VTT Technical Research Centre of Finland
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Chemical looping combustion of wood pellets (WP), wood char (WC), and straw pellets (SP) was conducted in a 60 kW CLC pilot with ilmenite and braunite oxygen carriers (OCs). Alkali emissions were investigated with impactor-based and surface ionization detector (SID) measurements. Particle size distributions for WP and WC fuels were dominated by coarse particles formed by refractory species. For SP fuel, the distribution was bimodal with a distinct fine particle mode formed by nucleation of volatile ash species. Thermodynamic modelling of stable alkali species at 800 °C predicted that high KOH(g) and lower concentrations of KCl(g) are stable for WP and WC fuels. For SP fuel, equilibrium K species were dominated by condensed-phase K species, followed by KCl(g), and KOH(g). Modelling of fuel-OC interactions showed that ilmenite decreases equilibrium levels of KOH(g) and KCl(g). Braunite impacted only KOH(g) levels. Impactor sample leachate analysis showed that for WP-braunite operation, the leachate contained KCl, NaCl, KOH, and NaOH, in decreasing order. For WC-ilmenite operation, the samples contained KOH and KCl. For SP fuel, most detected alkalis were KCl. For most cases, speciation of impactor samples qualitatively agreed with modelling predictions. Impactor and SID alkali measurements showed reasonable agreement for WC-braunite and SP-braunite tests.
Driven by digitalization, the emergence of startups, and regulatory changes, the banking industry is undergoing a “fintech revolution” where the competitive advantages of incumbents are disrupted. In response, banks collaborate with startups by organizing accelerators and incubators to promote corporate innovation. A critical challenge is achieving a strategic fit with startups. In this research, a longitudinal case study of Nordea, the largest retail bank in the Nordics, was conducted. Three startup programs between 2015 and 2018 during a major fintech boom were investigated, and how the programs implement corporate sponsorship and enable corporate innovation was analyzed. We found that achieving a strategic fit was an iterative process fueled by the accumulation of technological and market knowledge from the startups, where Nordea adjusted its mode of startup collaboration according to the phase of the disruption to meet its evolving learning goals.
In this article, we discuss the technical and business risks associated with long-lasting functional digital twins, and describe different strategies for their alleviation. Functional digital twins are based on physics-based simulation models and are operated alongside the life cycle of their physical counterparts. These simulation-based digital twins are built using a simulation software. The problems with most of the commercial modeling and simulation tools are their black box nature and storing data in protective formats, leading to poor interoperability. Since the digital twins of certain assets need to be operated for a long period, even for several decades, there is a possibility that the computing infrastructure, i.e., the computing hardware and software, may not remain the same throughout the product or system life cycle. The computer hardware and operating systems are usually third-party components with limited choices for their users, whereas the selection of simulation tools is more flexible and the designer can choose from, for example, commercial, open-source, or in-house solutions. To avoid substantial costs or business disruption, the digital twin providers must be able to reproduce the underlying simulation models with up-to-date tools and adopt alternative solutions whenever needed. The findings of the study are presented in the form of propositions throughout the article.
Most of the power-to-heat and thermal energy storage technologies are mature and impact the European energy transition. However, detailed models of these technologies are usually very complex, making it challenging to implement them in large-scale energy models, where simplicity, e.g., linearity and appropriate accuracy, are desirable due to computational limitations. In the literature, the main power-to-heat and thermal energy storage technologies across all sectors have not been clearly identified and characterized. Their potential roles have not been fully discussed from the European perspective, and their mathematical modeling equations have not been presented in a compiled form. This paper contributes to the research gap in three main parts. First, it identifies and classifies the major power-to-heat and thermal energy storage technologies that are climate-neutral, efficient, and technologically matured to supplement or substitute the current fossil fuel-based heating. The second part presents the technology readiness levels of the identified technologies and discusses their potential role in a sustainable European energy system. The third part presents the mathematical modeling equations for the technologies in large-scale optimization energy models. We identified electric heat pumps, electric boilers, electric resistance heaters, and hybrid heating systems as the most promising power-to-heat options. We grouped the most promising thermal energy storage technologies under four major categories. Low-temperature electric heat pumps, electric boilers, electric resistance heaters, and sensible and latent heat storage show high technology readiness levels to facilitate a large share of the heat demand. Finally, the mathematical formulations capture the main effects of the identified technologies.
Inattentiveness of road users on approach to passive railway crossings represents a major threat to level crossing safety. An auxiliary strobe light system installed on trains in addition to existing headlights may help address this issue by providing an ergonomic way of attracting human attention to the level crossing and to the train. The objective of this paper was to investigate the ergonomics and safety potential of auxiliary strobe light systems. A system was implemented on a real railway vehicle and in the virtual environment of a driving simulator. Acceptance of the system, including its usefulness and perceived benefits and drawbacks, as well as its objective effectiveness, were evaluated using questionnaires, behavioural measures, and eye tracking. The safety potential of the system was evaluated with respect to fatal level crossing accidents. The auxiliary strobe lights were preferred over normal lights and were rated as useful, reducing driving speeds, increasing visual scanning at level crossings, and thus aiding detection of a train. The system has the potential to prevent 6–30% of level crossing accidents in Europe. The results suggest that it might be worthwhile to test auxiliary strobe lights in a larger scale real-world experiment. Especially on railway lines with a high number of passive level crossings, this system can be expected to increase safety by supporting timely detection by road users and preventing accidents caused by inattentiveness.
The transition towards more sustainable energy systems poses new requirements on energy system models. New challenges include representing more uncertainties, including short-term detail in long-term planning models, allowing for more integration across energy sectors, and dealing with increased model complexities. SpineOpt is a flexible, open-source, energy system modelling framework for performing operational and planning studies, consisting of a wide spectrum of novel tools and functionalities. The most salient features of SpineOpt include a generic data structure, flexible temporal and spatial structures, a comprehensive representation of uncertainties, and model decomposition capabilities to reduce the computational complexity. These enable the implementation of highly diverse case studies. SpineOpt’s features are presented through several publicly-available applications. An illustrative case study presents the impact of different temporal resolutions and stochastic structures in a co-optimised electricity and gas network. Using a lower temporal resolution in different parts of the model leads to a lower computational time (44%–98% reductions), while the total system cost varies only slightly (-1.22–1.39%). This implies that modellers experiencing computational issues should choose a high level of temporal accuracy only when needed.
The main contribution of this research is the evaluation of emerging technological opportunities for improving risk awareness and resilience of vulnerable people in disasters. The evaluation considered a survey, end-user evaluation, and co-creative workshops on technologies and was targeted to estimate the innovation potential, usefulness, importance, applicability, risks for vulnerable people, and ethical acceptability. The capabilities of the Mobile Positioning Data tool (1) to increase risk awareness in rescue planning and emergency management in cyber-hazard situations and (2) to help in locating and evacuating tourists and other vulnerable people in disasters was evaluated. The capabilities of the Trasim tool to help in crisis communications training for improving preparedness and tackling mis/disinformation through simulated responses. The evaluation indicated that there are a lot of innovation potential and useful technical enablers for improving disaster risk awareness and resilience, but there are also ethical challenges, and risks for misuse. The digital divide between people in the unequal distribution of skills and access to technological means and tools remains an essential future challenge, especially with vulnerable people in a crisis. Issues of fairness and inclusivity need great attention in the application of these technologies in crises or disasters in order not to overlook such vulnerable people. The failure in critical infrastructures (e.g., energy and communications) seems to increase risks and exacerbate the situation of vulnerable people in crises. However, the potential of technological opportunities for improving operation in disasters is so essential that significant investment in research and development is recommended.
Scope: Fermentation improves many food characteristics using microbes, such as lactic acid bacteria (LAB). Recent studies suggest fermentation may also enhance the health properties, but mechanistic evidence is lacking. We aimed to identify a metabolite pattern reproducibly produced during sourdough and in vitro colonic fermentation of various whole-grain rye products and how it affects the growth of bacterial species of potential importance to health and disease. Methods and results: We used Lactiplantibacillus plantarum DSMZ 13890 strain, previously shown to favour rye as its substrate. Using LC-MS metabolomics, we found seven microbial metabolites commonly produced during the fermentations, including dihydroferulic acid, dihydrocaffeic acid, and five amino acid metabolites, and stronger inhibition was achieved when exposing the bacteria to a mixture of the metabolites in vitro compared to individual compound exposures. Conclusion: Our study suggests that metabolites produced by LAB may synergistically modulate the local microbial ecology, such as in the gut. This could provide new hypotheses on how fermented foods influence human health via diet-microbiota interactions. This article is protected by copyright. All rights reserved.
Quantum effects in novel functional materials and new device concepts represent a potential breakthrough for the development of new information processing technologies based on quantum phenomena. Among the emerging technologies, memristive elements which exhibit resistive switching that relies on the electrochemical formation/rupture of conductive nanofilaments, exhibit quantum conductance effects at room temperature. Despite the underlying resistive switching mechanism having been exploited for the realization of next‐generation memories and neuromorphic computing architectures, the potentialities of quantum effects in memristive devices are still rather unexplored. Here, we present a comprehensive review on memristive quantum devices where quantum conductance effects can be observed by coupling ionics with electronics. Fundamental electrochemical and physicochemical phenomena underlying device functionalities are introduced, together with fundamentals of electronic ballistic conduction transport in nanofilaments. Quantum conductance effects including quantum mode splitting, stability and random telegraph noise are analyzed, reporting experimental techniques and challenges of nanoscale metrology for the characterization of memristive phenomena. Finally, potential applications and future perspectives are envisioned, including how memristive devices with controllable atomic‐sized conductive filaments can represent not only suitable platforms for the investigation of quantum phenomena but also promising building blocks for the realization of integrated quantum systems working in air at room temperature. This article is protected by copyright. All rights reserved
The employment of atomic layer deposition and spin coating techniques for preparing inorganic–organic hybrid multilayer structures of alternating ZnO-CNC layers was explored in this study. Helium ion microscopy and X-ray reflectivity showed the superlattice formation for the nanolaminate structures and atomic force microscopy established the efficient control of the CNCs surface coverage on the Al-doped ΖnO by manipulating the concentration of the spin coating solution. Thickness characterization of the hybrid structures was performed via both ellipsometry and X-ray reflectivity and the thermal conductivity was examined by time domain thermoreflectance technique. It appears that even the incorporation of a limited amount of CNCs between the ZnO laminates strongly suppresses the thermal conductivity. Even small, submonolayer amounts of CNCs worked as a more efficient insulating material than hydroquinone or cellulose nanofibers which have been employed in previous studies.
Xylomyrocins, a unique group of nonribosomal peptide secondary metabolites, were discovered in Paramyrothecium and Colletotrichum spp. fungi by employing a combination of high-resolution tandem mass spectrometry (HRMS/MS)–based chemometrics, comparative genome mining, gene disruption, stable isotope feeding, and chemical complementation techniques. These polyol cyclodepsipeptides all feature an unprecedented d -xylonic acid moiety as part of their macrocyclic scaffold. This biosynthon is derived from d -xylose supplied by xylooligosaccharide catabolic enzymes encoded in the xylomyrocin biosynthetic gene cluster, revealing a novel link between carbohydrate catabolism and nonribosomal peptide biosynthesis. Xylomyrocins from different fungal isolates differ in the number and nature of their amino acid building blocks that are nevertheless incorporated by orthologous nonribosomal peptide synthetase (NRPS) enzymes. Another source of structural diversity is the variable choice of the nucleophile for intramolecular macrocyclic ester formation during xylomyrocin chain termination. This nucleophile is selected from the multiple available alcohol functionalities of the polyol moiety, revealing a surprising polyspecificity for the NRPS terminal condensation domain. Some xylomyrocin congeners also feature N- methylated amino acid residues in positions where the corresponding NRPS modules lack N- methyltransferase (M) domains, providing a rare example of promiscuous methylation in the context of an NRPS with an otherwise canonical, collinear biosynthetic program.
We investigate the interplay between cellulose crystallization and aggregation with interfibrillar interactions, shear forces, and the local changes in the medium's acidity. The latter is affected by the CO2 chemisorbed from the surrounding atmosphere, which, combined with shear forces, explain cellulose gelation. Herein, rheology, nuclear magnetic resonance (NMR), small and wide-angle X-ray scattering (SAXS/WAXS), and focused ion beam scanning electron microscopy (FIB-SEM) are combined to unveil the fundamental factors that limit cellulose gelation and maximize its dissolution in NaOH(aq). The obtained solutions are then proposed for developing green and environmentally friendly cellulose-based materials.
Keratin is a potential raw material to meet the growing demand for bio-based materials with special properties. Keratin can be obtained from feathers, a by-product from the poultry industry. One approach for keratin valorization is to use the protein to improve the properties of already existing cellulose and lignin-based materials to meet the requirements for replacing fossil-based plastics. To ensure a successful combination of keratin with lignocellulosic building blocks, keratin must have an affinity to these substrates. Hence, we used quartz crystal microbalance with a dissipation monitoring (QCM-D) technique to get a detailed understanding of the adsorption of keratin peptides onto lignocellulosic substrates and how the morphology of the substrate, pH, ionic strength, and keratin properties affected the adsorption. Keratin was fractionated from feathers with a scalable and environmentally friendly deep eutectic solvent process. The keratin fraction used in the adsorption studies consisted of different sized keratin peptides (about 1-4 kDa), which had adopted a random coil conformation as observed by circular dichroism (CD). Measuring keratin adsorption to different lignocellulosic substrates by QCM-D revealed a significant affinity of keratin peptides for lignin, both as smooth films and in the form of nanoparticles but only a weak interaction between cellulose and keratin. Systematic evaluation of the effect of surface, media, and protein properties enabled us to obtain a deeper understanding of the driving force for adsorption. Both the structure and size of the keratin peptides appeared to play an important role in its adsorption. The keratin-lignin combination is an attractive option for advanced material applications. For improved adsorption on cellulose, modifications of either keratin or cellulose would be required.
We examined the usefulness of dried spot blood and saliva samples in SARS-CoV-2 antibody analyses. We analyzed 1231 self-collected dried spot blood and saliva samples from healthcare workers. Participants filled in a questionnaire on their COVID-19 exposures, infections, and vaccinations. Anti-SARS-CoV-2 IgG, IgA, and IgM levels were determined from both samples using the GSP/DELFIA method. The level of exposure was the strongest determinant of all blood antibody classes and saliva IgG, increasing as follows: (1) no exposure (healthy, non-vaccinated), (2) exposed, (3) former COVID-19 infection, (4) one vaccination, (5) two vaccinations, and (6) vaccination and former infection. While the blood IgG assay had a 99.5% sensitivity and 75.3% specificity to distinguish participants with two vaccinations from all other types of exposure, the corresponding percentages for saliva IgG were 85.3% and 65.7%. Both blood and saliva IgG-seropositivity proportions followed similar trends to the exposures reported in the questionnaires. Self-collected dry blood and saliva spot samples combined with the GSP/DELFIA technique comprise a valuable tool to investigate an individual’s immune response to SARS-CoV-2 exposure or vaccination. Saliva IgG has high potential to monitor vaccination response wane, since the sample is non-invasive and easy to collect.
We report the design and synthesis of three star‐shaped non‐fullerene (NFA) acceptors, TPA‐2T‐INCN, TPA‐2T‐BAB, and TPA‐T‐INCN, based on triphenylamine (TPA) core and linked through π‐conjugated thiophene (T) spacers to different terminal units (3‐oxo‐2,3‐dihydro‐1H‐inden‐1‐ylidene) malononitrile, INCN, and 1,3‐dimethylbarbituric acid, BAB). These materials were blended with the widely used poly(3‐hexylthiophene‐2,5‐diyl) (P3HT) donor polymer and tested in flexible organic photovoltaics (OPVs). The NFAs capped with the strong electron withdrawing INCN unit performed best in OPVs. Both P3HT:TPA‐T‐INCN, and P3HT:TPA‐2T‐INCN blends also showed the highest photoluminescence quenching efficiency (95.8% and 92.6%, respectively). Surprisingly, when reducing the number of T spacers from 2 to 1, the solubility of the NFAs in o‐dichlorobenzene increased, leading to easier processing during the OPV fabrication and better surface morphology. This explains the best performance of TPA‐T‐INCN‐based blends in OPVs, with a champion power conversion efficiency of 1.13%. This article is protected by copyright. All rights reserved.
The use of automated vehicles (AVs) may enable drivers to focus on non-driving related activities while travelling and reduce the unwanted efforts of the driving task. This is expected to make using a car more attractive, or at least less unpleasant compared to manually driven vehicles. Consequently, the number and length of car trips may increase. The aim of this study was to identify the main contributors to travelling more by AV. We analysed the L3Pilot project’s pilot site questionnaire data from 359 respondents who had ridden in a conditionally automated car (SAE level 3) either as a driver or as a passenger. The questionnaire queried the respondents’ user experience with the automated driving function, current barriers of travelling by car, previous experience with advanced driving assistance systems, and general priorities in travelling. The answers to these questions were used to predict willingness to travel more or longer trips by AV, and to use AVs on currently undertaken trips. The most predictive subset of variables was identified using Bayesian cumulative ordinal regression with a shrinkage prior (regularised horseshoe). The current study found that conditionally automated cars have a substantial potential to increase travelling by car once they become available. Willingness to perform leisure activities during automated driving, experienced usefulness of the system, and unmet travel needs, which AVs could address by making travelling easier, were the main contributors to expecting to travel more by AV. For using AVs on current trips, leisure activities, trust in AVs, satisfaction with the system, and traffic jams as barriers to current car use were important contributors. In other words, perceived usefulness motivated travelling more by AV and using AVs on current trips, but also other factors were important for using them on current trips. This suggests that one way to limit the growth of traffic with private AVs could be to address currently unmet travel needs with alternative, more sustainable travel modes.
There is a growing appreciation for the role that yeast play in biotransformation of flavour compounds during beverage fermentations. This is particularly the case for brewing due to the continued popularity of aromatic beers produced via the dry-hopping process. Here, we review the current literature pertaining to biotransformation reactions mediated by fermentative yeasts. These reactions are diverse and include the liberation of thiols from cysteine or glutathione-bound adducts, as well as the release of glycosidically bound terpene alcohols. These changes serve generally to increase the fruit and floral aromas in beverages. This is particularly the case for the thiol compounds released via yeast β-lyase activity due to their low flavour thresholds. The role of yeast β-glucosidases in increasing terpene alcohols is less clear, at least with respect to fermentation of brewer’s wort. Yeast acetyl transferase and acetate esterase also have an impact on the quality and perceptibility of flavour compounds. Isomerization and reduction reactions, e.g. the conversion of geraniol (rose) to β-citronellol (citrus), also have potential to alter significantly flavour profiles. A greater understanding of biotransformation reactions is expected to not only facilitate greater control of beverage flavour profiles, but also to allow for more efficient exploitation of raw materials and thereby greater process sustainability. Key points • Yeast can alter and boost grape- and hop-derived flavour compounds in wine and beer • β-lyase activity can release fruit-flavoured thiols with low flavour thresholds • Floral and citrus-flavoured terpene alcohols can be released or interconverted
The local approach has been successful in evaluating the brittle fracture probability of nuclear pressure vessel steels by establishing a link between microstructural defects and the macroscopic fracture behaviour. The evaluation of fracture probabilities relies on the applied stress on the smallest representative elementary volume. A proper description of the stress heterogeneities in polycrystals helps refine the prediction. The current work investigates the effect of carbon macro-segregation in heavy forgings and demonstrates a workflow combining crystal plasticity with the Microstructure Informed Brittle Fracture (MIBF) local approach model in fracture toughness prediction. The microstructural and mechanical properties of low alloy steels with different segregation levels were evaluated. A dislocation-density based crystal plasticity model which contains carbide strengthening contribution was identified and applied for modelling microstructure influence on local stress distributions. Results show that the microstructural evolution observed at high carbon levels has a significant influence on local stress distributions, which in turn affects the fracture toughness. The simulation results also demonstrate that, with proper input of microstructural information, the MIBF model is capable to predict the shift of the brittle-to-ductile transition zone with the variation of carbon and alloying elements and gives insights about factors affecting the resistance of materials.
Quantum Conductance In article number 2201248, Gianluca Milano, Ilia Valov, and co‐workers review the state‐of‐the‐art of quantum conductance effects in memristive devices. Besides analyzing fundamental physicochemical phenomena and electronic ballistic transport in nanofilaments, recent developments in experimental observation of quantum effects in memristive devices and related challenges are discussed. Representing suitable platforms for investigating quantum phenomena at room temperature, future perspectives of memristive devices in quantum and neuromorphic systems are envisioned.
Excessive nitrogen (N) uptake for nutrient use in food production and industry and increased N losses to the environment severely interfere with nutrient cycles and harm the environment and thus, closing N cycles through N recovery and recycling is required to improve N use efficiency. To quantify positive impacts enhancing N cycles, this study suggests a novel N handprint approach, which combines life cycle assessment based nutrient footprint and carbon handprint approaches. The N handprint comprises of a set of indicators providing a wide systemic view on changes in N cycles. The case study demonstrates that the N handprint is created when a recycled N nutrient product is used instead of a virgin N nutrient for the needs of a pulp and paper mill wastewater treatment. According to our results, the handprint equals a reduction of 454 kg of virgin N inputs, and 5.6 kg of total N inputs for daily treated wastewater. Additionally, global warming potential is 91%, and the eutrophication potential 48% lower for the recycled N nutrient than for the virgin N nutrient. These results can be used to promote the use of recycled N on similar occasions in order to improve nutrient use efficiency.
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