Nanocarbon materials, including single-walled carbon nanotubes (SWCNTs) and graphene, promise various novel biomedical applications (e.g., nanoelectronic biosensing). In this Letter, we study the ability of SWCNT networks and reduced graphene oxide (rGO) films in interfacing several types of cells, such as neuroendocrine PC12 cells, oligodendroglia cells, and osteoblasts. It was found that rGO is biocompatible with all these cell types, whereas the SWCNT network is inhibitory to the proliferation, viability, and neuritegenesis of PC12 cells, and the proliferation of osteoblasts. These observations could be attributed to the distinct nanotopographic features of these two kinds of nanocarbon substrates.

Mastery of the structure of nanomaterials enables control of their properties to enhance their performance for a given application. Herein we demonstrate the synthesis of metal nanomaterials with hollow interiors or cage-bell structures based on the inside-out diffusion of Ag in core-shell structured nanoparticles. It begins with the synthesis of core-shell Ag-M or core-shell-shell M(A)-Ag-M(B) nanoparticles in an organic solvent. Ag is then extracted from the core or the inner shell by bis(p-sulfonatophenyl)phenylphosphane, which binds strongly with Ag(I)/Ag(0) to allow the complete removal of Ag in 24-48 h, leaving behind an organosol of hollow or cage-bell structured metal nanomaterials. Because of their relatively lower densities, which usually translate to a higher surface area than their solid counterparts, the hollow and cage-bell structured metal nanomaterials are especially relevant to catalysis. For example, cage-bell structured Pt-Ru nanoparticles were found to display outstanding methanol tolerance for the cathode reaction of direct methanol fuel cells (DMFCs) as a result of the differential diffusion of methanol and oxygen in the cage-bell structure.

No abstract

No abstract

Biodegradable polycaprolactone (PCL) has been widely applied as a scaffold material in tissue engineering. However, the PCL surface is hydrophobic and adsorbs nonspecific proteins. Some traditional antifouling modifications using hydrophilic moieties have been successful but inhibit cell adhesion, which is not ideal for tissue engineering. The PCL surface is modified with bioinspired zwitterionic poly[2‐(methacryloyloxy)ethyl choline phosphate] (PMCP) via surface‐initiated atom transfer radical polymerization to improve cell adhesion through the unique interaction between choline phosphate (CP, on PMCP) and phosphate choline (PC, on cell membranes). The hydrophilicity of the PCL surface is significantly enhanced after surface modification. The PCL‐PMCP surface reduces nonspecific protein adsorption (e.g., up to 91.7% for bovine serum albumin) due to the zwitterionic property of PMCP. The adhesion and proliferation of bone marrow mesenchymal stem cells on the modified surface is remarkably improved, and osteogenic differentiation signs are detected, even without adding any osteogenesis‐inducing supplements. Moreover, the PCL‐PMCP films are more stable at the early stage of degradation. Therefore, the PMCP‐functionalized PCL surface promotes cell adhesion and osteogenic differentiation, with an antifouling background, and exhibits great potential in tissue engineering.

No abstract

Chemical incompatibility and low thermal conductivity issues of molten‐salt‐based thermal energy storage materials can be addressed by using microstructured composites. Using a eutectic mixture of lithium and sodium carbonates as molten salt, magnesium oxide as supporting material, and graphite as thermal conductivity enhancer, the microstructural development, chemical compatibility, thermal stability, thermal conductivity, and thermal energy storage performance of composite materials are investigated. The ceramic supporting material is essential for preventing salt leakage and hence provides a solution to the chemical incompatibility issue. The use of graphite gives a significant enhancement on the thermal conductivity of the composite. Analyses suggest that the experimentally observed microstructural development of the composite is associated with the wettability of the salt on the ceramic substrate and that on the thermal conduction enhancer.

BackgroundChemical compounds and drugs (together called chemical entities) embedded in scientific articles are crucial for many information extraction tasks in the biomedical domain. However, only a very limited number of chemical entity recognition systems are publically available, probably due to the lack of large manually annotated corpora. To accelerate the development of chemical entity recognition systems, the Spanish National Cancer Research Center (CNIO) and The University of Navarra organized a challenge on Chemical and Drug Named Entity Recognition (CHEMDNER). The CHEMDNER challenge contains two individual subtasks: 1) Chemical Entity Mention recognition (CEM); and 2) Chemical Document Indexing (CDI). Our study proposes machine learning-based systems for the CEM task.MethodsThe 2013 CHEMDNER challenge organizers provided a manually annotated 10,000 UTF8-encoded PubMed abstracts according to a predefined annotation guideline: a training set of 3,500 abstracts, a development set of 3,500 abstracts and a test set of 3,000 abstracts. We developed machine learning-based systems, based on conditional random fields (CRF) and structured support vector machines (SSVM) respectively, for the CEM task for this data set. The effects of three types of word representation (WR) features, generated by Brown clustering, random indexing and skip-gram, on both two machine learning-based systems were also investigated. The performance of our system was evaluated on the test set using scripts provided by the CHEMDNER challenge organizers. Primary evaluation measures were micro Precision, Recall, and F-measure.ResultsOur best system was among the top ranked systems with an official micro F-measure of 85.05%. Fixing a bug caused by inconsistent features marginally improved the performance (micro F-measure of 85.20%) of the system.ConclusionsThe SSVM-based CEM systems outperformed the CRF-based CEM systems when using the same features. Each type of the WR feature was beneficial to the CEM task. Both the CRF-based and SSVM-based systems using the all three types of WR features showed better performance than the systems using only one type of the WR feature.