Development of CNT-silicon nitrides with impro ved mechanical and electrical properties”, Advances Sci

Abstract. This work is focusing on exploring preparing processes to tailor the microstructure of carbon nanotube (CNT) reinforced silicon nitride-based ceramic composites. Samples with different porosity's and different amount (1, 3 or 5 wt%) of carbon nanotubes have been prepared by using gas pressure sintering or hot isostatic pressing. In comparison, composites with 1wt%, 5wt% or 10wt% carbon black and graphite have been manufactured. We measured the room temperature mechanical and electrical properties, examined the micro and nano structure by X-ray diffraction and electron microscopy. It was found that it is possible to develop CNT-silicon nitride composite for applications where a decent electric conductivity and good mechanical properties are required

Combined Model of Intrinsic and Extrinsic Variability for Computational Network Design with Application to Synthetic Biology

Biological systems are inherently variable, with their dynamics influenced by intrinsic and extrinsic sources. These systems are often only partially characterized, with large uncertainties about specific sources of extrinsic variability and biochemical properties. Moreover, it is not yet well understood how different sources of variability combine and affect biological systems in concert. To successfully design biomedical therapies or synthetic circuits with robust performance, it is crucial to account for uncertainty and effects of variability. Here we introduce an efficient modeling and simulation framework to study systems that are simultaneously subject to multiple sources of variability, and apply it to make design decisions on small genetic networks that play a role of basic design elements of synthetic circuits. Specifically, the framework was used to explore the effect of transcriptional and post-transcriptional autoregulation on fluctuations in protein expression in simple genetic networks. We found that autoregulation could either suppress or increase the output variability, depending on specific noise sources and network parameters. We showed that transcriptional autoregulation was more successful than post-transcriptional in suppressing variability across a wide range of intrinsic and extrinsic magnitudes and sources. We derived the following design principles to guide the design of circuits that best suppress variability: (i) high protein cooperativity and low miRNA cooperativity, (ii) imperfect complementarity between miRNA and mRNA was preferred to perfect complementarity, and (iii) correlated expression of mRNA and miRNA – for example, on the same transcript – was best for suppression of protein variability. Results further showed that correlations in kinetic parameters between cells affected the ability to suppress variability, and that variability in transient states did not necessarily follow the same principles as variability in the steady state. Our model and findings provide a general framework to guide design principles in synthetic biology.National Institutes of Health (U.S.) (CA112967)Singapore-MIT Alliance for Research and Technolog