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This paper presents an investigation of a fluidic-based impedance biosensor for rapid and accurate detection of Salmonella Typhimurium in raw chicken carcass rinsate. The biosensor is engineered with multiple distinct regions that concentrates Salmonella antigens to a detectable level, subsequently trapping the concentrated Salmonella samples on top of the detection interdigitated electrode array coated with a specific Salmonella antibody, maximizing the number of captured antigens. Detection is achieved through the antibody-antigen binding process, where binding events changes impedance values, providing a reliable method for identifying and quantifying Salmonella. The biosensor demonstrated a low limit of detection (LOD) of 1–2 cells/ml within 40–50 min. The findings demonstrated that the biosensor distinguishes low concentrations of live Salmonella cells, even in the presence of high concentrations of dead Salmonella cells, and non-specific binding pathogens viz., Listeria monocytogenes and E. coli O157:H7. 

This study presents the development of a highly sensitive microfluidic-based impedance biosensor designed for rapid detection and identification of Salmonella Infantis in raw turkey samples, with a limit of detection (LOD) as low as 1 CFU/ml in 70 minutes detection time. The biosensor is equipped with novel focusing and trapping regions, significantly enhancing its sensitivity by concentrating and trapping Salmonella cells in the detection region. Salmonella cells labeled with fluorescent dyes were used to validate the functionality of the focusing and trapping mechanism, confirming the biosensor's ability to concentrate and trap Salmonella cells. 

Abstract. A portion of Alaska's Fairbanks North Star Borough was designated as nonattainment for the 2006 24 h fine particulate matter 2.5 µm or less in diameter (PM2.5) National Ambient Air Quality Standards (NAAQS) in 2009. PM2.5 NAAQS exceedances in Fairbanks mainly occur during dark and cold winters, when temperature inversions form and trap high emissions at the surface. Sulfate (SO42-), often the second-largest contributor to PM2.5 mass during these wintertime PM episodes, is underpredicted by atmospheric chemical transport models (CTMs). Most CTMs account for primary SO42- and secondary SO42- formed via gas-phase oxidation of sulfur dioxide (SO2) and in-cloud aqueous oxidation of dissolved S(IV). Dissolution and reaction of SO2 in aqueous aerosols are generally not included in CTMs but can be represented as heterogeneous reactive uptake and may help better represent the high SO42- concentrations observed during Fairbanks winters. In addition, hydroxymethanesulfonate (HMS), a particulate sulfur species sometimes misidentified as SO42-, is known to form during Fairbanks winters. Heterogeneous formation of SO42- and HMS in aerosol liquid water (ALW) was implemented in the Community Multiscale Air Quality (CMAQ) modeling system. CMAQ simulations were performed for wintertime PM episodes in Fairbanks (2008) as well as over the Northern Hemisphere and Contiguous United States (CONUS) for 2015–2016. The added heterogeneous sulfur chemistry reduced model mean sulfate bias by ∼ 0.6 µg m−3 during a cold winter PM episode in Fairbanks, AK. Improvements in model performance are also seen in Beijing during wintertime haze events (reducing model mean sulfate bias by ∼ 2.9 µg S m−3). This additional sulfur chemistry also improves modeled summertime SO42- bias in the southeastern US, with implications for future modeling of biogenic organosulfates.