Preparation and characterization of carbon-encapsulated iron nanoparticles and their catalytic activity in the hydrogenation of levulinic acid

This study describes the synthesis of carbon-encapsulated iron nanoparticles using an ultrasonic method and also investigates their catalytic activity. These nanoparticles have been prepared using ultrasonic irradiation followed by annealing at various temperatures. As the annealing temperature of as-prepared α-Fe₂O₃ nanoparticles increased, the sample transformed into γ-Fe₂O₃, Fe₃O₄, and Fe nanoparticles via the reduction process without requiring any additional reducing agents such as H₂ gas, thus, creating a carbon shell surrounding the nanoparticles. By controlling the experimental conditions, Fe nanoparticles of various sizes can be formed with diameters in the range 100–800 nm; these nanoparticles are tightly encapsulated by 20-nm-thick carbon shells. Because of their high saturation magnetization 212 emu g^?1, the carbon-encapsulated Fe nanoparticles can be used for magnetic resonance imaging with a dramatically enhanced efficiency compared to commercially available T ₂ contrast agents. Moreover, the carbon-encapsulated Fe nanoparticles showed its superior catalytic activity and reusability for the hydrogenation of biomass-derived levulinic acid to GVL (99.6 %) in liquid phase.     

Visualization Approaches for Evaluating Ridge Regression Estimators in Mixture and Mixture‐Process Experiments

When the component proportions in mixture experiments are restricted by lower and upper bounds, the input space of a designed experiment space can become an irregular region that can induce multicollinearity problems when estimating the component proportion parameters. Thus, ridge regression provides a beneficial means of stabilizing the coefficient estimates in the fitted model. Previous research has focused on using prediction variance as a metric for determining an appropriate value of the ridge constant, k. We use visualization techniques to illustrate and evaluate ridge regression estimators and the robustness of estimation with respect to the variance and the bias. The addition of bias allows better balancing between the stability of the estimators and minimally changing the estimates. We illustrate the graphical methods with mixture and mixture‐process examples from the literature. Copyright © 2014 John Wiley & Sons, Ltd.     

DNA/Tannic Acid Hybrid Gel Exhibiting Biodegradability,Extensibility, Tissue Adhesiveness,and Hemostatic Ability

DNA has emerged as a novel material in many areas of materials science due to its programmability. Especially, DNA hydrogels have been studied to incorporate new functions into gels. To date, only a few methods have been developed for fabricating DNA hydrogels, such as the use of complementary sequences or covalent bond. Herein, it is demonstrated that one of the most well‐known plant‐derived polyphenols, tannic acid (TA), can form a DNA hydrogel which is named TNA hydrogel ( T A + D NA ). TA plays a role as a “molecular glue” by a new mode of action reversibly connecting between phosphodiester bonds, which is different from the crosslinking utilizing complementary sequences. TA intrinsically degrades due to ester bonds connecting between pyrogallol groups, causing a degradable DNA hydrogel. Furthermore, TNA gel is multifunctional in that the gel is extensible upon pulling and adhesive to tissues because of the rich polyphenol groups in TA (ten phenols per TA). Unexpectedly, TNA gel exhibits superior in vivo hemostatic ability that can be useful for biomedical applications. This new DNA hydrogel preparation method represents a new technique for fabricating a large amount of DNA‐based hemostatic hydrogel without chemically modifying DNA or requiring the crosslinking by complementary sequences.     

Highly Stretchable and Sensitive Strain Sensors Using Fragmentized Graphene Foam

Stretchable electronics have recently been extensively investigated for the development of highly advanced human‐interactive devices. Here, a highly stretchable and sensitive strain sensor is fabricated based on the composite of fragmentized graphene foam (FGF) and polydimethylsiloxane (PDMS). A graphene foam (GF) is disintegrated into 200–300 μm sized fragments while maintaining its 3D structure by using a vortex mixer, forming a percolation network of the FGFs. The strain sensor shows high sensitivity with a gauge factor of 15 to 29, which is much higher compared to the GF/PDMS strain sensor with a gauge factor of 2.2. It is attributed to the great change in the contact resistance between FGFs over the large contact area, when stretched. In addition to the high sensitivity, the FGF/PDMS strain sensor exhibits high stretchability over 70% and high durability over 10 000 stretching‐releasing cycles. When the sensor is attached to the human body, it functions as a health‐monitoring device by detecting various human motions such as the bending of elbows and fingers in addition to the pulse of radial artery. Finally, by using the FGF, PDMS, and μ‐LEDs, a stretchable touch sensor array is fabricated, thus demonstrating its potential application as an artificial skin.