We demonstrate a new, versatile class of nanoscale chemical sensors based on singlestranded DNA (ss-DNA) as the chemical recognition site and single-walled carbon nanotube field effect transistors (swCN-FETs) as the electronic read-out component. SwCN-FETs with a nanoscale coating of ss-DNA respond to gas odors that do not cause a detectable conductivity change in bare devices. Responses of ss-DNA/swCN-FETs differ in sign and magnitude for different gases, and can be tuned by choosing the base sequence of the ss-DNA. Ss-DNA/swCN-FET sensors detect a variety of odors, with rapid response and recovery times on the scale of seconds. The sensor surface is selfregenerating: samples maintain a constant response with no need for sensor refreshing through at least 50 gas exposure cycles. This remarkable set of attributes makes sensors based on ss-DNA decorated nanotubes very promising for "electronic nose" and "electronic tongue" applications ranging from homeland security to disease diagnosis.The one-dimensional carbon cage structure of semiconducting single-walled carbon nanotubes (swCNs) makes their physical properties exquisitely sensitive to variations in the surrounding electrostatic environment, whether the swCNs are suspended in liquid or incorporated into field effect transistor (FET) circuits on a substrate.

Key Points Natural silk protein sponge and vascular tubes reproduce human bone marrow niche environments for functional platelet generation ex vivo. Programmable bioengineered model for the investigation and therapeutic targeting of altered platelet formation.

Detailed knowledge of mechanical parameters such as cell elasticity, stiffness of the growth substrate, or traction stresses generated during axonal extensions is essential for understanding the mechanisms that control neuronal growth. Here, we combine atomic force microscopy-based force spectroscopy with fluorescence microscopy to produce systematic, high-resolution elasticity maps for three different types of live neuronal cells: cortical (embryonic rat), embryonic chick dorsal root ganglion, and P-19 (mouse embryonic carcinoma stem cells) neurons. We measure how the stiffness of neurons changes both during neurite outgrowth and upon disruption of microtubules of the cell. We find reversible local stiffening of the cell during growth, and show that the increase in local elastic modulus is primarily due to the formation of microtubules. We also report that cortical and P-19 neurons have similar elasticity maps, with elastic moduli in the range 0.1-2 kPa, with typical average values of 0.4 kPa (P-19) and 0.2 kPa (cortical). In contrast, dorsal root ganglion neurons are stiffer than P-19 and cortical cells, yielding elastic moduli in the range 0.1-8 kPa, with typical average values of 0.9 kPa. Finally, we report no measurable influence of substrate protein coating on cell body elasticity for the three types of neurons.

We fabricate and electrically characterize electrospun nanofibers of doped polyaniline/polyethylene oxide (PAn/PEO) blend with sub-30 nm diameter. Fiber diameters near 5 nm are obtained for optimized process parameters. Scanning conductance microscopy (SCM) shows that fibers with diameter below 15 nm are electrically insulating; the small diameter may allow complete dedoping in air or be smaller than phase-separated grains of PAn and PEO. Electrical contacts to nanofibers are made by shadow mask evaporation with no chemical or thermal damage to the fibers. Single fiber I–V characteristics show that thin fibers conduct more poorly than thick ones, in agreement with SCM data. I–Vs of asymmetric fibers are rectifying, consistent with formation of Schottky barriers at the nanofiber-metal contacts.

Scalable computational modelling tools are required to guide the rational design of complex hierarchical materials with predictable functions. Here, we utilize mesoscopic modelling, integrated with genetic block copolymer synthesis and bioinspired spinning process, to demonstrate de novo materials design that incorporates chemistry, processing and material characterization. We find that intermediate hydrophobic/hydrophilic block ratios observed in natural spider silks and longer chain lengths lead to outstanding silk fibre formation. This design by nature is based on the optimal combination of protein solubility, self-assembled aggregate size and polymer network topology. The original homogeneous network structure becomes heterogeneous after spinning, enhancing the anisotropic network connectivity along the shear flow direction. Extending beyond the classical polymer theory, with insights from the percolation network model, we illustrate the direct proportionality between network conductance and fibre Young's modulus. This integrated approach provides a general path towards de novo functional network materials with enhanced mechanical properties and beyond (optical, electrical or thermal) as we have experimentally verified.