Multiplexed DNA-functionalized graphene sensor with artificial intelligence-based discrimination performance for analyzing chemical vapor compositions.

This study presents a new technology that can detect and discriminate individual chemical vapors to determine the chemical vapor composition of mixed chemical composition in situ based on a multiplexed DNA-functionalized graphene (MDFG) nanoelectrode without the need to condense the original vapor or target dilution. To the best of our knowledge, our artificial intelligence (AI)-operated arrayed electrodes were capable of identifying the compositions of mixed chemical gases with a mixed ratio in the early stage. This innovative technology comprised an optimized combination of nanodeposited arrayed electrodes and artificial intelligence techniques with advanced sensing capabilities that could operate within biological limits, resulting in the verification of mixed vapor chemical components. Highly selective sensors that are tolerant to high humidity levels provide a target for "breath chemovapor fingerprinting" for the early diagnosis of diseases. The feature selection analysis achieved recognition rates of 99% and above under low-humidity conditions and 98% and above under humid conditions for mixed chemical compositions. The 1D convolutional neural network analysis performed better, discriminating the compositional state of chemical vapor under low- and high-humidity conditions almost perfectly. This study provides a basis for the use of a multiplexed DNA-functionalized graphene gas sensor array and artificial intelligence-based discrimination of chemical vapor compositions in breath analysis applications.

Identification of Chemical Vapor Mixture Assisted by Artificially Extended Database for Environmental Monitoring.

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 NO2, HCHO, and NH3 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.

On-Chip Chemiresistive Sensor Array for On-Road NO x Monitoring with Quantification.

The adverse effects of air pollution on respiratory health make air quality monitoring with high spatial and temporal resolutions essential especially in cities. Despite considerable interest and efforts, the application of various types of sensors is considered immature owing to insufficient sensitivity and cross-interference under ambient conditions. Here, a fully integrated chemiresistive sensor array (CSA) with parts-per-trillion sensitivity is demonstrated with its application for on-road NO x monitoring. An analytical model is suggested to describe the kinetics of the sensor responses and quantify molecular binding affinities. Finally, the full characterization of the system is connected to implement on-road measurements on NO x vapor with quantification as its ultimate field application. The obtained results suggest that the CSA shows potential as an essential unit to realize an air-quality monitoring network with high spatial and temporal resolutions.

Determination of the molecular assembly of actin and actin-binding proteins using photoluminescence.

Actin, the most abundant protein in cells, polymerizes into filaments that play key roles in many cellular dynamics. To understand cell dynamics and functions, it is essential to examine the cytoskeleton structure organized by actin and actin-binding proteins. Here, we pave the way for determining the molecular assembly of the actin cytoskeleton using direct photonic in situ analysis, providing the photoluminescence characteristics of actin as a function of filament length and bundling, without labeling. In experiments for monomeric and filamentous actin reconstituted in vitro, structural forms of actin are identified from the peak positions and intensities of photoluminescence. Actin monomers exhibited small intensity emission peaks at 334 nm, whereas filamentous and bundled actin showed a shifted peak at 323 nm with higher intensity. The peak shift was found to be proportional to the length of the actin filament. With probing structural change of actin in various cells in vivo, our study provides an efficient and precise analytical in situ tool to examine the cytoskeleton structure. It will be beneficial for elucidating the mechanism of various cellular functions such as cell migration, differentiation, cytokinesis and adhesion. Furthermore, our technique can be applied to detect the alterations in the cell cytoskeleton that can occur during many pathological processes.

Hollow Pt-Functionalized SnO2 Hemipill Network Formation Using a Bacterial Skeleton for the Noninvasive Diagnosis of Diabetes.

Hollow-structured nanomaterials are presented as an outstanding sensing platform because of their unique combination of high porosity in both the micro- and nanoscale, their biocompatibility, and flexible template applicability. Herein, we introduce a bacterial skeleton method allowing for cost-effective fabrication with nanoscale precision. As a proof-of-concept, we fabricated a hollow SnO2 hemipill network (HSHN) and a hollow Pt-functionalized SnO2 hemipill network (HPN). A superior detecting capability of HPN toward acetone, a diabetes biomarker, was demonstrated at low concentration (200 ppb) under high humidity (RH 80%). The detection limit reaches 3.6 ppb, a level satisfying the minimum requirement for diabetes breath diagnosis. High selectivity of the HPN sensor against C6H6, C7H8, CO, and NO vapors is demonstrated using principal component analysis (PCA), suggesting new applications of HPN for human-activity monitoring and a personal healthcare tool for diagnosing diabetes. The skeleton method can be further employed to mimic nanostructures of biomaterials with unique functionality for broad applications.

Chemiresistive Electronic Nose toward Detection of Biomarkers in Exhaled Breath.

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.

Molecular dynamics simulations of nanoscale and sub-nanoscale friction behavior between graphene and a silicon tip: analysis of tip apex motion.

A sliding object on a crystal surface with a nanoscale contact will always experience stick-slip movement. However, investigation of the slip motion itself is rarely performed due to the short slip duration. In this study, we performed molecular dynamics simulation and frictional force microscopy experiments for the precise observation of slip motion between a graphene layer and a crystalline silicon tip. The simulation results revealed a hierarchical structure of stick and slip motion. Nanoscale stick and slip motion is composed of sub-nanoscale stick and slip motion. Sub-nanoscale stick and slip motion occurred on a timescale of a few ps and a force scale of 10(-1) nN. The relationship between the trajectories of the silicon tip and stick-slip peak revealed that in-plane and vertical motions of the tip provide information about stick and slip motion in the sub-nanoscale and nanoscale ranges, respectively. Parametric studies including tip size, scan angle, layer thickness, and flexibility of the substrate were also carried out to compare the simulation results with findings on lateral force microscopy.