Recent Researches

In a chemical sensor array, sensing signals are obtained by measuring physicochemical interactions induced by the sorption of target analytes in sensing materials. Recently, we have established a reverse approach, that is, pattern recognition of solid materials by multiple probe gases [1]. As the sensing signals of chemical sensors are based on the interaction between gases and solids, a sensing material and a target analyte should be exchangeable (i.e., solid materials as target analytes and gaseous molecules as sensing probes), leading to the pattern recognition of solid materials by the probe gases. By using this approach, it is possible to discriminate not only among different polymers, but also very minute differences of rice.

Since any kind of gaseous or volatile molecules can be potentially utilized as a probe for the pattern recognition-based solid sensing, this approach possesses unlimited possibilities for differentiating solid materials. Importantly, this approach combined with machine learning technique can discriminate and/or identify different solid properties by changing the combination of two or three selected probe gases.

1. K. Minami et al. Mater. Horiz. 2019, 6, 580–586; IEEE Sens. Lett. 2023, 7, 4502404; ECS Meeting Abst. 2020, MA2020-01, 2260.

Nanomechanical sensors and their arrays have been attracting significant attention for detecting, distinguishing, and identifying target analytes. In the static mode operation, sensing signals are obtained by a concentration-dependent sorption-induced mechanical strain/stress. Additionally, the receptor materials often show viscoelastic behaviors, and hence the dynamic behaviors of nanomechanical sensing reflect the sorption kinetics along with the viscoelastic stress relaxation.

On the basis of the theoretical model proposed by Wenzel et al. [1], we recently formulated a new model based on the sorption kinetics and viscoelastic behaviors [2].

1. M. J. Wenzel et al. J. Appl. Phys. 2008, 103, 064913.

2. K. Minami et al. J. Appl. Phys. 2021, 129, 124503; IEEE ISOEN2022.

Nanomechanical sensors and their arrays have attracted significant attention for detecting, distinguishing, and identifying target analytes, especially odors that are composed of a complex mixture of gaseous molecules. Dr. Yoshikawa has developed an optimized nanomechanical sensor platform, that is, Membrane-type Surface stress Sensor (MSS) [1], which has great advantages for artificial olfaction including high sensitivity, compactness, quick response, low power consumption, and stability.

To develop the artificial olfaction, we are working on comprehensive investigations:
• Synthesis of novel receptor materials [2]
• Machine-learning based analysis of odor detection [3]
• Structural mechanics using Finite Element Analysis [4]
• Design & construction of sensor devices [5]
To integrate all the required cutting-edge technologies, we launched an industry-academia-government collaboration; MSS alliance and MSS forum [6].

1. G. Yoshikawa et al. Nano Lett. 2011, 11, 1044; see also Review: K. Minami et al. Biosensors 2022, 12, 762.

2. For example: a) M. Nishikawa et al. Bull. Chem. Soc. Jpn. 2021, 94, 648; b) G. Imamura et al. Chemosensors 2020, 8, 82; c) H. H.-M. Yeung et al. J. Mater. Chem. A 2020, 8, 18007.

3. For example: a)  H. Xu et al.  Sci. Rep. 2021, 11, 12070; b) K. Shiba et al. ACS Sens. 2018, 3, 1592.

4. For example: a) K. Minami et al. Sens. Actuators A: Phys. 2021, 319, 112533; b) K. Minami et al. Sensors 2020, 20, 1518.

5. For example: a) C. Zhuang et al. ACS Appl. Eng. Mater. 2023, 1, 1213–1219; b) C. Zhuang et al. Nano Express 2023, 4, 035007.

6. See also MSS forum.