The Nanomechanical Bit Kim, Chulki; Marsland, Robert; Blick, Robert H.
Small,
08/2020, Letnik:
16, Številka:
33
Journal Article
Recenzirano
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The applicability of nanomechanical devices for computational approaches is reviewed. The focus is on the representation and processing of information based on nanomechanical bits. Several device ...concepts are discussed ranging from nano‐electromechanical systems in silicon to circuits based on carbon nano‐tube switches, combinations of nanomechanical resonators and traditional transistors, and integration into a computing architecture. The strengths of mechanical systems include their scalability, robustness to external electrical shocks, and their low‐energy consumption. Hence, they may lead the way to new forms of ultradense memory and alternative routes of computing. In conjunction with quantum mechanical single electron circuits, nano‐electromechanical systems may also have potential for quantum computational circuits.
The potential of nanomechanical devices for nanomechanical memory and logic operations is considered. Various device approaches are discussed including nano‐electromechanical systems in silicon and carbon nano‐tube switches, and combinations of nanomechanical resonators. With their scalability, robustness, and outstanding power efficiency, a new avenue for computing devices is naturally expected in conjunction with quantum mechanical circuits.
Toward the development of surface-sensitive analytical techniques for biosensors and diagnostic biochip assays, a local integration of low-concentration target materials into the sensing region of ...interest is essential to improve the sensitivity and reliability of the devices. As a result, the dynamic process of sorting and accurate positioning the nanoparticulate biomolecules within pre-defined micro/nanostructures is critical, however, it remains a huge hurdle for the realization of practical surface-sensitive biosensors and biochips. A scalable, massive, and non-destructive trapping methodology based on dielectrophoretic forces is highly demanded for assembling nanoparticles and biosensing tools. Herein, we propose a vertical nanogap architecture with an electrode-insulator-electrode stack structure, facilitating the generation of strong dielectrophoretic forces at low voltages, to precisely capture and spatiotemporally manipulate nanoparticles and molecular assemblies, including lipid vesicles and amyloid-beta protofibrils/oligomers. Our vertical nanogap platform, allowing low-voltage nanoparticle captures on optical metasurface designs, provides new opportunities for constructing advanced surface-sensitive optoelectronic sensors.
Molecular recognition and discrimination of carbohydrates are important because carbohydrates perform essential roles in most living organisms for energy metabolism and cell-to-cell communication. ...Nevertheless, it is difficult to identify or distinguish various carbohydrate molecules owing to the lack of a significant distinction in the physical or chemical characteristics. Although there has been considerable effort to develop a sensing platform for individual carbohydrates selectively using chemical receptors or an ensemble array, their detection and discrimination limits have been as high in the millimolar concentration range. Here we show a highly sensitive and selective detection method for the discrimination of carbohydrate molecules using nano-slot-antenna array-based sensing chips which operate in the terahertz (THz) frequency range (0.5-2.5 THz). This THz metamaterial sensing tool recognizes various types of carbohydrate molecules over a wide range of molecular concentrations. Strongly localized and enhanced terahertz transmission by nano-antennas can effectively increase the molecular absorption cross sections, thereby enabling the detection of these molecules even at low concentrations. We verified the performance of nano-antenna sensing chip by both THz spectra and images of transmittance. Screening and identification of various carbohydrates can be applied to test even real market beverages with a high sensitivity and selectivity.
Abstract
Despite technological advances in biomolecule detections, evaluation of molecular interactions via potentiometric devices under ion-enriched solutions has remained a long-standing problem. ...To avoid severe performance degradation of bioelectronics by ionic screening effects, we cover probe surfaces of field effect transistors with a single film of the supported lipid bilayer, and realize respectable potentiometric signals from receptor–ligand bindings irrespective of ionic strength of bulky solutions by placing an ion-free water layer underneath the supported lipid bilayer. High-energy X-ray reflectometry together with the circuit analysis and molecular dynamics simulation discovered biochemical findings that effective electrical signals dominantly originated from the sub-nanoscale conformational change of lipids in the course of receptor–ligand bindings. Beyond thorough analysis on the underlying mechanism at the molecular level, the proposed supported lipid bilayer-field effect transistor platform ensures the world-record level of sensitivity in molecular detection with excellent reproducibility regardless of molecular charges and environmental ionic conditions.
Detection of gas-phase chemicals finds a wide variety of applications, including food and beverages, fragrances, environmental monitoring, chemical and biochemical processing, medical diagnostics, ...and transportation. One approach for these tasks is to use arrays of highly sensitive and selective sensors as an electronic nose. Here, we present a high performance chemiresistive electronic nose (CEN) based on an array of metal oxide thin films, metal-catalyzed thin films, and nanostructured thin films. The gas sensing properties of the CEN show enhanced sensitive detection of H2S, NH3, and NO in an 80% relative humidity (RH) atmosphere similar to the composition of exhaled breath. The detection limits of the sensor elements we fabricated are in the following ranges: 534 ppt to 2.87 ppb for H2S, 4.45 to 42.29 ppb for NH3, and 206 ppt to 2.06 ppb for NO. The enhanced sensitivity is attributed to the spillover effect by Au nanoparticles and the high porosity of villi-like nanostructures, providing a large surface-to-volume ratio. The remarkable selectivity based on the collection of sensor responses manifests itself in the principal component analysis (PCA). The excellent sensing performance indicates that the CEN can detect the biomarkers of H2S, NH3, and NO in exhaled breath and even distinguish them clearly in the PCA. Our results show high potential of the CEN as an inexpensive and noninvasive diagnostic tool for halitosis, kidney disorder, and asthma.
A motion recognition-based 3D pedestrian navigation system that employs a smartphone is presented. In existing inertial measurement unit (IMU)-based pedestrian dead-reckoning (PDR) systems, sensor ...axes are fixed regardless of user motion, because the IMU is mounted on the shoes or helmet. On the other hand, the sensor axes of a smartphone are changed according to the walking motion of the user, because the smartphone is usually carried by hand or kept in the pocket. Therefore, the conventional PDR method cannot apply to the smartphone-based PDR system. To overcome this limitation, the walking status is detected using a motion recognition algorithm with sensor measurements from the smartphone. Then, different PDR algorithms are applied according to the recognized pattern of the pedestrian motion. The height information of the pedestrian is also estimated using the on-board barometric pressure sensor of the smartphone. The 3D position, which consists of the 2D position calculated by the PDR and the height information, is provided to the pedestrian. The proposed system has several advantages in terms of cost and accessibility. It requires no additional peripheral devices except for the smartphone, because smartphones are equipped with all the necessary sensors, such as an accelerometer, magnetometer, gyroscope, and barometric pressure sensor. This paper implements the proposed system as an android-based application. The experimental results demonstrate the performance of the proposed system and reveal a high positioning accuracy.
Some of the shopping malls, airports, hospitals, etc. have underground parking lots where hundreds of vehicles can be parked. However, first-time visitors find it difficult to determine their current ...location and need to keep moving the vehicle to find an empty parking space. Moreover, they need to remember the parked location, and find a nearby staircase or elevator to move toward the destination. In such a situation, if the user location can be estimated, a new navigation system can be offered, which can assist users. This study presents an underground parking lot navigation system using long-term evolution (LTE) signals. As the proposed system utilizes LTE network signals for which the infrastructure is already installed, no additional infrastructure is required. To estimate the location of the vehicle, the signal strength of the LTE signal is accumulated, and the location of the vehicle is estimated by comparing it with the previously stored database of the LTE received signal strength (RSS). In addition, the acceleration and gyroscope sensors of a smartphone are used to improve the vehicle position estimation performance. The effectiveness of the proposed system is verified by conducting an experiment in a large shopping-mall underground parking lot where approximately 500 vehicles can be parked. From the results of the experiment, an error of less than an average of 10 m was obtained, which shows that seamless navigation is possible using the proposed system even in an environment where GNSS does not function.
A fully integrated sensor array assisted by pattern recognition algorithm has been a primary candidate for the assessment of complex vapor mixtures based on their chemical fingerprints. Diverse ...prototypes of electronic nose systems consisting of a multisensory device and a post processing engine have been developed. However, their precision and validity in recognizing chemical vapors are often limited by the collected database and applied classifiers. Here, we present a novel way of preparing the database and distinguishing chemical vapor mixtures with small data acquisition for chemical vapors and their mixtures of interest. The database for individual vapor analytes is expanded and the one for their mixtures is prepared in the first-order approximation. Recognition of individual target vapors of NO
, HCHO, and NH
and their mixtures was evaluated by applying the support vector machine (SVM) classifier in different conditions of temperature and humidity. The suggested method demonstrated the recognition accuracy of 95.24%. The suggested method can pave a way to analyze gas mixtures in a variety of industrial and safety applications.
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