Photoluminescence spectroscopy of YVO4_{4}:Eu3+^{3+} nanoparticles with aromatic linker molecules: a precursor to biomedical functionalization

Photoluminescence spectra of YVO$_{4}$:Eu$^{3+}$ nanoparticles are presented,with and without the attachment of of organic linker molecules that are proposed for linking to biomolecules. YVO$_{4}$:Eu$^{3+}$ nanoparticles with 5% dopant concentration were synthesized by wet chemical synthesis. X-ray diffraction and transmission electron microscopy show the expected wakefieldite structure of tetragonal particles with an average size of 17 nm. Fourier-transform infrared spectroscopy determines that metal-carboxylate coordination is successful in replacing the native metal-hydroxyl bonds with three organic linkers, namely benzoic acid, 3-nitro 4-chloro-benzoic acid and 3,4-dihydroxybenzoic acid, in separate treatments. UV-excitation photoluminescence spectra show that the position and intensity of dominant $^{5}D_{0}-^{7}F_{2}$ electric-dipole transition at 619 nm is unaffected by the benzoic acid and 3-nitro 4-chloro-benzoic acid treatments. Attachment of the 3,4-dihydroxybenzoic acid produces an order-of-magnitude quenching of the photoluminescence, due to the presence of high-frequency modes in the linker. Ratios of the dominant electric- and magnetic-dipole transitions confirm infrared measurements, which indicate that the bulk crystal of the nanoparticle is unchanged by all three treatments.Comment: 9 pages, 5 figures, journal articl

Cranial nerve outcomes in regionally recurrent head & neck melanoma after sentinel lymph node biopsy

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/156007/1/lary28243.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/156007/2/lary28243_am.pd

Wear response of impregnated diamond bits

© 2018 Elsevier B.V. The drilling response of impregnated diamond (ID) bits is controlled by processes involved in the rock fragmentation, and also the wear processes that continuously change the bearing surface of diamonds and bonding matrix. Due to the co-existence of these two processes both affecting the overall drilling response but differently based on drilling conditions (operating parameters, bit, rock and drilling fluid), the response of ID bits has been found inconsistent and difficult to interpret. In this paper, wear mechanisms of impregnated diamond bits are studied using a series of precise state of the art cutting and drilling experiments conducted with impregnated diamond bits and segments. The wear responses are decomposed into three phases of polishing, fracturing and sharpening, and the response in each phase is analyzed using interface laws in terms of depth of cut, and the extents of frictional contacts. The variation of the response at various operating parameters (weight on bit and depth of cut) are obtained and conceptual models characterizing the drilling response under both weight-on-bit and depth of cut conditions are presented

Metabolic dysfunction in obese Hispanic women with PCOS

Study question: Are certain ethnic groups with PCOS at increased risk of metabolic disorders? Summary answer: Obese Hispanic women with PCOS are at increased risk of metabolic disorders compared to age- and BMI-matched obese non-Hispanic white women with PCOS in the United States. What is known already: Ethnic differences in body composition and metabolic risk are well established. Polycystic ovary syndrome (PCOS) is a common disorder in reproductive age women and is associated with high rates of insulin resistance, glucose intolerance anddyslipidemia. Study design, size, duration: A cross-sectional observational study was performed at an Academic Medical Center on 60 reproductive age women with PCOS in the United States. Participants/materials, setting, methods: Fasting blood was obtained from 17 Hispanic, 22 non-Hispanic black and 21 non-Hispanic white women with PCOS who were similar in age and BMI. Anthropometric parameters, insulin, lipid and lipoprotein levels by nuclear magnetic resonance were compared between the 3 groups. Main results and the role of chance: Age and BMI were similar between the groups (P=0.52 for age and P=0.60 for BMI). Hispanic women with PCOS had higher waist to hip ratio (WHR) (P=0.02), HOMA-IR (P=0.03), and a more atherogenic lipid and lipoprotein profile consisting of lower HDL (P=0.02), higher LDL particle number (P=0.02), higher VLDL particle size (P=0.03) and lower LDL (P=0.03) and HDL particle size (P=0.005) compared to non-Hispanic white women. The differences in HDL, HOMA-IR, VLDL and LDL size did not persist after adjustment for WHR while differences in LDL particle number (P=0.04) and HDL size (P=0.01) persisted

Predicting entropy and heat capacity of hydrocarbons using machine learning

Chemical substances are essential in all aspects of human life, and understanding their properties is essential for developing chemical systems. The properties of chemical species can be accurately obtained by experiments or ab initio computational calculations; however, these are time-consuming and costly. In this work, machine learning models (ML) for estimating entropy, S, and constant pressure heat capacity, Cp, at 298.15 K, are developed for alkanes, alkenes, and alkynes. The training data for entropy and heat capacity are collected from the literature. Molecular descriptors generated using alvaDesc software are used as input features for the ML models. Support vector regression (SVR), v-support vector regression (v-SVR), and random forest regression (RFR) algorithms were trained with K-fold cross-validation on two levels. The first level assessed the models' performance, and the second level generated the final models. Between the three ML models chosen, SVR shows better performance on the test dataset. The SVR model was then compared against traditional Benson's group additivity to illustrate the advantages of using the ML model. Finally, a sensitivity analysis is performed to find the most critical descriptors in the property estimations

Screening gas‐phase chemical kinetic models: Collision limit compliance and ultrafast timescales

Detailed gas‐phase chemical kinetic models are widely used in combustion research, and many new mechanisms for different fuels and reacting conditions are developed each year. Recent works have highlighted the need for error checking when preparing such models, but a useful community tool to perform such analysis is missing. In this work, we present a simple online tool to screen chemical kinetic mechanisms for bimolecular reactions exceeding collision limits. The tool is implemented on a user‐friendly website, cloudflame.kaust.edu.sa, and checks three different classes of bimolecular reactions; (ie, pressure independent, pressure‐dependent falloff, and pressure‐dependent PLOG). In addition, two other online modules are provided to check thermodynamic properties and transport parameters to help kinetic model developers determine the sources of errors for reactions that are not collision limit compliant. Furthermore, issues related to unphysically fast timescales can remain an issue even if all bimolecular reactions are within collision limits. Therefore, we also present a procedure to screen ultrafast reaction timescales using computational singular perturbation. For demonstration purposes only, three versions of the rigorously developed AramcoMech are screened for collision limit compliance and ultrafast timescales, and recommendations are made for improving the models. Larger models for biodiesel surrogates, tetrahydropyran, and gasoline surrogates are also analyzed for exemplary purposes. Numerical simulations with updated kinetic parameters are presented to show improvements in wall‐clock time when resolving ultrafast timescales

Hypoglossal Nerve Stimulator Explantation: A Case Series

Hypoglossal nerve stimulation (HGNS) has emerged as a successful surgical treatment strategy for moderate to severe obstructive sleep apnea in patients failing first-line positive airway pressure therapy. HGNS explantation due to adverse events such as pain and infection is rare and has yet to be well described. Here, our correspondence describes the first case series of patients who have undergone explantation of the Inspire HGNS system. Five patients were identified who underwent HGNS explantation. Three patients underwent explantation due to magnetic resonance imaging (MRI) incompatibility. One patient underwent explantation due to poor cosmesis. One patient underwent explantation due to surgical site infection. Average operative explant time was 163 minutes. MRI incompatibility, poor cosmesis, and device-related infection are reasons for HGNS explantation. Future need for MRI or chest wall surgery should be considered in patients being evaluated for HGNS implants

Uncertainty quantification of a deep learning fuel property prediction model

Deep learning models are being widely used in the field of combustion. Given the black-box nature of typical neural network based models, uncertainty quantification (UQ) is critical to ensure the reliability of predictions as well as the training datasets, and for a principled quantification of noise and its various sources. Deep learning surrogate models for predicting properties of chemical compounds and mixtures have been recently shown to be promising for enabling data-driven fuel design and optimization, with the ultimate goal of improving efficiency and lowering emissions from combustion engines. In this study, UQ is performed for a multi-task deep learning model that simultaneously predicts the research octane number (RON), Motor Octane Number (MON), and Yield Sooting Index (YSI) of pure components and multicomponent blends. The deep learning model is comprised of three smaller networks: Extractor 1, Extractor 2, and Predictor, and a mixing operator. The molecular fingerprints of individual components are encoded via Extractor 1 and Extractor 2, the mixing operator generates fingerprints for mixtures/blends based on linear mixing operation, and the predictor maps the fingerprint to the target properties. Two different classes of UQ methods, Monte Carlo ensemble methods and Bayesian neural networks (BNNs), are employed for quantifying the epistemic uncertainty. Combinations of Bernoulli and Gaussian distributions with DropConnect and DropOut techniques are explored as ensemble methods. All the DropConnect, DropOut and Bayesian layers are applied to the predictor network. Aleatoric uncertainty is modeled by assuming that each data point has an independent uncertainty associated with it. The results of the UQ study are further analyzed to compare the performance of BNN and ensemble methods. Although this study is confined to UQ of fuel property prediction, the methodologies are applicable to other deep learning frameworks that are being widely used in the combustion community