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Tuning physical performance of gelatin-cellulose nanocrystals hydrogels
No full textStimuli-responsive hydrogels are interesting, particularly in the realm of biomedicals, but often the fundamental response of their key physical properties is not simultaneously monitored. Here, we investigated the pH response on the porosity, rheological behavior, mechanical performance, and molecular diffusivity of a hydrogel system composed of two bio-based components: gelatin and rod-like cellulose nanocrystals (CNCs). By leveraging the pH-responsive nature of gelatin, we systematically examined the structural properties of these hydrogels formed under three pH conditions: below (pH 5), above (pH 11), and at the isoelectric point (pH 8) of type A gelatin. All hydrogels exhibited a distinct cellular architecture, characterized by micron-scale tubular pores with embedded mesopores. Increasing pH upon the hydrogel crosslinking promoted the formation of more porous structures with significantly enhanced mechanical performance. The effect on the Young's modulus was significant: with a 3-fold increase compared to its counterparts, the hydrogel fabricated at pH 11 exhibited the stiffest structure. This improvement in hydrogel stiffness with pH further restricted the molecular diffusivity within the hydrogels to some extent, as evidenced by Fluorescence Recovery After Photobleaching analysis using fluorescein isothiocyanate-dextran as a diffusion probe. Overall, this study presents a straightforward and effective strategy for fabricating pH-tunable hydrogels, providing valuable insights for the design of responsive biomaterials with potential applications in soft tissue engineering and drug delivery.</p
Optimizing an iron- and manganese-based electrocatalyst for the oxygen evolution reaction in a proton exchange membrane electrolyzer
No full textThe development of electrocatalysts based on earth-abundant elements has gained significant attention due to the scarcity and high cost of Ir- and Ru-based materials typically used in proton exchange membrane electrolyzers (PEMELs). This study focuses on Fe-Mn-based catalysts for promoting the oxygen evolution reaction (OER), the sluggish four-electron process at the anode of PEMELs, where acidic conditions and high anodic potentials (1.6–2.0 V RHE) often accelerate corrosion. The catalysts were synthesized via a hydrothermal method and optimized using a response surface design of experiments (DOE), followed by detailed physicochemical characterization. The optimized composition ( [Figure presented] ) demonstrates good electrochemical activity and stability, maintaining performance over 10,000 potential cycles between 1.2 and 2.0 V RHE, with a moderate shift in the overpotential required to reach 10 mAcm −2 (from 1.78 V iR−corr vs RHE to 1.84 V iR−corr vs RHE). Inductively coupled plasma mass spectrometry of flow cells scanning (SFC-ICP-MS) confirms high stability at elevated potentials, showing reduced [Figure presented] to [Figure presented] oxidation. At lower potentials (≤1.4V RHE), dissolution signals indicate reductive leaching of Fe and Mn. Integration of the catalyst into a laboratory PEMEL demonstrate operational stability, sustaining 10 mAcm −2 at 50 °C over 80 h, with a 50 mV increase in iR-corrected potential (1.82 to 1.87 V).</p
Parametric study of sewage sludge gasification in air and steam environments:Experimental and process simulation
No full textGasification of sewage sludge (SS) is a thermochemical process which converts sludge into a value-added syngas, offering a sustainable alternative treatment to conventional disposal methods such as landfilling, land application, and incineration. This study investigates the gasification of dried sewage sludge in bubbling fluidized bed conditions, primarily focusing on the effects of key operating parameters such as bed temperature, equivalence ratio (ER) and steam-to-fuel ratio (S/F) on syngas composition. A total of 36 experiments were conducted, varying the bed temperatures (650 °C, 750 °C, and 850 °C), ER (0.2, 0.3, and 0.4) and S/F (0.5, 1, and 1.5). The results indicate a direct correlation between the bed temperature and the production of hydrogen and carbon monoxide, while carbon dioxide and methane concentrations decreased with the increasing the bed temperature. The optimum ER was found to be at 0.2, yielding the highest hydrogen and carbon monoxide production. Increasing S/F favored hydrogen generation through the water gas shift reaction (H2O(g) + C(s) → H2 + CO). In addition, experimental results were further validated using Aspen Plus process simulation, which exhibited matching trends in syngas composition.</p
An integrated approach to structure, texture and nutritional quality in high-moisture extruded meat analogues from faba protein concentrate and single-cell proteins
No full textThis study evaluated meat analogues using high-moisture extrusion (HME) using faba protein concentrate (FPC) alone (Control) and blends with single-cell proteins (SCPs): microalgae Chlorella vulgaris (SCP1) and bacteria Xanthobacter spp. (SCP2). Three blends were formulated via linear programming based on the beneficial nutrients content in meat (beef, pork and chicken): Blend1 (60% FPC + 40% SCP1), Blend2 (22.5% FPC + 77.5% SCP2), and Blend3 (13.5% FPC + 11% SCP1 + 75.5% SCP2). Composition, texture, phytic acid and in vitro digestibility analyses assessed protein quality and mineral bioaccessibility. Samples were oven cooked before assays to simulate typical consumption. Cooking caused minor structural changes, without significantly affecting protein denaturation or phytic acid levels, as extrusion was the dominant thermal process. Protein digestibility was high (close to 100%) across all samples and generally unaffected by cooking. SCP inclusion significantly improved amino acid profiles, with Blend1 and Blend2 classified as excellent sources and Control and Blend3 as good sources of essential amino acids. Minerals such as manganese and potassium showed enhanced bioaccessibility linked to reduced phytic acid levels due to SCP incorporation and extrusion. Compared to average meat and dietary reference values, extruded blends demonstrated promising nutritional equivalency, supporting their potential as sustainable, nutrient-dense meat analogues. This study highlights the benefit of combining alternative protein blends with high-impact extrusion to enhance meat substitute nutritional quality
Elemental analysis of divertor marker tiles exposed during the 2018 (C3), 2019 (C4) and 2020 (C5) WEST campaigns
No full textErosion marker tiles mounted in the lower divertor of WEST were exposed during Phase 1 of plasma operations to evaluate poloidal erosion and re-deposition profiles on the tiles. Previous analyses performed to the exposed tiles have shown distinct erosion- or deposition-dominated patterns on them. Afterwards, core-drilled disks cut from the tiles were sent to different laboratories for further and detailed analysis. The present work relates the main results achieved from five characteristic regions of the tiles after completion of the C3, C4, and C5 experimental campaigns on WEST. SIMS and complementary IBA measurements were carried out and the corresponding elemental depth profiles strongly agree, confirming the main earlier conclusions. Deposits are composed of 2H, B, C, O, Mo and W, mainly. Low amounts of Cr, Fe, Ni and Cu were identified as additional metallic impurities. The research confirmed the locations of thin deposition zones nearby the inner and outer divertor limits: at the inner region, the deposition of B and C is particularly enhanced after C4 and C5. Strong erosion zones are located at the inner and outer strike point (ISP and OSP, respectively) areas: only a small erosion occurred after C3, which evolved after C4; nevertheless, the deposition of B and C is enhanced at the OSP edge after C5 nearby the thin deposition zone. Thick deposits appear in the neighborhood of ISP, towards the high field side, and evolve significantly after C4. The amount of O follows the deposition of B. Low retained amounts of 2H were quantified.</p
Futures of Everyday Life:A Qualitative Content Analysis of Future Personas in Scenarios
No full textScenario reports, holding a long-standing tradition in foresight and futures studies, act as an essential document for organizations to prepare for possible, plausible, and alternative futures. Focusing on descriptions and representations of everyday life, we examined 29 future persona narratives from six publications—covering a wide field from public to private sector—through qualitative content analysis. Our guiding question is: How can anthropological perspectives such as cultural relativism or postcolonial discourses contribute to an in-depth, qualitative interpretation depictions of future everyday life? Acknowledging anthropology's colonial origins and its growing commitment to the interests of indigenous and other marginalized groups, we offer alternative readings of prominent scenario reports. Our findings suggest that scenario reports, in addition to anticipating possible futures, construct certain futures based on a systematic analysis of empirical data but also speculative interpretation. The results of these interpretative acts often appear elitist, stereotypical, and technocratic, often replicating dominant societal narratives rather than fostering substantive shifts in how the future is imagined. We therefore call for a more polyphonic representation of futures in scenario writing and foresight work that can produce more discontinuous and transformative images of the future. We understand polyphonic representations as coined by various independent, predominant as well as subaltern perspectives on the same issue at stake while being offered the same amount of space. Therefore, as we will indicate in our analysis, most of the reports referred to are rather monophonic and do not offer discuptive perspectives on the future of everyday life. As an avenue of methodological development, we propose a more nuanced and comprehensive perception of culture and social structures in scenario narrative writing. In addition, ethnographic methods could increase our understanding of how futures are collaboratively constructed and produced by different actors and their respective backgrounds and knowledge in scenario processes.</p
Searching for a needle in a haystack - Sampling the ultra-low biomass deep subsurface environment for microbiology from the ultra-deep drillholes in Otaniemi, Finland
No full textDigitalisation in geosciences for environmental protection
No full textData Science (Digitalization and Artificial Intelligence) became more than an important facilitator in various domains in fundamental and applied sciences as well as industry and is disrupting the way of research already to a large extent. Originally, data sciences were viewed to be well-suited, especially, for data-intensive applications such as image processing, pattern recognition, etc. In the recent past, particularly, data-driven and physics-inspired machine learning methods have been developed to an extent that they accelerate numerical simulations and became directly applied in the nuclear waste management cycle. In addition to process-based approaches for creating surrogate models, other disciplines such as virtual reality methods and high-performance computing are leveraging the potential of data sciences more and more. The present challenge is utilizing of the best experimental and monitoring data as well as model concepts and tools to integrate multi-chemical-physical, coupled processes, multi-scale and probabilistic simulations in Digital Twins (DT) able to mirror or predict the performance of its corresponding existing or future physical implementations including workflows. The call for the Topical Collection was initiated from different actors, including research entities, technical support organizations and nuclear waste management organizations of the European projects EURAD (European Joint Programme on Radioactive Waste Management) and PREDIS (Pre-disposal Management of Radioactive Waste). The Topical Collection attracted a large number of manuscripts, more than eighty of which were published. These articles reveal a strong academic focus on using machine learning to map and assess soil and groundwater resources, hydrology and land use, landslides, and climate protection. They also highlight the core theme of nuclear waste management.</p
Large-scale forest resource mapping with spatial gaps in the training data:Comparison of different modeling approaches
No full textForest attribute maps are essential for supporting local decision-making regarding forest resource use. Such maps are produced by combining remote sensing and field data through various modeling approaches. When mapping across large areas, spatial gaps in field data used for model training are common. Our study evaluates the performance of three methods—k-Nearest Neighbor (k-NN), Random Forests (RF), and Multi-Layer Perceptron (MLP)—for forest resource mapping across Norway, Sweden, and Finland in an experimental setup with respect to availability of field data around the target area. Models were trained with sample plot sizes (N) ranging from 100 to 3000. RF consistently produced the most accurate predictions in terms of relative bias and RMSE. While spatial gaps in the training data (radius: 7–141 km) affected %RMSE of broad-leaved above ground biomass (AGB), they had minimal impact on %RMSE of both local and country-level predictions of total AGB and volume. For RF with N=3000, %RMSE of total AGB ranged between 53%–55% in Finland and Sweden, and 70%–72% in Norway across gap sizes. However, %bias increased for local predictions across the whole study region with larger gaps: RF with N=500 showed bias of −12%–12% (7 km gap) and −17%–28% (78 km gap). Similarly, country-level %bias of total AGB for Norway increased from −1.7% to −3.7% with larger gaps. In conclusion, spatial gaps in training data can significantly affect bias in predictions. Therefore, forest attribute maps should always be accompanied by metadata describing the training data used.</p
An Efficient Rescheduling Scheme for Prioritizing Safety Messages in VANETs
No full textThe growing demand among users of mobile devices to swiftly access information items, coupled with the swift creation of new services and applications in automotive environments has led to the introduction of road side units (RSUs) along the roads. These RSUs facilitate the broadcasting of data during communication between the infrastructure and the vehicle. Vehicular Ad-hoc Networks (VANETs) face challenges such as frequent connection changes, a sizeable topological region, location variations, and varying speed of vehicles. In vehicular scenarios, messages are typically categorized into safety and non-safety messages. Efficient broadcasting of safety messages in vehicular scenarios requires message scheduling, with the highest priority given to crucial messages. This paper proposes an Optimum efficient scheme for organizing messages in VANETs. This scheme reschedules messages based on parameters such as data size, quantity of data sought, speed of vehicle, and message deadline. To differentiate messages pertaining to safety with those from non-safety messages, a message factor is taken into consideration. Priorities for service requests or messages are established using these parameters, and messages are rescheduled accordingly. Simulation results demonstrate the superior performance of the proposed algorithm compared to recent and relevant schemes.</p