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Characterization of a precipitate sludge from a sulfuric acid plant
No full textMaterials characterization is essential for both waste management, but also as the first stage in determining the potential for waste reprocessing as part of the circular economy. This paper describes in detail the multi-method characterisation of a filter press sulfur sludge sample from Boliden’s Harjavalta Smelter in Finland. This material represents the filter press cake precipitate after it has been clarified and filtered from the sulfuric acid plant. The sample was characterized geochemically and mineralogically, as well as for Acid Mine Drainage (AMD) potential. Magnetic and gravity separation process tests were also conducted to further investigate processing options for extracting any valuable metals. The study showed the sludge is chemically highly complex and mineralogically/materially challenging, mainly because of its extreme composition. In conclusion, it is suggested that a hydrometallurgical process path to neutralize this sample is the best way forward, which will be developed in future work.</p
Boosting softwood hemicellulose hydrolysis:Enzymes from a new fungi Penicillium rotoruae remarkably improve CTec-2 hydrolysis efficiency and reduce sugar production costs
No full textEconomic production of fermentable sugars from lignocellulosic biomass is critical for the biorefinery applications in the bioeconomy industry. This study demonstrates effective enzymatic hydrolysis of recalcitrant softwood using newly identified fungus Penicillium rotoruae. Initially, nineteen fungal isolates were screened on softwood galactoglucomannan (GGM), with nine showing strong responses in the liquid culture. Trichoderma viride, Penicillium rotoruae, and Amorphotheca resinae showed highest β-mannanase, β-mannosidase, and α-galactosidase activities. P. rotoruae demonstrated superior main chain cleaving enzyme activities, while A. resinae excelled in the side chain cleaving activity. The crude enzyme of P. rotoruae was evaluated on two Pinus radiata substrates. Using soluble GGM, P. rotoruae released 34.3 % monomeric sugars (32.1 g/L reducing sugars), outperforming commercial CTec-2 (22.9 % and 23.2 g/L respectively). Co-application of CTec-2 with P. rotoruae enzymes increased monomeric sugar yield to 56.3 %, with galactose, mannose, and glucose increasing 20-, 3.6-, and 2.2-fold respectively. Using insoluble pulp, co-application yielded 88 % of monomeric sugars (20.2 g/L reducing sugars) representing an increase of 20 % soluble sugars relative to CTec-2 used alone. Techno-economic analysis indicated an increase in annual EBITDA, a positive ROCE and sugar cost savings of NZD 125/t demonstrating significant economic potential for softwood biorefineries.</p
Exhaled CO<sub>2</sub> and aerosol dispersion on a cruise ship:Airflow and infection risk insights
No full textUnderstanding airborne pathogen transmission in cruise ship environments remains a critical challenge due to the confined nature of indoor spaces, high occupancy, and limited access for real-world experimentation. This study addresses the gap in empirical data on particulate matter and CO2 dynamics aboard operational cruise ships, providing a high-resolution dataset that can be used for the validation of Computational Fluid Dynamics (CFD) models and informing infection probability risk assessments. An experimental trial was designed for two mechanically ventilated cruise ship rooms (R01, R02), instrumented at ten locations under eight ventilation scenarios: R01 with 100 % (S1a) and 50 % (S1b) design flow rates; R02 with 100 % (S2a), 50 % (S2b) and 10 % (S2c) design flow rates; R01 with high aerosol rate and 50 % flow rate (S3); and R01 with an air purifier at maximum (S4a, 1300 m3 h−1) and minimum (S4b, 422 m3 h−1) clean air delivery rate (CADR). A live UK-EU cruise hosted the experimental trial. Particulate matter and CO2 concentration, temperature and relative humidity were collected using portable sensors to build a unique dataset to validate subsequent computational modelling of aerosol dispersion, infection probability and transmission prevention, mitigation and management (PMM) approaches in arbitrary passenger ship spaces. As expected, PM and CO2 were markedly reduced under 100 % design flow ventilation compared with 50 %. Maximum PM2.5 reductions were 84 % during background, 29 % in build-up, and 72 % in decay experimental phases. An air purifier further reduced particulate matter, with peak PM reductions of 57 % (PM10), 48 % (PM2.5), and 45 % (PM1). These findings offer practical guidance for optimising air quality management strategies in cruise ships and other high-occupancy spaces, besides providing a crucial high-resolution dataset for validating numerical modelling. Moreover, this study provides valuable insights into mechanically ventilated shipboard airflow behaviour.</p
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
Deep Life Under Pressure: Hydrogen Formation and Microbial Signatures in Precambrian Crystalline Bedrock
No full textIntegrating nuclear Small Modular Reactors into low-carbon energy systems:an illustration using a recent European R&D initiative
No full textThe race to develop Small Modular Reactors (SMRs) is in full swing around the world. SMRs are nuclear reactors with a power output of a few hundred MWe incorporating high modularisation and standardisation by design, thus facilitating economies of in-series production. SMR technologies have the potential to strongly contribute to decarbonisation of the energy sector but are yet to be deployed. Considered at a local or regional scale, SMRs can be fully integrated in innovative hybrid energy systems (HES), including variable renewables and nuclear energy in the form of electricity, heat or hydrogen, energy storage systems, heat networks, and power grids. These systems must operate flexibly to ensure the stability of energy networks. These integrated energy systems are currently under development, however, in Europe, studies on such systems remain limited. In this context, a European Industrial Alliance on Small Modular Reactors, launched by the European Commission in 2024, pointed out significant R&D gaps to be tackled to make these energy systems ready for deployment. Therefore, TANDEM, a Euratom-funded project was carried out between 2022 and 2025 to help fill these gaps. The project has delivered methodologies and tools for the assessment of HES and validated and demonstrated them on case studies for decarbonisation. The project enabled first evaluation considerations of the technical performance and economic viability of such systems. It then covered nuclear safety aspects and environmental impact. Finally, it investigated citizen engagement and Education & Training needs to prepare the workforce required for developing and deploying these energy systems.</p
Decoding acceptance of driver monitoring systems:Evaluating alternative measurement models, cross-country variations, and behavioural intention
No full textDriver monitoring systems (DMS) demonstrate significant potential for enhancing road safety. It is imperative to comprehend potential users’ attitudes towards DMS to optimise their benefits and increase public acceptance. This study investigates potential users’ acceptance of DMS in conditionally automated driving systems (SAE level 3) by evaluating alternative measurement models and assessing cross-country variations across nine countries (i.e., Germany, Spain, France, Japan, Poland, Sweden, the United Kingdom, the United States, and China). Utilising survey data from 9025 drivers, we compared the principal component analysis and the four models (a single-factor model, a six factors model, a two higher-order factors model, and a two lower-order factors model) via structural equation modelling. A model with two correlated factors, General Acceptance and Concerns, emerged as the optimal solution with high reliability across constructs. Significant cross-country differences in all constructs were found, although only 0.3% of the variance in behavioural intention was attributable to country-level differences. A linear mixed model demonstrated that the general acceptance factor positively related to behavioural intention, whereas concerns had a small but significant negative effect. The implications for research and practice suggest that while individual-level perceptions are paramount, country context also plays a role, albeit a modest one, in shaping users’ willingness to adopt DMS technologies.</p
Business Model (BM) Transformations in Business-to-Business (B2B) Digital Multimodal Logistics Platform Ecosystem:Insights from Prospective Marketplace Sellers and Buyers
No full textDigitalization and sustainability imperatives are transforming the logistics industry; however, the evolution of business models (BMs) in emerging business-to-business (B2B) digital multimodal marketplaces has not been thoroughly explored. This study investigates how one of the first B2B digital multimodal marketplace ecosystems – designed to calculate emissions and promote more sustainable logistics services – might affect the BMs of sellers and buyers. We used a qualitative research design based on the Business Model Canvas (BMC). To gather data, we analyzed the current (AS-IS) and future (TO-BE) BMs. Additionally, we created detailed questionnaires structured around the BMC framework, which were completed by representatives from seller and buyer actor groups. A hybrid deductive-inductive coding approach allowed us to integrate the established BMC framework with emergent themes. Our analysis reveals significant potential transformations in the key activities, followed by potential changes in key resources, channels and revenue streams. Most BM changes centre on adding emission calculation as a new value-proposition element, which in turn triggers adjustments in the other BMC blocks. These insights deepen the theoretical understanding of how digital logistics marketplace ecosystem might drive BM transformation, while also highlighting the anticipated challenges, risks, and necessary adjustments managers in the logistics industry should address when integrating a digital marketplace ecosystem. This study is one of the first to analyze likely changes in the BMs of companies adopting a B2B digital logistics platform ecosystem. Additionally, it is the first to explore a multimodal and environmentally conscious platform ecosystem
WeTRaC: Scalable EV charging demand forecasting for heavy-duty fleets
Get PDFThe rapid expansion of electric vehicles (EVs) in response to stricter emissions targets presents formidable challenges for power systems, particularly in scaling EV charging infrastructure to meet growing demands from heavy-duty fleets. Such demands are shaped by complex spatio-temporal interdependencies, such as weather conditions, traffic density, routes, and charging infrastructure, leading to imprecise charging demand predictions by the existing models that do not fully address all factors. This study introduces the Weather Traffic Routes and Chargers (WeTRaC), a predictive framework that unifies graph neural networks (GNNs) with physics-based vehicle simulations and open global data to produce high-precision forecasts of heavy-duty (i.e., buses and trucks) EV charging needs. Forecasts are generated at the vehicle level along routes and then aggregated to fleet- or corridor-level demand using probabilistic priors over vehicle attributes. We validate its performance through large-scale simulations (including ten international virtual corridor case studies) and real-world truck data from Finland, revealing a 500-fold computational speedup over conventional physics-based approaches at only a marginal (4%) accuracy trade-off. By identifying peak periods and locations of corridor demand for specified fleets, WeTRaC can effectively mitigate grid overload and accelerate the transition toward zero-emission transport