Advanced Test Research Articles

The Fruits of an Enduring Research Program Benefiting from the Coupling of Advanced Characterization and Testing with Material Deformation Modeling

The Fruits of an Enduring Research Program Benefiting from the Coupling of Advanced Characterization and Testing with Material Deformation Modeling

Photoluminescence Spectroscopy Sheds New Light on Silicon Microchip Functional Properties.

In this study, we demonstrate that photoluminescence spectroscopy probing local interaction and dynamics at the fundamental level is a versatile tool for testing and evaluation of semiconducting materials as well as microscale chips based on them. Monocrystalline silicon, which is still an undisputed leader among semiconductors in microelectronics, exhibits very low photoluminescence emission additionally shielded by the metallization and passivating layers at the integrated circuit level. To unleash the full potential of photoluminescence spectroscopy for advanced testing and evaluation of the functional properties of the silicon microchips, new essential conceptually built approaches are required that overcome this problem. Here, we report on the first fundamental research-based application of a potentiometric dye to sense the electric field of operational silicon chips. Furthermore, we demonstrate high sensitivity of crystalline silicon phonon-assisted photoluminescence to local temperature in the 27-64 °C range. Our results show that sensing and mapping of thermal and electric field distributions using photoluminescence can enable precision testing of the structure, function, operation, and security, not only at the component level but also at the level of the entire integrated circuit.

Hydrogen solubility in different chemicals: A modelling approach and review of literature data

Hydrogen (H2) solubility is a crucial parameter for industrial processes. This study utilises various Machine Learning (ML) techniques, including Decision Tree (DT), Multilayer Perceptron (MLP), Random Forest (RF), and Extremely Randomized Trees (ET), to predict H2 solubility across diverse chemical classes, encompassing n-alkanes, aliphatic and aromatic hydrocarbons, alcohols, aldehydes, diols, esters, ethers, ketones, and halides. An extensive database of 4337 samples of H2 solubility in 100 different chemicals was compiled from open literature. Problematic samples were identified through preprocessing. Three input sets were utilised for model development to determine the optimal combination of regressors. It was found that including dimensionless pressure and temperature significantly improved modelling accuracy. Higher accuracy was achieved by employing polynomial extracted features. 76 chemicals trained the model, and 20 tested it, with none overlapping. High accuracy on the test set shows applicability to a diverse chemical range beyond the study's dataset. The study's modelling results indicate RF and ET ensemble methods outperform DT and MLP in accuracy and stability, with ET being the most accurate. This study improves our understanding of H2 solubility in various chemicals, crucial for industrial processes like designing H2-based renewable energy plants. Our study advances ML in predicting chemical solubility by introducing advanced feature extraction and testing models with unseen chemicals. This not only validates our models' predictive capability but also yields deeper insights into H2 solubility. With standardized data preprocessing and a model adept at handling diverse chemicals, we demonstrate a substantial advancement in the field through a meticulous testing methodology.

Transient thermal management of laser systems using Plate-Fin phase change heat Exchangers: Experimental and computational study

To mitigate transient thermal shocks in lasers and reduce thermal stresses caused by temperature fluctuations, the use of phase change materials (PCMs) in thermal management systems is a viable solution. This study proposes an innovative two-dimensional transient heat transfer model specifically designed for plate-fin phase change heat exchangers (PFPCHEs). The model meticulously simulates the complex heat transfer phenomena within the heat exchanger, including fluid convection, solid thermal conduction, and the phase change processes of PCM. The convective heat transfer coefficient between fluid and plate is calculated using the Wieting correlation. An advanced pulse-mode experimental test platform was constructed to validate the model, dynamically monitoring and recording outlet temperatures and heat exchange performance for stringent experimental comparison. The research explores the impact of key operating parameters such as initial temperature, flow rate, and inlet temperature of the cooling cycle on the performance of the heat exchanger, providing valuable design and control strategies for transient thermal management of laser systems. The experimental results confirm that the model accurately predicts the dynamic response characteristics and temperature distribution of PFPCHEs under multi-cycle pulse loads, with 96% of the predictions within a 10% error margin. A significant finding is that the initial temperature has negligible influence on the heat transfer characteristics when it is below the solid phase temperature of the PCM. Moreover, by meticulously adjusting the flow rate and inlet temperature of cooling cycle, it is possible to effectively maintain the stability of the outlet temperature throughout the entire pulse cycle. This is crucial for minimizing temperature fluctuations within the thermal management system and extending the service life of electronic components.

Inspection of aluminum sheets using a multi-element eddy current sensor: 2d and 3d imaging of surface defects of various sizes and internal defects at various depths

In the industrial sector, ensuring reliability and durability is of paramount importance. Our research aims to advance beyond conventional non-destructive testing methods by focusing on thorough defect detection and imaging. We utilize advanced, sensor-enhanced eddy current testing, featuring multiple elements arranged in a cutting-edge serial array. This innovative configuration addresses the issue of magnetic repulsion between sensor elements, thereby speeding up the testing process and ensuring precise results through both 3D and 2D imaging. This sophisticated approach allows us to more effectively characterize defects of varying sizes and depths in aluminum sheets. By meticulously collecting and analyzing data from the sensors, we can identify the appearance and nature of these defects with greater clarity. Our findings introduce a pioneering method for defect detection, highlighting the efficacy of our advanced testing technique. Our research underscores the potential of multi-element eddy current sensors in revolutionizing the inspection process. The ability to produce detailed 3D and 2D images of surface and internal defects represents a significant leap forward in non-destructive testing. This comprehensive imaging capability not only accelerates the detection process but also enhances the accuracy and reliability of defect characterization. By employing this state-of-the-art technology, we can detect even the smallest and most deeply embedded defects that traditional methods might miss. The precise imaging provided by our approach ensures that defects of various sizes and depths are accurately identified and characterized. This level of detail is crucial for maintaining the structural integrity and performance of aluminum sheets used in industrial applications. Our research demonstrates a groundbreaking approach to defect detection in aluminum sheets, leveraging the advanced capabilities of multi-element eddy current sensors. The innovative use of a serial array of sensors, combined with sophisticated data analysis techniques, allows for rapid, accurate, and detailed imaging of defects. These findings pave the way for improved reliability and durability in industrial applications, setting a new standard for non-destructive testing.

Superior passivation capability for biomedical-grade CoCrMo alloys fabricated by laser powder bed fusion with nitrogen doping

Nitrogen, a non-metallic element, is widely abundant in nature and possesses numerous advantageous properties for metals and alloys, and supersaturated nitrogen content can be obtained through non-equilibrium rapid solidification. In this work, the microstructure and passivation properties of CoCrMo alloys prepared by the laser powder bed fusion (LPBF) technique under nitrogen and argon atmospheres were compared through multi-scale microstructural characterization combined with advanced electrochemical testing methods. Results show that the N content in LPBF N2-CoCrMo alloy (0.1839 wt%) demonstrates more than one order of magnitude higher than that of LPBF Ar-CoCrMo counterparts (0.0046 wt%). Despite comparable sub-microstructures in grain structure and dislocation cells, the LPBF N2-CoCrMo alloy demonstrates a higher corrosion potential of −0.34 VSCE and lower corrosion current density in comparison to the LPBF Ar-CoCrMo in a chloride-containing solution. Moreover, the passive film of LPBF N2-CoCrMo alloys shows a low defect density with a high-content and dense Cr2O3 layer therein. Our research calls attention to alloy design enhancement by modifying protective atmospheres during the additive manufacturing process.

Pathogen-Agnostic Advanced Molecular Diagnostic Testing for Difficult-to-Diagnose Clinical Syndromes-Results of an Emerging Infections Network Survey of Frontline US Infectious Disease Clinicians, May 2023.

During routine clinical practice, infectious disease physicians encounter patients with difficult-to-diagnose clinical syndromes and may order advanced molecular testing to detect pathogens. These tests may identify potential infectious causes for illness and allow clinicians to adapt treatments or stop unnecessary antimicrobials. Cases of pathogen-agnostic disease testing also provide an important window into known, emerging, and reemerging pathogens and may be leveraged as part of national sentinel surveillance. A survey of Emerging Infections Network members, a group of infectious disease providers in North America, was conducted in May 2023. The objective of the survey was to gain insight into how and when infectious disease physicians use advanced molecular testing for patients with difficult-to-diagnose infectious diseases, as well as to explore the usefulness of advanced molecular testing and barriers to use. Overall, 643 providers answered at least some of the survey questions; 478 (74%) of those who completed the survey had ordered advanced molecular testing in the last two years, and formed the basis for this study. Respondents indicated that they most often ordered broad-range 16S rRNA gene sequencing, followed by metagenomic next-generation sequencing and whole genome sequencing; and commented that in clinical practice, some, but not all tests were useful. Many physicians also noted several barriers to use, including a lack of national guidelines and cost, while others commented that whole genome sequencing had potential for use in outbreak surveillance. Improving frontline physician access, availability, affordability, and developing clear national guidelines for interpretation and use of advanced molecular testing could potentially support clinical practice and public health surveillance.

Design of a first-of-A-kind instrumented advanced test reactor irradiation Capsule experiment for In situ thermal conductivity measurements of metallic fuel

Metallic fuel undergoes dramatic microstructural changes early in life due to fission gas swelling until ∼2–3 at% burnup which affects the conductivity of the material, however the evolution of metallic fuel thermal conductivity during this early phase burnup has never been successfully measured in situ. The Irradiated Material Properties Accelerated Characterization Test (IMPACT) experiment will be the first in a series of experiments to irradiate advanced nuclear metallic fuel specimens with novel embedded thermal conductivity probes in ATR. In the current work the IMPACT experiment final design and supporting analysis is reported in detail. Results are evaluated for various reactor operational conditions to meet the functional requirements of the experiment. The first iteration of this IMPACT experiment will provide data regarding thermal properties evolution in uranium-zirconium (U10Zr) fuel, but this experiment vehicle is envisioned for future advanced fuels and structural materials irradiations in ATR.

Thermal Model of the AGR-5/6/7 Experiment with Offset Gas Gaps

Because of their high impact on calculated temperatures, the offset gas gaps between the graphite holder and capsule wall are evaluated for the thermal model of the Advanced Gas Reactor 5/6/7 (AGR-5/6/7) irradiation experiment. Fuel compact temperatures are a crucial factor in assessing the irradiation performance of tristructural isotropic (TRISO)-coated-particle fuel for use in high-temperature gas-cooled reactors. TRISO fuel irradiation performance is a core tenet of the U.S. Department of Energy’s Advanced Gas Reactor Fuel Development and Qualification Program, conducted in the Advanced Test Reactor at Idaho National Laboratory. Specifically, the irradiation experiments were intended to provide irradiation performance data to support fuel process development, qualify fuel for normal operating conditions, support the development of fuel performance models and codes, and provide irradiated fuel and materials for postirradiation examination and safety testing. The thermal model used to predict the fuel compact temperatures was a three-dimensional finite element model that was subject to uncertainty. The most dominant factor in the uncertainty pertaining to calculated fuel temperatures is the gas gap uncertainty, which is caused by the graphite holder nub-to-capsule wall clearance. In AGR-5/6/7 capsules, the graphite holders can be shifted away from being perfectly centered inside the stainless steel capsule walls. This is due to irradiation-induced graphite shrinkage or to insufficient nubs on the graphite holder leading to azimuthally varying gas gaps during irradiation. A method was developed to find the best offset magnitude and direction at the top and bottom of the graphite holders in Capsules 1 and 2, which experienced higher temperatures than originally predicted among the five AGR-5/6/7 axially dispersed capsules irradiated between 2018 and 2020. The differences between the calculated and the measured temperatures of thermocouples (TCs) placed in the graphite holder near the fuel compacts were used as the basis for the holder offset determination. The best-fit offset assumes that the root-mean-square error between the measured and calculated TC temperatures is a minimum value. Typical offsets in the range of 0.002 to 0.004 in. increase the fuel compact temperatures in the range of 100°C to 200°C when compared to the zero offset for Capsules 1 and 2. Additionally, because of irradiation-induced changes in the capsule geometries, the holder offsets change throughout the irradiation period.

Advancements in integrated robotic sensing: A European perspective

Robotic Non-destructive Testing and Sensing stands at the forefront of technological innovation, offering capabilities in assessing structural integrity, safety, and material quality across diverse industries. This comprehensive review article provides a detailed exploration of the field, focusing on the substantial contributions of European researchers and institutions. The need for non-destructive testing has been a constant in industries that rely on structural integrity, including aerospace, manufacturing, energy, construction, and healthcare. Traditional testing methods, such as radiography, ultrasonic testing, magnetic particle testing, and dye penetrant testing, have been integral for quality control and safety assurance. However, the robotisation of such methods has marked a profound shift, enabling precise, fast, efficient, and repeatable testing while minimising human exposure to hazardous environments. European researchers and institutions have played an instrumental role in driving the evolution of integrated robotic sensing. The historical perspective of the field reveals the pioneering spirit of Europe, as collaborative initiatives led to the development of robotic platforms equipped with advanced sensors and testing techniques. A critical aspect of the European impact on robotic inspection applications lies in developing advanced sensors, innovative robotic platforms, novel robotic path-planning and control approaches and data collection and visualisation tools. These developments continue to influence the global landscape of robotic sensing. European researchers remain at the forefront of current trends and innovations as the field continues to evolve. This review article will delve into these recent advancements, highlighting Europe’s pivotal role in pushing the boundaries of technology and application. The implications and applications of robotic sensing reverberate across multiple sectors worldwide. From inspecting critical aerospace components to ensuring the quality of manufactured goods, these technologies underpin safety and quality standards.

Neurological manifestations of Flaviviridae, Togaviridae, and Peribunyaviridae as vector-borne viruses.

Vector-borne viruses pose a significant health problem worldwide, as they are transmitted to humans through the bite of infected arthropods such as mosquitoes and ticks. In recent years, emerging and re-emerging vector-borne diseases have gained attention as they can cause a wide spectrum of neurological manifestations. The neurological manifestations of vector-borne viruses encompass a board spectrum of clinical manifestations, ranging from mild and self-limiting symptoms to severe and life-threatening conditions. Common neurological complications include viral encephalitis, acute flaccid paralysis, aseptic meningitis, and various neuromuscular disorders. The specific viruses responsible for these neurological sequelae vary by geographic region and include Orthoflavivirus nilense, Zika virus, dengue virus, chikungunya virus, Japanese encephalitis virus, and tick-borne encephalitis virus. This review focuses on the pathogenesis of these neurologic complications and highlights the mechanisms by which vector-borne viruses invade the central nervous system and trigger neuroinflammatory responses. Diagnostic challenges and strategies for early detection of neurological manifestations are discussed, emphasising the importance of clinical suspicion and advanced laboratory testing.

A living lab for Structural Health Monitoring at the Nibelungen Bridge Worms for Transfer Learning of Structural Dynamics

The Nibelungen Bridge in Worms, Germany has been selected as a national demonstration structure for advanced non-destructive testing (NDT) and structural health monitoring concepts to extend the lifetime of civil structures and to optimize O&M actions. Parts of the research that involves this bridge as a demonstrator belong to the focus area program SPP100+. In this program, the bridges SHM System has been extended and combined with an additional setup of vibration sensors. The used digital smart sensor with pre-processing functions, the arrangement of the sensors at the structure and additional edge computing capability allows the investigation of transfer learning and other methods directly into the real structure. The living lab with seven triaxial accelerometers can be reconfigured in real-time and adjusted to the needs of AI models for classification. The comparison with the existing conventional SHM sensors has been made possible by hardware synchronization to the existing SHM System and collocating sensors at similar positions, so that a hardware exchange can be an investigated use-case for the transfer learning. During idle times, the system collects vibration data like a conventional SHM system. Keywords: Structural Health Monitoring (SHM), Data acquisition, Sensor technology.

Mitigation of Steel Corrosion Threshold Utilizing Plant-Based Green Corrosion Inhibitors through Electrochemical Techniques

Corrosion of steel reinforcement is a major cause of deterioration in reinforced concrete structures. The corrosion process is influenced by the concrete-steel interface, with the alkaline concrete pore solution initially providing passivation. However, ingress of aggressive substances like chlorides can disrupt the passive layer, initiating active pitting corrosion above a threshold level often taken as 0.4% chloride by cement weight. Once the chloride threshold is exceeded, corrosion propagation depends on oxygen and moisture availability. The resulting rust formation causes expansive cracking and spalling of the concrete cover. Corrosion damage can be mitigated through use of inhibitors like calcium nitrite though high dosages impair concrete strength. Recently, plant-based organic compounds have shown promise as green corrosion inhibitors. The corrosion behavior of steel in concrete can be evaluated through impressed accelerated corrosion testing along with electrochemical techniques like half-cell potential mapping and resistivity measurements. These allow assessment of the probability of corrosion and corrosion rate. Techniques like linear polarization resistance and electrochemical impedance spectroscopy can also quantify instantaneous corrosion rate. Proper structural condition assessment and repair using both conventional and green inhibitors is crucial to control steel corrosion, maintain service life and ensure safety. Further research is needed on green corrosion mitigation methods and advanced non-destructive testing techniques.

Mapping Prehospital Clinician Impression to Hospital-Based Diagnoses in Children Transported to the Hospital by Emergency Medical Services

Objectives Emergency medical services (EMS) serves a critical role in the delivery of services to children with out-of-hospital emergencies. The EMS clinicians’ initial field diagnoses, termed “impressions,” facilitate focused patient assessments, guide the application of prehospital treatment protocols, and help determine transport destination. We sought to evaluate the concordance of the EMS clinician impression to a child’s hospital-based diagnosis. Methods We retrospectively studied de-identified pediatric (<18 years old) scene runs transported to the hospital and with available linked hospital data from the 2021 ESO Data Collaborative, a multi-agency prehospital electronic health record dataset. EMS impressions and primary emergency department or admission-based diagnoses were categorized into one of twenty-one major groups in the Diagnosis Grouping System. We identified the most common hospital-based discharge diagnoses and evaluated for the agreement between EMS impression and hospital-based diagnosis using Cohen’s Kappa statistic. Results We included 35,833 pediatric transports from the scene with linked prehospital and in-hospital data (median age 11 years, interquartile range, 3-15 years; 50.9% male). The most common categories for both EMS impressions and hospital-based diagnoses were as follows respectively: trauma (26.1%; 24.6%), neurologic diseases (18.9%; 16.4%), psychiatric and behavioral diseases and substance use disorder (11.8%; 11.6%), and respiratory diseases (11.1% and 9.5%). A total of 23,224 out of 35,833 patients, or 64.8%, had concordant EMS impressions and hospital-based diagnoses. There was high agreement between common EMS impression and in-hospital diagnoses (trauma 77.3%; neurologic diseases 70.3%; respiratory diseases 64.5%; and psychiatric, behavioral disease and substance use disorder 73.9%). Hospital-based diagnoses demonstrated moderate concordance with prehospital data (Cohen’s κ = 0.59). Conclusions We found moderate concordance between EMS primary impression and hospital diagnoses. The EMS encounter is brief and without capabilities of advanced testing, but initial impressions may influence the basis of the triage assignment and interventions during the hospital-based encounter. By evaluating EMS impressions and ultimate hospital diagnoses, pediatric protocols may be streamlined, and specific training emphasized in pursuit of improving patient outcomes. Future work is needed to examine instances of discordance and evaluate the impact on patient care and outcomes.

The significance of multimodality approach in the management of non-lesional drug-resistant focal parietal lobe epilepsies.

Due to extensive connectivity of the parietal lobe, non-lesional drug-resistant (DRE) parietal lobe epilepsies (PLEs) are difficult to localize and often imitate other epilepsies. Therefore, patients with PLEs have low rates of seizure freedom following epilepsy surgery. Previous studies have highlighted the need to combine EEG and semiology for more accurate localization of PLEs. As sophisticated tools for localization become more available, the use of multiple different neuroimaging and neurophysiologic diagnostic tests may more readily identify PLE. We hereby report a unique case of a complex localization in a non-lesional PLE, which was initially falsely localized to frontal lobe. This case underscores the utility of voxel-based morphometry (VBM) in identifying an epileptogenic lesion on a non-lesional MRI and the significance of multimodality approach including PET, magnetoencephalopathy (MEG), interictal and ictal EEG, semiology and cortical stimulation for accurate localization of PLEs. Understanding epilepsy through multimodality approach in this fashion can help with accurate localization especially in difficulty to localize and deceptive non-lesional PLEs. PLAIN LANGUAGE SUMMARY: Parietal lobe epilepsies are hard to pinpoint in the brain and can mimic other types of epilepsy, especially when brain MRIs appear normal. As sophisticated tools for locating epilepsies in the brain become more available, using multiple diagnostic tests may help identify parietal lobe epilepsies more easily. We describe a unique case of a parietal lobe epilepsy patient with normal brain MRI whose epilepsy was initially misidentified as being in the frontal lobe. Using various advanced diagnostic tests, we accurately found the epilepsy's true location in the parietal lobe and successfully treated the patient with surgery.

Completion of Service in the Israel Defense Forces as a Volunteer with Autism vs Other Psychiatric Disorders: A Survival Analysis

Military service in the Israel Defense Forces (IDF) is compulsory. As part of the conscription process, individuals undergo a thorough occupational evaluation, which may include advanced psychiatric testing. As a result, some individuals are excluded based on presentations of psychiatric diagnoses, such as autism spectrum disorder (ASD), but they can volunteer with the IDF in special capacities (psychiatric volunteers). This study aimed to examine completion rates and correlates of psychiatric volunteers during the first year of service. We used historical data maintained by the IDF to identify psychiatric volunteers (N = 1,159, 76.9% male, M[SD] age = 19.65[0.94]). Overall, 32.3% (n = 374) of psychiatric volunteers had a diagnosis of ASD and 67.7% (n = 785) had other psychiatric diagnoses. We used survival analysis. Overall, 59.5% (n = 690) of the sample completed their first year of volunteering. Non-ASD compared with ASD psychiatric diagnoses (OR = 2.057) was at greater risk to dropout in the first year. Individuals with ASD are more likely to stay in the military as part of volunteer service than individuals with other psychiatric disorders. Individuals with ASD may meaningfully benefit from – and contribute to – military service.

Investigation of an Active Focusing Planar Piezoelectric Ultrasonic Transducer.

Ultrasonic focusing transducers have broad prospects in advanced ultrasonic non-destructive testing fields. However, conventional focusing methods that use acoustic concave lenses can disrupt the acoustic impedance matching condition, thereby adversely affecting the sensitivity of the transducers. In this paper, an active focusing planar ultrasonic transducer is designed and presented to achieve a focusing effect with a higher sensitivity. An electrode pattern consisting of multiple concentric rings is designed, which is inspired by the structure of Fresnel Zone Plates (FZP). The structural parameters are optimized using finite element simulation methods. A prototype of the transducer is manufactured with electrode patterns made of conductive silver paste using silk screen-printing technology. Conventional focusing transducers using an acoustic lens and an FZP baffle are also manufactured, and their focusing performances are comparatively tested. The experimental results show that our novel transducer has a focal length of 16 mm and a center frequency of 1.16 MHz, and that the sensitivity is improved by 23.3% compared with the conventional focusing transducers. This research provides a new approach for the design of focusing transducers.

A machine-learning-aided data recovery approach for predicting multi-material thermal behaviors in advanced test reactor capsules

Instrumented experiments conducted at test reactors are essential to the deployment of new advanced reactor systems. Designing new experiments and generating data on specific reactor conditions require significant investments in terms of both time and cost. Finite element analysis software can be used to create high-fidelity models of experiment environments in order to support the actual experiments, but computation time remains a concern in terms of applying outcomes to real-time usage of data (e.g., a digital twin [DT]). The present research proposes a machine-learning (ML) aided approach to making temperature and displacement predictions based on the thickness of the outer gas gap on the experimental capsule used for in-pile demonstration of a novel new thermal conductivity probe in the Advanced Test Reactor (ATR). This capsule consisted of U10Zr fuel, a rodlet, sodium, and inner and outer capsules. Gas gaps existed between the fuel and the rodlet, and between the inner and the outer capsule. The learning data pertained to an experimental capsule's radial distributions of temperature and displacement, as obtained based on Abaqus and the physical features. For the first step of ML sequence, the temperature was predicted using three positional parameters. Next, the displacement was predicted using seven additional parameters. Each physical feature was normalized in order to be both nondimensional and standardized. The temperature and displacement predictions showed good agreement with the simulation results in all cases involving interpolation and extrapolation. Furthermore, data similarity enhancement increased the similarity between the training and the target data, thereby increasing the predictive accuracy of the ML models. In certain extrapolation cases involving limited original ML model accuracy, data similarity enhancement and data recovery was able to somewhat improve this accuracy.

Advanced testing methods for proton exchange membrane electrolysis stacks

Research on proton exchange membrane water electrolysis for renewable hydrogen production is rapidly advancing worldwide, driven by the imperative to reduce costs and enhance efficiency through development of novel materials. However, to effectively evaluate and validate these advancements, standardized testing methods are essential, extending beyond single-cell analysis to encompass stack-level characterization.This paper proposes comprehensive characterization methods tailored for analysis of electrolysis stacks and their performance characteristics. Each method is introduced with a focus on its practical applicability, accompanied by detailed procedural guidelines for implementation. Furthermore, variations within each method are discussed, offering possibilities for gathering additional insights.Presenting a portfolio of different methods, ranging from standard to advanced techniques applicable at the stack level, the paper showcases results obtained through their application. These results, normalized to cell area, demonstrate the significance of each method in obtaining stack characteristics crucial for informed design decisions on material selection and subsequent integration into electrolysis systems.By illustrating results derived from various stacks, this study contributes valuable insights for evaluating design, material suitability, and operational performance, thereby advancing the development and deployment of proton exchange membrane water electrolysis technology for sustainable hydrogen production.

At-power subcritical multiplication in the Advanced Test Reactor during nuclear requalification testing

Power division information during nuclear requalification of the Advanced Test Reactor (ATR) is of considerable interest as an importance function for observed changes to core reactivity. The degree to which a given physical subdivision of a critical reactor acts as a neutron source for other lobes is not analytically characterized for general application. When ATR operates at power, individual power-producing lobes rely on each other as neutron sources in order to maintain constant power, which in general requires either exactly critical multiplication within a reactor or an external neutron source. This work shows that fuel element and lobe powers in ATR can be related with subcritical multiplication theory. Subcritical multiplication factors are computed with a physically validated analytical method based on actual at-power operation, quantifying for each lobe its dependence on other lobes as an external neutron source. This explanation is significant for ATR due to the desire to irradiate a large variety of experiments simultaneously, each having its impact on the core neutron population. For any physical subdivision of any other critical reactor, it is likewise true that the subdivision undergoes only subcritical multiplication.