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OpenTextile-NIR: Near-infrared hyperspectral imaging and photography dataset for optical identification of textiles
No full textSee description at dataset page in Zenodo: https://zenodo.org/records/1826917
Production of high-concentration CO2 from electrified limestone calcination for carbon capture applications
Get PDFOperating electrically heated kilns under high-CO2 atmospheres can increase CO2 capture efficiency but creates reducing conditions that drive CO formation. In this work, CO generation during limestone calcination in a 280 kW electrically heated rotary kiln at 75 vol-% CO2 and low O2 concentration is investigated. Equilibrium calculations indicate that sulphide and sulphite phases in limestone decompose, releasing SO2 and promoting CO formation. Complementary packed-bed experiments confirm that sulphur species are a major CO promoter and reveal a synergistic interaction between sulphur compounds and elevated CO2 levels. Using low-sulphur limestone could suppress CO emissions. Where low-sulphur feedstocks are unavailable, targeted electrolytic O2 or air injection coupled with indirect limestone preheating is proposed to strip sulphur and preserve the high-purity CO2 stream which will improve the efficiency of electrified kilns integrated with a carbon capture process
Large Eddy Simulation of environmental impacts on mass transport in laboratory-scale vertical farm
No full textThe impact of environmental factors on airflow and mass transport within a laboratory-scale vertical farm is investigated using Computational Fluid Dynamics. Large Eddy Simulation models complex airflow behaviour, while solving enthalpy and mass transport equations yields temperature, humidity, and CO2 concentration. The Eulerian-Lagrangian approach simulates the free-fall of water droplets in the dehumidifier-cooling system. Humidity and CO2 consumption/production by plants and utilities are modelled as volumetric sources/sinks. An experimental campaign is conducted to measure temperature, relative humidity, and CO2 above cultivation beds, validating the numerical setup with mean absolute errors of 0.8%, 2.2%, and 3.9%, respectively. Analysing the airflow shows that the free fall of droplets is the dominant mechanism driving airflow characteristics. We investigate the effects of wall confinement, number of lamps, and location of lamps on the mass transport. Curtains were used to divide each cultivation bed into three regions to assess the wall confinement effect. Results show the overall adverse effect of curtains on mass transport. In more detail, mass transport is enhanced when the curtains and streamlines are aligned parallel, whereas it is reduced when they are perpendicular. Increasing the number of operative lamps improves the uniformity of mass distribution on the upper cultivation beds due to a stronger positive buoyancy. Positioning lamp-induced buoyant flow within the droplet’s lateral momentum injection zone further enhances vertical mass transport. These findings highlight the impact of environmental factors on mass transport, offering insights for more efficient designs of indoor vertical farms.</p
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
Get PDFThe 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
Get PDFStable 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
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