatalysts for promoting boron removal via electrosorption
- Authors
- Shin, Yong-Uk; Yu, SungIl; Jeon, Junbeom; Kim, Hanwoong; Kim, Taehun; Cheng, Li-Hua; Bae, Hyokwan; Jang, Am
- Issue Date
- 1-Apr-2025
- Publisher
- Elsevier Ltd
- Keywords
- Annealed nanodiamond; Boron; Electrified water treatment; Flow-through electrosorption; Time-series machine learning algorithms
- Citation
- Water Research, v.273
- Indexed
- SCOPUS
- Journal Title
- Water Research
- Volume
- 273
- URI
- https://scholarx.skku.edu/handle/2021.sw.skku/119695
- DOI
- 10.1016/j.watres.2024.123080
- ISSN
- 0043-1354
1879-2448
- 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 N2 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. © 2024 Elsevier Ltd
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