The gas sensing properties of zeolite modified zinc oxide
04 Mar 2014-Journal of Materials Chemistry (The Royal Society of Chemistry)-Vol. 2, Iss: 13, pp 4758-4764
TL;DR: In this paper, an array of four thick film metal oxide semiconducting (MOS) sensors was fabricated, based on zinc oxide inks, using a commercially available screen printer, a 3 × 3 mm alumina substrate containing interdigitated electrodes and a platinum heater track.
Abstract: The illicit manufacture of drugs in the 21st century presents a danger to first responders, bystanders and the environment, making its detection important. Electronic noses based on metal oxide semiconducting (MOS) sensors present a potential technology to create devices for such purposes. An array of four thick film MOS gas sensors was fabricated, based on zinc oxide inks. Production took place using a commercially available screen printer, a 3 × 3 mm alumina substrate containing interdigitated electrodes and a platinum heater track. ZnO inks were modified using zeolite β, zeolite Y and mordenite admixtures. The sensors were exposed to four gases commonly found in the clandestine laboratory environment; these were nitrogen dioxide, ethanol, acetone and ammonia. Zeolite modification was found to increase the sensitivity of the sensor, compared to unmodified ZnO sensors, all of which showed strong responses to low ppm concentrations of acetone, ammonia and ethanol and to ppb concentrations of nitrogen dioxide. Machine learning techniques were incorporated to test the selectivity of the sensors. A high level of accuracy was achieved in determining the class of gas observed.
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TL;DR: This paper presents a meta-analyses of the chiral stationary phase transition of Na6(CO3)(SO4)/ Na2SO4 using a high-performance liquid chromatography apparatus for the determination of Na2CO3(SO4).
Abstract: Xin Zhou,†,‡ Songyi Lee,† Zhaochao Xu,* and Juyoung Yoon*,† †Department of Chemistry and Nanoscience, Ewha Womans University, Seoul 120-750, Republic of Korea ‡Research Center for Chemical Biology, Department of Chemistry, Yanbian University, Yanjii 133002, People’s Republic of China Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, 457 Zhongshan Road, Shahekou, Dalian, Liaoning, People’s Republic of China
631 citations
TL;DR: The current state-of-the-art in using porous materials for sensing the gases relevant to automotive emissions is surveyed, and both types of porous material reveal great promise for the fabrication of sensors for exhaust gases and vapours due to high selectivity and sensitivity.
Abstract: Improvements in the efficiency of combustion within a vehicle can lead to reductions in the emission of harmful pollutants and increased fuel efficiency. Gas sensors have a role to play in this process, since they can provide real time feedback to vehicular fuel and emissions management systems as well as reducing the discrepancy between emissions observed in factory tests and ‘real world’ scenarios. In this review we survey the current state-of-the-art in using porous materials for sensing the gases relevant to automotive emissions. Two broad classes of porous material – zeolites and metal–organic frameworks (MOFs) – are introduced, and their potential for gas sensing is discussed. The adsorptive, spectroscopic and electronic techniques for sensing gases using porous materials are summarised. Examples of the use of zeolites and MOFs in the sensing of water vapour, oxygen, NOx, carbon monoxide and carbon dioxide, hydrocarbons and volatile organic compounds, ammonia, hydrogen sulfide, sulfur dioxide and hydrogen are then detailed. Both types of porous material (zeolites and MOFs) reveal great promise for the fabrication of sensors for exhaust gases and vapours due to high selectivity and sensitivity. The size and shape selectivity of the zeolite and MOF materials are controlled by variation of pore dimensions, chemical composition (hydrophilicity/hydrophobicity), crystal size and orientation, thus enabling detection and differentiation between different gases and vapours.
386 citations
TL;DR: It was concluded that the material-sensor integration was also introduced to maintain the structural stability in the sensor fabrication process, ensuring the sensing stability of MOS-based gas sensors.
Abstract: Metal-oxide-semiconductor (MOS) based gas sensors have been considered a promising candidate for gas detection over the past few years. However, the sensing properties of MOS-based gas sensors also need to be further enhanced to satisfy the higher requirements for specific applications, such as medical diagnosis based on human breath, gas detection in harsh environments, etc. In these fields, excellent selectivity, low power consumption, a fast response/recovery rate, low humidity dependence and a low limit of detection concentration should be fulfilled simultaneously, which pose great challenges to the MOS-based gas sensors. Recently, in order to improve the sensing performances of MOS-based gas sensors, more and more researchers have carried out extensive research from theory to practice. For a similar purpose, on the basis of the whole fabrication process of gas sensors, this review gives a presentation of the important role of screening and the recent developments in high throughput screening. Subsequently, together with the sensing mechanism, the factors influencing the sensing properties of MOSs involved in material preparation processes were also discussed in detail. It was concluded that the sensing properties of MOSs not only depend on the morphological structure (particle size, morphology, pore size, etc.), but also rely on the defect structure and heterointerface structure (grain boundaries, heterointerfaces, defect concentrations, etc.). Therefore, the material-sensor integration was also introduced to maintain the structural stability in the sensor fabrication process, ensuring the sensing stability of MOS-based gas sensors. Finally, the perspectives of the MOS-based gas sensors in the aspects of fundamental research and the improvements in the sensing properties are pointed out.
363 citations
TL;DR: A comprehensive analysis of the emerging applications of microporous nanosized crystals in the field of semiconductor industry, optical materials, chemical sensors, medicine, cosmetics, and food industry is presented.
Abstract: This review highlights recent developments in the synthesis and unconventional applications of nanosized microporous crystals including framework (zeolites) and layered (clays) type materials. Owing to their microporous nature nanosized zeolites and clays exhibit novel properties, different from those of bulk materials. The factors controlling the formation of nanosized microporous crystals are first revised. The most promising approaches from the viewpoint of large-scale production of nanosized zeolites and clays are discussed in depth. The preparation and advanced applications of nanosized zeolites and clays in free (suspension and powder forms) and fixed (films) forms are summarized. Further the review emphasises the non-conventional applications of new porous materials. A comprehensive analysis of the emerging applications of microporous nanosized crystals in the field of semiconductor industry, optical materials, chemical sensors, medicine, cosmetics, and food industry is presented. Finally, the future needs and perspectives of nanosized microporous materials (zeolites and clays) are addressed.
278 citations
TL;DR: In this article, the effects of successive ion layer adsorption and reaction (SILAR) cycles on the structural, optical, surface morphological and electrical properties of nanostructured ZnO thin films were investigated.
Abstract: Zinc oxide (ZnO) thin films have been widely used as an effective gas sensor element. In the present study, nanostructured thin films of ZnO were prepared by using the simplistic and economical successive ion layer adsorption and reaction (SILAR) technique. The effects of SILAR cycles on the structural, optical, surface morphological and electrical properties of nanostructured ZnO thin films were investigated. Characterization techniques such as XRD, UV-vis, PL, FESEM, and Hall measurement were utilized to study the physical and chemical properties of the synthesized films. XRD confirms the formation of hexagonal phase structural ZnO thin films. FE-SEM analysis reveals the formation of well-dispersed ZnO nanoparticles having sizes of ∼18–40 nm. The SILAR cycles play a key role in the synthesis of nanostructured ZnO thin films and it is found that, with increasing SILAR cycles, the grain size continues increasing. Optical studies confirm the presence of oxygen vacancies in synthesized ZnO thin films. Finally, the ZnO thin films were exposed to NO 2 gas with a concentration of 100 ppb–200 ppm and the resulting resistance transient was recorded. The nanostructured ZnO thin films synthesized at 30 SILAR cycles displays an enhancement of gas sensing performance and exhibit significantly higher responses (∼5% per ppm). Moreover, our ZnO thin-film-based gas sensor is sensitive to very low concentrations of dangerous NO 2 (100 ppb). The sensitive gas sensor used to trace level NO 2 detection, synthesized via simple SILAR route proves the novelty of our work. The present report provides a new direction in fabricating nanostructured ZnO thin films for low-cost and efficient gas sensing applications.
197 citations
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01 Jul 1992
TL;DR: A training algorithm that maximizes the margin between the training patterns and the decision boundary is presented, applicable to a wide variety of the classification functions, including Perceptrons, polynomials, and Radial Basis Functions.
Abstract: A training algorithm that maximizes the margin between the training patterns and the decision boundary is presented. The technique is applicable to a wide variety of the classification functions, including Perceptrons, polynomials, and Radial Basis Functions. The effective number of parameters is adjusted automatically to match the complexity of the problem. The solution is expressed as a linear combination of supporting patterns. These are the subset of training patterns that are closest to the decision boundary. Bounds on the generalization performance based on the leave-one-out method and the VC-dimension are given. Experimental results on optical character recognition problems demonstrate the good generalization obtained when compared with other learning algorithms.
11,211 citations
TL;DR: Decomposition implementations for two "all-together" multiclass SVM methods are given and it is shown that for large problems methods by considering all data at once in general need fewer support vectors.
Abstract: Support vector machines (SVMs) were originally designed for binary classification. How to effectively extend it for multiclass classification is still an ongoing research issue. Several methods have been proposed where typically we construct a multiclass classifier by combining several binary classifiers. Some authors also proposed methods that consider all classes at once. As it is computationally more expensive to solve multiclass problems, comparisons of these methods using large-scale problems have not been seriously conducted. Especially for methods solving multiclass SVM in one step, a much larger optimization problem is required so up to now experiments are limited to small data sets. In this paper we give decomposition implementations for two such "all-together" methods. We then compare their performance with three methods based on binary classifications: "one-against-all," "one-against-one," and directed acyclic graph SVM (DAGSVM). Our experiments indicate that the "one-against-one" and DAG methods are more suitable for practical use than the other methods. Results also show that for large problems methods by considering all data at once in general need fewer support vectors.
6,562 citations
Journal Article•
TL;DR: The sequential minimal optimization (SMO) algorithm as mentioned in this paper uses a series of smallest possible QP problems to solve a large QP problem, which avoids using a time-consuming numerical QP optimization as an inner loop.
Abstract: This paper proposes a new algorithm for training support vector machines: Sequential Minimal Optimization, or SMO. Training a support vector machine requires the solution of a very large quadratic programming (QP) optimization problem. SMO breaks this large QP problem into a series of smallest possible QP problems. These small QP problems are solved analytically, which avoids using a time-consuming numerical QP optimization as an inner loop. The amount of memory required for SMO is linear in the training set size, which allows SMO to handle very large training sets. Because matrix computation is avoided, SMO scales somewhere between linear and quadratic in the training set size for various test problems, while the standard chunking SVM algorithm scales somewhere between linear and cubic in the training set size. SMO’s computation time is dominated by SVM evaluation, hence SMO is fastest for linear SVMs and sparse data sets. On realworld sparse data sets, SMO can be more than 1000 times faster than the chunking algorithm.
2,856 citations
TL;DR: This research presents a meta-analysis of 126 existing and new technologies in the gas chromatography field, and some new technologies that are being developed, as well as suggestions for further studies.
Abstract: 2.2. New Approaches 707 2.2.1. Optical Sensor Systems 707 2.2.2. Mass Spectrometry 708 2.2.3. Ion Mobility Spectrometry 708 2.2.4. Gas Chromatography 709 2.2.5. Infrared Spectroscopy 709 2.2.6. Use of Substance-Class-Specific Sensors 709 2.3. Combined Technologies 710 3. Companies 710 4. Application Areas 710 4.1. Food and Beverage 712 4.2. Environmental Monitoring 715 4.3. Disease Diagnosis 716 5. Research and Development Trends 718 5.1. Sample Handling 719 5.2. Filters and Analyte Gas Separation 719 5.3. Data Evaluation 720 6. Conclusion 721 7. References 722
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