Service-Oriented Factors Affecting the Adoption of Smartphones

This research investigates the adoption factors of smartphones focusing on the differences of smartphone and feature phone users. We used Technology Acceptance Model (TAM) which incorporates service-oriented and device-oriented functional attributes as exogenous variables for a product-service system such as smartphones. In addition, Decision Tree (DT) and customer surveys were conducted. As a study results, we found that the service-oriented functional attributes - ‘wireless internet’ and ‘mobile applications’ - affect the adoption of smartphones regardless of users. However, the DT results revealed that the more important factor is 'mobile applications' to smartphone users but 'wireless internet' for feature phone users. In conclusion, we discovered that a strategy emphasis on the service-oriented attributes is needed for the adoption of smartphones

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

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

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