While availability of nanoscale fabrication tools has uncovered a rich area of physical phenomena with applications including sensing, energy, and imaging - scalable nanomanufacturing techniques allowing for technological impact still remain elusive. Self-assembly of nanoarchitectured systems, with control on atomic and molecular length scales, not only hold promise for device fabrication but offer new functionality for probing and interacting with molecular systems. For example, understanding hierarchical driving forces in assembly of nanospheres from colloid enables arranging 2D ‘metamolecule’ building blocks where the geometry of resultant oligomers, gap spacing, and dielectric environment provide additional degrees of freedom for tuning electromagnetic response. I will present metasurface geometries exhibiting magnetic fields at optical frequency and billon-fold electric field enhancements in nanogaps.
The reproducibility offered by controlling nanogap spacing with chemical crosslinkers allows for acquisition of large data sets needed for machine learning analysis. Our group has recently demonstrated that plasmonic nanoantennas enhance surface enhanced Raman scattering (SERS) signals sufficiently for continuously monitoring metabolites produced by bacteria. Multivariate statistical analysis of SERS data from nanogaps incorporated in microfluidic devices shows bacterial metabolite concentration can be quantified across five orders of magnitude and detected in supernatant from Pseudomonas aeruginosa cultures as early as three hours after innoculation. Bacteria exposed to a bactericidal antibiotic were differentially less susceptible after 10 h of growth, indicating that these devices may be useful for early intervention of bacterial infections. Analysis with artificial neural networks pushes quantification down to the femtomolar regime offering the promise of quantification down to the single molecule limit. We will also show results demonstrating the ability to discriminate antibiotic resistance to rifampicin and susceptibility to carbenicillin in Psuedomonas Aeruginosa through SERS analysis of metabolites in cellular lysate. Discrimination accuracies greater than 99% are achieved using big data machine learning techniques like convolutional neural networks. Yet these techniques require large quantities of labeled data, which is extraordinarily expensive to acquire for medical diagnostics due to the need for experts to culture and analyse bacterial samples. Thus we have also introduced few shot and semi-supervised machine learning techniques in the analysis of SERS spectra to greatly reduce the amount of labeled data. We have demonstrated an increase in one shot classification of over 10% through the use of a semi-supervised variational autoencoder and a spike timing plasticity dependent model designed for few shot learning. These results demonstrate that SERS is a fast, accurate, and facile method for identification of pathogenic states by analysis of unknown metabolites. The ability of clinicians to quickly determine the susceptibility of an infection to antibiotic therapy is critical to limit the spread of antibiotic resistant bacterial strains.
Surface enhanced Raman scattering (SERS) is a vibrational spectroscopy method that enables the quantification of the concentration of small molecules. SERS sensing has been demonstrated in a wide variety of applications, from explosive and drug detection, to monitoring of bacteria growth. Underpinning SERS sensing are the sensor surfaces that are composed of vast quantities of metal nanostructures which confine light into small gaps called “hotspots”, enhancing Raman scattering. While these surfaces are essential for increasing Raman scattering intensity so that analyte signal may be observed in small concentrations, they introduce signal variations due to spatial distributions of Raman enhancement and hotspot volume. In this work, we introduce a convolutional neural network model that improves concentration regressions in SERS sensors by learning the distributions of sensor surface dependent latent variables. We demonstrate that this model significantly improves predictions compared to a traditional multilayer perceptron approach, and that the model uses analyte spectral information and is capable of reasonable interpolations.
Over twenty years have elapsed since the first report of single molecule surface enhanced Raman spectroscopy (SERS) yet quantitative sensing in the single molecule regime remains elusive. We have recently introduced a new self-assembly method for fabricating sensor surfaces capable of single-molecule SERS with uniform SERS enhancements of 109 over 100s of μm. Yet, the main challenge in quantitative single molecule SERS is the complex, system dependent, hotspot occupancy rate of analyte. In this work, we solve this problem using a new big data method for quantifying analyte concentration in the single molecule regime. Specifically, deep convolutional neural networks are trained with SERS spectra acquired across large sensor surfaces. The universal approximation property of neural networks is used to automatically fit the hotspot occupancy rate of any molecule. We demonstrate quantitative detection of rhodamine 800 as low as 1 femtomolar, where the single molecule regime begins at approximately 100 picomolar concentrations. This method is validated by comparison with traditional, non-quantitative methods of single molecule SERS detection. Further, we use SERS’s rich spectral information and label free detection to demonstrate the simultaneous quantification of multiple analyte molecules. Finally, this new quantification method is used to sense small molecules produced by bacterial biofilms. The proposed method is not system specific and is thus broadly applicable to any SERS sensor capable of large-area, uniform single molecule detection.
We demonstrate the advantage of using machine learning for surface enhanced Raman scattering (SERS) spectral analysis for quantitative detection of pyocyanin in Luria-Bertani media. Planar Au nanoparticle clusters were selfassembled on PS-b-PMMA diblock copolymer template using EDC crosslinking chemistry and electrohydrodynamic flow to fabricate SERS substrates. Resulting substrates produce uniform SERS response over large area with signal relative standard deviation of 10.8 % over 50 μm × 50 μm region. Taking advantage of the uniformity, 400 SERS spectra were collected at each pyocyanin concentration as training dataset. Tracking the intensity of pyocyanin 1350 cm-1 vibrational band shows linear regime beginning at 10 ppb. PLS analysis was also performed on the same training dataset. Without being explicitly “told” which spectrum to look for, PLS analysis recognizes the SERS spectrum of pyocyanin as its first loading vector even in the presence of other molecules in LB media. PLS regression enables quantitative detection at 1 ppb, 1 order of magnitude earlier than univariate regression. We hope this work will fuel a push toward wider adoption of more sophisticated machine learning algorithms for quantitative analysis of SERS spectra.
Colloidal self-assembly combined with templated surfaces holds the promise of fabricating large area
devices in a low cost facile manner. This directed assembly approach improves the complexity of assemblies that
can be achieved with self-assembly while maintaining advantages of molecular scale control. In this work,
electrokinetic driving forces, i.e., electrohydrodynamic flow, are paired with chemical crosslinking between
colloidal particles to form close-packed plasmonic metamolecules. This method addresses challenges of obtaining
uniformity in nanostructure geometry and nanometer scale gap spacings in structures. Electrohydrodynamic flows
yield robust driving forces between the template and nanoparticles as well as between nanoparticles on the surface
promoting the assembly of close-packed metamolecules. Here, electron beam lithography defined Au pillars are
used as seed structures that generate electrohydrodynamic flows. Chemical crosslinking between Au surfaces
enables molecular control over gap spacings between nanoparticles and Au pillars. An as-fabricated structure is
analyzed via full wave electromagnetic simulations and shown to produce large magnetic field enhancements on the
order of 3.5 at optical frequencies. This novel method for directed self-assembly demonstrates the synergy between
colloidal driving forces and chemical crosslinking for the fabrication of plasmonic metamolecules with unique
<i>Pseudomonas aeruginosa</i> (PA), a biofilm forming bacterium, commonly affects cystic fibrosis, burn victims, and immunocompromised patients. PA produces pyocyanin, an aromatic, redox active, secondary metabolite as part of its quorum sensing signaling system activated during biofilm formation. Surface enhanced Raman scattering (SERS) sensors composed of Au nanospheres chemically assembled into clusters on diblock copolymer templates were fabricated and the ability to detect pyocyanin to monitor biofilm formation was investigated. Electromagnetic full wave simulations of clusters observed in scanning electron microcopy images show that the localized surface plasmon resonance wavelength is 696 nm for a dimer with a gap spacing of 1 nm in an average dielectric environment of the polymer and analyte; the local electric field enhancement is on the order of 400 at resonance, relative to free space. SERS data acquired at 785 nm excitation from a monolayer of benzenethiol on fabricated samples was compared with Raman data of pure benzenethiol and enhancement factors as large as 8×10<sup>9</sup> were calculated that are consistent with simulated field enhancements. Using this system, the limit of detection of pyocyanin in pure gradients was determined to be 10 parts per billion. In SERS data of the supernatant from the time dependent growth of PA shaking cultures, pyocyanin vibrational modes were clearly observable during the logarithmic growth phase corresponding to activation of genes related to biofilm formation. These results pave the way for the use of SERS sensors for the early detection of biofilm formation, leading to reduced healthcare costs and better patient outcomes.