Well-defined palladium–gold nanoparticles (PdAuNPs) with randomly alloyed structures and broadly tunable compositions were studied in catalytic nitrite (NO2–) reduction. The catalysts were synthesized using a microwave-assisted polyol coreduction method. PdxAu100–xNPs with systematically varied compositions (x = 18–83) were supported on amorphous silica (SiO2) and studied as model catalysts for aqueous NO2– reduction in a batch reactor, using H2 as the electron donor. The reactions followed pseudo-first-order kinetics for ≥80% NO2– conversion. The PdxAu100–xNP-SiO2 catalysts showed a volcano-like correlation between NO2– reduction activity and x; the highest activity was observed for Pd53Au47, with an associated first-order rate constant of 5.12 L min–1 gmetal–1. Alloy NPs with greater proportions of Au were found to reduce the loss in catalytic activity due to sulfide fouling. Density functional theory calculations indicate that this is because Au weakens sulfur binding at PdAuNP surfaces due to atomic ensemble, electronic, and strain effects and thus reduces sulfur poisoning. The environmental relevance of the most active supported catalyst was evaluated by subjecting it to five cycles of catalytic NO2– reduction. The catalytic activity decreased over multiple cycles, but analysis of the postreaction PdxAu100–xNP-SiO2 materials using complementary techniques indicated that there were no significant structural changes. Most importantly, we show that PdxAu100–xNP-SiO2 alloys are significantly more active NO2– reduction catalysts in comparison to pure Pd catalysts.
Diffuse reflectance spectroscopy offers a noninvasive, fast, and low-cost alternative to visual screening and biopsy for skin cancer diagnosis. We have previously acquired reflectance spectra from 137 lesions in 76 patients and determined the capability of spectral diagnosis using principal component analysis (PCA). However, it is not well elucidated why spectral analysis enables tissue classification. To provide the physiological basis, we used the Monte Carlo look-up table (MCLUT) model to extract physiological parameters from those clinical data. The MCLUT model results in the following physiological parameters: oxygen saturation, hemoglobin concentration, melanin concentration, vessel radius, and scattering parameters. Physiological parameters show that cancerous skin tissue has lower scattering and larger vessel radii, compared to normal tissue. These results demonstrate the potential of diffuse reflectance spectroscopy for detection of early precancerous changes in tissue. In the future, a diagnostic algorithm that combines these physiological parameters could be enable non-invasive diagnosis of skin cancer.
Aromatic molecules with pi electrons are commonly involved in chemical and biological recognitions. For example, nucleobases play central roles in DNA/RNA structure and their interactions with proteins. The delocalization of the pi electrons is responsible for the high polarizability of aromatic molecules. In this work, the AMOEBA force field has been developed and applied to 5 regular nucleobases and 12 aromatic molecules. The permanent electrostatic energy is expressed as atomic multipole interactions between atom pairs, and many-body polarization is accounted for by mutually induced atomic dipoles. We have systematically investigated aromatic ring stacking and aromatic-water interactions for nucleobases and aromatic molecules, as well as base-base hydrogen-bonding pair interactions, all at various distances and orientations. van der Waals parameters were determined by comparison to the quantum mechanical interaction energy of these dimers and fine-tuned using condensed phase simulation. By comparing to quantum mechanical calculations, we show that the resulting classical potential is able to accurately describe molecular polarizability, molecular vibrational frequency, and dimer interaction energy of these aromatic systems. Condensed phase properties, including hydration free energy, liquid density, and heat of vaporization, are also in good overall agreement with experimental values. The structures of benzene liquid phase and benzene-water solution were also investigated by simulation and compared with experimental and PDB structure derived statistical results.
Raman spectroscopy (RS) has shown great potential in noninvasive cancer screening. Statistically based algorithms, such as principal component analysis, are commonly employed to provide tissue classification; however, they are difficult to relate to the chemical and morphological basis of the spectroscopic features and underlying disease. As a result, we propose the first Raman biophysical model applied to in vivo skin cancer screening data. We expand upon previous models by utilizing in situ skin constituents as the building blocks, and validate the model using previous clinical screening data collected from a Raman optical fiber probe. We built an 830nm confocal Raman microscope integrated with a confocal laser-scanning microscope. Raman imaging was performed on skin sections spanning various disease states, and multivariate curve resolution (MCR) analysis was used to resolve the Raman spectra of individual in situ skin constituents. The basis spectra of the most relevant skin constituents were combined linearly to fit in vivo human skin spectra. Our results suggest collagen, elastin, keratin, cell nucleus, triolein, ceramide, melanin and water are the most important model components. We make available for download (see supplemental information) a database of Raman spectra for these eight components for others to use as a reference. Our model reveals the biochemical and structural makeup of normal, nonmelanoma and melanoma skin cancers, and precancers and paves the way for future development of this approach to noninvasive skin cancer diagnosis.
Physical, chemical, and biological interactions between groundwater and sedimentary rock directly control the fundamental subsurface properties such as porosity, permeability, and flow. This is true for a variety of subsurface scenarios, ranging from shallow groundwater aquifers to deeply buried hydrocarbon reservoirs. Microfluidic flow cells are now commonly being used to study these processes at the pore scale in simplified pore structures meant to mimic subsurface reservoirs. However, these micromodels are typically fabricated from glass, silicon, or polydimethylsiloxane (PDMS), and are therefore incapable of replicating the geochemical reactivity and complex three-dimensional pore networks present in subsurface lithologies. To address these limitations, we developed a new microfluidic experimental test bed, herein called the Real Rock-Microfluidic Flow Cell (RR-MFC). A porous 500 μm-thick real rock sample of the Clair Group sandstone from a subsurface hydrocarbon reservoir of the North Sea was prepared and mounted inside a PDMS microfluidic channel, creating a dynamic flow-through experimental platform for real-time tracking of subsurface reactive transport. Transmitted and reflected microscopy, cathodoluminescence microscopy, Raman spectroscopy, and confocal laser microscopy techniques were used to (1) determine the mineralogy, geochemistry, and pore networks within the sandstone inserted in the RR-MFC, (2) analyze non-reactive tracer breakthrough in two- and (depth-limited) three-dimensions, and (3) characterize multiphase flow. The RR-MFC is the first microfluidic experimental platform that allows direct visualization of flow and transport in the pore space of a real subsurface reservoir rock sample, and holds potential to advance our understandings of reactive transport and other subsurface processes relevant to pollutant transport and cleanup in groundwater, as well as energy recovery.
Skin cancer is the most common form of cancer in the United States and is a recognized public health issue. Diagnosis of skin cancer involves biopsy of the suspicious lesion followed by histopathology. Biopsies, which involve excision of the lesion, are invasive, at times unnecessary, and are costly procedures ( $2.8B/year in the US). An unmet critical need exists to develop a non-invasive and inexpensive screening method that can eliminate the need for unnecessary biopsies. To address this need, our group has reported on the continued development of a noninvasive method that utilizes multimodal spectroscopy towards the goal of a “spectral biopsy” of skin. Our approach combines Raman spectroscopy, fluorescence spectroscopy, and diffuse reflectance spectroscopy to collect comprehensive optical property information from suspicious skin lesions. We previously described an updated spectral biopsy system that allows acquisition of all three forms of spectroscopy through a single fiber optic probe and is composed of off-the-shelf OEM components that are smaller, cheaper, and enable a more clinic-friendly system. We present initial patient data acquired with the spectral biopsy system, the first from an extensive clinical study (n = 250) to characterize its performance in identifying skin cancers (basal cell carcinoma, squamous cell carcinoma, and melanoma). We also present our first attempts at analyzing this initial set of clinical data using statistical-based models, and with models currently being developed to extract biophysical information from the collected spectra, all towards the goal of noninvasive skin cancer diagnosis.