Breath analysis has attracted human beings for centuries. It was one of the simplest methods to detect various diseases by using human smell sense only. Advances in technology enable to use more reliable and standardized methods, based on different gas sensing systems. Breath analysis requires the detection of volatile organic compounds (VOCs) of the concentrations below individual ppm (parts per million). Therefore, advanced detection methods have been proposed. Some of these methods use expensive and bulky equipment (e.g. optical sensors, mass spectrometry –MS), and require time-consuming analysis. Less accurate, but much cheaper, are resistive gas sensors. These sensors use porous materials and adsorptiondesorption processes, determining their physical parameters.We consider the problems of applying resistive gas sensors to breath analysis. Recent advances were underlined, showing that these economical gas sensors can be efficiently employed to analyse breath samples. General problems of applying resistive gas sensors are considered and illustrated with examples, predominantly related to commercial sensors and their long-term performance. A setup for collection of breath samples is considered and presented to point out the crucial parts and problematic issues.
We present the results of a numerical analysis of a two-dimensional photonic crystal with line defect for a laser gas sensor working in a slow light regime. The geometrical parameters of photonic crystals with three different line defects were numerically analyzed: a missing row of holes, a row of holes with changed diameter and air channel. Antireflection sections were also analyzed. The simulations were carried out by MEEP and MPB programs, with the aim to get the values of a group refractive index, transmission and a light-gas overlap as high as possible. The effective refractive index method was used to reduce the simulation time and required computing power. We also described numerical simulation details such as required conditions to work in the slow light regime and the analyzed parameters values’ dependency of the simulation resolution that may influence the accuracy of the results.
This paper presents a portable exhaled breath analyser, developed to detect selected diseases. The set-up
employs resistive gas sensors: commercial MEMS sensors and prototype gas sensors made of WO3 gas
sensing layers doped with various metal ingredients. The set-up can modulate the gas sensors by applying
UV light to induce physical changes of the gas sensing layers. The sensors are placed in a tiny gas
chamber of a volume of about 22 ml. Breath samples can be either injected or blown into the gas chamber
when an additional pump is used to select the last breath phase. DC resistance and resistance fluctuations
of selected sensors using separate channels are recorded by an external data acquisition board. Low-noise
amplifiers with a selected gain were used together with a necessary bias circuit. The set-up monitors other
atmospheric parameters interacting with the responses of resistive gas sensors (humidity, temperature, atmospheric
pressure). The recorded data may be further analysed to determine optimal detection methods.
Semiconductive - resistive sensors of toxic and explosive gases were fabricated from nanograins of SnO2 using thick-.lm technology. Sensitivity, selectivityand stabilityof sensors working in di.erent temperature depend on the way the tin dioxide and additives were prepared. A construction also plays an important role. The paper presents an attitude towards the evaluation of transport of electrical charges in semiconductive grain layer of SnO2, when dangerous gases appear in the surrounding atmosphere.
A layered sensor structure of metal-free phthalocyanine H2Pc (~160 nm) with a very thin film of palladium (Pd ~20 nm) on the top, has been studied for hydrogen gas-sensing application at relatively low temperatures of about 30°C and about 40°C. The layered structure was obtained by vacuum deposition (first the phthalocyanine Pc and than the Pd film) onto a LiNbO3Y- cut Z-propagating substrate, making use of the Surface Acoustic Wave method, and additionally (in this same technological processes) onto a glass substrate with a planar microelectrode array for simultaneous monitoring of the planar resistance of the layered structure. In such a layered structure we can detect hydrogen in a medium concentration range (from 0.5 to 3% in air) even at about 30°C. At elevated temperature up to about 40°C the differential frequency increases proportionally (almost linearly) to the hydrogen concentration and the response reaches its steady state very quickly. The response times are about 18 s at the lowest 0.5% hydrogen concentration to about 42 s at 4% (defined as reaching 100% of the steady state). In the case of the investigated layered structure a very good correlation has been observed between the two utilized methods - the frequency changes in the SAW method correlate quite well with the decreases of the layered structure resistance.
Alternating current a.c. measurements enable to understand the physical and chemical processes occurring in semiconductor materials. Impedance spectroscopy has been successfully applied to study the responses of gas sensors based on metal oxides, such as TiO2, SnO2 and TiO2/SnO2 nanocomposites. This work is devoted to dynamic measurements of hydrogen sensor behaviour over the temperature range of 300–450◦C. Frequency dependence of the impedance signal gives evidence that 50 mol% TiO2/50 mol% SnO2 nanocomposites should be treated as resistive-type sensors. Temporal evolution of the response to 500 ppm H2 at 320◦C indicates a very short response time and much longer recovery.
In recent years, smog and poor air quality have become a growing environmental problem. There is a need to continuously monitor the quality of the air. The lack of selectivity is one of the most important problems limiting the use of gas sensors for this purpose. In this study, the selectivity of six amperometric gas sensors is investigated. First, the sensors were calibrated in order to find a correlation between the concentration level and sensor output. Afterwards, the responses of each sensor to single or multicomponent gas mixtures with concentrations from 50 ppb to 1 ppm were measured. The sensors were studied under controlled conditions, a constant gas flow rate of 100 mL/min and 50 % relative humidity. Single Gas Sensor Response Interpretation, Multiple Linear Regression, and Artificial Neural Network algorithms were used to predict the concentrations of SO2 and NO2. The main goal was to study different interactions between sensors and gases in multicomponent gas mixtures and show that it is insufficient to calibrate sensors in only a single gas.
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