Impact of Porous Silica Nanosphere Architectures on the Catalytic Performance of Supported Sulphonic Acid Sites for Fructose Dehydration to 5-Hydroxymethylfurfural.

5-hydroxymethylfurfural represents a key chemical in the drive towards a sustainable circular economy within the chemical industry. The final step in 5-hydroxymethylfurfural production is the acid catalysed dehydration of fructose, for which supported organoacids are excellent potential catalyst candidates. Here we report a range of solid acid catalysis based on sulphonic acid grafted onto different porous silica nanosphere architectures, as confirmed by TEM, N2 porosimetry, XPS and ATR-IR. All four catalysts display enhanced active site normalised activity and productivity, relative to alternative silica supported equivalent systems in the literature, with in-pore diffusion of both substrate and product key to both performance and humin formation pathway. An increase in-pore diffusion coefficient of 5-hydroxymethylfurfural within wormlike and stellate structures results in optimal productivity. In contrast, poor diffusion within a raspberry-like morphology decreases rates of 5-hydroxymethylfurfural production and increases its consumption within humin formation.

Serum Metabolic Fingerprints on Bowl-Shaped Submicroreactor Chip for Chemotherapy Monitoring.

Chemotherapy is a primary cancer treatment strategy, the monitoring of which is critical to enhancing the survival rate and quality of life of cancer patients. However, current chemotherapy monitoring mainly relies on imaging tools with inefficient sensitivity and radiation invasiveness. Herein, we develop the bowl-shaped submicroreactor chip of Au-loaded 3-aminophenol formaldehyde resin (denoted as APF-bowl&Au) with a specifically designed structure and Au loading content. The obtained APF-bowl&Au, used as the matrix of laser desorption/ionization mass spectrometry (LDI MS), possesses an enhanced localized electromagnetic field for strengthened small metabolite detection. The APF-bowl&Au enables the extraction of serum metabolic fingerprints (SMFs), and machine learning of the SMFs achieves chemotherapy monitoring of ovarian cancer with area-under-the-curve (AUC) of 0.81-0.98. Furthermore, a serum metabolic biomarker panel is preliminarily identified, exhibiting gradual changes as the chemotherapy cycles proceed. This work provides insights into the development of nanochips and contributes to a universal detection platform for chemotherapy monitoring.