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- ItemPhase Transitions in Low-Dimensional Layered Double Perovskites: The Role of the Organic Moieties(Washington, DC : ACS, 2021) Martín-García, Beatriz; Spirito, Davide; Biffi, Giulia; Artyukhin, Sergey; Francesco Bonaccorso, null; Krahne, RomanHalide double perovskites are an interesting alternative to Pb-containing counterparts as active materials in optoelectronic devices. Low-dimensional double perovskites are fabricated by introducing large organic cations, resulting in organic/inorganic architectures with one or more inorganic octahedra layers separated by organic cations. Here, we synthesized layered double perovskites based on 3D Cs2AgBiBr6, consisting of double (2L) or single (1L) inorganic octahedra layers, using ammonium cations of different sizes and chemical structures. Temperature-dependent Raman spectroscopy revealed phase transition signatures in both inorganic lattice and organic moieties by detecting variations in their vibrational modes. Changes in the conformational arrangement of the organic cations to an ordered state coincided with a phase transition in the 1L systems with the shortest ammonium moieties. Significant changes of photoluminescence intensity observed around the transition temperature suggest that optical properties may be affected by the octahedral tilts emerging at the phase transition.
- ItemAuthor Correction: Influence of plasma treatment on SiO2/Si and Si3N4/Si substrates for large-scale transfer of graphene([London] : Macmillan Publishers Limited, part of Springer Nature, 2021) Lukose, R.; Lisker, M.; Akhtar, F.; Fraschke, M.; Grabolla, T.; Mai, A.; Lukosius, M.The original version of this Article omitted an affiliation for M. Lisker. The correct affiliations for M. Lisker are listed below: IHP- Leibniz Institut für innovative Mikroelektronik, Im Technologiepark 25, 15236, Frankfurt (Oder), Germany Technical University of Applied Science Wildau, Hochschulring 1, 15745, Wildau, Germany The original Article and accompanying Supplementary Information file have been corrected.
- ItemEffectiveness of myAirCoach: A mHealth Self-Management System in Asthma(Amsterdam [u.a.] : Elsevier, 2020) Khusial, Rishi J.; Honkoop, Persijn J.; Usmani, Omar; Soares, Marcia; Simpson, Andrew; Biddiscombe, Martyn; Meah, Sally; Bonini, Matteo; Lalas, Antonios; Polychronidou, Eleftheria; Koopmans, Julia G.; Moustakas, Konstantinos; Snoeck-Stroband, Jiska B.; Ortmann, Steffen; Votis, Konstantinos; Tzovaras, Dimitrios; Chung, Kian Fan; Fowler, Stephen; Sont, Jacob K.Background: Self-management programs have beneficial effects on asthma control, but their implementation in clinical practice is poor. Mobile health (mHealth) could play an important role in enhancing self-management. Objective: To assess the clinical effectiveness and technology acceptance of myAirCoach-supported self-management on top of usual care in patients with asthma using inhalation medication. Methods: Patients were recruited in 2 separate studies. The myAirCoach system consisted of an inhaler adapter, an indoor air-quality monitor, a physical activity tracker, a portable spirometer, a fraction exhaled nitric oxide device, and an app. The primary outcome was asthma control; secondary outcomes were exacerbations, quality of life, and technology acceptance. In study 1, 30 participants were randomized to either usual care or myAirCoach support for 3 to 6 months; in study 2, 12 participants were provided with the myAirCoach system in a 3-month before-after study. Results: In study 1, asthma control improved in the intervention group compared with controls (Asthma Control Questionnaire difference, 0.70; P = .006). A total of 6 exacerbations occurred in the intervention group compared with 12 in the control group (hazard ratio, 0.31; P = .06). Asthma-related quality of life improved (mini Asthma-related Quality of Life Questionnaire difference, 0.53; P = .04), but forced expiratory volume in 1 second was unchanged. In study 2, asthma control improved by 0.86 compared with baseline (P = .007) and quality of life by 0.16 (P = .64). Participants reported positive attitudes toward the system. Discussion: Using the myAirCoach support system improves asthma control and quality of life, with a reduction in severe asthma exacerbations. Well-validated mHealth technologies should therefore be further studied. © 2020 The Authors
- ItemInfluence of plasma treatment on SiO2/Si and Si3N4/Si substrates for large-scale transfer of graphene([London] : Macmillan Publishers Limited, part of Springer Nature, 2021) Lukose, R.; Lisker, M.; Akhtar, F.; Fraschke, M.; Grabolla, T.; Mai, A.; Lukosius, M.One of the limiting factors of graphene integration into electronic, photonic, or sensing devices is the unavailability of large-scale graphene directly grown on the isolators. Therefore, it is necessary to transfer graphene from the donor growth wafers onto the isolating target wafers. In the present research, graphene was transferred from the chemical vapor deposited 200 mm Germanium/Silicon (Ge/Si) wafers onto isolating (SiO2/Si and Si3N4/Si) wafers by electrochemical delamination procedure, employing poly(methylmethacrylate) as an intermediate support layer. In order to influence the adhesion properties of graphene, the wettability properties of the target substrates were investigated in this study. To increase the adhesion of the graphene on the isolating surfaces, they were pre-treated with oxygen plasma prior the transfer process of graphene. The wetting contact angle measurements revealed the increase of the hydrophilicity after surface interaction with oxygen plasma, leading to improved adhesion of the graphene on 200 mm target wafers and possible proof-of-concept development of graphene-based devices in standard Si technologies.
- ItemPrediction of Pest Insect Appearance Using Sensors and Machine Learning(Basel : MDPI, 2021) Marković, Dušan; Vujičić, Dejan; Tanasković, Snežana; Đorđević, Borislav; Ranđić, Siniša; Stamenković, ZoranThe appearance of pest insects can lead to a loss in yield if farmers do not respond in a timely manner to suppress their spread. Occurrences and numbers of insects can be monitored through insect traps, which include their permanent touring and checking of their condition. Another more efficient way is to set up sensor devices with a camera at the traps that will photograph the traps and forward the images to the Internet, where the pest insect’s appearance will be predicted by image analysis. Weather conditions, temperature and relative humidity are the parameters that affect the appearance of some pests, such as Helicoverpa armigera. This paper presents a model of machine learning that can predict the appearance of insects during a season on a daily basis, taking into account the air temperature and relative humidity. Several machine learning algorithms for classification were applied and their accuracy for the prediction of insect occurrence was presented (up to 76.5%). Since the data used for testing were given in chronological order according to the days when the measurement was performed, the existing model was expanded to take into account the periods of three and five days. The extended method showed better accuracy of prediction and a lower percentage of false detections. In the case of a period of five days, the accuracy of the affected detections was 86.3%, while the percentage of false detections was 11%. The proposed model of machine learning can help farmers to detect the occurrence of pests and save the time and resources needed to check the fields.
- ItemLow Complexity Radar Gesture Recognition Using Synthetic Training Data(Basel : MDPI, 2022) Zhao, Yanhua; Sark, Vladica; Krstic, Milos; Grass, EckhardDevelopments in radio detection and ranging (radar) technology have made hand gesture recognition feasible. In heat map-based gesture recognition, feature images have a large size and require complex neural networks to extract information. Machine learning methods typically require large amounts of data and collecting hand gestures with radar is time- and energy-consuming. Therefore, a low computational complexity algorithm for hand gesture recognition based on a frequency-modulated continuous-wave (FMCW) radar and a synthetic hand gesture feature generator are proposed. In the low computational complexity algorithm, two-dimensional Fast Fourier Transform is implemented on the radar raw data to generate a range-Doppler matrix. After that, background modelling is applied to separate the dynamic object and the static background. Then a bin with the highest magnitude in the range-Doppler matrix is selected to locate the target and obtain its range and velocity. The bins at this location along the dimension of the antenna can be utilised to calculate the angle of the target using Fourier beam steering. In the synthetic generator, the Blender software is used to generate different hand gestures and trajectories and then the range, velocity and angle of targets are extracted directly from the trajectory. The experimental results demonstrate that the average recognition accuracy of the model on the test set can reach 89.13% when the synthetic data are used as the training set and the real data are used as the test set. This indicates that the generation of synthetic data can make a meaningful contribution in the pre-training phase.
- ItemNeuromorphic on-chip recognition of saliva samples of COPD and healthy controls using memristive devices([London] : Macmillan Publishers Limited, part of Springer Nature, 2020) Zarrin, Pouya Soltani; Zahari, Finn; Mahadevaiah, Mamathamba K.; Perez, Eduardo; Kohlstedt, Hermann; Wenger, ChristianChronic Obstructive Pulmonary Disease (COPD) is a life-threatening lung disease, affecting millions of people worldwide. Implementation of Machine Learning (ML) techniques is crucial for the effective management of COPD in home-care environments. However, shortcomings of cloud-based ML tools in terms of data safety and energy efficiency limit their integration with low-power medical devices. To address this, energy efficient neuromorphic platforms can be used for the hardware-based implementation of ML methods. Therefore, a memristive neuromorphic platform is presented in this paper for the on-chip recognition of saliva samples of COPD patients and healthy controls. Results of its performance evaluations showed that the digital neuromorphic chip is capable of recognizing unseen COPD samples with accuracy and sensitivity values of 89% and 86%, respectively. Integration of this technology into personalized healthcare devices will enable the better management of chronic diseases such as COPD. © 2020, The Author(s).
- ItemAnalogue pattern recognition with stochastic switching binary CMOS-integrated memristive devices([London] : Macmillan Publishers Limited, part of Springer Nature, 2020) Zahari, Finn; Pérez, Eduardo; Mahadevaiah, Mamathamba Kalishettyhalli; Kohlstedt, Hermann; Wenger, Christian; Ziegler, MartinBiological neural networks outperform current computer technology in terms of power consumption and computing speed while performing associative tasks, such as pattern recognition. The analogue and massive parallel in-memory computing in biology differs strongly from conventional transistor electronics that rely on the von Neumann architecture. Therefore, novel bio-inspired computing architectures have been attracting a lot of attention in the field of neuromorphic computing. Here, memristive devices, which serve as non-volatile resistive memory, are employed to emulate the plastic behaviour of biological synapses. In particular, CMOS integrated resistive random access memory (RRAM) devices are promising candidates to extend conventional CMOS technology to neuromorphic systems. However, dealing with the inherent stochasticity of resistive switching can be challenging for network performance. In this work, the probabilistic switching is exploited to emulate stochastic plasticity with fully CMOS integrated binary RRAM devices. Two different RRAM technologies with different device variabilities are investigated in detail, and their potential applications in stochastic artificial neural networks (StochANNs) capable of solving MNIST pattern recognition tasks is examined. A mixed-signal implementation with hardware synapses and software neurons combined with numerical simulations shows that the proposed concept of stochastic computing is able to process analogue data with binary memory cells. © 2020, The Author(s).
- ItemSemiconductor Gas Sensors: Materials, Technology, Design, and Application(Basel : MDPI, 2020) Nikolic, Maria Vesna; Milovanovic, Vladimir; Vasiljevic, Zorka Z.; Stamenkovic, ZoranThis paper presents an overview of semiconductor materials used in gas sensors, their technology, design, and application. Semiconductor materials include metal oxides, conducting polymers, carbon nanotubes, and 2D materials. Metal oxides are most often the first choice due to their ease of fabrication, low cost, high sensitivity, and stability. Some of their disadvantages are low selectivity and high operating temperature. Conducting polymers have the advantage of a low operating temperature and can detect many organic vapors. They are flexible but affected by humidity. Carbon nanotubes are chemically and mechanically stable and are sensitive towards NO and NH3, but need dopants or modifications to sense other gases. Graphene, transition metal chalcogenides, boron nitride, transition metal carbides/nitrides, metal organic frameworks, and metal oxide nanosheets as 2D materials represent gas-sensing materials of the future, especially in medical devices, such as breath sensing. This overview covers the most used semiconducting materials in gas sensing, their synthesis methods and morphology, especially oxide nanostructures, heterostructures, and 2D materials, as well as sensor technology and design, application in advance electronic circuits and systems, and research challenges from the perspective of emerging technologies. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.
- ItemStrain Engineered Electrically Pumped SiGeSn Microring Lasers on Si(Washington, DC : ACS, 2022) Marzban, Bahareh; Seidel, Lukas; Liu, Teren; Wu, Kui; Kiyek, Vivien; Zoellner, Marvin Hartwig; Ikonic, Zoran; Schulze, Joerg; Grützmacher, Detlev; Capellini, Giovanni; Oehme, Michael; Witzens, Jeremy; Buca, DanSiGeSn holds great promise for enabling fully group-IV integrated photonics operating at wavelengths extending in the mid-infrared range. Here, we demonstrate an electrically pumped GeSn microring laser based on SiGeSn/GeSn heterostructures. The ring shape allows for enhanced strain relaxation, leading to enhanced optical properties, and better guiding of the carriers into the optically active region. We have engineered a partial undercut of the ring to further promote strain relaxation while maintaining adequate heat sinking. Lasing is measured up to 90 K, with a 75 K T0. Scaling of the threshold current density as the inverse of the outer circumference is linked to optical losses at the etched surface, limiting device performance. Modeling is consistent with experiments across the range of explored inner and outer radii. These results will guide additional device optimization, aiming at improving electrical injection and using stressors to increase the bandgap directness of the active material.