Comment on " epsilon expansion for the conductivity of a random resistor network"

Bioelectronic nose with high sensitivity and selectivity using chemically functionalized carbon nanotube combined with human olfactory receptor

Single-walled carbon nanotubes (swCNTs) hold great promise for use as molecular wires because they exhibit high electrical conductivity and chemical stability. However, constructing swCNT-based transducer devices requires controlled strategies for assembling biomolecules on swCNTs. In this study, we proposed a chemically modified swCNT. The swCNT was functionalized with 1,5-diaminonaphthalene via π-stacking, for reliable attachment of the human olfactory receptor 2AG1 (hOR2AG1). The human olfactory receptor was then anchored. We investigated the use of this functionalized CNT in the fabrication of a highly sensitive and selective bioelectronic nose. For the bioelectronic nose, the swCNT-field effect transistor (FET) platform was composed of polyethylene glycol (PEG)-coated regions to prevent non-specific absorption and chemically modified swCNTs regions containing hOR2AG1, which can bind to the specific odorant. This approach allowed us to create well-defined micron-scale patterns of hOR2AG1 on the swCNTs. Our bioelectronic nose displayed ultrahigh sensitivity down to concentrations as low as 1fM due to the enhanced hOR2AG1-odorant interaction through the tight binding of hOR2AG1 on the chemically modified swCNTs. In addition, the approach described here may provide an alternative route for multiplexed detection of diverse odorants and to improve the sensitivity of sensor devices.

Universal parameters for carbon nanotube network-based sensors: can nanotube sensors be reproducible?

Carbon nanotube (CNT) network-based sensors have been often considered unsuitable for practical applications due to their unpredictable characteristics. Herein, we report the study of universal parameters which can be used to characterize CNT network-based sensors and make their response predictable. A theoretical model is proposed to explain these parameters, and sensing experiments for mercury (Hg(2+)) and ammonium (NH(4)(+)) ions using CNT network-based sensors were performed to confirm the validity of our model.

Biosensor system-on-a-chip including CMOS-based signal processing circuits and 64 carbon nanotube-based sensors for the detection of a neurotransmitter

We developed a carbon nanotube (CNT)-based biosensor system-on-a-chip (SoC) for the detection of a neurotransmitter. Here, 64 CNT-based sensors were integrated with silicon-based signal processing circuits in a single chip, which was made possible by combining several technological breakthroughs such as efficient signal processing, uniform CNT networks, and biocompatible functionalization of CNT-based sensors. The chip was utilized to detect glutamate, a neurotransmitter, where ammonia, a byproduct of the enzymatic reaction of glutamate and glutamate oxidase on CNT-based sensors, modulated the conductance signals to the CNT-based sensors. This is a major technological advancement in the integration of CNT-based sensors with microelectronics, and this chip can be readily integrated with larger scale lab-on-a-chip (LoC) systems for various applications such as LoC systems for neural networks.

100 nm scale low-noise sensors based on aligned carbon nanotube networks: overcoming the fundamental limitation of network-based sensors

Nanoscale sensors based on single-walled carbon nanotube (SWNT) networks have been considered impractical due to several fundamental limitations such as a poor sensitivity and small signal-to-noise ratio. Herein, we present a strategy to overcome these fundamental problems and build highly-sensitive low-noise nanoscale sensors simply by controlling the structure of the SWNT networks. In this strategy, we prepared nanoscale width channels based on aligned SWNT networks using a directed assembly strategy. Significantly, the aligned network-based sensors with narrower channels exhibited even better signal-to-noise ratio than those with wider channels, which is opposite to conventional random network-based sensors. As a proof of concept, we demonstrated 100 nm scale low-noise sensors to detect mercury ions with the detection limit of approximately 1 pM, which is superior to any state-of-the-art portable detection system and is below the allowable limit of mercury ions in drinking water set by most government environmental protection agencies. This is the first demonstration of 100 nm scale low-noise sensors based on SWNT networks. Considering the increased interests in high-density sensor arrays for healthcare and environmental protection, our strategy should have a significant impact on various industrial applications.

Distribution functions in percolation problems

Percolation clusters are random fractals whose geometrical and transport properties can be characterized with the help of probability distribution functions. Using renormalized field theory, we determine the asymptotic form of various such distribution functions in the limits where certain scaling variables become small or large. Our study includes the pair-connection probability, the distributions of the fractal masses of the backbone, the red bonds, and the shortest, the longest, and the average self-avoiding walk between any two points on a cluster, as well as the distribution of the total resistance in the random resistor network. Our analysis draws solely on general, structural features of the underlying diagrammatic perturbation theory, and hence our main results are valid to arbitrary loop order.

Scalable assembly method of vertically-suspended and stretched carbon nanotube network devices for nanoscale electro-mechanical sensing components

For the first time, vertically suspended and stretched carbon nanotube network junctions were fabricated in large quantity via the directed assembly strategy using only conventional microfabrication facilities. In this process, surface molecular patterns on the side-wall of the Al structures were utilized to guide the assembly and alignment of carbon nanotubes in the solution. We also performed extensive experimental (electrical and mechanical) analysis and theoretical simulation about the vertically suspended single-walled carbon nanotube network junctions. The junctions exhibited semiconductor-like conductance behavior. Furthermore, we demonstrated gas sensing and electromechanical sensing using these devices.

Corrections to scaling in random resistor networks and diluted continuous spin models near the percolation threshold

We investigate corrections to scaling induced by irrelevant operators in randomly diluted systems near the percolation threshold. The specific systems that we consider are the random resistor network and a class of continuous spin systems, such as the x-y model. We focus on a family of least irrelevant operators and determine the corrections to scaling that originate from this family. Our field theoretic analysis carefully takes into account that irrelevant operators mix under renormalization. It turns out that long standing results on corrections to scaling are respectively incorrect (random resistor networks) or incomplete (continuous spin systems).

Critical exponents for diluted resistor networks

An approach by Stephen [Phys. Rev. B 17, 4444 (1978)] is used to investigate the critical properties of randomly diluted resistor networks near the percolation threshold by means of renormalized field theory. We reformulate an existing field theory by Harris and Lubensky [Phys. Rev. B 35, 6964 (1987)]. By a decomposition of the principal Feynman diagrams, we obtain diagrams which again can be interpreted as resistor networks. This interpretation provides for an alternative way of evaluating the Feynman diagrams for random resistor networks. We calculate the resistance crossover exponent phi up to second order in epsilon=6-d, where d is the spatial dimension. Our result phi=1+epsilon/42+4epsilon(2)/3087 verifies a previous calculation by Lubensky and Wang, which itself was based on the Potts-model formulation of the random resistor network.