Uptake of Fluoride from Aqueous Solution on Nano-Sized Hydroxyapatite: Examination of a Fluoridated Surface Layer

Hydroxyapatite (Ca₁₀(PO₄)₆(OH)₂, HAP), both as a synthetic material and as a constituent of bone char, can serve as an effective and relatively inexpensive filter material for fluoride (F^–) removal from drinking water in low-income countries. Fluoride uptake on HAP can occur through different mechanisms, which are, in principle, influenced by solution composition. Suspensions of HAP (2 g L^–1) were equilibrated under controlled pH conditions (pH 6.5, 7.3, 9.5) at 25 °C for 28 d after the addition of different F^– concentrations (0.5–7.0 mM). The reacted HAP solids were examined with Transmission Electron Microscopy (TEM), Fourier Transform Infrared Spectroscopy (FTIR), X-ray Photoelectron Spectroscopy (XPS), and Nano Secondary Ion Mass Spectroscopy (NanoSIMS). Fluoride uptake on HAP was dependent on pH, with the highest capacity at pH 6.5; the lowest uptake was found at pH 9.5. Under all experimental conditions, the thermodynamically stable mineral phase was fluorapatite, (Ca₁₀(PO₄)₆F₂, FAP). Fluoride uptake capacity was quantified on the basis of FTIR and XPS analysis, which was consistent with F^– uptake from solution. The results of XPS and NanoSIMS analyses indicate that a fluoridated surface layer with a thickness of several nanometers is formed on nanosized HAP

Ionic silver-mediated expression regulation.

<p>(A) Hierarchical clustering of expression ratios between growth conditions with 5.0, 6.5 or 8.5 μM of AgNO₃ in the medium and the control (i.e., no ionic silver added) for the 3,486 differentially-expressed genes in one of the three ionic-silver mediated stress conditions. The resulting heat map shows the samples in rows and the genes in columns, red indicates up-regulation, green down-regulation, and black no change. (B) The expression of genes in each cluster is plotted as a function of the concentration of AgNO₃ in the medium to show the general trend of expression regulation. The red line in the individual clusters represents the median calculated from the expression levels of the genes. Cluster 1: 1,131 genes, cluster 2: 746 genes, cluster 3: 516 genes, cluster 4: 76 genes, cluster 5: 881 genes and cluster 6: 136 genes.</p

Distribution maps of CN (in grey), P (in blue), S (in green) and Ag (in red) elements detected by NanoSIMS analysis in <i>E</i>. <i>coli</i> cells after 3 h of culture without ionic silver (control) or in the presence of 5.0, 6.5 and 8.5 μM of AgNO₃.

<p>The fourth column corresponds to overlapping of the three-coloured images (Composite Image mode [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0145748#pone.0145748.ref023" target="_blank">23</a>]). Scale bar: 2 μm.</p

Relevance networks resulting from the sPLSR approach between the Y matrix composed of selected sFTIR data in the region of fatty acids (3,100–2,800 cm^-1) and the X matrix corresponding to the expression of genes involved in fatty acid metabolism.

<p>The gene list was restricted to the 106 differentially-expressed genes annotated as involved in fatty acid and phospholipid metabolism and lipid transport (GO:0006631, GO:0006644 and GO:0006869). A threshold of 0.5 was used for relevant correlations. Green and red edges indicate positive and negative correlations respectively. Wavenumbers are represented as ellipses and genes as rectangles.</p

Results of the sPLSR integrative approach between the Y matrix, corresponding to AgNO₃ concentrations and physiological parameters (i.e., specific growth rate μ_{t = 3h}, number of cultivable cells (CFU/mL/ODU) and intracellular silver content illustrated by the Ag/CN ratio at 3 h culture) and the X matrix composed of the selected sFTIR data (i.e. the regions 3,100–2,800 cm^-1, 1,700–1,600 cm^-1, 1,600–1,480 cm^-1 and 1,300–1,150 cm^-1, representative of fatty acids, C = O stretching vibration, amide I and amide II bands and PO2- groups and C-O stretching mode, respectively).

<p>The regions 2,800–2,700 cm^-1, 1,800–1,700 cm^-1 and 1,480–1,300 cm^-1 are grouped under “Others”. (A) Correlation circle plots for dimensions 1 and 2. The subsets of correlated wavenumbers are represented by thick points coloured according to the legend, while the physiological parameters are represented by their name in black. (B) Relevance networks resulting from the sPLSR approach. A threshold of 0.5 was used for relevant correlations. Green and red edges indicate positive and negative correlations, respectively. Physiological parameters and wavenumbers are represented as circles and ellipses, respectively.</p

Variation in the Ag/CN ratio with the extracellular AgNO₃ concentration deduced from NanoSIMS analysis.

<p>For each condition, the number of bacterial cells used for calculation (n) is indicated.</p

Relevance networks resulting from the sPLSR approach between the Y matrix corresponding to AgNO₃ concentrations and physiological parameters (i.e., specific growth rate μ_{t = 3h}, number of cultivable cells (CFU/mL/ODU) and intracellular silver content illustrated by the Ag/CN ratio at 3 h culture) and the X matrix representing the expressions of the 3,486 differentially expressed genes in one of the three AgNO₃-mediated stress growth conditions.

<p>A threshold of 0.5 was used for relevant correlations. Green and red edges indicate positive and negative correlations, respectively. Physiological parameters are represented as circles and genes as rectangles.</p

Correlative Microscopy Combining Secondary Ion Mass Spectrometry and Electron Microscopy: Comparison of Intensity–Hue–Saturation and Laplacian Pyramid Methods for Image Fusion

Correlative microscopy combining various imaging modalities offers powerful insights into obtaining a comprehensive understanding of physical, chemical, and biological phenomena. In this article, we investigate two approaches for image fusion in the context of combining the inherently lower-resolution chemical images obtained using secondary ion mass spectrometry (SIMS) with the high-resolution ultrastructural images obtained using electron microscopy (EM). We evaluate the image fusion methods with three different case studies selected to broadly represent the typical samples in life science research: (i) histology (unlabeled tissue), (ii) nanotoxicology, and (iii) metabolism (isotopically labeled tissue). We show that the intensity–hue–saturation fusion method often applied for EM-sharpening can result in serious image artifacts, especially in cases where different contrast mechanisms interplay. Here, we introduce and demonstrate Laplacian pyramid fusion as a powerful and more robust alternative method for image fusion. Both physical and technical aspects of correlative image overlay and image fusion specific to SIMS-based correlative microscopy are discussed in detail alongside the advantages, limitations, and the potential artifacts. Quantitative metrics to evaluate the results of image fusion are also discussed

Additional file 1: Figure S1. of Effects of silver nanoparticles and ions on a co-culture model for the gastrointestinal epithelium

Mucus layer characterization (Alcian blue staining). Figure S2. Mucus layer characterization (Toluidine blue staining and TEM). Figure S3. Mucus layer characterization (Toluidine blue staining, top view). Figure S4. Cell monolayer integrity evaluation (TEER). Figure S5. Cell-free DCFH-DA assay. Figure S6. TEM images of cells in co-culture exposed to Ag particles. Figure S7. Hierarchical clustering. Table S1. Detailed information on protein identification. Table S2. Cellular Ag content determination. Table S3. KEGG enrichment analysis. (DOCX 1.17 mb