Chemoresistive Sensor Readout Circuit Design for Detecting Gases with Slow Response Time Characteristics.

Based on an analysis of the signal characteristics of gas sensors, this work presents a chemoresistive sensor readout circuit design for detecting gases with slow response time characteristics. The proposed readout circuit directly generates a reference voltage corresponding to the initial value of the gas sensor and extracts only the amount of gas concentration change in the sensor. Because the proposed readout circuit can adaptively regenerate the suitable reference voltage under various changing ambient conditions, it can alleviate the variation in output values at the same gas concentration caused by non-uniformities among gas sensors. Furthermore, this readout circuit effectively eliminates the initial value shifts due to the poor reproducibility of the gas sensor itself without requiring complex digital signal calibrations. This work focuses on a commercially viable readout circuit structure that can effectively obtain slow response gas information without requiring a large capacitor. The proposed readout circuit operation was verified by simulations using spectre in cadence simulation software. It was then implemented on a printed circuit board with discrete components to confirm the effectiveness with existing gas sensor systems and its commercial viability.

Quasi-SMILES-Based Nano-Quantitative Structure-Activity Relationship Model to Predict the Cytotoxicity of Multiwalled Carbon Nanotubes to Human Lung Cells.

Quantitative structure-activity relationship (QSAR) models for nanomaterials (nano-QSAR) were developed to predict the cytotoxicity of 20 different types of multiwalled carbon nanotubes (MWCNTs) to human lung cells by using quasi-SMILES. The optimal descriptors, recorded as quasi-SMILES, were encoded to represent the physicochemical properties and experimental conditions for the MWCNTs from 276 data records collected from previously published studies. The quasi-SMILES used to build the optimal descriptors were (i) diameter, (ii) length, (iii) surface area, (iv) in vitro toxicity assay, (v) cell line, (vi) exposure time, and (vii) dose. The model calculations were performed by using the Monte Carlo method and computed with CORAL software ( www.insilico.eu/coral ). The quasi-SMILES-based nano-QSAR model provided satisfactory statistical results ( R2 for internal validation data sets: 0.60-0.80; R2pred for external validation data sets: 0.81-0.88). The model showed potential for use in the estimation of human lung cell viability after exposure to MWCNTs with the following properties: diameter, 12-74 nm; length, 0.19-20.25 µm; surface area, 11.3-380.0 m2/g; and dose, 0-200 ppm.

Tailored Graphene Micropatterns by Wafer-Scale Direct Transfer for Flexible Chemical Sensor Platform.

2D materials, such as graphene, exhibit great potential as functional materials for numerous novel applications due to their excellent properties. The grafting of conventional micropatterning techniques on new types of electronic devices is required to fully utilize the unique nature of graphene. However, the conventional lithography and polymer-supported transfer methods often induce the contamination and damage of the graphene surface due to polymer residues and harsh wet-transfer conditions. Herein, a novel strategy to obtain micropatterned graphene on polymer substrates using a direct curing process is demonstrated. Employing this method, entirely flexible, transparent, well-defined self-activated graphene sensor arrays, capable of gas discrimination without external heating, are fabricated on 4 in. wafer-scale substrates. Finite element method simulations show the potential of this patterning technique to maximize the performance of the sensor devices when the active channels of the 2D material are suspended and nanoscaled. This study contributes considerably to the development of flexible functional electronic devices based on 2D materials.

Implementation of Complementary Model using Optimal Combination of Hematological Parameters for Sepsis Screening in Patients with Fever.

The early detection and timely treatment are the most important factors for improving the outcome of patients with sepsis. Sepsis-related clinical score, such as SIRS, SOFA and LODS, were defined to identify patients with suspected infection and to predict severity and mortality. A few hematological parameters associated with organ dysfunction and infection were included in the score although various clinical pathology parameters (hematology, serum chemistry and plasma coagulation) in blood sample have been found to be associated with outcome in patients with sepsis. The investigation of the parameters facilitates the implementation of a complementary model for screening sepsis to existing sepsis clinical criteria and other laboratory signs. In this study, statistical analysis on the multiple clinical pathology parameters obtained from two groups, patients with sepsis and patients with fever, was performed and the complementary model was elaborated by stepwise parameter selection and machine learning. The complementary model showed statistically better performance (AUC 0.86 vs. 0.74-0.51) than models built up with specific hematology parameters involved in each existing sepsis-related clinical score. Our study presents the complementary model based on the optimal combination of hematological parameters for sepsis screening in patients with fever.

Quasi-QSAR for predicting the cell viability of human lung and skin cells exposed to different metal oxide nanomaterials.

A quasi-QSAR model was developed to predict the cell viability of human lung (BEAS-2B) and skin (HaCaT) cells exposed to 21 types of metal oxide nanomaterials. A wide range of toxicity datasets obtained from the S2NANO (www.s2nano.org) database was used. The data of descriptors representing the physicochemical properties and experimental conditions were coded to quasi-SMILES. In particular, hierarchical cluster analysis (HCA) and min-max normalization method were respectively used in assigning alphanumeric codes for numerical descriptors (e.g., core size, hydrodynamic size, surface charge, and dose) and then quasi-QSAR model performances for both methods were compared. The quasi-QSAR models were developed using CORAL software (www.insilico.eu/coral). Quasi-QSAR model built using quasi-SMILES generated by means of HCA showed better performance than the min-max normalization method. The model showed satisfactory statistical results (Radj2 for the training dataset: 0.71-0.73; Radj2 for the calibration dataset: 0.74-0.82; and Radj2 for the validation dataset: 0.70-0.76).

Toxicity Classification of Oxide Nanomaterials: Effects of Data Gap Filling and PChem Score-based Screening Approaches.

Development of nanotoxicity prediction models is becoming increasingly important in the risk assessment of engineered nanomaterials. However, it has significant obstacles caused by the wide heterogeneities of published literature in terms of data completeness and quality. Here, we performed a meta-analysis of 216 published articles on oxide nanoparticles using 14 attributes of physicochemical, toxicological and quantum-mechanical properties. Particularly, to improve completeness and quality of the extracted dataset, we adapted two preprocessing approaches: data gap-filling and physicochemical property based scoring. Performances of nano-SAR classification models revealed that the dataset with the highest score value resulted in the best predictivity with compromise in its applicability domain. The combination of physicochemical and toxicological attributes was proved to be more relevant to toxicity classification than quantum-mechanical attributes. Overall, by adapting these two preprocessing methods, we demonstrated that meta-analysis of nanotoxicity literatures could provide an effective alternative for the risk assessment of engineered nanomaterials.

Towards a generalized toxicity prediction model for oxide nanomaterials using integrated data from different sources.

A generalized toxicity classification model for 7 different oxide nanomaterials is presented in this study. A data set extracted from multiple literature sources and screened by physicochemical property based quality scores were used for model development. Moreover, a few more preprocessing techniques, such as synthetic minority over-sampling technique, were applied to address the imbalanced class problem in the data set. Then, classification models using four different algorithms, such as generalized linear model, support vector machine, random forest, and neural network, were developed and their performances were compared to find the best performing preprocessing methods as well as algorithms. The neural network model built using the balanced data set was identified as the model with best predictive performance, while applicability domain was defined using k-nearest neighbours algorithm. The analysis of relative attribute importance for the built neural network model identified dose, formation enthalpy, exposure time, and hydrodynamic size as the four most important attributes. As the presented model can predict the toxicity of the nanomaterials in consideration of various experimental conditions, it has the advantage of having a broader and more general applicability domain than the existing quantitative structure-activity relationship model.

p-p Heterojunction of Nickel Oxide-Decorated Cobalt Oxide Nanorods for Enhanced Sensitivity and Selectivity toward Volatile Organic Compounds.

The utilization of p-p isotype heterojunctions is an effective strategy to enhance the gas sensing properties of metal-oxide semiconductors, but most previous studies focused on p-n heterojunctions owing to their simple mechanism of formation of depletion layers. However, a proper choice of isotype semiconductors with appropriate energy bands can also contribute to the enhancement of the gas sensing performance. Herein, we report nickel oxide (NiO)-decorated cobalt oxide (Co3O4) nanorods (NRs) fabricated using the multiple-step glancing angle deposition method. The effective decoration of NiO on the entire surface of Co3O4 NRs enabled the formation of numerous p-p heterojunctions, and they exhibited a 16.78 times higher gas response to 50 ppm of C6H6 at 350 °C compared to that of bare Co3O4 NRs with the calculated detection limit of approximately 13.91 ppb. Apart from the p-p heterojunctions, increased active sites owing to the changes in the orientation of the exposed lattice surface and the catalytic effects of NiO also contributed to the enhanced gas sensing properties. The advantages of p-p heterojunctions for gas sensing applications demonstrated in this work will provide a new perspective of heterostructured metal-oxide nanostructures for sensitive and selective gas sensing.

The definition of basic TEDS of IEEE 1451.4 for sensors for an electronic tongue and the proposal of new template TEDS for electrochemical devices.

It is important to define a standard method to store basic sensor information, such as the type and the structure of sensors for an electronic tongue system and there is no such method defined in the IEEE 1451.4 transducer electronic data sheet (TEDS) so far. The major challenge is to choose a suitable standard template that can be used with sensors for electronic tongues. However, the standard templates provide an imprecise specification when used with sensing devices for electronic tongues. In this paper, we present definitions of the basic TEDS of IEEE 1451.4 for sensors for an electronic tongue system and propose a new template TEDS for IEEE 1451.4 for potentiometric devices.

A simple taste analyzing system design for visual and quantitative analysis of different tastes using multi-array chemical sensors and pattern recognition techniques.

In this paper, we describe design of a simple taste analyzing system using sensory system based on a multi-array chemical sensor (MACS) and personal digital assistant (PDA) for visual and quantitative analysis of different tastes using pattern recognition techniques. The sensory system is communicated with PDA, which has several interesting benefits for data analysis and display, via wireless using the Bluetooth. A various pattern recognition techniques are adapted including spider map, principal component analysis (PCA) and fuzzy C-means (FCM) algorithm to classify visually data patterns detected by the sensory system. The proposed techniques can be determined the cluster centers and membership grade of patterns through the unsupervised way. The membership grade of an unknown pattern, which does not shown previously, can be visually and analytically determined. Throughout the experimental trails, the taste analyzing system is demonstrated robust performance through data acquisition via wireless communication and visual and quantitative analysis of different tastes for the liquids. The system, which is implemented as a simple hand-held taste analyzing instrument, can be applicable to on-site taste monitoring.