For plasmonic alloy nanoparticles, theoretical modeling and experimental characterization are both central to our capabilities involving predictable synthesis and targeted applications. This article uses composition-tunable colloidal Au−Cu nanoparticles as a model system for exploring the issue of reliable experimental determination of composition in plasmonic alloy nanoparticles and correlation of this experimental data with theoretical predictions. Highly uniform spherical Au1-x Cu x alloy nanoparticles were synthesized with compositions ranging from x = 0 to 0.5. The particle compositions were analyzed independently using both powder X-ray diffraction (XRD) and energy dispersive X-ray spectroscopy (EDS), which represent two of the most common nanoparticle composition analysis techniques. The plasmon resonance frequencies, determined experimentally for each sample using UV−vis spectroscopy, red shift with increasing copper content as expected. These experimentally determined plasmon resonance frequencies were then compared to the values predicted theoretically based upon the XRD and EDS composition measurements. Although EDS and XRD are both found to be acceptable methods for experimentally determining the composition, careful data analysis suggests that XRD composition measurements are more accurate for smaller values of x, whereas EDS measurements are more accurate for larger values of x. In addition, some discrepancies between the experimentally determined plasmon resonance frequencies and those predicted by theory suggest inaccuracies in using a simple linear mixing rule to determine the dielectric constant of the Au−Cu alloys.

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In this work we present a detailed investigation of the Raman properties of a dithienylethene photoswitch interacting with a small gold cluster (Au(19)(+)) using time-dependent density functional theory (TD-DFT). The enhancement mechanism (CHEM) due to the molecule-surface chemical coupling in surface-enhanced Raman scattering (SERS) has been characterized for this system. We demonstrate that it is possible to control the CHEM enhancement by switching the photoswitch from its closed form to its open form. The open form of the photoswitch is found to be the strongest Raman scatterer when adsorbed on the surface whereas the opposite is found for the free molecule. This trend is explained using a simple two-state approximation. In this model the CHEM enhancement scales roughly as (omega(X)/omega(e)(4)), where omega(X) is the HOMO-LUMO gap of the free molecule and omega(e) is an average excitation between the HOMO of the photoswitch and the LUMO of the metal. We propose that the ability of this photoswitch to switch reversibly from open to closed will make it an excellent probe to control the CHEM enhancement of SERS.

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Epithelial ovarian cancer (EOC) mortality rates are strongly correlated with the stage at which it is diagnosed. Detection of EOC prior to its dissemination from the site of origin is known to significantly improve the patient outcome. However, there are currently no effective methods for early detection of the most common and lethal subtype of EOC. We sought to determine whether laser-induced breakdown spectroscopy (LIBS) and classification techniques such as linear discriminant analysis (LDA) and random forest (RF) could classify and differentiate blood plasma specimens from transgenic mice with ovarian carcinoma and wild type control mice. Herein we report results using this approach to distinguish blood plasma samples obtained from serially bled (at 8, 12, and 16 weeks) tumor-bearing TgMISIIR-TAg transgenic and wild type cancer-free littermate control mice. We have calculated the age-specific accuracy of classification using 18,000 laser-induced breakdown spectra of the blood plasma samples from tumor-bearing mice and wild type controls. When the analysis is performed in the spectral range 250 nm to 680 nm using LDA, these are 76.7 (±2.6)%, 71.2 (±1.3)%, and 73.1 (±1.4)%, for the 8, 12 and 16 weeks. When the RF classifier is used, we obtain values of 78.5 (±2.3)%, 76.9 (±2.1)% and 75.4 (±2.0)% in the spectral range of 250 nm to 680 nm, and 81.0 (±1.8)%, 80.4 (±2.1)% and 79.6 (±3.5)% in 220 nm to 850 nm. In addition, we report, the positive and negative predictive values of the classification of the two classes of blood plasma samples. The approach used in this study is rapid, requires only 5 μL of blood plasma, and is based on the use of unsupervised and widely accepted multivariate analysis algorithms. These findings suggest that LIBS and multivariate analysis may be a novel approach for detecting EOC.

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As the SARS-CoV-2 pandemic persists, methods that can quickly and reliably confirm infection and immune status is extremely urgently and critically needed. In this contribution we show that combining laser induced breakdown spectroscopy (LIBS) with machine learning can distinguish plasma of donors who previously tested positive for SARS-CoV-2 by RT-PCR from those who did not, with up to 95% accuracy. The samples were also analyzed by LIBS-ICP-MS in tandem mode, implicating a depletion of Zn and Ba in samples of SARS-CoV-2 positive subjects that inversely correlate with CN lines in the LIBS spectra.

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