Direct Detection of Tobacco Mosaic Virus in Infected Plants with SERS-Sensing Hydrogels.

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.

Polydimethylsiloxane Polymerized Emulsions for Acoustic Materials Prepared Using Reactive Triblock Copolymer Surfactants.

Porous polymers have interesting acoustic properties including wave dampening and acoustic impedance matching and may be used in numerous acoustic applications, e.g., waveguiding or acoustic cloaking. These materials can be prepared by the inclusion of gas-filled voids, or pores, within an elastic polymer network; therefore, porous polymers that have controlled porosity values and a wide range of possible mechanical properties are needed, as these are key factors that impact the sound-dampening properties. Here, the synthesis of acoustic materials with varying porosities and mechanical properties that could be controlled independent of the pore morphology using emulsion-templated polymerizations is described. Polydimethylsiloxane-based ABA triblock copolymer surfactants were prepared using reversible addition-fragmentation chain transfer polymerizations to control the emulsion template and act as an additional cross-linker in the polymerization. Acoustic materials prepared with reactive surfactants possessed a storage modulus of ∼300 kPa at a total porosity of 71% compared to materials prepared using analogous nonreactive surfactants that possessed storage modulus values of ∼150 kPa at similar porosities. These materials display very low longitudinal sound speeds of ∼35 m/s at ultrasonic frequencies, making them excellent candidates in the preparation of acoustic devices such as metasurfaces or lenses.

Design, Rationalization, and Automation of a Catalytic Sensing Mechanism for Homogeneous SERS Biosensors.

The current pandemic has shown that we need sensitive and deployable diagnostic technologies. Surface-enhanced Raman scattering (SERS) sensors can be an ideal solution for developing such advanced point-of-need (PON) diagnostic tests. Homogeneous (reagentless) SERS sensors work by directly responding to the target without any processing step, making them capable for simple one-pot assays, but their limitation is the achievable sensitivity, insufficient compared to what is needed for sensing of viral biomarkers. Noncovalent DNA catalysis mechanisms have been recently exploited for catalytic amplification in SERS assays. These advances used catalytic hairpin assembly (CHA) and other DNA self-assembly processes to develop sensing mechanisms with improved sensitivities. However, these mechanisms have not been used in OFF-to-ON homogeneous sensors, and they often target the same biomarker, likely due to the complexity of the mechanism design. There is still a strong need for a catalytic SERS sensor with a homogeneous mechanism and a rationalization of the catalytic sensing mechanism to translate this sensing strategy to different targets and applications. We developed and investigated a homogeneous SERS sensing mechanism that uses catalytic amplification based on DNA self-assembly. We systematically investigated the role of three domains in the fuel strand (internal loop, stem, and toehold), which drives the catalytic mechanism. The thermodynamic parameters determined in our studies were used to build an algorithm for automated design of catalytic sensors that we validated on target sequences associated with malaria and SARS-CoV-2 strains. With our mechanism, we were able to achieve an amplification level of 20-fold for conventional DNA and of 36-fold using locked nucleic acids (LNAs), with corresponding improvements observed in the sensor limit of detection (LOD). We also show a single-base sequence specificity for a sensor targeting a sequence associated with the omicron variant, tested against a delta variant target. This work on catalytic amplification of homogeneous SERS sensors has the potential to enable the use of this sensing modality in new applications, such as infectious disease surveillance, by improving the LOD while conserving the sensor's homogeneous character.

Optimized micro-sampling and computational analysis for SERS identification of red organic dyes on prints.

The goals of this study were to develop a robust methodology and data analysis procedure to identify red dyes in artwork where dye collection is inaccessible by traditional methods. With Surface-Enhanced Raman Spectroscopy (SERS) it is possible to obtain identifying molecular information from dilute and degraded dyes. A minimally invasive, soft-mechanical sampling method to gently contact printed paper is described; using a customized polymeric hydrogel surface with an exposure area of about 1 mm2, micrometer-diameter colorant particles were collected. To validate that the sample collection methodology is minimally invasive, test papers were photographed before and after sampling under UV and white light; and DART-MS analysis of the sampled area was conducted. A reference library of SERS spectra from binder (hide glue), dyes (safflower, sappan, and madder), and binder-dye mixtures was built and used by a spectral-matching genetic algorithm (GA). Fifty individual GA runs returned results that precisely matched at least one dye component in 48-50 of the 50 runs, and matched both dyes in a mixture between 29 and 50 of the 50 runs. Finally, in an artwork application, the methodologies were demonstrated on micro-samples from three areas of an 18th century Japanese woodblock print by Suzuki Harunobu in the collection of the Portland Art Museum, on which, madder dyes were positively identified. Conclusions and extensions from this work are expected to contribute to the body of knowledge about 18th c. Japanese woodblock prints.