Ultrasmall Superparamagnetic Iron Oxide Nanoparticles with Europium(III) DO3A as a Bimodal Imaging Probe

A new prototype consisting of ultrasmall superparamagnetic iron oxide (USPIO) nanoparticles decorated with europium(III) ions encapsulated in a DO3A organic scaffold was designed as a platform for further development of bimodal contrast agents for MRI and optical imaging. The USPIO nanoparticles act as negative MRI contrast agents, whereas the europium(III) ion is a luminophore that is suitable for use in optical imaging detection. The functionalized USPIO nanoparticles were characterized by TEM, DLS, XRD, FTIR, and TXRF analysis, and a full investigation of the relaxometric and optical properties was conducted. The typical luminescence emission of europium(III) was observed and the main red emission wavelength was found at 614 nm. The relaxometric study of these ultrasmall nanoparticles showed r₂ values of 114.8 mm ^?1_Fes^?1 at 60 MHz, which is nearly double the r₂ relaxivity of Sinerem^®.     

Parallelizing Nonlinear Least-Squares Regression with Application to Analyses of Microalgae

Nonlinear least-squares regression is a valuable tool for gaining chemical insights into complex systems. Yet, the success of nonlinear regression as measured by residual sum of squares (RSS), correlation, and reproducibility of fit parameters strongly depends on the availability of a good initial solution. Without such, iterative algorithms quickly become trapped in an unfavorable local RSS-minimum. For determining an initial solution, a high-dimensional parameter space needs to be screened, a process that is very time-consuming but can be parallelized. Another advantage of parallelization is equally important: After determining initial solutions, the used processors can be tasked to each optimize an initial guess. Even if several of these optimizations become stuck in a shallow local RSS-minimum, other processors continue and improve the regression outcome. A software package for parallel processing-based constrained nonlinear regression (RegressionLab) has been developed, implemented, and tested on a variety of hardware configurations. As proof-of-principle, microalgae to environment interactions have been studied by infrared attenuated total reflection spectroscopy. Additionally, light microscopy has been used to monitor cell production. It is shown that spectroscopic data sets with 10,000?s of data points and >1000 nonlinear model parameters as well as imaging data with 100,000s of data points and >2000 nonlinear model parameters may now be investigated by constrained nonlinear regression. Acceleration factors of up to 8.1 have been obtained which is of high practical relevance when computations take weeks on single-processor machines. Solely using parallel processing, the RSS values may be improved up to a factor of 5.5.     

On approximate analytical solutions of differential equations in enzyme kinetics using homotopy perturbation method

Homotopy perturbation method is used to extend the approximate analytical solutions of non-linear reaction equations describing enzyme kinetics for combinations of parameters for which solutions obtained in previous works are not valid. Also, by constructing a new homotopy, alternative approximate analytical expressions for substrate, substrate-enzyme complex and product concentrations are found. These first-order approximate solutions give more accurate results than the second-order approximations derived in previous works.     

Facile non‐lithographic route to highly aligned silica nanopatterns using unidirectionally aligned polystyrene‐block‐polydimethylsiloxane films

Thin films (monolayer and bilayer) of cylinder forming polystyrene‐block‐polydimethylsiloxane (PS‐b‐PDMS) were shear aligned by the swelling and deswelling of a crosslinked PDMS pad that was physically adhered to the film during solvent vapor annealing. The nanostructures formed by self‐assembly were exposed to ultraviolet‐ozone to partially oxidize the PDMS, followed by calcination in air at 500 °C. In this process, the PS segments were fully decomposed, while the PDMS yielded silica nanostructures. The highly aligned PDMS cylinders were thus deposited as silica nanolines on the silicon substrate. Using a bilayer film, the center‐to‐center distance of these features were effectively halved from 38 to 19 nm. Similarly, by sequential shear‐alignment of two distinct layers, a rhombic array of silica nanolines was fabricated. This methodology provides a facile route to fabricating complex topographically patterned nanostructures. © 2015 Wiley Periodicals, Inc. J. Polym. Sci., Part B: Polym. Phys. 2015 , 53, 1058–1064     

Structural rearrangement and stiffening of hydrophobically modified supramolecular hydrogels during thermal annealing

Dynamic crosslinks formed by thermoreversible associations provide an energy dissipation mechanism to toughen hydrogels. However, the details of the organization of these crosslinks impact the hydrogel properties through constraints on the network chain conformation. The physical crosslinks generated by hydrophobic association of the 2‐(N‐ethylperfluorooctane‐sulfonamido)ethyl methacrylate (FOSM) groups in a random copolymer of N,N‐dimethylacrylamide (DMA) and FOSM provide a simple system to investigate how the hydrogel structure (as determined from small angle neutron scattering impacts the mechanical properties of the hydrogel. The initial hydration of the copolymer at 25 °C leads to a kinetically trapped structure with large‐scale heterogeneities. Heating the hydrogel at 60 °C, which is above the glass transition temperature for the FOSM domains, allows the hydrogel structure to rearrange to reduce the density of network defects and the structural heterogeneities. That effectively increases the crosslink density of the network, which stiffens the hydrogel and decreases the swelling at equilibrium at 25 °C. The processing history determines how the hydrophobes aggregate to form the physically crosslinked network, whose structure defines the mechanical properties of these hydrogels. © 2017 Wiley Periodicals, Inc. J. Polym. Sci., Part B: Polym. Phys. 2017 , 55, 1036–1044     

Impact of chain architecture (branching) on the thermal and mechanical behavior of polystyrene thin films

The modulus and glass transition temperature (T_g) of ultrathin films of polystyrene (PS) with different branching architectures are examined via surface wrinkling and the discontinuity in the thermal expansion as determined from spectroscopic ellipsometry, respectively. Branching of the PS is systematically varied using multifunctional monomers to create comb, centipede, and star architectures with similar molecular masses. The bulk‐like (thick film) T_g for these polymers is 103 ± 2 °C and independent of branching and all films thinner than 40 nm exhibit reductions in T_g. There are subtle differences between the architectures with reductions in T_g for linear (25 °C), centipede (40 °C), comb (9 °C), and 4 armed star (9 °C) PS for ≈ 5 nm films. Interestingly, the room temperature modulus of the thick films is dependent upon the chain architecture with the star and comb polymers being the most compliant (≈2 GPa) whereas the centipede PS is most rigid (≈4 GPa). The comb PS exhibits no thickness dependence in moduli, whereas all other PS architectures examined show a decrease in modulus as the film thickness is decreased below ～40 nm. We hypothesize that the chain conformation leads to the apparent susceptibility of the polymer to reductions in moduli in thin films. These results provide insight into potential origins for thickness dependent properties of polymer thin films. © 2011 Wiley Periodicals, Inc. J Polym Sci Part B: Polym Phys, 2012     

Unsupervised cell identification on multidimensional X‐ray fluorescence datasets

A novel approach to locate, identify and refine positions and whole areas of cell structures based on elemental contents measured by X‐ray fluorescence microscopy is introduced. It is shown that, by initializing with only a handful of prototypical cell regions, this approach can obtain consistent identification of whole cells, even when cells are overlapping, without training by explicit annotation. It is robust both to different measurements on the same sample and to different initializations. This effort provides a versatile framework to identify targeted cellular structures from datasets too complex for manual analysis, like most X‐ray fluorescence microscopy data. Possible future extensions are also discussed.