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Production of nitroaryl secondary metabolites by wood-decaying fungi of<i> Phlebia</i> spp.
No full textFilamentous fungi produce secondary metabolites with multiple biochemical activities. For wood-decaying fungi of Basidiomycota, some of these compounds may act as redox-active mediators involved in biodegradation of lignocelluloses and biopolymers. Our aim was to identify natural aromatic compounds produced by white rot fungi of the genus Phlebia (Meruliaceae, Polyporales, Agaricomycetes), which comprises efficient decomposers of wood, wastes, and xenobiotics. Naturally produced aryl compounds were obtained by cultivating the fungi on a defined low-nitrogen liquid medium with glucose as carbon source. Culture supernatants were extracted and analyzed with UPLC-MS (ultra-performance liquid chromatography–mass spectrometry) and NMR (nuclear magnetic resonance). Enzyme assays, cultivation with 15N isotope–labeled nitrogen supplement, and aryl compound–feeding experiments were performed to assess biosynthesis mechanisms. Together with the well-known secondary metabolite veratryl alcohol and its enzymatic oxidation product veratraldehyde, we identified two nitroaryl derivatives, 6-nitroveratryl alcohol and 4-nitroveratrole, accumulating in culture supernatants of Phlebia spp. Cultivation of P. radiata isolate 2776 with NH4NO3 caused higher product yield of the nitroaryl compounds than 15NH4Cl supplementation, suggesting a role of nitrate ions in formation of nitroaryl products. With 15N-labeled supplementation, however, incorporation of nitrogen also from ammonium ions was observed. Although lignin peroxidase (LiP) enzyme activities correlated with appearance of nitroaryl compounds, their formation from veratryl alcohol by LiP was not accomplished in vitro in reaction mixtures with extracellular supernatants. In compound-feeding experiments, additional glycosylated derivative of 6-nitroveratryl alcohol was detected in P. radiata cultures, and nitroguaiacol was formed from nitroveratrole. These results indicate multiple pathways including both intra- and extracellular metabolism in biosynthesis and bioconversion of monoaromatic aryl compounds and their derivatives in fungi of Phlebia
Property Variations of Binder-Free Lignin-Rich Fiber Networks Driven by Forming Processes and Hot Pressing
No full textSheets made from lignin-rich fiber raw materials can be bonded by hot pressing without external binders. This paper explores how air-laid, foam-laid, and water-laid web formation methods, initial sheet moisture content, as well as hot-pressing conditions (5 MPa, 100–260 °C, 1–60 s), impact the physical properties of board-like materials made of chemi-thermomechanical softwood fibers. In addition to the structural characterization of the hot-pressed materials by X-ray microtomography, air permeance, water contact angle, dry and wet tensile strength, and in-plane compression properties were measured. Despite the significant structural densification, characteristics of the forming method were retained after hot pressing in the final sheet properties. The compressed air-laid sheets had the highest air permeance and the smallest mean pore size, which could be beneficial for particle filtering. At moderate pressing temperatures and times, the significant proportion of large pores in the foam-laid sheets made them weaker than the corresponding water-laid sheets. However, under extreme pressing conditions, the foam- and water-laid sheets reached similar values of high tensile and in-plane compression strength. This suggests that polymer interdiffusion becomes the dominant factor for material strength under these conditions, superimposing the hydrogen bonding created during aqueous forming
Self-supervised representation learning for cloud detection using Sentinel-2 images
Full text linkThe unavoidable presence of clouds and their shadows in optical satellite imagery hinders the true spectral response of the Earth underlying surface. Accurate cloud and cloud shadow detection is therefore a crucial preprocessing step for optical satellite images and any downstream analysis. Various methods have been developed to address this critical task and can be broadly categorized in physical rule-based methods and learning based methods. In recent years, machine learning based methods, particularly deep learning frameworks, have proven to outperform physical rule-based models. However, these approaches are mostly fully supervised and require a large amount of pixel-level annotations whose obtention is costly and time consuming. In this work, we propose to deal with cloud and cloud shadow detection in optical satellite images using self-supervised representation learning, a machine learning paradigm that focuses on extracting relevant representations from unlabeled data, which can then be used as an effective starting point to fine-tune models with few labeled data in a supervised fashion. These approaches were shown to perform competitively with fully supervised methods without the requirement of large annotation datasets. Particularly, we assessed two self-supervised representation learning methods that use different philosophies about self-supervision: Momentum Contrast (MoCo), based on contrastive learning and DeepCluster, based on clustering. Using two publicly available Sentinel-2 cloud datasets, namely WHUS2-CD+ and CloudSEN12, we show that MoCo and DeepCluster, trained with only 25% of the annotated data, can perform better than physical rule-based methods such as FMask and Sen2Cor, weakly supervised methods and even several fully supervised methods. These results point out the strong applicability of self-supervised representation learning methods to the task of cloud and cloud shadow detection with self-supervised pretraining leading to fine-tuned models that outperform industry standards and achieve near state-of-the-art performances with a fraction of the data
Investigating the failure mechanisms of screen-printed reference electrodes
Full text linkStable reference electrodes are essential for reliable electrochemical measurements, including in electroanalytical devices, and for continuous environmental monitoring in particular, yet many SPREs are optimised for short-term, disposable use and their stability over multi-day operation remains limited. In this work, we target continuous monitoring, on the timescale of hours to weeks, where a compact, low-cost reference capable of maintaining a stable potential over time without requiring recalibration is essential. This study builds on our previous work on SPREs with polydimethylsiloxane (PDMS) junctions by systematically investigating their degradation mechanisms and the factors controlling operational lifetime. SPREs were fabricated on polyethylene terephthalate (PET) substrates using a KCl/poly(vinyl acetate) (KCl/PVAc) electrolyte reservoir and a PDMS junction.Electrochemical characterisation demonstrated that depletion of the internal KCl reservoir is the dominant failure mechanism, with reference potential drift exceeding 1 mV h−1 once the electrolyte is no longer able to maintain saturation. Incorporating a PDMS junction markedly reduced Cl− leaching, extending operational lifetimes from <0.2 days to over 18 days in 3 M KCl solution. Electrochemical impedance spectroscopy and SEM–EDS analyses indicated that, beyond electrolyte depletion, localised AgCl degradation also contributes to long-term instability.By quantifying the relationship between electrolyte volume, chloride retention, and potential drift, this work establishes direct links between SPRE structure, composition, and performance. These insights support improved SPRE designs for continuous monitoring applications and highlight the importance of junction integrity, water-resistant polymer components, and reproducible fabrication
Qualification of affordable open-source analog and digital Sun sensors for CubeSats
No full textThis paper presents the qualification of affordable, open-source analog (PSS) and digital Sun sensors (DSS) for CubeSats, which aim to provide cost-effective and accessible attitude determination solutions for small satellite missions. The study evaluates the performance, reliability, and suitability of these sensors in space-like conditions, addressing key factors such as accuracy, thermal stability, and radiation tolerance. Experimental results demonstrate that the PSS can achieve better than 5° precision over a field of view of 100° while maintaining low costs and power consumption. The DSS shows a precision better than 0.5° over a field of view of 36° using a photolithographically patterned optical aperture acting as a pinhole. The research highlights the potential of these sensors to democratize access to space technology, supporting academic and commercial CubeSat missions with accessible and effective attitude determination solutions
Combining cellulose substrates and perovskites in sustainable solar cells is possible:a systematic literature review offering realistic solutions
No full textThe aim of this article is to provide direction for the advancement of cellulose films as sustainable substrates for perovskite solar cells (PSCs). Cellulose, the most abundant biopolymer on Earth, represents a viable, renewable alternative to glass and synthetic polymers when subjected to appropriate modifications. It can be customized via crosslinking, plasticization, and functionalization to increase flexibility and solvent resistance while decreasing gas permeation, surface roughness, and thermal expansion. The adoption of cellulose can drive transformative changes in PSC processing, facilitating the integration of sustainable electrode materials and greener alternatives to toxic solvents, as well as the replacement of high-temperature treatments. Although the literature contains numerous solutions to specific challenges, these findings are scattered across different fields and must be critically assessed for PSC suitability. In this article, we critically review alternative fabrication methods and form a step-by-step multidisciplinary strategy to alter both cellulose and PSC fabrication protocols for the development of sustainable next-generation solar cells.</p
Strategic niobium integration and thermomechanical processing in the advancement of novel CMnSiAlPMo TRIP-aided bainitic steel
No full textThis study examines the effects of niobium (Nb) addition and different thermomechanical controlled processing (TMCP) regimes on the flow stress behaviour and microstructure evolution of a newly developed CMnSiAlPMo TRIP-aided bainitic steel. TMCP tests were conducted with various hot deformation passes, followed by austempering at 400 °C for 10 min using a Gleeble 3800 thermomechanical simulator. Microstructures were analysed using scanning electron microscopy with electron backscattering diffraction and X-ray diffraction. Results showed that increasing the number of passes and reducing the final deformation temperature (FDT) enhanced the flow behaviour for both 0Nb and 0.05Nb alloys, with strain hardening being the dominant mechanism across all regimes. The four-pass regime with an FDT of 850 °C for the 0Nb alloy achieved the highest hardness (457 HV), attributed to grain refinement, which was more influential than the retained austenite fraction. For the 0.05Nb alloy, the two-pass regime at 1050 °C showed the highest hardness (428 HV), resulting from a lower retained austenite fraction. Additionally, Nb addition significantly refined the microstructure and increased the peak flow stress from 385 MPa to 421 MPa for the four-pass regime. The prior austenite grain size decreased from 23 to 12 μm in the single-pass regime, and the largest grain size in the cumulative grain size distribution (D90%) decreased from 8.45 to 7.49 μm.</p
From Leaves to Breezes: Machine learning based prediction of nitrogen dioxide concentration from surrounding urban greenery and meteorological, spatial, and traffic characteristics in Berlin, Germany
No full textThis study compares two machine learning models, a Random Forest (RF) and a spatial Graph Neural Network (GNN), for predicting nitrogen dioxide (NO) concentrations across diverse urban conditions in Berlin, Germany. Therefore, both models use information on local land-use characteristics, meteorological conditions, and seasonal greenery, which enables a post-hoc analysis of high-concentration scenarios under varying environmental factors. Unlike most previous approaches to air-pollution estimation, this study explicitly considers the interaction between urban greenery and its seasonal variation. The analysis is based on a self-curated, high-resolution site-level environmental dataset that captures hourly NO observations from sixteen monitoring stations across Berlin in 2023 with detailed land-use, traffic, and architectural data obtained from the Berlin Geoportal. This dataset is supplemented with multiple meteorological records from the Deutscher Wetterdienst (DWD). While both models achieve comparable accuracy (R 0.6), the GNN shows a tendency toward less variation of predictive accuracy across test sites, suggesting potential spatial robustness. For explainability, only the RF model allows for local interpretability via Shapley values, which indicate that urban greenery helps mitigate NO levels depending on seasonal changes in leaf area. However, additional statistical testing does not support this observed trend. Beyond the conducted assessment, this research contributes a comprehensive environmental dataset that links air quality, land-use, and meteorological variables at hourly resolution. This resource supports future investigations into how environmental and spatial factors jointly influence pollutant dispersion and decomposition in urban environments
Kiinnostus fuusioenergiaa kohtaan kasvaa
No full textFuusioenergiaa on pitkään pidetty tulevaisuuden suurena toivona - vaihtoehtona, jonka avulla voidaan tuottaa mittavia määriä puhdasta energiaa. Viime vuosina alalla on nähty enemmän kehitystä kuin useisiin aiempiin vuosikymmeniin yhteensä: uusia maailmanennätyksiä on syntynyt, ja yli 50 uutta yritystä on perustettu