Solid biomass oxy-fuel combustion with external recirculation was demonstrated in a 100-kW atmospheric, down-fired combustor operated close to stoichiometry. The external recirculation setup comprised a particulate filter, a condenser, a fan and O2 addition before the burner inlet. A theoretical description of the recirculation process is presented and validated. Two fuels, softwood (SW) and forest residues (FR), with similar residence times, were compared. Gaseous species, including potassium (K) compounds (atomic K, KOH, and KCl), and gas temperature were quantified in real-time by tunable diode laser absorption spectroscopy (TDLAS) and photofragmentation TDLAS at two locations in the reactor core. Major species (CO2, H2O, O2, and N2) were also measured at the exhaust. Flue gas particles collected with a low-pressure impactor at the exhaust were analyzed by X-ray powder diffraction and scanning electron microscopy. The average CO2 purity (dry) was 90 % for SW and 86 % for FR. The NO concentration was higher for FR due to the larger nitrogen content in the fuel. The gaseous K species concentrations were higher for FR than for SW (factor 2–3), but not as high as expected from the difference in fuel K content (factor 7), likely due to the high content of Si and Al in FR. Gas-phase K was significantly lower than predicted by thermodynamic equilibrium calculations (TEC), probably due to K adsorption by soot particles. The fine and coarse particle concentrations were significantly higher for FR than for SW due to the higher ash content of FR. The FR fine mode particles consisted mainly of K2SO4 and KCl, in good quantitative agreement with TEC of gas phase condensation. Apatite, Ca5(PO4)3OH, likely formed from vaporized Ca and P, was found in the fine mode in all recirculation cases.
Biochar performance in applications like soil amendment depends on properties including porosity, surface area, and pore structure, which are influenced by both process conditions and feedstock characteristics such as wood anatomy. However, their effects across micro- and nanometre length scales remain poorly understood. This study therefore investigates how feedstock and process conditions affect properties of pine wood biochar produced in industrially relevant pilot-scale continuous reactor for torrefaction (291–315°C, 6–12 min) and pyrolysis (350–400°C, 25 min). High-resolution X-ray microtomography combined with AI-assisted image analysis was used to quantify earlywood–latewood distribution, porosity, pore size, cell wall thickness, and micrometre-scale surface area, while N2 and CO2 sorption probed nanometre-scale structure. The wood microstructure was largely preserved across treatments, resulting in highly anisotropic pore networks. Porosity remained high (33–77%), primarily influenced by earlywood–latewood variability rather than temperature. From torrefaction to pyrolysis, cell wall thickness and pore diameter decreased, while microporosity (<∼0.7 nm) and accessible surface area increased significantly above 350°C. Discrepancies between N2 and CO2 measurements indicate differences in pore accessibility, likely associated with ultramicroporosity, pore constrictions and/or partially inaccessible pores. Water holding capacity ranged from 1.7 to 5.3 times dry weight, with highest average at 350°C, although differences were not significant. Combined use of microtomography and gas sorption provides a multiscale framework for linking biochar structure across micro- and nanometre scales to performance-relevant properties. These findings demonstrate that wood anatomy dominates over process conditions under mild thermal treatment, highlighting new possibilities for tailoring process design through consideration of wood anatomy.
Large Language Models (LLM) have experienced strong development in recent years, with varied applications. This paper uses LLMs to develop a post-hoc process that provides more elaborated explanations of the results of food recommendation systems. By combining LLM with a hybrid extraction of key variables using SHAP, we obtain dynamic, convincing and more comprehensive explanations to lay user, compared to those in the literature. This approach enhances user trust and transparency by making complex recommendation outcomes easier to understand for a lay user.
Rapid advancements in artificial intelligence (AI), in combination with increased availability of rich large-scale clinical data, has paved the way for promising implementations in both diagnosing/subtyping as well as managing of sleep disordered breathing (SDB). A central strength of AI in this regard is how it facilitates analysis of complex multidimensional/modal data, for example pertaining to comorbidities and so-called treatable traits. However, the utility of such applications remains somewhat limited, as most AI models are so-called “black boxes”, for which it is not intuitive to assess how an output was arrived at. This poses challenges for ensuring patient trust and controlling bias. Explainable AI (XAI), which constitutes techniques to either distill “black box” models to simpler ones or to elucidate influential patterns by surrogate “white box” models, may provide such insights. Adoption of XAI within this field, however, is still at a nascent stage, and overall, the optimal role of AI alongside clinicians continues to be unclear. This review provides a clinically oriented overview of state-of-the-art implementations of AI and XAI in SDB. In addition, we discuss limitations and trade-offs with XAI and propose a general framework for personalized management of obstructive sleep apnea using XAI and treatable traits.
In an era of misinformation, pseudoscience, and declining public trust in expert knowledge, science education must help students develop the competences needed to navigate post-truth realities. Drawing on recent literature, we identify four key challenges: (1) strengthening students’ media and digital literacy; (2) enhancing their understanding of scientific practices and the social construction of knowledge; (3) nurturing habits of mind grounded in intellectual virtues; and (4) building their capacity for socio-scientific decision-making and constructive dialogue in the context of epistemic disagreement. Together, these challenges point to a set of essential competences for contemporary science education. To explore how existing frameworks respond to these needs, we analyse the affordances and limitations of scientific literacy visions I–III. Building on this dual analysis, we present the EVIDENCE approach—a pedagogical model developed specifically for upper-secondary science education to address evolving epistemic and socio-political demands. In doing so, we seek to enrich existing conceptualisations of post-truth competences and, through our analysis of the scientific literacy visions, offer insights to inform curriculum development and pedagogical practice, while acknowledging the limitations of our analysis.
