2022, Wang, Lei, Torkamanzadeh, Mohammad, Majed, Ahmad, Zhang, Yuan, Wang, Qingsong, Breitung, Ben, Feng, Guang, Naguib, Michael, Presser, Volker

Electrochemical ion separation is a promising technology to recover valuable ionic species from water. Pseudocapacitive materials, especially 2D materials, are up-and-coming electrodes for electrochemical ion separation. For implementation, it is essential to understand the interplay of the intrinsic preference of a specific ion (by charge/size), kinetic ion preference (by mobility), and crystal structure changes. Ti3C2Tz MXene is chosen here to investigate its selective behavior toward alkali and alkaline earth cations. Utilizing an online inductively coupled plasma system, it is found that Ti3C2Tz shows a time-dependent selectivity feature. In the early stage of charging (up to about 50 min), K+ is preferred, while ultimately Ca2+ and Mg2+ uptake dominate; this unique phenomenon is related to dehydration energy barriers and the ion exchange effect between divalent and monovalent cations. Given the wide variety of MXenes, this work opens the door to a new avenue where selective ion-separation with MXene can be further engineered and optimized.

2022, Faye, Babacar, Webber, Heidi, Gaiser, Thomas, Müller, Christoph, Zhang, Yinan, Stella, Tommaso, Latka, Catharina, Reckling, Moritz, Heckelei, Thomas, Helming, Katharina, Ewert, Frank

Most large scale studies assessing climate change impacts on crops are performed with simulations of single crops and with annual re-initialization of the initial soil conditions. This is in contrast to the reality that crops are grown in rotations, often with sizable proportion of the preceding crop residue to be left in the fields and varying soil initial conditions from year to year. In this study, the sensitivity of climate change impacts on crop yield and soil organic carbon to assumptions about annual model re-initialization, specification of crop rotations and the amount of residue retained in fields was assessed for seven main crops across Europe. Simulations were conducted for a scenario period 2040–2065 relative to a baseline from 1980 to 2005 using the SIMPLACE1 framework. Results indicated across Europe positive climate change impacts on yield for C3 crops and negative impacts for maize. The consideration of simulating rotations did not have a benefit on yield variability but on relative yield change in response to climate change which slightly increased for C3 crops and decreased for C4 crops when rotation was considered. Soil organic carbon decreased under climate change in both simulations assuming a continuous monocrop and plausible rotations by between 1% and 2% depending on the residue management strategy.

2022, Karimi, Hadi, Navid, Hossein, Dammer, Karl-Heinz

Because of insufficient effectiveness after herbicide application in autumn, bur chervil (Anthriscus caucalis M. Bieb.) is often present in cereal fields in spring. A second reason for spreading is the warm winter in Europe due to climate change. This weed continues to germinate from autumn to spring. To prevent further spreading, a site-specific control in spring is reasonable. Color imagery would offer cheap and complete monitoring of entire fields. In this study, an end-to-end fully convolutional network approach is presented to detect bur chervil within color images. The dataset consisted of images taken at three sampling dates in spring 2018 in winter wheat and at one date in 2019 in winter rye from the same field. Pixels representing bur chervil were manually annotated in all images. After a random image augmentation was done, a Unet-based convolutional neural network model was trained using 560 (80%) of the sub-images from 2018 (training images). The power of the trained model at the three different sampling dates in 2018 was evaluated at 141 (20%) of the manually annotated sub-images from 2018 and all (100%) sub-images from 2019 (test images). Comparing the estimated and the manually annotated weed plants in the test images the Intersection over Union (Jaccard index) showed mean values in the range of 0.9628 to 0.9909 for the three sampling dates in 2018, and a value of 0.9292 for the one date in 2019. The Dice coefficients yielded mean values in the range of 0.9801 to 0.9954 for 2018 and a value of 0.9605 in 2019.

2022, Ji, Ran, Zhang, Zongbao, Hofstetter, Yvonne J., Buschbeck, Robin, Hänisch, Christian, Paulus, Fabian, Vaynzof, Yana

Modern photovoltaic devices are often based on a heterojunction structure where two components with different optoelectronic properties are interfaced. The properties of each side of the junction can be tuned by either utilizing different materials (for example, donor/acceptor) or doping (for example, p–n junction) or even varying their dimensionality (for example, 3D/2D). Here we demonstrate the concept of phase heterojunction (PHJ) solar cells by utilizing two polymorphs of the same material. We demonstrate the approach by forming γ-CsPbI3/β-CsPbI3 perovskite PHJ solar cells. We find that all of the photovoltaic parameters of the PHJ device significantly surpass those of each of the single-phase devices, resulting in a maximum power conversion efficiency of 20.1%. These improvements originate from the efficient passivation of the β-CsPbI3 by the larger bandgap γ-CsPbI3, the increase in the built-in potential of the PHJ devices enabled by the energetic alignment between the two phases and the enhanced absorption of light by the PHJ structure. The approach demonstrated here offers new possibilities for the development of photovoltaic devices based on polymorphic materials.

2022, Huang, Yang, Lu, Qiongqiong, Wu, Dianlun, Jiang, Yue, Liu, Zhenjie, Chen, Bin, Zhu, Minshen, Schmidt, Oliver G.

MXenes add dozens of metallic conductors to the family of two-dimensional (2D) materials. A top-down synthesis approach removing A-layer atoms (e.g., Al, Si, and Ga) in MAX phases to produce 2D flakes attaches various surface terminations to MXenes. With these terminations, MXenes show tunable properties, promising a range of applications from energy storage devices to electronics, including sensors, transistors, and antennas. MXenes are also excellent building blocks to create flexible films used for flexible and wearable devices. This article summarizes the synthesis of MXene flakes and highlights aspects that need attention for flexible devices. Rather than listing the development of energy storage devices in detail, we focus on the main challenges of and solutions for constructing high-performance devices. Moreover, we show the applications of MXene films in electronics to call on designs to construct a complete system based on MXene with good flexibility, which consists of a power source, sensors, transistors, and wireless communications.

2022, Salamut, Christian, Kohnert, Iris, Landwehr, Niels, Pflanz, Michael, Schirrmann, Michael, Zare, Mohammad

Insect populations appear with a high spatial, temporal and type-specific diversity in orchards. One of the many monitoring tools for pest management is the manual assessment of sticky traps. However, this type of assessment is laborious and time-consuming so that only a few locations can be controlled in an orchard. The aim of this study is to test state-of-the art object detection algorithms from deep learning to automatically detect cherry fruit flies (Rhagoletis cerasi), a common insect pest in cherry plantations, within images from yellow sticky traps. An image annotation database was built with images taken from yellow sticky traps with more than 1600 annotated cherry fruit flies. For better handling in the computational algorithms, the images were augmented to smaller ones by the known image preparation methods “flipping” and “cropping” before performing the deep learning. Five deep learning image recognition models were tested including Faster Region-based Convolutional Neural Network (R-CNN) with two different methods of pretraining, Single Shot Detector (SSD), RetinaNet, and You Only Look Once version 5 (YOLOv5). R‑CNN and RetinaNet models outperformed other ones with a detection average precision of 0.9. The results indicate that deep learning can act as an integral component of an automated system for high-throughput assessment of pest insects in orchards. Therefore, this can reduce the time for repetitive and laborious trap assessment but also increase the observed amount of sticky traps

2022, Wadas, Sonja Halina, von Hartmann, Hartwig

The Molasse Basin is one of the most promising areas for deep geothermal exploitation in Germany and the target horizon is the aquifer in the Upper Jurassic carbonates. Carbonate deposits can be very heterogeneous even over a small area due to diagenetic processes and varying depositional environments. The preferential targets for geothermal exploitation in carbonate deposits are fault zones, reef facies and karstified areas, since they are expected to act as hydraulically permeable zones due to high porosity and high permeability. Therefore, identifying these structures and characterizing, e.g., their internal porosity distribution are of high importance. This can be accomplished using 3D reflection seismic data. Besides structural information, 3D seismic surveys provide important reservoir properties, such as acoustic impedance, from which a porosity model can be derived. In our study area in Munich we carried out a seismic amplitude inversion to get an acoustic impedance model of the Upper Jurassic carbonate reservoir using a 3D seismic data set, a corresponding structural geological model, and logging data from six wells at the ‘Schäftlarnstraße’ geothermal site. The impedance model and porosity logs were than used to calculate a porosity model. The model shows a wide porosity range from 0 to 20% for the entire reservoir zone and the lithology along the wells reveals that dolomitic limestone has the highest porosities and calcareous dolomite has the lowest porosities. The study area is cut by a large W–E striking fault, the Munich Fault, and the footwall north of it shows higher porosities and more intense karstification than the hanging wall to the south. Considering the entire study area, an increase in porosity from east to west is observed. Furthermore, we identified a complex porosity distribution in reef buildups and pinnacle reefs. The reef cores have mostly low porosities of, e.g., < 3% and the highest porosities of up to 7 to 14% are observed at the reef caps and on the reef slopes. The reef slopes show a characteristic interfingering of the reef facies with the surrounding bedded facies, which indicates a syn-sedimentary reef development with slightly varying build up growth rates. We also assessed the reservoir quality with regard to porosity distribution and determined areas with moderate to good quality for geothermal exploitation by defining porosity evaluation levels. The porosity evaluation maps show that the carbonate rocks of Berriasian to Malm ζ1 are preferential targets for exploitation, especially in the footwall of the Munich Fault and to the west of the hanging wall, because these areas are characterized by high porosities due to intense karstification of bedded and massive facies, although the latter is mainly restricted to reef caps and reef slopes.

2022, Sen, Ömer, van der Velde, Dennis, Lühman, Maik, Sprünken, Florian, Hacker, Immanuel, Ulbig, Andreas, Andres, Michael, Henze, Martin

The transformation of power grids into intelligent cyber-physical systems brings numerous benefits, but also significantly increases the surface for cyber-attacks, demanding appropriate countermeasures. However, the development, validation, and testing of data-driven countermeasures against cyber-attacks, such as machine learning-based detection approaches, lack important data from real-world cyber incidents. Unlike attack data from real-world cyber incidents, infrastructure knowledge and standards are accessible through expert and domain knowledge. Our proposed approach uses domain knowledge to define the behavior of a smart grid under non-attack conditions and detect attack patterns and anomalies. Using a graph-based specification formalism, we combine cross-domain knowledge that enables the generation of whitelisting rules not only for statically defined protocol fields but also for communication flows and technical operation boundaries. Finally, we evaluate our specification-based intrusion detection system against various attack scenarios and assess detection quality and performance. In particular, we investigate a data manipulation attack in a future-orientated use case of an IEC 60870-based SCADA system that controls distributed energy resources in the distribution grid. Our approach can detect severe data manipulation attacks with high accuracy in a timely and reliable manner.

2022, Alirezazadeh, Pendar, Schirrmann, Michael, Stolzenburg, Frieder

In recent years, deep learning-based plant disease classification has been widely developed. However, it is challenging to collect sufficient annotated image data to effectively train deep learning models for plant disease recognition. The attention mechanism in deep learning assists the model to focus on the informative data segments and extract the discriminative features of inputs to enhance training performance. This paper investigates the Convolutional Block Attention Module (CBAM) to improve classification with CNNs, which is a lightweight attention module that can be plugged into any CNN architecture with negligible overhead. Specifically, CBAM is applied to the output feature map of CNNs to highlight important local regions and extract more discriminative features. Well-known CNN models (i.e. EfficientNetB0, MobileNetV2, ResNet50, InceptionV3, and VGG19) were applied to do transfer learning for plant disease classification and then fine-tuned by a publicly available plant disease dataset of foliar diseases in pear trees called DiaMOS Plant. Amongst others, this dataset contains 3006 images of leaves affected by different stress symptoms. Among the tested CNNs, EfficientNetB0 has shown the best performance. EfficientNetB0+CBAM has outperformed EfficientNetB0 and obtained 86.89% classification accuracy. Experimental results show the effectiveness of the attention mechanism to improve the recognition accuracy of pre-trained CNNs when there are few training data.

2022, Dammer, Karl-Heinz

At the beginning of an epidemic, the Colorado beetle occur sparsely on few potato plants in the field. A target-orientated crop protection applies insecticides only on infested plants. For this, a complete monitoring of the whole field is required, which can be done by camera-sensors attached to tractors or unmanned aerial vehicles (UAVs). The gathered images have to be analyzed using appropriate classification methods preferably in real-time to recognize the different stages of the beetle in high precision. In the paper, the methodology of the application of one multispectral and three commercially available color cameras (RGB) and the results from field tests for recognizing the development stages of the beetle along the vegetation period of the potato crop are presented. Compared to multispectral cameras color cameras are low-cost. The use of artificial neural network for classification of the larvae within the RGB-images are discussed. At the bottom side of the potato leaves the eggs are deposited. Sensor based monitoring from above the crop canopy cannot detect the eggs and the hatching first instar. The ATB developed a camera equipped vertical sensor for scanning the bottom of the leaves. This provide a time advantage for the spray decision of the farmer (e.g. planning of the machine employment, purchase of insecticides). In this paper, example images and a possible future use of the presented monitoring methods above and below the crop surface are presented and discussed.