Towards Circular Steelmaking: Hydrogen-Based Valorization of Electric Arc Furnace Dust

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Here, we show that hydrogen reduction, under industrially relevant conditions, offers a transformative solution for EAFD valorization. Compared to conventional carbothermic reduction, hydrogen achieved higher zinc recovery (98.8–99.7% vs. 86.2–90.0%), enhanced iron metallization, and superior removal of sulfur and phosphorus. This is attributed to hydrogen's enhanced diffusivity and reactivity, which prevents the microstructural degradation (pore closure) that limits carbothermic reduction. Importantly, we demonstrate the feasibility of selective zinc extraction, allowing EAFD recycling even without simultaneous iron reduction. This work provides a pathway for transforming EAFD from a hazardous waste into valuable resources, contributing to a lower-carbon and more resource-efficient steel production process. Physical sciences/Engineering/Chemical engineering Physical sciences/Chemistry/Chemical engineering Electric Arc Furnace Dust (EAFD) Hydrogen Reduction Zinc Recovery Circular Economy Sustainable Steelmaking Waste Valorization Figures Figure 1 Figure 2 Figure 3 Introduction The global steel industry, a cornerstone of modern infrastructure, is also a major contributor to anthropogenic CO2 emissions, accounting for approximately 7.2% of the global total (1.9 Gt of steel produced in 2019) 1 . A significant portion of this steel is galvanized for corrosion protection, and its recycling in electric arc furnaces (EAFs) is crucial for resource efficiency. However, EAF steelmaking generates substantial quantities of hazardous electric arc furnace dust (EAFD), with a global output of 5.5 to 11 million tons annually (based on 558.4 Mt of EAF steel production and EAFD generation rates of 10–20 kg/ton) 2 , 3 . The currently dominant technology for EAFD treatment, the Waelz process, relies on carbothermic reduction, which presents major obstacles to achieving a truly circular economy 4 , 5 . This process is not only carbon-intensive, requiring approximately 150 kg of coke 6 and emitting nearly 2000 kg of CO2 per ton of recovered zinc, but also achieves only around 90% zinc recovery. Furthermore, it generates 600–650 kg of slag per ton of EAFD 7 , a byproduct containing residual zinc (2–5%), substantial iron (40–60% as both oxides and metal), and various impurities (SiO 2 , CaO, MnO, Cr 2 O 3 , Cu, Ni, and P) 8 – 10 . This slag is largely unsuitable for direct recycling because of its high sulfur content and low degree of iron metallization (the fraction of the total iron present as metallic Fe rather than iron oxides) that prevent effective iron recovery and integration back into the electric arc furnace, hindering the closed-loop material flow essential for a circular economy 11 . Driven by the imperative for industrial decarbonization and a circular economy, hydrogen-based reduction is emerging as a transformative technology for metal recovery 12 – 14 . Replacing carbon with hydrogen eliminates direct CO 2 emissions, a fundamental advantage. Furthermore, hydrogen reduction offers the potential for improved reaction kinetics, leading to more efficient metal extraction 15 . While the basic principles of hydrogen reduction are known, its specific application to complex hazardous waste materials like EAFD, particularly under dynamic, industrially relevant conditions, has not been thoroughly investigated. This study directly addresses this critical gap, providing a comprehensive analysis of hydrogen-based EAFD processing compared to conventional carbothermic methods. To directly compare hydrogen and carbothermic reduction of EAFD under realistic industrial conditions, we systematically investigated the reduction kinetics of two distinct steelmaking dusts using a dynamic temperature-gas composition profile. This profile mimicked the continuously changing environment a material undergoes within industrial reactors. Our approach combined thermogravimetric analysis (TGA) for kinetic quantification with microstructural and elemental analysis using scanning electron microscopy (SEM) with backscattered electron detector (BED) and SEM with energy-dispersive X-ray spectroscopy (EDX). A key innovation was the transformation of the 2D spatial elemental distribution data into a series of scatter plots, allowing us to identify correlations between elements and infer the presence of specific compounds (e.g., a positive correlation between zinc and sulfur with a 1:1 atomic ratio indicates ZnS). This revealed that CO-based reduction led to denser, less reactive oxide structures, while hydrogen preserved microstructural features conducive to continued reaction. Consequently, hydrogen reduction achieved superior Zn recovery, enhanced iron metallization, and lower residual sulfur and phosphorus. These results demonstrate the clear environmental and process engineering benefits of hydrogen-based EAFD processing, offering a pathway towards more sustainable steelmaking. Results and Discussion We used TGA to reduce two EAFD samples comparing hydrogen and carbon-based reduction. The samples differed primarily in their ZnO content (29.6% and 40.7%). The experiments featured a dynamic temperature-gas composition profile, mirroring the changing environment in industrial counter-current reactors. The gas composition was linearly transitioned from a H₂O/CO₂-rich mixture to pure H₂/CO during heating. This allowed direct comparison of H₂/H₂O and CO/CO₂ reduction kinetics under comparable conditions. Post-reduction, SEM-BED imaging enabled phase segmentation (distinguishing porosity, slag, and metal based on brightness) and quantification of macro-porosity and metallization. SEM-EDX mapping provided spatial elemental concentration profiles. Across multiple metrics – including reaction kinetics, microstructure preservation, and impurity removal – hydrogen reduction consistently outperformed carbothermic reduction. Comparing the reduction of EAFD1 in H₂/H₂O and CO/CO₂ atmospheres revealed significant differences in reaction kinetics and microstructural evolution (Fig. 1 ). The H₂/H₂O system consistently exhibited superior performance, achieving a higher overall mass loss (Fig. 1 a) and greater mass loss rates when compared at equivalent extents of reaction (Fig. 1 b). Notably, the maximum reaction rate for the CO/CO₂ system occurred at 998°C with 74% of the sample mass remaining, whereas the peak rate for the H₂/H₂O system shifted to a higher temperature (1030°C) and lower residual mass (61%). This difference in the temperature and extent of reaction at the peak rate suggests that, in the CO/CO₂ system, reduction kinetics are limited by microstructural changes that occur at elevated temperatures, rather than by the simple depletion of reducible compounds SEM-BED imaging, distinguishing phases (void/porosity, slag, and metal) based on brightness differences, confirmed these kinetic differences. Porosity mapping (using 10 x 10 pixel regions, approximately 9 µm x 9 µm) revealed a significantly larger fraction of low-porosity regions in the CO/CO₂-reduced sample. Specifically, 50% of the analyzed area in the CO/CO₂ sample exhibited a porosity of 3% or less, whereas only 19% of the analyzed area in the H₂/H₂O sample had such low porosity. Furthermore, metallization in the H₂/H₂O system was pervasive and uniformly distributed with a fine particle size distribution, while in the CO/CO₂ sample, it was limited to the outer surface, forming a dense contour around the non-metallized matrix. This combination of lower overall porosity and restricted metallization in the CO/CO₂ sample directly explains the observed slower reduction kinetics, as gas diffusion to and from reaction sites is hindered. SEM-EDX elemental mapping provided further insights into the reduction mechanisms. The CO/CO₂ sample displayed a clear Zn concentration gradient (lower at the surface, higher in the center), while the H₂/H₂O sample showed nearly complete Zn removal. Quantitatively, the Zn concentration decreased from an initial 23.7 wt.-% to 0.1% in the hydrogen system (with 52.3% mass loss), yielding a zinc recovery rate of 99.7%. In contrast, the carbon-based system achieved only 90.0% recovery, with a final Zn concentration of 3.7% (and 43% mass loss) – a value comparable to those obtained in state-of-the-art industrial processes. Turning to the iron behavior, Fe concentration histograms derived from the EDX data (Fig. 1 h) confirmed the findings from the SEM-BED images. The CO/CO₂ system maintained a stable wüstite region at approximately 40 at.-% Fe, with evident MnO dissolution into the FeO matrix (Fig. 1 d) and a corresponding accumulation of MgO (Fig. 1 e). MnO and MgO stabilize the oxide phase, reducing its reactivity and lowering the liquidus temperature, thus promoting earlier sintering and contributing to the observed dense microstructure. In contrast, the H₂/H₂O sample lacked these correlations and instead exhibited significant metallic Fe formation. The behavior of sulfur and phosphorus further highlighted the distinct reduction mechanisms in the two atmospheres. In the CO/CO₂ sample, sulfur was primarily present as ZnS (Fig. 1 f). In contrast, the H₂/H₂O system lacked this correlation and exhibited a significantly lower bulk sulfur concentration (0.3% vs. 0.8%), likely caused by the formation of H₂S. Phosphorus behaved differently: in the CO/CO₂ atmosphere, it formed apatite-type phases with calcium (Ca:P ratio of 5:3), while this association was largely absent in the H₂/H₂O environment, suggesting the formation of volatile phosphorus species (likely PH3). Although final bulk phosphorus concentrations were similar (0.6% vs. 0.7%), the greater mass loss in the H₂/H₂O system (52.3% vs. 43%) indicates more effective overall phosphorus removal. The lower residual sulfur and phosphorus content in the hydrogen-reduced material significantly benefits its potential for recycling in electric arc furnaces, which have limited capabilities for removing these elements. However, the process design must account for the potential generation of hazardous H 2 S and PH 3 gases during hydrogen-based EAFD treatment. As with EAFD1, the second dust sample (EAFD2, Fig. 2 ) showed significantly better reduction performance with H₂/H₂O compared to CO/CO₂. The H₂/H₂O system achieved a maximum reaction rate of 2.4 mass-%/min, twice that of the CO/CO₂ system (1.2%/min). The peak reaction rate occurred at a lower temperature and higher residual mass for CO/CO₂ (983°C, 76% residual mass) than for H₂/H₂O (1016°C, 56% residual mass). This difference indicates that while reactant depletion primarily controls the kinetics in the H₂/H₂O system, pore structure collapse due to sintering limits the reaction progress in the CO/CO₂ system. The impact of this sintering-induced pore collapse is observed in both samples earlier in the CO/CO₂ system, likely due to the larger size of CO and CO₂ molecules compared to H₂ and H₂O, leading to more significant diffusion limitations as pores shrink. SEM analysis using the backscatter detector showed that both reduction conditions resulted in dense microstructures with low median porosity (≤ 2%). However, iron metallization was significantly different: substantial and pervasive in the H₂/H₂O sample, but minimal in the CO/CO₂ sample. Zinc distribution also differed greatly. The CO/CO₂ sample showed a steep Zn concentration gradient (surface to core), while the H₂ system achieved near-complete Zn removal (0.9% bulk concentration, 98.8% recovery, versus 18.5% and 86.2% for CO/CO₂). Examining iron phases, the CO/CO₂ sample retained a substantial FeO region with MnO dissolution (Fig. 2 d, 2 h). Conversely, the H₂/H₂O sample consisted mainly of metallic Fe with limited, MnO-enriched FeO. The differences in reduction behavior between EAFD2 and EAFD1 likely stem from the higher initial Zn concentration and longer residence time in the FeO-region in EAFD2 Analysis of the elemental correlations, derived from the EDX, revealed a correlation between sulfur and copper under H₂ reduction, indicating CuS₂ formation (Fig. 2 e). A possible explanation is that H₂S forms with a higher partial pressure within the dense microstructure, which further reacts with copper. The data also suggest the presence of Fe₂S. In contrast, sulfur remained primarily bound to zinc as ZnS in the CO/CO₂ sample (Fig. 2 f). The bulk sulfur concentrations were 0.9% for the H₂ reduction and 1.2% for the CO-based system. After adjusting for the different mass losses, the hydrogen-reduced sample contained 48% less sulfur. Phosphorus showed a strong correlation with calcium in the CO/CO₂ sample (Fig. 2 g). This correlation was significantly weaker in the H₂/H₂O sample, suggesting partial phosphorus removal, likely as volatile PH₃. We hypothesize that the denser microstructure in the H₂-reduced sample may have limited PH₃ formation due to restricted diffusion through the collapsed micropore structure. Summary and Conclusion Hydrogen-based reduction offers a transformative approach to EAFD valorization, significantly outperforming conventional carbothermic methods. Our study, using dynamic temperature-gas profiles to mimic industrial reactor conditions, demonstrates that H₂/H₂O reduction achieves superior zinc recovery (98.8–99.7% vs. 86.2–90.0% for CO/CO₂), enhanced iron metallization, and more effective removal of detrimental elements like sulfur and phosphorus. These improvements stem from hydrogen's smaller molecular size, higher diffusivity, and greater reactivity, which promote more efficient gas-solid interactions and prevent the sintering-induced pore collapse that hinders reduction in CO/CO₂ environments. Crucially, our findings reveal the potential for selective zinc extraction without iron reduction, opening a pathway for EAFD recycling even before widespread adoption of hydrogen-based ironmaking becomes economically feasible. This work establishes a foundation for optimizing hydrogen-based EAFD processing, contributing to a more sustainable and circular steel industry. Future research should focus on detailed kinetic modeling, the effect of additives on kinetics, selectivity and impurity removal and upscaled validation to assess economic feasibility. Materials and Methods Two different EAFD materials were used in this study. Material 1 contained 18.8 wt.-% Zn, while Material 2 had a higher Zn concentration of 30.3 wt.-%, measured by X-ray fluorescence (XRF). In both materials, zinc was primarily present as franklinite (ZnFe₂O₄) and zinc oxide (ZnO). Iron was present primarily as Fe³⁺ in the form of franklinite and hematite (Fe₂O₃). The detailed chemical compositions of the EAFD samples, determined by XRF, inductively coupled plasma mass spectrometry (ICP-MS), and SEM-EDX are presented in Table 1 . Table 1 Chemical analysis of initial dust samples in wt.-% Mat Meth Zn Fe CaO MgO SiO 2 Al 2 O 3 MnO Cr 2 O 3 S Cu K Cl 1 XRF 18.8 31.0 3.52 1.8 3.5 1.0 2.96 1.02 0.4 0.40 0.6 0.7 1 ICP 19.8 31.1 6.48 3.5 3.0 0.6 3.27 1.04 0.4 0.40 0.9 2.4 1 EDX 21.2 30.8 3.42 1.4 3.6 1.2 3.07 1.12 0.5 0.47 0.9 2.4 2 XRF 30.3 21.0 2.46 1.2 3.4 1.2 1.43 0.55 0.6 0.35 0.6 2.0 2 ICP 30.9 19.5 4.56 3.6 2.8 0.9 1.42 0.51 0.7 0.44 1.8 5.4 2 EDX 29.2 18.1 2.74 1.9 4.6 2.3 1.32 0.52 1.0 0.49 2.6 5.9 Sample Preparation EAFD samples were dried at 105°C for 24 hours in a laboratory oven to remove moisture. For each experiment, 350 mg of dried EAFD was compacted using a custom-designed electromechanical uniaxial press at 14 MPa. The resulting cylindrical specimens had a diameter of 7.5 mm and a height of 3.75 mm, yielding a geometric surface area of 176.7 mm² and a bulk density of 2.1 g/cm³. These dimensions resulted in uniform gas diffusion paths of ≤ 3.75 mm through the sample's macroporous structure. Sample morphology and exact dimensions were documented using a Keyence VHX-7000 digital microscope prior to reduction experiments. Thermogravimetric Reduction Procedure Reduction experiments were conducted in a customized Linseis TGA, as described in detail by Brandner et al. 16 . Temperature control was achieved using a type C (tungsten-rhenium) thermocouple integrated into the sample holder. The TGA was configured to simulate the conditions experienced by material in a counter-current reactor, where the temperature increases and the gas composition transitions from oxidizing (H₂O/CO₂-rich) to reducing (H₂/CO-rich). It is important to note that while the volumetric gas flow rates of CO/CO₂ and H₂/H₂O were the same, the different molar masses and reaction stoichiometries result in different thermodynamic equilibria. The experimental profile (Fig. 3 a) consisted of three zones: a drying zone (up to 200°C), a heating zone with constant gas composition (up to 800°C), and a reduction zone with a continuously changing gas composition. Samples were heated to 300°C at 25 K/min in a 61% H₂O and 39% H₂ atmosphere, followed by heating rates of 20 K/min to 500°C and 12.5 K/min to 800°C. From 800°C to 1150°C, the temperature increased at 5.83 K/min over one hour. During this final heating segment, the H₂O/CO₂ flow rate decreased linearly from 50 ml/min to 0 ml/min, while the H₂/CO flow rate increased linearly from 30 ml/min to 80 ml/min. Consequently, the inflowing gas consisted of 100% H₂/CO once the sample reached 1150°C. Samples were subsequently cooled at 25 K/min under an inert argon atmosphere. The thermodynamic conditions for the H₂ system (Fig. 3 b) indicate that wüstite (FeO) becomes stable at approximately 800°C. The reduction of ZnO to gaseous Zn depends on the partial pressure of Zn(g); at a partial pressure of 0.01 bar, Zn(g) becomes thermodynamically stable at around 900°C, corresponding to the onset of significant zinc fuming observed in the TGA. Conditions favorable for FeO reduction to Fe occur at approximately 940°C. In the CO system (Fig. 3 c), wüstite becomes stable earlier (at around 740°C), Zn(g) fuming (at 0.01 bar) begins at around 910°C, and FeO reduction starts at approximately 990°C. Therefore, from a purely thermodynamic perspective, the sample in the CO system resides in the wüstite stability region for at least 37 minutes, compared to 24 minutes for the H₂ system. SEM-EDX Analysis Samples were characterized after reduction using a JEOL IT300 scanning electron microscope. Backscatter images were acquired with a pixel step size of 0.9 µm per pixel, while EDX mappings (Oxford X-Max50 EDX Detector) used a step size of 29.3 µm per pixel with at least 6000 counts per pixel. Quantification of EDX mappings was performed using Aztec 6.0 SP2 with the Extended Set of Quant Standardizations. EDX maps were exported as 16-bit TIFF files, and metadata including stage position, count statistics, and BED data were exported using the HDF5 export feature of Aztec. Further evaluation of the data, including image stitching and cluster analysis, was performed using Python References International Energy Agency (IEA). Iron and Steel Technology Roadmap - Towards more sustainable steelmaking (2020) Hundt C, Pothen F (2025) European Post-Consumer Steel Scrap in 2050: A Review of Estimates and Modeling Assumptions. Recycling 10:21. 10.3390/recycling10010021 Guézennec A-G et al (2005) Dust formation in Electric Arc Furnace: Birth of the particles. Powder Technol 157:2–11. 10.1016/j.powtec.2005.05.006 Mager K et al (2000) Recovery of Zinc Oxide from Secondary Raw Materials: New Developments of the Waelz Process. In Recycling of Metals and Engineered Materials , edited by D. L. Stewart, J. C. Daley & R. L. Stephens (John Wiley & Sons, Inc, Hoboken, NJ, USA, 329–344 (2020) Antrekowitsch J, Rösler G, Steinacker S (2015) State of the Art in Steel Mill Dust Recycling. Chem Ing Tech 87:1498–1503. 10.1002/cite.201500073 Ruh A, Kim D-S The Waelz Process - Worldwide Most Used Process for the Recycling of Zinc Containing Residues. In Proceedings of European Metallurgical Conference (GDMB) Grudinsky PI, Zinoveev DV, Dyubanov VG, Kozlov PA (2019) State of the Art and Prospect for Recycling of Waelz Slag from Electric Arc Furnace Dust Processing. Inorg Mater Appl Res 10:1220–1226. 10.1134/S2075113319050071 Mombelli D, Mapelli C, Barella S, Gruttadauria A, Di Landro U (2015) Laboratory investigation of Waelz slag stabilization. Process Saf Environ Prot 94:227–238. 10.1016/j.psep.2014.06.015 Cifrian E, Coronado M, Quijorna N, Alonso-Santurde R, Andrés A (2019) Waelz slag-based construction ceramics: effect of the trial scale on technological and environmental properties. J Mater Cycles Waste Manag 18. 10.1007/s10163-019-00896-4 Barna R et al (2000) Assessment of chemical sensitivity of Waelz slag. Waste Manag 20:115–124. 10.1016/S0956-053X(99)00310-4 Maczek H, Kola R (1980) Recovery of Zinc and Lead from Electric-Furnace Steelmaking Dust at Berzelius. JOM 32:53–58. 10.1007/BF03354543 Brandner U, Antrekowitsch J, Leuchtenmueller M (2021) A review on the fundamentals of hydrogen-based reduction and recycling concepts for electric arc furnace dust extended by a novel conceptualization. Int J Hydrog Energy 46:31894–31902. 10.1016/j.ijhydene.2021.07.062 Guggilam CS (1990) Recycling of Electric Arc Furnace Dust. ITT Research Institute Palzer P, Hand Köhne S Method For Processing A Residue Mixture Containing The Elements Iron And/Or Calcium , And Corresponding Processing Plant Brandner U, Leuchtenmueller M (2024) Comparison of reduction kinetics of Fe2O3, ZnOFe2O3 and ZnO with hydrogen (H2) and carbon monoxide (CO). Int J Hydrog Energy 49:775–785. 10.1016/j.ijhydene.2023.07.189 Brandner U, Antrekowitsch J, Hoffelner F, Leuchtenmueller MA, Tailor-Made (2022) Experimental Setup for Thermogravimetric Analysis of the Hydrogen- and Carbon Monoxide-Based Reduction of Iron (III) Oxide (Fe2O3) and Zinc Ferrite (ZnOFe2O3). In TMS 2022 151st Annual Meeting & Exhibition Supplemental Proceedings Springer International Publishing, Cham, pp. 917–926 Additional Declarations There is NO Competing Interest. Cite Share Download PDF Status: Published Journal Publication published 18 Nov, 2025 Read the published version in Communications Materials → Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6154360","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":428471585,"identity":"e8f8d414-e90e-473f-9030-df9a5d209715","order_by":0,"name":"Manuel Leuchtenmüller","email":"data:image/png;base64,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","orcid":"https://orcid.org/0000-0001-8330-8358","institution":"Montanuniversitaet Leoben","correspondingAuthor":true,"prefix":"","firstName":"Manuel","middleName":"","lastName":"Leuchtenmüller","suffix":""},{"id":428471586,"identity":"be54b0dc-22bf-461d-b1a5-1b9946b8e041","order_by":1,"name":"Eleonora Shpilevaia","email":"","orcid":"https://orcid.org/0009-0004-8078-9335","institution":"Montanuniversitaet Leoben","correspondingAuthor":false,"prefix":"","firstName":"Eleonora","middleName":"","lastName":"Shpilevaia","suffix":""}],"badges":[],"createdAt":"2025-03-04 12:16:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6154360/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6154360/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s43246-025-00980-3","type":"published","date":"2025-11-18T05:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":81637302,"identity":"72315a90-1405-4d8f-9f2d-acd77531b5dc","added_by":"auto","created_at":"2025-04-29 12:51:36","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1028311,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eReduction behavior of EAFD1 under H₂/H₂O and CO/CO₂ atmospheres.\u003c/strong\u003e(a) Sample mass as a function of temperature during thermogravimetric analysis (TGA). The blue line represents the H₂/H₂O system, and the red line represents the CO/CO₂ system. Samples were heated under a dynamic temperature-gas composition profile simulating conditions in a counter-current reactor (see Materials and Methods and Figure 3a for details). (b) Mass loss rate as a function of temperature (left y-axis) and residual sample mass (right y-axis). The H₂/H₂O system (blue) shows significantly higher mass loss rates at equivalent extents of reaction compared to the CO/CO₂system (red). (c) Cumulative porosity distribution after reduction, determined from SEM-BED image analysis. The x-axis represents porosity (%), and the y-axis represents the cumulative percentage of the analyzed sample area exhibiting that porosity or less. The H₂/H₂O system shows significantly higher overall porosity. (d-g) Scatter plots of elemental correlations from SEM-EDX data, showing atomic percentages: (d) Mn vs. Fe, (e) Mg vs. Fe, (f) S vs. Zn, (g) Ca vs. P. Correlations indicate the presence of specific compounds or solid solutions. (h) Histogram of Fe concentration (atomic %) derived from SEM-EDX data. The H₂/H₂O system shows a peak corresponding to metallic Fe, while the CO/CO₂system shows a peak at 40 at.-% corresponding to FeO with dissolved MnO and MgO.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6154360/v1/04dff7dd6a39fee12f8ea144.png"},{"id":81638230,"identity":"00ef830f-8619-4ead-886f-1102a14e5cb6","added_by":"auto","created_at":"2025-04-29 12:59:36","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":883252,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eReduction behavior of EAFD2 under H₂/H₂O and CO/CO₂ atmospheres.\u003c/strong\u003e(a) Sample mass as a function of temperature during TGA. Blue line: H₂/H₂O system; red line: CO/CO₂system. Samples were heated under a dynamic temperature-gas composition profile (see Materials and Methods and Figure 3a). (b) Mass loss rate as a function of temperature (left y-axis) and residual sample mass (right y-axis). The H₂/H₂O system (blue) shows significantly higher mass loss rates at equivalent extents of reaction compared to the CO/CO₂system (red). (c) Cumulative porosity distribution after reduction, determined from SEM-BED image analysis. The x-axis represents porosity (%), and the y-axis represents the cumulative percentage of the analyzed sample area. (d-g) Scatter plots of elemental correlations from SEM-EDX data, showing atomic percentages: (d) Mn vs. Fe, (e) S vs. Cu, (f) S vs. Zn, (g) Ca vs. P. Correlations indicate the presence of specific compounds. (h) Histogram of Fe concentration (atomic %) derived from SEM-EDX data. The H₂/H₂O system shows a dominant peak corresponding to metallic Fe. The CO/CO₂system shows a lower extent of reaction, less porosity and less iron metallization with slower reduction kinetics, like EAFD1, although both samples show low overall porosity. The H₂/H₂O system exhibits significantly higher Zn recovery\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6154360/v1/25b0687e8ca3b1cd59b5dfbc.png"},{"id":81637308,"identity":"6bfa2d90-334a-4948-9442-68fc0f9d34bf","added_by":"auto","created_at":"2025-04-29 12:51:36","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":156089,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eExperimental conditions during thermogravimetric analysis (TGA).\u003c/strong\u003e(a) Temperature and gas composition profile used in the reduction experiments. The temperature increases in four stages, simulating the conditions in a counter-current reactor. The gas composition transitions linearly from 61% H₂O / 39% H₂ (or 61% CO₂ / 39% CO) at 800°C to 100% H₂ (or 100% CO) at 1150°C. (b) Baur Glaessner diagram showing the stability regions of ZnO, Zn(g), Fe₂O₃, FeO, and Fe in the H₂/H₂O system. Lines indicating Zn(g) partial pressures of 0.01, 0.1, and 1 bar are shown. (c) Baur Glaessner diagram showing the stability regions of ZnO, Zn(g), Fe₂O₃, FeO, and Fe in the CO/CO₂ system. Lines indicating Zn(g) partial pressures of 0.01, 0.1, and 1 bar are shown. The diagrams illustrate the thermodynamic driving forces for zinc and iron reduction under the experimental conditions.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-6154360/v1/3690573fb5b965cd3c64ce23.png"},{"id":96363539,"identity":"b3465e7d-8ef2-4c3c-a727-12f1f3b744af","added_by":"auto","created_at":"2025-11-20 10:07:17","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2367134,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6154360/v1/408280b4-1263-4093-b310-e391f48b4709.pdf"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Towards Circular Steelmaking: Hydrogen-Based Valorization of Electric Arc Furnace Dust","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe global steel industry, a cornerstone of modern infrastructure, is also a major contributor to anthropogenic CO2 emissions, accounting for approximately 7.2% of the global total (1.9 Gt of steel produced in 2019) \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. A significant portion of this steel is galvanized for corrosion protection, and its recycling in electric arc furnaces (EAFs) is crucial for resource efficiency. However, EAF steelmaking generates substantial quantities of hazardous electric arc furnace dust (EAFD), with a global output of 5.5 to 11\u0026nbsp;million tons annually (based on 558.4 Mt of EAF steel production and EAFD generation rates of 10\u0026ndash;20 kg/ton) \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. The currently dominant technology for EAFD treatment, the Waelz process, relies on carbothermic reduction, which presents major obstacles to achieving a truly circular economy \u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. This process is not only carbon-intensive, requiring approximately 150 kg of coke \u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e and emitting nearly 2000 kg of CO2 per ton of recovered zinc, but also achieves only around 90% zinc recovery. Furthermore, it generates 600\u0026ndash;650 kg of slag per ton of EAFD \u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e, a byproduct containing residual zinc (2\u0026ndash;5%), substantial iron (40\u0026ndash;60% as both oxides and metal), and various impurities (SiO\u003csub\u003e2\u003c/sub\u003e, CaO, MnO, Cr\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e3\u003c/sub\u003e, Cu, Ni, and P)\u003csup\u003e\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. This slag is largely unsuitable for direct recycling because of its high sulfur content and low degree of iron metallization (the fraction of the total iron present as metallic Fe rather than iron oxides) that prevent effective iron recovery and integration back into the electric arc furnace, hindering the closed-loop material flow essential for a circular economy \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eDriven by the imperative for industrial decarbonization and a circular economy, hydrogen-based reduction is emerging as a transformative technology for metal recovery \u003csup\u003e\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. Replacing carbon with hydrogen eliminates direct CO\u003csub\u003e2\u003c/sub\u003e emissions, a fundamental advantage. Furthermore, hydrogen reduction offers the potential for improved reaction kinetics, leading to more efficient metal extraction \u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. While the basic principles of hydrogen reduction are known, its specific application to complex hazardous waste materials like EAFD, particularly under dynamic, industrially relevant conditions, has not been thoroughly investigated. This study directly addresses this critical gap, providing a comprehensive analysis of hydrogen-based EAFD processing compared to conventional carbothermic methods.\u003c/p\u003e \u003cp\u003eTo directly compare hydrogen and carbothermic reduction of EAFD under realistic industrial conditions, we systematically investigated the reduction kinetics of two distinct steelmaking dusts using a dynamic temperature-gas composition profile. This profile mimicked the continuously changing environment a material undergoes within industrial reactors. Our approach combined thermogravimetric analysis (TGA) for kinetic quantification with microstructural and elemental analysis using scanning electron microscopy (SEM) with backscattered electron detector (BED) and SEM with energy-dispersive X-ray spectroscopy (EDX). A key innovation was the transformation of the 2D spatial elemental distribution data into a series of scatter plots, allowing us to identify correlations between elements and infer the presence of specific compounds (e.g., a positive correlation between zinc and sulfur with a 1:1 atomic ratio indicates ZnS). This revealed that CO-based reduction led to denser, less reactive oxide structures, while hydrogen preserved microstructural features conducive to continued reaction. Consequently, hydrogen reduction achieved superior Zn recovery, enhanced iron metallization, and lower residual sulfur and phosphorus. These results demonstrate the clear environmental and process engineering benefits of hydrogen-based EAFD processing, offering a pathway towards more sustainable steelmaking.\u003c/p\u003e"},{"header":"Results and Discussion","content":"\u003cp\u003eWe used TGA to reduce two EAFD samples comparing hydrogen and carbon-based reduction. The samples differed primarily in their ZnO content (29.6% and 40.7%). The experiments featured a dynamic temperature-gas composition profile, mirroring the changing environment in industrial counter-current reactors. The gas composition was linearly transitioned from a H₂O/CO₂-rich mixture to pure H₂/CO during heating. This allowed direct comparison of H₂/H₂O and CO/CO₂ reduction kinetics under comparable conditions. Post-reduction, SEM-BED imaging enabled phase segmentation (distinguishing porosity, slag, and metal based on brightness) and quantification of macro-porosity and metallization. SEM-EDX mapping provided spatial elemental concentration profiles. Across multiple metrics – including reaction kinetics, microstructure preservation, and impurity removal – hydrogen reduction consistently outperformed carbothermic reduction.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eComparing the reduction of EAFD1 in H₂/H₂O and CO/CO₂ atmospheres revealed significant differences in reaction kinetics and microstructural evolution (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The H₂/H₂O system consistently exhibited superior performance, achieving a higher overall mass loss (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea) and greater mass loss rates when compared at equivalent extents of reaction (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb). Notably, the maximum reaction rate for the CO/CO₂ system occurred at 998°C with 74% of the sample mass remaining, whereas the peak rate for the H₂/H₂O system shifted to a higher temperature (1030°C) and lower residual mass (61%). This difference in the temperature and extent of reaction at the peak rate suggests that, in the CO/CO₂ system, reduction kinetics are limited by microstructural changes that occur at elevated temperatures, rather than by the simple depletion of reducible compounds\u003c/p\u003e \u003cp\u003eSEM-BED imaging, distinguishing phases (void/porosity, slag, and metal) based on brightness differences, confirmed these kinetic differences. Porosity mapping (using 10 x 10 pixel regions, approximately 9 µm x 9 µm) revealed a significantly larger fraction of low-porosity regions in the CO/CO₂-reduced sample. Specifically, 50% of the analyzed area in the CO/CO₂ sample exhibited a porosity of 3% or less, whereas only 19% of the analyzed area in the H₂/H₂O sample had such low porosity. Furthermore, metallization in the H₂/H₂O system was pervasive and uniformly distributed with a fine particle size distribution, while in the CO/CO₂ sample, it was limited to the outer surface, forming a dense contour around the non-metallized matrix. This combination of lower overall porosity and restricted metallization in the CO/CO₂ sample directly explains the observed slower reduction kinetics, as gas diffusion to and from reaction sites is hindered.\u003c/p\u003e \u003cp\u003eSEM-EDX elemental mapping provided further insights into the reduction mechanisms. The CO/CO₂ sample displayed a clear Zn concentration gradient (lower at the surface, higher in the center), while the H₂/H₂O sample showed nearly complete Zn removal. Quantitatively, the Zn concentration decreased from an initial 23.7 wt.-% to 0.1% in the hydrogen system (with 52.3% mass loss), yielding a zinc recovery rate of 99.7%. In contrast, the carbon-based system achieved only 90.0% recovery, with a final Zn concentration of 3.7% (and 43% mass loss) – a value comparable to those obtained in state-of-the-art industrial processes. Turning to the iron behavior, Fe concentration histograms derived from the EDX data (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eh) confirmed the findings from the SEM-BED images. The CO/CO₂ system maintained a stable wüstite region at approximately 40 at.-% Fe, with evident MnO dissolution into the FeO matrix (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ed) and a corresponding accumulation of MgO (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ee). MnO and MgO stabilize the oxide phase, reducing its reactivity and lowering the liquidus temperature, thus promoting earlier sintering and contributing to the observed dense microstructure. In contrast, the H₂/H₂O sample lacked these correlations and instead exhibited significant metallic Fe formation.\u003c/p\u003e \u003cp\u003eThe behavior of sulfur and phosphorus further highlighted the distinct reduction mechanisms in the two atmospheres. In the CO/CO₂ sample, sulfur was primarily present as ZnS (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ef). In contrast, the H₂/H₂O system lacked this correlation and exhibited a significantly lower bulk sulfur concentration (0.3% vs. 0.8%), likely caused by the formation of H₂S. Phosphorus behaved differently: in the CO/CO₂ atmosphere, it formed apatite-type phases with calcium (Ca:P ratio of 5:3), while this association was largely absent in the H₂/H₂O environment, suggesting the formation of volatile phosphorus species (likely PH3). Although final bulk phosphorus concentrations were similar (0.6% vs. 0.7%), the greater mass loss in the H₂/H₂O system (52.3% vs. 43%) indicates more effective overall phosphorus removal. The lower residual sulfur and phosphorus content in the hydrogen-reduced material significantly benefits its potential for recycling in electric arc furnaces, which have limited capabilities for removing these elements. However, the process design must account for the potential generation of hazardous H\u003csub\u003e2\u003c/sub\u003eS and PH\u003csub\u003e3\u003c/sub\u003e gases during hydrogen-based EAFD treatment.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAs with EAFD1, the second dust sample (EAFD2, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) showed significantly better reduction performance with H₂/H₂O compared to CO/CO₂. The H₂/H₂O system achieved a maximum reaction rate of 2.4 mass-%/min, twice that of the CO/CO₂ system (1.2%/min). The peak reaction rate occurred at a lower temperature and higher residual mass for CO/CO₂ (983°C, 76% residual mass) than for H₂/H₂O (1016°C, 56% residual mass). This difference indicates that while reactant depletion primarily controls the kinetics in the H₂/H₂O system, pore structure collapse due to sintering limits the reaction progress in the CO/CO₂ system. The impact of this sintering-induced pore collapse is observed in both samples earlier in the CO/CO₂ system, likely due to the larger size of CO and CO₂ molecules compared to H₂ and H₂O, leading to more significant diffusion limitations as pores shrink.\u003c/p\u003e \u003cp\u003eSEM analysis using the backscatter detector showed that both reduction conditions resulted in dense microstructures with low median porosity (≤ 2%). However, iron metallization was significantly different: substantial and pervasive in the H₂/H₂O sample, but minimal in the CO/CO₂ sample. Zinc distribution also differed greatly. The CO/CO₂ sample showed a steep Zn concentration gradient (surface to core), while the H₂ system achieved near-complete Zn removal (0.9% bulk concentration, 98.8% recovery, versus 18.5% and 86.2% for CO/CO₂). Examining iron phases, the CO/CO₂ sample retained a substantial FeO region with MnO dissolution (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed, \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eh). Conversely, the H₂/H₂O sample consisted mainly of metallic Fe with limited, MnO-enriched FeO. The differences in reduction behavior between EAFD2 and EAFD1 likely stem from the higher initial Zn concentration and longer residence time in the FeO-region in EAFD2\u003c/p\u003e \u003cp\u003eAnalysis of the elemental correlations, derived from the EDX, revealed a correlation between sulfur and copper under H₂ reduction, indicating CuS₂ formation (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ee). A possible explanation is that H₂S forms with a higher partial pressure within the dense microstructure, which further reacts with copper. The data also suggest the presence of Fe₂S. In contrast, sulfur remained primarily bound to zinc as ZnS in the CO/CO₂ sample (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ef). The bulk sulfur concentrations were 0.9% for the H₂ reduction and 1.2% for the CO-based system. After adjusting for the different mass losses, the hydrogen-reduced sample contained 48% less sulfur. Phosphorus showed a strong correlation with calcium in the CO/CO₂ sample (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eg). This correlation was significantly weaker in the H₂/H₂O sample, suggesting partial phosphorus removal, likely as volatile PH₃. We hypothesize that the denser microstructure in the H₂-reduced sample may have limited PH₃ formation due to restricted diffusion through the collapsed micropore structure.\u003c/p\u003e "},{"header":"Summary and Conclusion","content":"\u003cp\u003eHydrogen-based reduction offers a transformative approach to EAFD valorization, significantly outperforming conventional carbothermic methods. Our study, using dynamic temperature-gas profiles to mimic industrial reactor conditions, demonstrates that H₂/H₂O reduction achieves superior zinc recovery (98.8–99.7% vs. 86.2–90.0% for CO/CO₂), enhanced iron metallization, and more effective removal of detrimental elements like sulfur and phosphorus. These improvements stem from hydrogen's smaller molecular size, higher diffusivity, and greater reactivity, which promote more efficient gas-solid interactions and prevent the sintering-induced pore collapse that hinders reduction in CO/CO₂ environments. Crucially, our findings reveal the potential for selective zinc extraction without iron reduction, opening a pathway for EAFD recycling even before widespread adoption of hydrogen-based ironmaking becomes economically feasible. This work establishes a foundation for optimizing hydrogen-based EAFD processing, contributing to a more sustainable and circular steel industry. Future research should focus on detailed kinetic modeling, the effect of additives on kinetics, selectivity and impurity removal and upscaled validation to assess economic feasibility.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003eTwo different EAFD materials were used in this study. Material 1 contained 18.8 wt.-% Zn, while Material 2 had a higher Zn concentration of 30.3 wt.-%, measured by X-ray fluorescence (XRF). In both materials, zinc was primarily present as franklinite (ZnFe₂O₄) and zinc oxide (ZnO). Iron was present primarily as Fe\u0026sup3;⁺ in the form of franklinite and hematite (Fe₂O₃). The detailed chemical compositions of the EAFD samples, determined by XRF, inductively coupled plasma mass spectrometry (ICP-MS), and SEM-EDX are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eChemical analysis of initial dust samples in wt.-%\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"14\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMat\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMeth\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eZn\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFe\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCaO\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMgO\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSiO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAl\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eMnO\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eCr\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003eCu\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\u003e \u003cp\u003eK\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c14\"\u003e \u003cp\u003eCl\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eXRF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e31.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e2.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e0.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eICP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e31.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e3.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e2.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEDX\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e30.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e3.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e2.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eXRF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e30.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e21.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e2.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eICP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e30.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e19.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e1.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e5.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEDX\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e29.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e2.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e5.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eSample Preparation\u003c/h3\u003e\n\u003cp\u003eEAFD samples were dried at 105\u0026deg;C for 24 hours in a laboratory oven to remove moisture. For each experiment, 350 mg of dried EAFD was compacted using a custom-designed electromechanical uniaxial press at 14 MPa. The resulting cylindrical specimens had a diameter of 7.5 mm and a height of 3.75 mm, yielding a geometric surface area of 176.7 mm\u0026sup2; and a bulk density of 2.1 g/cm\u0026sup3;. These dimensions resulted in uniform gas diffusion paths of \u0026le;\u0026thinsp;3.75 mm through the sample's macroporous structure. Sample morphology and exact dimensions were documented using a Keyence VHX-7000 digital microscope prior to reduction experiments.\u003c/p\u003e\n\u003ch3\u003eThermogravimetric Reduction Procedure\u003c/h3\u003e\n\u003cp\u003eReduction experiments were conducted in a customized Linseis TGA, as described in detail by Brandner et al. \u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. Temperature control was achieved using a type C (tungsten-rhenium) thermocouple integrated into the sample holder. The TGA was configured to simulate the conditions experienced by material in a counter-current reactor, where the temperature increases and the gas composition transitions from oxidizing (H₂O/CO₂-rich) to reducing (H₂/CO-rich). It is important to note that while the volumetric gas flow rates of CO/CO₂ and H₂/H₂O were the same, the different molar masses and reaction stoichiometries result in different thermodynamic equilibria.\u003c/p\u003e \u003cp\u003eThe experimental profile (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea) consisted of three zones: a drying zone (up to 200\u0026deg;C), a heating zone with constant gas composition (up to 800\u0026deg;C), and a reduction zone with a continuously changing gas composition. Samples were heated to 300\u0026deg;C at 25 K/min in a 61% H₂O and 39% H₂ atmosphere, followed by heating rates of 20 K/min to 500\u0026deg;C and 12.5 K/min to 800\u0026deg;C. From 800\u0026deg;C to 1150\u0026deg;C, the temperature increased at 5.83 K/min over one hour. During this final heating segment, the H₂O/CO₂ flow rate decreased linearly from 50 ml/min to 0 ml/min, while the H₂/CO flow rate increased linearly from 30 ml/min to 80 ml/min. Consequently, the inflowing gas consisted of 100% H₂/CO once the sample reached 1150\u0026deg;C. Samples were subsequently cooled at 25 K/min under an inert argon atmosphere.\u003c/p\u003e \u003cp\u003eThe thermodynamic conditions for the H₂ system (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb) indicate that w\u0026uuml;stite (FeO) becomes stable at approximately 800\u0026deg;C. The reduction of ZnO to gaseous Zn depends on the partial pressure of Zn(g); at a partial pressure of 0.01 bar, Zn(g) becomes thermodynamically stable at around 900\u0026deg;C, corresponding to the onset of significant zinc fuming observed in the TGA. Conditions favorable for FeO reduction to Fe occur at approximately 940\u0026deg;C. In the CO system (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec), w\u0026uuml;stite becomes stable earlier (at around 740\u0026deg;C), Zn(g) fuming (at 0.01 bar) begins at around 910\u0026deg;C, and FeO reduction starts at approximately 990\u0026deg;C. Therefore, from a purely thermodynamic perspective, the sample in the CO system resides in the w\u0026uuml;stite stability region for at least 37 minutes, compared to 24 minutes for the H₂ system.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eSEM-EDX Analysis\u003c/h3\u003e\n\u003cp\u003eSamples were characterized after reduction using a JEOL IT300 scanning electron microscope. Backscatter images were acquired with a pixel step size of 0.9 \u0026micro;m per pixel, while EDX mappings (Oxford X-Max50 EDX Detector) used a step size of 29.3 \u0026micro;m per pixel with at least 6000 counts per pixel. Quantification of EDX mappings was performed using Aztec 6.0 SP2 with the Extended Set of Quant Standardizations. EDX maps were exported as 16-bit TIFF files, and metadata including stage position, count statistics, and BED data were exported using the HDF5 export feature of Aztec. Further evaluation of the data, including image stitching and cluster analysis, was performed using Python\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eInternational Energy Agency (IEA). Iron and Steel Technology Roadmap - Towards more sustainable steelmaking (2020)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHundt C, Pothen F (2025) European Post-Consumer Steel Scrap in 2050: A Review of Estimates and Modeling Assumptions. Recycling 10:21. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/recycling10010021\u003c/span\u003e\u003cspan address=\"10.3390/recycling10010021\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGu\u0026eacute;zennec A-G et al (2005) Dust formation in Electric Arc Furnace: Birth of the particles. 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In \u003cem\u003eTMS 2022 151st Annual Meeting \u0026amp; Exhibition Supplemental Proceedings\u003c/em\u003eSpringer International Publishing, Cham, pp. 917\u0026ndash;926\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Electric Arc Furnace Dust (EAFD), Hydrogen Reduction, Zinc Recovery, Circular Economy, Sustainable Steelmaking, Waste Valorization ","lastPublishedDoi":"10.21203/rs.3.rs-6154360/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6154360/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eElectric arc furnace dust (EAFD), a hazardous waste from galvanized steel recycling, presents a significant challenge to sustainable steel production. Here, we show that hydrogen reduction, under industrially relevant conditions, offers a transformative solution for EAFD valorization. Compared to conventional carbothermic reduction, hydrogen achieved higher zinc recovery (98.8\u0026ndash;99.7% vs. 86.2\u0026ndash;90.0%), enhanced iron metallization, and superior removal of sulfur and phosphorus. This is attributed to hydrogen's enhanced diffusivity and reactivity, which prevents the microstructural degradation (pore closure) that limits carbothermic reduction. Importantly, we demonstrate the feasibility of selective zinc extraction, allowing EAFD recycling even without simultaneous iron reduction. This work provides a pathway for transforming EAFD from a hazardous waste into valuable resources, contributing to a lower-carbon and more resource-efficient steel production process.\u003c/p\u003e","manuscriptTitle":"Towards Circular Steelmaking: Hydrogen-Based Valorization of Electric Arc Furnace Dust","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-29 12:51:31","doi":"10.21203/rs.3.rs-6154360/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"communications-materials","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"commsmat","sideBox":"Learn more about [Communications Materials](https://www.nature.com/commsmat/)","snPcode":"","submissionUrl":"","title":"Communications Materials","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Communications Series","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"03f7d949-37b8-4b84-a98f-5594cadb6f13","owner":[],"postedDate":"April 29th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":45650319,"name":"Physical sciences/Engineering/Chemical engineering"},{"id":45650320,"name":"Physical sciences/Chemistry/Chemical engineering"}],"tags":[],"updatedAt":"2025-11-19T08:06:30+00:00","versionOfRecord":{"articleIdentity":"rs-6154360","link":"https://doi.org/10.1038/s43246-025-00980-3","journal":{"identity":"communications-materials","isVorOnly":false,"title":"Communications Materials"},"publishedOn":"2025-11-18 05:00:00","publishedOnDateReadable":"November 18th, 2025"},"versionCreatedAt":"2025-04-29 12:51:31","video":"","vorDoi":"10.1038/s43246-025-00980-3","vorDoiUrl":"https://doi.org/10.1038/s43246-025-00980-3","workflowStages":[]},"version":"v1","identity":"rs-6154360","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6154360","identity":"rs-6154360","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}