The impact of plant pathogens on global crop yields is a major societal concern. The current agricultural diagnostic paradigm involves either visual inspection (inaccurate) or laboratory molecular tests (burdensome). While field-ready diagnostic methods have advanced in recent years, issues remain with detection of presymptomatic infections, multiplexed analysis, and requirement for in-field sample processing. To overcome these issues, we developed surface-enhanced Raman scattering (SERS)-sensing hydrogels that detect pathogens through simple contact with a leaf. In this work, we developed a novel reagentless SERS sensor for the detection of tobacco mosaic virus (TMV) and embedded it in an optimized hydrogel material to produce sensing hydrogels. To test the diagnostic application of our sensing hydrogels, we demonstrate their use to detect TMV infection in tobacco plants. This technology has the potential to shift the current agricultural diagnostic paradigm by offering a field-deployable tool for presymptomatic and multiplexed molecular identification of pathogens.
Hydrogels , Tobacco Mosaic Virus , Plants , Nicotiana , Plant Leaves
Nanopore sensing has been successfully used to characterize biological molecules with single-molecule resolution based on the resistive pulse sensing approach. However, its use in nanoparticle characterization has been constrained by the need to tailor the nanopore aperture size to the size of the analyte, precluding the analysis of heterogeneous samples. Additionally, nanopore sensors often require the use of high salt concentrations to improve the signal-to-noise ratio, which further limits their ability to study a wide range of nanoparticles that are unstable at high ionic strength. Here, a new paradigm in nanopore research that takes advantage of a polymer electrolyte system to comprise a conductive pulse sensing approach is presented. A finite element model is developed to explain the conductive pulse signals observed and compare these results with experiments. This system enables the analytical characterization of heterogeneous nanoparticle mixtures at low ionic strength . Furthermore, the wide applicability of the method is demonstrated by characterizing metallic nanospheres of varied sizes, plasmonic nanostars with various degrees of branching, and protein-based spherical nucleic acids with different oligonucleotide loadings. This system will complement the toolbox of nanomaterials characterization techniques to enable real-time optimization workflow for engineering a wide range of nanomaterials.
Nanoparticles , Nanopores , Nucleic Acids , Proteins , Nanotechnology
Gold nanostars (NS) are emerging as a versatile tool in surface-enhanced Raman scattering (SERS) applications because of their wide localized surface plasmon resonance (LSPR) tunability, simple synthesis procedure, and high SERS enhancement. These particles are commonly used in solutions with a stabilizing coating shell (e.g., thiolated molecules or silver shell). However, coatings cannot be used for the fabrication of SERS substrates as the NS have to interact with the substrate planar surface. Without coating, NS have been observed to change over time, leading to a hypochromic shift of the LSPR. To understand this shift, we synthesized surfactant-free gold NS with different spike morphologies and investigated their reshaping morphology and kinetics. Using TEM, the NS sharp spike features were observed to reshape over time. The kinetics of this process were analyzed and determined by monitoring the LSPR, which was observed to follow an exponential decay over time. We used an empirical fit for the LSPR-shift data as a function of time, which permits to predict the LSPR at a specific time based only on the initial LSPR (independently of the initial spike morphology). We show the effect of the LSPR on the SERS signal for the NS and how the SERS signal correlated to our prediction. Finally, we evaluated our approach by fabricating SERS substrates with immobilized NS and collecting the reflectance spectra. We were able to predict the substrate LSPR and aim for an optimal LSPR with an average 3% deviation. These new insights on NS reshaping can permit the fabrication of NS-based substrates with desirable optical/plasmonic properties.
