Die Yang

One-step green synthesis of bimetallic Fe/Pd nanoparticles used to degrade Orange II

To reduce cost and enhance reactivity, bimetallic Fe/Pd nanoparticles (NPs) were firstly synthesized using grape leaf aqueous extract to remove Orange II. Green synthesized bimetallic Fe/Pd NPs (98.0%) demonstrated a far higher ability to remove Orange II in 12h compared to Fe NPs (16.0%). Meanwhile, all precursors, e.g., grape leaf extract, Fe(2+) and Pd(2+), had no obvious effect on removing Orange II since less than 2.0% was removed. Kinetics study revealed that the removal rate fitted well to the pseudo-first-order reduction and pseudo-second-order adsorption model, meaning that removing Orange II via Fe/Pd NPs involved both adsorption and catalytic reduction. The remarkable stability of Fe/Pd NPs showed the potential application for removing azo dyes. Furthermore, Scanning Electron Microscopy (SEM) and Fourier Transform Infrared Spectroscopy (FTIR) confirmed the changes in Fe/Pd NPs before and after reaction with Orange II. High Performance Liquid Chromatography-Mass Spectrum (HPLC-MS) identified the degraded products in the removal of Orange II, and finally a removal mechanism was proposed. This one-step strategy using grape leaf aqueous extract to synthesize Fe/Pd NPs is simple, cost-effective and environmentally benign, making possible the large-scale production of Fe/Pd NPs for field remediation.

The mechanism for degrading Orange II based on adsorption and reduction by ion-based nanoparticles synthesized by grape leaf extract.

Biomolecules taken from plant extracts have often been used in the single-step synthesis of iron-based nanoparticles (Fe NPs) due to their low cost, environmental safety and sustainable properties. However, the composition of Fe NPs and the degradation mechanism of organic contaminants by them are limited because these are linked to the reactivity of Fe NPs. In this study, Fe NPs synthesized by grape leaf extract served to remove Orange II. Batch experiments showed that more than 92% of Orange II was removed by Fe NPs at high temperature based on adsorption and reduction and confirmed by kinetic studies. To understand the role of Fe NPs in the removal process of azo dye, surface analysis via X-ray photoelectron spectroscopy (XPS) and transmission electron microscopy (TEM) were employed, showing that the Fe NPs were composed of biomolecules, hydrous iron oxides and Fe(0), thus providing evidence for the adsorption of Orange II onto hydrous iron oxides and its reduction by Fe(0). Degraded products such as 2-naphthol were identified using LC-MS analysis. A degradation mechanism based on asymmetrical azo bond cleavage for the removal of Orange II was proposed.

Characterization of bimetallic Fe/Pd nanoparticles by grape leaf aqueous extract and identification of active biomolecules involved in the synthesis.

This paper reports the detailed composition and morphology of one-step green synthesized bimetallic Fe/Pd nanoparticles (NPs) using grape leaf aqueous extract and identification of active biomolecules involved in the synthesis employing various techniques. Transmission Electron Microscopy (TEM) and Scanning Electron Microscopy (SEM) revealed that Fe/Pd NPs were polydispersed and quasi-spherical with a diameter ranging from 2 to 20nm. X-ray Photoelectron Spectroscopy (XPS) and Energy Dispersive X-ray Spectroscopy (EDS) provided evidence for the composition of Fe and Pd and for their species existing on the surface of Fe/Pd NPs. In addition, biomolecules in the grape leaf aqueous extract were identified but their functions are still unclear. Biomolecules in the aqueous extract such as methoxy-phenyl-oxime, N-benzoyl-2-cyano-histamine, 2-ethyl-phenol, 1,2-benzenediol, β-hydroxyquebracamine, hydroquinone, 2-methoxy-4-vinylphenol, 5-methyl-2-furancarboxaldehyde, 4-(3-hydroxybutyl)-3,5,5-trimethyl-2-cyclohexen and some polyphenolic compounds were identified as reducing and capping agents, which were studied by Chromatography-Mass Spectroscopy (GC-MS), XPS and Fourier Transform Infrared Spectroscopy (FTIR). Our finding suggests a new insight into cost-effective, simple, and environmentally benign production of bimetallic Fe/Pd NPs.

Speciation analysis of inorganic tin by on-column complexation ion chromatography with inductively coupled plasma mass spectrometry and electrospray mass spectrometry.

Inductively coupled plasma-mass spectrometry (ICP-MS) and electrospray mass spectrometry (ESI-MS) were used as complementary methods to identify Sn-pentaacetic acid (DTPA) complex formation. ESI-MS was used to initially confirm the formation of [Sn(DTPA)](3-) and [Sn(DTPA)](1-) and their MS spectra suggest these tin complexes were stable in solution. On-column complexation of tin with DTPA and the separation of [Sn(DTPA)](3-) and [Sn(DTPA)](1-) was performed on anion-exchange chromatography with an mobile phase containing 20mM NH4NO3 and 3mM DTPA at pH 6.0, and the subsequent detection of [Sn(DTPA)](3-) and [Sn(DTPA)](1-) was achieved by ICP-MS. Linear plots were obtained in a concentration range of 1.0-1000 μg L(-1) with detection limits ranging from 0.1 to 0.3 μg L(-1). The developed procedure allows the simultaneous determination of [Sn(DTPA)](3-) and [Sn(DTPA)](1-) in less than 5 min with a RSD between 2.1 and 2.7%. The recoveries of [Sn(DTPA)](3-) and [Sn(DTPA)](1-) were found to be 96.8 and 99.4%, respectively, when the sample was spiked with 20 μg L(-1) standard. Finally, the proposed procedure was used for the determination of tin species in contaminated water.

Application of neural networks with novel independent component analysis methodologies to a Prussian blue modified glassy carbon electrode array

Sodium potassium absorption ratio (SPAR) is an important measure of agricultural water quality, wherein four exchangeable cations (K(+), Na(+), Ca(2+) and Mg(2+)) should be simultaneously determined. An ISE-array is suitable for this application because its simplicity, rapid response characteristics and lower cost. However, cross-interferences caused by the poor selectivity of ISEs need to be overcome using multivariate chemometric methods. In this paper, a solid contact ISE array, based on a Prussian blue modified glassy carbon electrode (PB-GCE), was applied with a novel chemometric strategy. One of the most popular independent component analysis (ICA) methods, the fast fixed-point algorithm for ICA (fastICA), was implemented by the genetic algorithm (geneticICA) to avoid the local maxima problem commonly observed with fastICA. This geneticICA can be implemented as a data preprocessing method to improve the prediction accuracy of the Back-propagation neural network (BPNN). The ISE array system was validated using 20 real irrigation water samples from South Australia, and acceptable prediction accuracies were obtained.

Application of neural networks with novel independent component analysis methodologies for the simultaneous determination of cadmium, copper, and lead using an ISE array

The paper introduces a novel chemometric strategy based on independent component analysis (ICA) coupled with a back‐propagation neural network. In this approach, one of the most popular ICA methods, the fast fixed‐point algorithm for ICA (fastICA), was implemented by the genetic algorithm (geneticICA) to avoid the local maxima problem commonly observed with fastICA. As a case study, an ion‐selective electrode (ISE) array, consisting of three working electrodes and one reference electrode, was used for the simultaneous determination of three heavy metals (cadmium, copper, and lead) in aqueous solutions, which are normally prone to severe interferences. The robustness and appropriateness of the approach were assessed using the average mean of relative error (MRE) of triplicated external validation. After configuration and optimization, the average MRE for Cu was <5%. For the determination of Cd and Pb, whose ISEs normally cannot tolerate Cu ions even at the microgram per liter levels, the MREs were 8%. This article demonstrated that this approach can be applied to the detection of heavy metal contamination in industrial wastewater with prediction accuracies comparable with other popular quantitative chemometric neural network methods. Copyright