Over the past few decades, research studies have established that the mechanical properties of hydrogels can be largely impacted by the addition of nanoparticles. However, the exact mechanisms behind such enhancements are not yet fully understood. To further explore the role of nanoparticles on the enhanced mechanical properties of hydrogel nanocomposites, we used chemically crosslinked polyacrylamide hydrogels incorporating silica nanoparticles as the model system. Rheological measurements indicate that nanoparticle-mediated increases in hydrogel elastic modulus can exceed the maximum modulus that can be obtained through purely chemical crosslinking. Moreover, the data reveal that nanoparticle, monomer, and chemical crosslinker concentrations can all play an important role on the nanoparticle mediated-enhancements in mechanical properties. These results also demonstrate a strong role for pseudo crosslinking facilitated by polymer–particle interactions on the observed enhancements in elastic moduli. Taken together, our work delves into the role of nanoparticles on enhancing hydrogel properties, which is vital to the development of hydrogel nanocomposites with a wide range of specific mechanical properties.

Rapid and low-cost pathogen diagnostic approaches are critical for clinical decision-making procedures. Cultivating bacteria often takes days to identify pathogens and provide antimicrobial susceptibilities. The delay in diagnosis may result in compromised treatment and inappropriate antibiotic use. Over the past decades, molecular-based techniques have significantly shortened pathogen identification turnaround time with high accuracy. However, these assays often use complex fluorescent labeling and nucleic acid amplification processes, which limit their use in resource-limited settings. In this work, we demonstrate a wash-free molecular agglutination assay with a straightforward mixing and incubation step that significantly simplifies procedures of molecular testing. By targeting the 16S rRNA gene of pathogens, we perform a rapid pathogen identification within 30 min on a dark-field imaging microfluidic cytometry platform. The dark-field images with low background noise can be obtained using a narrow beam scanning technique with off-the-shelf complementary metal oxide semiconductor (CMOS) imagers such as smartphone cameras. We utilize a machine learning algorithm to deconvolute topological features of agglutinated clusters and thus quantify the abundance of bacteria. Consequently, we unambiguously distinguish Escherichia coli positive from other E. coli negative among 50 clinical urinary tract infection samples with 96% sensitivity and 100% specificity. Furthermore, we also apply this quantitative detection approach to achieve rapid antimicrobial susceptibility testing within 3 h. This work exhibits easy-to-use protocols, high sensitivity, and short turnaround time for point-of-care testing uses.

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