scholarly journals Nonlinear inverse dynamic models of gas sensing systems based on chemical sensor arrays for quantitative measurements

1998 ◽  
Vol 47 (3) ◽  
pp. 644-651 ◽  
Author(s):  
A. Pardo ◽  
S. Marco ◽  
J. Samitier
Keyword(s):  
Dynamic Models ◽  
Chemical Sensor ◽  
Gas Sensing ◽  
Sensor Arrays ◽  
Inverse Dynamic ◽  
Quantitative Measurements ◽  
Sensing Systems

scholarly journals Highly Sensitive and Selective H2S Chemical Sensor Based on ZnO Nanomaterial

2019 ◽  
Vol 9 (6) ◽  
pp. 1167 ◽  
Author(s):  
Vardan Galstyan ◽  
Nicola Poli ◽  
Elisabetta Comini
Keyword(s):  
Chemical Sensor ◽  
Gas Sensing ◽  
Chemical Properties ◽  
Device Fabrication ◽  
Sensing Systems ◽  
Highly Sensitive ◽  
Prepared Material ◽  
Significant Step ◽  
Reducing Gases ◽  
Physical And Chemical

ZnO is worth evaluating for chemical sensing due to its outstanding physical and chemical properties. We report the fabrication and study of the gas sensing properties of ZnO nanomaterial for the detection of hydrogen sulfide (H2S). This prepared material exhibited a 7400 gas sensing response when exposed to 30 ppm of H2S in air. In addition, the structure showed a high selectivity towards H2S against other reducing gases. The high sensing performance of the structure was attributed to its nanoscale size, morphology and the disparity in the sensing mechanism between the H2S and other reducing gases. We suggest that the work reported here including the simplicity of device fabrication is a significant step toward the application of ZnO nanomaterials in chemical gas sensing systems for the real-time detection of H2S.

scholarly journals Affinity Ionic Liquids for Chemoselective Gas Sensing

Molecules ◽  
2018 ◽  
Vol 23 (9) ◽  
pp. 2380 ◽  
Author(s):  
Albert Chang ◽  
Hsin-Yi Li ◽  
I-Nan Chang ◽  
Yen-Ho Chu
Keyword(s):  
Ionic Liquids ◽  
Chemical Sensor ◽  
Gas Sensing ◽  
Sensor Arrays ◽  
Structural Diversity ◽  
Cost Effective ◽  
Gas Analysis ◽  
Single Digit ◽  
Military Industry ◽  
Gaseous Analytes

Selective gas sensing is of great importance for applications in health, safety, military, industry and environment. Many man-made and naturally occurring volatile organic compounds (VOCs) can harmfully affect human health or cause impairment to the environment. Gas analysis based on different principles has been developed to convert gaseous analytes into readable output signals. However, gas sensors such as metal-oxide semiconductors suffer from high operating temperatures that are impractical and therefore have limited its applications. The cost-effective quartz crystal microbalance (QCM) device represents an excellent platform if sensitive, selective and versatile sensing materials were available. Recent advances in affinity ionic liquids (AILs) have led them to incorporation with QCM to be highly sensitive for real-time detection of target gases at ambient temperature. The tailorable functional groups in AIL structures allow for chemoselective reaction with target analytes for single digit parts-per-billion detection on mass-sensitive QCM. This structural diversity makes AILs promising for the creation of a library of chemical sensor arrays that could be designed to efficiently detect gas mixtures simultaneously as a potential electronic in future. This review first provides brief introduction to some conventional gas sensing technologies and then delivers the latest results on our development of chemoselective AIL-on-QCM methods.

scholarly journals Classification of Tea Aromas Using Multi-Nanoparticle Based Chemiresistor Arrays

Sensors ◽  
2019 ◽  
Vol 19 (11) ◽  
pp. 2547 ◽  
Author(s):  
Tuo Gao ◽  
Yongchen Wang ◽  
Chengwu Zhang ◽  
Zachariah A. Pittman ◽  
Alexandra M. Oliveira ◽  
...  
Keyword(s):  
Chemical Sensor ◽  
Sensor Arrays ◽  
Machine Learning Algorithms ◽  
Single Type ◽  
Multivariate Techniques ◽  
Integrated Sensor ◽  
Resistance Change ◽  
Linear Discriminant ◽  
Lift Off

Nanoparticle based chemical sensor arrays with four types of organo-functionalized gold nanoparticles (AuNPs) were introduced to classify 35 different teas, including black teas, green teas, and herbal teas. Integrated sensor arrays were made using microfabrication methods including photolithography and lift-off processing. Different types of nanoparticle solutions were drop-cast on separate active regions of each sensor chip. Sensor responses, expressed as the ratio of resistance change to baseline resistance (ΔR/R0), were used as input data to discriminate different aromas by statistical analysis using multivariate techniques and machine learning algorithms. With five-fold cross validation, linear discriminant analysis (LDA) gave 99% accuracy for classification of all 35 teas, and 98% and 100% accuracy for separate datasets of herbal teas, and black and green teas, respectively. We find that classification accuracy improves significantly by using multiple types of nanoparticles compared to single type nanoparticle arrays. The results suggest a promising approach to monitor the freshness and quality of tea products.

scholarly journals Applications of Electronic-Nose Technologies for Noninvasive Early Detection of Plant, Animal and Human Diseases

Chemosensors ◽  
2018 ◽  
Vol 6 (4) ◽  
pp. 45 ◽  
Author(s):  
Alphus Dan Wilson
Keyword(s):  
Electronic Nose ◽  
Gas Sensing ◽  
Sensor Arrays ◽  
Human Diseases ◽  
Detection Methods ◽  
Research Progress ◽  
Disease Detection ◽  
New Information ◽  
Disease Diagnostics ◽  
Diagnostic Applications

The development of electronic-nose (e-nose) technologies for disease diagnostics was initiated in the biomedical field for detection of biotic (microbial) causes of human diseases during the mid-1980s. The use of e-nose devices for disease-diagnostic applications subsequently was extended to plant and animal hosts through the invention of new gas-sensing instrument types and disease-detection methods with sensor arrays developed and adapted for additional host types and chemical classes of volatile organic compounds (VOCs) closely associated with individual diseases. Considerable progress in animal disease detection using e-noses in combination with metabolomics has been accomplished in the field of veterinary medicine with new important discoveries of biomarker metabolites and aroma profiles for major infectious diseases of livestock, wildlife, and fish from both terrestrial and aquaculture pathology research. Progress in the discovery of new e-nose technologies developed for biomedical applications has exploded with new information and methods for diagnostic sampling and disease detection, identification of key chemical disease biomarkers, improvements in sensor designs, algorithms for discriminant analysis, and greater, more widespread testing of efficacy in clinical trials. This review summarizes progressive advancements in utilizing these specialized gas-sensing devices for numerous diagnostic applications involving noninvasive early detections of plant, animal, and human diseases.

ODOR MIXTURES AND CHEMOSENSORY ADAPTATION IN GAS SENSOR ARRAYS

2003 ◽  
Vol 12 (01) ◽  
pp. 1-16 ◽  
Author(s):  
RICARDO GUTIERREZ-OSUNA ◽  
NILESH U. POWAR
Keyword(s):  
Chemical Sensor ◽  
Sensor Arrays ◽  
Ternary Mixtures ◽  
Discriminant Functions ◽  
Linear Discriminant ◽  
Chemical Sensor Array ◽  
Binary And Ternary Mixtures ◽  
Olfactory Systems ◽  
Odor Mixtures ◽  
Input Adaptation

Inspired by the process of olfactory adaptation in biological olfactory systems, this article presents two algorithms that allow a chemical sensor array to reduce its sensitivity to odors previously detected in the environment. The first algorithm is based on a committee machine of linear discriminant functions that operate on multiple subsets of the overall sensory input. Adaptation occurs by depressing the voting strength of discriminant functions that display higher sensitivity to previously detected odors. The second algorithm is based on a topology-preserving linear projection derived from Fisher's class separability criteria. In this case, the process of adaptation is implemented through a reformulation of the between-to-within-class scatter eigenvalue problem. The proposed algorithms are validated on two datasets of binary and ternary mixtures of organic solvents using an array of temperature-modulated metal-oxide chemoresistors.

scholarly journals Curating Metal-Organic Frameworks to Compose Robust Gas Sensor Arrays in Dilute Conditions

2019 ◽  
Author(s):  
Arni Sturluson ◽  
Rachel Sousa ◽  
Yujing Zhang ◽  
Melanie T. Huynh ◽  
Caleb Laird ◽  
...  
Keyword(s):  
Sensor Array ◽  
Gas Sensing ◽  
Metal Organic Frameworks ◽  
Sensor Arrays ◽  
Gas Composition ◽  
Composition Space ◽  
The Matrix ◽  
Metal Organic ◽  
Analyte Detection ◽  
Value Decomposition

Metal-organic frameworks (MOFs)-- tunable, nano-porous materials-- are alluring recognition elements for gas sensing. Mimicking human olfaction, an array of cross-sensitive, MOF-based sensors could enable analyte detection in complex, variable gas mixtures containing confounding gas species. Herein, we address the question: given a set of MOF candidates and their adsorption properties, how do we select the optimal subset to compose a sensor array that accurately and robustly predicts the gas composition via monitoring the adsorbed mass in each MOF? We first mathematically formulate the MOF-based sensor array problem under dilute conditions. Instructively, the sensor array can be viewed as a linear map from <i>gas composition space</i> to <i>sensor array response space</i> defined by the matrix <b>H</b> of Henry coefficients of the gases in the MOFs. Characterizing this mapping, the singular value decomposition of <b>H </b>is a useful tool for evaluating MOF subsets for sensor arrays, as it determines the sensitivity of the predicted gas composition to measurement error, quantifies the magnitude of the response to changes in composition, and recovers which direction in gas composition space elicits the largest/smallest response. To illustrate, on the basis of experimental adsorption data, we curate MOFs for a sensor array with the objective of determining the concentration of CO<sub>2</sub> and SO<sub>2</sub> in the gas phase.

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