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The simultaneous operation of multiple different semiconducting metal oxide (MOX) gas sensors is demanding for the readout circuitry. The challenge results from the strongly varying signal intensities of the various sensor types to the target gas. While some sensors change their resistance only slightly, other types can react with a resistive change over a range of several decades. Therefore, a suitable readout circuit has to be able to capture all these resistive variations, requiring it to have a very large dynamic range. This work presents a compact embedded system that provides a full, high range input interface (readout and heater management) for MOX sensor operation. The system is modular and consists of a central mainboard that holds up to eight sensor-modules, each capable of supporting up to two MOX sensors, therefore supporting a total maximum of 16 different sensors. Its wide input range is archived using the resistance-to-time measurement method. The system is solely built with commercial off-the-shelf components and tested over a range spanning from 100Ω to 5 GΩ (9.7 decades) with an average measurement error of 0.27% and a maximum error of 2.11%. The heater management uses a well-tested power-circuit and supports multiple modes of operation, hence enabling the system to be used in highly automated measurement applications. The experimental part of this work presents the results of an exemplary screening of 16 sensors, which was performed to evaluate the system’s performance.
The choice of suitable semiconducting metal oxide (MOX) gas sensors for the detection of a specific gas or gas mixture is time-consuming since the sensor’s sensitivity needs to be characterized at multiple temperatures to find its optimal operating conditions. To obtain reliable measurement results, it is very important that the power for the sensor’s integrated heater is stable, regulated and error-free (or error-tolerant). Especially the error-free requirement can be only be achieved if the power supply implements failure-avoiding and failure-detection methods. The biggest challenge is deriving multiple different voltages from a common supply in an efficient way while keeping the system as small and lightweight as possible. This work presents a reliable, compact, embedded system that addresses the power supply requirements for fully automated simultaneous sensor characterization for up to 16 sensors at multiple temperatures. The system implements efficient (avg. 83.3% efficiency) voltage conversion with low ripple output (<32 mV) and supports static or temperature-cycled heating modes. Voltage and current of each channel are constantly monitored and regulated to guarantee reliable operation. To evaluate the proposed design, 16 sensors were screened. The results are shown in the experimental part of this work.
With the increasing demand for ultrapure water in the pharmaceutical and semiconductor industry, the need for precise measuring instruments for those applications is also growing. One critical parameter of water quality is the amount of total organic carbon (TOC). This work presents a system that uses the advantage of the increased oxidation power achieved with UV/O3 advanced oxidation process (AOP) for TOC measurement in combination with a significant miniaturization compared to the state of the art. The miniaturization is achieved by using polymer-electrolyte membrane (PEM) electrolysis cells for ozone generation in combination with UV-LEDs for irradiation of the measuring solution, as both components are significantly smaller than standard equipment. Conductivity measurement after oxidation is the measuring principle and measurements were carried out in the TOC range between 10 and 1000 ppb TOC. The suitability of the system for TOC measurement is demonstrated using the oxidation by ozonation combined with UV irradiation of defined concentrations of isopropyl alcohol (IPA).
In the context of the Franco-German research project Re(h)strain, this work focuses on a global system analysis integrating both safety and security analysis of international and/or urban railway stations. The Re(h)strain project focuses on terrorist attacks on high speed train systems and investigates prevention and mitigation measures to reduce the overall vulnerability and strengthen the system resilience. One main criterion regarding public transport issues is the number of passengers. For example, the railway station of Paris “Gare du Nord” deals with a bigger number of passengers than the biggest airport in the world (SNCF open Data 2014), the Atlanta airport, but in terms of passengers, it is only around the 23rd rank railway station in the world. Due to the enormous mass of people, this leads to the system approach of breaking out the station into several classes of zones, e.g. entrance, main hall, quays, trains, etc. All classes are analysed considering state-of-the-art parameters, like targets attractiveness, feasibility of attack, possible damage, possible mitigation and defences. Then, safety incidence of security defence is discussed in order to refine security requirement with regard to the considered zone. Finally, global requirements of security defence correlated to the corresponding class of zones are proposed.
Timely recognition of threats can be significantly supported by security assistance systems that work continuously in time and call the security personnel in case of anomalous events in the surveillance area. We describe the concept and the realization of an indoor security assistance system for real-time decision support. The system consists of a computer vision module and a person classification module. The computer vision module provides a video event analysis of the entrance region in front of the demonstrator. After entering the control corridor, the persons are tracked, classified, and potential threats are localized inside the demonstrator. Data for the person classification are provided by chemical sensors detecting hazardous materials. Due to their limited spatio-temporal resolution, a single chemical sensor cannot localize this material and associate it with a person. We compensate this deficiency by fusing the output of multiple, distributed chemical sensors with kinematical data from laser-range scanners. Considering both the computer vision formation and the results of the person classification affords the localization of threats and a timely reaction of the security personnel.
The following work presents algorithms for semi-automatic validation, feature extraction and ranking of time series measurements acquired from MOX gas sensors. Semi-automatic measurement validation is accomplished by extending established curve similarity algorithms with a slope-based signature calculation. Furthermore, a feature-based ranking metric is introduced. It allows for individual prioritization of each feature and can be used to find the best performing sensors regarding multiple research questions. Finally, the functionality of the algorithms, as well as the developed software suite, are demonstrated with an exemplary scenario, illustrating how to find the most power-efficient MOX gas sensor in a data set collected during an extensive screening consisting of 16,320 measurements, all taken with different sensors at various temperatures and analytes.
Detection of triacetone triperoxide using temperature cycled metal-oxide semiconductor gas sensors
(2015)
Hydrophilic surface-enhanced Raman spectroscopy (SERS) substrates were prepared by a combination of TiO2-coatings of aluminium plates through a direct titanium tetraisopropoxide (TTIP) coating and drop coated by synthesised gold nanoparticles (AuNPs). Differences between the wettability of the untreated substrates, the slowly dried Ti(OH)4 substrates and calcinated as well as plasma treated TiO2 substrates were analysed by water contact angle (WCA) measurements. The hydrophilic behaviour of the developed substrates helped to improve the distribution of the AuNPs, which reflects in overall higher lateral SERS enhancement. Surface enhancement of the substrates was tested with target molecule rhodamine 6G (R6G) and a fibre-coupled 638 nm Raman spectrometer. Additionally, the morphology of the substrates was characterised using scanning electron microscopy (SEM) and Raman microscopy. The studies showed a reduced influence of the coffee ring effect on the particle distribution, resulting in a more broadly distributed edge region, which increased the spatial reproducibility of the measured SERS signal in the surface-enhanced Raman mapping measurements on mm scale.
Surface-enhanced Raman spectroscopy (SERS) with subsequent chemometric evaluation was performed for the rapid and non-destructive differentiation of seven important meat-associated microorganisms, namely Brochothrix thermosphacta DSM 20171, Pseudomonas fluorescens DSM 4358, Salmonella enterica subsp. enterica sv. Enteritidis DSM 14221, Listeria monocytogenes DSM 19094, Micrococcus luteus DSM 20030, Escherichia coli HB101 and Bacillus thuringiensis sv. israelensis DSM 5724. A simple method for collecting spectra from commercial paper-based SERS substrates without any laborious pre-treatments was used. In order to prepare the spectroscopic data for classification at genera level with a subsequent chemometric evaluation consisting of principal component analysis and discriminant analysis, a pre-processing method with spike correction and sum normalisation was performed. Because of the spike correction rather than exclusion, and therefore the use of a balanced data set, the multivariate analysis of the data is significantly resilient and meaningful. The analysis showed that the differentiation of meat-associated microorganisms and thereby the detection of important meat-related pathogenic bacteria was successful on genera level and a cross-validation as well as a classification of ungrouped data showed promising results, with 99.5 % and 97.5 %, respectively.
Discrimination of Stressed and Non-Stressed Food-Related Bacteria Using Raman-Microspectroscopy
(2022)
As the identification of microorganisms becomes more significant in industry, so does the utilization of microspectroscopy and the development of effective chemometric models for data analysis and classification. Since only microorganisms cultivated under laboratory conditions can be identified, but they are exposed to a variety of stress factors, such as temperature differences, there is a demand for a method that can take these stress factors and the associated reactions of the bacteria into account. Therefore, bacterial stress reactions to lifetime conditions (regular treatment, 25 °C, HCl, 2-propanol, NaOH) and sampling conditions (cold sampling, desiccation, heat drying) were induced to explore the effects on Raman spectra in order to improve the chemometric models. As a result, in this study nine food-relevant bacteria were exposed to seven stress conditions in addition to routine cultivation as a control. Spectral alterations in lipids, polysaccharides, nucleic acids, and proteins were observed when compared to normal growth circumstances without stresses. Regardless of the involvement of several stress factors and storage times, a model for differentiating the analyzed microorganisms from genus down to strain level was developed. Classification of the independent training dataset at genus and species level for Escherichia coli and at strain level for the other food relevant microorganisms showed a classification rate of 97.6%.
Hydrogen‐Bonded Cholesteric Liquid Crystals—A Modular Approach Toward Responsive Photonic Materials
(2022)
A supramolecular approach for photonic materials based on hydrogen-bonded cholesteric liquid crystals is presented. The modular toolbox of low-molecular-weight hydrogen-bond donors and acceptors provides a simple route toward liquid crystalline materials with tailor-made thermal and photonic properties. Initial studies reveal broad application potential of the liquid crystalline thin films for chemo- and thermosensing. The chemosensing performance is based on the interruption of the intermolecular forces between the donor and acceptor moieties by interference with halogen-bond donors. Future studies will expand the scope of analytes and sensing in aqueous media. In addition, the implementation of the reported materials in additive manufacturing and printed photonic devices is planned.
Explorative experiments were done to figure out differences in the emission of volatile organic compounds (VOCs) of not infested trees and trees infested by Anoplophora glabripennis (Asian longhorn beetle, ALB), a quarantine pest. Therefore, VOCs from some native insect species, Anoplophora glabripennis infested Acer, stressed Acer, healthy Acer, Populus and Salix were obtained by enrichment on adsorbents. Qualitative analysis was done by thermal desorption gas chromatography coupled with a mass selective detector (TD-GC/MS). Altogether 169 substances were identified. 11 substances occur from ALB infested or mechanically damaged trees i.e. stressed trees, but not from healthy trees. (+)-Cyclosativene, (+)-α-longipinene, copaene and caryophyllene are detectable only from ALB-infested Acer not from mechanically damaged or healthy Acer. However, these substances are also emitted by healthy Salix. 2,4-Dimethyl-1-heptene is among all tree samples exclusively present in the ambience of ALB-infested trees. It´s rarely detectable from native insect species’ samples.
Because the robust and rapid determination of spoilage microorganisms is becoming increasingly important in industry, the use of IR microspectroscopy, and the establishment of robust and versatile chemometric models for data processing and classification, is gaining importance. To further improve the chemometric models, bacterial stress responses were induced, to study the effect on the IR spectra and to improve the chemometric model. Thus, in this work, nine important food-relevant microorganisms were subjected to eight stress conditions, besides the regular culturing as a reference. Spectral changes compared to normal growth conditions without stressors were found in the spectral regions of 900–1500 cm−1 and 1500–1700 cm−1. These differences might stem from changes in the protein secondary structure, exopolymer production, and concentration of nucleic acids, lipids, and polysaccharides. As a result, a model for the discrimination of the studied microorganisms at the genus, species and strain level was established, with an accuracy of 96.6%. This was achieved despite the inclusion of various stress conditions and times after incubation of the bacteria. In addition, a model was developed for each individual microorganism, to separate each stress condition or regular treatment with 100% accuracy.
Optical gas sensors based on chiral-nematic liquid crystals (N* LCs) forming one-dimensional photonic crystals do not require electrical energy and have a considerable potential to supplement established types of sensors. A chiral-nematic phase with tunable selective reflection is induced in a nematic host LC by adding reactive chiral dopants. The selective chemical reaction between dopant and analyte is capable to vary the pitch length (the lattice constant) of the soft, self-assembled, one-dimensional photonic crystal. The progress of the ongoing chemical reaction can be observed even by naked eye because the color of the samples varies. In this work, we encapsulate the responsive N* LC in microscale polyvinylpyrrolidone (PVP) fibers via coaxial electrospinning. The sensor is, thus, given a solid form and has an improved stability against nonavoidable environmental influences. The reaction behavior of encapsulated and nonencapsulated N* LC toward a gaseous analyte is compared, systematically. Making use of the encapsulation is an important step to improve the applicability.
A series of reactive binaphthyl‐diimine‐based dopants is prepared and investigated with respect to their potential for the chiral induction of structural coloration in nematic liquid crystal mixture E7 and the selective photonic sensing of nitrogen dioxide (NO2). Studies of the helical twisting power (HTP) in 4‐cyano‐4′‐pentylbiphenyl (5CB) reveal HTP values as high as 375 µm‐1 and the tremendous impact of structural compatibility and changes of the dihedral binaphthyl angle on the efficiency of the chiral transfer. Detailed investigation of the sensing capabilities of the systems reveals an extraordinarily high selectivity for NO2 and a response to concentrations as low as 100 ppm. The systems show a direct response to the analyte gas leading to a concentration‐dependent shift of the reflectance wavelength of up to several hundred nanometers. Incorporation of copper ions remarkably improves the sensor's properties in terms of sensitivity and selectivity, enabling the tailored tweaking of the system's properties.
Polymer fibers with liquid crystals (LCs) in the core have potential as autonomous sensors of airborne volatile organic compounds (VOCs), with a high surface-to-volume ratio enabling fast and sensitive response and an attractive non-woven textile form factor. We demonstrate their ability to continuously and quantitatively measure the concentration of toluene, cyclohexane, and isopropanol as representative VOCs, via the impact of each VOC on the LC birefringence. The response is fully reversible and repeatable over several cycles, the response time can be as low as seconds, and high sensitivity is achieved when the operating temperature is near the LC-isotropic transition temperature. We propose that a broad operating temperature range can be realized by combining fibers with different LC mixtures, yielding autonomous VOC sensors suitable for integration in apparel or in furniture that can compete with existing consumer-grade electronic VOC sensors in terms of sensitivity and response speed.
Raman-microspectroscopy was used for the non-destructive characterization and differentiation of six different meat spoilage associated microorganisms, namely Brochothrix thermosphacta DSM 20171, Micrococcus luteus, Pseudomonas fluorescens DSM 4358, Escherichia coli Top10 and K12 and Pseudomonas fluorescens DSM 50090. To evaluate and classify the Raman-spectroscopic data at species and strain level an adequate preprocessing and subsequent principal component analysis was used. The same procedure was extended to an independent test data set, which could be successfully assigned to the correct bacterial species and even to the right strain. The evaluation was not only successful in differentiation of gram-positive and gram-negative bacteria but also the discrimination between the different bacterial species and strains was possible. This means that the training data set, the preprocessing method and the evaluation of the data lead to a robust principal component analysis. Even the correct assignment of unknown samples is possible. The results show that Raman-microspectroscopy in combination with an appropriate chemometric treatment can be a good tool for a rapid examination and classification of microbial cultures.