Narrow atomically precise graphene nanoribbons hold great promise for electronic and optoelectronic applications, but the previously demonstrated nanoribbon-based devices typically suffer from low currents and mobilities. In this study, we explored the idea of lateral extension of graphene nanoribbons for improving their electrical conductivity. We started with a conventional chevron graphene nanoribbon, and designed its laterally extended variant. We synthesized these new graphene nanoribbons in solution and found that the lateral extension results in decrease of their electronic bandgap and improvement in the electrical conductivity of nanoribbon-based thin films. These films were employed in gas sensors and an electronic nose system, which showed improved responsivities to low molecular weight alcohols compared to similar sensors based on benchmark graphitic materials, such as graphene and reduced graphene oxide, and a reliable analyte recognition. This study shows the methodology for designing new atomically precise graphene nanoribbons with improved properties, their bottom-up synthesis, characterization, processing and implementation in electronic devices.

Reliable environmental monitoring requires cost effective but highly sensitive and selective gas sensors. While the sensitivity of the sensors is improved by reducing the characteristic dimensions of the gas-sensing material, the selectivity is often approached by combining the sensors into multisensor arrays. The development of scalable methods to manufacture such arrays based on low-dimensional structures offers new perspectives for gas sensing applications. Here we examine an approach to produce multisensor array chips based on the TiO_xnanotube layers segmented by multiple Pt strip electrodes. We study the sensitivity and selectivity of the developed chip at operating temperatures up to 400 °C towards organic vapors in the ppm range. The results indicate that the titania nanotubes are a promising material platform for novel cost-effective and powerful gas-analytical multisensor units.

We report a deposition of the tin oxide/hydroxide nanostructured layer by the potentiodynamic method from acidic nitrate solutions directly over the substrate, equipped with multiple strip electrodes which is employed as a gas-analytical multisensor array chip. The electrochemical synthesis is set to favor the growth of the tin oxide/hydroxide phase, while the appearance of metallic Sn is suppressed by cycling. The as-synthesized tin oxide/hydroxide layer is characterized by mesoporous morphology with grains, 250–300 nm diameter, which are further crystallized into fine SnO₂ poly-nanocrystals following heating to 300 °C for 24 h just on the chip. The fabricated layer exhibits chemiresistive properties under exposure to organic vapors, which allows the generation of a multisensor vector signal capable of selectively distinguishing various vapors.

The development of portable gas-sensing units implies a special care of their power efficiency, which is often approached by operation at room temperature. This issue primarily appeals to a choice of suitable materials whose functional properties are sensitive toward gas vapors at these conditions. While the gas sensitivity is nowadays advanced by employing the materials at nano-dimensional domain, the room temperature operation might be targeted via the application of layered solid-state electrolytes, like titanates. Here, we report gas-sensitive properties of potassium titanate whiskers, which are placed over a multielectrode chip by drop casting from suspension to yield a matrix mono-layer of varied density. The material synthesis conditions are straightforward both to get stable single-crystalline quasi-one-dimensional whiskers with a great extent of potassium replacement and to favor the increase of specific surface area of the structures. The whisker layer is found to be sensitive towards volatile organic compounds (ethanol, isopropanol, acetone) in the mixture with air at room temperature. The vapor identification is obtained via processing the vector signal generated by sensor array of the multielectrode chip with the help of pattern recognition algorithms.