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Controlling trace humidity is vital for both the fabrication and long-term stability of metal halide perovskite (MHP) solar cells. Relevant humidity levels are typically below 10 ppmV, especially in glovebox-based processing and in well-encapsulated devices. Even minute amounts during fabrication can influence crystallization, introducing defects and lowering efficiency. Over time, humidity accelerates degradation of the perovskite layer and internal interfaces, ultimately reducing operational lifetime. Probing these effects at low concentrations under operando conditions is therefore essential for advancing device performance and durability. In this work, we employed a high-precision transfer standard dew point hygrometer to investigate humidity levels between 5 and 35 ppmV in non-encapsulated MHP solar cells. To permit unobstructed water migration during operation, we fabricated interdigital back contact devices. Operando measurements revealed water transport through the perovskite layer and enabled quantification of outgassing. Under trace-humidified conditions, devices exhibited initial charge-carrier quenching, followed by gradual recovery. Notably, the photocurrent response to humidified nitrogen demonstrated that the MHP layer behaves fully reversibly within the explored timescale and across the investigated humidity levels and conditions. These findings establish a systematic operando framework for examining extrinsic stressors in perovskites and highlight opportunities for assessing passivation strategies.
We introduce a thermographic dataset for subsurface defect detection in radioactive waste storage drums, comprising thermal sequences from 7 barrel specimens with artificially manufactured internal defects. The dataset was acquired using cost-effective halogen-lamp excitation (2kW per lamp) as an alternative to laser-based systems, with dual-camera thermal imaging (CMOS and bolometric) to enable performance comparison across imaging modalities. The specimens include both new and aged barrel types with controlled defects — FBHs, lines, crosses, triangles, and rectangles — simulating internal corrosion at varying scales (4mm to 60mm). Three heating regimes (both lamps, left only, right only) were systematically applied across multiple measurement regions per sample, yielding normalized thermal sequences. To lower the barrier for machine learning practitioners without thermography expertise, the dataset provides pre-computed features derived from principal component analysis, pulse phase thermography, and independent component analysis extracted using experimentally optimized time windows. Ground-truth binary masks mapping defect locations are included to enable supervised learning. This resource is designed to support the development and benchmarking of automated defect detection algorithms for non-destructive testing of curved, thin-walled metallic structures under realistic surface conditions (paint inhomogeneity, dirt, geometric artifacts), while validating low-cost thermographic inspection alternatives for industrial deployment.
This dataset accompanies the study on sequential learning–based optimisation of bio-ash–cement binder formulations under seasonally varying material availability. It provides a fully synthetic but chemically inspired benchmark design space for evaluating data-driven optimisation strategies in cementitious materials research.
The dataset comprises 5,006 unique binder formulations, each defined by the mass fractions of cement and five bio-based ash components (A1–A5). Ash components represent generic bio-ash types derived from agricultural residues (e.g. rice husk ash, cassava peel ash), and their internal proportions are systematically varied under mass-balance constraints. Cement content ranges from 0 to 100 wt% in discrete steps.
To reflect dynamic supply conditions, the dataset includes season-specific ash usage metrics for four seasons (S1–S4), expressing the fraction of available ash resources consumed by each formulation. A synthetic compressive strength value is assigned to every formulation using a nonlinear scoring function based on chemically inspired descriptors, with added noise to generate a structured yet non-trivial optimisation landscape. These strength values do not represent calibrated physical predictions and are intended solely as a hidden objective function for benchmarking sequential learning algorithms.
The dataset is designed for in silico benchmarking, reproducibility studies, and methodological comparisons of optimisation and active learning strategies. It enables systematic evaluation of algorithmic performance without the need for physical experiments.
Atomically dispersed Fe in N-doped carbon (Fe-N-C) catalysts are leading platinum-group-metal-free candidates for the O2 reduction reaction in proton exchange membrane fuel cells (PEMFCs). Zeolitic imidazolate framework (ZIF-8) derived Fe-N-C present the most promising performance; however, they possess a narrow distribution of small micropores, which limits active site accessibility. Here, to induce hierarchical porosity in Fe-N-C, we report a systematic study on MgCl₂·6H₂O-templated ZIF-8-derived Fe-N-C catalysts for the O2 reduction reaction. MgCl₂·6H₂O addition induced complete Zn removal, collapse of the ZIF-8 framework, and formation of large micro- and mesopores, with graphene-like structures. N content was markedly reduced, with conversion from pyridinic to pyrrolic N species. Rotating disc electrode tests showed a progressive increase in O2 reduction activity with MgCl₂·6H₂O, which is strongly correlated (R2 = 0.98) to the formation of large micropores and small mesopores (1-4 nm). This introduces a clear structure-activity design principle for Fe-N-Cs. The enhanced Fe-N-C porosity also leads to increased degradation rates under accelerated stress test conditions, which we attributed to the oxidation of disordered carbon domains and active Fe loss. This study highlights a key trade-off between porosity-driven O2 reduction activity and durability in Fe-N-C catalysts.
BAM Reference Data: High Temperature Tensile Data of Single-Crystal Ni-Based Superalloy CMSX-6
(2025)
This publication provides comprehensive metadata and test results of tensile tests at elevated temperature according to DIN EN ISO 6892-2:2018-09 on the single crystal Ni-based superalloy CMSX-6 at T = 980 °C. The tests were performed in an ISO 17025-accredited test laboratory. The calibrations of measuring equipment are documented, meet the requirements of the measurement standard, and are metrologically traceable. The provided data were audited and are BAM reference data.
This is a python library for finite element (FE) modelling of static/quasi-static structural mechanics problems with legacy FEniCS, which contains the following main modules. Module structure: for defining a structural mechanics experiment, including geometry, mesh, boundary conditions (BCs), and time-varying loadings. The time is quasi-static, i.e. no dynamic (inertia) effects will be accounted for in the problem module as follows. Module material: for handling constitutive laws such as elasticity, gradient damage, plasticity, etc. Module problem: for establishing structural mechanics problems for desired structures and material laws coming from the two above modules, and solving the problems built up. These can be performed for two main cases: static (no time-evolution) that also includes homogenization, and quasi-static (QS).
A python implementation of an analytical variational Bayes algorithm of "Variational Bayesian inference for a nonlinear forward model", Chappell, Michael A., Adrian R. Groves, Brandon Whitcher, and Mark W. Woolrich, IEEE Transactions on Signal Processing 57, no. 1 (2008): 223-236, with an updated free energy equation to correctly capture the log evidence. The algorithm requires a user-defined model error allowing an arbitrary combination of custom forward models and measured data.
Strength characteristics, such as proof strength and tensile strength, of metallic materials are usually measured at room temperature. However, for high-temperature materials, the values at operational temperatures are equally important for analyzing the mechanical behavior of components or for designing purposes. Elevated temperature tensile tests (often referred to as hot tensile tests) generally enable the measurement of the same material parameters as those obtained at room temperature. Typically, tensile and proof strength, elongation after fracture, and reduction of area are analyzed, with the focus often being on proof strength.
The attached data schema for elevated temperature tensile tests was developed within the German NFDI-MatWerk initiative (https://nfdi-matwerk.de/). It builds on a previously published creep data schema and follows a reference data methodology that has also been previously outlined.
The presented schema constitutes a structured approach for collecting all relevant information on an elevated temperature tensile test experiment. Overall, this development, as defined in the previously published data schema for creep data, aims to provide a comprehensive hierarchical data description that can be implemented in data management platforms (such as electronic laboratory notebooks), facilitate consistent quality assessment across different users and data providers, and promote the alignment of datasets to the FAIR principles by providing easy interoperability and full reusability.
The presented schema was initially developed for datasets of Ni-based high-temperature alloys. However, thanks to its modular structure, it can also be applied to tensile tests of various metallic and other materials. Although the focus is on elevated temperature tensile testing, the data schema also offers a solid foundation for documenting room temperature tensile tests. In such cases, an adjustment of the requirement profile might be necessary, and categories or entries specific to mechanical testing at elevated temperatures, such as “temperature-measuring system”, “specified temperature”, or “soaking time”, become irrelevant.
Overall, a detailed approach was followed to ensure the collection of all relevant information, including both metadata and test results. This includes, for example, comprehensive descriptions of the material’s manufacturing history and of the laboratory equipment, and basic strength and deformation characteristic values. The resulting data schema aims to support the description and identification of high-quality datasets, which may qualify as reference data of materials. Our current definition of reference data of materials has been published elsewhere. It should be noted, however, that not all research datasets —depending on their origin and purpose— require this full level of detail. Nevertheless, the presented data schema can support data providers in evaluating the completeness and value of their datasets.
This version of the data schema covers elevated temperature tensile tests on both single- and polycrystalline specimen materials. The terminology is aligned with DIN EN ISO 6892-1 and DIN EN ISO 6892-2. It is designed to record the use of temperature measurement using thermocouples and the use of contacting extensometer systems.
The requirement profile refers to the highest quality class of reference data, taken from calibrated instruments, and which shall enable the following usages:
1. Checking one's own elevated temperature tensile test results on nominally similar material
2. Verification of own testing set-up (e.g., by testing the same or a similar material)
3. Using the data as input data for simulations in the context of design or alloy development
Biocides are used in large amounts in industrial, medical, and domestic settings. Benzalkonium chloride (BAC) is a commonly used biocide, for which previous research revealed that Escherichia coli can rapidly adapt to tolerate BAC-disinfection, with consequences for antibiotic susceptibility. However, the consequences of BAC- tolerance for selection dynamics and resistance evolution to antibiotics remain unknown. Here, we investigated the effect of BAC -tolerance in E. coli on its response upon challenge with different antibiotics. Competition assays showed that subinhibitory concentrations of ciprofloxacin—but not ampicillin, colistin and gentamicin—select for the BAC-tolerant strain over the BAC-sensitive ancestor at a minimal selective concentration of 0.0013–0.0022 µg∙mL−1. In contrast, the BAC-sensitive ancestor was more likely to evolve resistance to ciprofloxacin, colistin and gentamicin than the BAC-tolerant strain when adapted to higher concentrations of antibiotics in a serial transfer laboratory evolution experiment. The observed difference in the evolvability of resistance to ciprofloxacin was partly explained by an epistatic interaction between the mutations conferring BAC -tolerance and a knockout mutation in ompF encoding for the outer membrane porin F. Taken together, these findings suggest that BAC -tolerance can be stabilized in environments containing low concentrations of ciprofloxacin, while it also constrains evolutionary pathways towards antibiotic resistance.