Laser-induced breakdown spectroscopy quantitative analysis method via adaptive analytical line selection and relevance vector machine regression model

Abstract A new LIBS quantitative analysis method based on analytical line adaptive selection and Relevance Vector Machine (RVM) regression model is proposed. First, a scheme of adaptively selecting analytical line is put forward in order to overcome the drawback of high dependency on a priori knowledge. The candidate analytical lines are automatically selected based on the built-in characteristics of spectral lines, such as spectral intensity, wavelength and width at half height. The analytical lines which will be used as input variables of regression model are determined adaptively according to the samples for both training and testing. Second, an LIBS quantitative analysis method based on RVM is presented. The intensities of analytical lines and the elemental concentrations of certified standard samples are used to train the RVM regression model. The predicted elemental concentration analysis results will be given with a form of confidence interval of probabilistic distribution, which is helpful for evaluating the uncertainness contained in the measured spectra. Chromium concentration analysis experiments of 23 certified standard high-alloy steel samples have been carried out. The multiple correlation coefficient of the prediction was up to 98.85%, and the average relative error of the prediction was 4.01%. The experiment results showed that the proposed LIBS quantitative analysis method achieved better prediction accuracy and better modeling robustness compared with the methods based on partial least squares regression, artificial neural network and standard support vector machine.

Concentration polarization model of spiral-wound membrane modules with application to batch-mode RO desalination of brackish water

Batch-mode reverse osmosis (batch-RO) operation is considered a promising desalination method due to its low energy requirement compared to other RO system arrangements. To improve and predict batch-RO performance, studies on concentration polarization (CP) are carried out. The Kimura-Sourirajan mass-transfer model is applied and validated by experimentation with two different spiral-wound RO elements. Explicit analytical Sherwood correlations are derived based on experimental results. For batch-RO operation, a new genetic algorithm method is developed to estimate the Sherwood correlation parameters, taking into account the effects of variation in operating parameters. Analytical procedures are presented, then the mass transfer coefficient models are developed for different operation processes, i.e., batch-RO and continuous RO. The CP related energy loss in batch-RO operation is quantified based on the resulting relationship between feed flow rates and mass transfer coefficients. It is found that CP increases energy consumption in batch-RO by about 25% compared to the ideal case in which CP is absent. For continuous RO process, the derived Sherwood correlation predicted CP accurately. In addition, we determined the optimum feed flow rate of our batch-RO system.

Hydroxyl radical generation by cactus-like copper oxide nanoporous carbon catalysts for microcystin-LR environmental remediation

Copper oxide supported on nanoporous activated carbon (CuO-NPAC) is reported for the aqueous phase catalytic degradation of cyanotoxin microcystin-LR (MC-LR). The loading and spatial distribution of CuO throughout the NPAC matrix strongly influence the catalytic efficiency. CuO-NPAC synthesis was optimized with respect to the copper loading and thermal processing, and the physicochemical properties of the resulting materials were characterized by XRD, BET, TEM, SEM, EPR, TGA, XPS and FT-IR spectroscopy. EPR spin trapping and fluorescence spectroscopy showed in situ ˙OH formation via H2O2 over CuO-NPAC as the catalytically relevant oxidant. The impact of reaction conditions, notably CuO-NPAC loading, H2O2 concentration and solution pH, is discussed in relation to the reaction kinetics for MC-LR remediation.

Biosensor based on excessively tilted fiber grating in thin-cladding optical fiber for sensitive and selective detection of low glucose concentration

We report a highly sensitive, high Q-factor, label free and selective glucose sensor by using excessively tilted fiber grating (Ex-TFG) inscribed in the thin-cladding optical fiber (TCOF). Glucose oxidase (GOD) was covalently immobilized on optical fiber surface and the effectiveness of GOD immobilization was investigated by the fluorescence microscopy and highly accurate spectral interrogation method. In contrast to the long period grating (LPG) and optical fiber (OF) surface Plasmon resonance (SPR) based glucose sensors, the Ex-TFG configuration has merits of nearly independent cross sensitivity of the environmental temperature, simple fabrication method (no noble metal deposition or cladding etching) and high detection accuracy (or Q-factor). Our experimental results have shown that Ex-TFG in TCOF based sensor has a reliable and fast detection for the glucose concentration as low as 0.1~2.5mg/ml and a high sensitivity of ~1.514nm·(mg/ml)−1, which the detection accuracy is ~0.2857nm−1 at pH 5.2, and the limit of detection (LOD) is 0.013~0.02mg/ml at the pH range of 5.2~7.4 by using an optical spectrum analyzer with a resolution of 0.02nm.