Search

Alkaline anion exchange membranes (AAEMs) are an enabling component for next generation electrochemical applications, including alkaline fuel cells, alkaline water electrolyzers, CO2 electrochemical reduction, and flow batteries. While commercial systems, notably fuel cells, have traditionally relied on proton-exchange membranes (PEMs), hydroxide-ion conducting AAEMs hold promise as a way to reduce cost-per-device by enabling the use of less expensive non-platinum group electrodes and cheaper cell components. AAEMs have undergone significant material development over the past two decades resulting in substantial improvements in hydroxide conductivity, alkaline stability, and dimensional stability. Despite these advances, challenges still remain in the areas of durability, water management, high temperature performance, and selectivity. In this review we discuss crosslinking as a synthesis tool for tuning various AAEM material properties, such as water uptake, conductivity, alkaline stability, and selectivity, and we describe synthetic strategies for incorporating crosslinks during membrane fabrication.

Bridging polymer design with catalyst surface science is a promising direction for tuning and optimizing electrochemical reactors that could impact long-term goals in energy and sustainability. Particularly, the interaction between inorganic catalyst surfaces and organic-based ionomers provides an avenue to both steer reaction selectivity and promote activity. Here, we studied the role of imidazolium-based ionomers for electrocatalytic CO2 reduction to CO (CO2R) on Ag surfaces and found that they produce no effect on CO2R activity yet strongly promote the competing hydrogen evolution reaction (HER). By examining the dependence of HER and CO2R rates on concentrations of CO2 and HCO3-, we developed a kinetic model that attributes HER promotion to intrinsic promotion of HCO3- reduction by imidazolium ionomers. We also show that varying the ionomer structure by changing substituents on the imidazolium ring modulates the HER promotion. This ionomer-structure dependence was analyzed via Taft steric parameters and density functional theory calculations, which suggest that steric bulk from functionalities on the imidazolium ring reduces access of the ionomer to both HCO3- and the Ag surface, thus limiting the promotional effect. Our results help develop design rules for ionomer-catalyst interactions in CO2R and motivate further work into precisely uncovering the interplay between primary and secondary coordination in determining electrocatalytic behavior.

At the start of the COVID-19 pandemic, the US faced nationwide shortages of nasopharyngeal swabs due to both overwhelmed supply chains and an increase in demand. To address this shortfall, multiple 3D printed swabs were ultimately produced and sold for COVID-19 testing. In this work, we present a framework for mechanical and functional bench-testing of nasopharyngeal swabs using standard and widely available material testing equipment. Using this framework, we offer a comprehensive, quantitative comparison of the 3D printed swabs to benchmark their performance against traditional flocked swabs. The test protocols were designed to emulate the clinical use of the nasopharyngeal swabs and to evaluate potential failure modes. Overall, the 3D printed swabs performed comparably to, or outperformed, the traditional swabs in all mechanical tests. While traditional swabs outperformed some of the new 3D printed swabs in terms of sample uptake and retention, similar amounts of RNA were recovered from both 3D printed and traditional swabs. Graphic abstract

Advanced manufacturing (AM) represents an appealing approach for creating novel electrochemical systems for chemicals synthesis. In this work, we demonstrate AM for rapid development and testing for improved performance for the carbon dioxide reduction reaction across an evolution of vapor-fed reactor designs. In our final design, we observe activation- and mixed-control regimes for a variety of operating conditions via inlet CO2 flow rate and electrochemical potential. Furthermore, we define a dimensionless number (Da) to identify mass transport regimes by exploring the impact of hypothesized underlying mass transport mechanisms, including consumption of CO2via OH−, increased local temperatures, and partial penetration of electrolyte into gas diffusion layer. The accelerated pace of reactor design and development led to high geometric current densities (500 mA cm−2), heightened selectivity (85.5% FE C2+ products), and increased carbon dioxide conversion (16.6%) and cathodic energy efficiency (49.6% CO2R). Using AM vapor-fed reactors, we attain high ethylene (3.67%) and record ethanol (3.66%) yields compared to the literature. This work underscores the promise of AM for accelerating reactor design, understanding of governing phenomena, and improving the performance of catalytic systems.

Additive Manufacturing (AM), or 3D printing, is a powerful technology that is revolutionizing the way designers create products across many industries. AM technology is severely limited by the lack of effective methods for in situ characterization of multi-material properties and composition during printing. The ability to detect the composition of multi-material printed inks in real time is an emerging need for a wide range of manufacturing applications. In this study, dielectric properties of embedded metal microparticles in a dielectric matrix are measured and characterized as a function of particle size, shape, volume percentage and frequency. Unexpectedly, there was no observation of any percolation threshold. The impedance was found to decrease with the percentage of metal filler as expected. While particle shape seemed to have significant effect on the impedance, there was little to no correlation between particle size and impedance. The resulting data can be used to generate a calibration curve correlating metal loading with impedance or capacitance. We discuss how this data can be used for in situ sensing of local ink composition, which does not currently exist, to facilitate greater control over the resulting properties and functionality of printed materials.

Formate is an important value-added chemical that can be produced via electrochemical CO2 reduction reactions (CO2RR). Cu2O-based catalysts have previously demonstrated decent activity for formate ...

Engineering the electrolyte microenvironment represents an attractive route to tuning the selectivity of electrocatalytic reactions beyond catalyst composition and morphology. However, harnessing t...

Three-dimensional (3D) hierarchical nanoporous Cu (3DHNP-Cu) is synthesized using a combination of direct ink writing (DIW) based 3D printing, thermal sintering, and chemical dealloying of Mn–Cu alloys. Through tuning processing conditions such as ink composition and cooling rate (after sintering), with consideration of Mn loss during sintering, 3DHNP-Cu with fully bicontinuous nanostructures and negligible residual Mn can be produced after dealloying. The 3DHNP-Cu is comprised of structural features that span seven orders of magnitude, where DIW digitally controls macroscale porous features, thermal sintering and degradation of the binding polymer determines microscale porous features, and dealloying gives rise to nanoscale pores.

Bridging polymer design with catalyst surface science is a promising direction for tuning and optimizing electrochemical reactors that could impact long-term goals in energy and sustainability. Particularly, the interaction between inorganic catalyst surfaces and organic-based ionomers provides an avenue to both steer reaction selectivity and promote activity. Here, we studied the role of imidazolium-based ionomers for electrocatalytic CO

Electrochemical reactors utilizing flow-through electrodes (FTEs) provide an attractive path toward the efficient utilization of electrical energy, but their commercial viability and ultimate adoption hinge on attaining high currents to drive productivity and cost competitiveness. Conventional FTEs composed of random, porous media provide limited opportunity for architectural control and engineering of microscale transport. Alternatively, the design freedom engendered by additively manufacturing FTEs yields additional opportunities to further drive performance via flow engineering. Through experiment and validated continuum computation we analyze the mass transfer in three-dimensional (3D)-printed porous FTEs with periodic lattice structures and show that, in contrast to conventional electrodes, the mesoscopic length scales in 3D-printed electrodes lead to an increase in the mass correlation exponent as inertial flow effects dominate. The inertially enhanced mass transport yields mass transfer coefficients that exceed previously reported 3D-printed FTEs by 10 to 100 times, bringing 3D-printed FTE performance on par with conventional materials.

CO

Recent advances in additive manufacturing, specifically direct ink writing (DIW) and ink-jetting, have enabled the production of elastomeric silicone parts with deterministic control over the structure, shape, and mechanical properties. These new technologies offer rapid prototyping advantages and find applications in various fields, including biomedical devices, prosthetics, metamaterials, and soft robotics. Stereolithography (SLA) is a complementary approach with the ability to print with finer features and potentially higher throughput. However, all high-performance silicone elastomers are composites of polysiloxane networks reinforced with particulate filler, and consequently, silicone resins tend to have high viscosities (gel- or paste-like), which complicates or completely inhibits the layer-by-layer recoating process central to most SLA technologies. Herein, the design and build of a digital light projection SLA printer suitable for handling high-viscosity resins is demonstrated. Further, a series of UV-curable silicone resins with thiol-ene crosslinking and reinforced by a combination of fumed silica and MQ resins are also described. The resulting silicone elastomers are shown to have tunable mechanical properties, with 100–350% elongation and ultimate tensile strength from 1 to 2.5 MPa. Three-dimensional printed features of 0.4 mm were achieved, and complexity is demonstrated by octet-truss lattices that display negative stiffness.

Inks compounded of short carbon fibers suspended in polymer resin can be extruded to produce composite materials during additive manufacturing or 3D printing processes. The flow process induces anisotropic orientation of the fibers which is set into the matrix and significantly affects the mechanical and physical properties of the final product. Therefore, the flow of fiber suspensions needs to be understood in order to predict the orientation distribution of the fibers during such manufacturing processes. There is still a lack of knowledge for extrusion of the complex mixture of a non-Newtonian polymer containing a high-volume fraction of fibers with high fiber aspect ratio, where both inter-particle, fluid-particle and fiber-wall interactions are computationally evaluated. This paper presents predictive numerical simulations and experimental results of such confined flow in a concentrated regime. The code is based on Lagrange multiplier technique and resolves each particle and interaction between the fibers and surrounding fluid and nozzle walls. We investigate numerically how the fiber length impacts the fiber alignment during extrusion. We found that a fiber length above 67% of the nozzle's diameter induces dramatic change in the fiber flow, causing fibers to concentrate at the nozzle boundaries with very low concentration of fibers in the center of the nozzle. It was found that the best fiber alignment is reached for fiber lengths equal to 40–50% of the nozzle diameters. Numerical findings are supported by experimental results. This work improves understanding of fiber orientation during 3D printing and is an important milestone for the prediction of the complex mechanical properties of additively manufactured fiber composites.

Summary Retaining sound electrochemical performance of electrodes at high mass loading holds significant importance to energy storage. Pseudocapacitive materials such as manganese oxide (MnO2) deposited on current collectors have achieved outstanding gravimetric capacitances, sometimes even close to their theoretical values. Yet, this is only achievable with very small mass loading of active material typically less than 1 mg cm−2. Increasing mass loading often leads to drastic decay of capacitive performance due to sluggish ion diffusion in bulk material. Here, we demonstrate a 3D printed graphene aerogel electrode with MnO2 loading of 182.2 mg cm−2, which achieves a record-high areal capacitance of 44.13 F cm−2. Most importantly, this 3D printed graphene aerogel/MnO2 electrode can simultaneously achieve excellent capacitance normalized to area, gravimetry, and volume, which is the trade-off for most electrodes. This work successfully validates the feasibility of printing practical pseudocapacitive electrodes, which might revolutionize pseudocapacitor fabrication.

Microfluidic-based microencapsulation requires significant oversight to prevent material and quality loss due to sporadic disruptions in fluid flow that routinely arise. State-of-the-art microcapsule production is laborious and relies on experts to monitor the process, e.g. through a microscope. Unnoticed defects diminish the quality of collected material and/or may cause irreversible clogging. To address these issues, we developed an automated monitoring and sorting system that operates on consumer-grade hardware in real-time. Using human-labeled microscope images acquired during typical operation, we train a convolutional neural network that assesses microencapsulation. Based on output from the machine learning algorithm, an integrated valving system collects desirable microcapsules or diverts waste material accordingly. Although the system notifies operators to make necessary adjustments to restore microencapsulation, we can extend the system to automate corrections. Since microfluidic-based production platforms customarily collect image and sensor data, machine learning can help to scale up and improve microfluidic techniques beyond microencapsulation.

The criteria dictating the performance of gas diffusion electrodes (GDEs) for CO2 electrolysis are not well understood, due to the complex and highly coupled relationships of the underlying physical and chemical phenomena. In addition, a number of key performance indicators (KPIs) have been identified (e.g., Faradaic efficiency, reaction selectivity, single-pass conversion, and productivity), and optimizing for any single metric often leads to inherent tradeoffs. Consequently, much recent work has focused on understanding how operational and architectural parameters control GDE performance. The porous catalyst supports and diffusion media in these devices are critical components as they mediate the transport and reactive processes. The microstructure of these components influences the balance between the electrochemical surface area, which dictates CO2 consumption, and mass transfer of the aqueous and gaseous species. Traditional porous media is often spatially homogeneous and provides limited opportunity to tailor this balance. In contrast, novel advanced manufacturing methods have now enabled researchers to create electrodes with variable porosity that can be tuned for optimal performance. To explore the impact of locally altering the porous media structure, we leverage previous modeling work by Weng et al. to allow for spatially varying porosity. Further, we couple the forward simulation to adjoint based optimization algorithms to determine optimal porosity distribution in the diffusion media and catalyst layer of a GDE. The cost functions for the optimization are derived from the previously mentioned KPIs. Finally, the performance of the resultant spatially varying porosity GDEs is compared to the performance of homogenous porosity GDEs, and we identify key features of the porosity distribution leading to improved performance. Weng, Lien-Chun, Alexis T. Bell, and Adam Z. Weber. "Modeling gas-diffusion electrodes for CO2 reduction." Physical Chemistry Chemical Physics 20.25 (2018): 16973-16984. 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-830082

Power-efficient energy storage is important for the successful integration of renewables into the electrical grid, and one promising storage device is the redox flow battery. In this presentation, we discuss the design of the flow field component of a redox flow battery using topology optimization. The flow field is a flow manifold that distributes and guides fluid with reactant both into and out of the reactive porous electrode. The popular two-dimensional interdigitated flow field, where a series of interlocking flow channels force fluid into the electrode, is a design that is commonly used in the lab, and we perform our optimization to improve upon this design. Specifically, we aim to minimize the electrical and flow pressure power losses, and we find that different operating conditions, i.e., flow rate and current density, lead to different optimal designs. We observe fully three-dimensional features in our optimized designs, where primary flow channels appear along with smaller branching channels that guide fluid to the electrode. This provides evidence that three-dimensional structures can provide improvement over conventional two-dimensional designs. While high power efficiency can certainly be attained using a traditional interdigitated flow field, the dimensions of the fluid channels and solid lands need to be carefully chosen for the desired operating conditions. In this presentation, we show that our method can computationally guide this design process. 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 release number: LLNL-ABS-830167.

Three-dimensional (3D) printed, hierarchically porous nickel molybdenum (NiMo) electrocatalysts were synthesized and evaluated in a flow-through configuration for the hydrogen evolution reaction (HER) in 1.0 M KOH(aq) in a simple electrochemical H-cell. 3D NiMo electrodes possess hierarchically porous structures because of the resol-based aerogel precursor, which generates superporous carbon aerogel as a catalyst support. Relative to a traditional planar electrode configuration, the flow-through configuration allowed efficient removal of the hydrogen bubbles from the catalyst surface, especially at high operating current densities, and significantly decreased the overpotentials required for HER. An analytical model that accounted for the electrokinetics of HER as well as the mass transport with or without the flow-through configuration was developed to quantitatively evaluate voltage losses associated with kinetic overpotentials and ohmic resistance due to bubble formation in the porous electrodes. The chemical composition, electrochemical surface area (ECSA), and roughness factor (RF) were also systematically studied to assess the electrocatalytic performance of the 3D printed, hierarchically porous NiMo electrodes. An ECSA of 25163 cm2 was obtained with the highly porous structures, and an average overpotential of 45 mV at 10 mA cm-2 was achieved over 24 h by using the flow-through configuration. The flow-through configuration evaluated in the simple H-cell achieved high electrochemical accessible surface areas for electrochemical reactions and provided useful information for adaption of the porous electrodes in flow cells.

Large-scale renewable energy must overcome conversion and storage challenges before it can replace fossil fuels due to its intermittent nature. However, current sustainable energy devices still suffer from high cost, low efficiency, and poor service life problems. Recently, porous metal-based materials have been widely used as desirable cross-functional platforms for electrochemical and photochemical energy systems for their unique electrical conductivity, catalytic activity, and chemical stability. To tailor the porosity length scale, ordering, and compositions, 3D printing has been applied as a disruptive manufacturing revolution to create complex architected components by directly joining sequential layers into designed structures. This article intends to summarize cutting-edge advances of metal-based materials for renewable energy devices (e.g., fuel cells, solar cells, supercapacitors, and batteries) over the past decade.

As power generated from renewables becomes more readily available, the need for power-efficient energy storage devices, such as redox flow batteries, becomes critical for successful integration of renewables into the electrical grid. An important aspect of a redox flow battery is the planar flow field, which is usually composed of two-dimensional channels etched into a backing plate. As reactant-laden electrolyte flows into the flow battery, the channels in the flow field distribute the fluid throughout the reactive porous electrode. We utilize topology optimization to design flow fields with full three-dimensional geometry variation, i.e., 3D flow fields. Specifically, we focus on vanadium redox flow batteries and use the optimization algorithm to generate 3D flow fields evolved from standard interdigitated flow fields by minimizing the electrical and flow pressure power losses. To understand how these 3D designs improve performance, we analyze the polarization of the reactant concentration and exchange current within the electrode to highlight how the designed flow fields mitigate the presence of electrode dead zones. While interdigitated flow fields can be heuristically engineered to yield high performance by tuning channel and land dimensions, such a process can be laborious; this work provides a framework for automating that design process.