The electrochemical reduction of carbon dioxide (CO2) to ethylene presents a promising route for utilizing exhaust gases to produce value-added chemicals with broad manufacturing applications. While zero-gap electrolyzer architectures show great potential to enable commercial-scale CO2-to-ethylene conversion, their performance is often limited by failure within the first 100 hours. In this work, we demonstrate that a low-frequency electrochemical pulsing protocol effectively mitigates carbonate salt precipitation and flooding by managing water transport to and through the cathode gas diffusion electrode and associated flow fields. Operando neutron imaging further reveals the dynamics of water crossover and flooding, emphasizing the intricate interplay between electrochemical operation and ionic transport. By mitigating short-term flooding and salt precipitation failure modes, this study establishes a foundation for understanding long-term degradation mechanisms and advancing the practical viability of CO2 electrolyzers for industrial-scale applications.

Energy recovery from gas-phase waste streams is essential for reducing environmental impact, promoting sustainable industrial practices, and increasing profit margins. Compared to thermochemical pathways, biocatalytic conversions offer a compelling alternative due to their mild operating conditions and high specificity. However, conventional systems are hindered by slow gas-to-liquid mass transfer, resulting in high energy consumption and low productivity. Here, we demonstrate a new solid-state bioreactor (SSB) technology through a case study of methane-to-succinate conversion using methanotrophs. SSBs immobilize high densities of methanotrophs within gas-permeable, 3D-printed geometries that operate under gas-phase and static conditions. These reactors exhibit a 1–2 order of magnitude increase in biocatalytic performance compared to traditional liquid-phase reactors. Computational models of the SSB are developed and benchmarked against conventional stirred-tank reactor models to highlight design advantages.

Electrochemical CO2 reduction (eCO2R) holds promise for decarbonizing industrial sectors by producing valuable commodities, such as ethylene. Incorporating polymer electrolyte ionomers onto Cu-based eCO2R cathodes is crucial for enhancing eCO2R efficiency. These ionomers control mass transport, surface chemistry, and water uptake at the cathode, enabling selectivity tuning toward desired C2 products. Complexities and interdependence of interfacial properties have led to challenges within the field to define design properties of catalyst layer ionomers that can enhance the performance of Cu-based catalysts. Herein, we present a systematic investigation into ionomer properties and their relationship to electrochemical performance and demonstrate a 14.3% energy efficiency for ethylene selectivity at 200 mA cm-2. Through multi-physics modeling, we elucidated that the role of the water content of the ionomer is to mitigate flooding and control the local water concentration at the catalyst surface. Translating knowledge from this study will stimulate the synthesis of ionomers tailored for eCO2R.

Gas bubble flows in porous media often exhibit complex and seemingly unpredictable behaviors that are difficult to control. This lack of control limits the ability to design effective devices which manage multiphase flows. We show how the design of 3D printed pores can deterministically control the flow path of an injected gas stream. Open cell structures can be designed to shape the gas/liquid interface with fidelity to control how the two phases are distributed throughout a porous material. The distributed gas volume is free to interact physically and chemically with the surrounding liquid phase, an effect we exploit to create a logical control gate to redirect flows within a lattice. This also allows us to design architectures for reactive capture and aerating bioreactors, resulting in patterned boundaries which can make more effective use of the liquid and gas reagents.

The ethylene industry has contributed over 260 million tons of CO2 annually, warranting a more sustainable approach. The conversion of CO2 and H2O into ethylene is an appealing technology capable of decoupling chemical production from fossil fuels. However, the large energy demand from this process can potentially lead to adverse environmental impacts. In this article, we critically analyze the economic viability, environmental impact, and scalability of the conversion of CO2 to ethylene via electrochemical reduction (CO2R) and compare this with those of CO2-neutral fossil routes utilizing carbon capture and direct air capture. Ethylene derived from CO2 may be economically competitive under optimistic conditions; however, its large energy requirements pose environmental and scalability challenges. Meeting forecast 2050 ethylene demand using CO2R would require half of all electricity produced globally today, and, if powered by solar PV, may have greater CO2 emissions than current petrochemical ethylene production, negating the purpose of this technology. Using Carbon Capture and Storage and Direct Air Capture to decarbonize petrochemical pathways would require roughly an order of magnitude less energy but would have disproportionate health and climate impacts. Lastly, the analysis highlights the importance of low-carbon energy sources to ensure sustainable CO2R ethylene production.

The electrochemical reduction of CO 2 provides a sustainable route to produce key building blocks in the petrochemical and manufacturing industries, thereby reducing carbon emissions and dependence on non-renewable sources. Zero gap electrolyzers are more energy efficient compared to traditional electrolyzers due to significant reduction in ohmic losses. However, the challenge of salting out limits the lifetimes of high performance zero gap electrolyzers to a few hours, hindering their adoption into industry. Pulsed currents have been shown to increase the electrochemical stability of copper-based electrodes during the electrochemical CO 2 reduction reaction (CO 2 RR). High operating current provides favorable CO 2 RR product efficiencies, but also increases the salt crossover from the anolyte to the cathode where the salt precipitation may lead to blocking the CO 2 flow fields causing the electrolyzer to fail. We demonstrate an operating protocol that incorporates a short period of lower current which reduces CO 2 consumption and allows for dissolution of carbonate salt on the cathode surface by reducing cation crossover. This is followed by a higher current to maximize CO 2 RR products. Intermittent pulsing reverses some of the carbonate salt production at the cathode, which allows the electrolyzer cell to restore itself, thereby increasing lifetime. The method is demonstrated in a 5cm 2 zero-gap cell, with KOH, CsOH and CsHCO 3 to establish the extent of this method’s utility with different cations and the effect of solubility limits on this salting behavior. By employing this pulsing protocol, we observe significantly increased cell lifetimes at a high current density of 200mA/cm 2 . This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344. LLNL-ABS-847304

Silicone elastomers have a broad variety of applications, such as soft robotics, biomedical devices, and structural metamaterials. The extrusion-based method known as direct ink write (DIW) has enabled the production of additively manufactured silicone structures. However, this method is limited to manufacturing mostly planar or pseudo-3D structures. Due to the low self-supporting capabilities of extruded strands for traditional silicone-based "inks," obtaining tall or overhanging structures, or structures comprised by thin walls is not feasible. Here, a novel Fast Cure silicone-based ink is demonstrated that enables manufacturing of complex 3D structures. The Fast Cure ink is a two-part mixture and silicone structures are produced by inline mixing and coextrusion of a part containing a catalyst (part A) and a part containing a crosslinker (part B). By the virtue of crosslinking, the extruded strands rapidly rigidize, increasing their self-supportive capacity. Hence, structures can be obtained with superior shape retention and previously unobtainable parts are realized that are tall, with aspect ratios higher than 3, and have overhanging features, achieving inclination angles smaller than 35 degrees with respect to the printing plane. These minimal sag parts are achieved without requiring extra curing or mechanisms, support structures, or suspension baths. Fast Cure silicone in direct ink write additive manufacturing enables the obtention of previously unattainable structures, such as tall or overhanging structures, or structures comprised by thin walls. Thanks to the quick gelling process, extruded strands rigidize, enabling the attainment of such structures, without requiring an extra curing mechanism or a suspension bath. image

Numerous cutting-edge scientific technologies originate at the laboratory scale, but transitioning them to practical industry applications is a formidable challenge. Traditional pilot projects at intermediate scales are costly and time-consuming. An alternative, the E-pilot, relies on high-fidelity numerical simulations, but even these simulations can be computationally prohibitive at larger scales. To overcome these limitations, we propose a scalable, physics-constrained reduced order model (ROM) method. ROM identifies critical physics modes from small-scale unit components, projecting governing equations onto these modes to create a reduced model that retains essential physics details. We also employ Discontinuous Galerkin Domain Decomposition (DG-DD) to apply ROM to unit components and interfaces, enabling the construction of large-scale global systems without data at such large scales. This method is demonstrated on the Poisson and Stokes flow equations, showing that it can solve equations about $15 - 40$ times faster with only $\sim$ $1\%$ relative error. Furthermore, ROM takes one order of magnitude less memory than the full order model, enabling larger scale predictions at a given memory limitation.

Additive manufacturing of freeform structures containing multiple materials with deterministic spatial arrangement and interactions remains a challenge for most 3D printing processes, due to complex fabrication tool requirements and limitations in printability of some material classes. Here, a versatile method is reported to produce architected composites using the concept of cellular fluidics, in which lattices of unit cells are used as templating scaffolds to guide flowable infill materials in a programmed spatial pattern, upon which they are cured in place to produce a deterministically ordered multimaterial solid. The lattice design relies on the unit cell size, type, strut diameter, surface wetting, and distribution of cellular structures to control liquid flow and retention. Individual unit cells are tuned to achieve reliable infilling and combined into higher-order architectures to achieve multiscale composite materials with disparate mechanical properties, including those considered non-printable. Lattice design considerations for leveraging capillary phenomena and demonstrate several methods of patterning polymers in 3D-printed cellular fluidic structures are presented. The concept of tuning the compressive response of an architected composite using a flexible-elastomer as the lattice and a stiff-epoxy as the infill material is illustrated. The concept of cellular fluidics is applied and expanded to demonstrate a facile unit-cell-based polymer composite manufacturing technique. Different unit cells are used to selectively place flowable, non-printable infill materials within a larger architecture. By layering geometric hierarchy, 1.4x increase in both stiffness and ultimate strength is realized for an example composite architecture with a similar overall material composition. image

AbstractLattices remain an attractive class of structures due to their design versatility; however, rapidly designing lattice structures with tailored or optimal mechanical properties remains a significant challenge. With each added design variable, the design space quickly becomes intractable. To address this challenge, research efforts have sought to combine computational approaches with machine learning (ML)-based approaches to reduce the computational cost of the design process and accelerate mechanical design. While these efforts have made substantial progress, significant challenges remain in (1) building and interpreting the ML-based surrogate models and (2) iteratively and efficiently curating training datasets for optimization tasks. Here, we address the first challenge by combining ML-based surrogate modeling and Shapley additive explanation (SHAP) analysis to interpret the impact of each design variable. We find that our ML-based surrogate models achieve excellent prediction capabilities (R2 > 0.95) and SHAP values aid in uncovering design variables influencing performance. We address the second challenge by utilizing active learning-based methods, such as Bayesian optimization, to explore the design space and report a 5 × reduction in simulations relative to grid-based search. Collectively, these results underscore the value of building intelligent design systems that leverage ML-based methods for uncovering key design variables and accelerating design.

Invited for this issue's Front Cover are researchers from the Carbon Initiative at Lawrence Livermore National Laboratory and the SUNCAT Center at Stanford University. The front cover shows a cross-section of the cathode of a membrane electrode assembly for CO2 electrolysis looking down the feed channel. CO2 molecules flow down the channel and diffuse up through the gas diffusion layer to the silver catalyst, where CO2 reacts to form CO, while also competing against hydrogen reduction from water. Some of the CO2 molecules react to form bicarbonate and carbonate ions, which can diffuse across the membrane, where they react at the anode to form CO2 again. The top of the image shows CO2 that has crossed over through the membrane into the anolyte. Cover design by Brendan Thompson. Read the full text of the Research Article at 10.1002/celc.202300566.

Mass transfer is critical for the reaction kinetics and efficiency of alkaline water splitting (AWS). For AWS to operate at high current densities (hundreds of mA/cm2), the device architecture must ensure a large catalytic surface area, rapid ion diffusion, and minimal solution and charge transfer resistances. Effective electrodes should also facilitate gas bubble detachment and release. 3D-printed electrodes have shown promise, but stacking them increases the ion diffusion length and solution resistance. We demonstrate a new device architecture with interpenetrating gyroid electrodes, providing a large ion-accessible surface area and gas diffusion channels. This design significantly reduces the interelectrode distance, lowering ion diffusion length and solution resistance. Simulations show faster ion diffusion and higher current density in the interpenetrating configuration compared with separate electrodes. This improved performance, especially at low temperatures and high current densities, highlights a promising strategy for enhancing AWS and other electrochemical systems limited by slow ion diffusion.

Recent advances in 3D printing have enabled the manufacture of porous electrodes which cannot be machined using traditional methods. With micron-scale precision, the pore structure of an electrode can now be designed for optimal energy efficiency, and a 3D printed electrode is not limited to a single uniform porosity. As these electrodes scale in size, however, the total number of possible pore designs can be intractable; choosing an appropriate pore distribution manually can be a complex task. To address this challenge, we adopt an inverse design approach. Using physics-based models, the electrode structure is optimized to minimize power losses in a flow reactor. The computer-generated structure is then printed and benchmarked against homogeneous porosity electrodes. We show how an optimized electrode decreases the power requirements by 16% compared to the best-case homogeneous porosity. Future work could apply this approach to flow batteries, electrolyzers, and fuel cells to accelerate their design and implementation.

Low-temperature CO 2 electrolyzers transform captured CO 2 into more useful chemicals, such as syngas (CO and H 2 ) for systems with silver or gold catalysts and ethylene and other C 2+ products for systems with copper catalysts. At low overpotentials, bicarbonate ions act as proton donors and have been shown to have enhanced activity 1 , leading to nonlinear Tafel slopes for the hydrogen evolution reaction (HER). We have developed a 1D planar electrode model for CO 2 electrolysis, similar to the model developed by Gupta et al., 2 including a thin ionomer layer on top of a silver catalyst layer surface. The model includes buffer chemistry and ion transport in the boundary layer as well as the ionomer thin-film layer. Tafel parameters are fit for both bicarbonate and water as proton donors for HER using the model results to account for mass transport in the boundary layer. A comparison against a bare silver electrode shows that the Donnan potential across the ionomer/electrolyte interface has a significant effect on the ion concentrations at the catalyst layer surface, leading to enhanced HER from bicarbonate at low overpotentials. These results are then compared against Butler-Volmer parameters that are derived from a full microkinetic model. The Tafel kinetic parameters were then used in a 1D full-cell membrane electrode assembly (MEA) model, and we found good agreement with experimental data, illustrating that the ionomer in contact with the catalyst layer surface impacts the underlying kinetics both in planar electrodes and industrially-relevant MEA systems. References D. M. Koshy et al., J. Am. Chem. Soc. , 143 , 14712–14725 (2021) https://doi.org/10.1021/jacs.1c06212. N. Gupta, M. Gattrell, and B. MacDougall, J. Appl. Electrochem. , 36 , 161–172 (2006). This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344 and was partially supported by a Cooperative Research and Development Agreement (CRADA) between Lawrence Livermore National Laboratory, Stanford University, and TotalEnergies American Services, Inc. (affiliate of TotalEnergies SE) under Agreement No. TC02307 and Laboratory Directed Research and Development (LDRD) funding under project 22-SI-006. LLNL-ABS-857478 Figure 1

Recent developments in additive manufacturing (AM) of glass via silica-filled inks have facilitated fabrication of previously unattainable geometries and compositions. However, the maximum processable size of 15 mm limits the use of these prints in applications such as optics. A key limitation lies in the trade-off between material printability and green strength: increasing silica content in the feedstock improves crack resistance and reduces shrinkage but results in dramatic changes in viscoelastic properties that hinder flowability. This paper presents a novel approach that offers expanded versatility in processable size, feedstock formulation, and printing. Described here is a direct ink writing (DIW) system coupled with an active high-shear micromixer and UV light source, capable of simultaneously printing multiple inks with a wide range of rheological properties. Choice of silica sourc, solvent, UV-curable binder, and dispersant is used to tune the ink rheology and improve printability and mechanical properties. Imparting high shear with the micromixer while UV-curing the extrudate allows for increased ink viscosities and reduced nozzle diameters, enabling printing finer feature sizes. With these advances, thin-walled high-aspect ratio structures and a crack-free glass disk measuring 44 mm in diameter are demonstrated, an increase of 3x in the greatest dimension compared to current state-of-the-art.

Avoiding the worst effects of climate change depends on our ability to scale and deploy technologies faster than ever before. Scale-up has largely been the domain of industrial research and development teams, but advances in modeling and experimental techniques increasingly allow early-stage researchers to contribute to the process. Here we argue that early assessments of technology market fit and how the physics governing system performance evolves with scale can de-risk technology development and accelerate deployment. We highlight tools and processes that can be used to assess both these factors at an early stage. By bringing together technical risk assessments, scaled physics modeling, data analysis and in situ experimentation within multidisciplinary teams, new technologies can be invented, developed and deployed on a shorter timetable with greater probability of success. This Perspective argues that early assessments of technology-market fit, as well as how the physics governing system performance evolves with scale, can de-risk technology development and accelerate deployment. The authors highlight tools and processes that can be used to assess both these factors at an early stage.

The accelerated scale-up and deployment of carbon management technologies are critical steps to avoid the worst impacts of climate change. Many of these technologies involve highly coupled multiphysics phenomena, making the prediction of scaling behaviors using standard engineering methods such as dynamic similitude highly challenging. In this article, we demonstrate the application of the concept of dynamic similitude to a carbon capture process. Specifically, we show that a lab-scale (0.6 m) carbon capture column is able to match previously published thermal profiles of a pilot-scale column—at several operating conditions—by matching dimensionless groups as informed by a 1D absorber model. We describe the various experimental aspects that required extra care in order to capture dynamically similar conditions; most notably, we developed a high-surface-area porous packing that enhanced liquid spreading in the lab-scale column. We conclude by discussing the opportunities and challenges in applying this concept to other decarbonization technologies.

Nanostructured multi-principal element alloys (MPEAs) have been explored as next-generation engineering materials due to unique mechanical and functional properties which have significant advantages over traditional dilute alloys. However, the practical applications of nanostructured MPEAs are still limited due to the lack of scalable processing approaches to prepare a large quantity of nanostructured MPEAs, as well as lack of an efficient pathway for high-throughput discovery of better functional nanostructured MPEAs within their vast compositional space. Here we tackle these challenges by presenting an integrated approach by combining direct-ink-writing-based additive manufacturing, solid-state sintering, and chemical dealloying to manufacture hierarchically porous MPEAs. The hierarchical structure is comprised of macro- and micro-scale pores introduced via extrusion printing and polymer decomposition during sintering, as well as nanoscale pores formed via chemical dealloying. The macro- and micro-scale pores allow efficient dealloying of a large mass of material as the diffusion length that the corroding medium must penetrate remains at the scale of the ligaments formed after sintering (similar to 10 mu m), despite the large volume of the 3D-printed samples. In addition, this integrated approach enables versatile control of the alloy composition via precisely tuning the ratio of elemental powders in the starting ink, thus offering a pathway for high-throughput discovery of novel functional MPEAs. As a case study, multiscale macro/micro/nanoporous NiFeMn MPEAs with three different compositions were investigated as catalysts to reduce the overpotential of oxygen evolution reaction (OER), where NiFeMn-based electrocatalysts display composition-dependent performance such that the overpotential measured at a current of 0.5 A g(-1) for OER increases in the order of Ni58Fe29Mn13 <= Ni64Fe26Mn10 < Ni76Fe18Mn6. This introduced manufacturing process offers new opportunities for scalable fabrication and rapid screening of nanostructured multi-component complex alloys.