Lutein Loaded in ß-Cyclodextrin Metal-Organic Frameworks for Stability and Solubility Enhancements.

Lutein (Lut) is a recognized nutritional supplement known for its antioxidative and anti-inflammatory properties, crucial in mitigating ocular disease. However, enhancements to Lut stability and solubility remain challenges to be addressed in the healthcare industry. Herein, we fabricated and evaluated a food-grade highly porous ß-cyclodextrin metal-organic framework (ß-CD-MOF) for its ability to encapsulate Lut. Lut stability considerably improved when loaded into ß-CD-MOF to form a Lut@ß-CD-MOF complex, which exhibited better stability than Lut loaded into the γ-cyclodextrin metal-organic framework (Lut@γ-CD-MOF), Lut@ß-CD, and commercial product (Blackmores™) at 40°C, 60°C, and 70°C, respectively. The solubility of Lut@ß-CD-MOF in water increased by 26.8-fold compared to raw Lut at 37°C. Lut@ß-CD-MOF exhibited greater hydrophilicity, as determined by measuring the water contact angle. Molecular docking and other characterizations of Fourier transform infrared spectroscopy and powder X-ray diffraction confirmed that Lut was successfully encapsulated in the chamber formed by the three cyclodextrins in ß-CD-MOF. Thermogravimetric analysis and Raman spectroscopy demonstrated that Lut distributed in the ß-CD-MOF cavity deeply improved Lut stability and solubility. In conclusion, our findings underscored the function of ß-CD-MOF in enhancing Lut stability and solubility for formulation applications.

Purification of gamma-cyclodextrin via selective coordination with potassium ions to form metal-organic frameworks.

Efficient purification of gamma-cyclodextrin (γ-CD) is always challenging due to its structural similarity to other CDs and low crystallinity in water. In addressing this issue, an approach was proposed based on the formation mechanism of cyclodextrin metal-organic frameworks (CD-MOFs). This method involved the selective coordination of CDs mixture with potassium ions in water, facilitated by ethanol-induced crystallization, leading to the purification of γ-CD. The results showed that potassium ions enhanced γ-CD crystallization, and ethanol was crucial to selectively coordinating potassium ions with γ-CD. The characterizations revealed that the resulting CD-MOFs exhibited a small particle size, high surface area, and high thermal stability, and was identical to γ-CD-MOF, further indicating the final γ-CD with high purity. The separation factors of γ-CD/α-CD and γ-CD/ß-CD were 309 and 260, respectively. Moreover, this method was validated through its application to the industrial enzymatic CDs mixture. The purification of γ-CD could achieve 99.99 ± 0.01 % after four crystallization cycles. Therefore, selectively coordinating with potassium ions to form MOFs provided a valuable reference for the purification of γ-CD and even the direct synthesis of γ-CD-MOF from CDs mixture. This advancement will also benefit the future production and application of γ-CD.

Invariant Molecular Representations for Heterogeneous Catalysis.

Catalyst screening is a critical step in the discovery and development of heterogeneous catalysts, which are vital for a wide range of chemical processes. In recent years, computational catalyst screening, primarily through density functional theory (DFT), has gained significant attention as a method for identifying promising catalysts. However, the computation of adsorption energies for all likely chemical intermediates present in complex surface chemistries is computationally intensive and costly due to the expensive nature of these calculations and the intrinsic idiosyncrasies of the methods or data sets used. This study introduces a novel machine learning (ML) method to learn adsorption energies from multiple DFT functionals by using invariant molecular representations (IMRs). To do this, we first extract molecular fingerprints for the reaction intermediates and later use a Siamese-neural-network-based training strategy to learn invariant molecular representations or the IMR across all available functionals. Our Siamese network-based representations demonstrate superior performance in predicting adsorption energies compared with other molecular representations. Notably, when considering mean absolute values of adsorption energies as 0.43 eV (PBE-D3), 0.46 eV (BEEF-vdW), 0.81 eV (RPBE), and 0.37 eV (scan+rVV10), our IMR method has achieved the lowest mean absolute errors (MAEs) of 0.18 0.10, 0.16, and 0.18 eV, respectively. These results emphasize the superior predictive capacity of our Siamese network-based representations. The empirical findings in this study illuminate the efficacy, robustness, and dependability of our proposed ML paradigm in predicting adsorption energies, specifically for propane dehydrogenation on a platinum catalyst surface.

Benchmarking the Accuracy of Density Functional Theory against the Random Phase Approximation for the Ethane Dehydrogenation Network on Pt(111).

The Random Phase Approximation (RPA) is conceptually the most accurate Density Functional Approximation method, able to simultaneously predict both adsorbate and surface energies accurately; however, this work questions its superiority over DFT for catalytic application on hydrocarbon systems. This work uses microkinetic modeling to benchmark the accuracy of DFT functionals against that of RPA for the ethane dehydrogenation reaction on Pt(111). Eight different functionals, with and without dispersion corrections, across the GGA, meta-GGA and hybrid classes are evaluated: PBE, PBE-D3, RPBE, RPBE-D3, BEEF-vdW, SCAN, SCAN-rVV10, and HSE06. We show that PBE and RPBE, without dispersion correction, closely model RPA energies for adsorption, transition states, reaction, and activation energies. Next, RPA fails to describe the gas phase energy as unsaturation and chain-length increases in the hydrocarbon. Finally, we show that RPBE has the best accuracy-to-cost ratio, and RPA is likely not superior to RPBE or BEEF-vdW, which also gives a measure of uncertainty.

Co-delivery of superfine nano-silver and solubilized sulfadiazine for enhanced antibacterial functions.

For the effective treatment of bacterial infection, it is essential to find a new strategy to deliver antibacterial agents. In the present study, the co-delivery of superfine nano-silver with solubilized sulfadiazine (SD) using cyclodextrin metal-organic frameworks (CD-MOF) as a carrier exhibited superior antibacterial efficacy to insoluble silver sulfadiazine. The abundant hydroxyl moieties in CD-MOF were utilized to reduce Ag precursor into silver nanoparticles (Ag NPs) of 4-5 nm and immobilized within the nano-sized cavities. Microporous CD-MOF facilitated the inclusion of SD molecules in the hydrophobic cavities of γ-cyclodextrin (γ-CD) molecular pairs. The hydrophilic CD-MOF can easily dissolve within exudates at the wound region to release the drug. This approach improved the aqueous solubility of SD by 50 folds which subsequently enhanced SD release and their antibacterial activity. The CD framework also prevented the aggregation of nano-silver particles, stabilizing the particle size and enhancing the curative effects. Hence, the incorporation of superfine nano-silver with solubilized SD using CD-MOF could be an alternate strategy to co-deliver silver and SD with higher efficacy.