Mussels have a remarkable ability to bond to solid surfaces under water. From a microscopic perspective, the first step of this process is the adsorption of dopa molecules to the solid surface. In fact, it is the catechol part of the dopa molecule that is interacting with the surface. These molecules are able to make reversible bonds to a wide range of materials, even underwater. Previous experimental and theoretical efforts have produced only a limited understanding of the mechanism and quantitative details of the competitive adsorption of catechol and water on hydrophilic silica surfaces. In this work, we uncover the nature of this competitive absorption by atomic scale modeling of water and catechol adsorbed at the geminal (001) silica surface using density functional theory calculations. We find that catechol molecules displace preadsorbed water molecules and bond directly on the silica surface. Using molecular dynamics simulations, we observe this process in detail. We also calculate the interaction force as a function of distance, and observe a maximum of 0.5 nN of attraction. The catechol has a binding energy of 23 kcal/mol onto the silica surface with adsorbed water molecules.

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Herein, we have summarized and argued about biomarkers and indicators used for the detection of SARS-CoV-2. Antibody detection methods are not considered suitable to screen individuals at early stages and asymptomatic cases. The diagnosis of COVID-19 using biomarkers and indicators at point of care level is much crucial. Therefore, it is urgently needed to develop rapid and sensitive detection methods which can target antigens. We have critically elaborated key role of biosensors to cope the outbreak situation. In this review, the importance of biosensors including electrochemical, surface enhanced Raman scattering, field-effect transistor and surface plasmon resonance biosensors in the detection of SARS-CoV-2 has been underscored. Finally, we have outlined pros and cons of diagnostic approaches as-well-as future directions.

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Sepsis is a leading cause of death and is the most expensive condition to treat in U.S. hospitals. Despite targeted efforts to automate earlier detection of sepsis, current techniques rely exclusively on using either standard clinical data or novel biomarker measurements. In this study, we apply machine learning techniques to assess the predictive power of combining multiple biomarker measurements from a single blood sample with electronic medical record data (EMR) for the identification of patients in the early to peak phase of sepsis in a large community hospital setting. Combining biomarkers and EMR data achieved an area under the receiver operating characteristic (ROC) curve (AUC) of 0.81, while EMR data alone achieved an AUC of 0.75. Furthermore, a single measurement of six biomarkers (IL-6, nCD64, IL-1ra, PCT, MCP1, and G-CSF) yielded the same predictive power as collecting an additional 16 hours of EMR data(AUC of 0.80), suggesting that the biomarkers may be useful for identifying these patients earlier. Ultimately, supervised learning using a subset of biomarker and EMR data as features may be capable of identifying patients in the early to peak phase of sepsis in a diverse population and may provide a tool for more timely identification and intervention.

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Using a single-crystal wire fabricated through the crystal growth process, the contribution of grain boundaries (GBs) to electrical resistivity was investigated in copper. We developed a novel wire fabrication process that preserved the grain-free structure of single-crystal copper (SCC) grown by the Czochralski method. The resistivity of grain-free SCC showed a reduction of 9% compared to the international annealed copper standard (IACS) resistivity, with the resulting value smaller than that of silver. We also found that the GBs strongly influenced the resistivity above 70 K, but hardly contributed below 70 K, unlike the impurities. Insights into the GB effects could contribute to our understanding of conducting phenomena and the development of nanoscale analytical models.