Functionalized epitaxial graphene as versatile platform for air quality sensors

The work presented in this thesis focuses on epitaxial graphene on SiC as a platform for air quality sensors. Several approaches have been tested and evaluated to increase the sensitivity, selectivity, speed of response and stability of the sensors. The graphene surfaces have been functionalized, for example, with different metal oxide nanoparticles and nanolayers using hollow-cathode sputtering and pulsed laser deposition. The modified surfaces were investigated to-wards topography, integrity and chemical composition with characterization methods such as atomic force microscopy and Raman spectroscopy. Interaction energies between several analytes and nanoparticle-graphene-combinations were calculated by density functional theory to find the optimal material for specific target gases, and to verify the usefulness of this approach. The impact of environmental influences such as operating temperature, relative humidity and UV irradiation on sensing properties was investigated as well. To further enhance sensor performances, the first-order time-derivative of the sensor’s resistance was introduced to speed up sensor response and a temperature cycled operation mode was investigated towards selectivity. Applying these methods in laboratory conditions, sensors with a quantitative readout of single ppb benzene and formaldehyde were developed. These results show promise to fill the existing gap of low-cost but highly sensitive and fast gas sensors for air quality monitoring.Financial support by the Swedish Foundation for Strategic Research (SSF) through the grants GMT14-0077 and RMA15-024

Graphene Decorated with Iron Oxide Nanoparticles for Highly Sensitive Interaction with Volatile Organic Compounds

Gases, such as nitrogen dioxide, formaldehyde and benzene, are toxic even at very low concentrations. However, so far there are no low-cost sensors available with sufficiently low detection limits and desired response times, which are able to detect them in the ranges relevant for air quality control. In this work, we address both, detection of small gas amounts and fast response times, using epitaxially grown graphene decorated with iron oxide nanoparticles. This hybrid surface is used as a sensing layer to detect formaldehyde and benzene at concentrations of relevance (low parts per billion). The performance enhancement was additionally validated using density functional theory calculations to see the effect of decoration on binding energies between the gas molecules and the sensor surface. Moreover, the time constants can be drastically reduced using a derivative sensor signal readout, allowing the sensor to work at detection limits and sampling rates desired for air quality monitoring applications

Towards a versatile gas sensing platform with epitaxial graphene

The work presented in this thesis focuses on how to utilize epitaxially grown graphene on SiC as a basis for ultra-sensitive gas sensor. Several approaches have been tested and evaluated to increase the sensitivity, selectivity, speed of response and stability and of the graphene based gas sensors with a focus on air quality monitoring applications. The graphene surfaces have been functionalized with different metal oxide nanoparticles and nanolayers using hollow-cathode sputtering and pulsed laser deposition. The modified surface was investigated towards its topography, integrity and chemical composition with characterization methods such as AFM, Raman and XPS. Moreover, the binding energy was calculated with density functional theory for benzene and formaldehyde when reacting with pristine epitaxial graphene and iron oxide nanoparticle decorated graphene to verify the usefulness of this approach. The impact of environmental influences such as operating temperature, relative humidity and UV irradiation towards sensing properties was investigated as well. To further decrease time constants, the first-order time-derivative of the sensor’s resistance is introduced as an alternative sensor signal and evaluated towards its applicability. Applying these methods in laboratory conditions, sensors with a quantitative readout of single ppb benzene and formaldehyde were developed and time constants of less than one minute could be achieved with the first-order time-derivative signal. These results show promise to fill the existing gap of low-cost but highly sensitive and fast gas sensors for air quality monitoring

Functionalized epitaxial graphene as versatile platform for air quality sensors

The work presented in this thesis focuses on epitaxial graphene on SiC as a platform for air quality sensors. Several approaches have been tested and evaluated to increase the sensitivity, selectivity, speed of response and stability of the sensors. The graphene surfaces have been functionalized, for example, with different metal oxide nanoparticles and nanolayers using hollow-cathode sputtering and pulsed laser deposition. The modified surfaces were investigated towards topography, integrity and chemical composition with characterization methods such as atomic force microscopy and Raman spectroscopy. Interaction energies between several analytes and nanoparticle-graphene-combinations were calculated by density functional theory to find the optimal material for specific target gases, and to verify the usefulness of this approach. The impact of environmental influences such as operating temperature, relative humidity and UV irradiation on sensing properties was investigated as well. To further enhance sensor performances, the first-order time-derivative of the sensor’s resistance was introduced to speed up sensor response and a temperature cycled operation mode was investigated towards selectivity. Applying these methods in laboratory conditions, sensors with a quantitative readout of single ppb benzene and formaldehyde were developed. These results show promise to fill the existing gap of low-cost but highly sensitive and fast gas sensors for air quality monitoring.Der Fokus dieser Thesis liegt auf der Erforschung von epitaxialem Graphen auf SiC als Plattform für Luftgütesensoren. Diverse Ansätze wurden untersucht, um die Sensitivität, Selektivität, Reaktionsgeschwindigkeit und Stabilität der Sensoren zu verbessern. Die Graphenoberfläche wurde unter anderem mit Metalloxid-Nanopartikeln oder nanometerdünnen Schichten funktionalisiert. Die funktionalisierten Sensorschichten wurden hinsichtlich ihrer Oberflächenbeschaffenheit, Unversehrtheit und chemischen Zusammensetzung mittels Rasterkraftmikroskopie und Raman Spektroskopie untersucht. Die Reaktionsenergien zwischen verschiedenen Analyten und Nanopartikel-Graphen-Kombinationen wurden mit Dichtefunktionaltheorie berechnet, um das optimale Material für spezifische Gase zu finden und um die Brauchbarkeit dieser Funktionalisierungsmethode zu verifizieren. Der Einfluss von äußeren Parametern wie Sensortemperatur, Luftfeuchte und UV-Einstrahlung auf die Sensoreigenschaften wurde ebenfalls untersucht. Um die Sensorleistung zu verbessern, wurde die erste zeitliche Ableitung des Sensorwiderstands als zusätzliches Signal eingeführt und ein temperaturzyklischer Betriebsmodus hinsichtlich seiner Eignung erforscht. Durch die Anwendung dieser Methoden ist es möglich, einzelne ppbs Benzol und Formaldehyd unter Laborbedingungen zu detektieren. Diese Ergebnisse sind vielversprechend, um die bestehende Lücke der günstigen, aber sehr sensitiven Sensoren für Luftqualitätsüberwachung zu schließen.Arbetet som presenteras i denna avhandling fokuserar på epitaxiell grafen på SiC som en plattform för luftkvalitetssensorer. Flera tillvägagångssätt har testats och utvärderats för att öka känsligheten, selektiviteten, responstiden, och stabiliteten hos sensorerna. Grafenytorna har modifierats till exempel med olika metalloxid-nanopartiklar och nanolager med användning av hålkatodsputtring och PLD. De modifierade ytorna undersöktes mot topografi, strukturell integritet och kemisk sammansättning med karakteriseringsmetoder som atomkraftsmikroskopi och Ramanspektroskopi. Interaktionsenergier mellan flera analyter och nanopartiklar-grafen- materialkombinationer beräknades med täthetsfunktionalteori för att hitta de optimala materialkombinationerna för specifika målgaser och för att verifiera användbarheten av ytmodifieringarna. Effekten av externa faktorer som arbetstemperatur, relativ fuktighet och UV-bestrålning på avkänningsegenskaper undersöktes också. För att ytterligare förbättra sensorprestanda introducerades första ordningens tidsderivat av sensorns resistans för att snabbare utvärdera sensorns respons, och ett temperaturcyklat driftläge i kombination med multivariat dataanalys undersöktes mot selektivitet. Genom att använda dessa metoder under laboratorieförhållanden utvecklades sensorer med en kvantitativ avläsning av enstaka ppb bensen och formaldehyd. Dessa resultat visar på en möjlig lösning för att fylla det hålrum som finns i dagens sensorteknologier för luftkvalitetsövervakning, där flera relevanta gaser i dagsläget inte kan mätas med kostnadseffektiva men mycket känsliga och snabba gassensorer

First-order time-derivative readout of epitaxial graphene-based gas sensors for fast analyte determination

For many applications, gas sensors need to be very sensitive, selective and exhibit a good stability. Moreover, they should also be cheap and small, and allow a fast response time. Usually, sensors are optimized for specific applications with a compromise between the mentioned criteria. Here, we show a method that allows very sensitive, but rather slow, graphene metal oxide hybrid sensors to be used in a much faster and more effective way with a focus on targeting trace level concentrations of some common toxic air pollutants. By exploiting the first-order time-derivative of the measured resistance signal after a concentration step, the response peak is achieved much faster, while also being more robust against sensor exposure and relaxation times, and concomitantly maintaining the very high sensitivities inherent to graphene. We propose to use this method to generate an additional signal to allow using sensors that are normally rather slow in applications where steep concentration changes need to be detected with much faster time constants.Funding: Swedish Foundation for Strategic Research (SSF)Swedish Foundation for Strategic Research [GMT140077, RMA15-024]; Centre in Nanoscienceandtechnology(CeNano) throughtheproject"Graphene-nanoparticlehybridgassensor"</p

Machine Learning with Interdependent and Non-identically Distributed Data (Dagstuhl Seminar 15152)

One of the most common assumptions in many machine learning and data analysis tasks is that the given data points are realizations of independent and identically distributed (IID) random variables. However, this assumption is often violated, e.g., when training and test data come from different distributions (dataset bias or domain shift) or the data points are highly interdependent (e.g., when the data exhibits temporal or spatial correlations). Both scenarios are typical situations in visual recognition and computational biology. For instance, computer vision and image analysis models can be learned from object-centric internet resources, but are often rather applied to real-world scenes. In computational biology and personalized medicine, training data may be recorded at a particular hospital, but the model is applied to make predictions on data from different hospitals, where patients exhibit a different population structure. In the seminar report, we discuss, present, and explore new machine learning methods that can deal with non-i.i.d. data as well as new application scenarios

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In this manuscript, we explore the sensor properties of epitaxially grown graphene on silicon carbide decorated with nanolayers of CuO, Fe3O4, V2O5, or ZrO2. The sensor devices were investigated in regard to their response towards NH3 as a typical reducing gas and CO, C6H6, CH2O, and NO2 as gases of interest for air quality monitoring. Moreover, the impact of operating temperature, relative humidity, and additional UV irradiation as changes in the sensing environment have been explored towards their impact on sensing properties. Finally, a cross-laboratory study is presented, supporting stable sensor responses, and the final data is merged into a simplified sensor array. This study shows that sensors can be tailored not only by using different materials but also by applying different working conditions, according to the requirements of certain applications. Lastly, a combination of several different sensors into a sensor array leads to a well-performing sensor system that, with further development, could be suitable for several applications where there is no solution on the market today

Performance tuning of gas sensors based on epitaxial graphene on silicon carbide

In this study, we investigated means of performance enhancement in sensors based on epitaxial graphene on silicon carbide (SiC). Epitaxially grown graphene on SiC substrates were successfully decorated with metal oxide nanoparticles such as TiO2 and Fe3O4 using hollow cathode pulsed plasma sputtering. Atomic Force Microscopy and Raman data verified that no damage was added to the graphene surface. It could be shown that it was easily possible to detect benzene, which is one of the most dangerous volatile organic compounds, with the Fe3O4 decorated graphene sensor down to an ultra-low concentration of 5 ppb with a signal to noise ratio of 35 dB. Moreover, upon illumination with a UV light LED (265 nm) of the TiO2 decorated graphene sensor, the sensitivity towards a change of oxygen could be enhanced such that a clear sensor response could be seen which is a significant improvement over dark conditions, where almost no response occurred. As the last enhancement, the time derivative sensor signal was introduced for the sensor data evaluation, testing the response towards a change of oxygen. This sensor signal evaluation approach can be used to decrease the response time of the sensor by at least one order of magnitude. (C) 2018 Elsevier Ltd. All rights reserved.Funding Agencies|Swedish Foundation for Strategic research (SSF) [GMT14-0077]; Swedish Government Strategic Research Area in Materials Science on Functional Materials at Linkoping University [2009-00971]; Centre in Nano science and technology (CeNano)</p

Performance tuning of gas sensors based on epitaxial graphene on silicon carbide

In this study, we investigated means of performance enhancement in sensors based on epitaxial graphene on silicon carbide (SiC). Epitaxially grown graphene on SiC substrates were successfully decorated with metal oxide nanoparticles such as TiO2 and Fe3O4 using hollow cathode pulsed plasma sputtering. Atomic Force Microscopy and Raman data verified that no damage was added to the graphene surface. It could be shown that it was easily possible to detect benzene, which is one of the most dangerous volatile organic compounds, with the Fe3O4 decorated graphene sensor down to an ultra-low concentration of 5 ppb with a signal to noise ratio of 35 dB. Moreover, upon illumination with a UV light LED (265 nm) of the TiO2 decorated graphene sensor, the sensitivity towards a change of oxygen could be enhanced such that a clear sensor response could be seen which is a significant improvement over dark conditions, where almost no response occurred. As the last enhancement, the time derivative sensor signal was introduced for the sensor data evaluation, testing the response towards a change of oxygen. This sensor signal evaluation approach can be used to decrease the response time of the sensor by at least one order of magnitude. (C) 2018 Elsevier Ltd. All rights reserved.Funding Agencies|Swedish Foundation for Strategic research (SSF) [GMT14-0077]; Swedish Government Strategic Research Area in Materials Science on Functional Materials at Linkoping University [2009-00971]; Centre in Nano science and technology (CeNano)</p