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Mechanochemistry, a sustainable synthetic method that minimizes solvent use, has shown great promise in producing metal–organic framework (MOF)-based biocomposites through ball milling. While ball milling offers fast reaction times, biocompatible conditions, and access to previously unattainable biocomposites, it is a batch-type process typically limited to gram-scale production, which is insufficient to meet commercial capacity. We introduce a scalable approach for the continuous solid-state production of MOF-based biocomposites. Our study commences with model batch reactions to examine the encapsulation of various biomolecules into Zeolitic Imidazolate Framework-8 (ZIF-8) via hand mixing, establishing a foundation for upscaling. Subsequently, the process is scaled up using reactive extrusion, enabling continuous and reproducible kilogram-scale production of bovine serum albumin (BSA)@ZIF-8 with tunable protein loading. Furthermore, we achieve the one-step formation of shaped ZIF-8 extrudates encapsulating clinical therapeutic hyaluronic acid (HA). Upon release of HA from the composite, the molecular weight of HA is preserved, highlighting the industrial potential of reactive extrusion for the cost-effective and reliable manufacturing of biocomposites for drug-delivery applications.
Metal-organic framework-based biocomposites (MOF-biocomposites) are promising materials for biosensing, biocatalysis, and delivery of biopharmaceuticals. One of the most studied MOFs for bioapplications is ZIF-8 (zeolitic imidazolate framework 8) due to its high surface area, high thermal and chemical stability, and low cytotoxicity. The conventional synthesis of ZIF-8-biocomposites called biomimetic mineralization includes mixing selected biomolecules 2-methylimidazole, and soluble Zn2+ source in water. Despite the high efficiency of the method, it does not allow for large-scale production and is restricted to hydrophilic biomolecules. Aimed at developing a scalable and versatile approach, we adapted our recently-reported ZIF-8 reactive extrusion for biocomposite production.
We selected bovine serum albumin (BSA) as an inexpensive model biomacromolecule for the preparation of biocomposites. The synthesis of BSA@ZIF-8 was performed using a twin-screw extruder ZE 12 HMI (Three-Tec Gmbh) at a mild temperature of 40 °C. Automatic volumetric feeder ZD 12B (Three-Tec GmbH) was used to supply the reagent mixture consisting of 2-methylimidazole, zinc source, and BSA. To initiate the reaction, a catalytic amount of EtOH was added using a peristaltic pump BT-L (Lead Fluid, China). Powder X-Ray diffraction (PXRD), thermogravimetric analysis (TGA), FTIR, and N2 adsorption were used to characterize the extrudates.
Highly crystalline and pure BSA@ZIF-8 with different BSA loadings was isolated after washing the extrudate with EtOH and sodium dodecyl sulfate. The EtOH feeding rate was optimized by following the protein encapsulation efficiency at a BSA mass fraction of 10%. A continuous extruder operation under optimized conditions showed good reproducibility and capability of producing biocomposites on the kilograms scale. These results provide highly valuable information for cheap and large-scale production of ZIF-8-based biocomposites. Due to the lack of restrictions on molecule size and solubility, our proof-of-concept study may significantly expand the selection of biomolecules for immobilization in ZIF-8, making the method applicable to various functional applications
Transition metal phosphates (TMPs) are extensively explored for electrochemical and catalytical applications due to their structural versatility and chemical stability. Within this material class, novel high-entropy metal phosphates (HEMPs)─containing multiple transition metals combined into a single-phase structure─are particularly promising, as their compositional complexity can significantly enhance functional properties. However, the discovery of suitable HEMP compositions is hindered by the vast compositional design space and complex or very specific synthesis conditions. Here, we present a data-driven strategy combining automated wet-chemical synthesis with a Sequential Learning App for Materials Discovery (SLAMD) framework (Random Forest regression model) to efficiently explore and optimize HEMP compositions. Using a limited set of initial experiments, we identified multimetal compositions in a single-phase crystalline solid. The model successfully predicted a novel Co0.3Ni0.3Fe0.2Cd0.1Mn0.1 phosphate octahydrate phase, validated experimentally, demonstrating the effectiveness of the machine learning approach. This work highlights the potential of integrating automated synthesis platforms with data-driven algorithms to accelerate the discovery of high-entropy materials, offering an efficient design pathway to advanced functional materials.
To accelerate high-entropy metal phosphate (HEMP) discovery, we employed a Random Forest regression model within a SLAMD framework. Trained on limited initial data, the model efficiently explored the vast compositional space to predict a novel five-metal phosphate, which was then successfully synthesized and validated experimentally.
