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The development of base metal electrodes that can act as active and stable oxygen generating electrodes in water electrolysis systems, especially at low pH levels, remains a challenge. The use of suspensions as electrolytes for water splitting has until recently been limited to photoelectrocatalytic approaches. A high current density (j=30 mA/cm2) for water electrolysis has been achieved at a very low oxygen evolution reaction (OER) potential (E=1.36 V vs. RHE) using a SnO2/H2SO4 suspension-based electrolyte in combination with a steel anode. More importantly, the high charge-to-oxygen conversion rate (Faraday efficiency of 88% for OER at j=10 mA/cm2 current density). Since cyclic voltammetry (CV) experiments show that oxygen evolution starts at a low, but not exceptionally low, potential, the reason for the low potential in chronoamperometry (CP) tests is an increase in the active electrode area, which has been confirmed by various experiments. For the first time, the addition of a relatively small amount of solids to a clear electrolyte has been shown to significantly reduce the overpotential of the OER in water electrolysis down to the 100 mV region, resulting in a remarkable reduction in anode wear while maintaining a high current density.
Durch die Auswirkungen des Klimawandels – besonders durch Hitze – geraten viele indigene Baumarten innerhalb der nächsten 75 Jahre voraussichtlich an den Rand ihrer Existenz. Der Stadtstandort stellt eine zusätzliche Herausforderung dar, der durch menschliche Aktivitäten negativ, aber auch positiv beeinflusst werden kann. Besonders die Wasserverfügbarkeit kann durch geeignete vegetationstechnische Maßnahmen und intelligente Profilierung von Oberflächen befördert werden. Die Vegetation wird sich verändern. Mit gebietseinheimischen Genotypen und natürlicher Migration hitzeverträglicher Arten alleine lassen sich unsere Probleme nicht lösen. Wir brauchen Bäume in der Stadt, die beschatten und verdunsten.
Lösungsansätze sind die vielfältige Anpflanzung hitzeresistenter Genotypen indigener Arten, neuer, submediterraner Arten aus Süd- und Südosteuropa (assisted migration) sowie klimatoleranter Arten anderer Kontinente. Es ist allerdings davon auszugehen, dass sich diese Arten dann bei uns auch etablieren werden. Und das ist bei der durch die Eiszeiten verarmten Gehölzflora Mitteleuropas und für lebenswerte Städte auch gut so!
Aufgrund vermuteter, negativer Folgen, welche von der Etablierung und Ausbreitung gebietsfremder Art ausgehen können, ist um die Verwendung von Zukunftsbäumen im städtischen Raum eine Diskussion entstanden. Im Rahmen einer Forschungsarbeit an der Hochschule Osnabrück wurde daher untersucht, wie die Ausbreitungstendenz einzelner Arten von Mitarbeiter*innen in Grünflächenämtern und Botanischen Garten eingeschätzt wird.
Chlorophyllfluoreszenz als Werkzeug für die DCA-Lagerung von Äpfeln (Malus x domestica BORKH.)
(2024)
Hyperhydricity (HH) is one of the most important physiological disorders that negatively affects various plant tissue culture techniques. The objective of this study was to characterize optical features to allow an automated detection of HH. For this purpose, HH was induced in two plant species, apple and Arabidopsis thaliana, and the severity was quantified based on visual scoring and determination of apoplastic liquid volume. The comparison between the HH score and the apoplastic liquid volume revealed a significant correlation, but different response dynamics. Corresponding leaf reflectance spectra were collected and different approaches of spectral analyses were evaluated for their ability to identify HH-specific wavelengths. Statistical analysis of raw spectra showed significantly lower reflection of hyperhydric leaves in the VIS, NIR and SWIR region. Application of the continuum removal hull method to raw spectra identified HH-specific absorption features over time and major absorption peaks at 980 nm, 1150 nm, 1400 nm, 1520 nm, 1780 nm and 1930 nm for the various conducted experiments. Machine learning (ML) model spot checking specified the support vector machine to be most suited for classification of hyperhydric explants, with a test accuracy of 85% outperforming traditional classification via vegetation index with 63% test accuracy and the other ML models tested. Investigations on the predictor importance revealed 1950 nm, 1445 nm in SWIR region and 415 nm in the VIS region to be most important for classification. The validity of the developed spectral classifier was tested on an available hyperspectral image acquisition in the SWIR-region.
Background
The current development of sensor technologies towards ever more cost-effective and powerful systems is steadily increasing the application of low-cost sensors in different horticultural sectors. In plant in vitro culture, as a fundamental technique for plant breeding and plant propagation, the majority of evaluation methods to describe the performance of these cultures are based on destructive approaches, limiting data to unique endpoint measurements. Therefore, a non-destructive phenotyping system capable of automated, continuous and objective quantification of in vitro plant traits is desirable.
Results
An automated low-cost multi-sensor system acquiring phenotypic data of plant in vitro cultures was developed and evaluated. Unique hardware and software components were selected to construct a xyz-scanning system with an adequate accuracy for consistent data acquisition. Relevant plant growth predictors, such as projected area of explants and average canopy height were determined employing multi-sensory imaging and various developmental processes could be monitored and documented. The validation of the RGB image segmentation pipeline using a random forest classifier revealed very strong correlation with manual pixel annotation. Depth imaging by a laser distance sensor of plant in vitro cultures enabled the description of the dynamic behavior of the average canopy height, the maximum plant height, but also the culture media height and volume. Projected plant area in depth data by RANSAC (random sample consensus) segmentation approach well matched the projected plant area by RGB image processing pipeline. In addition, a successful proof of concept for in situ spectral fluorescence monitoring was achieved and challenges of thermal imaging were documented. Potential use cases for the digital quantification of key performance parameters in research and commercial application are discussed.
Conclusion
The technical realization of “Phenomenon” allows phenotyping of plant in vitro cultures under highly challenging conditions and enables multi-sensory monitoring through closed vessels, ensuring the aseptic status of the cultures. Automated sensor application in plant tissue culture promises great potential for a non-destructive growth analysis enhancing commercial propagation as well as enabling research with novel digital parameters recorded over time.
Klimabäume scheinen in der Mitte der Gesellschaft angekommen zu sein. Zahlreiche Empfehlungen finden sich in Onlinemedien und stellen die fünf oder zehn besten Baumarten vor. Dabei gibt es Schnittmengen. Interessanterweise enthält das Sortiment weitgehend bekannte und auch heute schon verwendete Arten. Es steht eine breite Palette an zukunftsfähigen Klimabäumen zur Verfügung, die teilweise noch gar nicht beachtet werden. In zwei Teilen sollen bekannte und weniger bekannte Klimabäume bezüglich ihrer Verwendbarkeit im urbanen Raum, ihrer Herkunft und ihres Ausbreitungspotenzials diskutiert werden.
Semi-natural grasslands (SNGs) are an essential part of European cultural landscapes. They are an important habitat for many animal and plant species and offer a variety of ecological functions. Diverse plant communities have evolved over time depending on environmental and management factors in grasslands. These different plant communities offer multiple ecosystem services and also have an effect on the forage value of fodder for domestic livestock. However, with increasing intensification in agriculture and the loss of SNGs, the biodiversity of grasslands continues to decline. In this paper, we present a method to spatially classify plant communities in grasslands in order to identify and map plant communities and weed species that occur in a semi-natural meadow. For this, high-resolution multispectral remote sensing data were captured by an unmanned aerial vehicle (UAV) in regular intervals and classified by a convolutional neural network (CNN). As the study area, a heterogeneous semi-natural hay meadow with first- and second-growth vegetation was chosen. Botanical relevés of fixed plots were used as ground truth and independent test data. Accuracies up to 88% on these independent test data were achieved, showing the great potential of the usage of CNNs for plant community mapping in high-resolution UAV data for ecological and agricultural applications.
Fütterung auf dem Prüfstand
(2023)
Weide für Trockensteher?
(2023)