Towards a hand-held, fast, and sensitive gas chromatograph-ion mobility spectrometer for detecting volatile compounds

Ion mobility spectrometers can detect gaseous compounds at atmospheric pressure in the range of parts per trillion within a second. Due to their fast response times, high sensitivity, and limited instrumental effort, they are used in a variety of applications, especially as mobile or hand-held devices. However, most real-life samples are gas mixtures, which can pose a challenge for IMS with atmospheric pressure chemical ionization mainly due to competing gas-phase ionization processes. Therefore, we present a miniaturized drift tube IMS coupled to a compact gas chromatograph for pre-separation, built of seven bundled standard GC columns (Rtx-Volatiles, Restek GmbH) with 250 μm ID and 1.07 m in length. Such pre-separation significantly reduces chemical cross sensitivities caused by competing gas-phase ionization processes and adds orthogonality. Our miniaturized GC-IMS system is characterized with alcohols, halocarbons, and ketones as model substances, reaching detection limits down to 70 pptv with IMS averaging times of just 125 ms. It separates test mixtures of ketones and halocarbons within 180 s and 50 s, respectively. The IMS has a short drift length of 40.6 mm and reaches a high resolving power of RP = 68. [Figure not available: see fulltext.]. © 2020, The Author(s)

Micro/Nano Manufacturing

Micro manufacturing involves dealing with the fabrication of structures in the size range of 0.1 to 1000 µm. The scope of nano manufacturing extends the size range of manufactured features to even smaller length scales—below 100 nm. A strict borderline between micro and nano manufacturing can hardly be drawn, such that both domains are treated as complementary and mutually beneficial within a closely interconnected scientific community. Both micro and nano manufacturing can be considered as important enablers for high-end products. This Special Issue of Applied Sciences is dedicated to recent advances in research and development within the field of micro and nano manufacturing. The included papers report recent findings and advances in manufacturing technologies for producing products with micro and nano scale features and structures as well as applications underpinned by the advances in these technologies

Miniaturized high-performance drift tube ion mobility spectrometer

Developing powerful hand-held drift tube ion mobility spectrometers (IMS) requires small, lightweight drift tubes with high analytical performance. In this work, we present an easy-to-manufacture, miniaturized drift tube ion mobility spectrometer, which is manufactured from polyether ether ketone, stainless steel foils and printed circuit boards. It is possible to operate the drift tube IMS with a radioactive 3H ionization source or a non-radioactive X-ray ionization source with 3 kV acceleration voltage. The drift tube design provides high resolving power of Rp = 63 at a drift length of just 40 mm, 15 mm × 15 mm in cross-section (outer dimensions) and a drift voltage of 2.5 kV. The limits of detection for less than one second of averaging are 40 pptv for dimethyl-methylphosphonate and 30 pptv for methyl salicylate. For demonstration, the miniaturized drift tube IMS is integrated into a stand-alone battery-powered mobile device, including a closed gas-loop, high performance driver electronics and wireless data transmission. In a proof-of-concept study, this device was tested in an international field evaluation exercise to detect the release of a volatile, hazardous substance inside a large entry hall

Arquitetura para ganho de eficiência energética em redes de sensores sem fios de próxima geração

Dissertação (mestrado) - Universidade Federal de Santa Catarina, Centro Tecnológico. Programa de Pós-Graduação em Ciência da Computação.A Nova Geração de Redes de Sensores Sem Fios irá integrar sistemas de comunicação além terceira geração. Como resultado desta integração, estes novos sistemas de comunicação serão alimentados pelas Redes de Sensores com informação colhida do ambiente, tornando-se cientes do contexto. Para alcançar a necessária conectividade entre redes IP e Redes de Sensores, o grupo de trabalho 6LoWPAN da Internet Engineering Task Force projetou uma camada de adaptação IPv6 para dispositivos de capacidades restritas. Esta pilha protocolar provê aos nós sensores interoperabilidade IPv6, evitando sobrecarga protocolar tanto quanto possível. Nesta dissertação, foram analisados e avaliados os possíveis cenários de comunicação IPv6 em Redes de Sensores sem Fios. Estas analises levaram a conclusão que, mesmo o 6LoWPAN sendo uma solução IPv6 leve o suficiente para ser executado em nós sensores, em cenários de comunicação global, isto é, quando há necessidade de utilização de endereçamento IPv6 globais e únicos, a sobrecarga protocolar é significativa e novas abordagens devem ser propostas. A fim de evitar sobrecarga de comunicação em Redes de Sensores Sem Fios quando há necessidade de comunicação com diferentes redes, foi proposta uma arquitetura que explora as técnicas do 6LoWPAN para supressão de cabeçalhos através de mecanismos de tradução transparente de endereços, alcançando maior eficiência energética. The next generation of wireless sensor networks will integrate communication systems beyond third generation paradigm. As a result of this integration, the new communication systems will be feed by the sensor networks with information gathered from the environment, achieving context awareness. To reach the necessary end-to-end connectivity between all-IP networks and sensor networks, the IETF 6LoWPAN working group designed an IPv6 adaptation layer for low power, low cost, low bit rate and short range devices. This protocol stack provides to the sensor networks IPv6 interoperability, avoiding overhead as much as possible. This document analyses and evaluates the associated IP overhead in different 6LoWPAN scenarios, from intra to inter network communication. We conclude that, even the 6LoWPAN being a light weight IP solution for link local sensor network communication, when data flows between different networks the overhead is too high and new approaches must be proposed. In order to avoid communication overhead when global IPv6 addressing is necessary in resource constrained networks, we propose an architecture to exploit 6LoWPAN crosslayering techniques to relief constrained networks from IP unnecessary headers through transparent address translation mechanisms

Novel ion drift tube for high-performance ion mobility spectrometers based on a composite material

Ion mobility spectrometers (IMS) are able to detect pptV-level concentrations of substances in gasses and in liquids within seconds. Due to the continuous increase in analytical performance and reduction of the instrument size, IMS are established nowadays in a variety of analytical field applications. In order to reduce the manufacturing effort and further enhance their widespread use, we have developed a simple manufacturing process for drift tubes based on a composite material. This composite material consists of alternating layers of metal sheets and insulator material, which are connected to each other in a mechanically stable and gastight manner. Furthermore, this approach allows the production of ion drift tubes in just a few steps from a single piece of material, thus reducing the manufacturing costs and efforts. Here, a drift tube ion mobility spectrometer based on such a composite material is presented. Although its outer dimensions are just 15 mm × 15 mm in cross section and 57 mm in length, it has high resolving power of Rp = 62 and detection limits in the pptV-range, demonstrated for ethanol and 1,2,3-trichloropropane. © 2020, The Author(s)

Screening accuracy of a 14-day smartphone ambulatory assessment of depression symptoms and mood dynamics in a general population sample: Comparison with the PHQ-9 depression screening

Introduction Major depression affects over 300 million people worldwide, but cases are often detected late or remain undetected. This increases the risk of symptom deterioration and chronification. Consequently, there is a high demand for low threshold but clinically sound approaches to depression detection. Recent studies show a great willingness among users of mobile health apps to assess daily depression symptoms. In this pilot study, we present a provisional validation of the depression screening app Moodpath. The app offers a 14-day ambulatory assessment (AA) of depression symptoms based on the ICD-10 criteria as well as ecologically momentary mood ratings that allow the study of short-term mood dynamics. Materials and methods N = 113 Moodpath users were selected through consecutive sampling and filled out the Patient Health Questionnaire (PHQ-9) after completing 14 days of AA with 3 question blocks (morning, midday, and evening) per day. The psychometric properties (sensitivity, specificity, accuracy) of the ambulatory Moodpath screening were assessed based on the retrospective PHQ-9 screening result. In addition, several indicators of mood dynamics (e.g. average, inertia, instability), were calculated and investigated for their individual and incremental predictive value using regression models. Results We found a strong linear relationship between the PHQ-9 score and the AA Moodpath depression score (r = .76, p < .001). The app-based screening demonstrated a high sensitivity (.879) and acceptable specificity (.745). Different indicators of mood dynamics covered substantial amounts of PHQ-9 variance, depending on the number of days with mood data that were included in the analyses. Discussion AA and PHQ-9 shared a large proportion of variance but may not measure exactly the same construct. This may be due to the differences in the underlying diagnostic systems or due to differences in momentary and retrospective assessments. Further validation through structured clinical interviews is indicated. The results suggest that ambulatory assessed mood indicators are a promising addition to multimodal depression screening tools. Improving app-based AA screenings requires adapted screening algorithms and corresponding methods for the analysis of dynamic processes over time

A candidate protostellar object in the L1457 / MBM12 cloud

The association of young T Tauri stars, MBM12A, indicates that L1457 was forming stars not too long ago. With our study we want to find out whether or not there are still signs for ongoing star formation in that cloud. Using the Max-Planck-Millimeter-Bolometer MAMBO at the IRAM 30m telescope we obtained a map of about 8' by 8' centered on L1457 in the dust continuum emission at 230 GHz. Towards the most intense regions in our bolometer map we obtained spectra at high angular resolution in the CS (2-1) and the N2H+(1-0) lines using the IRAM 30m telescope. We find that the cold dust in L1457 is concentrated in several small cores with high H2 column densities and solar masses. The density profiles of the cores are inconsistent with a sphere with constant density. These cores are closer to virial equilibrium than the cloud as a whole. Data from the VLA and Spitzer archives reveal two point sources in the direction of one dust core. One of the sources is probably a distant quasar, whereas the other source is projected right on a local maximum of our dust map and shows characteristics of a protostellar object.Comment: 4 pages, 4 figures, accepted by Astronomy & Astrophysic

Towards reliable parameter extraction in MEMS final module testing using Bayesian inference

In micro-electro-mechanical systems (MEMS) testing high overall precision and reliability are essential. Due to the additional requirement of runtime efficiency, machine learning methods have been investigated in recent years. However, these methods are often associated with inherent challenges concerning uncertainty quantification and guarantees of reliability. The goal of this paper is therefore to present a new machine learning approach in MEMS testing based on Bayesian inference to determine whether the estimation is trustworthy. The overall predictive performance as well as the uncertainty quantification are evaluated with four methods: Bayesian neural network, mixture density network, probabilistic Bayesian neural network and BayesFlow. They are investigated under the variation in training set size, different additive noise levels, and an out-of-distribution condition, namely the variation in the damping factor of the MEMS device. Furthermore, epistemic and aleatoric uncertainties are evaluated and discussed to encourage thorough inspection of models before deployment striving for reliable and efficient parameter estimation during final module testing of MEMS devices. BayesFlow consistently outperformed the other methods in terms of the predictive performance. As the probabilistic Bayesian neural network enables the distinction between epistemic and aleatoric uncertainty, their share of the total uncertainty has been intensively studied