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.

We present a novel system-based approach to augmenting olfactory sensor signal output by modulating carrier gas flow rates, enhancing odour differentiation performance through experimental, statistical and analytical validation.

Syneresis-Driven Self-Refilling Printing of Geometry/Component-Controlled Nano/Microstructures.

Nano/microfabrication is of fundamental importance both in scientific and industrial situations. There are, therefore, many attempts at realizing easier, quicker, and more precise fabrication of various structures; however, achieving this aim without a bulky and costly setup is still challenging. Here, we introduce a facile and versatile means of printing an ordered structure consisting of nanoscale stripes and more complicated geometries including pillars and wavy form with a lateral resolution of single micrometers. To this end, we prepare a polydimethylsiloxane (PDMS) slab with an oxygen plasma-induced wrinkled surface where liquid PDMS exudes by syneresis. Since this liquid PDMS is automatically loaded, the printing is repeatable without inking. A substrate moderately wettable to the liquid PDMS as well as amount/property-controlled syneresis is primarily important for the creation of well-defined structures. Precisely controlling these conditions will make this method universally applicable to diverse substrates and liquids including suspensions.

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.

Effects of fermentative quality of corn silage on lactation, blood metabolites, and rumen fermentation in dairy cows.

We investigated the effect of fermentation quality of corn silage on dry matter intake (DMI), milk yield, ruminal fermentation, methane (CH4 ) emissions, and plasma metabolites in lactating cows. Six lactating Holstein cows were used in a 2 × 2 crossover design with two dietary treatments containing high quality corn silage with lower pH (high group) or low quality corn silage with higher pH (low group). The cows were fed partial mixed ration (PMR containing 50%DM of each corn silage) ad libitum plus 0.7 kg/day of concentrates at milking. The DMI of cows in the low group (24.8 kg/day) tended to be lower (p < 0.10) than that in the high group (26.8 kg/day). The dietary treatment did not affect milk yield or milk fat, protein, or lactose concentrations. The ruminal acetic acid proportion of the low group was significantly higher (p < 0.05) than that of the high group. The CH4 emission per DMI of the low group tended to be higher (p < 0.10) than that of the high group. The plasma concentration of the total cholesterol (TCHO) and the activity of aspartate aminotransferase and alanine aminotransferase of the low group were significantly higher than those of the high group.

Repetitive Direct Comparison Method for Odor Sensing.

Olfactory sensors are one of the most anticipated applications of gas sensors. To distinguish odors-complex mixtures of gas species, it is necessary to extract sensor responses originating from the target odors. However, the responses of gas sensors tend to be affected by interfering gases with much higher concentrations than target odor molecules. To realize practical applications of olfactory sensors, extracting minute sensor responses of odors from major interfering gases is required. In this study, we propose a repetitive direct comparison (rDC) method, which can highlight the difference in odors by alternately injecting the two target odors into a gas sensor. We verified the feasibility of the rDC method on chocolates with two different flavors by using a sensor system based on membrane-type surface stress sensors (MSS). The odors of the chocolates were measured by the rDC method, and the signal-to-noise ratios (S/N) of the measurements were evaluated. The results showed that the rDC method achieved improved S/N compared to a typical measurement. The result also indicates that sensing signals could be enhanced for a specific combination of receptor materials of MSS and target odors.

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.

Visualization of Flow-Induced Strain Using Structural Color in Channel-Free Polydimethylsiloxane Devices.

Measuring flow of gases is of fundamental importance yet is typically done with complex equipment. There is, therefore, a longstanding need for a simple and inexpensive means of flow measurement. Here, gas flow is measured using an extremely simple device that consists of an Ar plasma-treated polydimethylsiloxane (PDMS) slab adhered on a glass substrate with a tight seal. This device does not even have a channel, instead, gas can flow between the PDMS and the glass by deforming the PDMS wall, in other words, by making an interstice as a temporary path for the flow. The formation of the temporary path results in a compressive bending stress at the inner wall of the path, which leads to the formation of well-ordered wrinkles, and hence, the emergence of structural color that changes the optical transmittance of the device. Although it is very simple, this setup works sufficiently well to measure arbitrary gases and analyzes their flow rates, densities, and viscosities based on the change in color. It is also demonstrated that this technique is applicable to the flow-induced display of a pattern such as a logo for advanced applications.

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.

2-step reaction kinetics for hydrogen absorption into bulk material via dissociative adsorption on the surface.

We have demonstrated that the process of hydrogen absorption into a solid experimentally follows a Langmuir-type (hyperbolic) function instead of Sieverts law. This can be explained by independent two theories. One is the well-known solubility theory which is the basis of Sieverts law. It explains that the amount of hydrogen absorption can be expressed as a Langmuir-type (hyperbolic) function of the square root of the hydrogen pressure. We have succeeded in drawing the same conclusion from the other theory. It is a 2-step reaction kinetics (2sRK) model that expresses absorption into the bulk via adsorption on the surface. The 2sRK model has an advantage to the solubility theory: Since it can describe the dynamic process, it can be used to discuss both the amount of hydrogen absorption and the absorption rate. Some phenomena with absorption via adsorption can be understood in a unified manner by the 2sRK model.

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.

The Evolution of Rag Gene Enhancers and Transcription Factor E and Id Proteins in the Adaptive Immune System.

Adaptive immunity relies on the V(D)J DNA recombination of immunoglobulin (Ig) and T cell receptor (TCR) genes, which enables the recognition of highly diverse antigens and the elicitation of antigen-specific immune responses. This process is mediated by recombination-activating gene (Rag) 1 and Rag2 (Rag1/2), whose expression is strictly controlled in a cell type-specific manner; the expression of Rag1/2 genes represents a hallmark of lymphoid lineage commitment. Although Rag genes are known to be evolutionally conserved among jawed vertebrates, how Rag genes are regulated by lineage-specific transcription factors (TFs) and how their regulatory system evolved among vertebrates have not been fully elucidated. Here, we reviewed the current body of knowledge concerning the cis-regulatory elements (CREs) of Rag genes and the evolution of the basic helix-loop-helix TF E protein regulating Rag gene CREs, as well as the evolution of the antagonist of this protein, the Id protein. This may help to understand how the adaptive immune system develops along with the evolution of responsible TFs and enhancers.

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.

Microchannel measurements of viscosity for both gases and liquids.

Quantifying the viscosity of a fluid is of great importance in determining its properties and can even be used to identify what the fluid is. While many techniques exist for measuring the viscosity of either gases or liquids, it is very challenging to probe both gases and liquids with a single approach because of the significant difference in their nature, and the vast difference in the values of their viscosities. We introduce a facile approach to measuring the viscosity of a Newtonian fluid, either a gas or a liquid, by flowing it through a deformable microchannel where the deformation depends on the pressure required to induce the flow, which, in turn, depends on the fluid viscosity. A strain gauge embedded just above and across the microchannel transduces the flow-induced deformation into strain. The strain is proportional to the square of the flow-induced deformation enabling us to precisely discriminate not only gases but also liquids based on their viscosities with the same device.

Amorphous thin-film oxide power devices operating beyond bulk single-crystal silicon limit.

Power devices (PD) are ubiquitous elements of the modern electronics industry that must satisfy the rigorous and diverse demands for robust power conversion systems that are essential for emerging technologies including Internet of Things (IoT), mobile electronics, and wearable devices. However, conventional PDs based on "bulk" and "single-crystal" semiconductors require high temperature (> 1000 °C) fabrication processing and a thick (typically a few tens to 100 µm) drift layer, thereby preventing their applications to compact devices, where PDs must be fabricated on a heat sensitive and flexible substrate. Here we report next-generation PDs based on "thin-films" of "amorphous" oxide semiconductors with the performance exceeding the silicon limit (a theoretical limit for a PD based on bulk single-crystal silicon). The breakthrough was achieved by the creation of an ideal Schottky interface without Fermi-level pinning at the interface, resulting in low specific on-resistance Ron,sp (< 1 × 10-4 Ω cm2) and high breakdown voltage VBD (~ 100 V). To demonstrate the unprecedented capability of the amorphous thin-film oxide power devices (ATOPs), we successfully fabricated a prototype on a flexible polyimide film, which is not compatible with the fabrication process of bulk single-crystal devices. The ATOP will play a central role in the development of next generation advanced technologies where devices require large area fabrication on flexible substrates and three-dimensional integration.

Autonomous Nanoscale Chemomechanical Oscillation on the Self-Oscillating Polymer Brush Surface by Precise Control of Graft Density.

As a novel functional surface, a self-oscillating polymer brush that undergoes autonomous, periodic swelling/deswelling during the Belousov-Zhabotinsky (BZ) reaction has been developed. Although extensive research has revealed how the fundamental aspects of the BZ reaction can be regulated based on the surface design of the self-oscillating polymer brush, design strategies for the induction of mechanical oscillation remain unexplored. Herein, we investigated the graft density effects on the phase transition behavior, which is an important design parameter for the mechanical oscillation of the modified polymer. The self-oscillating polymer-modified substrates with controlled graft densities were prepared by immobilizing various compositions of an initiator and a noninitiator followed by surface-initiated atom transfer radical polymerization of the self-oscillating polymer chains. In addition to the characterization of each prepared substrate, atomic force microscopy (AFM) and digital holographic microscopy (DHM) were employed to evaluate the density effects on the static and dynamic surface structures. AFM revealed that equilibrium swelling as well as thermoresponsive behavior is profoundly affected by the graft density. Moreover, using DHM, autonomous mechanical oscillation was captured only on the self-oscillating polymer brush with adequate graft density. Notably, the oscillation amplitude (150 nm) and the period (20 s) in this study were superior to those in a previous report on the self-oscillating polymer modified through the grafting-to method by 10- and 3-fold, respectively. This study presents design guidelines for future applications, such as autonomous transport devices.

Towards future physics and applications via two-dimensional material NEMS resonators.

Two-dimensional materials (2Dm) offer a unique insight into the world of quantum mechanics including van der Waals (vdWs) interactions, exciton dynamics and various other nanoscale phenomena. 2Dm are a growing family consisting of graphene, hexagonal-Boron Nitride (h-BN), transition metal dichalcogenides (TMDs), monochalcogenides (MNs), black phosphorus (BP), MXenes and 2D organic crystals such as small molecules (e.g., pentacene, C8 BTBT, perylene derivatives, etc.) and polymers (e.g., COF and MOF, etc.). They exhibit unique mechanical, electrical, optical and optoelectronic properties that are highly enhanced as the surface to volume ratio increases, resulting from the transition of bulk to the few- to mono- layer limit. Such unique attributes include the manifestation of highly tuneable bandgap semiconductors, reduced dielectric screening, highly enhanced many body interactions, the ability to withstand high strains, ferromagnetism, piezoelectric and flexoelectric effects. Using 2Dm for mechanical resonators has become a promising field in nanoelectromechanical systems (NEMS) for applications involving sensors and condensed matter physics investigations. 2Dm NEMS resonators react with their environment, exhibit highly nonlinear behaviour from tension induced stiffening effects and couple different physics domains. The small size and high stiffness of these devices possess the potential of highly enhanced force sensitivities for measuring a wide variety of un-investigated physical forces. This review highlights current research in 2Dm NEMS resonators from fundamental physics and an applications standpoint, as well as presenting future possibilities using these devices.

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.