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Deoxyribonucleic acid (DNA) is one of the most important biomolecules that govern the genetic information of life, and recently it has attracted attention as a functional material for electronic device applications such as sensors [1]. In the application of DNA to electronic devices, it is necessary to form a flat thin film at the nanometer (nm) level for heterogeneous film stacking and patterning. We have been developing nm flat DNA thin films by laser molecular beam deposition (LMBD) method, and also fabricated a quartz crystal microbalance sensor (QCM) for volatile organic compound (VOC) detection and evaluated its VOC detection characteristics to explore the possibility of sensor applications of the DNA solid-state thin films [2].

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.

We successfully fabricated a novel sensing platform, a Membrane-type Surface stress Sensor (MSS) coated with copper(I) complex bearing phen and BINAP ligands, [Cu(phen)((±)-BINAP)]PF6 (1, phen = 1,...

Development of human mesenchymal stem cell (hMSC)-based therapies is hindered by the limited availability of adult stem cells. Long-term retention of multipotency and stem-like phenotypes remains a...

Metal–organic frameworks (MOFs) have received much attention for their potential as chemical sensors, owing to unparalleled tunability of their host–guest response, high uptake and structural flexibility. However, because of the limited compatibility between MOF properties and sensor transduction mechanisms, very few MOFs have successfully been integrated into practical devices. We report the fabrication of the first strain-based sensor constructed from MOF nanoparticles deposited directly onto a membrane-type surface stress sensor (MSS) architecture, which exhibits unprecedented response times on the order of seconds and ppm-level sensitivity towards volatile organic compounds (VOCs). Finite element analysis is used to demonstrate that the sensor response is a result of analyte-induced strain in the MOF receptor layer. We show that an array of four types of MOF nanoparticles allows for clear discrimination between different classes of VOCs and even individual gases, using principal component analysis of their response profiles. This work opens up the possibility of VOC sensing using a wide range of MOFs, beyond those that are electrically conducting or those that form oriented thin films, with the added advantages of high sensitivity and rapid response compared to existing MOF strain-based sensors.

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.

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)&mdash, A kind of nanomechanical sensor operated in the static mode&mdash, 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.

Metal-organic frameworks (MOFs) have received much attention for their potential as chemical sensors, owing to unparalleled tunability of their host-guest response, high uptake and structural flexibility. However, because of the limited compatibility between MOF properties and sensor transduction mechanisms, very few MOFs have successfully been integrated into practical devices. We report the fabrication of the first strain-based sensor constructed from MOF nanoparticles deposited directly onto a membrane-type surface stress sensing architecture, which exhibits unprecedented response times on the order of seconds and ppm-level sensitivity towards volatile organic compounds (VOCs). Finite element analysis is used to demonstrate that the sensor response is a result of analyte-induced strain in the MOF receptor layer. We show that an array of four types of MOF nanoparticles allows for clear discrimination between different classes of VOCs and even individual gases, using principal component analysis of their response profiles. This work opens up the possibility of VOC sensing using a wide range of MOFs, beyond those that are electrically conducting or those that form oriented thin films, with the added advantages of high sensitivity and rapid response compared to existing MOF strain-based sensors.

A pattern recognition-based chemical sensor array is an efficient approach to discriminating odours or a complex mixture of gaseous molecules. In such an approach, solid materials are coated on surfaces of sensors as probe receptors, and gaseous molecules are exposed to those sensors as targets. Here, we propose the reverse approach, that is, gaseous molecules as probes and solid materials as targets, leading to pattern recognition of solid materials. Using a nanomechanical sensor as an example of a sensing platform, we have demonstrated that this approach can discriminate polymers with different molecular weights as well as those having slightly different functional groups evaluated through detailed classification using a support vector machine in addition to principal component analysis and linear discriminant analysis. Classification of those target solid materials with 100% accuracy has been achieved with some specific combinations of probe gases. Since any kind of gaseous molecule and any kind of chemical sensor can be utilized as the probe and sensing platform, respectively, this study will open a new horizon for comprehensive analysis of solid materials through a pattern formed by the gas–solid interaction.

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.

A pattern recognition-based chemical sensor array is an efficient approach to discriminating odours, while the fluctuation in the concentration of odorous molecules often results in concentration-dependent patterns rather than the ones based on chemical properties. Here, we present the discrimination of odorous molecules based on the chemical properties with reduced influence of their concentrations in a wide range from ppm to ppb levels using a nanomechanical sensor. Extraction of effective features, which are less affected by concentrations of odorous molecules, from sensing signals leads to successful discrimination of each chemical species even in a series with similar physical structures.

There is growing evidence to suggest that the prevailing physical microenvironment and mechanical stress regulate cellular functions, including adhesion, proliferation, and differentiation. Moreover, the physical microenvironment determines the stem-cell lineage depending on stiffness of the substrate relative to biological tissues as well as the stress relaxation properties of the viscoelastic substrates used for cell culture. However, there is little known regarding the biological effects of a fluid substrate, where viscoelastic stress is essentially absent. Here, we demonstrate the regulation of myogenic differentiation on fluid substrates by using a liquid-liquid interface as a scaffold. C2C12 myoblast cells were cultured using water-perfluorocarbon (PFC) interfaces as the fluid microenvironment. We found that, for controlled in vitro culture at water-PFC interfaces, expression of myogenin, myogenic regulatory factors (MRF) family gene, is remarkably attenuated even when myogenic differentiation was induced by reducing levels of growth factors, although MyoD was expressed at the usual level (MyoD up-regulates myogenin under an elastic and/or viscoelastic environment). These results strongly suggest that this unique regulation of myogenic differentiation can be attributed to the fluid microenvironment of the interfacial culture medium. This interfacial culture system represents a powerful tool for investigation of the mechanisms by which physical properties regulate cellular adhesion and proliferation as well as their differentiation. Furthermore, we successfully transferred the cells cultured at such interfaces using Langmuir-Blodgett (LB) techniques. The combination of the interfacial culture system with the LB approach enables investigation of the effects of mechanical compression on cell functions.

This review tests you for survival in modern science and technology.Q1: Can we operate molecular machines by our hands?Ans: YES (Hand-Operating Nanotechnology)!Q2: Can we mechanically control life and bio-events?Ans: YES (Mechanobiology)!Q3: What comes after nanotechnology?Ans: It is definitely Nanoarchitectonics.

Recently, the nanoarchitectonics concept has been proposed to fabricate functional materials on the basis of concerted harmonization actions to control materials organization. In this review, we introduce recent several sensor designs from viewpoints of nanoarchitectonics that are classified into three categories: (i) inside nanoarchitectonics, (ii) outside nanoarchitectonics, and (iii) hierarchic nanoarchitectonics. In addition, various nanocarbons, such as carbon nanotubes, graphene derivatives, fullerene assemblies and nanoporous carbons, are selected as standard materials. In the first section, design and fabrication of mesoporous or nanoporous materials for enhanced sensing are especially exemplified as inside nanoarchitectonics. In the next section, recent examples of layer-by-layer nanoarchitectures for sensor fabrications are explained for outside nanoarchitectonics. Finally, their combined strategies for hierarchic carbon nanoarchitectonics are introduced as advanced sensor materials designs.

In this short review article, we explained nanoarchitectonics approaches from very simple unit components (fullerenes and their derivatives) to complicated targets (bio-related applications). Application targets described herein are classified into bioimaging, biosensing, drug and gene delivery, photodynamic therapy, and cellular scaffold. Specific features of fullerenes in their electrical, electrochemical, photonic, mechanical and hydrophobic properties provide unique opportunities in these applications. Complexation of fullerenes with drugs often leads to better water dispersibility and target delivery. Photoelectrochemical properties are well used in various usages such as bioimaging, biosensing, and photodynamic therapy. Their supramolecular assemblies such as fullerene vesicles and fullerene nanowhiskers exhibit useful functions in drug/gene delivery and cell cultures with differentiation controls. From simple zero-dimensional fullerenes themselves to microscopic assembles, various types of functional fullerene materials are widely used for biological and biomedical applications.

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. The analytical models for the static mode nanomechanical sensing with viscoelastic receptor layers have been proposed, while they are not formulated for practical conditions, such as multistep injection-purge cycles. Here, we derive an analytical model of viscoelastic material-based nanomechanical sensing by extending the theoretical model via solving differential equations with recurrence relations. The presented model is capable of reproducing the transient behaviors observed in the experimental signal responses with multistep injection-purge cycles, including drifts and/or changes in the baseline. Moreover, this model can be utilized for extracting viscoelastic properties of the receptor material/analyte pairs as well as the concentrations of analytes accurately by fitting a couple of injection-purge curves obtained from the experimental data. The parameters of the model that best fit the data can be used for predicting the entire signal response.

In nanomechanical sensing, the interfacial structure between a receptor layer and an underlying substrate affects output signals, especially in the case of a partial attachment at the interface. Here, we present numerical simulations of the interfacial structures for the static deflection of a nanomechanical cantilever sensor by finite element analysis. A wide range of physical and structural parameters of receptor layers as well as interfacial structures are analyzed, while taking into account most of the practical conditions. The comprehensive analyses performed in this study confirmed the important requirements for effective transduction of the stress induced in a receptor layer through the interfacial structures, including partial and full attachments. Notably, the results indicate that some partially attached interfacial structures can even enhance the deflection of a cantilever.