Process Design Research Articles

Evaluation of the Design and Operating Conditions of an Adsorption Reactor for Hydrogen Sulfide Removal in Biogas

Purpose : This study aims to provide basic data for the design and operation of adsorption towers to effectively remove hydrogen sulfide (H2S) from biogas.Methods : Various adsorbents, such as activated carbon, zeolite, and iron sulfate-treated zeolite (Zeolite-Fe7), were used to evaluate the efficiency of H<sub>2</sub>S removal. The experiments were conducted under different operating conditions, including reactor length, gas flow rate, humidity, and the combined application of absorption and adsorption methods. Key factors influencing the performance of each adsorbent and hydrogen sulfide removal were analyzed.Results and Discussion : Activated carbon recorded the highest H<sub>2</sub>S removal efficiency at approximately 97.8%, while zeolite and Zeolite-Fe7 showed relatively lower efficiency. The gas flow rate significantly impacted the removal performance of the adsorbents, and reactor length and humidity had varying effects depending on the adsorbent. Additionally, when absorption and adsorption methods were applied simultaneously, a removal rate of about 95.6% was achieved.Conclusion : Activated carbon demonstrated higher efficiency and resistance to humidity compared to other adsorbents, making it the most suitable material for H<sub>2</sub>S removal from biogas. This study provides basic data for optimizing the design and operation conditions of adsorption towers and is expected to contribute to the future design of pre-treatment processes for H<sub>2</sub>S removal in biogas.

One-pot synthesis of TiO2–Fe3O4 bimetallic oxides decorated on carbon nanosheets as an efficient photothermal microwave absorber

Microwave absorbers with photothermal property have an important application prospect in extreme environment. However, developing multifunctional microwave absorbers usually requires complex procedures including synthesis process and component design. Here, TiO2–Fe3O4 bimetallic oxides decorated on carbon nanosheets (CNSs/TiO2/Fe3O4) was constructed through a simple one-pot method to apply as a highly efficient photothermal microwave absorber. The synergistic dielectric/magnetic properties of the multi-component composites, together with the visible light absorption of TiO2/Fe3O4, allow CNSs/TiO2/Fe3O4 to efficiently absorb microwave and visible light, and convert them into heat. As a result, CNSs/TiO2/Fe3O4 exhibits an exceptional microwave absorption capability, with a minimum reflection loss (RLmin) of −42.55 dB at a thin thickness of 1.6 mm and a maximum effective absorption bandwidth (EABmax) of 3.52 GHz. Furthermore, it demonstrates a good photothermal response (52.1 °C at 100 mW cm−2) and long-term cycling stability under simulated solar lighting conditions. The mechanisms of the microwave absorption and photothermal conversion were elucidated. In summary, this work presents a simple approach to decorate bimetallic oxides on carbon nanosheets and can provide a mind for designing high-efficiency microwave absorbers with photothermal property.

Application of BIM in Modeling Concrete Retaining Walls

During the hydraulic design phase, challenges often arise, such as poor coordination between different disciplines and ineffective information transfer. These issues can lead to errors in the design process and unreasonable structural designs. As the complexity of engineering projects continues to increase, traditional 2D design methods can no longer meet current demands. As a result, Building Information Modeling (BIM) technology has been introduced, shifting the design approach to 3D modeling. This transition has significantly improved design accuracy and construction efficiency. To further optimize this process, a user interface interaction system based on C# language and the Revit API was developed. Through this system, we successfully implemented the 3D automatic modeling of retaining wall components and a reinforcement plugin. These tools have greatly saved time on repetitive tasks for designers, improving work efficiency. At the same time, they have advanced the informatization of hydraulic concrete structure construction, making the construction process more efficient, precise, and controllable. The 3D deepening design can accurately express the geometric characteristics of the structure, enhancing precision and accelerating the informatization development of hydraulic concrete structure construction.

Controls on microbially-induced carbonate precipitation in geologic porous media

Microbially-induced carbonate precipitation (MICP) provides a natural biomineralization approach to secure the geologic storage of gases (e.g., carbon dioxide, hydrogen and methane). Cracks in embrittled wellbore cement, for example, provide a pathway for atmospheric gas leakage, while permeability heterogeneities in the storage reservoir leads to fingering effects that diminish the storage capacity. The design of MICP processes, however, remains a challenge due to limited understanding of the coupled nonlinear reaction kinetics and multiphase transport involved. Specifically, previous attempts at MICP through porous media have been encumbered by carbonate precipitation localized to the first ∼ cm of the bulk injection surface. In this study, we investigate the reactive transport controls on MICP necessary to enable deep MICP penetration into the formation. We use a micromodel with pore geometry and geochemistry representative of real geologic media to image direct pore- and pore-ensemble-level mineral, fluid, and microbial distributions. An approach to adsorb microbes uniformly across the micromodel, rather than local accumulation near the inlet, is developed that enables deep MICP penetration into the porous medium. A sensitivity analysis was performed to investigate the impact of injection conditions (e.g., rates, concentrations) required to maximize CaCO3 precipitation away from the injection site. With multiple cycles of MICP, a ∼ 78 % reduction in permeability was achieved with ∼8 % carbonate pore volume occupation. Overall, this study establishes the possibility of MICP as an effective and controllable method to enhance the security of gas storage in geologic media.

Developing a Weight-Neutral Health Intervention in Denmark: Protocol for a Co-Design Process.

Lifestyle interventions for weight loss are generally ineffective in achieving clinically meaningful long-term reductions in body weight and may contribute to negative behavior such as weight cycling or disordered eating. Negative focus on high weight may also contribute to weight stigma. Weight stigma includes negative attitudes and discriminatory behavior toward people with big bodies and can result in psychological stress and unfavorable health outcomes. Taken together, it is possible that the potential harms of lifestyle-based weight loss interventions may exceed the potential benefits. Weight-neutral health (WNH) has emerged as an alternative strategy advocating for size diversity, intuitive eating, and joyful physical movement, all without placing emphasis on weight reduction. This protocol outlines the study design for the co-design process of developing a WNH complex intervention, engaging relevant stakeholders in Denmark. We base our understanding of WNH on the principles from Health at Every Size: body acceptance, joyful movement, intuitive eating, and weight stigma reduction. The co-design development process is based on the Medical Research Council's framework for complex interventions and applies methods from human-centered design through 4 iterative design phases of engaging stakeholders-discover: search existing literature, and conduct interviews with Danish municipal stakeholders working with WNH and other expert stakeholders; define: coproduction of seminars with health professionals (HPs) with knowledge of WNH, and semistructured interviews with people with BMI≥30 kg/m2 who have participated in existing WNH interventions; design: content-creating workshops with HPs and people with BMI≥30 kg/m2; and validate: evaluate seminars, plan feasibility, and produce materials. The data will be analyzed thematically to build a scaffold for the intervention activities and components. In further analysis, we will explore how health is performed, meaning the actions and dialogues that arise when dealing with health guidelines, the societal body, weight, and health expectations, in the context of the intervention. The project is fully funded. As of August 2024, the co-design process was in the closing phase. In total, 15 HPs were included, some of whom have larger body sizes. This provides a dual perspective, combining their personal experiences of living with a high BMI with their professional expertise. In total, 16 people with BMI≥30 kg/m2 have generously shared their experiences with WNH programs, including the difficulties of moving away from external demands and personal wishes for weight loss. Their contributions have nuanced and unfolded our understanding of the principles of WNH in a Danish setting. The intervention designed in and from the co-design process will be tested for feasibility in 2025. The findings from the feasibility study will inform a future randomized controlled trial and present novel findings in the field of health management. The long-term goal is to implement the intervention in a Danish municipal setting free of charge.

Environmental insights of bioethanol production and phenolic compounds extraction from apple pomace-based biorefinery

Food waste is one of the main challenges of solid waste management throughout the food supply chain. Apples are one of the most widely consumed fruits worldwide, the production of which is accompanied by the generation of pomace as a by-product with valuable nutrients. This research presents the first environmental insights of a multiproduct apple pomace-based biorefinery through a life cycle perspective. The design and process modelling of this platform aims to produce bioethanol and extract total phenolic compounds (TPC) towards an efficient use of the pomace obtained from the apple manufacturing industry (juice production). Bioethanol production consist of pressing, fermentation, distillation as the main processes; while in the case of TPC, two extraction techniques were evaluated: i) solvent extraction (mainly water), and ii) the Soxhlet method. The life cycle analysis followed an attributional cradle-to-gate approach considering different midpoint impact categories from the ReCipe 2016 method such as Global Warming (GW), Eutrophication, Human Toxicity, Fossil Scarcity, among others. The results indicate that bioethanol production encompasses a GW profile of 3.17 kg of CO2 eq per kg of product, being the vinasse treatment (subproduct after distillation) the most impactful stage. Phenolic compounds extraction with water achieves a total value of 5.8 kg CO2 eq per g of TPC, while 0.22 kg CO2 eq per g of TPC is obtained with Soxhlet technique. Furthermore, a sensitivity analysis is addressed to improve the environmental profile of bioethanol and TPC. This research demonstrated the relevance of process design on the environmental performance of bioethanol and TPC, where stillage treatment has a key contribution to the results and the use of solvents in TPC extraction, although improving extraction yield, leads to a higher environmental impact.

Synthesis and Integrated Design of a Compact Azeotropic Process for EtAc–MeOH–Water Separation

Synthesis and Integrated Design of a Compact Azeotropic Process for EtAc–MeOH–Water Separation

Kinetic study of the double dehydration of sorbitol into isosorbide over commercial sulfonic acid resin

Biomass-derived chemicals show great promise as an alternative resource for the replacement of fossil fuel-derived chemicals. The double dehydration of sorbitol produces isosorbide, which is an important chemical building platform and a component in food, pharmaceuticals, and plasticizers. Homogeneous catalysts are used to perform this reaction due to their good performance, but they also exhibit some drawbacks. Therefore, in this study, the sulfonic acid resin Purolite CT269 was used as a heterogeneous catalyst to study isosorbide production. The reaction was carried out with a 4.43 wt.% catalyst loading, reaction temperature of 151 °C, and reaction time of 4 h under a vacuum. 100 % conversion of sorbitol was observed within 2 h, and an isosorbide yield of about 78 % was achieved after 4 h. The kinetics of the reaction were evaluated, the rate law followed a first-order reaction model, and the reaction rate depended on the concentration of reactant. The Langmuir–Hinshelwood–Hougen–Watson (LHHW) model was used to determine that the first dehydration was the rate-determining step of this reaction. The study also examined the deactivation of the catalyst using FE-SEM, N2 adsorption/desorption, DSC, TGA, and XPS, confirming that changes to the catalytic properties after the reaction negatively impacted the performance of the sorbitol dehydration reaction. The catalytic performance and kinetic analysis were used to explain the mechanism of the double dehydration of sorbitol into isosorbide, providing essential information for catalyst and isosorbide production process design.

Numerical study of the dynamic aggregation behaviors of oil droplets in the porous filter media consisting of basalt particles

Porous granular media based on the gravity-driven principle have been proven to be efficient for the separation of oil-water emulsions. However, the complexity of the relevant parameters and the intricate structure of porous media pose great challenges to the design and optimization of the separation process. In this study, the dynamic behavior of oil droplets within the porous structure was numerically analyzed based on the Euler-Euler model and the coupling of the two-phase flow and level set method. The oil-water separation efficiency and droplet trajectory were analyzed under different conditions, considering the deformation of the droplets, kinetic viscosity, and interactions among the droplets. The simulation results show the real-time movement, aggregation, and detachment of oil droplets within the porous filter. The results are useful for understanding the demulsification in a microscopic perspective and provide theoretical guidance for designing more efficient oil-water separation systems.

Machine Learning in Fuel Cell Stacks, Systems and Modeling

Machine learning (ML) has become a prominent tool for science and engineering as computing speed has increased over the last 5 to10 years. This paper describes our recent progress in applying ML to polymer electrolyte fuel cells (PEFC) systems prognostics and their research and development.A key requirement for ML is the availability of high quality, labeled data to train models. Plug Power has 60,000 fuel cells in the field for materials handling and small stationary power, a sufficient number of systems from which to collect data. However, cost effective, practical systems have limited on board computing power, and often can be difficult to retrofit with processors that are compatible with ML. Real-time high-speed data collection for 60,000 systems for offboard computing can also become prohibitively expensive. Despite the data challenges, we have been able to use onboard system data to inexpensively predict the failure of valves and coolant pumps from sensors already instrumented on the systems. The valves are solenoids, which fail catastrophically within microseconds. We have found that by training a long-short-term-memory (LSTM) model with 3 to 4 variables, we observe changes in the fuel cell balance of plant that predict an unhealthy valve ahead of valve failure. A similar approach is used for the coolant loop, however although this subsystem has different behavior than the valves, and oscillate between healthy and unhealthy, before gradually becoming too unhealthy to use. For both cases, we had to understand the system operation in combination with training on the system sensors to establish which variables were most relevant to the state of health of the system and component.ML is also becoming invaluable to modeling, particularly for expediting the cost and accuracy of the complex models that correlate the nano- and micro-structures of a fuel cell to its electrochemical performance. Computational simulation of fuel cells at the cell scale or stack level has traditionally required the use of computational fluid dynamics (CFD) models with reduced-order, analytical solutions for the catalyst layer phenomena. Examples include the agglomerate model that is used to model the coupled oxygen transport and oxygen reduction reaction in the catalyst agglomerates. However, as our fundamental understanding of the complex phenomena at the catalyst scale increases, including the impacts of local ionomer resistance, anion adsorption, and capillary condensation in the carbon-supported catalyst, there is an increasing amount of significant phenomena that should be incorporated into the models. The combined phenomena are beyond the capabilities of analytical solutions, and requires computational simulation to evaluate the local reaction rates. A valuable, emerging approach to addressing the significant length scale disparity is to train an ML model on a catalyst-scale simulation and embed the output of that model into a cell-scale model. We will highlight the use of this approach in evaluating the efficacy of high oxygen permeability ionomer (HOPI) with high surface area carbon supports versus low surface area supports.Emerging research is showing how to create the next generation of data-informed electrochemical models. Instead of separating data-driven models from physical models, a new paradigm for embedding data-driven elements within physical models promises increased accuracy and speed relative to standalone physical models, pure “black-box” ML or hybrid approaches that still enforce a strict separation between data-driven and physical elements. Embedded ML has already shown considerable promise in dynamic systems, outperforming state-of-the-art recurrent neural networks such as LSTM and gated recurring units (GRUs) on benchmark problems. Embedded methods require computationally compact data-driven functions such as Karhunen-Loève decomposed Gaussian processes and gradient boosted trees, which represent physically well-defined functions such as rate constants, equilibrium constants and transport coefficients in the context of chemical and electrochemical device-scale models. The connection to physical quantities creates opportunities for independent measurements via targeted experimentation. The combination of speed and accuracy offered by embedded ML is ideal for product and process design as well as control applications.

(Invited) bridging MOFs to Applications Via Machine Learning

MOFs (metal-organic frameworks) are materials with the potential to significantly improve several energy-related applications by allowing customization of their properties. However, traditional scientific methods make the process of discovery of scalable materials that perform well in industrial processes slow and complicated. This is due to the need for a holistic approach that considers the entire design process, including exploring vast materials spaces, linking them to their properties, synthesis, and process design and engineering. To tackle this challenge, we are developing tools using machine learning and artificial intelligence to help navigate the vast design space, uncover hidden relationships between material properties and performance, and accelerate MOF design.In this discussion, I'll elaborate on how these novel tools are being used to quantify the structural diversity of large MOF material databases, develop physics-constrained machine learning methods for low-data design tasks, learn from failures to design better experiments and perform the inverse design of MOFs.

Sustainable Desalination Waste Brine Management through Electrochemically Supported CO2 mineralization and Multiple Product Recovery

Seawater reverse osmosis desalination (SWRO) is a vital technology for low cost and energy requirement water production to ensure regional water security and continued economic development. However, the state-of-the-art technology typically exhibits a recovery rate of around 42% converting in 58% of the seawater input into desalination waste brine which are largely discharged into the sea1. Significant works on brine utilization have shown that SWRO desalination brine can be converted into usable feed for chlor-alkali process with simultaneous carbon capture capabilities2,3,4. In this work, the desalination effect of electrochemistry for simultaneous utilization and treatment of desalination brine (56.0 ± 2.1 g/L) into seawater level (32.0 ± 1.4 g/L) was explored. The results demonstrate chemical self-sufficiency for desalination brine treatment (removing Mg2+ and Ca2+ to below ppm level), electrochemically recovering chlorine (95% – 99% selectivity, and hydrogen gas (92% – 97% selectivity), and conversion of desalination brine into reclaimed seawater discharge (RSD). The RSD is further chemically dechlorinated and neutralised to pH 7.3 to be safe to discharge into the sea. The excess alkali brine is used to capture additional CO2 in the form of bicarbonates achieving net abatement in climate change impact (9.90 CO2 e/m3) after product carbon abatements are accounted. 1 Jones, E.; Qadir, M.; van Vliet, M. T. H.; Smakhtin, V.; Kang, S. The State of Desalination and Brine Production: A Global Outlook. Science of The Total Environment 2019, 657, 1343–1356. 2 Melián-Martel, N.; Sadhwani, J. J.; Ovidio Pérez Báez, S. Saline Waste Disposal Reuse for Desalination Plants for the Chlor-Alkali Industry: The Particular Case of Pozo Izquierdo SWRO Desalination Plant. Desalination 2011, 281, 35–41. 3 Du, F.; Warsinger, D. M.; Urmi, T. I.; Thiel, G. P.; Kumar, A.; Lienhard V, J. H. Sodium Hydroxide Production from Seawater Desalination Brine: Process Design and Energy Efficiency. Sci. Technol. 2018, 52 (10), 5949–5958. 4 Choi, W. Y.; Aravena, C.; Park, J.; Kang, D.; Yoo, Y. Performance Prediction and Evaluation of CO2 Utilization with Conjoined Electrolysis and Carbonation Using Desalinated Rejected Seawater Brine. Desalination 2021, 509, 115068. Figure 1

Multiscale modelling of bioprocess dynamics and cellular growth

BackgroundFermentation processes are essential for the production of small molecules, heterologous proteins and other commercially important products. Traditional bioprocess optimisation relies on phenomenological models that focus on macroscale variables like biomass growth and protein yield. However, these models often fail to consider the crucial link between macroscale dynamics and the intracellular activities that drive metabolism and proteins synthesis.ResultsWe introduce a multiscale model that not only captures batch bioreactor dynamics but also incorporates a coarse-grained approach to key intracellular processes such as gene expression, ribosome allocation and growth. Our model accurately fits biomass and substrate data across various Escherichia coli strains, effectively predicts acetate dynamics and evaluates the expression of heterologous proteins. By integrating construct-specific parameters like promoter strength and ribosomal binding sites, our model reveals several key interdependencies between gene expression parameters and outputs such as protein yield and acetate secretion.ConclusionsThis study presents a computational model that, with proper parameterisation, allows for the in silico analysis of genetic constructs. The focus is on combinations of ribosomal binding site (RBS) strength and promoters, assessing their impact on production. In this way, the ability to predict bioreactor dynamics from these genetic constructs can pave the way for more efficient design and optimisation of microbial fermentation processes, enhancing the production of valuable bioproducts.

(Invited) Digital Twin for Inverse Design of Battery Manufacturing Processes

The manufacturing process of lithium ion battery (LIB) electrodes impacts their architecture and the practical properties of the cells, such as their energy and power densities, their durability and safety. Therefore, it is crucial to optimize this manufacturing process in a proper manner. Such an optimization is highly complex because of the very significant amount of parameters and numerous interdependencies between the process steps, such as the slurry mixing, the casting, the drying, the calendering, the assembly, the electrolyte filling and the solid electrolyte interphase formation. As a consequence, traditional trial and error approaches can lead to high scrap rates in LIB cell manufacturing.ARTISTIC is a digital twin for inverse design of LIB manufacturing process that we keep developing since several years.[1] ARTISTIC is supported on a combination of physics-based models and machine learning. The physics-based models simulate the dynamics, with 3D resolution, of the slurry mixing, the slurry coating and its drying, the resulting electrode calendering, the cell infiltration by the liquid electrolyte, the formation step and the resulting electrochemical performance of the virtually produced electrodes and cells. These physics-based models are linked with each other in a sequential manner, and they are supported on a combination of methods, such as Coarse Grained Particle Dynamics, Discrete Element Method, Lattice Boltzmann Method and Finite Element Method. Deep learning is used to derive time-dependent 3D surrogate models mimicking the behavior of the physics-based models with much less computational cost. Furthermore, a wide diversity of machine learning techniques are used to accelerate the calibration of the physics-based models with experimental observables (e.g. slurry viscosity and density, electrode porosity, tortuosity factor and conductivity) and to unravel correlations between manufacturing parameters and electrode and cell properties. All these pieces are integrated in a single computational infrastructure performing the inverse design. For that purpose, the deep learning surrogates and the machine learning models are coupled to a multi-objective Bayesian Optimizer. The latter allows predicting which manufacturing parameters to adopt (e.g. slurry formulation, drying rate, calendering degree) in order to reach desired properties for the electrodes (e.g. loading, porosity, conductivity) and for the cells (energy and power densities). We have demonstrated this pionneering digital twin for multiple manufacturing steps and electrode chemistries (e.g. NMC111, NMC622, LFP, graphite, silicon-graphite blends), with the strong support of experimental databases acquired in our LIB manufacturing pilot line.[2] We have also demonstrated the transferability of our approach to Sodium Ion and Solid State Batteries, and we start to adapt it for dry processing methods. In my talk, I will present the latest updates of this research and development effort. Furthermore, I will also present the latest updates of the ARTISTIC Online Calculator, a free service to perform battery manufacturing simulations from an internet browser, as well as of our Virtual and Mixed Reality tools to train students, researchers and operators in the battery manufacturing field. These updates will include the presentation of the first "battery manufacturing metaverse", a multi-user battery manufacturing pilot line in Virtual Reality designed to perform practices with the MSc. students of the i-MESC Erasmus+ programme (Interdisciplinarity in Materials for Energy Storage and Coversion).[3]References[1] https://www.erc-artistic.eu/[2] https://www.modeling-electrochemistry.com/publications[3] https://i-mesc.eu/

Isobaric vapour-liquid equilibrium studies for binary systems of a green solvent furfuryl alcohol with aniline and substituted anilines at 101.31 kPa

New experimental vapour-liquid equilibrium (VLE) data, which is expected to be useful in industrial processes such as the design of separation processes is reported in the present article. Isobaric VLE data of an eco-sustainable, green solvent furfuryl alcohol (FFA) as a common component has been measured for three binary systems (i) furfuryl alcohol + aniline (AN), (ii) furfuryl alcohol + N-methylaniline (NMA) and (iii) furfuryl alcohol + N-ethylaniline (NEA) in the complete concentration of FFA at a pressure of 101.31 kPa. The Antoine equation has been used to calculate the vapour pressure of pure samples. The reported VLE data is thermodynamically consistent according to the van Ness, Fredenslund and Herington area tests. The experimental observations of the three systems displayed non-ideal nature and negative deviation from Raoult's law. The Wilson and UNIQUAC activity coefficient models have been applied to correlate the experimental data. The information of the boiling point temperature (T), activity coefficients (γi) and variation of excess Gibbs energy (GE) with the concentration of FFA indicated the presence of dipole-dipole interactions and hydrogen bonding between the molecules of FFA and the selected amines.

Repair and Reuse of Spent Lithium Battery Electrode Materials

With the popularity of new energy vehicles and various electronic devices, the use of lithium-ion batteries (LIBs) has shown explosive growth, which has resulted in a large number of spent LIBs. Spent LIBs contain a large amount of metal resources, and improper disposal will not only cause waste of resources, but also have potential environmental risks. The existing commercial recycling methods are mainly pyrometallurgy and hydrometallurgy recycling methods, both of which require re-extraction from the electrode material after destroying them to the atomic level for the preparation of new electrode materials, which is a long process with high cost, and involves the application of extreme conditions such as high temperature and strong acid, with inferior economic and environmental benefits. It is a major challenge in the field of battery recycling to develop innovative and clean recycling methods, to simplify the recycling process and to develop ways to reuse the recovered products. In view of the challenge of existing recycling methods, the reporters proposed the idea of direct recycling of electrode materials at the molecular scale, and designed innovative recycling methods such as direct repair of degraded lithium cobalt oxides with deep eutectic solvent (DES), repair of Ni-Mn-Co ternary (NCM) cathode with high failure degree by low temperature molten salt, and thermal regeneration of degraded lithium iron phosphate (LFP) with multifunctional solvent; and closed-loop design of the direct recycling process. In addition, we propose a high-value utilization path for the conversion of degraded electrode materials to high-performance electrode materials and nano-catalysts. It greatly simplifies the recycling process, avoids the use of extreme conditions, and provides a new technical system and theoretical guidance for battery recycling research and industry.

Multiscale Materials and Process Engineering for Efficient CO2 Recycling

Renewable electricity powered conversion of carbon dioxide (CO2) to fuels and chemicals has potential to close the anthropogenic carbon cycle at meaningful scales and achieve global sustainability goals. It provides an effective route to store renewable energy in chemical bonds for its modular utilization in a circular carbon economy. However, the technological progress of this complex chemical transformation is stymied due to the lack of systematic materials design principles, poor understanding of the convoluted reaction pathways that control efficiency and selectivity, and complexities associated with elementary electron and mass transfer in the reaction environment. At a system level, issues of carbon and energy conversion efficiencies needs to be addressed. Engineering of materials and interfaces can have profound effects on the correlated bulk and surface physicochemical properties for judicious tuning of these parameters and reaction pathways. In my talk, I will present examples of our research to build a broad perspective on how the rational design of materials and processes can solve some key scientific/technological challenges in electron-driven CO2 recycling pathways through understanding of structure-activity correlations. On the materials aspect, engineering of low dimensional carbon and copper based systems will be discussed. Integration of electrochemical CO2 conversion with capture and alternative anodic oxidation reactions will be showcased to highlight emerging approaches in addressing technological challenges from a process perspective.

Modeling Capacity Losses Due to Irreversible Volume Change in Lithium-Ion Batteries

During operation of lithium-ion batteries, cells experience volume change due to mechanical and electrochemical phenomena. This volume change can be described in two main modes: reversible and irreversible. Irreversible volume change permanently affects cell performance, often lowering the maximum capacity of cells over time making them less effective in storing energy. Irreversible volume change can come from a few different sources such as gasification of the electrolyte, dendritic growth or plating of lithium metal, and SEI growth on the active material. These phenomena reduce the effectiveness of battery cells by removing working ions from the electrolyte and blocking intercalation sites of the active material.Quantifying this behavior experimentally can be drawn-out, as cells must be properly cycled thousands of times as they would be in consumer applications. In order to speed up this process and test many different cell designs, simulations that can quantify the irreversible volume change and resultant capacitive losses should be developed. The 3D microstructure-based modeling method developed by Lopata offers a strong framework for development of these simulations. Through a combination of experimental cycling data and electrochemical simulations, volume change from irreversible sources can be accurately represented at a microscale level and extrapolated into cell level response.In this work, discrete element simulations (DEM) have been developed to quantify local irreversible volume change. These DEM simulations utilize data from electrochemical simulations with an equivalent microstructure, which have been informed from experimental measurements at a cell level. This can be coupled with reversible volume expansions in the DEM framework to give a full picture of volume change at the electrode/electrolyte microscale. The irreversible volume change predicted by the DEM simulations is used to estimate capacitive losses in the cell overtime.References T. R. Garrick, Y. Zeng, J. B. Siegel, and V. R. Subramanian, J. Electrochem. Soc., 170, 113502 (2023).U. Janakiraman, T. R. Garrick, and M. E. Fortier, J. Electrochem. Soc., 167, 160552 (2020).T. R. Garrick, J. Gao, X. Yang, and B. J. Koch, J. Electrochem. Soc., 168, 010530 (2021).T. R. Garrick et al., J. Electrochem. Soc., 170, 060548 (2023).A. Paul et al., J. Electrochem. Soc., 171, 023501 (2024).M. Song, Y. Hu, S.-Y. Choe, and T. R. Garrick, J. Electrochem. Soc., 167, 120503 (2020).S. T. Dix, J. S. Lowe, M. R. Avei, and T. R. Garrick, J. Electrochem. Soc., 170, 083503 (2023).T. F. Fuller, M. Doyle, and J. Newman, Journal of the electrochemical society, 141, 1 (1994).M. Doyle, T. F. Fuller, and J. Newman, Journal of the Electrochemical society, 140, 1526 (1993).J. S. Lopata et al., J. Electrochem. Soc., 170, 020530 (2023).S. Pannala, H. Movahedi, T. R. Garrick, A. G. Stefanopoulou, and J. B. Siegel, J. Electrochem. Soc., 171, 010532.T. R. Garrick, Y. Miao, E. Macciomei, M. Fernandez, and J. W. Weidner, J. Electrochem. Soc., 170, 100513 (2023)

Hydroformylation of Alkenes by Supported Ionic Liquid Phase (SILP) Catalysis

Hydroformylation of olefins forms aldehydes which are valuable final products as well as intermediates in synthesizing bulk chemicals like alcohols, amines, and esters with a yearly world production of about 12 Mt. Industrial hydroformylation processes are essentially homogeneously catalyzed systems with an economic incitement for improvement by easy separation of products and reuse of catalysts, high reactivity and selectivity by using new catalysts, novel processes and alternative methodologies.One such potential candidate for an alternative catalyst is a Supported Ionic Liquid- Phase (SILP) catalyst comprising “heterogenized” homogeneous ionic liquid (IL) catalyst systems on a porous support. Such catalysts make it possible to apply the much preferred fixed bed continuous flow process design which overcomes the troublesome and energy demanding separation of the products from the reaction mixture obtained in the traditional homogenous batch process.In our previous and very recent studies [1-2], we have prepared SILP catalysts containing immobilized Rh-complexes of TPPTS-Cs3 in 1-butyl-3-methylimidazolium octylsulfate ([BMIM][n-C8H17OSO3]) on SiO2 support and investigated the influence of IL loading on the catalytic activity. The influence of other supports : SBA-15, MCM-41, Al2O3, ZrO2 and TiO2 has also been studied and the catalytic activity compared to the SiO2 SILP catalyst. Finaly we will report on a comparison on the catalytic activity of the SiO2 SILP catalyst by use of the ligand precurser TPPTS-Cs3 and the much cheaper TPPTS-Na3 salt. References. H. N. T. Ha, D. T. Duc, T. V. Dao, M. T. Le, A. Riisager, R. Fehrmann. Catal. Commun. 25, 136-141 (2012)Nguyen, V. C., Do, V. H., Truong, D. D., Pham, T. T., Dinh, P. K., Vu, T. L., Garcia-Suarez, E. J., Riisager, A., Fehrmann, R., Le, M. T. Res. Chem. Int..49, 6, 2383-2398 (2023).

(Invited) Optimizing CO2 Reduction in Aprotic Media: Insights Via Kinetic Modeling of Bimetallic CuxZn1-X Electrocatalysts

Bimetallic CuxZn1-x electrocatalysts, as an earth-abundant material, have emerged as promising candidates for improving the efficiency and selectivity of CO2 reduction in aqueous electrolytes.[1] However, the use of aqueous electrolytes has several drawbacks, including limited CO2 transport due to its low solubility in the electrolyte solution and, most importantly, the competing hydrogen evolution reaction (HER).[2] These challenges can be effectively circumvented by using aprotic organic electrolytes instead, which offer higher CO2 solubility and minimize HER.[3] Aprotic electrolytes (e.g., acetonitrile) show distinct differences in the reaction pathway, yielding either CO or oxalate. Studies under these conditions are scarce, and fundamental knowledge of the performance-limiting processes at the electrode-electrolyte interface is lacking. This knowledge gap hinders further development and process design for efficient CO2R in aprotic media.In this contribution, we present a comprehensive analysis of CO2R on Cu and CuxZn1-x electrocatalysts in acetonitrile. By combining standard electrochemical methods such as cyclic voltammetry, polarization curve, and electrochemical impedance spectroscopy, with mathematical modeling that couples microkinetics with continuum transport, we elucidate reaction and transport phenomena and reveal the underlying performance limitations. Based on this approach, we provide insights into the local reaction environment and discuss in detail the rates, surface coverages of adsorbed species, and local concentration profiles at the electrode. We investigate Cu and α-brass at different CuxZn1-x ratios as planar as well as nanoparticle coated electrodes. For Cu, we find that adsorbed CO2 is being consumed faster than it replenishes, suggesting that CO2 adsorption is the bottleneck in aprotic electrolytes,[4] as opposed to CO2 transport as in alkaline solution[5]. With increasing Zn content of the α-brass, higher CO2R performance is achieved. According to our simulations this can be attributed to i) higher utilization of the electrocatalyst and ii) improved CO2 adsorption capability compared to pure Cu.The gained insights allow for a better understanding of the effects of the catalyst composition on the performance and highlight the importance of CO2 adsorption on the performance of CO2 electroreduction in organic electrolytes. This enables knowledge-based optimization of the electrocatalysts and tailoring of reaction conditions. Overall, our results provide valuable insights into the poorly understood organic electrolytes for CO2R as a viable alternative to aqueous electrolytes and pave the way for the analysis of more complex reaction systems such as electro-organic syntheses or CO2R with protic additives.