Extension of Novel Lanthanide Luminescent Mesoporous Nanostructures to Detect Fluoride

A novel polydentate type ligand derived from <i>N</i>²,<i>N</i>⁶-bis­(4,4-diethoxy-9-oxo-3-oxa-8,10-diaza-4-siladodecan-12-yl)­pyridine-2,6-dicarboxamide (<b>L</b>) has been designed, and it played essential roles in the assembly of new organic–inorganic functional materials. First, its multiple amide groups would coordinate to lanthanide ions firmly and transfer the absorbed energy to both Eu­(III) and Tb­(III) simultaneously. Second, the hydrogen-bond donor units showed strong affinity to guest anion (F^–). Third, the two silylated arms could induce the formation of sol–gel derived siloxane hybrid materials. Following this idea, two lanthanide luminescent amorphous particles (<b>ASNs-Eu</b> and <b>ASNs-Tb</b>) have been prepared for the recognition of fluoride ions. Further modification of the synthesis method and transformation to mesoporous network (<b>MSNs-Eu</b> and <b>MSNs-Tb</b>) led to much enhanced thermostabilities, larger specific surface area (from 78.5 to 515 m² g^–1 for Eu­(III); 89.6 to 487 m² g^–1 for Tb­(III)), and lower detection limits (2.5 × 10^–8 M for <b>MSNs-Eu</b> and 3.4 × 10^–8 M for <b>MSNs-Tb</b>) for the fluoride ion

Aggregation Induced Emission Mediated Controlled Release by Using a Built-In Functionalized Nanocluster with Theranostic Features

We report biological evaluation of a novel nanoparticle delivery system based on 1,1,2-triphenyl-2-(<i>p-</i>hydroxyphenyl)-ethene (TPE-OH, compound <b>1</b>), which has tunable aggregation-induced emission (AIE) characteristics. Compound <b>1</b> exhibited no emission in DMSO. In aqueous media, compound <b>1</b> aggregated, and luminescence was observed. The novel membrane–cytoplasm–nucleus sequential delivery strategy could induce apoptosis in four different kinds of cancer cells (including three adherent cell lines and one suspension cell line). The nanoparticles remained in the cytoplasm with intense blue emissions, whereas doxorubicin was observed in the nucleus with striking red luminescence. The nanoassembly was internalized in cells through an energy-dependent process. Three sorts of chemical inhibitors were used to clarify the endocytosis mechanism based on the AIE type prodrug. Furthermore, we have developed the first AIE theranostic system where drug targeting and release have been applied in an animal model

Illustrations of proposed algorithm.

<p>Illustrations of proposed algorithm.</p

Illustration of three simulated T1-weighted brain MR images with 9% noise and corresponding segmentation results obtained by each algorithm.

<p>In each subfigure, the images from left to right show: original image, segmentation results obtained by SCGM-EM, FRSCGMM, BAMM, BGGMM, GRFCM, proposed algorithm, and ground truth.</p

Computational complexity, converging time, number of iterations and per iteration time (average ± standard deviation, UNIT: Second) by applying five algorithms on BrainWeb dataset.

<p>Computational complexity, converging time, number of iterations and per iteration time (average ± standard deviation, UNIT: Second) by applying five algorithms on BrainWeb dataset.</p

Illustration of three rough regions.

<p>Illustration of three rough regions.</p

PRI values of image segmentation results on Berkeley’s color image dataset.

<p>PRI values of image segmentation results on Berkeley’s color image dataset.</p

Illustrations of estimated distributions on natural image.

<p>Illustrations of estimated distributions on natural image.</p

A Rough Set Bounded Spatially Constrained Asymmetric Gaussian Mixture Model for Image Segmentation - Fig 10

<p>DC values for: (a) GM segmentation, (b) WM segmentation, (c) CSF segmentation, (d) CCR values over the entire images obtained by applying six segmentation algorithms to simulated brain MR images with increasing noise levels.</p

Intra-class correlation coefficients (ICCs) and variances of country level random components in multilevel linear regressions.

*<p>p value<5 percent. “Intercept only” model is a multilevel model with no covariates other than the constant. “ICC” stands for “intraclass correlation coefficient”, which is calculated as the ratio of country-level variance versus total variance in the intercept-only model, and can be interpreted as the proportion of total variance attributed to the country level. The key independent variable in model 1 is “social support”; in model 2 the key independent variable is “volunteering”; in model 3 the key independent variable is “social trust”. All models control for age, gender, education, household income, marital status, religiosity, and year dummy variables.</p