No abstract

Surface-enhanced Raman spectroscopy (SERS) is an attractive method for bio-chemical sensing due to its potential for single molecule sensitivity and the prospect of DNA composition analysis. In this manuscript we leverage metal specific chemical enhancement effect to detect differences in SERS spectra of 200-base length single-stranded DNA (ssDNA) molecules adsorbed on gold or silver nanorod substrates, and then develop and train a linear regression as well as neural network models to predict the composition of ssDNA. Our results indicate that employing substrates of different metals that host a given adsorbed molecule leads to distinct SERS spectra, allowing to probe metal-molecule interactions under distinct chemical enhancement regimes. Leveraging this difference and combining spectra from different metals as an input for PCA (Principal Component Analysis) and NN (Neural Network) models, allows to significantly lower the detection errors compared to manual feature-choosing analysis as well as compared to the case where data from single metal is used. Furthermore, we show that NN model provides superior performance in the presence of complex noise and data dispersion factors that affect SERS signals collected from metal substrates fabricated on different days.

We report on the experimental study of an anomalous transmission effect in ultrathin metallic gratings, where the metal thickness is much thinner than the skin depth. In particular, incident transverse magnetic polarized waves are reflected while incident transverse electric polarized waves are transmitted. This anomalous effect is strongly dependent on the metal thickness and metal width. We systematically investigate and demonstrate the anomalous effect and find the optimized nanostrip thickness and width by introducing a shadow-mask fabrication approach. Our results demonstrate the possibility of developing ultrathin nanostrip based planar metasurfaces with low loss.

The high signal throughput of Fourier transform spectroscopy has proved a boon to overcoming the difficulties of otherwise overwhelming detector noise in chemical infrared spectroscopy and near-infrared Raman spectroscopy, and is desirable for in situ measurement. Here we introduce a stationary channel dispersed Fourier transform (CDFT) spectrometer that efficiently utilizes two-dimensional imagers to obtain high-resolution input spectra in low-pixel-density imagers. By angularly dispersing a vertical one-dimensional interference pattern along a horizontal axis, an array of bandlimited interferogram channels is measured with reduced sampling constraints in each channel. CDFT spectrometers can use existing imagers to measure spectra with 0.0025 cm −1 resolution over an 800 cm −1 bandwidth. Moreover, this approach reduces the quantization noise present in low dynamic range imagers significantly. To demonstrate the utility of CDFT spectroscopy, broadband spectral domain optical coherence and multicolor quantum dot photoluminescence are measured in low-pixel-density imagers.

We present an integrated label-free biosensor based on surface plasmon resonance (SPR) and Faradaic electrochemical impedance spectroscopy (f-EIS) sensing modalities, for the simultaneous detection of biological analytes. Analyte detection is based on the angular spectroscopy of surface plasmon resonance and the extraction of charge transfer resistance values from reduction-oxidation reactions at the gold surface, as responses to functionalized surface binding events. To collocate the measurement areas and fully integrate the modalities, holographically exposed thin-film gold SPR-transducer gratings are patterned into coplanar electrodes for tandem impedance sensing. Mutual non-interference between plasmonic and electrochemical measurement processes is shown, and using our scalable and compact detection system, we experimentally demonstrate biotinylated surface capture of neutravidin concentrations as low as 10 nM detection, with a 5.5 nM limit of detection.