Sep 2026· Journal of Cleaner Production· 90 references
Abstract
The removal of emerging contaminants from aqueous systems remains a significant environmental challenge. Among the adsorbent materials explored for water treatment, cryogels based on natural polymers have emerged as promising candidates due to their interconnected macroporous structure, enhanced mass-transfer properties, and environmentally friendly composition. In this study, a 3D-printed cryogel composed of gelatin, carboxymethylcellulose, and sodium alginate, and crosslinked with Fe 3+ was developed for tetracycline (TC) adsorption in aqueous solution. The Fe-crosslinked cryogel (Cryogel-Fe) was characterized regarding its microstructure, porosity, surface area, and physical, chemical, rheological, thermal, and adsorption properties. The material exhibited a hierarchical macroporous structure with porosity of approximately 91%, as well as stability and swelling capacity in aqueous media, good thermal stability, and excellent rheological properties. The Cryogel-Fe reached a maximum experimental adsorption capacity of 336.45 mg g −1 for TC, while the Langmuir model yielded a theoretical maximum capacity (q max ) of 445.61 mg g −1 . Adsorption analysis indicated that TC removal was governed by a combination of adsorption and diffusion processes, while FTIR, Raman, and XPS results supported the involvement of hydrogen bonding, electrostatic interactions, and possible Fe-assisted interactions. Furthermore, Cryogel-Fe retained more than 80% of its adsorption efficiency after three reuse cycles and maintained high TC removal efficiency in the presence of common coexisting ions and in real water samples. These results demonstrate that the combination of 3D printing, natural polymers, and Fe 3+ crosslinking provides an effective strategy for developing sustainable adsorbents for antibiotic removal.
The results are packaged in the Greenfield Startup Model (GSM), which explains the priority of startups to release the product as quickly as possible, and the need to shorten time-to-market, by speeding up the development through low-precision engineering activities.
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Microsoft Research Blog· microsoft.comOct 12, 2026
StudentSim is a research system for evaluating and improving AI tutoring tools before they are tested with learners. It models how individuals may engage with tutors, giving feedback to improve tutor performance across subjects like chess, writing, and mathematics. The post Simulating student learning for better AI tutoring appeared first on Microsoft Research.