In the dynamic and demanding environment of industrial plants, the reliability of machines is paramount. Ensuring that machinery operates reliably and efficiently is crucial for profitability of the plant. Reliability in industrial plants is beyond preventing failures and also about enhancing performance and extending the lifespan of equipment. By focusing on the most failure-prone components or systems, maintenance teams can prioritize their efforts and resources effectively, leading to significant improvements in overall reliability and total cost of ownership. This abstract delves into the critical role of reliability in industrial environments, emphasizing the importance of employing reliability growth models to systematically validate the effectiveness of solutions implemented to address machine reliability issues. For every unplanned events(trips), remote real-time data gathering and analysis conducted to identify the components or systems responsible for the trip. All the events and contributors are tracked and trended to identify top offenders. Identified top offenders are deeply investigated to find the solution & opportunity to develop the automatic diagnostic and prognostic tool based on remotely acquired time-series data. Based on outcome of Diagnostic and prognostic tools, identifying the degradation of equipment. Once a malfunction is identified, we analyze root causes, extract learnings, and develop targeted improvements. These improvements are first validated in controlled environments (e.g., lab or test bench), then implemented incrementally across the fleet. Each implementation cycle is tracked using reliability growth models to statistically measure the reduction in failure rates and validate the effectiveness of the solution over time. This process allows us to Diagnose and isolate malfunctions using real-time analytics, Generate and refine new analytics based on observed failure modes, Quantify reliability growth through Mean Time between failure (MTBF) decrease / Mean Time Between Trip (MTBT) improvements, Scale validated solutions from individual assets to the entire fleet. By integrating reliability growth models into our reliability process, we ensure that each improvement is measured and also predictively estimate the reliability improvement on any other unit in the fleet. This methodology has already demonstrated success, with MTBT improvements from 1,000 to 8,000 hours, showcasing the power of structured reliability growth modelling in complex, distributed systems.

Abstract While decarbonizing road transport is crucial for global climate goals, there is limited quantitative evidence on the economic viability and life-cycle emissions of low-carbon passenger vehicles in Africa, where motorization is rising. Here we study the economic cost and life-cycle greenhouse gas emissions of low-carbon passenger transport in Africa across six segments in 52 African countries through 2040. Using Monte Carlo and optimization models, we compare the total cost of ownership and life-cycle greenhouse gas emissions of battery electric vehicles powered by solar off-grid systems and synthetic fuelled vehicles to that of fossil-fuelled ones, neglecting policy-induced cost distortions. Whereas past reports suggested fossil fuel vehicles would dominate in Africa by mid-century, our results show that battery electric vehicles with solar off-grid chargers will have lower costs and negative greenhouse gas abatement costs well before 2040 in most countries and segments. Financing is identified as the key action point for governments and global financial institutions to accelerate Africa’s transition to battery electric vehicles with solar off-grid charging offering a cost-effective, viable solution to electricity infrastructure challenges.

Total cost of ownership of vehicle electrification and fuel switching options for light-duty and heavy-duty vehicles

This paper reviews an easily expandable plan for smart document handling across multiple cloud systems, aiming to make work easier to manage, more resilient to issues, and improve the total cost of ownership. The importance of this task stems from two factors: first, Intelligent Document Processing (IDP) tools are experiencing growth; second, multi-cloud use is expanding more widely. This increases the primary fight between wanting top-notch help for every step and the dangers of being stuck with one provider, having messy operations, and uneven safety rules. The study aims to create and support a complete design that can hide both setup and software links while offering complete control and standard protection in a mixed environment. The innovation is in the coherent four-layer model, which merges a general control plane atop Kubernetes and Crossplane with portable application runtime Dapr, exposing standard APIs for statelessness, messaging, and service invocation, decomposed IDP microservices, and an overlay layer for management and security. The key findings validate that only the combination of Crossplane at the level of the control plane with GitOps and OPA policies together with Dapr at the level of the application-API can provide real portability, elastic scaling, governed security, while maintaining freedom of choice between cloud services. It proves that workflows crossing provider boundaries can be orchestrated, thus reducing vendor lock-in. The article will be helpful to cloud-platform architects, IT executives, data and MLOps engineers, IDP product teams, and researchers in distributed systems and enterprise AI.

This article proposes and justifies a system-based approach to managing the development of mountain territories based on implementation of the unmanned aerial systems. The relevance of transitioning from a conceptual description of the unmanned aerial systems capabilities to a practical toolkit for planning, multi-criteria evaluation, and economic justification of projects is established. The study aims to present a comprehensive methodology that allows the ‘competent customers’, such as the regional authorities and corporations, to make well-founded investment decisions regarding the use of unmanned aerial systems for solving social and economic problems in conditions of complex terrain and limited accessibility. The research is based on the ‘Scenario Requirements Life Cycle Model’ methodological framework, adapted for the specific features of mountain regions. The key instruments proposed include task formalization via a “Scenario Passport”, operational risk analysis, application of multi-criteria decision making for selecting the optimal class of unmanned aerial systems, and economic modeling based on the Total Cost of Ownership and the Net Benefit calculations. The results demonstrate the methodology application in practical cases, including emergency delivery of medications and infrastructure monitoring. It is concluded that the proposed approach serves as an effective strategic planning tool that reduces the risks of inefficient investment and contributes to the sustainable and economically viable development of mountain territories

This study develops a dynamic, scenario-based simulation model to evaluate the techno-economic and environmental viability of transitioning urban rickshaw fleets from petrol to electric in Dharan, Nepal—a representative secondary city experiencing rapid urbanization. Integrating fleet dynamics, emission accounting, and total cost of ownership (TCO) within a Python-based Monte Carlo framework, the model assesses outcomes under Baseline, Conservative, and Policy Accelerated scenarios from 2016 to 2040. Findings reveal that the Policy Accelerated pathway, characterized by robust subsidies, fuel taxation, and synchronized grid decarbonization, can achieve up to 95% fleet electrification by 2040. This transition reduces well-to-wheel CO2, PM2.5, and NOx emissions by 72%, 91%, and 78% respectively, while also cutting the lifetime cost of e-rickshaws by 39% compared to business-as-usual. Economically, e-rickshaws achieve a positive net present value (NPV) of NPR 0.7 billion with an operator payback period of 5-7 years. Sensitivity analysis identifies fuel taxes and purchase incentives as the most effective policy levers. The results underscore that the greatest climate and health benefits are realized through an integrated strategy that concurrently targets vehicle electrification and power sector decarbonization. This study provides a replicable analytical framework and evidence-based policy roadmap for sustainable urban mobility in developing economies.

This study aims to provide a comprehensive and realistic evaluation of consumer and external costs associated with commercially available passenger cars. The central research question is: How do Total Cost of Ownership (TCO) and external costs differ between conventional vehicles, Battery Electric Vehicles (BEVs), and Fuel Cell Electric Vehicles (FCEVs) across various vehicle segments? The methodological approach includes the selection of 55 commonly registered vehicle variants in Germany and the calculation of TCO and external costs over a 16-year vehicle lifetime. TCO components include purchase price, governmental subsidies, remaining value, fuel or energy expenses, maintenance, insurance and taxes. External costs incorporate emissions, land use and the societal costs from purchase bonuses. Apart from the large quantity of considered vehicles and the depth of investigation, this study’s main contribution is the consideration of tax revenue as a negative external cost. The results show that BEVs consistently exhibit the lowest TCO and external cost across all segments. For example, a BEV in the E segment has 26 lower TCO and 14,300 lower external cost than an equivalent diesel vehicle. FCEVs show competitive results in both TCO and external costs, though limited by market availability. While higher in TCO, vehicles in higher segments generally lead to lower external cost due to higher tax revenue. The findings support the economic and ecological advantages of BEVs, which should therefore be primarily considered by consumers and policy-makers.

This study critically evaluates the challenge posed by Ubiquiti's UniFi Enterprise Fortress Gateway (EFG) to the established dominance of Fortinet's FortiGate in the African higher education context, using the University of Cape Coast (UCC) as a case study. It addresses the significant disconnect between globally prescribed, high-cost enterprise security models and the operational realities of African universities, which are characterized by chronic underfunding, limited technical staff, and infrastructural instability. Employing a comparative case study methodology, the research analyzes vendor datasheets, independent lab reports, and user reviews to assess both platforms across performance, total cost of ownership (TCO), and organizational fit, framed by the Technology-Organization-Environment (TOE) framework and Resource-Based View (RBV). The findings reveal a performance parity of approximately 90% between the EFG and FortiGate 600F in core security functions, coupled with a dramatic 75-80% reduction in TCO for the EFG. The study concludes that while FortiGate remains a powerful resource for well-resourced core networks, the EFG's "integrated convenience" model, characterized by operational simplicity, a capex-focused financial model, and built-in resilience, represents a strategically superior fit for the network edge of resource-constrained institutions. The research contributes a novel, empirically-grounded hybrid architectural model and provides policymakers with a context-driven framework for technology selection, advocating for a redefinition of "enterprise-grade" based on sustainable performance-to-cost and organizational alignment rather than vendor prestige.

Modern cryptographic systems increasingly depend on certified hardware modules to guarantee trustworthy key management, tamper resistance, and secure execution across Internet of Things (IoT), embedded, and cloud infrastructures. Although numerous FIPS 140-certified platforms exist, prior studies typically evaluate these solutions in isolation, offering limited insight into their cross-domain suitability and practical deployment trade-offs. This work addresses this gap by proposing a unified, multi-criteria evaluation framework aligned with the FIPS 140 standard family (including both FIPS 140-2 and FIPS 140-3), replacing the earlier formulation that assumed an exclusive FIPS 140-3 evaluation model. The framework systematically compares secure elements (SEs), Trusted Platform Modules (TPMs), embedded Systems-on-Chip (SoCs) with dedicated security coprocessors, enterprise-grade Hardware Security Modules (HSMs), and cloud-based trusted execution environments. It integrates certification analysis, performance normalization, physical-security assessment, integration complexity, and total cost of ownership. Validation is performed using verified CMVP certification records and harmonized performance benchmarks derived from publicly available FIPS datasets. The results reveal pronounced architectural trade-offs: lightweight SEs offer cost-efficient protection for large-scale IoT deployments, while enterprise HSMs and cloud enclaves provide high throughput and Level 3 assurance at the expense of increased operational and integration complexity. Quantitative comparison further shows that secure elements reduce active power consumption by approximately 80–85% compared to TPM 2.0 modules (<20 mW vs. 100–150 mW) but typically require 2–3× higher firmware-integration effort due to middleware dependencies. Likewise, SE050-based architectures deliver roughly 5× higher cryptographic throughput than TPMs (∼500 ops/s vs. ∼100 ops/s), whereas enterprise HSMs outperform all embedded platforms by two orders of magnitude (>10 000 ops/s). Because the evaluated platforms span both FIPS 140-2 and FIPS 140-3 certifications, the comparative analysis interprets their security guarantees in terms of requirements shared across the FIPS 140 standard family, rather than attributing all properties to FIPS 140-3 alone. No single architecture emerges as universally optimal; rather, platform suitability depends on the desired balance between assurance level, scalability, performance, and deployment constraints. The findings offer actionable guidance for engineers and system architects selecting FIPS-validated hardware for secure and compliant digital infrastructures.

Global megatrends have a great impact on future's powertrain concepts. Main drivers are the emission protection legislation, sustainability and, from a customer's view, competitive pressure, represented by the total cost of ownership. The conventional powertrain with the internal combustion engine will successively leave its dominant position. Electro mobility for city buses demands for hybrid systems, and, finally for electric busses. ZF was among the first to offer electric drivetrains for city busses in the late 90’s. The commercial breakthrough of electric drivetrains depends on legislation/subsidies and commercial advantage derived from the total cost of ownership. ZF has shown a variety of systems, beginning with electric axles, best suitable for all type of electric mobility, parallel hybrid transmissions and full electric drivetrain solutions. Full electric drives are a major future technology for commercial vehicles as they may significantly contribute to achieving future CO2 reduction targets as well as ambitious fuel consumption improvements. Hybrid solutions have also their markets, but not in city bus applications, Main market will be in intercity, coach and long haul truck applications. ZF will take an active part in the definition of future electric powertrains – mainly for city busses, but also intercity and potentially long distance traffic - and safeguard a fruitful cooperation with OEs and operators as well.

Traditional prosthetic limb manufacturing has long relied on traditional processes such as subtractive manufacturing, which has inherent limitations such as low customization, high cost, and long production cycle. In recent years, new manufacturing processes such as additive manufacturing and composite material forming have provided disruptive solutions for personalized, lightweight and functional prosthetic design. This article aims to systematically study the comprehensive impact of new manufacturing processes on the functionality, economy and user experience of prosthetic limbs. Through literature review, the evolution of technology is reviewed. Through case analysis, advanced prosthetic design cases are studied. A comprehensive performance index evaluation system and a total cost of ownership (TCO) model are constructed to quantitatively compare the differences between traditional and new manufacturing processes. The research results show that the design based on 3D printing and composite materials can significantly reduce the weight of prosthetic limbs, enhance structural strength, shorten the customization cycle and lower the cost per piece. In addition, the user experience has also significantly improved in terms of aesthetics, comfort and psychological acceptance. This paper ultimately proposes an optimization framework for prosthetic limbs that integrates materials, design and manufacturing processes, providing a theoretical basis and practical guidance for the future research and development of high-performance and highly accessible prosthetic limbs.

Healthcare simulation has become a central pillar of competency-based medical education (CBME) and patient-safety reform. In 2024, global investment in medical simulation exceeded $3 billion, reflecting double-digit growth and the rapid adoption of high-fidelity, immersive, and data-driven technologies. This paper examines turnkey simulation ecosystems, comprehensive, service-backed bundles that integrate technology, curriculum, analytics, faculty development, and lifecycle service, contrasted with modular, device-by-device procurement. Through a structured narrative review of peer-reviewed literature, updated standards (INACSL 2024, SSH 2024), and current market analyses, we evaluate educational outcomes, operational efficiencies, and economic implications of turnkey adoption. The article introduces an implementation roadmap for Eastern Europe and Ukraine, aligning with regional financing realities and capacity-building needs. Findings highlight that turnkey ecosystems reduce integration friction, enable standardized and analytics-ready programs, and support defensible assessment data, provided that contracts ensure interoperability and a transparent total cost of ownership (TCO). The discussion concludes with 2024-specific insights into market trajectories, implementation science metrics, and policy recommendations.

The cost economics of a self-propelled planter with an electronic seed metering system generally show promising benefits in terms of precision, operational efficiency, and economic viability. Such planters reduce seed wastage by providing precise seed metering, improve sowing efficiency, and reduce labor costs.The cost of the developed planter was assessed to be ₹ 32314.00 and the operational cost was observed to be ₹ 192.65 per hour. The saving over traditional planting was of 49.83% and payback period, break-even point and benefit-cost ratio were obtained to be 2-year, 50 h. yr-1 and 5.70. The self-propelled planter was precise in operation, ergonomically comfortable and cost effective. The initial investment cost of such planters is higher compared to conventional models, but savings on seeds, labor, and operational efficiency typically compensate over time. The purpose of this study is to conduct a comprehensive cost-benefit and investment analysis of the self-propelled planter with an electronic seed metering system, comparing it to traditional broadcasting method, in order to determine its economic viability and provide a practical decision-making framework for farmers and agribusinesses. By modeling the total cost of ownership—encompassing capital expenditure, operational costs, input savings, yield gains from improved accuracy, and the value of timeliness—the study aims to quantify key financial metrics such as payback period, break-even point and benefit cost ratio.

This article presents an approach to decision-making for the implementation of electric passenger cars in enterprise fleets, considering both economic and environmental factors. The literature review is focused on fleet composition and the impacts of technological change. The paper examines practical aspects of introducing alternatively powered vehicles into corporate fleets and proposes a model for optimal fleet composition. The final section provides a case study on the adoption of electric vehicles (BEV, HEV) in selected fleets in Poland. The conclusion highlights key opportunities and constraints associated with integrating electric vehicles into fleet operations. The model proposed in the article incorporates Total Cost Ownership (TCO) of fleet and environmental criteria, as well as various forms of vehicle financing and the resulting limitations on vehicle mileage and duration of use, together with budget constraints and the discounting of cash flows over time. Importantly, vehicle depreciation and several other parameters are treated as nonlinear functions of multiple variables. As the research presented in the paper demonstrates, the introduction of electric vehicles into fleets is currently unprofitable in Poland, particularly for short-term use in company car fleets. The persistently higher purchase prices of such vehicles compared with internal combustion vehicles of a similar standard, combined with their substantially higher depreciation during the initial period of use, are not offset by lower operating costs. Electric vehicles gain an advantage only when environmental objectives are assigned a sufficiently high weighting. At the same time, over sufficiently long operating periods, the economic performance of electric vehicles may prove more favourable, although there remains considerable market uncertainty concerning price formation and the residual values of these vehicles.

Abstract Reducing greenhouse gas emissions and achieving carbon neutrality require enhancing spark-ignition engine efficiency and compatibility with renewable fuels. However, electrode wear of spark plugs presents a significant challenge in hydrogen-fueled spark-ignition internal combustion engines. Such excessive wear increases the total cost of ownership and may delay the introduction of such lowemission transportation alternatives. Hence, understanding the interaction between spark discharges and the electrodes to reveal the mechanisms of such wear is crucial. Unlike conventional, ex-situ long-term tests, laser-induced fluorescence (LIF) can assess the wear process during the spark discharges by detecting the target species from the electrodes with high temporal resolution. In this work, spatiotemporal characteristics of gas phase nickel atoms originated from nickel-based alloy spark plug electrodes are performed with two-dimensional planar LIF in elevated pressures. A higher intensity and an earlier peak of laser-induced nickel fluorescence signal are observed under higher pressure. The spatial distribution of nickel atoms within the electrodes gap is observed to be different at varied pressures. Lengthening the dwell time, i.e. charging of the coil between DC spark discharges, and thus increasing the energy of sparks can significantly increase the loss of material. Similarly, increasing the peak current of AC sparks results in a higher power of spark discharges and thus increasing the removal of material. Moreover, the low signal intensity in pure nitrogen indicates that the existence of oxygen enhances the evaporation process and accelerates the erosion of the electrodes. The unique experimental data of electrode wear provides valuable insights not only into the development of next-generation ignition systems for renewable fuels, but also other aspects involving the interactions between the gas discharges and the electrodes, such as spark nanoparticle generation.

ABSTRACT Additive Manufacturing (AM) has revolutionized the automotive industry by enabling efficient production of complex and customized components while reducing dependency on multi-tier suppliers in the Supply Chain (SC). This study develops an agent-based simulation model of a two-tier Automotive Supply Chain (ASC) to quantify the impact of integrating AM into Original Equipment Manufacturers’ (OEM) operations. The model evaluates key performance indicators (KPIs) under various industry-relevant disruption scenarios—logistical and manufacturing—across two dimensions: sustainability and resilience. Sustainability is assessed via Total Cost of Ownership (TCO) and Total Carbon Footprint (TCF) from transportation; resilience is measured by Order Fulfillment Rate (OFR) from supplier to OEM. The simulation replicates end-to-end operations for both Traditional Supply Chains (TSC) and AM-enabled Supply Chains (AMSC), offering a decision-support tool for stakeholders to evaluate AM adoption based on TCO, TCF, and OFR trade-offs. The proposed framework empowers researchers and practitioners to make data-driven, KPI-based decisions regarding AM integration, particularly in OEM contexts. This work contributes to the growing literature on agent-based simulation in supply chain networks, demonstrating its utility for “what-if” disruption analysis and highlighting AM’s transformative potential in enhancing supply chain sustainability and resilience within the automotive sector.

Environmental concerns, technological factors, total cost of ownership, and charging infrastructure accelerate intention to electric vehicle adoption: A non-linear value-attitude-intention model

This study addresses a critical policy paradox in transport infrastructure planning: the necessity for substantial decarbonization investments amid declining freight demand forecasts in less developed territories. Despite reduced demand, such investments remain justified for advancing sustainability, regulatory compliance, and long-term system resilience. Herein, an integrated decision support framework is developed that optimizes infrastructure investment sequencing while maximizing private capital participation and ensuring technology–regulation alignment. Using comprehensive freight transport data from Latvia (2012–2023), a scenario tree analysis integrated with S-curve technology adoption models is employed to evaluate optimal infrastructure sequencing strategies for hydrogen fuel cell vehicles (HFCVs) and battery electric vehicles (BEVs). The methodology combines Autoregressive Integrated Moving Average (ARIMA) demand forecasting with total cost of ownership (TCO)-based technology adoption curves and hierarchical modal split modeling. The analysis further identifies distinct market segments and adoption trajectories, demonstrating how strategic infrastructure sequencing can accelerate low- and zero-emission technology uptake across different freight distances and policy scenarios. The results demonstrate that strategic sequencing generates net present value (NPV) savings of approximately EUR 18.2 million (at a 4% discount rate) compared to immediate full-scale deployment while maintaining regulatory compliance timelines. The framework provides policymakers with systematic evidence-based criteria for infrastructure investment timing, contributing to the efficient allocation of scarce public resources in the transition to sustainable freight transport.

Proton Exchange Membrane Fuel Cells (PEMFC) are emerging as a promising clean and efficient energy source for heavy-duty commercial vehicles, owing to their high efficiency, short refuelling times, and zero emissions. However, the widespread commercialization of PEMFCs faces significant challenges, particularly concerning the durability of the Membrane Electrode Assembly (MEA) and the high total cost of ownership (TCO).Carbon support is crucial for enhancing the dispersion of platinum nanoparticles (Pt NPs) and facilitating efficient electron transport within the Catalyst Layer (CL). High transient cathode potentials can arise during Start-Up (SU) and Shut-Down (SD), due to abrupt changes in the reactant distribution. During SD, oxygen diffuses to the anode, whereas during SU, residual oxygen may remain at the anode due to a delayed hydrogen supply. In both scenarios, the anode temporarily behaves as a secondary cathode and drives the cathode potential above 1.5V due to the reverse current flow [1]. As a result, the carbon becomes highly susceptible to corrosion leading to detachment of the Pt-NP from the support, collapse of the structure, and significant performance degradation. Although the corrosion of carbon is thermodynamically unfavourable, it occurs rapidly in these conditions in an acidic environment and is catalyzed by platinum [2].The performance of a Pt/C catalyst layer is critically dependent on efficient gas and proton transport to the catalytic sites. The agglomerate size formed during the catalyst ink process significantly affects the distribution of Pt NPs, pore structure, and mass transport properties. While previous studies have addressed the influence of CL microstructure on performance with various solvent compositions at the single-cell level, the effect of degradation at the ex-situ level remains unclear [3] [4]. This study primarily aims to investigate the effect of catalyst agglomerate size on the degradation behaviour at the ex-situ Rotating Disk Electrode (RDE) level.The RDE technique is used for its ability to efficiently evaluate catalyst activity and durability, as it allows for faster testing times while minimizing the use of precious materials before transitioning to single-cell MEA testing. Despite these advantages, RDE is limited in its ability to replicate the operating conditions in MEAs, including variations in relative humidity, high current densities, and elevated temperatures. These limitations have led to significant discrepancies between the durability and performance metrics obtained using RDE and those observed at the single-cell level, as reported in previous studies [5]. Here an AST protocol is proposed that could potentially bridge the gap between RDE and fuel cells for fast determination of carbon support durability.To evaluate the transferability of this AST various electrochemical measurement techniques were conducted, including Electrochemically Active Surface Area (ECSA), Mass Activity (MA), Double-Layer Capacitance (DLC), and Diffusion-Limiting Current. It is shown that the relative change in DLC can be used to determine the durability of the carbon support during AST with an RDE. This new fast screening approach with RDE is used to evaluate the carbon durability of different carbon aggregate sizes and to investigate the influence of surface oxidation of the carbon support on its durability.

Electrification of heavy-duty transportation is motivated by a need to increase energy efficiency and resource flexibility (nuclear, renewables, etc.). In addition, local air quality is negatively impacted in areas of significant heavy-duty diesel vehicle use (ports, diesel train depots, etc.), with commensurate local respiratory health impacts and costs. Using hydrogen fuel cells as the electric power source for heavy-duty transportation is further motivated by the challenges of applying batteries to their electrification. Using Li-ion batteries for heavy-duty, long-range vehicles is challenged by the global transition to a market dominated by lower-cost but heavier lithium iron phosphate chemistry. Heavy-duty vehicles also present one of the most achievable costs for low-carbon hydrogen among the hard-to-abate sectors. Therefore, the rapid commercialization of fuel cells for heavy-duty vehicles is a crucial step in increasing low-carbon hydrogen production and driving down hydrogen costs through economies of scale and thus enabling low-carbon hydrogen in more challenging sectors, such as ammonia production. Establishing widespread use of PEMFCs in heavy-duty trucks requires significant improvements in efficiency and durability, including achieving a challenging lifetime target of over 25,000 hours while also meeting acceptable values for total cost of ownership. This talk will highlight our lab’s recent efforts in addressing these challenges and our innovations in materials, imaging, diagnostics, and simulations for PEMFCs. These highlights will include unique ionomer integration strategies and ionomer durability assessment, image-based modeling from catalyst to stack-scale, where we integrate micro- and nano-scale resolution X-ray computed tomography (micro-CT and nano-CT) along with plasma-focused ion beam scanning electron microscopy (pFIB-SEM) into our model geometries. Image-based modeling enables the exploration of physically accurate geometries and heterogeneity versus ideal geometries. The talk will further highlight new innovations being developed in our laboratory, including cathodes with high oxygen permeability ionomer, novel mesoporous supports, and ionomer-free electrode concepts.