Fazel Yavari

High Sensitivity Gas Detection Using a Macroscopic Three-Dimensional Graphene Foam Network

Nanostructures are known to be exquisitely sensitive to the chemical environment and offer ultra-high sensitivity for gas-sensing. However, the fabrication and operation of devices that use individual nanostructures for sensing is complex, expensive and suffers from poor reliability due to contamination and large variability from sample-to-sample. By contrast, conventional solid-state and conducting-polymer sensors offer excellent reliability but suffer from reduced sensitivity at room-temperature. Here we report a macro graphene foam-like three-dimensional network which combines the best of both worlds. The walls of the foam are comprised of few-layer graphene sheets resulting in high sensitivity; we demonstrate parts-per-million level detection of NH3 and NO2 in air at room-temperature. Further, the foam is a mechanically robust and flexible macro-scale network that is easy to contact (without Lithography) and can rival the durability and affordability of traditional sensors. Moreover, Joule-heating expels chemisorbed molecules from the foams surface leading to fully-reversible and low-power operation.

Graphene-Based Chemical Sensors

Pioneering research in 2004 by Geim and Novoselov (2010 Nobel Prize winners in Physics) of the University of Manchester led to the isolation of a monolayer graphene sheet. Graphene is a single-atom-thick sheet of sp(2) hybridized carbon atoms that are packed in a hexagonal honeycomb crystalline structure. Graphene is the fundamental building block of all sp(2) carbon materials including single-walled carbon nanotubes, mutliwalled carbon nanotubes, and graphite and is therefore interesting from the fundamental standpoint as well as for practical applications. One of the most promising applications of graphene that has emerged so far is its utilization as an ultrasensitive chemical or gas sensor. In this article, we review some of the significant work performed with graphene and its derivatives for gas detection and provide a perspective on the challenges that need to be overcome to enable commercially viable graphene chemical sensor technologies.

Tunable Bandgap in Graphene by the Controlled Adsorption of Water Molecules

Graphene, a single-atom-thick layer of sp 2 -hybridized carbon atoms, has generated considerable excitement in the scientifi c community due to its peculiar electronic band structure, which leads to unusual phenomena such as the anomalous quantum Hall effect, [ 1,2 ] spin-resolved quantum interference, [ 3 ] ballistic electron transport, [ 4 ] and bipolar supercurrent. [ 5 ] However, pristine graphene is a semimetal with zero bandgap; the local density of states at the Fermi level is zero and conduction can only occur by the thermal excitation of electrons. [ 2 ] This lack of an electronic bandgap is the major obstacle limiting the utilization of graphene in nano-electronic and -photonic devices, [ 6,7 ] such as p–n junctions, transistors, photodiodes, and lasers. The graphene band structure is sensitive to lattice symmetry and several methods have been developed to break this symmetry and open an energy gap. These methods are based on a variety of techniques, such as defect generation, [ 8 ] doping (e.g., with potassium [ 9 ] ), applied bias, [ 10–12 ] and interaction with gases [ 13 ] (e.g., nitrogen dioxide). For instance, in reference [ 12 ] a tunable bandgap of up to 0.25 eV was achieved for electrically gated bilayer graphene by a variable external electric fi eld. Similarly, an internal electric fi eld produced by an imbalance of doped charge between two graphene layers has been shown to open a bandgap. [ 9 ] It has been demonstrated that a gap of ≈ 0.26 eV can be produced by growing graphene epitaxially on silicon carbide substrates. [ 14 ] This gap originated from the breaking of sublattice symmetry due to the graphene–substrate interaction. Patterned adsorption of atomic hydrogen onto the Moire superlattice positions of graphene [ 15 ] has resulted in a bandgap of ≈ 0.73 eV opening, while half-hydrogenated graphene [ 16 ] resulted in a bandgap of ≈ 0.43 eV. A graphene nanomesh structure [ 17 ] has also been shown to exhibit a bandgap. In this graphene structure, lateral quantum confi nement and localization effects due to

Enhanced electrical conductivity in polystyrene nanocomposites at ultra-low graphene content.

We compared the electrical conductivity of multiwalled-carbon-nanotube/polystyrene and graphene/polystyrene composites. The conductivity of polystyrene increases from ∼6.7 × 10(-14) to ∼3.49 S/m, with an increase in graphene content from ∼0.11 to ∼1.1 vol %. This is ∼2-4 orders of magnitude higher than for multiwalled-carbon-nanotube/polystyrene composites. Furthermore, we show that the conductivity of the graphene/polystyrene system can be significantly enhanced by incorporation of polylactic acid. The volume-exclusion principle forces graphene into the polystyrene-rich regions (selective localization) and generates ∼4.5-fold decrease in its percolation threshold from ∼0.33 to ∼0.075 vol %.

High sensitivity detection of NO2 and NH3 in air using chemical vapor deposition grown graphene

We show that graphene films synthesized by chemical-vapor-deposition enables detection of trace amounts of nitrogen dioxide (NO2) and ammonia (NH3) in air at room temperature and atmospheric pressure. The gas species are detected by monitoring changes in electrical resistance of the graphene film due to gas adsorption. The sensor response time was inversely proportional to the gas concentration. Heating the film expelled chemisorbed molecules from the graphene surface enabling reversible operation. The detection limits of ∼100 parts-per-billion (ppb) for NO2 and ∼500 ppb for NH3 obtained using our device are markedly superior to commercially available NO2 and NH3 detectors.

Feature extraction of forearm EMG signals for prosthetics

This paper presents a new technique for feature extraction of forearm electromyographic (EMG) signals using a proposed mother wavelet matrix (MWM). A MWM including 45 potential mother wavelets is suggested to help the classification of surface and intramuscular EMG signals recorded from multiple locations on the upper forearm for ten hand motions. Also, a surface electrode matrix (SEM) and a needle electrode matrix (NEM) are suggested to select the proper sensors for each pair of motions. For this purpose, EMG signals were recorded from sixteen locations on the forearms of six subjects in ten hand motion classes. The main goal in classification is to define a proper feature vector able to generate acceptable differences among the classes. The MWM includes the mother wavelets which make the highest difference between two particular classes. Six statistical feature vectors were compared using the continuous form of wavelet packet transform. The mother wavelet functions are selected with the aim of optimum classification between two classes using one of the feature vectors. The locations where the satisfactory signals are captured are selected from several mounted electrodes. Finally, three ten-by-ten symmetric MWM, SEM, and NEM represent the proper mother wavelet function and the surface and intramuscular selection for recording the ten hand motions.