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Permafrost is warming in the northern high latitudes, inducing highly dynamic thaw-related permafrost disturbances across the terrestrial Arctic. Monitoring and tracking of permafrost disturbances is important as they impact surrounding landscapes, ecosystems and infrastructure. Remote sensing provides the means to detect, map, and quantify these changes homogeneously across large regions and time scales. Existing Landsat-based algorithms assess different types of disturbances with similar spatiotemporal requirements. However, Landsat-based analyses are restricted in northern high latitudes due to the long repeat interval and frequent clouds, in particular at Arctic coastal sites. We therefore propose to combine Landsat and Sentinel-2 data for enhanced data coverage and present a combined annual mosaic workflow, expanding currently available algorithms, such as LandTrendr, to achieve more reliable time series analysis. We exemplary test the workflow for twelve sites across the northern high latitudes in Siberia. We assessed the number of images and cloud-free pixels, the spatial mosaic coverage and the mosaic quality with spectral comparisons. The number of available images increased steadily from 1999 to 2019 but especially from 2016 onward with the addition of Sentinel-2 images. Consequently, we have an increased number of cloud-free pixels even under challenging environmental conditions, which then serve as the input to the mosaicking process. In a comparison of annual mosaics, the Landsat+Sentinel-2 mosaics always fully covered the study areas (99.9–100 %), while Landsat-only mosaics contained data-gaps in the same years, only reaching coverage percentages of 27.2 %, 58.1 %, and 69.7 % for Sobo Sise, East Taymyr, and Kurungnakh in 2017, respectively. The spectral comparison of Landsat image, Sentinel-2 image, and Landsat+Sentinel-2 mosaic showed high correlation between the input images and mosaic bands (e.g., for Kurungnakh 0.91–0.97 between Landsat and Landsat+Sentinel-2 mosaic and 0.92–0.98 between Sentinel-2 and Landsat+Sentinel-2 mosaic) across all twelve study sites, testifying good quality mosaic results. Our results show that especially the results for northern, coastal areas was substantially improved with the Landsat+Sentinel-2 mosaics. By combining Landsat and Sentinel-2 data we accomplished to create reliably high spatial resolution input mosaics for time series analyses. Our approach allows to apply a high temporal continuous time series analysis to northern high latitude permafrost regions for the first time, overcoming substantial data gaps, and assess permafrost disturbance dynamics on an annual scale across large regions with algorithms such as LandTrendr by deriving the location, timing and progression of permafrost thaw disturbances
Electrochemical methods offer the simple characterization of the synthesis of molecularly imprinted polymers (MIPs) and the readouts of target binding. The binding of electroinactive analytes can be detected indirectly by their modulating effect on the diffusional permeability of a redox marker through thin MIP films. However, this process generates an overall signal, which may include nonspecific interactions with the nonimprinted surface and adsorption at the electrode surface in addition to (specific) binding to the cavities. Redox-active low-molecular-weight targets and metalloproteins enable a more specific direct quantification of their binding to MIPs by measuring the faradaic current. The in situ characterization of enzymes, MIP-based mimics of redox enzymes or enzyme-labeled targets, is based on the indication of an electroactive product. This approach allows the determination of both the activity of the bio(mimetic) catalyst and of the substrate concentration.
Electrochemical biosensors employing natural and artificial heme peroxidases on semiconductors
(2020)
Heme peroxidases are widely used as biological recognition elements in electrochemical biosensors for hydrogen peroxide and phenolic compounds. Various nature-derived and fully synthetic heme peroxidase mimics have been designed and their potential for replacing the natural enzymes in biosensors has been investigated. The use of semiconducting materials as transducers can thereby offer new opportunities with respect to catalyst immobilization, reaction stimulation, or read-out. This review focuses on approaches for the construction of electrochemical biosensors employing natural heme peroxidases as well as various mimics immobilized on semiconducting electrode surfaces. It will outline important advances made so far as well as the novel applications resulting thereof.
Nanoporous carbon materials (NCMs) provide the "function" of high specific surface area and thus have large interface area for interactions with surrounding species, which is of particular importance in applications related to adsorption processes. The strength and mechanism of adsorption depend on the pore architecture of the NCMs. In addition, chemical functionalization can be used to induce changes of electron density and/or electron density distribution in the pore walls, thus further modifying the interactions between carbons and guest species. Typical approaches for functionalization of nanoporous materials with regular atomic construction like porous silica, metal-organic frameworks, or zeolites, cannot be applied to NCMs due to their less defined local atomic construction and abundant defects. Therefore, synthetic strategies that offer a higher degree of control over the process of functionalization are needed. Synthetic approaches for covalent functionalization of NCMs, that is, for the incorporation of heteroatoms into the carbon backbone, are critically reviewed with a special focus on strategies following the concept "from molecules to materials." Approaches for coordinative functionalization with metallic species, and the functionalization by nanocomposite formation between pristine carbon materials and heteroatom-containing carbons, are introduced as well. Particular focus is given to the influences of these functionalizations in adsorption-related applications.
Cold plasma is considered to be a novel, non-thermal, chemical-free and eco-friendly disinfection and surface modification technology. Plasma treatment of air to generate the so called plasma processed air (PPA) induces the formation of reactive oxygen (ROS) and nitrogen species (RNS). As a result, PPA has a different chemical composition compared to untreated air and suits therefore as an alternative method for microbial disinfection. However, depending on the product properties of the food matrix and its composition, a number of plasmainduced reactions also need to be taken into consideration.
This necessitates also the elucidation and understanding of the basic interactions of plasma species with bioactive compounds. The intention here is to avoid the degradation of these valuable substances and to prevent other undesirable effects in future food related applications.
In the present study, the effects of PPA treatment on selected antioxidants such as pyrocatechol and derivatives of hydroxycinnimic acid were investigated in model systems to specify possible reactions induced. Antioxidant capacity, pH value, UV-Vis spectroscopy, RP-HPLC and LC-MS analysis were applied to identify reaction products providing information on possible changes induced in food matrices by PPA treatment.
Exposure to PPA caused a perceptible color change towards yellow-brown accompanied by a strong reduction of the pH and the formation of insoluble sediments in the model solutions. The accumulation of nitrate, nitrite, but not of hydrogen peroxide was shown. LC-MS analysis demonstrated the formation of plasma-modified derivatives in all tested systems. The main reactions in liquid model solutions exposed to PPA were attributed to oxidation, nitration and polymerization of the phenolic compounds.
Electrochemical methods offer the simple characterization of the synthesis of molecularly imprinted polymers (MIPs) and the readouts of target binding. The binding of electroinactive analytes can be detected indirectly by their modulating effect on the diffusional permeability of a redox marker through thin MIP films. However, this process generates an overall signal, which may include nonspecific interactions with the nonimprinted surface and adsorption at the electrode surface in addition to (specific) binding to the cavities. Redox-active low-molecular-weight targets and metalloproteins enable a more specific direct quantification of their binding to MIPs by measuring the faradaic current. The in situ characterization of enzymes, MIP-based mimics of redox enzymes or enzyme-labeled targets, is based on the indication of an electroactive product. This approach allows the determination of both the activity of the bio(mimetic) catalyst and of the substrate concentration.
Recent trends in ubiquitous computing have led to a proliferation of studies that focus on human activity recognition (HAR) utilizing inertial sensor data that consist of acceleration, orientation and angular velocity. However, the performances of such approaches are limited by the amount of annotated training data, especially in fields where annotating data is highly time-consuming and requires specialized professionals, such as in healthcare. In image classification, this limitation has been mitigated by powerful oversampling techniques such as data augmentation. Using this technique, this work evaluates to what extent transforming inertial sensor data into movement trajectories and into 2D heatmap images can be advantageous for HAR when data are scarce. A convolutional long short-term memory (ConvLSTM) network that incorporates spatiotemporal correlations was used to classify the heatmap images. Evaluation was carried out on Deep Inertial Poser (DIP), a known dataset composed of inertial sensor data. The results obtained suggest that for datasets with large numbers of subjects, using state-of-the-art methods remains the best alternative. However, a performance advantage was achieved for small datasets, which is usually the case in healthcare. Moreover, movement trajectories provide a visual representation of human activities, which can help researchers to better interpret and analyze motion patterns.
Nanoporous carbon materials (NCMs) provide the "function" of high specific surface area and thus have large interface area for interactions with surrounding species, which is of particular importance in applications related to adsorption processes. The strength and mechanism of adsorption depend on the pore architecture of the NCMs. In addition, chemical functionalization can be used to induce changes of electron density and/or electron density distribution in the pore walls, thus further modifying the interactions between carbons and guest species. Typical approaches for functionalization of nanoporous materials with regular atomic construction like porous silica, metal-organic frameworks, or zeolites, cannot be applied to NCMs due to their less defined local atomic construction and abundant defects. Therefore, synthetic strategies that offer a higher degree of control over the process of functionalization are needed. Synthetic approaches for covalent functionalization of NCMs, that is, for the incorporation of heteroatoms into the carbon backbone, are critically reviewed with a special focus on strategies following the concept "from molecules to materials." Approaches for coordinative functionalization with metallic species, and the functionalization by nanocomposite formation between pristine carbon materials and heteroatom-containing carbons, are introduced as well. Particular focus is given to the influences of these functionalizations in adsorption-related applications.
The usage of gamification in the contexts of commerce, consumption, innovation or eLearning in schools and universities has been extensively researched. However, the potentials of serious games to transfer and perpetuate knowledge and action patterns in learning factories have not been levered so far. The goal of this paper is to introduce a serious game as an instrument for knowledge transfer and perpetuation. Therefore, reqirements towards serious games in the context of learning factories are pointed out. As a result, that builds on these requirements, a serious learning game for the topic of Industry 4.0 is practically designed and evaluated.
Permafrost is warming in the northern high latitudes, inducing highly dynamic thaw-related permafrost disturbances across the terrestrial Arctic. Monitoring and tracking of permafrost disturbances is important as they impact surrounding landscapes, ecosystems and infrastructure. Remote sensing provides the means to detect, map, and quantify these changes homogeneously across large regions and time scales. Existing Landsat-based algorithms assess different types of disturbances with similar spatiotemporal requirements. However, Landsat-based analyses are restricted in northern high latitudes due to the long repeat interval and frequent clouds, in particular at Arctic coastal sites. We therefore propose to combine Landsat and Sentinel-2 data for enhanced data coverage and present a combined annual mosaic workflow, expanding currently available algorithms, such as LandTrendr, to achieve more reliable time series analysis. We exemplary test the workflow for twelve sites across the northern high latitudes in Siberia. We assessed the number of images and cloud-free pixels, the spatial mosaic coverage and the mosaic quality with spectral comparisons. The number of available images increased steadily from 1999 to 2019 but especially from 2016 onward with the addition of Sentinel-2 images. Consequently, we have an increased number of cloud-free pixels even under challenging environmental conditions, which then serve as the input to the mosaicking process. In a comparison of annual mosaics, the Landsat+Sentinel-2 mosaics always fully covered the study areas (99.9–100 %), while Landsat-only mosaics contained data-gaps in the same years, only reaching coverage percentages of 27.2 %, 58.1 %, and 69.7 % for Sobo Sise, East Taymyr, and Kurungnakh in 2017, respectively. The spectral comparison of Landsat image, Sentinel-2 image, and Landsat+Sentinel-2 mosaic showed high correlation between the input images and mosaic bands (e.g., for Kurungnakh 0.91–0.97 between Landsat and Landsat+Sentinel-2 mosaic and 0.92–0.98 between Sentinel-2 and Landsat+Sentinel-2 mosaic) across all twelve study sites, testifying good quality mosaic results. Our results show that especially the results for northern, coastal areas was substantially improved with the Landsat+Sentinel-2 mosaics. By combining Landsat and Sentinel-2 data we accomplished to create reliably high spatial resolution input mosaics for time series analyses. Our approach allows to apply a high temporal continuous time series analysis to northern high latitude permafrost regions for the first time, overcoming substantial data gaps, and assess permafrost disturbance dynamics on an annual scale across large regions with algorithms such as LandTrendr by deriving the location, timing and progression of permafrost thaw disturbances