Machine learning predicts heavy metal adsorption on iron (oxyhydr)oxides: A combined insight into the adsorption efficiency and binding configuration.

Sci Total Environ

School of Metallurgy and Environment, Central South University, Changsha, Hunan 410083, China; State Key Laboratory of Advanced Metallurgy for Non-ferrous Metals, Changsha 410083, China; Chinese National Engineering Research Center for Control & Treatment of Heavy Metal Pollution, Changsha, Hunan 410083, China.

Published: November 2024

The adsorption of heavy metal on iron (oxyhydr)oxides is one of the most vital geochemical/chemical processes controlling the environmental fate of these contaminants in natural and engineered systems. Traditional experimental methods to investigate this process are often time-consuming and labor-intensive due to the complexity of influencing factors. Herein, a comprehensive database containing the adsorption data of 11 heavy metals on 7 iron (oxyhydr)oxides was constructed, and the machine learning models was successfully developed to predict the adsorption efficiency. The random forest (RF) models achieved high prediction performance (R > 0.9, RMSE < 0.1, and MAE < 0.07) and interpretability. Key factors influencing heavy metal adsorption efficiency were identified as mineral surface area, solution pH, metal concentration, and mineral concentration. Additionally, by integrating our previous binding configuration models, we elucidated the simultaneous effects of input features on adsorption efficiency and binding configuration through partial dependence analysis. Higher pH simultaneously enhanced adsorption efficiency and affinity for cations, whereas lower pH benefited that for oxyanions. While higher mineral surface area improved the metal adsorption efficiency, the adsorption affinity could be weakened. This work presents a data-driven approach for investigating metal adsorption behavior and elucidating the influencing mechanisms from macroscopic to microcosmic scale, thereby offering comprehensive guidance for predicting and managing the environmental behavior of heavy metals.

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.scitotenv.2024.175370DOI Listing

Publication Analysis

Top Keywords

iron oxyhydroxides
12
machine learning
8
heavy metal
8
adsorption efficiency
8
adsorption
5
learning predicts
4
predicts heavy
4
metal adsorption
4
adsorption iron
4
oxyhydroxides combined
4

Similar Publications

Electrochemical water splitting is a promising method for the generation of "green hydrogen", a renewable and sustainable energy source. However, the complex, multistep synthesis processes, often involving hazardous or expensive chemicals, limit its broader adoption. Herein, a nitrate (NO) anion-intercalated nickel-iron-cerium mixed-metal (oxy)hydroxide heterostructure electrocatalyst is fabricated on nickel foam (NiFeCeOH@NF) via a simple electrodeposition method followed by cyclic voltammetry activation to enhance its surface properties.

View Article and Find Full Text PDF

Oxygen adsorption and activation control the photochemical activity of common iron oxyhydroxide polymorphs in mediating oxytetracycline degradation under visible light.

J Colloid Interface Sci

December 2024

MOE Laboratory for Earth Surface Processes, College of Urban and Environmental Sciences, Peking University, Beijing 100871, China. Electronic address:

The natural minerals with semiconducting properties possess photochemical activity through generating reactive oxygen species (ROSs) and affect the fate of adsorbed organic pollutants. Iron oxyhydroxides occur in different polymorphic structures under various geological and climatic conditions in natural environment. However, the difference in their photoactivity has not been well understood.

View Article and Find Full Text PDF

Immobilization or mobilization of heavy metal(loid)s in lake sediment-water interface: Roles of coupled transformation between iron (oxyhydr)oxides and natural organic matter.

Sci Total Environ

January 2025

Engineering Research Center of Watershed Carbon Neutralization, Ministry of Education, Nanchang University, Nanchang 330031, China; School of Resources and Environment, Nanchang University, Nanchang 330031, China. Electronic address:

Iron (Fe) (oxyhydr)oxides and natural organic matter (NOM) are active substances ubiquitously found in sediments. Their coupled transformation plays a crucial role in the fate and release risk of heavy metal(loid)s (HMs) in lake sediments. Therefore, it is essential to systematically obtain relevant knowledge to elucidate their potential mechanism, and whether HMs provide immobilization or mobilization effect in this ternary system.

View Article and Find Full Text PDF

Construction of crystalline/amorphous NiP/FePO/graphene heterostructure by microwave irradiation for efficient oxygen evolution.

J Colloid Interface Sci

December 2024

Key Laboratory of Synthetic and Natural Functional Molecule of the Ministry of Education, College of Chemistry and Materials Science, Northwest University, Xi'an 710127, China; Shaanxi Key Laboratory for Carbon Neutral Technology, Northwest University, Xi'an 710127, China. Electronic address:

The rational design of highly efficient and cost-effective oxygen evolution reaction (OER) electrocatalysts is crucial for hydrogen production through electrocatalytic water splitting. Although the crystalline/amorphous heterostructure shows great potential in enhancing OER activity, its fabrication presents significantly greater challenges compared to that of crystalline/crystalline heterostructures. Herein, a microwave irradiation strategy is developed to construct reduced graphene oxide supported crystalline NiP/amorphous FePO heterostructure (NiP/FePO/RGO) as an efficient OER electrocatalyst.

View Article and Find Full Text PDF

Coastal sediments are a key contributor to oceanic phosphorus (P) removal, impacting P bioavailability and primary productivity. Vivianite, an Fe(II)-phosphate mineral, can be a major P sink in nonsulfidic, reducing coastal sediments. Despite its importance, vivianite formation processes in sediments remain poorly understood.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!