Open Access

High-and medium entropy alloys have been investigated for more than two decades and their potential keeps being evaluated. Their “baseless” character distinguishes them from classic alloys that are characterized by one main element, such as steel – Fe based. The question has arisen whether our analysis methods are suited for alloys without a base element and is has been found that they are within the limitations of the methods. This dataset shows the compatibility between inductively coupled plasma optical emission spectrometry, combustion analysis, x-ray fluorescence analysis and energy dispersive x-ray spectroscope, measured in the scanning electron microscope. Four alloys from the well-studied Co-Cr-Fe-Ni medium entropy family have been used as testing materials.

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.

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.

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.

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.