Glucose Sensing Using Surface-Enhanced Raman-Mode Constraining.

Diabetes mellitus is a chronic disease, and its management focuses on monitoring and lowering a patient's glucose level to prevent further complications. By tracking the glucose-induced shift in the surface-enhanced Raman-scattering (SERS) emission of mercaptophenylboronic acid (MPBA), we have demonstrated fast and continuous glucose sensing in the physiologically relevant range from 0.1 to 30 mM and verified the underlying mechanism using numerical simulations. Bonding of glucose to MPBA suppresses the "breathing" mode of MPBA at 1071 cm-1 and energizes the constrained-bending mode at 1084 cm-1, causing the dominant peak to shift from 1071 to 1084 cm-1. MPBA-glucose bonding is also reversible, allowing continuous tracking of ambient glucose concentrations, and the MPBA-coated substrates showed very stable performance over a 30 day period, making the approach promising for long-term continuous glucose monitoring. Using Raman-mode-constrained, miniaturized SERS implants, we also successfully demonstrated intraocular glucose measurements in six ex vivo rabbit eyes within ±0.5 mM of readings obtained using a commercial glucose sensor.

Empirical optimization of DFT + U and HSE for the band structure of ZnO.

ZnO is a well-known wide band gap semiconductor with promising potential for applications in optoelectronics, transparent electronics, and spintronics. Computational simulations based on the density functional theory (DFT) play an important role in the research of ZnO, but the standard functionals, like Perdew-Burke-Erzenhof, result in largely underestimated values of the band gap and the binding energies of the Zn3d electrons. Methods like DFT + U and hybrid functionals are meant to remedy the weaknesses of plain DFT. However, both methods are not parameter-free. Direct comparison with experimental data is the best way to optimize the computational parameters. X-ray photoemission spectroscopy (XPS) is commonly considered as a benchmark for the computed electronic densities of states. In this work, both DFT + U and HSE methods were parametrized to fit almost exactly the binding energies of electrons in ZnO obtained by XPS. The optimized parameterizations of DFT + U and HSE lead to significantly worse results in reproducing the ion-clamped static dielectric tensor, compared to standard high-level calculations, including GW, which in turn yield a perfect match for the dielectric tensor. The failure of our XPS-based optimization reveals the fact that XPS does not report the ground state electronic structure for ZnO and should not be used for benchmarking ground state electronic structure calculations.

Revealing Interaction of Organic Adsorbates with Semiconductor Surfaces Using Chemically Enhanced Raman.

Surface enhanced Raman spectroscopy (SERS) is frequently associated with "chemical enhancement" (CE), which is an effect of the chemical coupling between reporting molecules and surfaces. While SERS technique is mainly attributed to the studies of metallic surfaces, chemical coupling must be present on semiconductor surfaces as well. Here, we examine binding of trans-1,2-two(4-pyridyl) ethylene (BPE) to various crystallographic facets of PbSe semiconductor. The calculated off-resonant Raman spectra vary significantly on different crystallographic facets of PbSe, correlating with the electronic structure of each type of semiconductor surface. We distinguish situations when the charge transfer is present and when it is not, which raises the question about what exactly should be called the "chemical enhancement". We attempt to clarify this situation by introducing the concept of the "charge-transfer" and "charge-transfer-less" chemical enhancement. We also demonstrate a transition between these two regimes, which exhibits a nonlinear behavior of the vibrational coupling and a significantly stronger contribution to the Raman intensity.

Harnessing Chemical Raman Enhancement for Understanding Organic Adsorbate Binding on Metal Surfaces.

Surface-enhanced Raman spectroscopy (SERS) is a known approach for detecting trace amounts of molecular species. Whereas SERS measurements have focused on enhancing the signal for sensing trace amounts of a chemical moiety, understanding how the substrate alters molecular Raman spectra can enable optical probing of analyte binding chemistry. Here we examine binding of trans-1,2-two(4-pyridyl) ethylene (BPE) to Au surfaces and understand variations in experimental data that arise from differences in how the molecule binds to the substrate. Monitoring differences in the SERS as a function of incubation time, a period of several hours in our case, reveals that the number of BPE molecules that chemically binds with the Au substrate increases with time. In addition, we introduce a direct method of accessing relative chemical enhancement from experiments that is in quantitative agreement with theory. The ability to probe optically specific details of metal/molecule interfaces opens up possibilities for using SERS in chemical analysis.

Role of confinement on diffusion barriers in semiconductor nanocrystals.

We find that quantum size effects not only play an important role in the electronic properties of defects in semiconductor nanocrystals, but also strongly affect the incorporation of defect atoms into the nanocrystals. In particular, using ab initio methods based on density functional theory, we predict that Mn defects will be energetically driven towards the surface of CdSe and ZnSe nanocrystals, and that the diffusion barrier of a Mn interstitial defect in a CdSe nanocrystal will be significantly lower than that in the bulk.

Algorithms for the electronic and vibrational properties of nanocrystals.

Solving the electronic structure problem for nanoscale systems remains a computationally challenging problem. The numerous degrees of freedom, both electronic and nuclear, make the problem impossible to solve without some effective approximations. Here we illustrate some advances in algorithm developments to solve the Kohn-Sham eigenvalue problem, i.e. we solve the electronic structure problem within density functional theory using pseudopotentials expressed in real space. Our algorithms are based on a nonlinear Chebyshev filtered subspace iteration method, which avoids computing explicit eigenvectors except at the first self-consistent-field iteration. Our method may be viewed as an approach to solve the original nonlinear Kohn-Sham equation by a nonlinear subspace iteration technique, without emphasizing the intermediate linearized Kohn-Sham eigenvalue problems. Replacing the standard iterative diagonalization at each self-consistent-field iteration by a Chebyshev subspace filtering step results in a significant speed-up, often an order of magnitude or more, over methods based on standard diagonalization. We illustrate this method by predicting the electronic and vibrational states for silicon nanocrystals.