Two dimensional array of piezoresistive nanomechanical Membrane-type Surface Stress Sensor (MSS) with improved sensitivity

Sensing signal augmentation by flow rate modulation of carrier gas for accurate differentiation of complex odours

For olfactory sensors, clear differentiation of complex odour samples requires diverse information. To obtain such information, hardware modifications, such as introducing additional channels with different physical/chemical properties, are usually needed. In this study, we present a new approach to augmenting the sensing signals of an olfactory sensor by modulating the flow rate of the carrier gas. The headspace vapour of complex odours is measured using a sensing system of nanomechanical sensor (Membrane-type Surface stress Sensor, MSS). The resulting data set is quantitatively evaluated using the Davies-Bouldin index (DBI) of principal component analysis (PCA). The increasing number of sensing signals obtained at different gas flow rates leads to a decrease in the DBI, achieving better cluster separation between different odours. Such gas flow effects can be attributed to several factors, including the sample evaporation and the equilibrium of the gas-liquid and gas-solid interfaces. Proton-transfer-reaction time-of-flight mass spectrometry (PTR-TOF-MS) experiments reveal that the compositions of odour samples vary with the different gas flow rates. It is demonstrated that a simple technique for modulating gas flow rates can significantly improve the differentiation performance of complex odours, providing an additional degree of freedom in olfactory sensing.

Lung cancer detection in perioperative patients' exhaled breath with nanomechanical sensor array

INTRODUCTION

Breath analysis using a chemical sensor array combined with machine learning algorithms may be applicable for detecting and screening lung cancer. In this study, we examined whether perioperative breath analysis can predict the presence of lung cancer using a Membrane-type Surface stress Sensor (MSS) array and machine learning.

METHODS

Patients who underwent lung cancer surgery at an academic medical center, Japan, between November 2018 and November 2019 were included. Exhaled breaths were collected just before surgery and about one month after surgery, and analyzed using an MSS array. The array had 12 channels with various receptor materials and provided 12 waveforms from a single exhaled breath sample. Boxplots of the perioperative changes in the expiratory waveforms of each channel were generated and Mann-Whitney U test were performed. An optimal lung cancer prediction model was created and validated using machine learning.

RESULTS

Sixty-six patients were enrolled of whom 57 were included in the analysis. Through the comprehensive analysis of the entire dataset, a prototype model for predicting lung cancer was created from the combination of array five channels. The optimal accuracy, sensitivity, specificity, positive predictive value, and negative predictive value were 0.809, 0.830, 0.807, 0.806, and 0.812, respectively.

CONCLUSION

Breath analysis with MSS and machine learning with careful control of both samples and measurement conditions provided a lung cancer prediction model, demonstrating its capacity for non-invasive screening of lung cancer.

Sorption Kinetic Parameters from Nanomechanical Sensing for Discrimination of 2-Nonenal from Saturated Aldehydes

Nanomechanical sensors have gained significant attention as promising platforms for artificial olfaction. Since sorption kinetic parameters that can be estimated from the sensing signals of nanomechanical sensors reflect the chemical and physicochemical interactions between the odorant and receptor material, the parameters can be utilized for the direct discrimination of each odorant. In this study, we demonstrated the discrimination of 20 vapors, including hydrocarbons, alcohols, organic acids, ketones, and aldehydes, which are reported as human body odor components, using the parameters extracted in the analytical solution of nanomechanical sensors based on sorption kinetics with viscoelastic behaviors. By using one of the specific nanomechanical sensors─membrane-type surface stress sensor─as a sensing unit, we successfully discriminated trans-2-nonenal known as an aging marker from other saturated aldehydes along with quantifying their concentrations.

Measurement of Volatile Fatty Acids in Silage through Odors with Nanomechanical Sensors

The measurement of volatile fatty acids (VFAs) is of great importance in the fields of food and agriculture. There are various methods to measure VFAs, but most methods require specific equipment, making on-site measurements difficult. In this work, we demonstrate the measurements of VFAs in a model sample, silage, through its vapor using an array of nanomechanical sensors-Membrane-type Surface stress Sensors (MSS). Focusing on relatively slow desorption behaviors of VFAs predicted with the sorption kinetics of nanomechanical sensing and the dissociation nature of VFAs, the VFAs can be efficiently measured by using features extracted from the decay curves of the sensing response, resulting in sufficient discrimination of the silage samples. Since the present sensing system does not require expensive, bulky setup and pre-treatment of samples, it has a great potential for practical applications including on-site measurements.

Detection of Trace Amounts of Water in Organic Solvents by DNA-Based Nanomechanical Sensors

The detection of trace amounts of water in organic solvents is of great importance in the field of chemistry and in the industry. Karl Fischer titration is known as a classic method and is widely used for detecting trace amounts of water; however, it has some limitations in terms of rapid and direct detection because of its time-consuming sample preparation and specific equipment requirements. Here, we found that a DNA-based nanomechanical sensor exhibits high sensitivity and selectivity to water vapor, leading to the detection and quantification of trace amounts of water in organic solvents as low as 12 ppm in THF, with a ppb level of LoD through their vapors. Since the present method is simple and rapid, it can be an alternative technique to the conventional Karl Fischer titration.

Recent Advances in Nanomechanical Membrane-Type Surface Stress Sensors towards Artificial Olfaction

Nanomechanical sensors have gained significant attention as powerful tools for detecting, distinguishing, and identifying target analytes, especially odors that are composed of a complex mixture of gaseous molecules. Nanomechanical sensors and their arrays are a promising platform for artificial olfaction in combination with data processing technologies, including machine learning techniques. This paper reviews the background of nanomechanical sensors, especially conventional cantilever-type sensors. Then, we focus on one of the optimized structures for static mode operation, a nanomechanical Membrane-type Surface stress Sensor (MSS), and discuss recent advances in MSS and their applications towards artificial olfaction.

Odor-Based Nanomechanical Discrimination of Fuel Oils Using a Single Type of Designed Nanoparticles with Nonlinear Viscoelasticity

Odors are one of the most diverse and complicated gaseous mixtures so that their discrimination is challenging yet attractive because of the rich information about their origin. The more similar the properties of odors are, the more difficult the discrimination becomes. The practical applications, however, often demand such discrimination, especially with a compact sensing platform. In this paper, we show that a nanomaterial designed for a specific type of odors can clearly discriminate them even with a single nanomechanical sensing channel. Fuel oils and their mixture are used as a model target that has similar chemical properties but different compositions mainly consisting of paraffinic, olefinic, naphthenic, and aromatic hydrocarbons. We demonstrate using octadecyl functionalized silica-titania nanoparticles that the difference in the compositions is successfully picked up based on their high affinity for the aliphatic hydrocarbons and alkyl chain length dependent nonlinear viscoelastic behavior. Such a properly designed material is proved to derive sufficient information from a series of analytes to discriminate them even with a single sensing element. This approach provides a guideline to prepare various sensors whose response properties are distinct and optimized depending on applications.

Statistical Evaluation of Total Expiratory Breath Samples Collected throughout a Year: Reproducibility and Applicability toward Olfactory Sensor-Based Breath Diagnostics

The endogenous volatile organic compounds (VOCs) in exhaled breath can be promising biomarkers for various diseases including cancers. An olfactory sensor has a possibility for extracting a specific feature from collective variations of the related VOCs with a certain health condition. For this approach, it is important to establish a feasible protocol for sampling exhaled breath in practical conditions to provide reproducible signal features. Here we report a robust protocol for the breath analysis, focusing on total expiratory breath measured by a Membrane-type Surface stress Sensor (MSS), which possesses practical characteristics for artificial olfactory systems. To assess its reproducibility, 83 exhaled breath samples were collected from one subject throughout more than a year. It has been confirmed that the reduction of humidity effects on the sensing signals either by controlling the humidity of purging room air or by normalizing the signal intensities leads to reasonable reproducibility verified by statistical analyses. We have also demonstrated the applicability of the protocol for detecting a target material by discriminating exhaled breaths collected from different subjects with pre- and post-alcohol ingestion on different occasions. This simple yet reproducible protocol based on the total expiratory breath measured by the MSS olfactory sensors will contribute to exploring the possibilities of clinical applications of breath diagnostics.

Determination of quasi-primary odors by endpoint detection

It is known that there are no primary odors that can represent any other odors with their combination. Here, we propose an alternative approach: "quasi" primary odors. This approach comprises the following condition and method: (1) within a collected dataset and (2) by the machine learning-based endpoint detection. The quasi-primary odors are selected from the odors included in a collected odor dataset according to the endpoint score. While it is limited within the given dataset, the combination of such quasi-primary odors with certain ratios can reproduce any other odor in the dataset. To visually demonstrate this approach, the three quasi-primary odors having top three high endpoint scores are assigned to the vertices of a chromaticity triangle with red, green, and blue. Then, the other odors in the dataset are projected onto the chromaticity triangle to have their unique colors. The number of quasi-primary odors is not limited to three but can be set to an arbitrary number. With this approach, one can first find "extreme" odors (i.e., quasi-primary odors) in a given odor dataset, and then, reproduce any other odor in the dataset or even synthesize a new arbitrary odor by combining such quasi-primary odors with certain ratios.

Label-free attomolar protein detection using a MEMS optical interferometric surface-stress immunosensor with a freestanding PMMA/parylene-C nanosheet

We demonstrated an optical interferometer-based surface-stress immunosensor using freestanding polymethyl methacrylate (PMMA)/parylene-C nanosheet with high sensitivity for detection of biomolecules. PMMA/parylene-C nanosheets were transferred onto a silicon substrate with microcavities to fabricate freestanding submicron-thick membrane with a sealed cavity structure. The adhesive force between the transferred parylene-C and binder parylene-C layer was measured to be 1.06-2.4 N/10 mm by tape test. Evading Debye shielding, these nanomechanical sensors allow detection of the adsorption on the membrane surface through changes in surface stress transduced by the electric charge. We optimized the density of receptors and mode of immobilization for high sensitivity. To evaluate the selectivity of the sensor, membrane deflections induced by various proteins were measured and the spectral shifts showed high selectivity only for the target antigen. The minimum limit of detection (LOD) of the sensor for human serum albumin antigen was 0.1-1 fg/mL (1.5-15 aM), which was 20,000 times lower than that of the conventional micro-cantilever sensor.

Development of a Mobile Device for Odor Identification and Optimization of Its Measurement Protocol Based on the Free-Hand Measurement

Practical applications of machine olfaction have been eagerly awaited. A free-hand measurement, in which a measurement device is manually exposed to sample odors, is expected to be a key technology to realize practical machine olfaction. To implement odor identification systems based on the free-hand measurement, the comprehensive development of a measurement system including hardware, measurement protocols, and data analysis is necessary. In this study, we developed palm-size wireless odor measurement devices equipped with Membrane-type Surface stress Sensors (MSS) and investigated the effect of measurement protocols and feature selection on odor identification. By using the device, we measured vapors of liquids as odor samples through the free-hand measurement in different protocols. From the measurement data obtained with these protocols, datasets of transfer function ratios (TFRs) were created and analyzed by clustering and machine learning classification. It has been revealed that TFRs in the low-frequency range below 1 Hz notably contributed to vapor identification because the frequency components in that range reflect the dynamics of the detection mechanism of MSS. We also showed the optimal measurement protocol for accurate classification. This study has shown a guideline of the free-hand measurement and will contribute to the practical implementation of machine olfaction in society.

Graphene Oxide as a Sensing Material for Gas Detection Based on Nanomechanical Sensors in the Static Mode

Graphene is a key material for gas sensing applications owing to its high specific surface area and vast chemical modification potential. To fully utilize the potential of graphene, a sensing platform independent of conductive properties is required. In this study, we employed membrane-type surface stress sensors (MSS)—A kind of nanomechanical sensor operated in the static mode—As a sensing platform and utilized graphene oxide (GO) as a gas sensing material. MSS detect surface stress caused by gas sorption; therefore, chemically modified graphene with low conductivity can be utilized as a gas sensing material. We evaluated the sensing performance of a GO-coated MSS by measuring its responses to five gases. We demonstrated with the GO-coated MSS the feasibility of GO as a gas sensing material for static mode nanomechanical sensors and revealed its high selectivity to water vapor. Moreover, we investigated the sensing mechanism of the GO-coated MSS by comparing it with the sensing performance of MSS coated with reduced graphene oxide and graphite powder and deduced key factors for sensitivity and selectivity. Considering the high sensitivity of the GO-coated MSS and the compact measurement system that MSS can realize, the present study provides a new perspective on the sensing applications of graphene.

Finite Element Analysis of Interface Dependence on Nanomechanical Sensing

Nanomechanical sensors and their arrays have been attracting significant attention for detecting, discriminating and identifying target analytes. The sensing responses can be partially explained by the physical properties of the receptor layers coated on the sensing elements. Analytical solutions of nanomechanical sensing are available for a simple cantilever model including the physical parameters of both a cantilever and a receptor layer. These analytical solutions generally rely on the simple structures, such that the sensing element and the receptor layer are fully attached at their boundary. However, an actual interface in a real system is not always fully attached because of inhomogeneous coatings with low affinity to the sensor surface or partial detachments caused by the exposure to some analytes, especially with high concentration. Here, we study the effects of such macroscopic interfacial structures, including partial attachments/detachments, for static nanomechanical sensing, focusing on a Membrane-type Surface stress Sensor (MSS), through finite element analysis (FEA). We simulate various macroscopic interfacial structures by changing the sizes, numbers and positions of the attachments as well as the elastic properties of receptor layers (e.g., Young’s modulus and Poisson’s ratio) and evaluate the effects on the sensitivity. It is found that specific interfacial structures lead to efficient sensing responses, providing a guideline for designing the coating films as well as optimizing the interfacial structures for higher sensitivity including surface modification of the substrate.

A CMOS MEMS-based Membrane-Bridge Nanomechanical Sensor for Small Molecule Detection

Small molecule compounds are necessary to detect with high sensitivity since they may cause a strong effect on the human body even in small concentrations. But existing methods used to evaluate small molecules in blood are inconvenient, costly, time-consuming, and do not allow for portable usage. In response to these shortcomings, we introduce a complementary metal-oxide-semiconductor bio-microelectromechanical system (CMOS BioMEMS) based piezoresistive membrane-bridge (MB) sensor for detecting small molecule (phenytoin) concentrations as the demonstration. Phenytoin is one of anticonvulsant drugs licensed for the management of seizures, which has a narrow therapeutic window hence a level of concentration monitoring was needed. The MB sensor was designed to enhance the structural stability and increase the sensitivity, which its signal response increased 2-fold higher than that of the microcantilever-based sensor. The MB sensor was used to detect phenytoin in different concentrations from 5 to 100  μg/mL. The limit of detection of the sensor was 4.06 ± 0.15  μg/mL and the linear detection range was 5–100  μg/mL, which was within the therapeutic range of phenytoin concentration (10–20  μg/mL). Furthermore, the MB sensor was integrated with an on-chip thermal effect eliminating modus and a reaction tank on a compact chip carrier for disposable utilization. The required amount of sample solution was only 10  μL and the response time of the sensor was about 25  minutes. The nano-mechanical MB sensing method with thermal effect compensation is specific, sensitive, robust, affordable and well reproducible; it is, therefore, an appropriate candidate for detecting small molecules.

Preparation and Characterization of Freely-Suspended Graphene Nanomechanical Membrane Devices with Quantum Dots for Point-of-Care Applications

We demonstrate freely suspended graphene-based nanomechanical membranes (NMMs) as acoustic sensors in the audible frequency range. Simple and low-cost procedures are used to fabricate NMMs with various thicknesses based on graphene layers grown by graphite exfoliation and solution processed graphene oxide. In addition, NMMs are grafted with quantum dots (QDs) for characterizing mass sensitive vibrational properties. Thickness, roughness, deformation, deflection and emissions of NMMs with attached QDs are experimented and analyzed by utilizing atomic force microscopy, Raman spectroscopy, laser induced deflection analyzer and spectrophotometers. Förster resonance energy transfer (FRET) is experimentally achieved between the QDs attached on NMMs and nearby glass surfaces for illustrating acousto-optic utilization in future experimental implementations combining vibrational properties of NMMs with optical emission properties of QDs. This property denoted as vibrating FRET (VFRET) is previously introduced in theoretical studies while important experimental steps are for the first time achieved in this study for future VFRET implementations. The proposed modeling and experimental methodology are promising for future novel applications such as NMM based biosensing, photonics and VFRET based point-of-care (PoC) devices.

Free-hand gas identification based on transfer function ratios without gas flow control

Gas identification is one of the most important functions of a gas sensor system. To identify gas species from sensing signals without gas flow control such as pumps or mass flow controllers, it is necessary to extract decisive dynamic features from complex sensing signals due to uncontrolled airflow. For that purpose, various analysis methods using system identification techniques have been proposed, whereas a method that is not affected by a gas input pattern has been demanded to enhance the robustness of gas identification. Here we develop a novel gas identification protocol based on a transfer function ratio (TFR) that is intrinsically independent of a gas input pattern. By combining the protocol with MEMS-based sensors-Membrane-type Surface stress Sensors (MSS), we have realized gas identification with a free-hand measurement, in which one can simply hold a small sensor chip near samples. From sensing signals obtained through the free-hand measurement, we have developed highly accurate machine learning models that can identify odors of spices and herbs as well as solvent vapors. Since no bulky gas flow control units are required, this protocol will expand the applicability of gas sensors to portable electronics, leading to practical artificial olfaction.

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.

Materials Nanoarchitectonics for Mechanical Tools in Chemical and Biological Sensing

In this Focus Review, nanoarchitectonic approaches for mechanical-action-based chemical and biological sensors are briefly discussed. In particular, recent examples of piezoelectric devices, such as quartz crystal microbalances (QCM and QCM-D) and a membrane-type surface stress sensor (MSS), are introduced. Sensors need well-designed nanostructured sensing materials for the sensitive and selective detection of specific targets. Nanoarchitectonic approaches for sensing materials, such as mesoporous materials, 2D materials, fullerene assemblies, supported lipid bilayers, and layer-by-layer assemblies, are highlighted. Based on these sensing approaches, examples of bioanalytical applications are presented for toxic gas detection, cell membrane interactions, label-free biomolecular assays, anticancer drug evaluation, complement activation-related multiprotein membrane attack complexes, and daily biodiagnosis, which are partially supported by data analysis, such as machine learning and principal component analysis.

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.

Deposition of a titania layer on spherical porous silica particles and their nanostructure-induced vapor sensing properties

A titania-stearic acid hybrid layer was deposited onto well-defined silica-hexadecyltrimethylammonium hybrid spherical particles with 854 nm size to obtain nanoporous particles with a useful hierarchical core-shell structure. The deposition of a 35 nm-thick titania layer was confirmed by transmission electron microscopy. The core-shell particles were washed with acidic ethanol (solvent extraction) and calcined at 550 °C for 5 h to remove the template, resulting in the formation of nanoporous titania coated nanoporous silica spherical particles, which have a bimodal pore size distribution attributed to the hierarchical porous core and porous shell structure. The nanoporous titania coated particles exhibited an unusual crystal phase transition; only anatase was present even after the calcination at 1000 °C for 1 h. This would be due to the interfacial bonding between the core silica and the shell titania, preventing the crystal phase transition from anatase to rutile. On the other hand, the direct calcination of the titania-stearic acid coated particles without solvent extraction led to a shell composed of both anatase and rutile. The transformation to rutile could be caused by the strong exothermic reaction during the oxidative decomposition of the occluded stearic acid. Furthermore, the intense exothermic reaction induced the formation of a yolk-shell structure, which played a role in the sensitive/selective sensing properties for acetic acid when the yolk-shell particles were coated onto a nanomechanical Membrane-type Surface stress Sensor (MSS).

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.

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.

Finite Element Analysis on Nanomechanical Sensing of Cellular Forces

Detection of cellular forces plays an important role in investigating the mechanical basis of cells. As nanomechanical sensors can directly detect surface stress, they can be utilized to detect cellular forces. In the present study, we perform quantitative simulations of nanomechanical sensors for the detection of cellular forces using finite element analyses (FEA). We focus on two types of nanomechanical sensors: a cantilever-type sensor and a membrane-type surface stress sensor (MSS). It is found that sensing signals can be obtained when cells on the nanomechanical sensors synchronize their motions. To effectively detect cellular forces on the nanomechanical sensors, we discuss the optimization scheme for a coating layer on the surface of the sensors.

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., Young's modulus and Poisson's ratio) on the sensitivity. It is found that a membrane-type surface stress sensors (MSS) has the potential for robust detection of small particles.

Nanomechanical Sensors

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.

Surface stress-based biosensors

Surface stress-based biosensors, as one kind of label-free biosensors, have attracted lots of attention in the process of information gathering and measurement for the biological, chemical and medical application with the development of technology and society. This kind of biosensors offers many advantages such as short response time (less than milliseconds) and a typical sensitivity at nanogram, picoliter, femtojoule and attomolar level. Furthermore, it simplifies sample preparation and testing procedures. In this work, progress made towards the use of surface stress-based biosensors for achieving better performance is critically reviewed, including our recent achievement, the optimally circular membrane-based biosensors and biosensor array. The further scientific and technological challenges in this field are also summarized. Critical remark and future steps towards the ultimate surface stress-based biosensors are addressed.

Double-side-coated nanomechanical membrane-type surface stress sensor (MSS) for one-chip-one-channel setup

With their capability for real-time and label-free detection of targets ranging from gases to biological molecules, nanomechanical sensors are expected to contribute to various fields, such as medicine, security, and environmental science. For practical applications, one of the major issues of nanomechanical sensors is the difficulty of coating receptor layers on their surfaces to which target molecules adsorb or react. To have measurable deflection, a single-side coating is commonly applied to cantilever-type geometry, and it requires specific methods or protocols, such as inkjet spotting or gold-thiol chemistry. If we can apply a double-side coating to nanomechanical sensors, it allows almost any kind of coating technique including dip coating methods, making nanomechanical sensors more useful with better user experiences. Here we address the feasibility of the double-side coating on nanomechanical sensors demonstrated by a membrane-type surface stress sensor (MSS) and verify its working principle by both finite element analysis (FEA) and experiments. In addition, simple hand-operated dip coating is demonstrated as a proof of concept, achieving practical receptor layers without any complex instrumentation. Because the double-side coating is compatible with batch protocols such as dip coating, double-side-coated MSS represents a new paradigm of one-chip-one-channel (channels on a chip are all coated with the same receptor layers) shifting from the conventional one-chip-multiple-channel (channels on a chip are coated with different receptor layers) paradigm.