Search

There is an unmet need for a low-cost instrumented technology for detecting sanitation-related malodor as an alert for maintenance around shared toilets and emerging technologies for onsite waste treatment. In this article, our approach to an electronic nose for sanitation-related malodor is based on the use of electrochemical gas sensors, and machine-learning techniques for sensor selection and odor classification. We screened 10 sensors from different vendors with specific target gases and recorded their response to malodor from fecal specimens and urine specimens, and confounding good odors such as popcorn. The analysis of 180 odor exposures data by two feature-selection methods based on mutual information indicates that, for malodor detection, the decision tree (DT) classifier with seven features from four sensors provides 88.0% balanced accuracy and 85.1% macro F1 score. However, the k-nearest-neighbors (KNN) classifier with only three features (from two sensors) obtains 83.3% balanced accuracy and 81.3% macro F1 score. For classification of urine against feces malodor, a balanced accuracy of 94.0% and a macro F1 score of 92.9% are achieved using only four features from three sensors and a logistic regression (LR) classifier.

Laying hens are chronically exposed to high levels of ammonia (NH3), one of the most abundant aerial pollutants in poultry houses. Tests for aversion to NH3 in laying hens have used artificially sourced NH3/air mixtures (i.e., from a gas cylinder) showing that birds prefer fresh air to NH3. However, artificially sourced NH3/air mixtures may not accurately reflect barn air conditions, where manure emits a variety of gases. Herein, we investigated whether laying hens differentiate between artificially and naturally sourced NH3/air mixtures and how exposure to NH3 affects foraging and aversive behavior. A total of 20 laying hens was exposed to artificially sourced [A] (from an anhydrous NH3 cylinder) and naturally sourced [N] (from conspecific laying hen excreta) gas mixtures. Hens were exposed to A and N mixtures with NH3 concentrations of 25 and 45 ppm, as well as fresh air [FA]. During the experiment, all birds were exposed to each treatment 3 times using a custom-built polycarbonate chamber, containing a foraging area (containing raisins, mealworms, and feed mix) and a gas delivery system. All testing sessions were video recorded, analyzed with INTERACT® software, and subjected to a GLIMMIX procedure in SAS. Our results showed that the laying hens spent less time foraging overall (P < 0.001) and were slower to commence foraging (P = 0.004) in ammoniated environments compared to the fresh air. Laying hens were more likely to forage for a longer time (with fewer interruptions) in N than in A treatments (P < 0.001). Laying hens also reacted with greater aversion towards treatment A compared to treatment N (P < 0.001). These findings suggest that the laying hens of our study preferred fresh to ammoniated air and that they behaved differently in artificially and naturally sourced NH3/air mixtures, possibly due to the presence of familiar stimuli from the excreta. These findings have implications for new developments in methodological approaches for behavioral testing and for recommendations regarding NH3 levels inside poultry barns., (© 2017 Poultry Science Association Inc.)

For many years there has been growing demand for gaseous reference materials, which is connected with development in many fields of science and technology. As a result, new methodological and instrumental solutions appear that can be used for this purpose. Appropriate quality assurance/quality control (QA/QC) must be used to make sure that measurement data are a reliable source of information. Reference materials are a significant element of such systems. In the case of gas samples, such materials are generally called reference gas mixtures. This article presents the application and classification of reference gas mixtures, which are a specific type of reference materials, and the methods for obtaining them are described. Construction solutions of devices for the production of reference gas mixtures are detailed, and a description of a prototype device for dynamic production of reference gas mixtures containing aroma compounds is presented.

A gas sensor array based on peptide modified gold nanoparticles deposited onto 20MHz quartz crystal microbalances has been realized. Glutathione and its constituting aminoacids and dipeptides have been used as ligands. A great increase in sensitivity (2 orders of magnitude) was achieved using gold nanoparticles versus monolayer modified QCMs. The sensors have been characterised in terms of sensitivity for hexane, water, trimethylammine and ethanol. Highest sensitivity was found for water. The ability to discriminate typical food aromas as cis-3-hexenol, isopentylacetate, ethylacetate, and terpinen-4-ol dissolved in different solvents was studied using a gas sensor array constituted by gold nanoparticles modified with the glutathione peptides, thioglycolic acid and an heptapeptide. The array was found able to discriminate the food aromas, the response being dependent on the polarity of the solvent used. Tests on real olive oil samples gave a satisfactory separation among samples having defects versus non defected samples demonstrating that this approach has high potential for the development of gas sensor arrays to be used in real samples., (Copyright © 2012 Elsevier B.V. All rights reserved.)

This paper develops a primitive gas recognition system for discriminating between industrial gas species. The system under investigation consists of an array of eight micro-hotplate-based SnO2 thin film gas sensors with different selectivity patterns. The output signals are processed through a signal conditioning and analyzing system. These signals feed a decision-making classifier, which is obtained via a Field Programmable Gate Array (FPGA) with Very High-Speed Integrated Circuit Hardware Description Language. The classifier relies on a multilayer neural network based on a back propagation algorithm with one hidden layer of four neurons and eight neurons at the input and five neurons at the output. The neural network designed after implementation consists of twenty thousand gates. The achieved experimental results seem to show the effectiveness of the proposed classifier, which can discriminate between five industrial gases.

This paper presents a new pattern recognition approach for enhancing the selectivity of gas sensor arrays for clustering intelligent odor detection. The aim of this approach was to accurately classify an odor using pattern recognition in order to enhance the selectivity of gas sensor arrays. This was achieved using an odor monitoring system with a newly developed neural-genetic classification algorithm (NGCA). The system shows the enhancement in the sensitivity of the detected gas. Experiments showed that the proposed NGCA delivered better performance than the previous genetic algorithm (GA) and artificial neural networks (ANN) methods. We also used PCA for data visualization. Our proposed system can enhance the reproducibility, reliability, and selectivity of odor sensor output, so it is expected to be applicable to diverse environmental problems including air pollution, and monitor the air quality of clean-air required buildings such as a kindergartens and hospitals.

We present an effective portable e-nose system that performs well even in noisy environments. Considering the characteristics of the e-nose data, we use an image covariance matrix-based method for extracting discriminant features for vapor classification. To construct composite vectors, primitive variables of the data measured by a sensor array are rearranged. Then, composite features are extracted by utilizing the information about the statistical dependency among multiple primitive variables, and a classifier for vapor classification is designed with these composite features. Experimental results with different volatile organic compounds data show that the proposed system has better classification performance than other methods in a noisy environment.

A catalytic sensor for the measurement of radical density in weakly ionized CO(2) plasmas, created in a low-pressure electrodeless discharge, is presented. The CO(2) plasma was created in a 4 cm wide borosilicate glass tube inside a copper coil connected to a radio frequency generator operating at 27.12 MHz with a nominal power of 250 W. The dissociation fraction of the CO(2) molecules was measured in the early afterglow at pressures ranging from 10 Pa to 100 Pa, and at distances of up to 35 cm along the gas stream from the glowing plasma. The radical density peaked (2 × 10(20) m-3) at 80 Pa. The density quickly decreased with increasing distance from the glowing plasma despite a rather large drift velocity. The dissociation fraction showed similar behavior, except that the maximum was obtained at somewhat lower pressure. The results were explained by rather intense surface recombination of radicals.

The role of contact between semiconducting nanowire and metal electrodes in a single nanowire field effect transistor (NW-FET) is investigated for the sensing of different type of gases. Two different types of In(2)O(3) nanowire devices, namely; Schottky contact device (SCD) and Ohmic contact device (OCD) are evaluated. SCD has shown a superior response to the reducing gas (CO) compared to oxidizing gas (NO), while OCD has shown high sensitivity towards oxidizing gas (NO) compared to the reducing gas (CO) under similar working conditions. The sensing mechanism is dominated by the contact resistance at the metal-semiconductor junction in SCD and the change in nanowire channel conductance dominates in OCD. The Schottky barrier height (SBH) was extracted using low temperature current voltage measurement which provided direct evidence for the notion that the barrier height plays a crucial role in the sensing of different types of gases. The sensing mechanism is illustrated in this work for both devices.

Cases of deaths in manure or septic tanks are rare in legal-medical practice, more frequently as unfortunate occupational accidents. Poisoning with toxic gases, especially with hydrogen sulfide, is reported as the cause of death, while the exhaustion of oxygen in the air is omitted with the simultaneous excess of carbon dioxide. In such cases, determination of the direct cause of death constitutes a big problem because post-mortem examination does not reveal the specific changes. A case of acute collective poisoning by gases in a manure storage tank is presented of 5 agricultural workers, 2 of whom died. While explaining the cause of poisoning and deaths, toxicological blood tests were performed in the victims of the accident, as well as gases inside the manure storage tank. The post-mortem examinations and toxicological blood tests performed did not allow determination of the direct cause of death. Toxicological tests of gases from inside the manure tank showed a very low concentration of oxygen, with a simultaneous very high concentration of carbon dioxide, and a considerable level of hydrogen sulfide. The cause of fainting of three and deaths of two workers was not the poisoning with hydrogen sulfide, but oxygen deficiency in the air of the tank.

We report on the development of a microelectromechanical systems (MEMS)-scale photoacoustic sensor for the detection of trace gases. A mid-infrared quantum cascade laser (QCL) was used to determine detection limits for acetic acid, acetone, 1,4-dioxane, and vinyl acetate. The source was continuously tunable from 1015 cm(-1) to 1240 cm(-1), allowing for the collection of photoacoustic vibrational spectra for these gases. Exceptional agreement between the measured photoacoustic spectra and the infrared spectra for acetic acid, acetone, 1,4-dioxane, and vinyl acetate was observed. Partial least-squares (PLS) regression was used to develop an algorithm for classification of these compounds based solely on photoacoustic spectra.

Electrochemical sensors composed of a ceramic-metallic (cermet) solid electrolyte are used for the detection of gaseous sulfur compounds SO(2), H(2)S, and CS(2) in a study involving 11 toxic industrial chemical (TIC) compounds. The study examines a sensor array containing four cermet sensors varying in electrode-electrolyte composition, designed to offer selectivity for multiple compounds. The sensors are driven by cyclic voltammetry to produce a current-voltage profile for each analyte. Raw voltammograms are processed by background subtraction of clean air, and the four sensor signals are concatenated to form one vector of points. The high-resolution signal is compressed by wavelet transformation and a probabilistic neural network is used for classification. In this study, training data from one sensor array was used to formulate models which were validated with data from a second sensor array. Of the 11 gases studied, 3 that contained sulfur produced the strongest responses and were successfully analyzed when the remaining compounds were treated as interferents. Analytes were measured from 10 to 200% of their threshold-limited value (TLV) according to the 8-h time weighted average (TWA) exposure limits defined by the National Institute of Occupational Safety and Health (NIOSH). True positive classification rates of 93.3, 96.7, and 76.7% for SO(2), H(2)S, and CS(2), respectively, were achieved for prediction of one sensor unit when a second sensor was used for modeling. True positive rates of 83.3, 90.0, and 90.0% for SO(2), H(2)S, and CS(2), respectively, were achieved for the second sensor unit when the first sensor unit was used for modeling. Most of the misclassifications were for low concentration levels (such 10-25% TLV) in which case the compound was classified as clean air. Between the two sensors, the false positive rates were 2.2% or lower for the three sulfur compounds, 0.9% or lower for the interferents (eight remaining analytes), and 5.8% or lower for clean air. The cermet sensor arrays used in this analysis are rugged, low cost, reusable, and show promise for multiple compound detection at parts-per-million (ppm) levels.

Carbon dioxide dilution effect on the flammability limits was measured for various flammable gases. The obtained values were analyzed using the extended Le Chatelier's formula developed in a previous study. As a result, it has been found that the flammability limits of methane, propane, propylene, methyl formate, and 1,1-difluoroethane are adequately explained by the extended Le Chatelier's formula using a common set of parameter values. Ethylene, dimethyl ether, and ammonia behave differently from these compounds. The present result is very consistent with what was obtained in the case of nitrogen dilution.

The flammability limits of methane and propane have been measured using cylindrical vessels of various sizes and one spherical vessel. An ac discharge ignition method has been employed. For a cylindrical vessel of small diameter with a large height, the flammability limits are primarily determined by the quenching effect of the wall. For cylindrical vessels of smaller heights, the experimental flammability limits are affected by hot gas accumulation at the vessel ceiling, unburned gas heating, self heating of the incipient flame by the reflection both from walls and ceiling, and the quenching effect of the walls. If the vessel size is large enough so that all these effects become negligible, the experimental values of flammability limits may approach to the values that would be obtained in free space. In order to approach this condition for a cylindrical vessel, it is desirable to use a container at least 30 cm in diameter and 60 cm in height. For comparison purpose, the measurement has also been done using ASHRAE type 12l spherical flask.

The ability to monitor air contaminants in the shuttle and the International Space Station is important to ensure the health and safety of astronauts, and equipment integrity. Three specific space applications have been identified that would benefit from a chemical monitor: (a) organic contaminants in space cabin air; (b) hypergolic propellant contaminants in the shuttle airlock; (c) pre-combustion signature vapors from electrical fires. NASA at Kennedy Space Center (KSC) is assessing several commercial and developing electronic noses (E-noses) for these applications. A short series of tests identified those E-noses that exhibited sufficient sensitivity to the vapors of interest. Only two E-noses exhibited sufficient sensitivity for hypergolic fuels at the required levels, while several commercial E-noses showed sufficient sensitivity of common organic vapors. These E-noses were subjected to further tests to assess their ability to identify vapors. Development and testing of E-nose models using vendor supplied software packages correctly identified vapors with an accuracy of 70-90%. In-house software improvements increased the identification rates between 90 and 100%. Further software enhancements are under development. Details on the experimental setup, test protocols, and results on E-nose performance are presented in this paper along with special emphasis on specific software enhancements., (c2003 Elsevier Science B.V. All rights reserved.)

Flammability limits measurement has been made by ASHRAE method for some 20 kinds of combustible gases and vapors. These compounds have been selected mainly because the literature values of flammability limits are not consistent with the F-number calculated ones [J. Hazard. Mater. A 82 (2001) 113]. As a result, it has been found that the newly obtained values of flammable range are classified into three groups. For the first group of compounds, the present values agree well to the literature values. For the second group, the present values do not agree to the literature values but agree with the calculated ones. For the third group ones, the present values neither agree to the literature values nor to the calculated ones. There are 4, 13, and 6 compounds in the respective groups.

Vapor detectors formed from composites of conductors and insulating organic polymers have been tailored to produce increased sensitivity toward specific classes of analyte vapors. Upon exposure to acetic acid at 1% of its vapor pressure, detectors consisting of linear poly(ethylenimine) (1-PEI)-carbon black composites showed an approximately 10(3) increase in signal/noise relative to the performance of typical insulating organic polymer-carbon black composite vapor detectors. Compositional diversity in an array of such vapor detectors was obtained by varying the degree of plasticization of the 1-PEI films. The resulting vapor detector array produced sensitive detection of, and robust discrimination between, various volatile organic acids and relatively little response from nonacidic organic vapors or from water vapor. Measurements of the mass uptake, thickness change, and electrical conductivity of such composites indicate that swelling of the polymer film, and thus its normalized resistance response, is beyond that expected by mass uptake alone upon exposure to acetic acid vapor. This additional thickness increase is attributed to charge-induced polymer swelling occurring from polymer-analyte interactions. Electrical percolation also plays a significant role in producing the large increase in normalized resistance response of these composites upon exposure to acetic acid vapor.

Neural networks were trained using whole ion mobility spectra from a standardized database of 3137 spectra for 204 chemicals at various concentrations. Performance of the network was measured by the success of classification into ten chemical classes. Eleven stages for evaluation of spectra and of spectral pre-processing were employed and minimums established for response thresholds and spectral purity. After optimization of the database, network, and pre-processing routines, the fraction of successful classifications by functional group was 0.91 throughout a range of concentrations. Network classification relied on a combination of features, including drift times, number of peaks, relative intensities, and other factors apparently including peak shape. The network was opportunistic, exploiting different features within different chemical classes. Application of neural networks in a two-tier design where chemicals were first identified by class and then individually eliminated all but one false positive out of 161 test spectra. These findings establish that ion mobility spectra, even with low resolution instrumentation, contain sufficient detail to permit the development of automated identification systems.

Bioregenerative life support systems (BLSS) being considered for long duration space missions will operate with limited resupply and utilize biological systems to revitalize the atmosphere, purify water, and produce food. The presence of man-made materials, plant and microbial communities, and human activities will result in the production of volatile organic compounds (VOCs). A database of VOC production from potential BLSS crops is being developed by the Breadboard Project at Kennedy Space Center. Most research to date has focused on the development of air revitalization systems that minimize the concentration of atmospheric contaminants in a closed environment. Similar approaches are being pursued in the design of atmospheric revitalization systems in bioregenerative life support systems. in a BLSS one must consider the effect of VOC concentration on the performance of plants being used for water and atmospheric purification processes. In addition to phytotoxic responses, the impact of removing biogenic compounds from the atmosphere on BLSS function needs to be assessed. This paper provides a synopsis of criteria for setting exposure limits, gives an overview of existing information, and discusses production of biogenic compounds from plants grown in the Biomass Production Chamber at Kennedy Space Center.

The classification of harmful gases may be envisaged in various ways according to the criteria considered. The two aspects adopted here concern chemical and toxicological classifications. Chemical classification is in general based upon the nature of the elements constituting the gases, e.g. carbon, nitrogen, sulphur, phosphorus, halogen compounds and halogenated compounds, etc. Toxicological classification is envisaged on the basis of the concepts of toxicity itself. It may then be used to establish the scale of its degree based upon two principal factors: concentration and duration of exposure. However, certain observations suggest complex intoxication phenomena related to an overall number of factors which are generally considered in isolation.