| 2024-01 | Modeling study of the effects of intrinsic membrane parameters on dilutive external concentration polarization occurring during forward and pressure-retarded osmosis | Chae, Sung Ho; Rho, Hojung; Moon, Seokyoon |
| 2024-05 | Adsorption of uranyl ion on hexagonal boron nitride for remediation of real U-contaminated soil and its interpretation using random forest | Jun, Byung-Moon; Chae, Sung Ho; Kim, Deokhwan; Kim, Tack-Jin; Jung, Jun-Young; Nam, Seong-Nam; Yoon, Yeomin; Park, Chanhyuk; Rho, Hojung |
| 2024-08 | Performance investigation of osmotically assisted reverse osmosis using explainable machine learning models: A comparative study | Chae, Sung Ho; Moon, Seokyoon; Hong, Seok Won; Lee, Chulmin; Son, Moon |
| 2024-09 | Metadata and feature importance analyses of membrane capacitive deionization models: Is a water treatment artificial intelligence panacea possible? | Chae, Sung Ho; Hong, Seok Won; Son, Moon |
| 2024-07 | Determining water and solute permeability of reverse osmosis membrane using a data-driven machine learning pipeline | Chae, Sung Ho; Hong, Seok Won; Son, Moon; Cho, Kyung Hwa |
| 2024-09 | Multimodal deep learning models incorporating the adsorption characteristics of the adsorbent for estimating the permeate flux in dynamic membranes | Jeong, Heewon; Yun, Byeongchan; Na, Seongyeon; Son, Moon; Chae, Sung Ho; Kim, Chang - Min; Cho, Kyung Hwa |
| 2024-09 | Autonomous real-time control for membrane capacitive deionization | Shim, Jaegyu; Lee, Suin; Yun, Nakyeong; Son, Moon; Chae, Sung Ho; Cho, Kyung Hwa |
| 2024-10 | Nitrosamine formation driven by electrochemical chlorination of urine-containing source waters: Effects of operational conditions | Seid, Mingizem Gashaw; Chae, Sung Ho; Lee, Changha; Cho, Kangwoo; Hong, Seok Won |
