@misc{ShafieeDorneanuArellanoGarcia, author = {Shafiee, Parisa and Dorneanu, Bogdan and Arellano-Garcia, Harvey}, title = {Improving catalysts and operating conditions using machine learning in Fischer-Tropsch synthesis of jet fuels (C8-C16)}, series = {Chemical Engineering Journal Advances}, volume = {21 (2025)}, journal = {Chemical Engineering Journal Advances}, publisher = {Elsevier}, doi = {10.1016/j.ceja.2024.100702}, pages = {25}, abstract = {Fischer-Tropsch synthesis (FTS) offers a promising route for producing sustainable jet fuels from syngas. However, optimizing catalyst design and operating conditions for the ideal C8-C16 jet fuel range is challenging. Thus, this work introduces a machine learning (ML) framework to enhance Co/Fe-supported FTS catalysts and optimize their operating conditions for a better jet fuel selectivity. For this purpose, a dataset was implemented with 21 features, including catalyst structure, preparation method, activation procedure, and FTS operating parameters. Moreover, various machine-learning models (Random Forest (RF), Gradient Boosted, CatBoost, and artificial neural networks (ANN)) were evaluated to predict CO conversion and C8-C16 selectivity. Among these, the CatBoost model achieved the highest accuracy (R2 = 0.99). Feature analysis revealed that FTS operational conditions mainly affect CO conversion (37.9 \%), while catalyst properties were primarily crucial for C8-C16 selectivity (40.6 \%). The proposed ML framework provides a first powerful tool for the rational design of FTS catalysts and operating conditions to maximize jet fuel productivity.}, language = {en} } @misc{ShezadSafdarArellanoGarciaetal., author = {Shezad, Nasir and Safdar, Muddasar and Arellano-Garcia, Harvey and Tai, Cheuk-Wai and Chen, Shaojiang and Seo, Dong-Kyun and You, Shujie and Vomiero, Alberto and Akhtar, Farid}, title = {Deciphering the role of APTES in tuning the metal support interaction of NiO nanolayers over hierarchical zeolite 13X for CO2 methanation}, series = {Carbon Capture Science \& Technology}, volume = {15}, journal = {Carbon Capture Science \& Technology}, publisher = {Elsevier}, address = {Amsterdam}, doi = {10.1016/j.ccst.2025.100424}, pages = {1 -- 11}, abstract = {The development of robust nickel catalysts on porous substrates offers great potential for converting carbon dioxide (CO2) into methane, thereby helping to address the global warming and sustainability challenges. This study investigates the dispersion and stability of Ni nanolayers by grafting bifunctional groups over the hierarchical zeolite 13X (h13X) support using (3-aminopropyl)triethoxysilane (APTES). The Ni nanolayers, with a thickness of 1.5-7 nm, were deposited around the edges of h13X and analyzed using STEM imaging. A clear shift in the binding energies was observed by XPS analysis, substantiating the enhanced metalsupport interaction (MSI) between NiO and h13X. The influence of reaction temperature on APTES incorporation into h13X was revealed by H2-TPR and CO2-TPD, with notable variations in the reducibility and surface basicity profiles of the catalysts. The optimized catalyst exhibited CO2 conversion of 61\% with CH4 selectivity of 97\% under GHSV of 60,000 mlgCat-1h-1 at 400 oC and 1 bar and demonstrated robust stability over a period of 150 h without discernible degradation. The enhanced performance could be attributed to the strengthened MSI and reduced size of Ni nanolayers over h13X. These findings highlight the development of robust heterogeneous catalysts by changing the surface chemistry of support material for various catalytic applications.}, language = {en} } @misc{MappasDorneanuNolascoetal., author = {Mappas, Vasileios and Dorneanu, Bogdan and Nolasco, Eduardo and Vassiliadis, Vassilios and Arellano-Garcia, Harvey}, title = {Towards scalable quantum annealing for pooling and blending problems : a methodological proof-of-concept}, series = {Chemical engineering research and design}, volume = {221}, journal = {Chemical engineering research and design}, publisher = {Elsevier BV}, address = {Amsterdam}, issn = {1744-3563}, doi = {10.1016/j.cherd.2025.08.031}, pages = {560 -- 576}, abstract = {Industrial optimization challenges, such as the pooling and blending problem (PBP), require advanced computational methods to address non-convexity and scalability limitations in classical solvers. This work introduces a novel methodological framework for solving PBPs using quantum annealing (QA) that transforms the PBP into quadratic unconstrained binary optimization (QUBO) formulations at two resolution levels, enabling direct deployment on quantum annealers. Key innovations include a discretization technique tailored for PBP's bilinear constraints and an embedding method optimized for current quantum hardware. Benchmarking against classical solvers focuses on Haverly's classical three-stream PBP, enabling transparent comparison and development of quantum embedding and solution techniques. The proposed framework offers a scalable template for adapting similar engineering systems to quantum annealing architectures. Addressing genuine industrial-scale instances will require future advances in quantum hardware and embedding algorithms. The results demonstrate that QA exhibits the best performance among the examined alternatives, providing foundational insights towards leveraging QA in Process Systems Engineering.}, language = {en} } @misc{MappasDorneanuHeinzelmannetal., author = {Mappas, Vasileios K. and Dorneanu, Bogdan and Heinzelmann, Norbert and Arellano-Garcia, Harvey}, title = {Capturing multiscale phenomena in trickle bed reactors : a flexible framework for flow and reaction analysis}, series = {Jahrestreffen der DECHEMA/VDI-Fachgruppe Fluidverfahrenstechnik 2025}, journal = {Jahrestreffen der DECHEMA/VDI-Fachgruppe Fluidverfahrenstechnik 2025}, abstract = {Multiphase systems, particularly trickle bed reactors (TBRs), are critical in various industrial applications and widely employed in catalytic processes such as hydrogenation and oxidation due to their high surface area, low operational and minimal catalyst loss. Despite advancements in modelling techniques, accurately capturing the complex multiphysics and multiscale phenomena remains challenging. Conventional approaches, relying on empirical correlations or Computational Fluid Dynamics (CFD) simulations, often fall short due to high computational demands, limited accuracy, and constraints on the number of catalytic particles that can be effectively simulated [3]. To address these limitations, this contribution presents a new framework tailored for the design and analysis of multiphase systems operating in the low-interaction regimes. This approach is based on the local structure of the packed bed and employs a Lagrangian approach, where flow dynamics within the reactor is represented by various discrete elements. The framework's modular and flexible setup enables the incorporation of multiscale information of both local and global levels, allowing for the additions of new modules or features to enhance modelling fidelity.}, language = {en} } @misc{ParkLeeKimetal., author = {Park, Haryn and Lee, Joowha and Kim, Jin-Kuk and Dorneanu, Bogdan and Arellano-Garcia, Harvey}, title = {Pathways to industrial decarbonization : renewable energy integration and electrified hydrogen production}, series = {Jahrestreffen der DECHEMA/VDI-Fachgruppe Fluidverfahrenstechnik 2025}, journal = {Jahrestreffen der DECHEMA/VDI-Fachgruppe Fluidverfahrenstechnik 2025}, pages = {1}, abstract = {Industrial sectors contribute substantially to global CO2 emissions, emphasizing the need for low-carbon, reliable energy supplies to meet operational demands. Achieving net-zero emissions in industrial processes involves transitioning from fossil fuels to renewable energy sources. However, the intermittent nature of renewables poses challenges to energy reliability and resilience, particularly in utility systems. This contribution addresses industrial decarbonisation and sustainable hydrogen production by developing a comprehensive design and optimization framework for integrating renewable energy systems into industrial operations. This framework incorporates energy storage and grid connections to improve flexibility and stability and is evaluated through two case studies. Both case studies analyse the operational and configurational changes necessary for renewable-powered hydrogen production, estimating the cost of hydrogen or CO2 avoidance cost to analyse economic viability. These insights provide guidelines for sustainable and economically viable energy management in industrial and hydrogen production sectors, supporting broader global energy transition goals.}, language = {en} } @misc{SchowarteRiedelSafdaretal., author = {Schowarte, Julia and Riedel, Ramona and Safdar, Muddasar and Helle, Sven and Fischer, Thomas and Arellano-Garc{\´i}a, Harvey}, title = {Photocatalytic degradation of PFOA with porous lanthanoid perovskites nano catalyst}, series = {Chemie - Ingenieur - Technik : CIT}, journal = {Chemie - Ingenieur - Technik : CIT}, publisher = {Wiley-VCH GmbH}, address = {Weinheim}, issn = {1522-2640}, doi = {10.1002/cite.70027}, pages = {1 -- 11}, abstract = {Perfluorooctanoic acid (PFOA), a persistent environmental pollutant, poses significant health and ecological risks. This study investigates for the first time the photocatalytic degradation of PFOA using novel doped perovskite catalysts under polychromatic UV-VIS irradiation with a peak emission at 366 nm. A series of nickel- and lanthanide-doped perovskites (NiMn2O4, LaMnO3, NdMnO3, and their nickel-doped variants) were synthesized via a facile co-precipitation technique and characterized using X-ray diffraction (XRD), UV-VIS diffuse reflectance spectroscopy (UV-VIS-DRS), scanning electron microscopy with energy-dispersive X-ray spectroscopy (SEM-EDX), N2-physisorption, and microwave plasma atomic emission spectroscopy (MP-AES). Photocatalytic experiments revealed that Ni/NdMnO3 exhibited the highest degradation efficiency toward PFOA, likely due to its small band gap energy of 1.5 eV, facilitating efficient C-C bond cleavage.}, language = {en} } @misc{YentumiJurischkaDorneanuetal., author = {Yentumi, Richard and Jurischka, Constantin and Dorneanu, Bogdan and Arellano-Garcia, Harvey}, title = {Optimal design and analysis of thermochemical storage and release of hydrogen via the reversible redox of iron oxide/iron}, series = {Systems and control transactions}, volume = {4}, journal = {Systems and control transactions}, publisher = {PSE Press}, address = {Notre Dame, IN}, isbn = {978-1-7779403-3-1}, issn = {2818-4734}, doi = {10.69997/sct.121492}, pages = {631 -- 636}, abstract = {In this contribution, a thermodynamic model-based approach for the optimal design of a solid-state hydrogen storage and release system utilizing the reversible iron oxide/iron thermochemical redox mechanism is presented. Existing storage processes using this mechanism face significant limitations, including low hydrogen conversion, high energy input requirements, limited storage density, and slow charging/discharging kinetics. To address these challenges, a custom thermodynamic model using NIST thermochemistry data is developed, enabling an in-depth analysis of redox reaction equilibria under different conditions. Unlike previous studies, this approach integrates a multi-objective optimization framework that explicitly balances competing objectives: maximizing hydrogen yield while minimizing thermal energy demand. By systematically identifying optimal trade-offs, the study provides new insights into improving process efficiency and reactor design for thermochemical hydrogen storage. These findings contribute to advancing energy-efficient and scalable hydrogen storage technologies.}, language = {en} } @misc{MappasDorneanuArellanoGarcia, author = {Mappas, Vasileios K. and Dorneanu, Bogdan and Arellano-Garcia, Harvey}, title = {Solving complex combinatorial optimization problems using quantum annealing approaches}, series = {Systems and control transactions}, volume = {4}, journal = {Systems and control transactions}, publisher = {PSE Press}, address = {Notre Dame, IN}, isbn = {978-1-7779403-3-1}, issn = {2818-4734}, doi = {10.69997/sct.188358}, pages = {1561 -- 1566}, abstract = {Currently, state-of-the-art approaches to solving complex optimization problems have focused solely on methods requiring high computational time and unable to find the global optimal solution. In this work, a methodology based on quantum computing is presented to overcome these drawbacks. The novelty of this framework stems from the quantum computer's architecture and taking into consideration the quantum phenomena that take place to solve optimization problems with specific structure. The proposed methodology includes steps for the transformation of the initial optimization problem into an unconstrainted optimization problem with binary variables and its embedding onto a quantum device. Moreover, different resolution levels for the transformation step and different architectures for the embedding process are utilized. To illustrate the procedure, a case study based on Haverly's pooling and blending problem is examined while demonstrating the potential of the proposed approach. The results indicate that the succinct formulation exhibited higher success rate during the embedding procedure for the different examined architectures, and the quantum annealing solver exhibited the best performance among the various solvers investigated. This highlights the potential of the approach for solving this type of problems with the rapid development and improvement of quantum hardware and expanding it to more complex chemical engineering optimization systems.}, language = {en} } @misc{ShafieeJafariSchowarteetal., author = {Shafiee, Parisa and Jafari, Mitra and Schowarte, Julia and Dorneanu, Bogdan and Arellano-Garcia, Harvey}, title = {Streamlining catalyst development through machine learning : insights from heterogeneous catalysis and photocatalysis}, series = {Systems and control transactions}, volume = {4}, journal = {Systems and control transactions}, publisher = {PSE Press}, address = {Notre Dame, IN}, isbn = {978-1-7779403-3-1}, issn = {2818-4734}, doi = {10.69997/sct.135551}, pages = {1866 -- 1871}, abstract = {Catalysis design and reaction condition optimization are considered the heart of many chemical and petrochemical processes and industries; however, there are still significant challenges in these fields. Advances in machine learning (ML) have provided researchers with new tools to address some of these obstacles, offering the ability to predict catalyst behaviour, optimal reaction conditions, and product distributions without the need for extensive laboratory experimentation. In this contribution, the potential applications of ML in heterogeneous catalysis and photocatalysis are explored by analysing datasets from different reactions, including Fischer-Tropsch synthesis and photocatalytic pollutant degradation. First, datasets were collected from literature. After cleaning and preparing the datasets, they were employed to train and test several models. The best model for each dataset was selected and applied for optimization.}, language = {en} } @misc{ParkLeeDorneanuetal., author = {Park, Haryn and Lee, Joowha and Dorneanu, Bogdan and Arellano-Garcia, Harvey and Kim, Jin-Kuk}, title = {Cost-effective process design and optimization for decarbonized utility systems integrated with renewable energy and carbon capture systems}, series = {Systems and control transactions}, volume = {4}, journal = {Systems and control transactions}, publisher = {PSE Press}, address = {Notre Dame, IN}, isbn = {978-1-7779403-3-1}, issn = {2818-4734}, doi = {10.69997/sct.107403}, pages = {1175 -- 1180}, abstract = {Industrial decarbonization is considered one of the key objectives in mitigating global climate change. To achieve a net-zero industry requires actively transitioning from fossil fuel-based energy sources to renewable alternatives. However, the intermittent nature of renewable energy sources poses challenges to a reliable and robust supply of energy for industrial sites. Therefore, the integration of renewable energy systems with existing industrial processes, subject to energy storage solutions and main grid interconnections, is essential to enhance operational reliability and overall energy resilience. This study proposes a novel framework for the design and optimization of industrial utility systems integrated with renewable energy sources. A monthly-based analysis is adopted to consider variable demand and non-constant availability in renewable energy supply. Moreover, carbon capture is considered in this work as a viable decarbonization measure, which can be strategically combined with renewable-based electrification. The proposed optimization model evaluates the economic trade-offs of integrating carbon capture, renewable energy, and energy storage. By applying this approach, systematic design guidelines are developed for the transition of a conventional steady-state utility system toward renewable energy integration, ensuring economically viable and sustainable energy management in process industries.}, language = {en} } @misc{MappasDorneanuHeinzelmannetal., author = {Mappas, Vasileios K. and Dorneanu, Bogdan and Heinzelmann, Norbert and Schnitzlein, Klaus and Arellano-Garcia, Harvey}, title = {An efficient and unified modeling framework for trickle bed reactors : a modular approach}, series = {Chemie - Ingenieur - Technik : CIT}, volume = {97}, journal = {Chemie - Ingenieur - Technik : CIT}, number = {11-12}, publisher = {Wiley}, address = {Weinheim}, issn = {1522-2640}, doi = {10.1002/cite.70035}, pages = {1110 -- 1126}, abstract = {Trickle bed reactors (TBRs) involve complex and multiscale dynamics that challenge their design, modeling, and optimization. Current approaches often suffer from high computational cost and limited scalability, restricting their applicability in large-scale cases. This work introduces a modular, computationally efficient framework to address these issues by systematically capturing key transport and reaction phenomena. Furthermore, it provides a critical review of existing modeling strategies for TBRs, outlining their strengths and limitations and highlighting opportunities for enhancement through modularization. By offering a structured and scalable approach, the proposed framework improves predictive capabilities and supports the development of optimized and adaptable reactor designs.}, language = {en} } @misc{CunhaCordeiroSafdarSantosdaSilvaetal., author = {Cunha Cordeiro, Jos{\´e} Luiz and Safdar, Muddasar and Santos da Silva, Jefferson and De Aquino, Gabrielle and Dos Santos, Mauricio and Cruz, Fernanda and Fiuza-Junior, Raildo A. and Dorneanu, Bogdan and Arellano-Garcia, Harvey and Pontes, Karen and Mascarenhas, Artur}, title = {Influ{\^e}ncia do Suporte em Catalisadores de Ni Obtidos Pelo M{\´e}todo da Combust{\~a}o na Reforma a Seco do Biog{\´a}s para Produ{\c{c}}{\~a}o de Hidrog{\^e}nio Sustent{\´a}vel}, series = {23º CBCAT : Congresso Brasileiro de Catalise}, volume = {1}, journal = {23º CBCAT : Congresso Brasileiro de Catalise}, number = {1}, pages = {1 -- 6}, abstract = {Este estudo avaliou catalisadores de NiO suportados em MgO, ZrO₂, Al₂O₃, La₂O₃ e CeO₂ para reforma a seco do biog{\´a}s. As caracteriza{\c{c}}{\~o}es revelaram varia{\c{c}}{\~o}es na dispers{\~a}o met{\´a}lica, {\´a}rea met{\´a}lica e morfologia superficial. Os catalisadores NiO-Al₂O₃ e NiO-CeO₂ apresentaram maior {\´a}rea met{\´a}lica e melhor dispers{\~a}o de Ni, favorecendo altas convers{\~o}es de CH₄ e CO₂ e bom rendimento em H₂. O NiO-Al₂O₃ foi o mais eficiente e est{\´a}vel por 8 horas de rea{\c{c}}{\~a}o. O NiO-La₂O₃ mostrou aumento progressivo da atividade e boa resist{\^e}ncia ao coque. O NiO-CeO₂, embora ativo no in{\´i}cio, desativou com o tempo devido {\`a} deposi{\c{c}}{\~a}o de coque (6,4\%). A an{\´a}lise p{\´o}s-rea{\c{c}}{\~a}o mostrou baixa forma{\c{c}}{\~a}o de coque na maioria dos catalisadores. Os resultados indicam que o suporte tem papel determinante na atividade, estabilidade e resist{\^e}ncia dos catalisadores na reforma a seco do biog{\´a}s. Palavras-chave: Hidrog{\^e}nio sustent{\´a}vel; Reforma a seco do biog{\´a}s; Catalisadores de NiO; efeito do suporte ABSTRACT-This study evaluates NiO-based catalysts supported on MgO, ZrO₂, Al₂O₃, La₂O₃, and CeO₂ for the dry reforming of biogas. Characterization of the samples revealed differences in metal dispersion, metallic area, and surface morphology. NiO-Al₂O₃ and NiO-CeO₂ show higher metallic areas and better Ni dispersion, leading to higher CH₄ and CO₂ conversions and good H₂ yield. NiO-Al₂O₃ is the most efficient and stable catalyst over 8 hours of reaction. NiO-La₂O₃ shows a gradual increase in activity and good coke resistance. Conversely, NiO-CeO₂, despite high initial activity, deactivates over time due to coke deposition (6.4\%). Post-reaction analysis confirmed low coke formation for most catalysts. The results indicate that the choice of support directly affects catalyst activity, stability, and resistance.}, language = {pt} } @misc{MbuyaPawarJafarietal., author = {Mbuya, Christel Olivier Lenge and Pawar, Kunal and Jafari, Mitra and Shafiee, Parisa and Okoye Chine, Chike George and Tarifa, Pilar and Dorneanu, Bogdan and Arellano-Garcia, Harvey}, title = {Tuning catalyst performance in methane dry reforming via microwave irradiation of Nickel-Silicon carbide systems}, series = {Journal of CO2 utilization}, volume = {102}, journal = {Journal of CO2 utilization}, publisher = {Elsevier BV}, address = {Amsterdam}, issn = {2212-9839}, doi = {10.1016/j.jcou.2025.103270}, pages = {1 -- 9}, abstract = {The dry reforming of methane (DRM) is a promising route for converting greenhouse gases such as methane (CH4) and carbon dioxide (CO2) into valuable syngas, hydrogen (H2) and carbon monoxide (CO). However, traditional nickel (Ni)-based catalysts suffer from rapid deactivation due to carbon deposition and sintering, especially when supported on low thermal conductivity materials. In this work, a novel post-synthesis microwave irradiation (MIR) treatment is introduced to systematically optimize the performance of Ni - β - SiC and Ni - Ti - Cβ - SiC catalysts for DRM. Unlike previous studies that have used MIR during reaction or with different supports, this approach tunes the metal - support interactions and textural properties of Ni - β - SiC and Ni - Ti - Cβ - SiC catalysts by varying the MIR exposure time after catalyst synthesis. MIR post-treatment (10-25 s) increased the CH4 conversion to 65 \% and the CO2 conversions to 62 \% for Ni-β-SiC catalysts and improved the H₂/CO ratio to 0.80, with stable performance over 20 h. For Ni-Ti-Cβ-SiC, MIR (10-20 s) maintained CH4 conversion up to 60 \% and CO2 conversion to 58 \% over 20 h, while the untreated catalyst, though initially higher, deactivated rapidly. Excessive MIR (30 s) reduced performance for both catalyst types, underscoring the need for optimal exposure time. These findings demonstrate post-synthesis MIR provides a tuneable approach for enhancing both the activity and durability of Ni/SiC - based DRM catalysts through controlled modification of metal - support interactions. This work offers new insights for the design of robust catalysts aimed at greenhouse gas utilization and sustainable syngas production, with activity and stability enhancements linked to controlled changes in metal - support interactions.}, language = {en} } @misc{CunhaCordeiroSafdarSantosdaSilvaetal., author = {Cunha Cordeiro, Jos{\´e} Luiz and Safdar, Muddasar and Santos da Silva, Jefferson and Silva de Aquino, Gabrielle and Vaz dos Santos Rios, Jo{\~a}o Gabriel and Brand{\~a}o dos Santos, Maur{\´i}cio and Teixeira Cruz, Fernanda and Alves Fiuza-Junio, Raildo and Dorneanu, Bogdan and Arellano-Garcia, Harvey and Valverde Pontes, Karen and Santos Mascarenhas, Artur Jos{\´e}}, title = {Effect of support on Ni catalysts prepared by the combustion method applied in the dry reforming of biogas for production of sustainable hydrogen}, series = {International journal of hydrogen energy}, volume = {204}, journal = {International journal of hydrogen energy}, publisher = {Elsevier BV}, address = {Amsterdam}, issn = {1879-3487}, doi = {10.1016/j.ijhydene.2025.153150}, pages = {1 -- 25}, abstract = {This work investigated Ni catalysts on different supports (MgO, ZrO2, NiAl2O4, CeO2 and La2O3) prepared by the combustion method aiming for sustainable hydrogen production via simulated biogas dry reforming. The Ni/NiAl2O4 catalyst stood out among the materials due to its high Ni dispersion, low crystallite size and strong metal-support interaction, being stable for 8 h of reaction with high H2 yield and low coke deposition. The Ni/CeO2 catalyst showed good catalytic activity, but with high coke deposition (11.7 \%). The Ni/La2O3 catalyst showed an increase over the reaction time, due to the dynamic reconstruction of the surface. The Ni/MgO and Ni/ZrO2 catalysts did not present satisfactory performance when compared to the other catalysts, due to the low Ni dispersion and high crystallite size. The Ni/NiAl2O4 catalyst is very promising, due to the high production of H2, low coke deposition, thermal stability, but new studies on durability and economic viability are necessary.}, language = {en} } @misc{LeeParkDorneanuetal., author = {Lee, Joohwa and Park, Haryn and Dorneanu, Bogdan and Kim, Jin-Kuk and Arellano-Garcia, Harvey}, title = {Decarbonized hydrogen production : integrating renewable energy into electrified SMR process with CO₂ capture}, series = {Systems and control transactions}, volume = {4}, journal = {Systems and control transactions}, publisher = {PSE Press}, address = {Notre Dame, IN}, isbn = {978-1-7779403-3-1}, issn = {2818-4734}, doi = {10.69997/sct.152295}, pages = {613 -- 618}, abstract = {Electrified steam methane reforming has emerged as a promising technology for electrifying the hydrogen production process industries. Unlike conventional fossil fuel-based steam methane reforming, the electrified steam methane reforming process relies exclusively on electrical heating, eliminating the need for fossil fuel combustion. Beyond that, however, significant amounts of electricity required for the electrified process should be imported from the renewable energy-based system rather than fossil fuel-based grid electricity to have an environmental advantage over the conventional process. This study suggests a framework for integrating renewable energy systems into the electrified process for decarbonized hydrogen production. Considering the variability of renewable energy, wind and solar power are supplemented by battery storage, to facilitate a stable electricity supply to the electrified hydrogen production process. A Mixed-Integer Linear Programming (MILP) model is developed to optimally size and operate both the renewable system and potential grid imports. Case studies under various carbon tax scenarios, using historical weather data from a region in Germany, are conducted, followed by a techno-economic assessment to estimate the Cost of Hydrogen (COH). The results show that higher carbon taxes and reduced capital costs for wind, solar, and storage technologies significantly increase the share of renewable-based electricity. These findings highlight the importance of more stringent carbon taxation and improvements in the technology readiness level (TRL) of renewable energy are critical for accelerating large-scale, clean hydrogen production and industrial decarbonization.}, language = {en} } @misc{DorneanuMappasArellanoGarcia, author = {Dorneanu, Bogdan and Mappas, Vasileios K. and Arellano-Garcia, Harvey}, title = {A novel approach to gradient evaluation and efficient deep learning : a hybrid method}, series = {Systems and control transactions}, volume = {4}, journal = {Systems and control transactions}, publisher = {PSE Press}, address = {Notre Dame, IN}, isbn = {978-1-7779403-3-1}, issn = {2818-4734}, doi = {10.69997/sct.120349}, pages = {1872 -- 1877}, abstract = {Deep learning faces significant challenges in efficiently training large-scale models. These issues are closely linked, as efficient training often depends on precise and computationally feasible gradient calculations. This work introduces innovative methodologies to improve deep learning network (DLN) training in complex systems. A novel approach to DLN training is proposed by adapting the block coordinate descent (BCD) method, which optimizes individual layers sequentially. This is combined with traditional batch-based training to create a hybrid method that harnesses the strengths of both techniques. Additionally, the study explores Iterated Control Random Search (ICRS) for initializing parameters and applies quasi-Newton methods like L-BFGS with restricted iterations to enhance optimization. By tackling DLN training efficiency, this contribution offers a comprehensive framework to address key challenges in modern machine learning. The proposed methods improve scalability and effectiveness, especially for handling complex real-world problems. Examples from Process Systems Engineering illustrate how these advancements can directly enhance the training of large-scale systems.}, language = {en} } @misc{ShezadSamikannuSafdaretal., author = {Shezad, Nasir and Samikannu, Ajaikumar and Safdar, Muddasar and Arellano-Garcia, Harvey and Mikkola, Jyri-Pekka and Seo, Dong-Kyun and Akhtar, Farid}, title = {Nickel supported over hierarchical zeolite 13X catalysts for enhanced conversion of carbon dioxide into methane}, series = {International journal of energy research}, volume = {2025}, journal = {International journal of energy research}, publisher = {Wiley}, address = {Hoboken, NJ}, issn = {1099-114X}, doi = {10.1155/er/4728304}, pages = {1 -- 14}, abstract = {Catalytic conversion of carbon dioxide (CO2) into useful chemicals such as methane (CH4) is a promising carbon utilization method that effectively mitigates CO2 and partially meets energy needs. The characteristics of commonly used nickel (Ni) supported meso/microporous catalysts for CO2 methanation can be tailored by tuning the structural properties of the support and adding promoters. This work investigated the Ni supported over hierarchical zeolite 13X (h13X) and incorporated with different promoters (Mg, Ca, Ce, and La) developed using the wet-impregnation method. The catalysts were thoroughly characterized using SEM, EDS, XRD, H2-TPR, CO2-TPD, thermogravimetric analysis (TGA), X-ray photoelectron spectroscopy (XPS), and N2 sorption and desorption techniques and evaluated for CO2 methanation. The impact of promoters on the characteristics of the catalysts was observed with improved surface basicity in CO2-TPD and metal-support interaction in H2-TPR analysis. Among the promoted catalysts, the NiLa/h13X catalyst exhibited the highest catalytic activity with a maximum conversion of 76\% and CH4 selectivity of 98.5\% at 400°C and 20 bar at GHSV of 60,000 mL gcat-1 h-1, respectively. Regarding stability, the Mg-promoted catalyst exhibited better stability during 24 h of reaction than other catalysts, demonstrating better resilience against deactivation. The enhanced performance of the NiLa/h13X catalyst could be credited to the increased surface basicity, high surface area, and dispersion. This study highlights the potential of hierarchical porous zeolites for CO2 methanation and other heterogeneous reactions.}, language = {en} } @misc{SafdarSherArellanoGarcia, author = {Safdar, Muddasar and Sher, Farooq and Arellano-Garcia, Harvey}, title = {Perovskite materials for catalytic CO₂ valorisation : structural characteristics, synthesis and lattice substitutions for gas-phase reactions}, series = {Journal of environmental chemical engineering}, volume = {14}, journal = {Journal of environmental chemical engineering}, number = {2}, publisher = {Elsevier BV}, address = {Amsterdam}, issn = {2213-3437}, doi = {10.1016/j.jece.2026.121473}, pages = {1 -- 35}, abstract = {Perovskites are emerging materials that are being extensively investigated for converting greenhouse gases (GHGs) through thermochemical processes due to their versatile properties. Given their distinct physical and chemical characteristics and their unique structure (ABO3, general formula), they are desirable candidates for designing state-of-the-art catalytic systems. For instance, they can be prepared with modified oxygen vacancies, enhanced redox potential, and tailored nanoparticle formulations for use in various catalytic gas-phase CO2 conversion processes, thereby facilitating the formation of valuable, renewable raw materials such as fuels and chemicals. This comprehensive review explains the perovskite structures, including their crystallographic properties, standard synthesis methods, recent advancements in A, B, and X-site substitutions, and their effectiveness in upgrading CO2 to produce valuable commodities via different synthetic routes in gas-phase reactions via methanation, reverse water gas shift reaction (rWGS), and dry reforming of methane (DRM). To achieve a sustainable clean energy supply, application-oriented, efficient, and advanced catalytic systems that support the necessary reaction conditions and serve as the most active and selective catalysts are reported in each synthetic gas-phase production section. This study highlights current advancements and optimised research efforts to design potential catalytic materials that meet future requirements for developing efficient decarbonised energy systems. The proposed synthesis methods are the most effective techniques for conserving time and energy. They can also yield favourable morphology and allow manipulation of nanoparticle size, both of which are essential for designing innovative catalysts. To address concerns about CO2 emissions harming the environment, this study focuses on adaptable, sustainable gas-phase reaction methods with diverse industrial applications. The primary emphasis is on effective, robust perovskite-based catalysts that enable the efficient conversion of CO2 into value-added chemicals and fuels, thereby supporting low-carbon energy and chemical technologies. This review delineates explicit correlations among synthesis, structure, properties, performance, and stability by relating perovskite lattice design, defect chemistry, and compositional flexibility to catalytic activity, selectivity, and durability in heterogeneous catalytic reactions.}, language = {en} } @misc{SchowarteRiedelSafdaretal., author = {Schowarte, Julia and Riedel, Ramona and Safdar, Muddasar and Helle, Sven and Fischer, Thomas and Arellano-Garcia, Harvey}, title = {Photocatalytic degradation of PFOA with porous lanthanoid perovskites nano catalyst}, series = {Chemie Ingenieur Technik}, volume = {98}, journal = {Chemie Ingenieur Technik}, number = {1-2}, publisher = {Wiley-VCH}, address = {Weinheim}, issn = {1522-2640}, doi = {10.1002/cite.70027}, pages = {7 -- 17}, abstract = {Perfluorooctanoic acid (PFOA), a persistent environmental pollutant, poses significant health and ecological risks. Thisstudy investigates for the first time the photocatalytic degradation of PFOA using novel doped perovskite catalysts underpolychromatic UV-VIS irradiation with a peak emission at 366 nm. A series of nickel- and lanthanide-doped perovskites(NiMn2 O 4 , LaMnO 3 , NdMnO 3 , and their nickel-doped variants) were synthesized via a facile co-precipitation techniqueand characterized using X-ray diffraction (XRD), UV-VIS diffuse reflectance spectroscopy (UV-VIS-DRS), scanning elec-tron microscopy with energy-dispersive X-ray spectroscopy (SEM-EDX), N2 -physisorption, and microwave plasma atomicemission spectroscopy (MP-AES). Photocatalytic experiments revealed that Ni/NdMnO3 exhibited the highest degradationefficiency toward PFOA, likely due to its small band gap energy of 1.5 eV, facilitating efficient C-C bond cleavage.}, language = {en} } @misc{YentumiJurischkaDorneanuetal., author = {Yentumi, Richard and Jurischka, Constantin and Dorneanu, Bogdan and Arellano-Garcia, Harvey}, title = {A comprehensive modelling approach to enhance performance and scalability of iron-oxide based hydrogen storage systems}, series = {PEMT 2025 - Annual Meeting of Process Engineering and Materials Technology}, journal = {PEMT 2025 - Annual Meeting of Process Engineering and Materials Technology}, address = {Frankfurt am Main}, pages = {1 -- 5}, abstract = {Hydrogen storage remains a major bottleneck in realizing a competitive hydrogen economy due to the energy intensity and economic limitations of existing solutions [1]. This contribution addresses these barriers through a model-driven optimization framework for a solid-state thermochemical hydrogen storage (TCS) based on reversible redox cycling of iron oxide/iron. While iron-based TCS offers inherent safety and scalability advantages, key limitations persist, including high reduction temperatures (requiring significant energy input), suboptimal energy storage density, and sluggish redox kinetics [2]. Kinetic parameters for the reduction and oxidation reactions were systematically derived through isothermal thermogravimetric analysis (TGA) coupled with kinetic model regression. A first-principles dynamic model of a fixed-bed reactor was developed, integrating mass, energy, and momentum balances, and validated against experimental data from a lab-scale apparatus. The experimental system featured precision gas flow control, an electric furnace reactor, rapid air-cooled condensation, molecular sieve dehydration, and online effluent analysis via flow meters and gas chromatography. Dynamic simulations were carried out to investigate the reactor's transient behaviour under variations in critical parameters, including H2/H2O partial pressures, reaction temperatures, and gas flow rates. These studies revealed trade-offs between energy efficiency (favoured by lower temperatures), and reaction rates (enhanced at higher temperatures), while identifying key bottlenecks in redox cycling. The model further demonstrated how optimizing feed composition and flow dynamics mitigates kinetic degradation during charge/discharge cycles. REFERENCES [1] Elberry A.M. et al.}, language = {en} } @misc{DorneanuMappasVassiliadisetal., author = {Dorneanu, Bogdan and Mappas, Vasileios K. and Vassiliadis, Vassilios S. and Arellano-Garcia, Harvey}, title = {Adjoint methods for fast sensitivity analysis in nonlinear multistage systems}, series = {PEMT 2025 - Annual Meeting of Process Engineering and Materials Technology}, journal = {PEMT 2025 - Annual Meeting of Process Engineering and Materials Technology}, address = {Frankfurt am Main}, pages = {1 -- 7}, abstract = {Parametric sensitivity analysis is critical for optimization, control, and decision-making in engineering systems, enabling precise understanding of system responses to changes in parameters [1]. In the case of large-scale multistage systems, characterized by interconnected components, high-dimensional parameter spaces, and nonlinear constraints, traditional gradient evaluation methods often face significant challenges in scalability, computational efficiency, and accuracy [2]. This contribution introduces a novel framework for evaluating parametric gradients tailored specifically for generally constrained multistage systems, which leverages adjoint-based techniques [3] to compute exact gradients efficiently, addressing the inherent complexity of these systems. This reduces the number of simulations required by direct numerical differentiation or finite difference methods. The framework accommodates continuous real-value parameters and is designed to handle high-dimensional spaces typical of multistage systems. The proposed methodology is validated through case studies involving large-scale modular systems with nonlinear constraints. Results demonstrate substantial improvements in computational efficiency and gradient accuracy compared to conventional techniques. These advancements enable optimization algorithms to converge more quickly and reliably while navigating complex solution spaces effectively. By facilitating accurate sensitivity analysis, the framework enhances the exploration of design alternatives and increases the likelihood of identifying globally optimal solutions.}, language = {en} } @misc{MbuyaJafariDorneanuetal., author = {Mbuya, Christel-Olivier Lenge and Jafari, Mitra and Dorneanu, Bogdan and Arellano-Garcia, Harvey}, title = {Structured FeMnK catalysts for aviation fuel production via Fischer-Tropsch synthesis : a channel geometry study}, series = {100 Years Fischer-Tropsch Process A Central Pillar of Future Energy Systems Conference}, journal = {100 Years Fischer-Tropsch Process A Central Pillar of Future Energy Systems Conference}, pages = {1 -- 3}, abstract = {Fischer-Tropsch synthesis (FTS) is a promising route for sustainable aviation fuel production but is often limited by heat and mass transfer constraints in fixed-bed reactors. This study investigates the role of structured catalyst geometry in intensifying FTS performance using FeMnK catalysts selectively tailored for aviation-range hydrocarbons. The catalyst was synthesized via organic combustion and further modified by microwave irradiation, with structural properties characterized by X-ray diffraction. Aluminum honeycomb monoliths with square, circular, and triangular channel geometries were designed and fabricated by 3D printing, followed by catalyst deposition through dip-coating. High-pressure FTS experiments conducted at 30 bar and 300 °C demonstrate that channel geometry significantly influences conversion and selectivity by affecting flow dynamics and reactant mixing. The results highlight the potential of geometry-optimized structured catalysts to enhance FTS efficiency and support advanced reactor designs for sustainable aviation fuel production.}, language = {en} } @misc{MappasDorneanuArellanoGarcia, author = {Mappas, Vasileios K. and Dorneanu, Bogdan and Arellano-Garcia, Harvey}, title = {Combinatorial optimization problems : a quantum-based approach}, series = {PEMT 2025 - Annual Meeting of Process Engineering and Materials Technology}, journal = {PEMT 2025 - Annual Meeting of Process Engineering and Materials Technology}, address = {Frankfurt am Main}, pages = {1 -- 7}, abstract = {Classical computing faces challenges in global optimization (GO) when addressing non-convex problems, where the number of solutions grows exponentially with problem size 1. Quantum computing emerges as a potential solution. Gate-based quantum computing offers a broader range of applicability due to its ability to implement general quantum algorithms, in contrast to quantum annealing which is more specialized 2,3. This contribution explores the application of gate-based quantum computing to solving combinatorial optimization (CO) problems, specifically focusing on overcoming the limitations of classical computing. The proposed approach utilizes gate-based quantum computers, using IBM's hardware and software as an example, and the quantum approximate optimization algorithm (QAOA) for solving the reformulated problem using the Qiskit toolbox. The methodology includes translating the quadratic unconstrainted optimization problem (QUBO) into an Ising Hamiltonian, constructing the QAOA ansatz circuit, and optimizing its parameters. Furthermore, real quantum device architectures are employed to solve the optimized QUBO formulations. To demonstrate the capabilities of the approach, Haverly's pooling-blending problem is selected as a case study. Through the application of different discretization strategies, resolution levels, and circuit architectures, a comparative analysis of solver performance is conducted. The resulting QUBO formulations, efficiently embedded and solved on real quantum devices, underscore the potential of gate-based quantum computing as a promising solution approach for complex CO challenges.}, language = {en} } @misc{SafdarSchowarteArellanoGarcia, author = {Safdar, Muddasar and Schowarte, Julia and Arellano-Garcia, Harvey}, title = {Sustainable production of synthetic natural gas : CO2 methanation on 3D-printed structured Ni-based perovskite catalysts}, series = {Annual Meeting on Reaction Engineering 2025}, journal = {Annual Meeting on Reaction Engineering 2025}, address = {W{\"u}rzburg}, pages = {1 -- 4}, abstract = {This study explores the thermo-catalytic conversion of captured CO₂ with renewable H₂ to produce synthetic natural gas (SNG) using Ni-based perovskite-type oxides (Ni-PTOs) as cost-effective and thermally stable catalysts in Power-to-Gas (PtG) applications. Structured monoliths improved the catalytic performance under reaction conditions. Use of 3D-printed geometries enhanced thermal/mechanical stability and process efficiency. Structured designs outperformed conventional powder-based configurations, indicating promise for scalable and sustainable SNG production.}, language = {en} } @misc{JafariSantosdaSilvaDorneanuetal., author = {Jafari, Mitra and Santos da Silva, Jefferson and Dorneanu, Bogdan and Valverde Pontes, Karen and Arellano-Garcia, Harvey}, title = {Towards digitalization of catalysis design and reaction engineering : data-driven insights into methanol to DME}, series = {58. Jahrestreffen Deutscher Katalytiker}, journal = {58. Jahrestreffen Deutscher Katalytiker}, pages = {1 -- 2}, abstract = {Dimethyl ether (DME, methoxymethane) is a clean-burning fuel and a promising alternative to conventional fossil fuels, especially in transportation and power generation. Its production from methanol through dehydration offers a viable pathway toward energy sustainability, not only because of its environmental benefits but also due to the high purity of the resulting products and the efficient conversion rate of methanol [1]. However, optimizing this process requires understanding the intricate dependencies among reaction parameters, including temperature, pressure, catalyst type, and feedstock composition [2]. Machine learning offers transformative potential in this context by identifying complex, non-linear interactions among variables and providing predictive insights that can improve reaction efficiency, yield, and product quality. Through predictive modeling, machine learning can significantly reduce the need for experimental trial-and-error by identifying optimal reaction conditions quickly, thereby decreasing costs, enhancing scalability, and supporting continuous, real-time process optimization [3, 4]. In this study, first a dataset is generated including different descriptors like catalyst formulation, pretreatment, characteristics, activation, and reaction conditions. This dataset is then preprocessed by encoding, imputation, and normalization to make it ready for modelling, followed by data analysis to identify patterns and dependencies. Different models, including Gradient Boosting Regressor, XGBoost, LightGBM, and neural networks, are applied to predict methanol conversion and DME yield based on input variables. The models were evaluated through cross-validation, achieving highaccuracy and underscoring the potential of data-driven optimization in enhancing DME production. These steps are illustrated in Figure 1. Finally, the prediction accuracy of each model is investigated, and the best algorithm is selected. The effect of different descriptors on the respond have also been assessed to find out the most effective parameters on the catalyst performance. The best model is then used to predict DME yield and optimize the catalyst and reaction parameters.}, language = {en} } @misc{HamdanHamdanDorneanuetal., author = {Hamdan, Mustapha and Hamdan, Malak and Dorneanu, Bogdan and Arellano-Garcia, Harvey}, title = {Modular high-temperature thermal energy storage for industrial decarbonisation using a particle-based heat battery}, series = {PEMT 2025 - Annual Meeting of Process Engineering and Materials Technology}, journal = {PEMT 2025 - Annual Meeting of Process Engineering and Materials Technology}, address = {Frankfurt am Main}, pages = {1 -- 3}, abstract = {Industrial heat contributes over 9 Gt of annual CO₂ emissions, with high-grade requirements (>1000°C) posing exceptional decarbonization challenges [1]. This contribution present the two-loop (2LP) Heat Battery, a particle-based thermal energy storage system delivering dispatchable zero-carbon heat and electricity at temperatures up to 1600°C. The modular design employs a dual-loop recirculating bed of advanced ceramic particles, achieving 98\% round-trip efficiency through controlled particle metering and high surface area heat transfer. Unlike bulk thermal storage systems that exhibit thermocline-induced temperature decay [2], the 2LP architecture maintains steady-state outlet temperatures during 24-hour discharge cycles. Key innovations include a volumetric energy density of 1280 kWh/m 3 (surpassing molten salts, lithium-ion batteries, and refractory brick systems) and thermal output density exceeding 1MWth/m 3. The technology reduces levelised cost of storage from €20/kWh (conventional molten salt) to below €3/kWh while supporting ultra-efficient supercritical CO2 Brayton cycles (thermal-to-electric efficiency >50\%). System performance exceeds EU SET Plan targets, achieving 98\% electro-thermal round-trip efficiency and 90\% combined heat and power efficiency. This scalable solution addresses critical gaps in industrial electrification, enabling grid congestion mitigation and providing a cost-effective pathway to decarbonize hard-to-abate sectors like steel and cement production. The 2LP Heat Battery demonstrates technical and economic viability to support EU Net Zero objectives through high-temperature electrification.}, language = {en} } @misc{MappasDorneanuHeinzelmannetal., author = {Mappas, Vasileios K. and Dorneanu, Bogdan and Heinzelmann, Norbert and Arellano-Garcia, Harvey}, title = {Modeling multiphase reactors with complex particle geometries}, series = {PEMT 2025 - Annual Meeting of Process Engineering and Materials Technology}, journal = {PEMT 2025 - Annual Meeting of Process Engineering and Materials Technology}, address = {Frankfurt am Main}, pages = {1 -- 4}, abstract = {Trickle bed reactors (TBRs) are the backbone of the catalytic multiphase reactors in industrial processes, owning to their simple design, flexible controllability and large surface area. One of the key aspects for designing TBRs is the flow simulation inside the reactor and the hydrodynamics phenomena that take place during its operation. Literature offers various methods for simulating the behaviour and the performance of TBRs based on empirical methods or Computational Fluid Dynamics (CFD) simulations leading to inaccurate results, high computational burden, case studies with small catalytic beds or considering only spherical particles particles 1. To overcome these drawbacks, a modular and flexible toolbox for the modelling and study of TBRs is proposed which is adapted to the local structure of the catalytic bed 2. To improve the contact point calculation and extend to more complex geometries (i.e., cylinders, Raschig rings, trilobes), an approach based on liquid element tracking (LET) is applied, where the particle's surface is discretised over a finite number of triangles. Therefore, a pointwise sequence of the fluid over individual partial surfaces, based on the applied forces, is implemented for the liquid flow path estimation. The benefits of this procedure lie in its effectiveness, modular interconnection, and robust capability to represent a diverse range of phenomena for simulating flow patterns during TBRs operation in the low-interaction regime. Furthermore, the required computational time is significantly reduced compared to CFD simulations and a large number of particles can be introduced in the examined case study. The new particle representation is successfully implemented and the results are in good agreement with the static holdup prediction and radial flow distribution based on the previous contact point model, based solely on geometric calculations of the distance between the spheres and the liquid-solid interactions. References [1] Fathiganjehlou, A., et al. (2024). Multi-scale pore network modeling of a reactive packed bed.}, language = {en} } @misc{JafariShafieeSantosdaSilvaetal., author = {Jafari, Mitra and Shafiee, Parisa and Santos da Silva, Jefferson and Dorneanu, Bogdan and Valverde Pontes, Karen and Arellano-Garcia, Harvey}, title = {Advancing catalyst design with machine learning : insights from FTS and DME production}, series = {Annual Meeting on Reaction Engineering 2025}, journal = {Annual Meeting on Reaction Engineering 2025}, pages = {1 -- 6}, abstract = {This contribution presents a ML-based framework to optimize heterogeneous catalysts, with a primary focus on the targeted application for FTS and DME production. However, the framework is designed to be generic, with applicability to a broader range of heterogeneous catalytic processes. The study analyzes a variety of catalyst parameters, including composition, pretreatment, and operating conditions, and employs advanced ML techniques such as regression models, ensemble learning, and neural networks to model the relationships between these factors and reaction outcomes. Hyperparameter optimization and performance evaluation using metrics like R², RMSE, MSE, and AIC further improve the accuracy and robustness of the models. In addition, the study provides a comparative analysis of applying this ML framework to both FTS and DME processes, highlighting the similarities and differences in optimizing catalyst performance and operating conditions. The study aims to identify the most influential parameters that drive catalyst performance and to assess the predictive power of each model. By enhancing understanding of how catalyst properties and operating conditions influence the efficiency of FTS and DME production, this work provides valuable insights into more efficient and sustainable catalytic process design.}, language = {en} } @misc{JafariAbadiMbuyaetal., author = {Jafari, Mitra and Abadi, Amirreza and Mbuya, Christel-Olivier Lenge and Dorneanu, Bogdan and Arellano-Garcia, Harvey}, title = {Fischer-Tropsch synthesis and hydrocracking process integration : a study on mesoporosity modification and acidity optimization}, series = {100 Years Fischer-Tropsch Process A Central Pillar of Future Energy Systems Conference}, journal = {100 Years Fischer-Tropsch Process A Central Pillar of Future Energy Systems Conference}, pages = {1 -- 3}, abstract = {Machine learning (ML) is employed to identify the key parameters influencing CO conversion and C5+ selectivity. Insights from both the literature review and ML analysis highlight pore volume and acidity as the most critical factors. Therefore, this study focuses on developing cobalt/beta zeolite catalysts with tailored mesoporosity and acidity to enhance catalytic performance. Specifically, the goal is to optimize the acidity of the zeolite by determining the ideal concentration of NH4+ during the ion-exchange step.}, language = {en} } @misc{RiedelSchowarteSemischetal., author = {Riedel, Ramona and Schowarte, Julia and Semisch, Laura and Gonzalez Castano, Miriam and Ivanova, Svetlana and Martienssen, Marion and Arellano-Garcia, Harvey}, title = {Improving the photocatalytic degradation of EDTMP : effect of doped NPs (Na, Y, and K) into the lattice of modified Au/TiO2 nano-catalysts}, series = {Chemical engineering journal}, volume = {506}, journal = {Chemical engineering journal}, doi = {10.1016/j.cej.2025.160109}, pages = {14}, abstract = {This study presents the photocatalytic degradation of the aminophosphonate ethylenediaminetetra(methylenephosphonic acid) (EDTMP) with a range of different doped nanoparticles (NP). The photocatalysts were based on TiO2 benchmark P25 and gold (Au) doped either with sodium (Na), potassium (K) or yttrium (Y). The synthesized photocatalysts were characterized via TEM, XRF, XRD, UV-DRS (band gap estimation) and N2-physisorption. Photocatalytic pre-screening at pH values of 3, 7 and 10 indicated highest o-PO4 release of EDTMP at pH 7 and 10 for NP either doped with K or Y. The results of LC/MS analysis showed that the NPs doped with 5 \% Y (Au2/Y5/P25) resulted in the fastest degradation of EDTMP. The target compound was completely degraded within 60 min, 4 times faster than photochemical treatment of unadulterated EDTMP. Importantly, also the transformation products were accelerated by the photocatalytic treatment with Au2/P25 either doped with 5 \% Y or 10 \% K. The results of scavenger experiments indicated that the enhanced photocatalytic degradation of EDTMP is primarily attributable to the presence of hydroxyl radicals in the bulk and to a lesser extent to •O2- and electron-holes (h+) at the surface of the catalysts. The study demonstrates that the catalytic efficiency of TiO2 nanocomposites is significantly influenced by the choice of dopants, which affect particle size, band gap, and photocatalytic activity. Yttrium at low concentrations (i.e., 5 wt\% Y) doping emerged as particularly effective, enhancing both the visible light absorption and h+ separation, leading to superior photocatalytic performance in the degradation of EDTMP. The Au content also plays a crucial role in enhancing the photocatalytic efficiency. However, the combination of Au and Na doping was found to be less effective for this photocatalysis in aqueous media, potentially due to larger particle sizes and insufficient dopant contents. In conclusion, the findings emphasise the necessity of optimising both the selection of dopants and the design of catalysts in order to enhance photocatalytic applications.}, language = {en} } @misc{SchowarteRiedelHelleetal., author = {Schowarte, Julia and Riedel, Ramona and Helle, Sven and Martienssen, Marion and Arellano-Garcia, Harvey}, title = {Synergistic enhancement of PFOA and 6:2-FTAB photodegradation using Au/Y-doped TiO₂ nanocatalysts}, series = {Chemical engineering journal advances}, volume = {26}, journal = {Chemical engineering journal advances}, publisher = {Elsevier BV}, address = {Amsterdam}, issn = {2666-8211}, doi = {10.1016/j.ceja.2026.101121}, pages = {1 -- 12}, abstract = {Efficient degradation of perfluoroalkyl substances (PFAS) requires photocatalysts capable of promoting strong C-F bond cleavage and selective interfacial charge transfer. In this proof-of-concept-study, a dual-doped TiO2 nanophotocatalyst (Au2/Y5/P25) was synthesized by combining gold (Au) nanoparticles and yttrium (Y) dopants to enhance charge separation and reactive oxygen species (ROS) generation. Structural characterization supported Au deposition on the TiO2 surface and Y incorporation into the lattice, accompanied by a slight band-gap narrowing. Under UV irradiation in aqueous solution (unbuffered pH 5.8, room temperature) the nanophotocatalyst exhibited distinct degradation pathways for 1000 µg L-1 of two representative PFAS, perfluorooctanoic acid (PFOA) and Capstone B (6:2 FTAB), reflecting environmentally prevalent groups. PFOA underwent 99 \% degradation within 100 min via a stepwise CF2-cleavage mechanism, generating a sequence of perfluorocarboxylic acids down to perfluorobutanoic acid (PFBA), consistent with enhanced electron-hole separation. In contrast, Capstone B showed rapid, single-step S-N bond cleavage to 6:2 perfluorooctanesulfonic acid (6:2 PFOS), primarily driven by hole- and •OH-mediated oxidation under oxygen-rich conditions. This process achieved 96 \% degradation within 20 min but did not proceed to further defluorination, indicating oxidative limitations. Dissolved oxygen analysis revealed efficient electron utilization and sustained oxidative turnover without excessive oxygen depletion. The findings demonstrate that Au/Y co-doping promotes selective PFAS activation, enabling rapid precursor oxidation while exposing the kinetic limits of secondary C-F bond cleavage. These discoveries offer new insights into the design of plasmonic-rare-earth-modified TiO2 photocatalysts for efficient PFAS degradation through interface-driven oxidation pathways.}, language = {en} }