Nanoplasmonic sensors based on surface-enhanced spectroscopies carry profound promise toward ultrasensitive detection of biomolecular analytes within miniaturized measurement footprints. High sensitivity in these sensors is achieved by intense electromagnetic (EM) enhancements at hot spots and colocalization of analytes with such hot spots. However, EM hot spots exhibiting high EM enhancements often present confined areas that render them inaccessible to large analytes such as biomolecules. Addressing this requires rational engineering of a nanoplasmonic interface that factors in the analyte surface concentrations and how they are distributed with respect to the EM hot spots. Here we demonstrate combination of metal-enhanced fluorescence (MEF) with a quartz crystal microbalance (QCM) through fabrication of highly resolved plasmonic nanoarrays directly on the QCM sensor. QCM and MEF are simultaneously employed to monitor in situ, real-time binding of fluorescently labeled protein to receptor-functionalized plasmonic arrays. The correlation between the QCM and MEF responses is used to quantify surface density of protein that contributes to the MEF signal intensities and obtain analyte distribution with respect to the EM hot spots by geometric modeling. Results further reveal the MEF assays to be sensitive down to ∼2 zmol of protein within measurement footprints that are 8 orders of magnitude smaller than that of the QCM.
Plasmonic metal nanoparticles (NPs) show promise in a variety of applications, ranging from theranostics to chemical sensing, with chemical sensing made possible by the sensitivity of the localized surface plasmon resonance (LSPR) to the surrounding dielectric environment. Au NPs have been the standard for LSPR sensing applications due to their narrow plasmon band, tunable LSPR maximum, and relative chemical stability; however, the comparatively low refractive index sensitivity (RIS) and morphological instability of Au nanostructures are limiting factors. Recent research has found that incorporating Pd into Au systems can increase RIS and impart multifunctionality, but how the distribution of Pd within Au-based nanostructures affects LSPR sensing is unclear. Here, Au-Pd heterostructures with different Au-Pd distributions were prepared to systematically study the effect of Pd distribution on RIS. Specifically, symmetrically branched Au nanocrystals with Oh symmetry (i.e., octopods) were selected as building blocks as their branch tips concentrate E-fields locally. Using these nanocrystals as seeds, Pd-tipped Au octopods and core@shell Au@Pd octopods were synthesized for comparison to alloy Au-Pd octopods and all-Au octopods. Through experiment and simulation, we show that RIS depends both on Pd loading and location in Au-Pd heterostructures, with the Pd-tipped Au octopods displaying the highest RIS while maintaining a moderate figure of merit. This systematic analysis highlights that localization of Pd at LSPR hotspots is critical to achieving the highest RIS, with this insight intended to guide design of future LSPR sensors that move beyond the all-Au standard.
We present an efficient approach to analyze CODEX-MAS exchange NMR data by comparing the experimental results with simulated data that are calculated from separately simulated motional trajectories rather than by application of the classic rate-equation approach. The trajectories can be calculated by random-walk simulations of dynamic processes of arbitrary motional geometries and distributions of exchange rates or taken from the result of MD simulations. The necessary computation time of this approach is independent of the complexity of the model and is particularly well-suited for the description of diffusive motions and/or complex models. It can also easily be expanded to intermediate motions. After comparison of trajectory and rate-equation approach, we discuss different dynamic models of discrete and diffusive motions and present applications to the model substance DMS and to scenarios that are applicable to polymeric or biological systems. As a proof of principle, we also calculate exchange-NMR data directly from a MD calculation of lipids; however, we adjusted its time scale to meet the dynamic range of the CODEX experiment.
Semiconductor-based heterogeneous photocatalysis has received much attention. A plasma-assisted method for synthesizing CuO was developed and used to decorate ZnO nanorods. High quality monoclinic CuO was synthesized in a highly excited oxygen-copper plasma generated by electron cyclotron resonance microwave discharge of O2 gas and pulsed laser ablation of a Cu target. The plasma and the CuO synthesis process were spectroscopically characterized by optical emission measurement. In the oxygen-copper plasma, surface decoration of hydrothermally grown ZnO nanorods with CuO was achieved. The CuO-decorated ZnO nanorods were systematically characterized for morphology, structure, photoexcitation, and light emission by field emission scanning electron microscopy, X-ray diffraction, Raman scattering spectroscopy, Fourier-transform infrared spectroscopy, light absorption, and photoluminescence measurements. Using the prepared samples as photocatalysts, degradation of organic rhodamine B dye was performed under ultraviolet or violet light illumination, showing that the CuO-decorated ZnO nanorods exhibit much higher photocatalytic activity than pure CuO films and bare ZnO nanorods. A Z-scheme electron transfer mechanism was proposed for the enhanced photocatalytic activity of nanostructured ZnO/CuO heterogeneous composites.
Predicting protein stability is a challenge due to the many competing thermodynamic effects. Through de novo protein design, one begins with a target structure and searches for a sequence that will fold into it. Previous work by Rocklin et al. introduced a data set of more than 16,000 miniproteins spanning four structural topologies with information on stability. These structures were characterized with a set of 46 structural descriptors, with no explicit inclusion of configurational entropy (Scnf). Our work focused on creating a set of 17 descriptors intended to capture variations in Scnf and its comparison to an extended set of 113 structural and energy model features that extend the Rocklin et al. feature set (R). The Scnf descriptors statistically discriminate between stable and unstable distributions within topologies and best describe EEHEE topology stability (where E = β sheet and H = α helix). Between 50 and 80% of the variation in each Scnf descriptor is described by linear combinations of R features. Despite containing useful information about minipeptide stability, providing Scnf features as inputs to machine learning models does not improve overall performance when predicting protein stability, as the R features sufficiently capture the implicit variations.
