ilian Hu

Sensitive determination of trace mercury by UV-visible diffuse reflectance spectroscopy after complexation and membrane filtration-enrichment.

A simple, sensitive and selective solid phase reflectometry method is proposed for the determination of trace mercury in aqueous samples. The complexation reagent dithizone was firstly injected into the properly buffered solution with vigorous stirring, which started a simultaneous formation of nanoparticles suspension of dithizone and its complexation reaction with the mercury(II) ions to make Hg-dithizone nanoparticles. After a definite time, the mixture was filtered with membrane, and then quantified directly on the surface of the membrane by using integrating sphere accessory of the UV-visible spectrophotometer. The quantitative analysis was carried out at a wavelength of 485 nm since it yielded the largest difference in diffuse reflectance spectra before and after reaction with mercury(II).A good linear correlation in the range of 0.2-4.0 μg/L with a squared correlation coefficient (R(2)) of 0.9944 and a detection limit of 0.12 μg/L were obtained. The accuracy of the method was evaluated by the analysis of spiked mercury(II) concentrations determined using this method along with those determined by the atomic fluorescence mercury vapourmeter and the results obtained were in good agreement. The proposed method was applied to the determination of mercury in tap water and river water samples with the recovery in an acceptable range (95.7-105.3%).

Development of on-line spectroscopic determination approach of dispersive liquid–liquid microextraction based on an effective device

A novel, rapid, simple, and low-cost on-line determination approach of dispersive liquid-liquid microextraction (DLLME) with low-density solvents was developed with the support of a specially designed effective homemade device. The proposed method surmounted the drawbacks of conventional DLLME of the need of high-density solvents as extractants, and the requirement of centrifugation operation to obtain phase separation, and the difficulties to realize on-line determination. The amount of sample utilized can conveniently change according to practical needs by varying the volume of the extraction tube of the device to perform a more effective DLLME. A case study was carried out to assess this method utilizing the dye rhodamine B as the model analyte. The experiment parameters influencing the extraction were systematically investigated. Under optimum conditions, the linearity was obtained in the range of 0.015-1.000 μg/mL with the correlation coefficient (r(2)) of 0.9980. The limit of detection and quantification were 6.1 and 20.4 μg/L, respectively. Good repeatability was achieved with the relative standard deviations (RSD) for five replicate measurements of different concentration samples less than 4.06%, and the presented method was successfully employed to quantify rhodamine B in three real samples.

Rapid determination of trace thiabendazole in apple juice utilizing dispersive liquid–liquid microextraction combined with fluorescence spectrophotometry

Food safety has become a large concern and prompts an urgent need for the development of rapid, simple and sensitive analytical methods that can monitor pesticide residues in foods. This study aimed to provide a method for quantitative determination of trace thiabendazole in apple juice. Due to its high sensitivity and selectivity, fluorescence spectrophotometry was utilized as a front end to dispersive liquid-liquid microextraction (DLLME). The experimental parameters that influenced the extraction were systematically investigated. Under optimum conditions, the whole procedure, including DLLME and analysis of one sample, was carried out within 5 min, and linearity was found in the 5-50 µg/L range with a correlation coefficient (r) of 0.9987. The limit of detection value was 2.2 µg/L. Good reproducibility was achieved based with a less than 4.5% relative standard deviation (RSD) for five replicates at different sample concentrations. This method was shown to be suitable for rapid and sensitive quantification of thiabendazole in apple juice.

Production of stable and sensitive SERS substrate based on commercialized porous material of silanized support

This paper reports accurate synthesis of a new type of surface-enhanced Raman spectroscopy (SERS) substrate based on gold nanoparticles decorated 201 red silanized diatomaceous supports. The developed SERS substrate is easy to fabricate, cost effective and offers sensitive rapid detection. The performance and stability of the SERS substrate was investigated and the results showed good SERS activity of substrate that can last for more than 6 months. Tiazophos and phosmet pesticides in aqueous solutions can be detected clearly at a low concentration of 0.01mg/L. The new substrate was applied to detect triazophos in apple pulp at 0.2mg/kg. The results indicate that this substrate have good potential in rapid monitoring of pesticide residues in fruits and could be suitable for field-based applications, and routine laboratory analysis of chemicals.

Identification of polymer building blocks by Py–GC/MS and MALDI-TOF MS

ABSTRACT The main goal of this work is to identify polyurethane (PU) building blocks by pyrolysis gas chromatography/mass spectrometry (Py–GC/MS) and matrix-assisted laser desorption/ionization-time of flight mass spectrometry (MALDI-TOF MS). Toluene diisocyanate (TDI) and diphenylmethane diisocyanate (MDI) are widely used polymer building blocks. Py–GC/MS and MALDI-TOF MS were proved to be powerful methods to distinguish TDI-PU and MDI-PU according to the characteristic pyrolysis products and the different repeated units, respectively. In Py–GC/MS, the specific pyrolyzates are TDI for TDI-PU and MDI for MDI-PU. In MALDI-TOF MS, the weights of repeated units are 264 g/mol for TDI-PU and 340 g/mol for MDI-PU.

Ensemble Regression Coefficient Analysis for Application to Near-Infrared Spectroscopy

A new variable selection method called ensemble regression coefficient analysis is reported on the basis of model population analysis. In order to construct ensemble regression coefficients, many subsets of variables are randomly selected to calibrate corresponding partial least square models. Based on ensemble theory, the mean of regression coefficients of the models is set as the ensemble regression coefficient. Subsequently, the absolute value of the ensemble regression coefficient can be applied as an informative vector for variable selection. The performance of ensemble regression coefficient analysis was assessed by four near infrared datasets: two simulated datasets, one wheat dataset, and one tobacco dataset. The results showed that this approach can select important variables to obtain fewer errors compared with regression coefficient analysis and Monte Carlo uninformative variable elimination.