Nanostructured forms of stoichiometric In 2 O 3 are proving to be efficacious catalysts for the gas-phase hydrogenation of CO 2. These conversions can be facilitated using either heat or light; however, until now, the limited optical absorption intensity evidenced by the pale-yellow color of In 2 O 3 has prevented the use of both together. To take advantage of the heat and light content of solar energy, it would be advantageous to make indium oxide black. Herein, we present a synthetic route to tune the color of In 2 O 3 to pitch black by controlling its degree of non-stoichiometry. Black indium oxide comprises amorphous non-stoichiometric domains of In 2 O 3-x on a core of crystalline stoichiometric In 2 O 3 , and has 100% selectivity towards the hydrogenation of CO 2 to CO with a turnover frequency of 2.44 s −1 .

Raman spectroscopy's capability to provide meaningful composition predictions is heavily reliant on a preprocessing step to remove insignificant spectral variation. This is crucial in biofluid analysis. Widespread adoption of diagnostics using Raman requires a robust model that can withstand routine spectra discrepancies due to unavoidable variations such as age, diet, and medical background. A wealth of preprocessing methods are available, and it is often up to trial-and-error or user experience to select the method that gives the best results. This process can be incredibly time consuming and inconsistent for multiple operators. In this study, we detail a method to analyze the statistical variability within a set of training spectra and determine suitability to form a robust model. This allows us to selectively qualify or exclude a preprocessing method, predetermine robustness, and simultaneously identify the number of components that will form the best predictive model. We demonstrate the ability of this technique to improve predictive models of two artificial biological fluids. Raman spectroscopy is ideal for noninvasive, nondestructive analysis. Routine health monitoring that maximizes comfort is increasingly crucial, particularly in epidemic-level diabetes diagnoses. High variability in spectra of biological samples can hinder Raman's adoption for these methods. Our technique allows the decision of optimal pretreatment method to be determined for the operator; model performance is no longer a function of user experience. We foresee this statistical technique being an instrumental element to widening the adoption of Raman as a monitoring tool in a field of biofluid analysis. KEYWORDS chemometrics, machine learning, preprocessing methods, quantiative biological analysis, Raman spectroscopy 958

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Optical techniques are useful for nanoparticle monitoring tasks. For example, photon correlation Fourier spectroscopy has been proven as a useful tool to extract single nanoparticle emission linewidths. [5] In addition, polarized photoluminescence has also been useful in studying the order and orientation of nanoparticles. [6] Another approach utilizes spontaneous Raman in, still and circulating fluid, liquid core waveguides and has enabled studies of nanomaterials with great sensitivity compared to conventional Raman. [7,8] Raman spectroscopy is a versatile characterization technique, non-destructively providing information on the molecular structure of materials with exceptional specificity. [9] Raman (inelastic) scattering, however, occurs at least 10 orders of magnitude less frequently than Rayleigh (elastic) scattering. The consequence is a faint scattering signal, which is particularly exacerbated in fluidic samples due to lower molecular density. As such, Raman is primarily associated with solid samples. Raman's potential in liquid and gaseous samples has yet to be fully realized: signal enhancement techniques such as Surface-Enhanced Raman Spectroscopy (SERS) require extensive substrate or sample preparation and lack repeatability, hindering reliable characterization. [10] We propose here that the pairing of spontaneous Raman with optofluidics provides the necessary enhancement, offering a means to repeatably and quantitatively identify trace unknown compounds which may pose a substantial threat. [11] Incorporating optofluidics with spontaneous Raman increases sensitivity for the full spectrum of Raman modes rather than a selection, without altering the native state of the analyte. [12] Both the laser light and the liquid of interest are confined to the same waveguiding cavity, enabling a strong and efficient process of light-matter interaction. A key challenge with spontaneous optofluidic Raman is a limit of sensitivity, on the order of millimolar (mM). [13] In this work we demonstrate an approach utilizing thermophoresis along with evaporation and capillary effects to extend the sensitivity of spontaneous Raman in fluidic samples to the nanomolar (nM) range for nanoparticle sensing. The technique promotes assembly of a thin-film of nanoparticles, by means of the laser source which is used to induce scattering. The coffeering effect acts to aggregate the solute atop a Hollow-Core Photonic Crystal Fiber (HC-PCF), and is depicted in Figure 1. The method is shown to be effective for a wide variety of samples both organic and inorganic.An approach to significantly enhance spontaneous Raman sensitivity through the formation of a thin film via thermophoresis along with evaporation at the facet of a hollow-core photonic crystal fiber is reported for the first time. Sensitivity of detection is increased by more than 6 orders of magnitude for both organic and inorganic nanoparticles, facilitating the search for trace analytes in solution. Detection of two nanoparticles, alumina and polystyrene, is demonstrated do...

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