Spectroscopy-Assisted Label-free Molecular Analysis of Live Cell Surface with Vertically Aligned Plasmonic Nanopillars.

A generalized label-free platform for surface-selective molecular sensing in living cells can transform the ability to examine complex events in the cell membrane. While vertically aligned semiconductor and metal-semiconductor hybrid nanopillars have rapidly surfaced for stimulating and probing the intracellular environment, the potential of such constructs for selectively interrogating the cell membrane is surprisingly underappreciated. In this work, a new platform, entitled nano-PROD (nano-pillar based Raman optical detection), enables molecular recording by probing fundamental vibrational modes of membrane constituents of cells adherent on a large-area silver-coated silicon nanopillar substrate fabricated using a precursor solution-based nanomanufacturing process. It is shown that the nano-PROD platform sustains live cells in near-physiological conditions, which can be directly profiled using surface-enhanced Raman spectroscopy due to the confined electromagnetic field enhancement. The experimental results highlight the utility of the platform in probing specific cell surface markers for accurately recognizing the phenotypically identical prostate cancer cells, differing only in prostate-specific membrane antigen expression. Due to its tunability, nano-PROD has the promise to be readily extendable to other applications that can leverage its unique combination of nanoscale topographic features and molecular sensing capabilities, from stain-free cytopathology inspection to understanding spatio-mechanical regulation in membrane receptor function.

Model research of electric coal calorific value based on near infrared frequency domain self-adaption analysis method.

At present, because the blending coal was taken in some power stations as the major fuel which has too complex physical and chemical characters to build accurate normal near infrared quantitative models in some cases, which brought difficulties for on-line electric coal calorific value detection. For this reason, it was carefully studied that the time domain and frequency domain properties of the power generation coal near infrared spectra, and was proposed that a new quantitative near infrared method named frequency domain self-adaption analysis. The first step, time domain near infrared spectra are converted into frequency domain near infrared signal by Fast Fourier Transform; The second step, the suitable frequency information range by means of valid spectra energy parameter ηE was obtained by this method; The third step, it was constructed that an information volume parameter which is formed by correlation coefficient, standard deviation spectra and coordinate of harmonic in frequency domain to initialize the regression model input parameters' position; Finally, the optimal model is established by way of discrete frequency domain scooping and synthesized performance function. At the same time, compared with the principle component regression, partial least squares regression, back propagation artificial network, support vector regression and partial least squares regression optimized by genetic algorithm models, it is acquired that a more accurate method which can effectively avoid over fitting and virtual effective models and has a very useful application prospect by verifying the electric coal calorific value. Additionally, this method can be used in other quantitative spectra analysis.