Nanomechanical cantilever sensors have been emerging as a key device for real-time and label-free detection of various analytes ranging from gaseous to biological molecules. The major sensing principle is based on the analyte-induced surface stress, which makes a cantilever bend. In this letter, we present a membrane-type surface stress sensor (MSS), which is based on the piezoresistive read-out integrated in the sensor chip. The MSS is not a simple "cantilever," rather it consists of an "adsorbate membrane" suspended by four piezoresistive "sensing beams," composing a full Wheatstone bridge. The whole analyte-induced isotropic surface stress on the membrane is efficiently transduced to the piezoresistive beams as an amplified uniaxial stress. Evaluation of a prototype MSS used in the present experiments demonstrates a high sensitivity which is comparable with that of optical methods and a factor of more than 20 higher than that obtained with a standard piezoresistive cantilever. The finite element analyses indicate that changing dimensions of the membrane and beams can substantially increase the sensitivity further. Given the various conveniences and advantages of the integrated piezoresistive read-out, this platform is expected to open a new era of surface stress-based sensing.

We present a new generation of piezoresistive nanomechanical Membrane-type Surface stress Sensor(MSS) chips, which consist of a two dimensional array of MSS on a single chip. The implementation of several optimization techniques in the design and microfabrication improved the piezoresistive sensitivity by 3∼4 times compared to the first generation MSS chip, resulting in a sensitivity about ∼100 times better than a standard cantilever-type sensor and a few times better than optical read-out methods in terms of experimental signal-to-noise ratio. Since the integrated piezoresistive read-out of the MSS can meet practical requirements, such as compactness and not requiring bulky and expensive peripheral devices, the MSS is a promising transducer for nanomechanical sensing in the rapidly growing application fields in medicine, biology, security, and the environment. Specifically, its system compactness due to the integrated piezoresistive sensing makes the MSS concept attractive for the instruments used in mobile applications. In addition, the MSS can operate in opaque liquids, such as blood, where optical read-out techniques cannot be applied.

The nanographite grains, the diameter of which was around 5 nm, were formed on Pt͑111͒ by exposing the Pt͑111͒ substrate to benzene gas at room temperature and annealing it up to 850 K. The increase of relative number of edge atoms enabled the observation of edge-derived electronic states. The measurement of ultraviolet photoelectron spectroscopy and near edge x-ray absorption fine structure on the nanographite revealed the appearance of the edge state located at the Fermi level.

Smells are known to be composed of thousands of chemicals with various concentrations, and thus, the extraction of specific information from such a complex system is still challenging. Herein, we report for the first time that the nanomechanical sensing combined with machine learning realizes the specific information extraction, e.g. alcohol content quantification as a proof-of-concept, from the smells of liquors. A newly developed nanomechanical sensor platform, a Membrane-type Surface stress Sensor (MSS), was utilized. Each MSS channel was coated with functional nanoparticles, covering diverse analytes. The smells of 35 liquid samples including water, teas, liquors, and water/EtOH mixtures were measured using the functionalized MSS array. We selected characteristic features from the measured responses and kernel ridge regression was used to predict the alcohol content of the samples, resulting in successful alcohol content quantification. Moreover, the present approach provided a guideline to improve the quantification accuracy; hydrophobic coating materials worked more effectively than hydrophilic ones. On the basis of the guideline, we experimentally demonstrated that additional materials, such as hydrophobic polymers, led to much better prediction accuracy. The applicability of this data-driven nanomechanical sensing is not limited to the alcohol content quantification but to various fields including food, security, environment, and medicine.