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High temperature fast pyrolysis of waste biomass in a solar-assisted quartz drop-tube reactor
No full textSolar-assisted pyrolysis is a sustainable process for converting biomass into syngas, bio-oil, and biochar using renewable solar thermal energy, with a potentially zero carbon footprint. It generates both high-value gaseous and liquid fuels while transforming the atmospheric CO2 captured in biomass in the form of solid carbon that can be long-term sequestrated or valorized. The EU's target of reducing net greenhouse gas emissions to at least 55% by 2030 sets the stage for effective measures to limit carbon emissions and achieve a sustainable future. This study presents the development of an innovative fast pyrolysis quartz drop-tube reactor using concentrated solar power and its performance for bio-waste valorization. Extensive raw material characterization was carried out, which provides valuable insights into demolition wood and rye straw feedstocks properties. Solar pyrolysis runs revealed key dependencies of product yields on operational parameters such as feedstock type, nitrogen gas flow rate (0.7-1.4 NL/min), and heating profile in the temperature range 800-900 °C. Operation at such high temperatures promoted gas production (>50% gas yield in mass) over liquid and solid products. In similar conditions, rye straw showed higher gas yield as compared to demolition wood. In addition, preheating or increasing the gas residence time favored gas production with negligible impact on gas composition. The solar drop tube pyrolysis reactor appears as a sustainable option to upgrade waste feedstocks into valuable products using concentrated solar energy with net zero CO2 emission
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
Trip chain characteristics and situational factors influencing private car mode choices – A survey study in two Finnish urban areas
Get PDFHuman mobility is often characterized by trip chains with multiple destinations. However, the reasons behind mode choices have been mostly studied in single-purpose travel contexts. To address this gap, we examine how trip chain length, complexity (number of trip legs), and purposes are linked to situational factors influencing the decision to use a private car (i.e., context-dependent considerations that affect travel decisions, such as the need to save time, avoid bad weather, or be able to relax). Data was collected through a survey where participants described a recent private car trip chain, including items about trip purposes, the number of trips and trip legs, trip length, and the importance of various situational factors on mode choice decision. The survey was conducted in two major urban areas in Finland, yielding 731 valid responses. The results of a regression analysis show that all examined trip chain characteristics impact on the prevalence of different situational factors. Leisure being a trip purpose was associated with non-utilitarian factors influencing car use, and errands and commuting with both utilitarian and non-utilitarian factors. Trips involving errands appeared more habitual than trips for other purposes. Furthermore, complexity was not only associated with utilitarian factors, but also non-utilitarian. The findings suggest that trip chain complexity and purposes should be considered by practitioners and policymakers in sustainable mobility campaigns, passenger information systems that account for trip chain characteristics should be developed, and targeted interventions to reduce habitual car use, especially for errands, should be created
On diffusion-controlled Li-trapping in high energy Li-ion cells under fast discharge and freezing conditions
No full textLi-ion batteries are promising energy storage devices owing to their high energy density which enables their use in different applications. Herein, we report on the electrochemical behavior of commercial high capacity 30 Ah pouch cells with energy density 275 Wh kg−1 over a wide temperature range (60 °C to −20 °C) using different discharging rates. A testing protocol was designed to understand the losses seen in discharge capacities (reduction charges) when the cells were discharged at fast rates (~16 % and 20 % capacity losses at discharge rates of 2C and 3C, respectively) or operated under freezing conditions (~17 % capacity loss at ~ −20 °C and C/5 discharge rate). The protocol involved monitoring the charge capacity (oxidation charge) before and after discharging as well as tracking the changes in open circuit voltage (OCV) for about 30 min after discharging. The accessible capacity was concluded to be limited not only by the developed iR-drop, but also by the diffusion-controlled lithium trapping as a result of the formation of concentration gradient of Li-ions. Notably, the surface temperatures of the cells were raised from 25 °C (environmental chamber temperature) to ~40 °C and 52 °C upon fast discharge at rates of 2C and 3C, respectively. Resting the cells for about 30 min after fast discharge was sufficient to drop the surface temperature back to 25 °C. This work provides insights for understanding the limitations of fast discharge and operating temperatures on industry relevant high energy Li-ion battery cells.</p
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
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
Machine-learning integrated multi-domain co-optimization for electrified heavy duty fleets
No full textDriven by global regulations and the urgent need for a sustainable transition to zero-emission fleets in the transport sector, revolutionizing powertrain systems and their respective development processes have become more and more prevalent. Ambitious goals have been established for the latest public-funded research projects, such as ESCALATE (Powering European Union Net Zero Future by Escalating Zero Emission Heavy Duty Vehicles (HDV) and Logistic Intelligence), to increase the efficiency of the powertrain by up to 10% and thus maximize the operational range above 750 km. All of this will be achieved by introducing cost-effective, modular, and scalable electric powertrain components combined with advanced system control algorithms, targeting a broad market coverage with flexible vehicle architectures. In this context, the paper presents a completely virtual frontloading strategy to create a modular and highly integrated e-Axle system, leveraging a dual permanent magnet synchronous machine configuration to improve multiple performance indicators. These are the performance output, in terms of power and torque, system efficiency, and noise-vibration-harshness (NVH) criteria. To allow for an holistic system parametrization, a combined electric machine and transmission synthesis, using an active learning-based, multi-layer nested optimization approach together with a model predictive control strategy for motion and thermal domain has been employed. This development framework is integrating electric machine dimensions and transmission gear ratios as design parameters, as well as thermal actuation and torque as control parameters, to ensure a system right-sizing in a given use-case environment. By including monetary considerations with genetic algorithms, an extension for a powertrain family identification to a complete HDV fleet is facilitated. To demonstrate the feasibility of this framework, a concept assessment and validation has been carried out. The key achievements include a close matching of the defined KPIs, namely the peak wheel torque of 56150 Nm and continuous power of 381 kW – about 2%, respectively 0.2% above the target – and an enhanced peak power capability of 536 kW. In terms of energy efficiency, the multi-stage gear boxes support a well optimized operation in the VECTO long haul cycle, indicating a 40-ton vehicle energy consumption of around 109.7 kWh per 100 km, while the 76-ton variant consumes approximately 204.6 kWh per 100 km. Further the predictive cruise control strategy led to a consumption reduction of about 2.6%–3.4%.</p
Supplementing XYR1-mutated Trichoderma reesei strain cultivation with (SO2-ethanol-water) softwood pulp improves cellulase production
No full textThe cellulolytic enzyme cost remains a major bottleneck in converting lignocellulose, especially softwoods, into fuels and chemicals. The aim of this study was to evaluate possibilities to increase enzyme production efficiency by using SO 2-ethanol-water (SEW) pretreated softwood pulp with a Trichoderma reesei strain that expresses a mutant form of the main transcriptional regulator, XYR1, of cellulase- and hemicellulase genes leading to loss of glucose repression/carbon catabolite repression. The (hemi)cellulase enzyme cocktail of this strain was improved by expressing three heterologous enzymes, a beta-glucosidase, a CEL6 (CBH2) exoglucanase and a lytic polysaccharide mono‑oxygenase. Seven bioreactor cultivations were performed using glucose and different cellulose supplementations and glucose feed strategies. We showed that adding 3 %-w/v cellulose to the glucose medium and starting the glucose feed when the glucose was consumed from the batch medium, improved the protein production rate by over 80 % during the first five days compared to total absence of cellulose. With only 3 % cellulose addition to the batch phase, we estimate that over one third of time and total carbon source, including cellulose, could be saved compared to a production process without cellulosic substrate supplementation. Additionally, enzymes produced with SEW pulp in 119 h and those produced with glucose alone in 193 h both achieved 90 % glucose conversion when used for SEW pulp hydrolysis at a protein loading of 4–5 mg/g cellulose. Herein, we have shown that the M2883 strain can produce more than 29 FPU/mL of the complete set of cellulase enzymes both with and without cellulose supplementation.</p
Effect of spatially non-uniform boronization on plasma restart in WEST
No full textThe recent ITER re-baseline with the adoption of a full-W wall calls for mandatory boronization studies. ITER pulses will be inboard limited on the W tiles of the central column for several seconds during the current ramp up phase. Our first question of this study is: will it be possible to efficiently start plasma operations in a full-W ITER without any boronization? In particular, throughout the start of research operations (SRO), ITER will be equipped with an asymmetric boronization system as glow anodes in the equatorial plane will not be uniformly distributed in the toroidal direction due to the limited availability of ports. According to recent simulations, such arrangement of the glow anodes could lead to a strongly non-uniform B layer with depleted regions. Our second question hence is: should a boronization be needed to start plasma operations in ITER, would a non-uniform B layer be enough? In November 2024, we attempted to restart WEST plasma operations without boronization after a vent and after installing new bulk W limiter tiles. In about 4 days of operation corresponding to 74 pulse attempts, we reached a maximum pulse duration of 1.55 s and a maximum plasma current of 600 kA. Plasmas were cold and dense, mostly detached from the inboard limiter and dominated by light impurities with radiated power fractions close to unity. No runaway electron beams were observed but the restart without boronization was not timely. We then carried out the first WEST boronization utilizing only 3 out of 6 diborane (B2D6) inlets (half torus), to deposit a non-uniform B layer. Repeatable, 10 s long, ohmic limiter pulses were immediately achieved with radiated power fractions between 50 % and 70 %. Through a separate experiment in February 2025, we achieved matching pulses before and after a second non-uniform boronization to better characterize its effects: the radiated fraction initially dropped by 22 % with the reduction mainly occurring in the central plasma and well correlating with lower UV signals for O, N and W. These effects almost vanished by the end of the first day after the non-uniform boronization corresponding to a cumulated injected energy of 0.7 GJ.</p
Defining Dimension Metrics for Evaluating Overall Prompting Effectiveness
No full textThe integration of Large Language Models (LLMs) into education, particularly in software engineering and IT, presents opportunities and challenges. While LLMs support problem-solving and code generation following the specifications, their effectiveness often depends on students’ ability to formulate precise and effective prompts. To address this, frameworks known as meta-prompts have been proposed. However, the impact of specific frameworks on students’ prompt-writing skills and learning outcomes remains underexplored. This study examines two didactic frameworks–Iterative Feedback and Reflection (IFR) and Adaptive Learning Progression (ALP)—to assess their effectiveness in enhancing prompt-writing skills and learning engagement. We propose complementary metrics within the Overall Prompting Effectiveness (OPE) framework, defined through three key dimensions: Adaptability, Relevance, and Efficiency. These dimensions encapsulate essential components for effective interaction with LLMs in educational contexts. The design of controlled experiment involves IT-engineering students divided into two groups, each using one of the two different didactical meta-prompt-enhanced frameworks. The IFR group engages in iterative cycles of prompt refinement and self-reflection, while the ALP group utilizes adaptive meta-prompts that dynamically adjust task complexity based on performance. Data collection focuses on OPE-aligned metrics, including the number of prompt iterations, time efficiency, response alignment, and learning progress self-rating, allowing for a comparative analysis of the frameworks’ impacts on learning outcomes. Our work establishes and evaluates these metrics, contributing to research in LLM-assisted learning. It addresses gaps in prompt engineering by showing how IFR and ALP frameworks can be utilized to enhance skill development and offers guidance on integrating LLMs into educational contexts for better interactive learning