Optimizing interlayer thickness for enhanced performance and chemical durability in sandwich-structured PEM fuel cells

Polymer electrolyte membrane (PEM) fuel cells are a leading technology for clean energy conversion, but their widespread adoption is hindered by the trade-off between high performance and long-term chemical durability. Here, we report an engineered multilayer PEM that sandwiches a gas barrier interlayer between cast Nafion outer layers. A blend of poly(vinyl alcohol) and poly(vinylsulfonic acid) (PVA/PVS) is used as the interlayer material, designed to suppress gas crossover and mitigate chemical attack without sacrificing ionic conductivity. The optimized membrane (designated PVA-100) has an interlayer loading of 100 μg/cm2 and achieves power density equivalent to pristine Nafion at 0.6 V. Crucially, under accelerated stress testing, this membrane exhibits 1.8x higher chemical durability compared with a conventional membrane, maintaining superior voltage stability and superior power output retention at 0.6 V. These findings establish interlayer engineering as a scalable and effective strategy to overcome the durability–performance trade-off in PEM fuel cells

The late Permian through Middle Triassic environmental crises in the Boreal Realm – Records of the Griesbachian, Dienerian, Smithian, and Spathian type sections in Arctic Canada

Our understanding of the greatest biologic catastrophe in Earth history, the Permian/Triassic mass extinction, is largely informed by extensive studies of highly fossiliferous sedimentary records from the Tethys Ocean. Deposited on the eastern equatorial margin of the Pangea supercontinent, Tethyan sediments record the dramatic devastation of ecosystems across the Permian/Triassic boundary, and the long, slow recovery that characterizes its aftermath. In contrast, the western margin of Pangea has a sparse fossil record, but it provides important insights into the dynamic changes in biogeochemical and nutrient cycles that occurred during the extinction. Here we examine chemostratigraphic and geochemical records from the northwestern margin of Pangea, with a focus on the type sections of the Griesbachian, Dienerian, Smithian, and Spathian (the four substages of the Lower Triassic), the sediments of which were deposited in the Sverdrup Basin which now lies in the Canadian Arctic Archipelago. We show that northwest Pangea, was under growing environmental stress prior to the mass extinction, where Boreal depositional systems were marked by the eradication of carbonate producers and progressive occupation of shallow shelf environments by siliceous sponges. The Siberian Traps erupted into this already stressed world driving shallow to deep water marine anoxia. Following the extinction, the Early Triassic was an interval of major perturbations in global biogeochemical cycles, defining several aborted recoveries including the Smithian hyperthermal event and subsequent Spathian negative carbon isotope excursion. Final stabilisation of marine environments occurred in the Middle Triassic, marked by massive carbon drawdown sequestered as marine algae

Algal reorganization in post-crisis Early Triassic oceans revealed by biomarker evidence

The end-Permian mass extinction (EPME) fundamentally reshaped marine ecosystems. However, the long-term response of eukaryotic algae, a key foundation for marine primary production, is poorly understood. To address this limited knowledge, we determine the long-term change in algal communities using molecular fossil steranes. We use samples that span the uppermost Permian to the Lower Triassic from sections that were located in Boreal Sea (Sverdrup Basin, Arctic Canada) as well as the tropical Tethys (Xiakou, South China), and complement these new data with published datasets. Sterane to hopane ratios, reflecting the relative contribution of eukaryotic algal to bacterial sources, vary in absolute values between sites but show no significant decrease in the earliest Griesbachian compared to the pre-crisis Permian. However, Early Triassic ratios changed dramatically. In the Sverdrup Basin, they were stable during the Griesbachian and, following an interval where both hopane and sterane concentrations diminished, became much higher in the late Spathian. This confirms suggestions that there was a major decline in algal productivity after the EPME that may have delayed recovery. Sterane C28/C29 ratios, which monitor algal composition, increase at the EPME level in Meishan and are generally higher in the rest of the Early Triassic in the Sverdrup Basin and Chaohu. The increase shows that algae that preferentially produce C28 over C29 sterols were thriving, possibly including those predominant in modern oceans. It further implies a reorganized marine algal community–apparently in the tropics and in the post-crisis interval in the Boreal realm. Our findings suggest that instead of a simple collapse and recovery, the Early Triassic saw a complicated reorganisation for algae

An innovative high-temperature pumped thermal energy storage driven by transcritical CO2 heat pump and steam Rankine cycles

Pumped thermal energy storage (PTES) is an emerging scheme for low-cost, site-independent, and environmentally friendly electricity storage. However, it faces critical technical challenges of low round-trip efficiency (generally<60 %) and significant irreversible loss during heat transfer process. This paper proposes an innovative high-temperature PTES coupling a transcritical CO2 (TCO2) heat pump cycle with a transcritical steam Rankine cycle (TSRC). It originally employs dual-storage fluids of molten salts and water with a four-tank structure, covering a wide temperature range from about 33 °C to 560 °C. Water is both low-temperature storage fluid and TSRC working fluid, thereby eliminating a secondary water-water heat transfer. In the charging process, CO2 at the compressor outlet releases heat to the molten salts and then splits into two streams. One stream increases water storage temperature, and the other preheats CO2 from the evaporator. Fundamentals of the PTES are illustrated, and mathematical models are built. The results show that the cascade sensible storage configuration can tackle the challenge of large throttling irreversibility and a high round-trip efficiency of 60.21 % can be achieved

The multifaceted implications of mental fatigue on women’s football players’ performance in small-sided games

Research shows that mental fatigue (MF) can negatively impact physical performance. However, the effects of MF during football match-play are not well understood, particularly in women, and its impact on psychological factors is less known (e.g., attentional focus). This study explored the physical and psychological effects of MF in women's football during 7 vs. 7 small-sided games (SSGs). 14 Women's National League players (M age = 25.9 ± 5.9 years) participated. A counterbalanced cross-over design was implemented involving a MF (30-min social media use), and a control condition (30-min sitting with teammates with no phone access) prior to 3 × 7-min SSGs, interspersed with 2-min rest. GPS was used to monitor work output. Participants had microphones attached and were asked to ‘think aloud’ (TA) during SSGs; content analysis was used to examine players' attentional focus and communication. MF (visual analogue scale) and fatigue (BRUMS) increased pre-to post-MF (+1.95 ± 1.45, p 0.05). Total TA was lower (p = .046) and there was less positive performance-related TA (p = .022) in MF (22.53 ± 13.11; 0.15 ± 0.38) vs. control (30.00 ± 17.84; 1.54 ± 2.11). There was more negative non-performance related communication (p = .031), and less joking with teammates (p = .020) with MF (0.85 ± 1.07; 1.69 ± 1.80) vs. control (0.08 ± 0.28; 4.39 ± 3.78). In sum, 30-min social media use was associated with reduced happiness, vigour and heightened perceptions of fatigue, and effected how able participants were to engage in TA, how positive their thoughts were, and how they communicated with teammates. Avoiding phone use prior to training and match-play may be worth considering. Further team-sport research could incorporate TA methods which the present study showed to be feasible, to understand more on players' cognitive processing in match-play

Effects of graded-porosity gas diffusion layers used in polymer electrolyte fuel cells

Optimising the design of gas diffusion layers (GDLs) is essential to enhance water management and reactant transport in polymer electrolyte fuel cells (PEFCs), which are critical renewable energy conversion technologies required to decarbonise electricity. In this work, a comprehensive three-dimensional model of a PEFC has been developed to analyse the sensitivity of fuel cell performance to graded-porosity cathode GDLs under various humidity conditions and GDL thicknesses. The results show that, for most humidity conditions, the fuel cell performs best when the cathode GDL has low porosity at the catalyst interface and high porosity at the bipolar plate interface. Under relatively low humidity conditions, fuel cell performance deteriorates when using graded-porosity GDLs with higher porosity near the catalyst layer. On the other hand, under high humidity conditions, a cathode GDL with a porosity gradient improves performance compared to a GDL with uniform porosity. Further, when the GDL thickness is reduced from 300 μm to 200 μm, the best performance is achieved with a GDL that has higher porosity near the catalyst layer. These findings are discussed and justified in the study providing valuable guidance for designing advanced GDL structures to improve PEFC efficiency, supporting their wider adoption in renewable energy systems

The Information Meta Model for Machine Learning IM³L: A Structured Approach to ML Integration in Engineering Systems

Machine learning (ML) has become an essential technology in the development of modern software-intensive systems, particularly in safety-critical domains such as autonomous driving. However, despite the maturity of model-driven and software engineering practices in these domains, the integration of ML components often remains unsystematic and poorly aligned with established engineering workflows. To address this challenge, this paper proposes the Information Meta Model for Machine Learning (IM3L), a conceptual modeling language that supports the structured design of ML components in complex system contexts. IM3L enables engineers to systematically capture and reason about key characteristics of ML-based functionality—including data structure and semantics, class and feature relationships, learning method, and relevant quality metrics—in a way that aligns with established model-driven engineering (MDE) practices. This approach fosters interdisciplinary alignment and establishes a robust foundation for traceability, comparability, and quality assurance within existing model-driving engineering (MDE) practices. To illustrate the practical application of the proposed approach, the paper presents a representative example utilizing the German Traffic Sign Recognition Benchmark (GTSRB) dataset within a prototypical object detection scenario. The example demonstrates how IM3L can be used to systematically document and structure the critical properties and underlying assumptions of an ML-based system. This facilitates a well-grounded understanding of the system’s intended functionality and its integration within the broader system context prior to implementation

Implications of retailer-owned digital twins services: The trade-offs between customer experience, misfit returns reduction, and investment costs

Many prominent retailers, including Walmart, Kroger, IKEA, and Amazon, utilize Digital Twins (DTs) to enhance customer experience and reduce misfit returns by creating virtual replicas of products, service systems, shopping environments, and customer interactions. However, prior studies on DTs have mainly centered on applications within the manufacturing sectors, thereby overlooking the development of DT services owned or operated by retailers. To fill this gap, our models analyze how retailer-owned DT service is shaped by the trade-off between customer experience, misfit returns reduction and investment expenditures, as well as investigate its impact on the manufacturer's equilibrium decisions and profits. Our analysis indicates that higher levels of product misfit and consumer return losses incentivize retailers to adopt DTs. Furthermore, although retailer-owned DT may benefit manufacturers and lead to Pareto improvements, manufacturers should be cautious, as higher DT adoption costs for ‘inefficient’ retailers can result in a win-lose situation. This occurs because retailer-owned DTs, while potentially enhancing the manufacturer's equilibrium quality and wholesale prices, can also incentivize ‘inefficient’ retailers to raise retail prices further, which reduces the optimal sales volume and negatively impacts the manufacturer's overall profits. In addition to confirming the robustness of our main findings, our two extensions further reveal that, (i) there is an inverted U-shaped relationship between retailers' incentives for DT adoption and consumers' privacy concerns, and (ii) previous-period misfit returns diminish retailers' incentives for DT adoption

The influence of reward qualifying conditions on participation in online referral programs

Online referral reward programs (RRPs) incentivize customers to promote products within their digital social networks by offering rewards, yet these programs often face persistently low participation. This research examines how online RRP qualifying conditions—specifically, whether rewards depend solely on the referrer’s own actions or require assistance from others—shape customers’ willingness to engage. Drawing on psychological reactance theory, the study investigates both the direct impact of assistance-based conditions and the mediating role of psychological reactance, as well as whether the timing of the reward offer (pre- vs. post-consumption) moderates these effects. Three scenario-based online RRP experiments conducted in gym, meal-kit, and coffee-shop contexts show that assistance-based conditions heighten reactance and reduce engagement, while post-consumption timing attenuates this reactance-driven decline. The findings advance understanding of consumer responses to online RRPs and provide actionable guidance for designing more effective digital referral strategies

Exploring the interaction between ethnicity, deprivation and the use of CGM on diabetes outcomes—A study from the Association of British Clinical Diabetologists

Aims: Ethnic and socioeconomic disparities in diabetes outcomes persist despite widespread adoption of technologies such as continuous glucose monitoring (CGM). We examined the independent and interactive effects of ethnicity and area-level deprivation on baseline glycaemic control and CGM effectiveness in a large UK diabetes cohort. Materials and Methods: This retrospective observational study used data from the Association of British Clinical Diabetologists (ABCD) national audit, including 18,139 adults with type 1 or type 2 diabetes initiating FreeStyle Libre. Ethnicity was categorised into White, Black, Indian, Pakistani/Bangladeshi, and Mixed/Other groups. Deprivation was assessed using hospital-level Index of Multiple Deprivation (IMD) rankings. Baseline and follow-up HbA1c and diabetes distress scores (DDS) were analysed. Linear regression assessed associations between predictors and HbA1c, and interaction effects were evaluated using both regression and Gradient Boosting Machine (GBM) models, with Friedman's H-statistic quantifying interaction strength. Results: Compared to White individuals, Black participants had significantly higher baseline HbA1c (β = 6.19, SE = 1.87, p 0.05); GBM analysis supported this with a low H-statistic of 0.088. CGM use resulted in significant HbA1c reductions across all IMD groups, most notably in very high deprivation areas (−8.54%, p < 0.001). Reduction was similar across ethnicities (all p = 1.0). Conclusions: Ethnicity and deprivation independently influence glycaemic control. However, CGM yields equitable HbA1c improvements across all subgroups