High Volume Production Research Articles

Optimizing Construction Spoil Reactivity for Cementitious Applications: Effects of Thermal Treatment and Alkaline Activation

Construction spoil (CS), a prevalent type of construction and demolition waste, is characterized by high production volumes and substantial stockpiles. It contaminates water, soil, and air, and it can also trigger natural disasters such as landslides and debris flows. With the advent of alkali activation technology, utilizing CS as a precursor for alkali-activated materials (AAMs) or supplementary cementitious materials (SCMs) presents a novel approach for managing this waste. Currently, the low reactivity of CS remains a significant constraint to its high-value-added resource utilization in the field of construction materials. Researchers have attempted various methods to enhance its reactivity, including grinding, calcination, and the addition of fluxing agents. However, there is no consensus on the optimal calcination temperature and alkali concentration, which significantly limits the large-scale application of CS. This study investigates the effects of the calcination temperature and alkali concentration on the mechanical properties of CS–cement mortar specimens and the ion dissolution performance of CS in alkali solutions. Mortar strength tests and ICP ion dissolution tests are conducted to quantitatively assess the reactivity of CS. The results indicate that, compared to uncalcined CS, the ion dissolution performance of calcined CS is significantly enhanced. The dissolution amounts of active aluminum, silicon, and calcium are increased by up to 420.06%, 195.81%, and 256.00%, respectively. The optimal calcination temperature for CS is determined to be 750 °C, and the most suitable alkali concentration is found to be 6 M. Furthermore, since the Al O bond is weaker and more easily broken than the Si O bond, the dissolution amount and release rate of active aluminum components in calcined CS are substantially higher than those of active silicon components. This finding indicates significant limitations in using CS solely as a precursor, emphasizing that an adequate supply of silicon and calcium sources is essential when preparing CS-dominated AAMs.

Comparison of the presence of high production volume chemicals in farmed and wild fish highly consumed in catalonia and their risk assessment

The decline in fish populations and the depletion of marine resources have sparked concerns about sustainable fish production, driving the innovation of new aquaculture methods. While some argue that wild fish are healthier than farmed fish due to less exposure to contaminants and pathogens, wild fish can accumulate contaminants from more contaminated water sources. The slower growth of wild fish and their longer exposure to the environment may contribute to higher pollutant levels in fish tissues. In this study, we focus on 25 contaminants considered as high production volume chemicals (HPVCs), such as organophosphate esters (OPEs), benzothiazoles (BTs), benzosulfonamides (BSAs) and phthalates (PAEs). The compounds were extracted from the edible part of the fish using the QuEChERS method and analysed by gas chromatography-tandem mass spectrometry.A total of 74 samples were analysed from three of the most commonly consumed species in Catalonia, Spain (turbot, sea bass and sea bream). Two samples of each species were collected each month, one form farmed and one from wild origin. In general, the compounds were found in all the samples in a wide concentrations range, although no significant differences were observed between the mean concentration of wild and farmed samples. Although similar mean concentrations for the OPEs, BTs and BSAs were found between farmed and wild origin samples, PAEs were more frequently detected in farmed samples. Di-n-octyl phthalate and diethyl phthalate showed the highest concentrations in all fish samples, with values up to 19505 and 17605 ng g−1 (d.w.), in sea bass and sea bream, respectively. Di-(2-ethylexyl)-adipate proved to be the most relevant carcinogenic compound, with no associated health risk. Despite the detection of the studied HPVCs, no health risk was associated with the consumption of these three fish species.

Expanding the horizons of metal additive manufacturing: A comprehensive multi-objective optimization model incorporating sustainability for SMEs

Metal Additive Manufacturing (MAM) has seen significant growth in recent years, with sub-processes like Metal Material Extrusion (MEX) reaching industrial readiness. MEX, known for its cost-effectiveness and ease of integration, targets a distinct market segment compared to established high-end MAM processes. However, despite technological improvements, its overall integration into the industry as a viable manufacturing technology remains incomplete. This paper investigates the competitiveness of MEX, specifically its integration into the supply chain and the implications on cost and carbon emissions. Utilizing real-world data, the research develops a multi-objective optimization (MOO) model for a four-echelon supply chain including suppliers, airports, production facilities, and customers. The optimization model is combined with a previously developed cost model for MEX to optimize facility location in Norway using the NSGA-II algorithm. Employing a case study approach, the paper examines the production of an industrial part using stainless steel 17-4PH, detailing concrete process costs and system-level costs across four different production scenarios: 10, 100, 1,000, and 10,000 parts. The findings indicate MEX’s potential for cost-effective production at low and diversified volumes, supporting the trend towards customization and manufacturing flexibility. However, the study also identifies significant challenges in maintaining competitiveness at higher production volumes. These challenges underline the necessity for further advancements in MEX technology and process optimization to enhance its applicability and efficiency in larger-scale production settings.

Relationship between the surface free energy of underlayers and the dissolution kinetics of poly(4-hydroxystyrene) partially protected by t-butoxycarbonyl groups in tetramethylammonium hydroxide aqueous developer

In the lithography used for the high-volume production of semiconductor devices, the photoresist film becomes thin with the reduction in pattern size to prevent the pattern collapse due to the surface tension of rinsing liquids. The interfacial effect becomes strong with the reduction in photoresist film thickness. In the development process, it is of importance to clarify the relationship between the photoresist and the underlayer for fine patterning. In this study, the dissolution kinetics of poly(4-hydroxystyrene) (PHS) partially protected by t-butoxycarbonyl (t-Boc) groups in tetramethylammonium hydroxide (TMAH) aqueous solution was found to be related to the surface free energy of the underlayer. The attenuation rate of developer viscosity first decreased and then increased with the polar-to-dispersion component ratio. An inflection point with the lowest rate existed. The TMAH concentration affected not only the attenuation rate but also the ratio of polar to dispersion components at the minimum attenuation rate.

Photopolymerization of Stainless Steel 420 Metal Suspension: Printing System and Process Development of Additive Manufacturing Technology toward High-Volume Production

As the metal additive manufacturing (AM) field evolves with an increasing demand for highly complex and customizable products, there is a critical need to close the gap in productivity between metal AM and traditional manufacturing (TM) processes such as continuous casting, machining, etc., designed for mass production. This paper presents the development of the scalable and expeditious additive manufacturing (SEAM) process, which hybridizes binder jet printing and stereolithography principles, and capitalizes on their advantages to improve productivity. The proposed SEAM process was applied to stainless steel 420 (SS420) and the processing conditions (green part printing, debinding, and sintering) were optimized. Finally, an SS420 turbine fabricated using these conditions successfully reached a relative density of 99.7%. The SEAM process is not only suitable for a high-volume production environment but is also capable of fabricating components with excellent accuracy and resolution. Once fully developed, the process is well-suited to bridge the productivity gap between metal AM and TM processes, making it an attractive candidate for further development and future commercialization as a feasible solution to high-volume production AM.

Case study of a batch asphalt mix plant: Energy consumption and emission allocation based on primary data

Determining the environmental impact of asphalt mixtures is crucial for sustainable infrastructure development. A dominant factor in the ecological performance of the building material is the heating process in the dryer drum of an asphalt mix plant. Since it depends on various parameters, large scattering can be observed. While most Product Category Rules (PCRs) only consider the mean consumption over a whole production year in Module A3 according EN 15804, and consequently assign the same value to each ton asphalt mixture produced, the advantages of some asphalt mix designs remain unconsidered. Based on primary data of a batch asphalt mix plant, this case study investigated the impact of production amount, asphalt mixture temperature, number of dryer drum starts and the addition of reclaimed asphalt pavement (RAP) on the energy consumption of asphalt mixture production. By applying Linear Discriminant Analysis (LDA), it was demonstrated that it is not possible to precisely determine the energy consumption in the dryer drum based on the available data. Thus, further parameters are necessary for a fully-fledged prediction model. Nevertheless, the finding of this study showed the significant impact of the daily production volume, which results in 22 % less fuel consumption for high production volumes compared to low volumes. When the production temperature is lowered from 186 °C to 162 °C, further fuel savings of up to 9 % can be achieved. Considering the Global Warming Potential (GWP), the highest saving potential (27 %) in the drying and heating process can be achieved by operating the dryer exclusively with natural gas instead of heating oil.

Ghost cells as a two-phase blood analog fluid-high-volume and high-concentration production.

Hemolysis in mechanical circulatory support systems is currently determined quantitatively. To also locally resolve hemolysis, we are developing a fluorescent hemolysis detection method. This requires a translucent two-phase blood analog fluid combined with particle image velocimetry, an optical flow field measurement. The blood analog fluid is composed of red blood cell surrogates. However, producing surrogates in sufficient volume is a challenge. We therefore present a high-volume and high-concentration production for our surrogates: ghost cells, hemoglobin-depleted erythrocytes. In the ghost cell production, the hemoglobin is removed by a repeated controlled osmolar lysis. We have varied the solution mixture, centrifugation time, and centrifugation force in order to increase production efficiency. The production is characterized by measurements of output volume, hematocrit, transparency, and rheology of the blood analog fluid. The volume of produced ghost cells was significantly increased, and reproducibility was improved. An average production of 389 mL of ghost cells were achieved per day. Those ghost cells diluted in plasma have a rheology similar to blood while being permeable to light. The volume of ghost cells produced is sufficient for optical measurements as particle image velocimetry in mechanical circulatory support systems. This makes further work on experimental measurements for a locally resolved hemolysis detection possible.

Impact of mixing and shaking on mRNA-LNP drug product quality characteristics

Since the COVID-19 pandemic, the interest in RNA-lipid nanoparticle (LNP) based drug products has increased drastically. While one RNA-LNP drug product, Onpattro, was already on the market in 2018, high volume manufacturing was only initiated end of 2020 with the approval of the mRNA-LNP vaccines, Comirnaty and Spikevax. As such, deep product knowledge for RNA-LNPs is continuously increasing. In this article the effect of large-scale mixing and lab-scale shaking on mRNA-LNP drug product quality characteristics is investigated. It is shown that mixing and shaking can have a profound impact on both LNP size distribution and mRNA encapsulation, suggesting a direct correlation between both quality characteristics, and further supported by a proposed underlying mechanism. An in-depth investigation of different drug product (DP) presentations reveals a consistent effect of headspace volume and LNP content on the shaking stress sensitivity. Results reported in this study are of utter importance for both small- and large-scale manufacturers but also for care givers and patients using these products.

Fabrication of nitrogen-hyperdoped silicon by high-pressure gas immersion excimer laser doping

In recent years, research on hyperdoped semiconductors has accelerated, displaying dopant concentrations far exceeding solubility limits to surpass the limitations of conventionally doped materials. Nitrogen defects in silicon have been extensively investigated for their unique characteristics compared to other pnictogen dopants. However, previous practical investigations have encountered challenges in achieving high nitrogen defect concentrations due to the low solubility and diffusivity of nitrogen in silicon, and the necessary non-equilibrium techniques, such as ion implantation, resulting in crystal damage and amorphisation. In this study, we present a single-step technique called high-pressure gas immersion excimer laser doping (HP-GIELD) to manufacture nitrogen-hyperdoped silicon. Our approach offers ultrafast processing, scalability, high control, and reproducibility. Employing HP-GIELD, we achieved nitrogen concentrations exceeding 6 at% (3.01 × 1021 at/cm3) in intrinsic silicon. Notably, nitrogen concentration remained above the liquid solubility limit to ~1 µm in depth. HP-GIELD’s high-pressure environment effectively suppressed physical surface damage and the generation of silicon dangling bonds, while the well-known effects of pulsed laser annealing (PLA) preserved crystallinity. Additionally, we conducted a theoretical analysis of light-matter interactions and thermal effects governing nitrogen diffusion during HP-GIELD, which provided insights into the doping mechanism. Leveraging excimer lasers, our method is well-suited for integration into high-volume semiconductor manufacturing, particularly front-end-of-line processes.

Miniaturized nanoelectrospray interface for coupling capillary electrophoresis with mass spectrometry detection.

A miniaturized electrospray interface consisting of a microfluidic nanosprayer and nanospray module is reported in the presented short communication. The nanosprayer was fabricated using silicon (Si) technology suitable for cost-efficient high-volume mass production. The nanospray module enabled the positioning of the nanosprayer in front of a mass spectrometry entrance and its coupling with capillary electrophoresis based on the liquid junction principle. A case study of top-down and bottom-up proteomic analyses of intact cytochrome c and its tryptic digest demonstrates the practical applicability of the developed interface.

High-Volume SiC Epitaxial Layer Manufacturing-Maintaining High Materials Quality of Lab Results in Production

Typically, research and development (R&D) results of epitaxial layer growth show superior properties of the grown layers compared to high volume results. Layer uniformities are excellent and achieved defect densities are low compared to typical results. In particular, the conversion of basal plane dislocations (BPD) from the silicon carbide (SiC) substrate is in focus to reduce bipolar degradation of p-n-junctions. It is a great challenge to maintain those excellent results in high-volume manufacturing considering all the factors that impact the properties of the epilayer. Thus, quality of the layers, high throughput and low cost have to be assessed to find a compromise between these key factors. In this paper we present results on the growth of epitaxial layers on 150 mm and 200 mm 4° off-oriented 4H-SiC substrates using warm-wall multi-wafer chemical vapor deposition (CVD) systems. Single wafer data of the key epitaxial layer parameters, thickness, doping and defect densities, are compared to batch and lot results, as well as to statistical data of several hundreds of wafers produced. Improvements in wafer-to-wafer (w-t-w) doping uniformity could be achieved for instance by implementation of an on-wafer temperature measurement. Substrate impact on defect levels is shown comparing X-ray topography (XRT) results of bare substrate wafers and defect analysis of epilayers on sister wafers from the same crystal. Statistical defect data and resulting predicted yield loss also show a dependence on substrate suppliers. For the first time we show w-t-w and run-to-run (r-t-r) results of doping and thickness measurements on 200 mm substrates. Also, defect results of epilayers on 200 mm wafers are compared to results on 150 mm.

Characterization and analysis of conduction welded thermoplastic composite joints considering the influence of manufacturing

Thermoplastic composite welding is a key technology that can help to make the aviation industry more sustainable, while at the same time enable high-volume production and cost-efficient manufacturing. In this work, characterization, testing and analysis of thermoplastic composite conduction welded joints is performed while accounting for the influence of the manufacturing process. Test specimens are designed from welds of a half a meter long welding tool that is developed to weld the stiffened structures of the next-generation thermoplastic composite fuselage. In the design, special attention is paid to the weldability of the laminates, while ensuring fracture occurs only at the welded interface. Two specimen configurations are evaluated for the Double Cantilever Beam and End-Notched Flexure characterization tests. Moreover, Single Lap-Shear specimens are tested in tension and in three-point-bending. Finally, the characterized material properties are introduced in finite element analyses to demonstrate that the cohesive zone modeling approach can be used to conservatively predict the strength of these welded joints. New insights are obtained in the relation between the manufacturing process, the quality of the weld and the mechanical properties of the joints, which are significantly different compared to autoclave consolidated composites.

(Invited) Chemical Mechanical Planarization Integrated Data Collaboration: Uncover Hidden Interactions for Predictive Maintenance and PCN

This article introduces an innovative analytical methodology that employs machine learning to optimize and predict consumable impacts for Chemical Mechanical Planarization (CMP). By harnessing fleet CMP equipment performance and individual equipment characteristics, our proposed approach aims to elevate wafer-level quality, maximize equipment utilization rates, and minimize unnecessary downtime. CMP, a multifaceted process, its performance is often influenced by strong interactions from its critical components including slurry, pad, machine recipe, and dresser usage. This requires control beyond hardware advancements, especially when equipment is pushed to its limits. Therefore, the demand for higher precision on-wafer performance mandates a data collaboration with sophisticated analytical strategy. In addition, a digitalized product change notification (PCN) concept is also introduced to demonstrate effectiveness of applying integrated data analytics collaboratively across semiconductor chain to accelerate material manufacturing process changes, minimizing impacts on wafer results and shortening the learning cycle and ramp time to full production.Our study leverages data analytics to explore differences between equipment within the same fleet of similar processing. Engineers can deploy a process based on the historical performance of specific equipment, supported by a model trained on data from the same fleet. Furthermore, our approach enables each equipment to predict its own fingerprinted consumable maintenance, accurately forecasting the maintenance time for consumable changes. This concept is equally applicable for consumable PCN migrations.The first use case is to demonstrate the use of integrated data for fleet matching of CMP equipment and/or consumable batches running similar processes. This is particularly crucial as the demand for process precision increases and the need to reduce post-CMP result variability in removal rates and wafer-level uniformity intensifies. This approach could add proactive data driven strategy for process engineers who traditionally concentrate on the removal rate, a highly influential factor, with the standard deviation of platen and machine data identified as a key parameter.The second use case employs heatmaps analysis and proposes scatter plots as an intuitive method to comprehend intricate connections between average removal rates and various parameters. Machine learning is particularly advantageous in this context, as no single parameter plays a dominant role. Preliminary observations suggested, for example that dresser and pad characteristics may have compounding impacts to the wafer level performance.In summary, this article provides a roadmap for in-depth CMP data analysis fed into machine learning, aiming to unravel trends and influential factors that can enhance process optimization and shorten the PCN qualification timeline to meet the increasing demands for CMP process precision and efficiency in High Volume Manufacturing (HVM).

Overview and Potential Solutions of Current and Future Challenges in CMP

This talk is dedicated to the special session in honor of Prof. Babu Shrink of critical dimensions and stringent technology specifications are posing several planarization challenges in technology development (TD) and high-volume manufacturing (HVM) phases of current and advanced technology nodes. In this talk, we will focus on process challenges related to achieving (a) minimum defects (b) high planarization efficiency with less than 1nm dishing (c) tighter WIW and WID uniformities (d) ultra-smooth post-CMP surface roughness in the order of sub-nanometer. In addition, identifying appropriate endpoint control techniques combined with advanced process control strategies to provide good process margin will also be discussed. Addressing the above challenges in a cost-effective manner is vital for HVM. Root cause analysis and probable working models will be discussed for high-risk process challenges. Based on our understanding, a jist shall be provided about challenges associated with future technology nodes in logic and memory industries, with focus on how (a) technical understanding, (b) change in CMP ecosystem, and (c) AI can help solving technology development and high-volume manufacturing problems.

(Invited) Past, Present and Future for Electrochemical Gas Sensors in Energy Applications

The markets for modern low-cost electrochemical gas sensors have been growing for longer than sensors have been around. Energy markets for gas sensors include the oil, gas and electricity industries as well as the new developing and fast growing green energy sectors. The primary gases to detect include combustible hydrocarbons, oxygen, and toxic gases. Specifically, we are interested in methane (blue and green), ammonia, and H2 for the renewable energy sectors but include hydrocarbon fuels and CO2 as a greenhouse gas emission. The reasons for monitoring crosscut all areas of the energy business and include applications in production, transport, storage and use. Primary sensor uses include: 1) health and safety (people and assets), 2) leak detection and isolation to help mitigate product losses, 3) monitoring to protect the environment and 4) measurements for process control and efficacy. Each of these areas of sensing have special requirements and demand cost-effective and time efficient sensor performance in many and varied real world scenarios.Electrochemical gas sensors have a long and rich history starting with the commercialization of the first practical potentiometric CO2 gas sensor by Severinghaus in 1954 and the O2 amperometric sensor by Clark in 1956 that launched the modern blood gas analysis industry. Additional milestones include the introduction of the “diffusion electrode” in 1968 creating the modern amperometric sensor which has produced a large array of sensors for toxic and hazardous gases including H2, ammonia, and other energy gases. Progress has been made in the field of electrochemical gas sensors, not only in improving performance, sensitivity, selectivity, response time, and stability, but also in logistical properties including miniaturization, lower power consumption, low cost as well as communication and computation to make automated-operational systems. New high-volume production has contributed to lower costs commensurate with a chip-based sensor mentality. The evolution of one of the gas sensor technologies is given below (the room temperature AGS or amperometric gas sensor) and similar progress has been seen in mixed potential and solid state gas sensors.The growing understanding of the sensor’s fundamental electrocatalytic reactions has led to tailored designs of electrode-electrolyte combinations and packages for the various applications. Often ignored in sensor publications of performance, the fundamental electrocatalytic studies are poised to make significant advances in energy gas sensor selectivity and sensitivity. The additional implementation of intelligent algorithms (AI/ML) to make “smart” sensors and sensor arrays complements advanced nano-materials and designs for improving sensor performance. One major improvement is our understanding of the sensor response mechanisms at the electrocatalytic level. This new research will enable new electrochemical sensor advances that are poised to impact the health and wellbeing of both people and the planet. Ideas and concepts that significantly contribute to the safe and efficient rollout of the newest green energy platforms are presented. Figure 1

(Invited) Challenges and Successes of an Industrial Start-up to Global Electrolyzer Company

Hydrogen generation via electrolysis has gained much attention internationally as not only a sustainable source of fuel for the transportation, but as a carrier of energy to capture stranded renewable energy due to the carbon-free chemical cycle and response characteristics of the technology. The barrier to entry preventing this technology from being widely adopted is the overall cost associated with both the upfront capital expense as well as the operating cost incurred over the product life. The majority of this cost is driven by 1) low electrical efficiencies due to the high ohmic resistance of thicker membranes required by electrolysis systems to electrochemically compress generated hydrogen for storage and the overpotential associated with the water splitting oxygen evolution reaction (OER) catalysts typically used, 2) high cost of system and cell stack materials, which need to be durable for up to 100,000hrs at potentials approaching 2V in both reducing and oxidizing environments, and 3) the lack of a robust supply chain for high volume manufacturing to further drive down cost through economies of scale.Nel Hydrogen has worked to commercialize this technology, originally developed for aerospace and miliary applications where cost and efficiency were not the drivers. Since the founding of Proton Energy Systems in 1996 and to the present as Nel Hydrogen we have worked to address these problem areas in an attempt to develop products that can serve cost sensitive markets, such as energy capture and storage. During this talk, I will look back on the challenges we faced as an industrial start-up dealing with uninterested suppliers, creating relationships, and establishing partnerships, all while developing our first 28cm2 products to the point today, where we are producing and siting multi-MW systems.

Changing the Linking Functionality in a Triblock Co-Polymer for Electrolyzer or Fuel Cell Membrane or Ionomer for Chemical Stability Improvements and Potentially Other Consequences.

We have developed a versatile scalable triblock polymer system for anion exchange membrane (AEM) based electrochemical energy conversion devices. This system is uniquely suited to solving both the membrane and ionomer challenges for the commercialization of AEM water electrolysis or fuel cells. Using our advanced fundamental understanding of AEMs based on our work which began in 2010, we can design systems from first principles to match the needs of the device. A triblock ABA polymer is readily fabricated that has both chemical and mechanical stability. The synthesis of these systems is robust, high yield and amenable to high volume production. It utilizes a novel dual chain transfer agent to mediate the chain-transfer ring-opening metathesis polymerization (ROMP) of cyclooctene (COE) or the polymerization of isoprene (IP) to give the hydrophobic B block of the polymer. This is followed by the reversible addition-fragmentation chain transfer radial polymerization of chloromethylstyrene (CMS). The polyCMS (pCMS) blocks are then quaternized with an amine to give the cationic hydrophilic A block. Before quaternization the B block is hydrogenated to give either semi-crystalline polyethylene (pE) or amorphous polymethylbutylene (pMB) from pCOE and pIP respectively. The obvious achilles heal in this system from a chemical durability standpoint is the linking ether functionality between the hydrophobic and hydrophilic blocks. The material we have synthesized to date have exceptional chemical and mechanical stability. It performs very well in ex-situ testing with KOH solutions and in device testing we have demonstrated >500 h at 50°C in water electrolysis in 5 cm2 cells in 1M carbonate electrolyte at 0.5 A cm-2. We attribute the chemical stability to the fact that we post quaternize the membrane to give methylpipiridinium cations and we assume that this reaction does not fully penetrate down the hydrophilic chain. This leaves a hydrophobic un-quaternized region close to the hydrophobic block that does not allow access to the ether linkage by the hydrated hydroxide anion. Recently a new approach to the telehelic mid block pCOE has been reported that has no ether linkages and allows the hydrophobic outer blocks to be linked by a methylene linker. This gives use an opportunity to directly contrast the chemical stability of two identical triblock co-polymers one with an ether linkage and one with a methylene linkage between blocks. In this presentation we will show the results from ex-situ chemical stability test one at low temperatures and higher hydroxide concentration and one at higher temperature and lower hydroxide concentration. There are many examples in polymer electrolyte chemistry where a simple change leads to unintended consequences. The geometry of the ether linkage versus the methylene linkage will change the way that the polymer chains will organize and entangle in ways that are not currently predictable. To probe the mechanical, chemical and morphological difference between these two polymers we will fully characterize and contrast both polymers physical chemical properties including water uptake, ionic conductivity, DSC, TGA, tensile strength and morphology through SAXS, WAXS and HRTEM. Data for the two polymer membranes will also be contrasted and compared in small laboratory 5 cm2 single cells for both electrolysis in dilute carbonate and H2/O2 fuel cells using conventional catalysts.

Finding the Fingerprint of an PEMWE Electrolyzer Using Applied Machine Learning Techniques

The energy transition is picking up pace and renewable energy sources are playing an increasing role in our energy supply. The use of green hydrogen is considered an important energy carrier, to store renewable energy and allow many industries to decarbonize. There are different ways to produce hydrogen, whereas low-temperature proton exchange membrane water electrolysis (PEMWE) is one of the more mature electrolysis technologies. The benefits of proton exchange membrane water electrolysis (PEMWE) include the ability to deliver pressurized hydrogen and/or oxygen, operate using intermittent and renewable power sources, and have a reduced footprint. To evaluate the characteristic performance of PEMWE, the voltage/current relation is an important metric. The voltage/current relation is commonly represented in jV-curves. jV-curves contain relevant information, and any issues or out-of-specification performance can be observed immediately. The shape of the curve, and indicated values allow to interpret the performance of the PEWME cell. Nevertheless, proper interpretation of jV-curves and understanding of the source leading to the out-of-specification performance requires expertise.This study aims to evaluate jV-curves and identify faults by applying machine learning (ML) techniques. A conventional approach has been used to create the model. In the model the Nernst voltage, activation voltage on the anode, and the voltage losses due to ohmic resistance are modeled. Implementation of optimization algorithms allow to fit measured jV-curve to the model. Literature has shown relevant techniques to apply these kinds of ML techniques to electrolysis data, for example Particle Swarm Optimization and a modified Honer Badger algorithm. The application of ML to electrolysis data enables the opportunity to develop online monitoring and automated interpretation of data. In this study particle Swarm Optimization (PSO) has been implemented to fit the model to the jV-curve and find the unknown parameters. Those parameters are unique, depending on the performance of the electrochemical cell, and can be interpreted at the fingerprint thereof.The data shown in the figure has been measured on a single-cell material screener using Nafion 117 CCMs. Although both CCMs have been produced in the same batch, a difference between the measurements can be observed. The jV-curve of sample 1 shows performance as expected. This expected performance is characterized by its linear behavior above 0.5 A/cm2. Observing sample 2 shows an increase in voltage at increasing current densities compared to sample 1. This increase in voltage results in increased energy consumption and therefore a less efficient electrolysis process. Both CCMs are measured under comparable conditions. The anode and cathode pressure were during the both tests close to atmospheric, and the average cell temperature was kept constant at 333.15 K.Despite high volume production processes for coating and manufacturing, the origin of the difference in performance remains unknown. The application of automated data analysis allows for the immediate evaluation of the parameters at fault. The preliminary results show a discrepancy in the modeling parameters relevant to the sub-model describing ohmic losses. Furthermore, ML allows for the creation of data sets that contain experiences from the past. This can help to develop faster performance evaluation protocols by reducing testing activities. For future work, alternative algorithms have to be considered as well. Utilizing ML can enable the opportunity for online monitoring, predictive maintenance, and optimizing control strategies. Figure 1

The Additive to Mass Manufacturing Process of Mixed Potential Electrochemical Sensors

Additive manufacturing allows for quick turn around and low cost of production of small numbers of parts. These attributes lend the technology to prototype development, where design changes are many and production numbers are low [1]. Once design parameters have been nailed down, additive manufacturing’s low throughput and poor reproducibility limit application to high volume production.Here, we discuss the implementation of direct-write extrusion of ceramic pastes and metal inks for prototype manufacturing of mixed potential electrochemical sensors and the transition to tape casting and screen printing for high volume production. Throughout the prototyping phase, direct write extrusion enabled quick and easy production of low numbers of parts, enabling various changes in sensor design and material to improve sensor performance. Sensor design evolved from a disk shape (Fig. 1 a) to a stick with integrated heater (Fig. 1 b, c). Direct write extrusion of substrate enabled quick testing of various stabilized zirconia ceramics to improve the sensitivity of the device [2]. Limitations of additive manufacturing became apparent when attempting to integrate the heater into the device, the inconsistent linewidths led to areas of high resistance and hot spots (Fig. 1 c). Tape-casting and screen printing of the devices was adopted for three reasons, first, to improve line fidelity, which is vitally important when printing small electrodes as well as heater traces (Fig. 1 d), second, to further decrease conductivity of substrate by laminating multiple layers of different ceramic materials, and finally to increase manufacturing throughput (Fig. 1 d, e).This work was supported by US Department of Energy Award DE-FE0031864References[1] T. D. Ngo, A. Kashani, G. Imbalzano, K. T. Q. Nguyen, and D. Hui, “Additive manufacturing (3D printing): A review of materials, methods, applications and challenges,” Compos. Part B Eng., vol. 143, pp. 172–196, Jun. 2018, doi: 10.1016/j.compositesb.2018.02.012.[2] S. Halley, K. Ramaiyan, F. Garzon, and L. Tsui, “Massive enhancement in sensitivity of mixed potential sensors towards methane and natural gas through magnesia stabilized zirconia low ionic conductivity substrate,” Sens. Actuators B Chem., vol. 392, p. 134031, Oct. 2023, doi: 10.1016/j.snb.2023.134031. Figure 1

Enhancing Efficiency and Durability of PEM Water Electrolysis with Low Iridium Loading through Nanofiber-Modified Porous Transport Electrodes

Hydrogen plays a significant role in the transition of the global energy system towards achieving net-zero emissions. According to the “Hydrogen for Net-Zero” report by Hydrogen Council and McKinsey & Company, the demand for clean hydrogen could reach 140 metric tons (MT) by 2030, with a further increase to 660 MT by 2050. For future high-volume production of green hydrogen, Proton Exchange Membrane Water Electrolysis (PEMWE) is a crucial technology due to its advantages of high-purity hydrogen, large operational current densities, and great energy conversion efficiency [1]. However, the use of precious metal-based catalysts for the sluggish oxygen evolution reaction (OER) on the anode side hinders the high-volume manufacturability of PEMWE. To ensure cost-effectiveness in large-scale PEMWE deployment for hydrogen production, developing strategies to reduce Iridium loading to 0.1 - 0.2 mg Ir/cm2 without compromising overall performance is essential. The challenge of reducing Ir loading also involves improving or maintaining the electrical contact between the catalyst layers and the substrate [2-3].Smoltek Hydrogen has made significant strides in achieving low iridium loading through our innovative Porous Transport Electrode (PTE) concept. This approach involves modifying a Porous Transport Layer (PTL) by growing vertically aligned Carbon Nanofibers (CNFs) using our in-house patented technology, efficiently increasing surface area [4]. Following this modification, a protective layer of corrosion-resistant and conductive material (e.g., Platinum) is applied to the CNFs. Subsequently, an OER catalyst layer is deposited utilizing a three-electrode cathodic electrodeposition method (Fig. 1a and 1b). The electrodeposited Ir catalyst exhibits strong adhesion to the CNF PTEs, resulting in a high electrical conductivity [5].In this work, we successfully reduced Ir loadings to 0.1 mg/cm2 in the PTE, demonstrating excellent mass activity in the half-cell tests (Fig. 1c). In the PEMWE tests, the polarization curves of electrodes with low Ir loading exhibited superior performance compared to cells with significantly higher Ir loading (Fig. 1d). The next step of our research involves conducting constant current durability tests for over 600 hours on PEMWE operation using low Ir loaded PTEs. The aim is to minimize degradation and overcome mass transport limitations, particularly at higher current densities.These unique PTEs developed by Smoltek Hydrogen, with CNF technology, maximize the electrode surface area and pave a new way to increase the utilization ratio of catalyst surface at a low loading amount, which makes it a competitive anode material in today’s PEMWE market. References https://www.iea.org/reports/hydrogen-2156#dashboardMöckl et al. J. Electrochem. Soc., 2022, 169, 064505.Bernt et al. Chem. Ing. Tech., 2020, 92, 31-39.https://www.smoltek.com/applications/electrolyzers/Krivina et al. Adv.Mater., 2022, 34, 2203033.https://publications.jrc.ec.europa.eu/repository/handle/JRC104045 Figure 1