Gaku Imamura

Highly Networked Capsular Silica–Porphyrin Hybrid Nanostructures as Efficient Materials for Acetone Vapor Sensing

The development of novel functional nanomaterials is critically important for the further evolution of advanced chemical sensor technology. For this purpose, metalloporphyrins offer unique binding properties as host molecules that can be tailored at the synthetic level and potentially improved by incorporation into inorganic materials. In this work, we present a novel hybrid nanosystem based on a highly networked silica nanoarchitecture conjugated through covalent bonding to an organic functional molecule, a tetraphenylporphyrin derivative, and its metal complexes. The sensing properties of the new hybrid materials were studied using a nanomechanical membrane-type surface stress sensor (MSS) with acetone and nitric oxide as model analytes. This hybrid inorganic-organic MSS-based system exhibited excellent performance for acetone sensing at low operating temperatures (37 °C), making it available for diagnostic monitoring. The hybridization of an inorganic substrate of large surface area with organic molecules of various functionalities results in sub-ppm detection of acetone vapors. Acetone is an important metabolite in lipid metabolism and can also be present in industrial environments at deleterious levels. Therefore, we believe that the analysis system presented by our work represents an excellent opportunity for the development of a portable, easy-to-use device for monitoring local acetone levels.

Smell identification of spices using nanomechanical membrane-type surface stress sensors

Artificial olfaction, that is, a chemical sensor system that identifies samples by smell, has not been fully achieved because of the complex perceptional mechanism of olfaction. To realize an artificial olfactory system, not only an array of chemical sensors but also a valid feature extraction method is required. In this study, we achieved the identification of spices by smell using nanomechanical membrane-type surface stress sensors (MSS). Features were extracted from the sensing signals obtained from four MSS coated with different types of polymers, focusing on the chemical interactions between polymers and odor molecules. The principal component analysis (PCA) of the dataset consisting of the extracted parameters demonstrated the separation of each spice on the scatter plot. We discuss the strategy for improving odor identification based on the relationship between the results of PCA and the chemical species in the odors.

Data-driven nanomechanical sensing: specific information extraction from a complex system

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.

Effects of Center Metals in Porphines on Nanomechanical Gas Sensing

Porphyrin is one of the most promising materials for realizing a practical artificial olfactory sensor system. In this study, we focus on non-substituted porphyrins—porphines—as receptor materials of nanomechanical membrane-type surface stress sensors (MSS) to investigate the effect of center metals on gas sensing. By omitting the substituents on the tetrapyrrole macrocycle of porphyrin, the peripheral interference by substituents can be avoided. Zinc, nickel, and iron were chosen for the center metals as these metalloporphines show different properties compared to free-base porphine. The present study revealed that iron insertion enhanced sensitivity to various gases, while zinc and nickel insertion led to equivalent or less sensitivity than free-base porphine. Based on the experimental results, we discuss the role of center metals for gas uptake from the view point of molecular interaction. We also report the high robustness of the iron porphine to humidity, showing the high feasibility of porphine-based nanomechanical sensor devices for practical applications in ambient conditions.

Analysis of nanomechanical sensing signals; physical parameter estimation for gas identification

Nanomechanical sensors—emerging chemical sensors which detect changes in mechanical properties caused by gas sorption—have been attracting much attention owing to their high sensitivity and versatility. In the data analysis of sensing signals, empirically extracted signal features have been commonly employed to identify the gas species. Such an empiric approach cannot be optimized further without a solid guideline, resulting in a limited identification performance. Therefore, a new analytical protocol based on intrinsic physical properties of a target gas and a receptor material has been highly demanded. In this study, we have developed a parameter estimation protocol based on a theoretical model for a cantilever-type nanomechanical sensor coated with a viscoelastic material. This protocol provides a practical estimation method for intrinsic parameters, which can be used for gas identification. As a demonstration of gas identification based on intrinsic parameters, we focused on the time constant for gas diffusion τs, which reflects the physicochemical interaction between gas species and a receptor material. Based on τs estimated from different receptor materials, we succeeded in the identification of solvent vapors. This parameter estimation protocol not only enables the gas identification based on the intrinsic property of target gases, but also provides a scientific guideline for the selection and optimization of receptor materials for nanomechanical sensors.Nanomechanical sensors—emerging chemical sensors which detect changes in mechanical properties caused by gas sorption—have been attracting much attention owing to their high sensitivity and versatility. In the data analysis of sensing signals, empirically extracted signal features have been commonly employed to identify the gas species. Such an empiric approach cannot be optimized further without a solid guideline, resulting in a limited identification performance. Therefore, a new analytical protocol based on intrinsic physical properties of a target gas and a receptor material has been highly demanded. In this study, we have developed a parameter estimation protocol based on a theoretical model for a cantilever-type nanomechanical sensor coated with a viscoelastic material. This protocol provides a practical estimation method for intrinsic parameters, which can be used for gas identification. As a demonstration of gas identification based on intrinsic parameters, we focused on the time constant for gas ...

Functional Nanoparticles-Coated Nanomechanical Sensor Arrays for Machine Learning-Based Quantitative Odor Analysis

A sensing signal obtained by measuring an odor usually contains varied information that reflects an origin of the odor itself, while an effective approach is required to reasonably analyze informative data to derive the desired information. Herein, we demonstrate that quantitative odor analysis was achieved through systematic material design-based nanomechanical sensing combined with machine learning. A ternary mixture consisting of water, ethanol, and methanol was selected as a model system where a target molecule coexists with structurally similar species in a humidified condition. To predict the concentration of each species in the system via the data-driven approach, six types of nanoparticles functionalized with hydroxyl, aminopropyl, phenyl, and/or octadecyl groups were synthesized as a receptor coating of a nanomechanical sensor. Then, a machine learning model based on Gaussian process regression was trained with sensing data sets obtained from the samples with diverse concentrations. As a result, the octadecyl-modified nanoparticles enhanced prediction accuracy for water while the use of both octadecyl and aminopropyl groups was indicated to be a key for a better prediction accuracy for ethanol and methanol. As the prediction accuracy for ethanol and methanol was improved by introducing two additional nanoparticles with finely controlled octadecyl and aminopropyl amount, the feedback obtained by the present machine learning was effectively utilized to optimize material design for better performance. We demonstrate through this study that various information which was extracted from plenty of experimental data sets was successfully combined with our knowledge to produce wisdom for addressing a critical issue in gas phase sensing.

Finite Element Analysis on Nanomechanical Detection of Small Particles: Toward Virus Detection

Detection of small particles, including viruses and particulate matter (PM), has been attracting much attention in light of increasing need for environmental monitoring. Owing to their high versatility, a nanomechanical sensor is one of the most promising sensors which can be adapted to various monitoring systems. In this study, we present an optimization strategy to efficiently detect small particles with nanomechanical sensors. Adsorption of particles on the receptor layer of nanomechanical sensors and the resultant signal are analyzed using finite element analysis (FEA). We investigate the effect of structural parameters (e.g., adsorption position and embedded depth of a particle and thickness of the receptor layer) and elastic properties of the receptor layer (e.g., Youngs modulus and Poissons ratio) on the sensitivity. It is found that a membrane-type surface stress sensors (MSS) has the potential for robust detection of small particles.

4.3 – Nanomechanical Sensors

Abstract This chapter introduces nanomechanical sensors and their applications. All molecules have “volume” and “mass.” Direct measurement of these fundamental parameters can realize label-free and real-time measurements. Nanomechanical sensors have been emerging as a key device for such label-free and real-time measurements with their multiple operation modes; static and dynamic modes for detecting volume- and mass-related features, respectively. A cantilever array sensor is a representative example among various geometries, while structural optimization can enhance the scope of nanomechanical sensors in both academic and industrial applications. One of the most advanced sensing platforms is a membrane-type surface stress sensor (MSS), which realizes both high sensitivity and compact system at the same time. The MSS is also expected to contribute to addressing nanomechanical behavior of living cells and their network.