MOF–Metal Ion Adsorption Capacity Dataset (Aqueous Systems; N=209, 10 studies)

This dataset contains n = 209 observations of equilibrium adsorption capacity for metal ions adsorbed by metal–organic frameworks (MOFs) in aqueous systems, obtained from 10 peer-reviewed studies. Each row corresponds to a unique experimental condition (MOF, metal ion, solution pH, initial concentration C₀, adsorbent dose). Input parameters span three descriptor groups used for machine-learning modeling and interpretability: MOF structural: BET (m² g⁻¹), PoreSize (nm), PoreVolume (cm³ g⁻¹), pHpzc Experimental: pH, C₀ (mg L⁻¹), AdsorbentDose(g L⁻¹) Ion properties: OxidationState, AtomicMass (g mol⁻¹), Electronegativity, IonizationEnergy (eV), AtomicRadius (Å) Intended use. Interpretable ML (e.g., XGBoost + SHAP) to predict metal ion adsorption capacity of MOFs and to identify governing factors across materials and operating conditions. Extrapolation beyond observed ranges is discouraged. Files in this deposit: dataset.xlsx (human-readable) and dataset.csv (UTF-8, machine-readable) README.md (context, usage) data_dictionary.md (definitions/units/ranges) references.docx and references.csv (StudyID ↔ full citation ↔ DOI mapping) code/train_ML_shap.py and requirements.txt mof-adsorption-dataset-v1.zip (all the files zipped together for one-click download)