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The following laboratory procedure provides students with hands-on experience in nanomaterial chemistry and characterization. This three-day protocol is easy to follow for undergraduates with basic chemistry or materials science backgrounds and is suitable for inclusion in upper-division courses in inorganic chemistry or materials science. Students use air-free chemistry procedures to synthesize and separate iron oxide magnetic nanoparticles and subsequently modify the nanoparticle surface by using a chemical stripping agent. The morphology and chemical composition of the nanoparticles are characterized using electron microscopy and dynamic light scattering measurements. Additionally, magnetic characterization of the particles is performed using an inexpensive open-source (3D-printed) magnetophotometer. Possible modifications to the synthesis procedure, including the incorporation of dopants to modify the magnetic response and alternative characterization techniques, are discussed. The three-day synthesis, purification, and characterization laboratory will prepare students with crucial skills for advanced technology industries such as semiconductor manufacturing, nanomedicine, and green chemistry. 

Ascertaining the collective viability of cells in different cell culture conditions has typically relied on averaging colorimetric indicators and is often reported out in simple binary readouts. Recent research has combined viability assessment techniques with image-based deep-learning models to automate the characterization of cellular properties. However, further development of viability measurements to assess the continuity of possible cellular states and responses to perturbation across cell culture conditions is needed. In this work, we demonstrate an image processing algorithm for quantifying features associated with cellular viability in 3D cultures without the need for assay-based indicators. We show that our algorithm performs similarly to a pair of human experts in whole-well images over a range of days and culture matrix compositions. To demonstrate potential utility, we perform a longitudinal study investigating the impact of a known therapeutic on pancreatic cancer spheroids. Using images taken with a high content imaging system, the algorithm successfully tracks viability at the individual spheroid and whole-well level. The method we propose reduces analysis time by 97% in comparison with the experts. Because the method is independent of the microscope or imaging system used, this approach lays the foundation for accelerating progress in and for improving the robustness and reproducibility of 3D culture analysis across biological and clinical research.