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Search results for: AIR QUALITY MONITORING, NITROGEN DIOXIDE, COST-EFFICIENT SENSORS, SENSOR CORRECTION, MACHINE LEARNING
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Taguchi sensors under temperature modulation
PublicationSemiconductor gas sensors are widely used in gas- analyzing applications for various gas species determination due to their low cost and possibility to detect number of different gases. However, one of the main problems with such sensors is their lack of selectivity. To overcome this issue different ap- proaches can be used. One of them is the operation with sensor temperature modulation combined with dedicated data process- ing...
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A Proposed Machine Learning Model for Forecasting Impact of Traffic-Induced Vibrations on Buildings
PublicationTraffic-induced vibrations may cause various damages to buildings located near the road, including cracking of plaster, cracks in load-bearing elements or even collapse of the whole structure. Measurements of vibrations of real buildings are costly and laborious. Therefore the aim of the research is to propose the original numerical algorithm which allows us to predict, with high probability, the nega-tive dynamic impact of traffic-induced...
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Cost-Efficient Optical Fronthaul Architectures for 5G and Future 6G Networks
PublicationFifth-generation and Beyond (5GB) wireless networks have introduced new centralized architectures such as cloud radio access network (CRAN), which necessitate extremely high-capacity low latency Fronthaul (FH). CRAN has many advantageous features in terms of cost reduction, performance enhancement, ease of deployment, and centralization of network management. Nevertheless, designing and deploying a cost-efficient FH is still a...
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Explainable machine learning for diffraction patterns
PublicationSerial crystallography experiments at X-ray free-electron laser facilities produce massive amounts of data but only a fraction of these data are useful for downstream analysis. Thus, it is essential to differentiate between acceptable and unacceptable data, generally known as ‘hit’ and ‘miss’, respectively. Image classification methods from artificial intelligence, or more specifically convolutional neural networks (CNNs), classify...
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Machine Learning for Sensorless Temperature Estimation of a BLDC Motor
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Machine Learning Modelling and Feature Engineering in Seismology Experiment
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Different Ways to Apply a Measurement Instrument of E-Nose Type to Evaluate Ambient Air Quality with Respect to Odour Nuisance in a Vicinity of Municipal Processing Plants
PublicationThis review paper presents different ways to apply a measurement instrument of e-nose type to evaluate ambient air with respect to detection of the odorants characterized by unpleasant odour in a vicinity of municipal processing plants. An emphasis was put on the following applications of the electronic nose instruments: monitoring networks, remote controlled robots and drones as well as portable devices. Moreover, this paper presents...
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Comparison of the Measurement Techniques Employed for Evaluation of Ambient Air Odour Quality Influenced by Operation of Industrial Sewage Treatment Plant
PublicationThe paper presents the results of investigation on ambient air quality evaluation with respect to concentration of odorants in a vicinity of a sewage treatment plant of the LOTOS Group S.A. petroleum plant. The investigation was performed during winter season using a prototype of electronic nose and the Nasal Ranger field olfactometers. The prototype was equipped with a set of six semiconductor sensors by FIGARO Co. and one PID-type...
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Mobile monitoring system for air quality control along traffic routes = Monitoring zanieczyszczeń wzdłuż ciągów komunikacyjnych
PublicationW referacie przedstawiono budowę i zasadę działania mobilnej stacji monitoringu do badania i analizy zanieczyszczeń powietrza atmosferycznego wzdłuż ciagów komunikacyjnych. Przedstawiono również wyniki badań imisji substancji emitowanych z pojazdów poruszających się wzdłuż układu komunikacyjnego Trójmiasta stosując urzadzenie ETL 2000 Bus, zamontowane na dachu samochodu osobowego. Wcześniej, uzyskane wyniki porównano z wynikami...
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Prediction of energy consumption and evaluation of affecting factors in a full-scale WWTP using a machine learning approach
PublicationTreatment of municipal wastewater to meet the stringent effluent quality standards is an energy-intensive process and the main contributor to the costs of wastewater treatment plants (WWTPs). Analysis and prediction of energy consumption (EC) are essential in designing and operating sustainable energy-saving WWTPs. In this study, the effect of wastewater, hydraulic, and climate-based parameters on the daily consumption of EC by...
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Sounding Mechanism of a Flue Organ Pipe—A Multi-Sensor Measurement Approach
PublicationThis work presents an approach that integrates the results of measuring, analyzing, and modeling air flow phenomena driven by pressurized air in a flue organ pipe. The investigation concerns a Bourdon organ pipe. Measurements are performed in an anechoic chamber using the Cartesian robot equipped with a 3D acoustic vector sensor (AVS) that acquires both acoustic pressure and air particle velocity. Also, a high-speed camera is employed...
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Optical monitoring of electrochemical processes with ITO-based lossy-mode resonance optical fiber sensor applied as an electrode
PublicationIn this work we discuss the application of optical fiber sensors based on lossy-mode resonance (LMR) phenomenon for real-time optical monitoring of electrochemical processes. The sensors were obtained by a reactive high power impulse magnetron sputtering of indium tin oxide (ITO) on a 2.5 cm-long core of polymer-clad silica fibers. The LMR effect made monitoring of changes in optical properties of both ITO and its surrounding medium...
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Study of various machine learning approaches for Sentinel-2 derived bathymetry
PublicationIn recent years precise and up-to-date information regarding seabed depth has become more and more important for companies and institutions that operate on coastlines. While direct, in-situ measurements are performed regularly, they are expensive, time-consuming and impractical to be performed in short time intervals. At the same time, an ever-increasing amount of satellite imaging data becomes available. With these images, it...
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Predicting emotion from color present in images and video excerpts by machine learning
PublicationThis work aims at predicting emotion based on the colors present in images and video excerpts using a machine-learning approach. The purpose of this paper is threefold: (a) to develop a machine-learning algorithm that classifies emotions based on the color present in an image, (b) to select the best-performing algorithm from the first phase and apply it to film excerpt emotion analysis based on colors, (c) to design an online survey...
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Road traffic can be predicted by machine learning equally effectively as by complex microscopic model
PublicationSince high-quality real data acquired from selected road sections are not always available, a traffic control solution can use data from software traffic simulators working offline. The results show that in contrast to microscopic traffic simulation, the algorithms employing neural networks can work in real-time, so they can be used, among others, to determine the speed displayed on variable message road signs. This paper describes...
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Cost Effective Corrosion and Fatigue Monitoring for Marine Transport Products
PublicationW artykule omówiono genezę i główne założenia projektu badawczego "Cost Effective Corrosion and Fatigue Monitoring for Transport Products'' o kryptonimie CORFAT, realizowanego w ramach VII Programu Ramowego Unii Europejskiej. Przedstawiono cele i metodykę badań, zalety nowej nowej metodyki monitorowania statków, zadania projektu, zalety i ograniczenia metody Emisji Akustycznej (EA), opis innych metod badań nieniszczących (NNDT)....
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SENSORS AND ACTUATORS B-CHEMICAL
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Jerzy Konorski dr hab. inż.
PeopleJerzy Konorski received his M. Sc. degree in telecommunications from Gdansk University of Technology, Poland, and his Ph. D. degree in computer science from the Polish Academy of Sciences, Warsaw, Poland. In 2007, he defended his D. Sc. thesis at the Faculty of Electronics, Telecommunications and Informatics, Gdansk University of Technology. He has authored over 150 papers, led scientific projects funded by the European Union,...
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Gas sampling system for matrix of semiconductor gas sensors
PublicationSemiconductor gas sensors are popular commercial sensors applied in numerous gas detection systems. They are reliable, small, rugged and inexpensive. However, there are a few problem limiting the wider use of such sensors. Semiconductor gas sensor usually exhibits a low selectivity, low repeatability, drift of response, strong temperature and moisture influence on sensor properties. Sample flow rate is one of the parameters that...
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Currently Commercially Available Chemical Sensors Employed for Detection of Volatile Organic Compounds in Outdoor and Indoor Air
PublicationThe paper presents principle of operation and design of the most popular chemical sensors for measurement of volatile organic compounds (VOCs) in outdoor and indoor air. It describes the sensors for evaluation of explosion risk including pellistors and IR-absorption sensors as well as the sensors for detection of toxic compounds such as electrochemical (amperometric), photoionization and semiconductor with solid electrolyte ones....
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Horizontally-split-drain MAGFET - a highly sensitive magnetic field sensor
PublicationWe propose a novel magnetic field sensitive semiconductor device, viz., Horizontally-Split-Drain Magnetic-Field Sensitive Field-Effect Transistor (HSDMAGFET) which can be used to measure or detect steady or variable magnetic fields. Operating principle of the transistor is based on one of the galvanomagnetic phenomena and a Gradual Channel Detachment Effect (GCDE) and is very similar to that of Popovic and Baltes's SDMAGFET. The...
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Application of Passive Samplers in Monitoring of Organic Constituents of Air
PublicationThe principles of passive dosimetry, which has been known for over 100 years, are finding an ever increasing use in analytical practice and are being used as a convenient technique for isolation and enrichment of analytes from various environmental media. Due to its simplicity, a variety of designs, as well as the possibility of using a number of different final determination techniques, passive dosimetry has been applied in...
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Evaluation of the prediction ability of air pollutants based on the electronic nose responses
PublicationElectronic noses are able to perform on-line measurements of the toxic volatile compounds in air. Due to their low cost and compact size they can be placed in the areas exposed to pollution, outside the laboratory. Those advantages, on the other side, force the need for development of the reliable sensors data analysis procedures. One of the most important issues connected with electronic noses is the lack of stability of the gas...
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The Relationships Between BTEX, NOx, and O3 Concentrations in Urban Air in Gdansk and Gdynia, Poland
PublicationThis paper presents the results of atmospheric air quality research conducted in the areas of two shipyard cities: Gdansk and Gdynia (Poland), in the period between March and December 2011. The purpose of the research was focused on determination of benzene, toluene, ethylbenzene, and xylenes (BTEX) compounds in atmospheric air. Passive sampling technique, with Radiello® diffusive passive samplers, was used for BTEX sample collection...
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Evaluation of Machine Learning Methods for the Experimental Classification and Clustering of Higher Education Institutions
PublicationHigher education institutions have a big impact on the future of skills supplied on the labour market. It means that depending on the changes in labour market, higher education institutions are making changes to fields of study or adding new ones to fulfil the demand on labour market. The significant changes on labour market caused by digital transformation, resulted in new jobs and new skills. Because of the necessity of computer...
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Efficient knowledge-based optimization of expensive computational models using adaptive response correction
PublicationComputer simulation has become an indispensable tool in engineering design as they allow an accurate evaluation of the system performance. This is critical in order to carry out the design process in a reliable manner without costly prototyping and physical measurements. However, high-fidelity computer simulations are computationally expensive. This turns to be a fundamental bottleneck when it comes to design automation using numerical...
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Unsupervised machine-learning classification of electrophysiologically active electrodes during human cognitive task performance
PublicationIdentification of active electrodes that record task-relevant neurophysiological activity is needed for clinical and industrial applications as well as for investigating brain functions. We developed an unsupervised, fully automated approach to classify active electrodes showing event-related intracranial EEG (iEEG) responses from 115 patients performing a free recall verbal memory task. Our approach employed new interpretable...
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Preeclampsia Risk Prediction Using Machine Learning Methods Trained on Synthetic Data
PublicationThis paper describes a research study that investigates the use of machine learning algorithms on synthetic data to classify the risk of developing preeclampsia by pregnant women. Synthetic datasets were generated based on parameter distributions from three real patient studies. Four models were compared: XGBoost, Support Vector Machine (SVM), Random Forest, and Explainable Boosting Machines (EBM). The study found that the XGBoost...
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Solubility of dapsone in deep eutectic solvents: Experimental analysis, molecular insights and machine learning predictions
PublicationBackground. Dapsone (DAP) is an anti-inflammatory and antimicrobial active pharmaceutical ingredient used to treat, e.g., AIDS-related diseases. However, low solubility is a feature hampering its efficient use. Objectives. First, deep eutectic solvents...
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Agnieszka Landowska dr hab. inż.
PeopleAgnieszka Landowska works for Gdansk University of Technology, FETI, Department of Software Engineering. Her research concentrates on usability, accessibility and technology adoption, as well as affective computing methods. She initiated Emotions in HCI Research Group and conducts resarch on User eXperiene evaluation of applications and other technologies.
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Noise profiling for speech enhancement employing machine learning models
PublicationThis paper aims to propose a noise profiling method that can be performed in near real-time based on machine learning (ML). To address challenges related to noise profiling effectively, we start with a critical review of the literature background. Then, we outline the experiment performed consisting of two parts. The first part concerns the noise recognition model built upon several baseline classifiers and noise signal features...
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ZnO ALD-Coated Microsphere-Based Sensors for Temperature Measurements
PublicationIn this paper, the application of a microsphere-based fiber-optic sensor with a 200 nm zinc oxide (ZnO) coating, deposited by the Atomic Layer Deposition (ALD) method, for temperature measurements between 100 and 300°C, is presented. The main advantage of integrating a fiber-optic microsphere with a sensing device is the possibility of monitoring the integrity of the sensor head in real-time, which allows for higher accuracy during...
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Interactive information and decision support system for urban and industrial air quality management based on multi-agent system
PublicationThis article presents conception of interactive information and decision support system for urban and industrial air quality management. The emphasis of the project is on real-time analysis and multi-media information, and the support of distributed and mobile clients through the Internet. The approach integrates meteorological data and forecasts, air quality and emission monitoring, dynamic 3D simulation modelling and forecasting,...
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Computational Complexity and Its Influence on Predictive Capabilities of Machine Learning Models for Concrete Mix Design
PublicationThe design of concrete mixtures is crucial in concrete technology, aiming to produce concrete that meets specific quality and performance criteria. Modern standards require not only strength but also eco-friendliness and production efficiency. Based on the Three Equation Method, conventional mix design methods involve analytical and laboratory procedures but are insufficient for contemporary concrete technology, leading to overengineering...
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Indoor air quality of everyday use spaces dedicated to specific purposes—a review
PublicationAccording to literature data, some of the main factors which significantly affect the quality of the indoor environment in residential households or apartments are human activities such as cooking, smoking, cleaning, and indoor exercising. The paper presents a literature overview related to air quality in everyday use spaces dedicated to specific purposes which are integral parts of residential buildings, such as kitchens, basements,...
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Machine learning-based prediction of preplaced aggregate concrete characteristics
PublicationPreplaced-Aggregate Concrete (PAC) is a type of preplaced concrete where coarse aggregate is placed in the mold and a Portland cement-sand grout with admixtures is injected to fill the voids. Due to the complex nature of PAC, many studies were conducted to determine the effects of admixtures and the compressive and tensile strengths of PAC. Considering that a prediction tool is needed to estimate the compressive and tensile...
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Machine learning-based prediction of preplaced aggregate concrete characteristics
PublicationPreplaced-Aggregate Concrete (PAC) is a type of preplaced concrete where coarse aggregate is placed in the mold and a Portland cement-sand grout with admixtures is injected to fill the voids. Due to the complex nature of PAC, many studies were conducted to determine the effects of admixtures and the compressive and tensile strengths of PAC. Considering that a prediction tool is needed to estimate the compressive and tensile strengths...
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A calibration model for gas sensor array in varying environmental conditions
PublicationAbstract: Gas-analyzing systems based on gas sensors, commonly referred to as electronic noses, are the systems which enable the recognition of volatile compounds in their working environment and provide the on-line results of analysis. The most commonly used type of sensors in such systems is semiconductor gas sensors. They are considered to be the most reliable in the long-term applications (more than 1 year), however,...
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A calibration model for gas sensor array in varying environmental conditions
PublicationAbstract: Gas-analyzing systems based on gas sensors, commonly referred to as electronic noses, are the systems which enable the recognition of volatile compounds in their working environment and provide the on-line results of analysis. The most commonly used type of sensors in such systems is semiconductor gas sensors. They are considered to be the most reliable in the long-term applications (more than 1 year), however,...
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Photocatalytic degradation using nitrogen-modified titanium dioxide
PublicationPrzepływowy reaktor z wypełnieniem został zastosowany do przeprowadzenia fotodegradacji 4-chlorofenolu za pomocą światła widzialnego. Fotokatalizator w postaci tlenku tytanu(IV) modyfikowanego azotem, został naniesiony na powierzchnię kulek szklanych. Źródło światła widzialnego stanowiła 8W lampa fluorescencyjna. Powietrze było w sposób ciągły przepuszczane przez zawartość reaktora.
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DNAffinity: a machine-learning approach to predict DNA binding affinities of transcription factors
PublicationWe present a physics-based machine learning approach to predict in vitro transcription factor binding affinities from structural and mechanical DNA properties directly derived from atomistic molecular dynamics simulations. The method is able to predict affinities obtained with techniques as different as uPBM, gcPBM and HT-SELEX with an excellent performance, much better than existing algorithms. Due to its nature, the method can...
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Properties of Nasicon-based CO2 sensor with Bi8Nb2O17 reference electrode
PublicationGas sensors are useful for the carbon dioxide concentration monitoring in many applications. The major challenge is to develop a potentiometric sensor working without the necessity of a reference gas and without a need of the reference electrode encapsulation. Important issue is a selection of reference electrode material, which should provide stable reference potential. For example as reference electrode material in sensor based...
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Machine-learning methods for estimating compressive strength of high-performance alkali-activated concrete
PublicationHigh-performance alkali-activated concrete (HP-AAC) is acknowledged as a cementless and environmentally friendly material. It has recently received a substantial amount of interest not only due to the potential it has for being used instead of ordinary concrete but also owing to the concerns associated with climate change, sustainability, reduction of CO2 emissions, and energy consumption. The characteristics and amounts of the...
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Monitoring of volatile organic compounds (VOCs) in atmospheric air. Part II. Sample collection and preparation
PublicationThe paper reviews literature information on air sampling techniques commonly used for monitoring volatile organic compounds (VOCs) levels in atmospheric air. It describes containers for collecting samples of atmospheric air, such as vacuum canisters and bags made from synthetic materials. It discusses dynamic, passive and denudational techniques for sampling analytes from air combining isolation with preliminary enrichment, and...
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Forewarned Is Forearmed: Machine Learning Algorithms for the Prediction of Catheter-Induced Coronary and Aortic Injuries
PublicationCatheter-induced dissections (CID) of coronary arteries and/or the aorta are among the most dangerous complications of percutaneous coronary procedures, yet the data on their risk factors are anecdotal. Logistic regression and five more advanced machine learning techniques were applied to determine the most significant predictors of dissection. Model performance comparison and feature importance ranking were evaluated. We identified...
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Cost-Efficient Design Methodology for Compact Rat-Race Couplers
PublicationIn this article, a reliable and low-cost design methodology for simulation-driven optimization of miniaturized rat-race couplers (RRCs) is presented. We exploit a two-stage design approach, where a composite structure (a basic building block of the RRC structure) is first optimized using a pattern search algorithm, and, subsequently, the entire coupler is tuned by means of surrogate-based optimization (SBO) procedure. SBO is executed...
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Hardware-Software Implementation of a Sensor Network for CityTraffic Monitoring Using the FPGA- and ASIC-Based Sensor Nodes
PublicationArtykuł opisuje prototypową sieć sensorową do monitorowania ruchu pojazdów w mieście. Węzły sieci sensorowej, wyposażone w kamerę o niskiej rozdzielczości, obserwują ulice i wykrywają poruszające się obiekty. Detekcja obiektów jest realizowana w oparciu o własny algorytm segmentacji obrazów, wykorzystujący podwójne odejmowanie tła, wykrywanie krawędzi i cieni, działający na dedykowanym systemie mikroelektronicznym typu ''System...
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MACHINE VISION DETECTION OF THE CIRCULAR SAW VIBRATIONS
PublicationDynamical properties of rotating circular saw blades are crucial for both production quality and personnel safety. This paper presents a novel method for monitoring circular saw vibrations and deviations. A machine vision system uses a camera and a laser line projected on the saw’s surface to estimate vibration range. Changes of the dynamic behaviour of the saw were measured as a function of the rotational speed. The critical rotational...
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Thermogravimetric analysis data of hydration in air and nitrogen for BaCe0.6Zr0.2Y0.1M0.1O3-δ (M = Fe, Pr, Tb)
Open Research DataThe dataset consists of 6 files of thermogravimetric analysis (TGA) data. The TGA experiments of hydration for BaCe0.6Zr0.2Y0.1Fe0.1O3-δ (BCZYFe), BaCe0.6Zr0.2Y0.1Pr0.1O3-δ (BCZYPr), and BaCe0.6Zr0.2Y0.1Tb0.1O3-δ (BCZYTb) were conducted on Netzsch STA 449.
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Combined chemoresistive and in situ FTIR spectroscopy study of nanoporous NiO films for light-activated nitrogen dioxide and acetone gas sensing
PublicationThe chemoresistive sensor response of nanoporous NiO films prepared by advanced gas deposition was investigated by combined resistivity and in situ FTIR spectroscopy, with and without simultaneous light illumination, to detect NO2 and acetone gases. The sensitivity towards NO2 increased dramatically under UV irradiation employing 275 nm light. Improved sensitivity was observed at an elevated temperature of 150 °C. In situ FTIR...