Rapid Synthesis of Dual-Responsive Hollow Capsules with Controllable Membrane Thickness by Surface-Initiated SET-LRP Polymerization

We present a facile and highly effective route to construct a dual-responsive polymeric capsule with photo-cross-linkable property based on surface-initiated single electron transfer living radical polymerization (SI-SET-LRP) exploiting silica particles as templates, dimethyl­aminoethyl methacrylate (DMAEMA) as dual-responsive component, and 2-hydroxy-4-(methacryloyloxy)­benzophenone (BMA) as an effective photo-cross-linker. This approach is highly efficient with complete monomer conversion in 15 min at ambient temperature resulting in wall thickness of 55 nm and usable in technical applications. Hollow capsules are available after photo-cross-linking of polymeric shell and removing silica particle, of which the morphology and composition were confirmed by employing a range of techniques, such as FTIR, TGA, TEM, SEM, cryo-TEM, DLS, GPC, and UV–vis spectroscopy. Thus, it represents a significant advance in the development of complex polymeric capsules synthesis usable for various applications (e.g., biotechnology and systems biology)

Fluorescence Detection of a Broad Class of Explosives with One Zinc(II)-Coordination Nanofiber

In this work, we report the development of one fluorescent carbazole-based oligomer <b>1</b>-zinc­(II) coordination nanofiber which enabled the detection of five classes of explosives, i.e., nitroaromatics (dinitrotoluene, DNT, and trinitrotoluene, TNT), aliphatic nitro-organics (2,3-dimethyl-2,3-dinitrobutane, DMNB), nitramines (cyclotrimethylenetrinitramine, RDX), nitro-esters (pentaerythritol tetranitrate, PETN), and black powder (sulfur). We demonstrate that the coordination of zinc ion with a carbazole-based oligomer <b>1</b> allows the formation of the Lewis acid–base complex between explosives and the nanofiber that enhances the electron-accepting ability of the nitro-based explosives and the binding interactions between the sensing nanofibers and explosives. Furthermore, the resulting nanofiber-based sensor exhibited highly sensitive fluorescence quenching when exposed to trace sulfur, thereby enabling the sensitive detection of black powder. Herein, we present a new fluorescent sensor for five classes of explosives, which represents an important advance toward a richer identification of threats

Relationships between best-corrected visual acuity (BCVA) and orientation discrimination threshold (ODT) in patients with different classifications of age-related macular degeneration (AMD).

<p>Relationships between best-corrected visual acuity (BCVA) and orientation discrimination threshold (ODT) in patients with different classifications of age-related macular degeneration (AMD).</p

Sensitive Detection of a Nerve-Agent Simulant through Retightening Internanofiber Binding for Fluorescence Enhancement

In this work, we develop fluorescent hierarchical nanofiber bundles <b>1</b>, which involve the internanofiber hydrogen-bonding interactions, for rapid and sensitive detection of diethyl chlorophosphate (DCP) vapor. First, the internanofiber hydrogen-bonding strength can be weakened by photoexcitation, which thereby increases the internanofiber spacing and decreases the fluorescence intensity. Then, when exposed to trace DCP vapor, the strong interactions between DCP and hydroxyl groups on the nanofibers can effectively retighten the nanofibers and enhance the fluorescence as the detection signal. In contrast, the interferences, such as common organic solvents, cannot retighten nanofiber bundles <b>1</b> because of the lack of strong interactions with the nanofibers. On the basis of this novel detection mechanism, fluorescence detection of DCP with rapid signal response (ca. 3 s) and high sensitivity (15 ppb) is achieved

Method to calculate the normalized effective area size (NEAS) of each lesion.

<p><b>(A)</b> A circular grid representing the macular region with the center at the foveola. The inner circle represents the central 4 deg and outer circle extends to the central 8 deg. Each sector of the inner circle (sectors 1–4) carries a weight of 1/6 and each sector of the outer circle (sectors 5–12) carries a weight of 1/24. As a whole, the central 8 deg had a weight of 1 (4 × 1/6 + 8 × 1/24 = 1). <b>(B)</b> Example of using the grid to estimate the NEAS of a pigment epithelium detachment lesion (PED, 0.833). <b>(C)</b> Example of using the grid to estimate the NEAS of a sub-retinal fluid lesion (SRF, 0.920). The images in panels B and C correspond to the lesions shown in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0185070#pone.0185070.g003" target="_blank">Fig 3E and 3F</a>.</p

Optical coherence tomography (OCT) images show examples of lesions in different classifications of age-related macular degeneration (AMD).

<p><b>(A)</b> Dry AMD. Dark arrows indicate drusens; <b>(B)</b> Non-active wet AMD, yellow arrow indicates a pigment epithelium detachment (PED); <b>(C-F)</b> Active wet AMD. Blue stars indicate sub-retinal fluid (SRF), the red star indicates intra-retinal fluid (IRF), and the green triangle indicates scarring.</p

Demographic data, macular lesions, and their locations, size, and associated normalized effective area size (NEAS) of age-related macular degeneration (AMD) patients with different classifications.

<p>Demographic data, macular lesions, and their locations, size, and associated normalized effective area size (NEAS) of age-related macular degeneration (AMD) patients with different classifications.</p

Smoothed probability density plots showing the distribution of best-corrected visual acuity (BCVA) (A) and orientation discrimination threshold (ODT) (B).

<p>Smoothed probability density plots showing the distribution of best-corrected visual acuity (BCVA) (A) and orientation discrimination threshold (ODT) (B).</p

Normalized multivariate-regression weights of different types of lesions for the variation of orientation discrimination threshold (ODT) and best-corrected visual acuity (BCVA).

<p>Normalized weights of lesions’ locations <b>(A)</b>, sizes <b>(B)</b>, and NEAS <b>(C)</b> for ODT; normalized weights of lesions’ locations <b>(D)</b>, sizes <b>(E)</b>, and NEAS <b>(F)</b> for BCVA. A filled bar indicates a significant contribution to the regression model.</p

Relationships between best-corrected visual acuity (BCVA) and orientation discrimination threshold (ODT) in patients with different classifications of age-related macular degeneration (AMD).

<p>Relationships between best-corrected visual acuity (BCVA) and orientation discrimination threshold (ODT) in patients with different classifications of age-related macular degeneration (AMD).</p