Synthesis, Structure, and Reactivity Study of Iron(II) Complexes with Bulky Bis(anilido)thioether Ligation

The synthesis, molecular structure, and ligand substitution reactivity of iron­(II) complexes bearing the bulky <i>N</i>,<i>N</i>′-dimesityl-2,2′-diamidophenyl sulfide ligand have been studied. The ligand H₂(^mesNSN) (<b>1</b>) was synthesized by a Pd-mediated Buchwald–Hartwig amination method. An amine elimination reaction between <b>1</b> and [Fe­(NTMS₂)₂]₂ afforded the high-spin complex [(^mesNSN)­Fe­(THF)] (<b>2</b>), displaying a distorted trigonal-monopyramidal geometry. Interaction of <b>2</b> with PMe₃ and 2,5-di-<i>tert</i>-butylimidazol-1-ylidene (IBu^<i>t</i>) gave the ligand substitution products [(^mesNSN)­Fe­(PMe₃)] (<b>3</b>) and [(^mesNSN)­Fe­(IBu^<i>t</i>)] (<b>4</b>), respectively. Both <b>3</b> and <b>4</b> are high spin and display molecular geometry similar to that of <b>2</b>. The reaction of <b>2</b> with 3 equiv of isocyanide gave the low-spin complexes [(^mesNSN)­Fe­(CNR)₃] (R = Bu^<i>t</i> (<b>5</b>), Ph-2,6-Me₂ (<b>6</b>)). Recrystallization of <b>6</b> has led to the isolation of the carbon–sulfur bond cleavage product [(^mesNS)­Fe­(CNPh-2,6-Me₂)₃] (<b>7</b>). Quite unexpectedly, the interaction of <b>2</b> with 3-hexyne and deuterated benzene could induce Fe–N­(amido) bond cleavage, giving [(^mesHNSN)₂Fe­(THF)] (<b>8</b>) and [(^mesHNSN)₂Fe] (<b>9</b>), respectively. The formation of <b>7</b>–<b>9</b> suggests the lability of the [(^mesNSN)­Fe] fragment, which could suffer from degradation in the presence of bulky strong field ligands

Multiscale Modeling and Optimization of Nanoclearcoat Curing for Energy Efficient and Quality Assured Coating Manufacturing

Nanopaint is a new type of coating material that could offer significantly improved coating performance and/or a number of new functionalities, such as super-scratch-resistance, self-healing, surface texture alteration control, and toxic chemical/acid/corrosive agent repelling. However, how to ensure the achievement of the anticipated nanocoating performance and functionalities in coating manufacturing is a great challenge. In this paper, we introduce a multiscale modeling and analysis methodology for characterizing nanoclearcoat curing in a multistage manufacturing system. The methodology provides an opportunity to gain a deep understanding of the nanocoating formation process, which involves solvent evaporation, cross-linking reaction, and film thickness change at the presence of nanoparticles in the coating layer. The information provided by the integrated model facilitates the analysis of nanocoating quality and the development of optimal operation strategies for energy efficient coating manufacturing. Methodological efficacy is demonstrated through a comprehensive case study

Accurate Construction of Photoactivated Localization Microscopy (PALM) Images for Quantitative Measurements

<div><p>Localization-based superresolution microscopy techniques such as Photoactivated Localization Microscopy (PALM) and Stochastic Optical Reconstruction Microscopy (STORM) have allowed investigations of cellular structures with unprecedented optical resolutions. One major obstacle to interpreting superresolution images, however, is the overcounting of molecule numbers caused by fluorophore photoblinking. Using both experimental and simulated images, we determined the effects of photoblinking on the accurate reconstruction of superresolution images and on quantitative measurements of structural dimension and molecule density made from those images. We found that structural dimension and relative density measurements can be made reliably from images that contain photoblinking-related overcounting, but accurate absolute density measurements, and consequently faithful representations of molecule counts and positions in cellular structures, require the application of a clustering algorithm to group localizations that originate from the same molecule. We analyzed how applying a simple algorithm with different clustering thresholds (<em>t_Thresh</em> and <em>d_Thresh</em>) affects the accuracy of reconstructed images, and developed an easy method to select optimal thresholds. We also identified an empirical criterion to evaluate whether an imaging condition is appropriate for accurate superresolution image reconstruction with the clustering algorithm. Both the threshold selection method and imaging condition criterion are easy to implement within existing PALM clustering algorithms and experimental conditions. The main advantage of our method is that it generates a superresolution image and molecule position list that faithfully represents molecule counts and positions within a cellular structure, rather than only summarizing structural properties into ensemble parameters. This feature makes it particularly useful for cellular structures of heterogeneous densities and irregular geometries, and allows a variety of quantitative measurements tailored to specific needs of different biological systems.</p> </div

Quantitative measurements of a simulated cluster dataset.

<p>(A) Representative cluster diameter measurement for a reference image with no repeat localizations. Each cluster is identified by eye, and then fit to a two-dimensional, symmetrical Gaussian distribution (blue mesh). The cluster diameter is measured as the FWHM, calculated as 2.35*σ, where σ is the fitted Gaussian standard deviation. The average FWHM of these four clusters is 74±1 nm. (B) Cluster diameter values (average of four clusters) calculated from images generated by applying different threshold pairs to the same simulated dataset. The measured diameters decrease with increasing threshold values, similarly to the Z-ring width measurement. (C) The fraction of molecules located in clusters (<i>f_cluster</i>) is most similar to that measured in the reference image (0.47) for low values of both <i>d_Thresh</i> and <i>t_Thresh</i>. (D) As with the Z-ring simulation, fractional difference between each reconstructed image and the number of molecules in the reference image (<i>N_ref</i>  = 1212) is lowest along two intersecting valleys. (E) The Jaccard index peak position for the cluster simulation is similar to that in the Z-ring simulation where identical kinetic parameters were used (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0051725#pone-0051725-g005" target="_blank">Figure 5B</a>). This simulated dataset was generated using the following parameters: N_total  = 2000 (50% in clusters),  = 200, FWHM_cluster  = 50 nm, σ  = 15 nm, blink>  = 2, <τ_off>  = 1 frame, <τ_on>  = 1 frame, <τ⁰_act>  = 5 frames (1 frame  = 50 ms).</p

<i>In vitro</i> characterization of mEos2.

<p>(A) A typical <i>in vitro</i> image of purified mEos2 molecules sparsely distributed on a cover glass, acquired using the same PALM imaging condition as the <i>in vivo</i> cell sample. All localized positions are indicated by small, filled circles that are colored by detection time. Localizations belonging to the same molecule are enclosed in a larger, open circle, which is colored by the mean detection time of all the enclosed localizations. The inset shows details of a single cluster, which contains four localizations (filled circles with black outlines). (B) Histogram of localizations per molecule from 515 molecules fitted with an exponential distribution (red line), which yielded a mean of 0.9±0.1 localizations per molecule. The value of α (2.4±2.8) represents the mean of observed molecules that lasted at least one frame, and is consequently larger than the fitted mean.</p

Effects of threshold selection on molecule density distribution in the Z-ring.

<p>(A) Histogram (gray bars) of molecules per pixel (15 nm ×15 nm) inside the Z-ring of a simulated image that was not processed with a clustering algorithm. (B) Histogram (gray bars) of molecules per pixel of the corresponding reference image, where each molecule is represented only once. Poisson distributions simulated with the sample means, 3.9 (A) and 1.2 (B) molecules per pixel, are shown in red. The ratio of mean values reflects the localization of each molecule approximately three times due to the simulated photoblinking kinetics (blink>  = 2, <τ_off>  = 1 frame, <τ_on>  = 1 frame). Poisson goodness-of-fit tests resulted in p_GOF  = 0 for distribution in (A), suggesting that blinking results in deviations from a Poisson density distribution (p_GOF  = 0.74 for the reference distribution in (B)). Insets show the cropped Z-ring regions used to generate the histograms. (C) p-values from the KS-test when the molecule density distribution of the Z-ring generated by the reference image (B) is compared with distributions in images generated with different threshold pairs. Distributions that resulted in p_KS >0.05 are not significantly different from the distribution in the reference image. Dataset analyzed is the same simulated dataset shown in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0051725#pone-0051725-g002" target="_blank">Figure 2</a>–<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0051725#pone-0051725-g003" target="_blank">3</a>.</p

Effects of threshold selection on mean and relative molecule density.

<p>(A) and (B) Total number of molecules, <i>N</i>, in images generated by applying different threshold pairs to an experimental dataset (A) and a simulated dataset (B). (C) Fractional difference |(<i>N-N_ref)/N_ref</i>| between each reconstructed simulated image and the number of molecules in the reference simulated image (<i>N_ref</i>  = 1248). Images with small fractional differences (dark areas) are generated from threshold pairs found along two intersecting valleys. (D) and (E) Fraction of molecules located at the midplane (<i>f_midcell</i>) in images generated by applying different threshold pairs for an experimental dataset (D) and a simulated dataset (E). In the reference image, <i>f_midcell</i>  = 0.53, which is most similar to the values calculated from images generated using low values of both <i>d_Thresh</i> and <i>t_Thresh</i>. Datasets analyzed are the same as those shown in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0051725#pone-0051725-g002" target="_blank">Figure 2</a>.</p

<b>Table1.</b> Quantitative measurements made from the optimized experimental image.

*<p>True values for ring width and <i>f_midcell</i> are those measured from the original image. **True value for <i>N</i> is the number of molecules in the original image divided by α_mEos2 (1204/2.2 = 547).</p

Fluorophore blinking affects superresolution image quality.

<p>(A) Simplified kinetic scheme of a photoactivatable fluorophore such as mEos2. The fluorophore is irreversibly photoactivated with rate constant k₁, can transiently access a nonfluorescent state with rate constant k₂, return to the fluorescent state with rate constant k₃, and irreversibly photobleach with rate constant k₄. (B) Superresolution image of an <i>E. coli</i> cell expressing FtsZ-mEos2 generated with conventional clustering thresholds: spots within 167 nm (1 camera pixel) and 50 ms (1 frame) of each other were grouped together and plotted once. The cytoplasmic cluster (left inset) consists of spots detected very closely in time, suggesting that they came from the same fluorophore, whereas a dense section inside the Z-ring (right inset) contains spots detected throughout the experiment. Scale bar, 500 nm. Inset grid size, 30 nm.</p

Relationship between Jaccard index, measurement error, and activation rate across different simulated datasets.

<p>(A) Minimum combined measurement error, ε_all, for each dataset plotted against and the corresponding Jaccard index value. ε_all was defined as the worst fractional measurement error of the three bulk measurements: <i>N, f_midcell</i>, and ring width when compared to the reference image. Images with low measurement error do not always correlate with high clustering accuracy (Jaccard index), and thus cannot ensure reliable lists of molecule counts and positions. (B) Maximum Jaccard index plotted against the ratio of the average time between localizations in the 255 nm ×255 nm maximum density region, <i>Δt_max</i>, and the average time between repeat localizations of the same molecules, <i>Δt_repeat</i>, calculated for each simulated dataset. Simulations with higher ratios of <i>Δt_max</i>/<i>Δt_repeat</i> result in higher Jaccard index values. (C) Comparison of maximum Jaccard index with Jaccard index identified at the intersection of the |(<i>N-N_ref</i>)<i>/N_ref</i>| plot for each simulated dataset. The two values agree well when the maximum Jaccard index is greater than 0.8. Simulation parameters can be found in Table S1 and S2. In all plots, Z-ring simulations are shown in blue and cluster simulations are shown in red.</p