AUTHOR=Kwon Sunki , Jung Eunji , Kong Jeongmin , Seoung Donghoon , Yang Kiho , Jung Jaewoo , Ko Youngtak , Lee Yongmoon 

TITLE=Deep-sea environmental conditions reflected in mineral phases of manganese nodules and their implications for Ni, Co, and Cu geochemistry

JOURNAL=Frontiers in Marine Science

VOLUME=Volume 12 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/marine-science/articles/10.3389/fmars.2025.1707329

DOI=10.3389/fmars.2025.1707329

ISSN=2296-7745

ABSTRACT=Manganese (Mn) nodules are deep-sea mineral resources that contain critical elements for modern industries, such as nickel (Ni), cobalt (Co), and copper (Cu). As a result, extensive global research has been conducted on the exploration and formation mechanisms of Mn nodules. However, studies investigating the correlation between manganate minerals and critical elements, as well as the deep-sea environment represented by these mineral phases, have been limited. Here, we show the significance of phase transformations in manganate minerals within Mn nodules, in relation to deep-sea formation environments and the geochemical behavior of Ni, Co, and Cu, using basic statistical analysis, machine learning, and hydrothermal experiments. The statistical analysis and machine learning models revealed that the trace amounts of Ni, Co, and Cu exhibit a positive correlation with vernadite, a dominant manganate mineral in Mn nodules. Additionally, hydrothermal experiments demonstrated that under suboxic conditions at temperatures between 120°C and 150°C, Mn nodules undergo mineral phase transformations, forming various minerals, including todorokite, Mn ferrite, fluorapatite, hematite, and goethite. During these transformations, Ni, Co, and Cu were incorporated into the mineral structures of todorokite, fluorapatite, and Mn ferrite, respectively. Therefore, the mineral phases within Mn nodules serve as key indicators not only for understanding their formation environment but also for tracking changes in the geochemical behavior of Ni, Co, and Cu. Furthermore, our methodological approach demonstrates to many researchers how correlations between materials can be validated and utilized using statistical analysis and machine learning techniques.