Pubmed

Nat Protoc. 2026 Aug 24. doi: 10.1038/s41596-026-01425-y. Online ahead of print.

ABSTRACT

Modern water treatment and resource recovery demand materials that combine high performance with real-world durability. Traditional remediation approaches (e.g., precipitation and coagulation) have drawbacks (e.g., poor selectivity) that can be overcome using adsorption-based strategies. Metal-organic frameworks (MOFs) offer high tunability, high uptake capacities and the potential for regeneration. Translating MOF adsorbents into scalable, reliable technologies requires consistent method reporting and improved mechanistic insight, toward improving long-term stability in realistic water matrices. In this protocol, we describe how to deploy and assess the performance of MOF-based adsorbents for simultaneous heavy-metal sequestration (e.g., Pb(II), Cd(II), Ni(II) and Mn(II)) and rare-earth element recovery (e.g., Nd(III), Y(III) and Dy(III)) from complex water matrices. The workflow is broadly applicable across MOF chemistries and is illustrated using Cu(II)-based frameworks as representative model systems, synthesized at gram scale using commercially available precursors. We stabilize these frameworks through controlled defect engineering (e.g., partial metal substitution) to mitigate hydrolytic degradation and prolong operation time. We further tune morphology (e.g., nanosheets) to enhance surface accessibility and enable recyclability. For industrial applicability, we shape the MOFs into macrobeads via a green process. The procedure comprises: (i) MOF synthesis; (ii) comprehensive pre-adsorption characterization to assess crystallinity, porosity, morphology and composition using powder X-ray diffraction, nitrogen adsorption-desorption, scanning electron microscopy and inductively coupled plasma optical emission spectrometry; (iii) mechanistic adsorption assessment with kinetic, isotherm, thermodynamic, pH and selectivity analyses; (iv) regeneration and recovery workflows; and (v) deployment considerations in complex aqueous matrices, including industrial effluents, saline waters and e-waste leachates. The protocol provides a reproducible framework for implementing MOF-based adsorption technologies in water remediation and circular resource applications.

PMID:42637844 | DOI:10.1038/s41596-026-01425-y

Nanoscale. 2026 Aug 6;18(30):16310-16332. doi: 10.1039/d5nr04817f.

ABSTRACT

A traditional MOF design often maximizes generic metrics such as BET surface area and crystallinity, assuming they universally predict performance. In this study, we present a machine-learning-guided optimization framework for MIL-100(Fe) from experimentally synthesized samples. All materials were synthesized via an acid-free, water-based hydrothermal route. We trained small-data ML models to link synthesis parameters, including temperature, time, metal-to-ligand molar ratio, and ion concentration, to key properties comprising surface area, total pore volume, average crystallite size, crystallinity, yield, and methylene blue (MB) removal. SHAP analysis showed that time and the metal-to-ligand molar ratio dominated dye removal, whereas surface area was more sensitive to temperature and time. The most accurate model, Gaussian process regression, was coupled with a genetic algorithm (GA) to optimize synthesis for property-specific targets. Through optimization, the BET-optimized sample increased the surface area from the highest baseline value in the initial experimental dataset, 1748 to 1841.9 m2 g-1, corresponding to a 5.37% relative increase. The MB-optimized sample increased MB removal from the highest baseline value in the initial experimental dataset, 88.6% to 98.3%, corresponding to a 9.7 percentage-point improvement and a 10.9% relative increase. The MB removal optimized sample with a surface area of 1274.3 m2 g-1 and 17.4% crystallinity delivered the highest MB uptake of 98.3%, corresponding to the highest adsorption capacity under the benchmark test conditions. While the optimized sample for surface area reached 1841.9 m2 g-1 (about 44% higher) with 34.9% crystallinity but achieved only 85.1% removal, about 13% lower than that of the optimized MB sample, indicating that even substantial increases in surface area do not govern adsorption performance. A qualitative t-SNE embedding of the descriptor space shows that the optimized samples occupy distinct neighborhoods, elucidating that MOF synthesis should be tailored to the target application rather than a single metric such as surface area.

PMID:42421429 | DOI:10.1039/d5nr04817f