Operational Criteria Research Articles

Strategies for introducing and implementing vaccines for adults into national immunization programs in Europe: Good practices and key insights of the adult immunization board meeting.

In April 2024, the Adult Immunization Board convened a technical meeting to explore the latest strategies and identify exemplary approaches regarding the implementation of vaccines for adults into Europe's National Immunization Programs (NIPs). The meeting was built around three pillars: decision making for introducing a new vaccine, implementation, monitoring, and evaluation. The increasing number of new vaccines available in a context of competing health priorities warrants transparent and evidence-based decision-making processes for vaccine introduction. In Europe, burden of disease, vaccine efficacy or effectiveness, and safety are universally used decision-making criteria. While economic evaluations and the quality of evidence are being increasingly considered, public acceptance, equity, and operational criteria remain underutilized. Vaccine implementation requires planning and coordination. Implementation activities discussed during the meeting were vaccine program goals, target population identification, communication, training of healthcare professionals, and involvement of pharmacists. Once operational, NIPs are to be monitored in terms of safety, effectiveness, and impact. Implementation science as well as behavioral and cultural insights can be used to identify tangible interventions to improve uptake. Meeting discussions allowed to identify adult vaccine implementation barriers and possible strategies to overcome them. Enhanced cooperation and coordination throughout the Europe region were recognized as critical components of progress.

Securing Cyber Physical Systems: Lightweight Industrial Internet of Things Authentication (LI2A) for Critical Infrastructure and Manufacturing

The increasing incorporation of Industrial Internet of Things (IIoT) devices into critical industrial operations and critical infrastructures necessitates robust security measures to safeguard confidential information and ensure dependable connectivity. Particularly in Cyber Physical Systems (CPSs), IIoT system security becomes critical as systems become more interconnected and digital. This paper introduces a novel Lightweight Industrial IoT Authentication (LI2A) method as a solution to address security concerns in the industrial sector and smart city infrastructure. Mutual authentication, authenticated message integrity, key agreement, soundness, forward secrecy, resistance to a variety of assaults, and minimal resource consumption are all features offered by LI2A. Critical to CPS operations, the approach prevents impersonation, man-in-the-middle, replay, eavesdropping, and modification assaults, according to a security study. The method proposed herein ensures the integrity of CPS networks by verifying communication reliability, identifying unauthorized message modifications, establishing a shared session key between users and IIoT devices, and periodically updating keys to ensure sustained security. A comprehensive assessment of performance takes into account each aspect of storage, communication, and computation. The communication and computing capabilities of LI2A, which are critical for the operation of CPS infrastructure, are demonstrated through comparisons with state-of-the-art systems from the literature. LI2A can be implemented in resource-constrained IIoT devices found in CPS and industrial environments, according to the results. By integrating IIoT devices into critical processes in CPS, it is possible to enhance security while also promoting urban digitalization and sustainability.

The architecture design of emergency logistics management system based on the Internet of Things

The Internet of Things (IoT) aims to connect intelligent products with network functions through both wireless and wired technologies, enabling the safe and efficient distribution of services across global networks, including computers, mobile phones, and other electronic devices. The emergence of network devices with communication, sensing, and actuation capabilities since 2005 has led to a wide range of applications, such as inventory management, environmental monitoring, and tracking moving objects. However, the progress of IoT technology is hindered by the lack of adequate infrastructure capable of supporting trillions of devices. This paper explores the role of radio frequency identification (RFID) sensor networks within the IoT architecture for logistics management, positioning these networks as key “network-enabled” devices. The study investigates the components of the system, which include the domain sensor name server (DSNS), sensor service publisher (SSP), historical database, and RFID sensors. The strategic and operational implications of IoT architecture in logistics management are analyzed, with a particular focus on emergency response scenarios. The findings highlight the potential of IoT to improve resource allocation, enhance situational awareness, and support real-time decision-making during critical operations. This research offers valuable insights into the scalability challenges and the strategic benefits of IoT in logistics management, particularly in emergency contexts where timely data can significantly influence outcomes.

Non-technological determinants of inter-industry knowledge sharing among port workers: Strategic insights from Malaysia

Efficient port operations rely on knowledge exchange among industry workers. Previous studies have identified several factors influencing this exchange. For instance, Caporuscio et al. (2020) noted that port operators often have limited industry-specific knowledge, which can hinder effective knowledge sharing. Additionally, there is a recognized gap in understanding the non-technological elements that shape the exchange of inter-industry information among port operators. Al-Busaidi and Olfman (2017) highlighted the crucial roles played by individuals, peers, and organizations in facilitating information-sharing processes. This study examines how individual, peer, and organizational non-technological factors impact port workers’ willingness to share their expertise across industries. Using a sample of seven critical operators from Malaysia’s federal port, the research employs a revised tool to measure these factors and promote information flow. Survey data from knowledge workers in Malaysia’s port operator sector further supports the findings, indicating that individual characteristics notably influence knowledge sharing among port personnel. This study contributes valuable insights to the broader understanding of how these factors influence information exchange among port workers across different industries.

Disaster-Resilient Telecommunication with Optical Technologies

Telecommunication services enable transmission of data, voice, video, and other forms of communication over long distances through various technologies such as telephone lines, satellites, fiber optics, and wireless systems. These services are fundamental in enabling communication across different sectors, including healthcare. In disaster scenarios, the rapid and reliable delivery of healthcare services is critical for saving lives and ensuring the well-being of affected populations. Telemedicine, enabled by advanced telecommunication systems, plays a pivotal role in providing remote healthcare services when traditional medical facilities are inaccessible. This paper explores the integration of optical communication technologies, such Free Space Optics (FSO), into telemedicine systems to enhance their effectiveness in disaster response. Optical wireless communication (OWC) technologies offer high-speed, secure, and resilient communication channels, making them ideal for transmitting large volumes of medical data, real-time video consultations, and remote patient monitoring in challenging environments. By leveraging the bandwidth, low latency, and reliability of optical communication, telemedicine can support critical care operations, including remote diagnostics, emergency triage, and real-time collaboration between healthcare providers. This paper examines the potential of optical communication technologies to strengthen telemedicine services in disaster scenarios, ensuring that healthcare remains accessible, efficient, and secure, even under the most adverse conditions

Between global risk reduction goals, scientific–technical capabilities and local realities: a modular approach for user-centric multi-risk assessment

Abstract. We live in a rapidly changing and globalized society. The increasing interdependence and interconnection of our economic, social and technical systems; growing urbanization; and increasing vulnerability to natural hazards (including climate change) are leading to ever more complex risk situations. This paper presents a conceptual approach for user-centred multi-risk assessment aimed to support potential users like disaster risk managers, urban planners or critical infrastructure operators. Based on the latest scientific and technical capabilities, we developed a method that enables the simulation and visualization of a range of scenarios with different intensities. It is based on a modular and decentralized system architecture using distributed web services that are published online, including a user-friendly interface. The approach is demonstrated using the example of earthquakes and tsunamis for the Lima metropolitan area (Peru), a megacity exposed to various cascading natural hazards. The development involved a wider group of Peruvian stakeholders from research and practice in a structured, iterative and participative feedback process over a period of 2.5 years to capture the needs and requirements from the user perspective. Results from the feedback process, including 94 responses to 5 questionnaires, confirmed the potential of the demonstrator as a complementary analysis and visualization tool. Together with the visualization of cascading processes, the ability to simulate and compare scenarios of varying severity was considered relevant and useful for improving the understanding of and preparedness for complex multi-risk situations, in practical applications, especially at the local level.

Multi-Objective Optimization of Manufacturing Process Using Artificial Neural Networks

This paper focuses on the optimization of a critical operation in the furniture manufacturing process, identifying it as a key priority for improvement by applying Systems Theory. The primary objective of this study is to develop a mathematical model for optimizing the detected key process by employing artificial neural networks (ANNs) which mirror adaptive management principles. Three input and three output parameters significantly impacting the effectiveness of this key process have been systematically identified and experimentally measured. It was necessary to perform multi-objective optimization (MOO), which consisted in achieving the minimum values of cost and process time and the maximum value of the quality index through the optimal setting of the input parameters (cutting speed, feed rate, and volume of removed material). The application of ANNs in MOO in this research study is a novelty in this field. The results obtained through application of the ANN method reveal the optimal values of the examined parameters, which represent the best combination of input technical variables leading to the best results in output economic parameters. This multi-objective optimizing solution facilitates enhanced process efficiency. By integrating Systems Theory, Complexity Theory, and adaptive management, this research advances sustainable process improvements by minimizing resource use, reducing waste, and enhancing overall system efficiency.

Postcolonial technoscience revisited.

What does a postcolonial inquiry into technoscience do? And what is it for? I develop these questions by reconsidering one powerful idea: that science and technology studies (STS) is postcolonial when it elucidates the hybridity, heterogeneity, and indeterminacy of global technoscientific formations, and does so to falsify colonial fantasies of hegemony expressed in imperious conceptual generalities and sovereign universalisms. Revisiting Warwick Anderson's expositions of postcolonial STS-initiated in this journal two decades ago-I reflect on the form and force of this critical operation. Despite an animating aversion to universalisms, the pursuit of hybridity and heterogeneity may ultimately universalize a liberal metaphysics of agency. This paradox suggests limits to the critical operation that pits hybridity and indeterminacy against hegemony in a postcolonial spirit.

Combining CFD and AI/ML Modeling to Improve the Performance of Polypropylene Fluidized Bed Reactors

Polypropylene is one of the most widely used polymers in various applications, ranging from packaging materials to automotive components. This paper proposes the Computational Fluid Dynamics (CFD) and AI/ML simulation of a polypropylene fluidized bed reactor to reduce reactor loss and facilitate process understanding. COMSOL Multiphysics 6.2® solves a 2D multiphase CFD model for the reactor’s complex gas–solid interactions and fluid flows. The model is compared to experimental results and shows excellent predictions of gas distribution, fluid velocity, and temperature gradients. Critical operating parameters like feed temperature, catalyst feed rate, and propylene inlet concentration are all tested to determine their impact on the single-pass conversion of the reactor. The simulation simulates their effects on polypropylene yield and reactor efficiency. It also combines CFD with artificial intelligence and machine learning (AI/ML) algorithms, like artificial neural networks (ANN), resulting in a powerful predictive tool for accurately predicting reactor metrics based on operating conditions. The multifaceted CFD-AI/ML tool provides deep insight into improving reactor design, and it also helps save computing time and resources, giving industrial polypropylene plant growth a considerable lift.

Enhanced model predictive control using artificial neural networks for grid-connected two-level inverters

Integrating artificial intelligence (AI) to improve the performance of photovoltaic systems is compelling for two main reasons. First, AI technologies are making significant progress and are widely applied across various fields, such as medicine and agriculture. Second, the world is experiencing an increasing demand for energy while simultaneously facing the challenge of reducing reliance on traditional energy sources that exacerbate carbon emissions and climate change. This emphasizes the critical importance of transitioning to sustainable and clean energy solutions. In this context, photovoltaic panels have emerged as one of the most promising and widely adopted renewable energy sources. As the global reliance on solar energy grows, enhancing the efficiency and reliability of solar energy systems has become imperative. However, this transition is accompanied by significant challenges, particularly in integrating these systems with existing power grids. The complexities involved in achieving seamless integration, maintaining consistent performance, and preventing system failures necessitate the adoption of advanced technologies. These challenges are further exacerbated by the need to manage fluctuations in energy production, optimize energy storage, and ensure the long-term sustainability of solar power. This paper presents a novel approach for controlling ANN-MPC solar inverters, leveraging artificial neural networks (ANN) to address the challenges related to the efficiency of photovoltaic systems and their integration with the grid, ultimately reducing dependence on conventional energy sources. Initially, the ANN-based model is trained using large datasets derived from the system inputs and outputs, taking into account all critical operating conditions, such as sudden changes in current. Following the training process, the model acquires extensive data, enabling it to adapt to all transient conditions the system may encounter. The model continuously evolves to improve the efficiency of grid-connected photovoltaic systems. Additionally, experimental validation and performance testing of the proposed model are conducted to verify the effectiveness and flexibility of the ANN-based approach. The experimental results confirm the proposed approach's ability to enhance the performance of grid-connected electrical systems, reduce computational costs, and achieve model-free control.

Security and safety concerns in the age of AI

Artificial Intelligence (AI) is transforming industries at an astonishing rate, reshaping how we live, work, and interact with technology. Yet, as AI becomes more pervasive, it brings urgent questions about security and safety. This article explores these critical issues, drawing a clear distinction between AI security and AI safety—two concepts that are often misunderstood but are crucial for responsible AI deployment. AI security focuses on protecting systems from external threats like data breaches, adversarial attacks, and unauthorized access. As AI systems increasingly handle sensitive data and control critical operations, securing them against such risks is essential. A breach or failure could compromise not only privacy but also the integrity of critical infrastructures. On the other hand, AI safety extends beyond technical defenses to the broader societal implications of AI. Issues like algorithmic bias, ethical decision-making, and unintended consequences of AI systems highlight the risks to human well-being. As AI becomes more autonomous, its alignment with human values and societal norms becomes paramount. Furthermore, the existential risks posed by advanced AI—such as loss of control or unintended outcomes—raise profound questions about the future of human-AI coexistence. This article delves into real-world case studies of AI failures and near-misses, offering tangible insights into the potential consequences of unchecked AI growth. It also explores strategies for mitigating these risks, balancing the pursuit of innovation with the need for transparency, accountability, and ethical oversight. As we look to the future, international cooperation and robust regulatory frameworks are essential to managing AI’s growing influence. By examining both technical and ethical dimensions, this article equips readers with a comprehensive understanding of AI security and safety, urging a proactive approach to managing the risks and harnessing the potential of this powerful technology.

Thickness monitoring of threshing mixture on the oscillating plate of corn grain harvester

Cleaning is a critical operation in the corn grain harvesting process, and the operation is seriously affected by the uneven distribution of the threshing mixture on the oscillating plate. To monitor the thickness of the threshing mixture on the oscillating plate of the corn grain harvester, in this study, we adopted the lever principle to design a corn threshing mixture thickness monitoring device based on the piezoresistive effect and theoretically analyzed the lever impact mechanics. The factors affecting the force on the sensor of the monitoring device are the vibration frequency of the oscillating plate, the thickness of the threshing mixture, and grain moisture content, which were obtained through theoretical analysis. The operational performance of the corn threshing mixture thickness monitoring device was assessed through a set of single-factor experiments, revealing the influence laws of three factors on the impact voltage: vibration frequency, thickness of the threshing mixture, and grain moisture content. A back-propagation (BP)11Back-propagation (BP) neural network prediction model was established using an orthogonal experiment with impact voltage, vibration frequency, and grain moisture content as input layers and the thickness of the corn threshing mixture as the output layer. An STM32 microcontroller was used as the primary control unit to build a thickness monitoring data acquisition and processing system that could process and operate the impact voltage collected by the pressure sensor and the vibration frequency signal collected by a Hall sensor. The data were wirelessly transmitted to a PC to predict the thickness of the threshing mixture by using the trained BP neural network. The experimental results of monitoring the variation in corn threshing mixture thickness demonstrated that the device could effectively monitor changes in thickness. The monitoring accuracy of the monitoring device was found to be over 90%, and the system can be used for monitoring the threshing mixture thickness on the oscillating plate of the corn grain harvester.

Randomized trial investigating the utility of a liver tissue transcriptional biomarker in identifying adult liver transplant recipients not requiring maintenance immunosuppression.

The maintenance of stable allograft status in the absence of immunosuppression, known as operational tolerance, can be achieved in a small proportion of liver transplant recipients, but we lack reliable tools to predict its spontaneous development. We conducted a prospective, multi-center, biomarker-strategy design, immunosuppression withdrawal clinical trial to determine the utility of a predictive biomarker of operational tolerance. The biomarker test, originally identified in a patient cohort with high operational tolerance prevalence, consisted of a 5-gene transcriptional signature measured in liver tissue collected before initiating immunosuppression weaning. 116 adult stable liver transplant recipients were randomized 1:1 to either Arm A (immunosuppression withdrawal regardless of biomarker status) or Arm B (immunosuppression withdrawal in biomarker-positive recipients). Immunosuppression withdrawal was initiated in 82 participants, rejection occurred in 54 (67.5%), successful discontinuation of immunosuppression was achieved in 22 (27.5%), but only 13 (16.3%) met operational tolerance histological criteria (10 in Arm A, 3 in Arm B). The biomarker test did not yield useful information in selecting patients able to successfully discontinue immunosuppression. Operational tolerance was associated with time post-transplantation, recipient age, presence of circulating exhausted CD8+ T cells, and a reduced number of immune synapses within the graft. TRIAL REGISTRATION: ISRCTN 47808000 and EudraCT 2014-004557-14.

A team-based approach to type 2 diabetes and cardiovascular care.

The Center for Integrated and Novel Approaches in Vascular-Metabolic Disease (CINEMA) program is an innovative, patient-centered system of care developed by the University Hospitals Harrington Heart and Vascular Institute in Cleveland, Ohio in the US for the management of high-risk patients with type 2 diabetes (T2D) and prediabetes at high risk for cardiovascular-kidney-metabolic (CKM) syndrome and its consequences. At its core, CINEMA is a multidisciplinary team of care experts, working together outside of traditional silos. The patient meets with the entire team up to 4 times each year to address all aspects of cardiovascular (CV) and T2D care. At the first visit, the team formulates a personalized approach that is evidence based and centered on optimal strategies to improve the patient's lifestyle, reduce their risk of CV and kidney disease events, and increase their access and adherence to guideline-directed pharmacologic therapies. A community health worker is utilized to address social determinants of health as needed. The program has a substantial research component, with the intent of developing evidence for novel care paradigms. The multiyear results of the CINEMA program indicate that a multidisciplinary approach to management of high-risk patients is highly effective in reducing CKM syndrome risk factors and increases use of guideline-directed therapies. The aim of this review is to describe the structure, operation, and eligibility criteria for admission to the CINEMA program, provide an overview of how CKM syndrome risks are determined and managed for each patient, and discuss how the integrated approach to care is supported by current recommendations from professional societies and results from other coordinated care/multidisciplinary programs. Lastly, the scalability challenges of a wider rollout of the CINEMA program are considered.

Critical Design and Operating Parameters of Active Waveguide Bragg Gratings for Laser Performance.

Active waveguide Bragg gratings (AWBGs) are promising photonic structures that combine the very efficient reflective properties of a Bragg grating with the power amplification character of rare earths. This combination may lead to a potential monolithic laser under the proper conditions. However, the photonic response of these structures highly depends on the grating design and operating parameters, so modeling its response for their laser performance is a must. In this work, a numerical method is employed to calculate the optical power propagation along an Er/Yb-codoped integrated AWBG. A complete database of the AWBG response as a function of the most relevant operational parameters is obtained. As a result, the critical values of each design and operating parameter to achieve its laser performance have been determined, which represents very useful information for further experimental design, optimization, and fabrication of these photonic structures.

Multi-Objective NSGA-II Optimization of Single- and Dual-Fluid ORC-VCC Systems Using Butane and Isobutane.

The urgent need for environmentally sustainable cooling technologies, driven by global regulatory constraints, has intensified the search for natural refrigerants with low global warming potential. This study evaluates the potential of natural refrigerants, specifically butane and isobutane, in advanced single- and dual-fluid Organic Rankine Cycle-Vapor Compression Cycle (ORC-VCC) systems to enhance energy efficiency and environmental sustainability. Using the Non-dominated Sorting Genetic Algorithm II (NSGA-II) within a multi-objective framework, the optimization maximizes key performance metrics such as coefficient of performance (COP) and cooling power, while the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method enables a refined ranking of optimal solutions. Findings reveal that the isobutane (ORC)-butane (VCC) dual-fluid configuration achieves the highest overall COP of 0.447 and a cooling capacity of 35.517 kW, surpassing the reference fluid R1233zd, which attains a COP of 0.374 and a cooling capacity of 30.361 kW. Isobutane-based configurations consistently deliver higher COP and cooling capacities than R1233zd, highlighting isobutane's suitability for applications demanding high energy efficiency. Pressure analysis revealed that R1233zd exhibits the highest pressure ratio of 4.10, necessitating more complex compressor designs. In contrast, isobutane configurations offer favorable pressure ratios and similar pressure parameters in both single and dual setups, simplifying compressor design requirements. This research provides valuable guidance for developing sustainable ORC-VCC systems by combining effective fluid selection and advanced multi-objective optimization techniques to meet both environmental and operational criteria.

A switched model predictive control with parametric weights-based mode transition strategy for a novel parallel hybrid electric vehicle

In a novel parallel hybrid electric vehicle (HEV) configuration, the transition from pure electric mode to hybrid mode encompasses critical operations such as engine startup, coordinated control of motor and engine torque, and engagement of the clutch. Addressing the intricate challenges associated with enhancing speed tracking performance during and after mode transition, mitigating jerk during mode transition, and minimizing mode transition time, this paper conducts a meticulous analysis of the vehicle configuration and mode transition process. The mode transition process is systematically delineated into four stages, with each stage characterized by the establishment of dynamic models. Subsequently, a mode transition strategy is proposed, leveraging switched model predictive control with parametric weights (SMPC-PW). This controller framework includes the design of two model predictive controllers (MPC) tailored for two pivotal stages, the formulation of a parametric weights pattern based on pre-transition acceleration, and the development of a stage switching strategy to ensure seamless switches between controllers. The efficacy of the proposed strategy is validated through co-simulations in the Simulink and GT-Power environment. The fine-tuning of MPC parameters is grounded in multiple sets of prediction horizons and sampling time simulation results. In comparison to strategies based on MPC and PID under various acceleration scenarios, the SMPC-PW strategy consistently maintains acceleration control below 10 m/s3. It not only achieves superior speed tracking during and after mode transition but also reduces mode switch time by 0.1 s-0.3 s. These compelling results unequivocally demonstrate that the proposed mode transition strategy significantly elevates the quality of mode transition for this specific parallel HEV configuration.

Current practices for the management of advanced high-grade epithelial ovarian cancer in the UK: OC-NOW survey (2023).

Aim: To investigate current management of advanced epithelial ovarian cancer (OC) in the UK.Materials & methods: A cross-sectional survey with 55 healthcare professionals involved in the care of OC patients was conducted via telephone/videoconference in March/May 2023.Results: Respondents reported that homologous recombination deficiency (HRD) status and brca mutations were routinely tested before planning maintenance treatment. All respondents agreed that cytoreductive surgery should be considered at first recurrence, and 65% recommended using the Descriptive Evaluation of Preoperative Selection Criteria for Operability in Recurrent Ovarian Cancer (DESKTOP) III criteria to guide secondary cytoreduction. Platinum responders typically receive poly (ADP-ribose) polymerase inhibitor maintenance therapy, regardless of HRD status.Conclusion: Respondents reinforce that most primary OC patients in the UK have known HRD and BRCA mutation status, and the role of secondary cytoreduction is increasingly recognized.

Towards a Deeper Understanding of Catalyst Degradation during Intermittent Operation at Different Operating Conditions of Proton Exchange Membrane Water Electrolyzers

Low-temperature water electrolysis with a proton exchange membrane (PEM) enables the production of green hydrogen without direct emissions of CO2. Besides the ability to generate hydrogen at elevated pressure and very high current densities, PEM water electrolyzers (PEMWEs) offer a high load flexibility [1]. This facilitates a coupling to renewable energy sources and opens pathways for the large-scale implementation of the PEM water electrolysis technology. Considering the limited supply of iridium, current efforts target reduced iridium loadings and even higher current densities while maintaining high performance, along with the implementation of iridium recycling loops [2]. To nevertheless ensure the durability of iridium-based catalysts over the desired >50,000 hours of PEMWE lifetime, it is crucial to fundamentally understand the various degradation mechanisms as well as lifetime-affecting operating parameters and conditions. Therefore, growing efforts are put into the development of accelerated stress tests (ASTs) to predict and understand the degradation of PEMWE components and materials within reasonably short experiments. While various anode catalyst materials have for example been screened by Alia et al. in galvanostatic and potentiostatic ASTs with intermittent operation or simulated start-stop cycles [3, 4], the underlying degradation mechanisms of iridium-based catalysts for the oxygen evolution reaction (OER) in PEMWE anodes are still not fully understood.The here presented work aims for a better understanding of anode catalyst degradation during intermittent operation of PEMWEs. Performance changes and catalyst durability of a TiO2 supported IrO2 anode catalyst are investigated in 5 cm2 single-cells at loadings of 1.7-1.8 mgIr cm-2 over the course of an AST, consisting of operation periods at 3 A cm-2 and 0.1 A cm-2 and of periods at open circuit voltage (OCV), which was previously developed by our group [5]. To mitigate the growing contact resistances between the Ti-based porous transport layer (PTL) and the anode electrode that was observed by Weiß et al. [5] and to focus on the performance changes resulting from catalyst layer degradation, a platinum-coated PTL is used here. While the catalyst activity and therefore the performance is increasing in the first cycles of the OCV-AST due to a transition of the IrO2-catalyst surface to a hydrous iridium oxide, a significant subsequent performance loss is observed upon further cycling (see blue symbols in Fig. 1). On the other hand, for an AST where the cell voltage during idle periods is held at 1.3 V to prevent the intermittent reduction of the iridium catalyst (caused by hydrogen crossover during OCV periods), the high-frequency resistance-corrected cell voltage at 0.1 A cm-2 initially remains at high values but increases by only ~5 mV over 660 cycles (see red symbols).Besides presenting factors that affect the initial performance improvement during the OCV-AST to better understand the performance gain, we will investigate the impact of different operating conditions such as maximum current density or operating temperature on the performance decay after prolonged cycling. With different in-situ characterization methods like electrochemical impedance spectroscopy or cyclic voltammetry, we will disentangle and elucidate possible reasons for the observed voltage losses to better understand catalyst degradation and critical lifetime-affecting operating parameters and conditions.

Numerical study on heat and ion transport characteristics enables optimal design of aqueous thermocells for low-grade heat recovery

As a cutting-edge heat-to-electricity technology, thermogalvanic cells (thermocells) have great prospects in low-grade heat recovery due to its high Seebeck coefficient (Se), high scalability and low cost. Most of previous studies about aqueous thermocells have been focused on the overall performance by experimentally exploring advanced electrode and electrolyte materials, while very few simulation studies were reported before, leading to the unclear mechanisms of heat and ion transport inside the thermocell. In view of these challenges, this study aims to reveal the heat and ion transport characteristics of aqueous thermocells under various critical operating parameters, providing theoretical guidelines for further design and optimization of aqueous thermocells with fixed electrode and electrolyte materials. Firstly, a multi-physical model considering the diffusion, migration and convection was established and validated. Then, the effects of hot electrode temperature, electrode spacing and electrode orientation were evaluated on the thermocell performance from the aspects of distributions of multi-physical fields, overpotentials and overall performance. Finally, a prototype aqueous thermocell was proposed based on the understandings of restrictions associated with these operating parameters. Results indicated that each operating parameter can attribute to the variation of natural convection from the intensity and forms, and then affected the ion transport flux and overpotentials, and thus determined the power density of thermocells. These findings prompted the design and optimization of new aqueous thermocells, and the proposed prototype thermocell delivered the maximum power density of 0.43 W/m2, which was 115 % higher than that of the basic rectangular thermocell.