VTT Technical Research Centre of Finland

The exceptional ability of liposomes to mimic a cellular lipid membrane makes them invaluable tools in biomembrane studies and bottom-up synthetic biology. Microfluidics provides a promising toolkit for creating giant liposomes in a controlled manner. Nevertheless, challenges associated with the microfluidic formation of double emulsions, as precursors to giant liposomes, limit the full exploration of this potential. In this study, we propose a PDMS-glass capillary hybrid device as a facile and versatile tool for the formation of double emulsions which not only eliminates the need for selective surface treatment, a well-known problem with PDMS formation chips, but also provides fabrication simplicity and reusability compared to the glass-capillary formation chips. These advantages make the presented device a versatile tool for forming double emulsions with varying sizes (spanning two orders of magnitude in diameter), shell thickness, number of compartments, and choice of solvents. We achieved robust thin shell double emulsion formation by operating the hybrid chip in double dripping mode without performing hydrophilic/phobic treatment a priori. In addition, as an alternative to the conventional, time-consuming density-based separation method, a tandem separation chip is developed to deliver double emulsions free of any oil droplet contamination in a continuous and rapid manner without any need for operator handling. The applicability of the device was demonstrated by forming giant liposomes using the solvent extraction method. This easy-to-replicate, flexible, and reliable microfluidic platform for the formation and separation of double emulsion templates paves the way for the high-throughput microfluidic generation of giant liposomes and synthetic cells, opening exciting avenues for biomimetic research.

Radiocarbon analysis of nuclear waste produced in nuclear facilities lacks fast, in situ detection methods. Moreover, the amount of radiocarbon desorbing from graphitic waste is not well known. In this study, we demonstrate the use of mid-infrared cavity ring-down spectroscopy combined with an automatic sample processing unit as a method to examine radiocarbon concentration in three types of nuclear waste: spent ion-exchange resin, graphite, and graphite outgassing in sealed storage crates. The solid samples were gasified, which allowed analyzing the effect of heating on the radiocarbon outgassing from the samples. The presented method also enabled examination of molecular speciation of the radiocarbon in the samples. The method performed well with the graphite and gaseous samples, but the analysis of the spent ion-exchange resin did not produce repeatable results due to high N2O concentrations. In the future, the presented method can be used in situ at nuclear facilities and expanded to a wider variety of sample materials than those presented here.

The European Space Agency (ESA) has designed a 70–95 GHz gallium-nitride-on-silicon (GaN-on-Si) monolithic microwave integrated circuit (MMIC) low noise amplifier (LNA) for future spaceborne cloud profiling radar (CPR) instruments at 94 GHz, like Wivern. The MMIC LNA has also been designed to be compatible with the W -band telecommunication feeder down (71–76 GHz) and up-links (81–86 GHz). The MMIC test results show an average noise figure of 3.9 dB and an average gain of 22.5 dB. The minimum measured noise figure is 2.8 dB at 77 GHz. The MMIC was subject to an input overdrive test and survived the application of a pulsed CPR representative signal with a level of + 23 dBm without showing any subsequent malfunction. To the best knowledge of the authors, these results show: 1) the best overall performance for any GaN-on-Si LNA reported to date in the 70–95 GHz range and 2) the first reporting in the open literature of an input overdrive test in a W-band GaN LNA.

This paper presents a comprehensive overview of the preliminary divertor design and plasma exhaust scenario for the reactor-class Spherical Tokamak for Energy Production project. Due to the smaller size of the machine, with a major radius less than half that of most DEMO concepts, the current design features a double-null divertor geometry, comprising tightly baffled extended outer legs and shorter inner legs approaching an X-divertor. Leveraging a significant database of SOLPS-ITER simulations, the exhaust operational space is mapped out, offering valuable insights into the plasma exhaust dynamics. An approach involving the validation of simple, yet robust models capable of accurately predicting key exhaust parameters is detailed, thereby streamlining the design process. The simple models are used to simulate the entire plasma scenario from the plasma current ramp-up, through the burning phase, to the plasma current ramp-down. Notably, the findings suggest that pronounced detachment, with peak heat loads below engineering limits and electron temperatures below 5 eV, is achievable with a divertor neutral pressure between 10 Pa and 15 Pa during the burning phase, and pressures below 5 Pa during the ramp-up to maximise the auxiliary current-drive efficiency. Throughout the scenario, an Ar concentration of ≈3% in the scrape-off layer (SOL) is required, in combination with a core radiation fraction of 70% driven by intrinsic emission and extrinsic injection of Xe seeded fuelling pellets. However, significant uncertainties remain regarding key parameters such as the SOL heat flux width, Ar screening, and plasma kinetic effects.

The smart city infrastructures, such as digital platforms, edge computing, and fast 5G/6G networks, bring new possibilities to use near-real-time sensor data in digital twins, AR applications, and Machine-to-Machine applications. In addition, AI offers new capabilities for data analytics, data adaptation, event/anomaly detection, and prediction. However, novel data supply and use strategies are needed when going toward higher-granularity data trade, in which a high volume of short-term data products is traded automatically in dynamic environments. This paper presents offering-driven data supply (ODS), demand-driven data supply (DDS), event and offering-driven data supply (EODS), and event and demand-driven data supply (EDDS) strategies for high-granularity data trade. Computer simulation was used as a method to evaluate the use of these strategies in supply of air quality data for four user groups with different requirements for the data quality, freshness, and price. The simulation results were stored as CSV files and analyzed and visualized in Excel. The simulation results and SWOT-analysis of the suggested strategies show that the choice between the strategies is case-specific. DDS increased efficiency in data supply in the simulated scenarios. There was higher profit and revenues and lower costs in DDS than in ODS. However, there are use cases that require the use of ODS, as DDS does not offer ready prepared data for instant use of data. EDDS increased efficiency in data supply in the simulated scenarios. The costs were lower in EODS, but EDDS produced clearly higher revenues and profits.

Accurate forecasts of renewable and nonrenewable energy output are essential for meeting global energy needs and resolving environmental issues. Energy sources like the sun and wind are variable, making forecasting difficult. Changes in weather, demand, and energy policy exacerbate this unpredictability. These challenges will be addressed by the bidirectional gated recurrent unit (Bi‐GRU) model, which forecasts power‐generating outcomes more efficiently. The investigation is done over a health data set from 2000 to 2023, including the energy states of the United Kingdom, Finland, Germany, and Switzerland. The comparison of our model (Bi‐GRU) performance with other popular models, including bidirectional long short‐term memory (Bi‐LSTM), ensemble techniques combining convolutional neural networks (CNN) and Bi‐LSTM, and CNNs, make the study more interesting. The performance remains better with a mean absolute percentage error (MAPE) of 2.75%, root mean square error (RMSE) of 0.0414, mean squared error (MSE) of 0.0017, and authentify that Bi‐GRU performs much better than others. This model's superior prediction accuracy significantly enhances our ability to forecast renewable and nonrenewable energy outputs in European states, contributing to more effective energy management strategies.

This paper takes a multi-perspective approach to understand drivers and barriers of climate action on the neighbourhood level. We start with the assumption that climate actions on the level of citizens are most motivating and promising, when conducted jointly within established social systems like neighbourhoods. A survey implemented in neighbourhoods (3 in Austria, 2 in Norway, 2 in Italy, 2 in Finland). The neighbourhoods were partly in rural communities (4) and partly in urban or semi-urban areas (5). In total, 1.084 answers were retained between summer 2022 and summer 2023. The impact of factors from the different perspectives on the self-reported number of implemented climate actions were tested in a stepwise structural-equation-modelling-approach. The analyses show that intentions to act both on the individual and collective level impact climate actions as represented by behaviour in four domains (travel, diet, protest, and general climate action) implemented by citizens in the neighbourhoods, but individual intentions are more important. In addition, local cultural aspects have an impact on climate action, as indicated by the two extremely rural Finnish neighbourhoods being different on many variables. On the socio-structural level, males and households with younger children report less climate action, whereas larger households in general and people with university degree report more. Intentions to act individually are mostly determined by perceived individual efficacy and attitudes, but also selected cultural and socio-structural factors. Collective intentions to act depend on the social capital in the neighbourhood, collective efficacy, and social norms, as well as selected socio-structural and cultural factors. Concluding, this paper emphasises that in order to understand and stimulate climate-related action of citizens, the individual, collective, cultural and socio-structural factors must be taken into account and that the level of neighbourhoods, where everyday action takes place, is a relevant unit of analysis to do so.

Background Ceramide and phosphatidylcholine lipids‐based risk score (CERT2) has shown a strong prognostic value in predicting cardiovascular (CV) events in patients with ischemic heart disease. This study aimed to investigate the prognostic value of CERT2 risk score in patients with heart failure (HF). Methods The current study combines data for 4234 subjects from the COMMANDER‐HF trial and 1227 subjects from the GISSI‐HF trial, which enrolled patients with a history of HF. The CERT2 risk score was calculated for all the participants as previously described. The primary outcome was CV death, but all‐cause death and major adverse CV events (three‐point MACE) were analysed as well. Results After adjustment for established CV risk factors and potential confounders, patients with the highest CERT2 risk category remained at almost three‐fold higher risk of CV death (COMMANDER‐HF: HR 2.80, 95% CI 2.18–3.60, GISSI‐HF: 2.84, 95% CI 1.70–4.74), all‐cause death (COMMANDER‐HF: HR 2.97, 95% CI 2.36–3.75, GISSI‐HF: 2.83, 95% CI 1.83–4.38) and MACE (COMMANDER‐HF: HR 2.73, 95% CI 2.20–3.38, GISSI‐HF: 2.67, 95% CI 1.67–4.26) compared to those with the lowest CERT2 risk category. Conclusions The CERT2 risk score is strongly associated with an increased risk of CV death, all‐cause death and MACE in patients with HF.

This article contributes to expanding the literature on and understanding about urban circular economy (CE) transitions towards circular cities, with a particular focus on the circularity of critical raw materials (CRMs), by identifying barriers in the transition’s exploration phase. We collected our empirical research data from 7 Finnish cities by interviewing 14 administrative officers responsible for procurement and for CE development and strategies. According to our findings, financial, institutional, policy and regulatory, technical, knowledge, and social factors are both internal and external barriers that city governments face in preventing urban CE transition of CRMs. Our findings suggest that an overarching problem with the identified barriers is regarding knowledge. Furthermore, we argue that intervening in local transformation paths towards circular cities requires the understanding and development of multilevel interactions between actors and their possibly conflicting interests. This contributes to the current understanding of early phases of urban CE transitions, that is, how knowledge deficits between multilevel systemic urban CE transitions should be addressed.

The friction and wear behavior of laser powder bed fusion (L-PBF) 316L stainless steels (SSs) with different post-manufacturing heat treatments (PMHTs) under various normal loads were investigated and compared with conventional wrought 316L SS. The dominant wear mechanism was adhesive wear for L-PBF 316L SS with as-built and stress relief PMHT conditions, but abrasive wear for wrought 316L SS. L-PBF 316L SS with solution annealing PMHT exhibited both adhesive and abrasive wear mechanisms. As built L-PBF 316L SS presented superior or close wear resistance to wrought 316L SS under low and medium normal loads, but the opposite under a high normal load. PMHTs gradually lowered compressive stress levels and decreased dislocation density in L-PBF 316L SS, inducing the degraded wear resistance of L-PBF 316L SS, which was more significant at a high normal load. The differentiated wear performance, i.e., better or worse wear resistances, among wrought and L-PBF 316L SSs not only depends on the applied normal loads during the wear test but is also controlled by the intrinsic microstructural features of materials and exhibited wear mechanisms.

We show that the mode strengths of a guided field in an arbitrary asymmetric channel waveguide can be uniquely determined from self-referencing interferometric measurements at the exit plane of the waveguide. This requires knowledge of both the amplitude and phase of the complex electric field distribution. Although the amplitude can be obtained from the measured intensity profile easily, the phase retrieval is usually non-trivial. We develop an innovative, alternative and promising technique, where the complex cross-spectral density (CSD) function is measured using a customized wavefront folding interferometer. We then construct the total electric field (complex valued), from which we can determine the strengths of the allowed modes for an asymmetric strip waveguide. Our retrieval algorithm also provides the phase information (intermodal dispersion) associated with each mode, directly from the measured electric field distribution. Moreover, we experimentally demonstrate the developed scheme for different in-coupling (butt-coupling) conditions, resulting in different modal strength distributions.

The development of eco‐friendly indoor photovoltaics (IPVs) for Internet‐of‐Things (IoT) devices is booming. Emerging IPVs, especially those based on lead halide perovskites (LHPs), outperform the industry standard of amorphous hydrogenated silicon (a‐Si:H). However, the toxic lead in LHPs drives the search for safer alternatives. Perovskite‐inspired materials (PIMs) containing bismuth (Bi) and antimony (Sb) have shown promise, achieving indoor power conversion efficiencies (PCE) approaching 10% despite early research stages. This is promising due to their eco‐friendlier light‐harvesting layers compared to LHPs. Yet, the environmental footprint of pnictogen‐based PIM over their lifecycle remains unassessed. This study conducts a life‐cycle assessment (LCA) of the best‐performing Sb‐ and Bi‐PIMs, considering PCE, raw material availability, energy consumption, and waste generation. It is find that PCE plays a decisive role in identifying the PIM for IPVs with minimized environmental impact, namely a Bi‐Sb alloy. Extended LCA simulations for industrial‐scale processing show that the most promising Bi‐PIM has a reduced environmental burden compared to a‐Si:H. It is also explore challenges and solutions for enhancing Bi‐and Sb‐PIMs’ sustainability. Overall, this study provides the first evidence of the potential of pnictogen‐based PIMs as a sustainable IPV technology, addressing whether lead‐free PIMs are truly eco‐friendly, thus contributing toward battery‐less IoT applications.

Wireless sensor network (WSN) cluster‐based architecture is a system designed to control and monitor specific events or phenomena remotely, and one of the important concerns that need quick attention is security risks such as an intrusion in WSN traffic. At the same time, a high‐level security method may refer to an intrusion detection system|intrusion detection systems (IDS), which may be employed effectively to achieve a higher level of security in detecting an intruder attack or any attack initiated within a WSN system. The significance of the detection of network intrusions on heterogeneous cluster‐based sensor networks with wireless connections, as well as the approaches to machine learning utilised in IDS model development, were discussed. In addition, this research conducted several comparative studies of feature selection techniques and machine learning methodologies in the development of intrusion detection systems. The authors used a bibliometric indicator to identify the leading trends when it comes to IDS, and the VOS viewer was used to create a spatial mapping of co‐authorship, co‐occurrence, and citation types of analysis with their respective units of study. The purpose of this research paper is to generate relevant findings and a research problem formulation that can lead to a research gap in the research topic's domain area.

The emergence of cracks in cementitious composites and concrete, stemming from both autogenous processes and external stressors, poses a significant challenge to the long-term durability and safety of structures. In response to this challenge, researchers have developed an innovative method featuring inherent capabilities for autonomous crack repair, known as self-healing concrete, which offers promising benefits for enhancing structural longevity and reducing environmental impact. This method offers not only an autogenous healing mechanism but also the potential for autonomous repair, significantly reducing the need for maintenance while concurrently extending the lifespan of concrete structures. This paper comprehensively reviews the working principles, fabrication techniques, and self-healing capabilities, specifically in relation to crack recovery and mechanical performance, of concrete incorporated with chemical-based self-healing agents. These agents encompass a range of materials, such as sodium silicate, calcium nitrate, dicyclopentadiene, calcium hydroxide, calcium sulfoaluminate (CSA), silica, and superabsorbent polymers. In this review, both autogenous and autonomous self-healing were considered, and literature studies suggested cracks can effectively heal, and mechanical performances can be significantly restored. Healing performance and mechanical performance restoration can vary significantly with the type of healing agents and their concentrations. Many researchers have achieved more than 100% restoration of mechanical performance and fully healed crack depths. Superabsorbent polymers (SAP) are highly effective self-healing agents, particularly when combined with autogenous healing agents like calcium nitrate or CSA. This combination can lead to significant healing in cracks up to 750 μm. Chemical-based self-healing agents can be an efficient way to enhance the durability of concrete, ensuring safety and promoting their practical application.

Microbial production of aromatic compounds from renewable feedstocks has gained increasing interest as a means towards sustainable production of chemicals. The potential of filamentous fungi for production of aromatic compounds has nonetheless not yet been widely exploited. Notably, many filamentous fungi can naturally break down lignin and metabolize lignin-derived aromatic compounds. A few examples where a fungal cell factory, often of Aspergillus spp., is used to produce an aromatic compound, typically through the conversion of one compound to another, have already been reported. In this review, we summarize fungal biosynthesis of biotechnologically interesting aromatic compounds. The focus is on compounds produced from the shikimate pathway. Biorefinery-relevant efforts for valorizing residual biomass or lignin derived compounds are also discussed. The advancement in engineering tools combined with the increasing amounts of data supporting the discovery of new enzymes and development of new bioprocesses has led to an increased range of potential production hosts and products. This is expected to translate into a wider utilization of fungal cell factories for production of aromatic compounds.

As the network continues to become more complex due to the increased number of devices and ubiquitous connectivity, the trend is shifting from a centralized implementation to decentralization. Similarly, strategies to secure networks are increasingly leaning towards decentralization for its potential to enhance security in future networks with the help of Machine Learning (ML) techniques. In this regard, Distributed Machine Learning (DML) techniques, such as Federated Learning (FL) and Split Learning (SL), are at the forefront of this shift, offering collaborative learning capabilities across network nodes while maintaining data privacy. However, ML requires vast amounts of dedicated computing, memory, bandwidth, and as a consequence, energy resources. Moreover, resource consumption ML techniques used for network security have mostly been overlooked, which presents a glaring challenge for future networks in terms of overall resource utilization. This research emphasizes the importance of understanding the resource consumption patterns of two important DML techniques, i.e., FL and SL, to analyze the consumption of critical resources when deployed for network security. Furthermore, this research draws important insights from a practical comparative analysis of FL and SL in terms of resource consumption patterns and discusses their scope for future network security, such as in 6G, and stirs further research in this area.

This special issue of the Journal of Advanced Concrete Technology features invited papers that originated from the International Conference on Non-destructive Evaluation of Concrete in Nuclear Applications, held in Espoo, Finland, from 25 - 27 January 2023. These papers were selected by the Scientific Committee after a careful review process, and they were chosen based on the recommendations of the session chairpersons, from a total of 48 accepted contributions. The event centered on the non-destructive evaluation (NDE) of concrete in nuclear applications, addressing both the safety of nuclear power plants and the management of radioactive waste. The primary aim was to share NDE solutions that tackle real-world challenges faced at nuclear power plants and waste management facilities. NDE techniques are crucial in the nuclear industry as they allow for the inspection of concrete structures without causing damage, ensuring the integrity and safety of these facilities. A significant emphasis was placed on interdisciplinary approaches, bringing together experts from different fields to foster collaboration and innovation. This interdisciplinary focus is essential for developing new NDE techniques and improving existing ones, ensuring they can effectively address the unique challenges posed by nuclear applications. Additionally, the event highlighted novel research areas that promise innovative NDE applications. Advancements in digital imaging and machine learning, for instance, are opening new possibilities for more accurate and efficient evaluation of concrete structures. These technologies enhance the ability to detect and analyze defects, leading to better maintenance and safety practices in the nuclear industry. This is noticeable through the selected journal papers highlighted in this issue.