Olga Lyandres

Biosensing with plasmonic nanosensors

Recent developments have greatly improved the sensitivity of optical sensors based on metal nanoparticle arrays and single nanoparticles. We introduce the localized surface plasmon resonance (LSPR) sensor and describe how its exquisite sensitivity to size, shape and environment can be harnessed to detect molecular binding events and changes in molecular conformation. We then describe recent progress in three areas representing the most significant challenges: pushing sensitivity towards the single-molecule detection limit, combining LSPR with complementary molecular identification techniques such as surface-enhanced Raman spectroscopy, and practical development of sensors and instrumentation for routine use and high-throughput detection. This review highlights several exceptionally promising research directions and discusses how diverse applications of plasmonic nanoparticles can be integrated in the near future.

Effect of Dimensionality on the Photocatalytic Behavior of Carbon–Titania Nanosheet Composites: Charge Transfer at Nanomaterial Interfaces

Due to their unique optoelectronic structure and large specific surface area, carbon nanomaterials have been integrated with titania to enhance photocatalysis. In particular, recent work has shown that nanocomposite photocatalytic performance can be improved by minimizing the covalent defect density of the carbon component. Herein, carbon nanotube-titania nanosheet and graphene-titania nanosheet composites with low carbon defect densities are compared to investigate the role of carbon nanomaterial dimensionality on photocatalytic response. The resulting 2D-2D graphene-titania nanosheet composites yield superior electronic coupling compared to 1D-2D carbon nanotube-titania nanosheet composites, leading to greater enhancement factors for CO2 photoreduction under ultraviolet irradiation. On the other hand, 1D carbon nanotubes are shown to be more effective titania photosensitizers, leading to greater photoactivity enhancement factors under visible illumination. Overall, this work suggests that carbon nanomaterial dimensionality is a key factor in determining the spectral response and reaction specificity of carbon-titania nanosheet composite photocatalysts.

Progress Toward an In Vivo Surface-Enhanced Raman Spectroscopy Glucose Sensor

BACKGROUND In this report, we detail our current work towards developing a surface-enhanced Raman spectroscopy (SERS) based sensor for in vivo glucose detection. Despite years of innovations in the development of blood glucose monitors, there remains a need for accurate continuous glucose sensors to provide care to rising numbers of diagnosed diabetes patients and mitigate secondary health complications associated with this metabolic disorder. METHODS SERS is a highly specific and sensitive optical technique suitable for direct detection of glucose. The SERS effect is highly distance dependent, thus the glucose molecules need to be within a few nanometers or adsorbed to an SERS-active surface. In our sensor, this is achieved with a self-assembled monolayer (SAM) that facilitates reversible interactions between glucose molecules and the surface. The amount of glucose near the surface is proportional to its concentration in the surrounding environment. RESULTS We determined that the SAM-functionalized surface is stable for at least 10 days and provides rapid, reversible partitioning. In vitro experiments in bovine plasma as well as in vivo experiments in rats demonstrated quantitative detection. CONCLUSIONS We show successful use of the SERS glucose sensor in rats, making it the first in vivo SERS sensor. Furthermore, we demonstrate free space transdermal detection of a SERS signal through the rats skin as an initial step toward developing a transcutaneous sensor.

Glucose Sensing with Surface-Enhanced Raman Spectroscopy

Since the discovery of SERS nearly thirty years ago, it has progressed from model-system studies of pyridine to state-of-the-art surface-science studies coupled with real-world applications. We have demonstrated a SERS-based glucose sensor as an example of the latter. A SERS-active surface functionalized with a mixed SAM was shown to partition and departition glucose efficiently. The two components of the SAM, DT and MH, provide the appropriate balance of hydrophobic and hydrophilic groups. The DT/MH-functionalized SERS surface partitioned and departitioned glucose in less than 1 min, which indicates that the sensor can be used in real-time, continuous sensing. Furthermore, quantitative glucose measurements, in the physiological concentration range, in a mixture of interfering analytes and in bovine plasma were also demonstrated. Finally, the DT/MH-functionalized SERS surface showed temporal stability for at least 10 days in bovine plasma, making it a potential candidate for implantable sensing.

Prediction range estimation from noisy Raman spectra with robust optimization.

Inferences need to be drawn in biological systems using experimental multivariate data. The number of samples collected in many such experiments is small, and the data are noisy. We present and study the performance of a robust optimization (RO) model for such situations. We adapt this model to generate a minimum and a maximum estimation of analyte concentration for a given sample, producing a prediction range. The calibration model was applied to sets of Raman spectra. In particular we used normal Raman measurements of pyridine/deuterated pyridine mixtures and spectra from a more complex glucose detection system based on surface-enhanced Raman spectroscopy. The results from the RO model were compared with prediction intervals estimated from partial least squares (PLS) method. We find that the RO prediction ranges included the actual concentration value of the sample more consistently than the 99% prediction intervals built with PLS methods.