Alexander Colsmann

To investigate the photoresponse of metal‐ferroelectric‐metal (MFM) devices, we sandwich polycrystalline bismuth ferrite between two electrodes of similar work function. We use calcium‐substitution on the A‐site to tailor the properties of bismuth ferrite Bi _1‐x Ca _x FeO _3‐δ from ferroelectric ( x < 0.2) to nonferroelectric ( x > 0.2). Devices with nonferroelectric Bi _1‐x Ca _x FeO _3‐δ show symmetric current density‐voltage ( J–V ) curves and no photoresponse, resembling their symmetric electronic properties that originate from two opposing Schottky junctions at the symmetric electrodes. In contrast, the J–V curves of ferroelectric Bi _1‐x Ca _x FeO _3‐δ are governed by the polarization direction, introducing directionality through modification of the Schottky barriers. Under illumination, the polarization induced modulation of the Schottky barriers promotes directionality of the photogenerated charge carriers and leverages enhanced photocurrents.

Solution‐processed electrochromic materials offer high potential for energy‐efficient smart windows and displays. Their performance varies with material choice and processing conditions. Electrochromic thin‐film electrodes require a smooth, defect‐free coating for optimal contrast between bleached and colored states. The complexity of optimizing the spin‐coated electrochromic thin layer poses challenges for rapid development. This study demonstrates the use of self‐driving laboratories to accelerate the development of electrochromic coatings by coupling automation with machine learning. Our system combines automated data acquisition, image processing, spectral analysis, and Bayesian optimization to explore processing parameters efficiently. This approach not only increases throughput but also enables a pointed search for optimal processing parameters. The approach can be applied to various solution‐processed materials, highlighting the potential of self‐driving labs in enhancing materials discovery and process optimization.

Due to an exponential increase in published research articles, it is impossible for individual scientists to read all publications, even within their own research field. Here we investigate the use of large language models to extract the main concepts and semantic information from scientific abstracts in the domain of materials science to identify links that were not noticed by humans and to suggest inspiring near and/or mid-term future research directions. We show that large language models can extract concepts more efficiently than automated keyword extraction methods to build a concept graph as an abstraction of the scientific literature. A machine learning model is trained to predict emerging combinations of concepts, that is, new research ideas, based on historical data. We demonstrate that integrating semantic concept information leads to increased prediction performance. The applicability of our model is demonstrated in qualitative interviews with domain experts based on individualized model suggestions. We show that the model can inspire materials scientists in their creative thinking process by predicting innovative combinations of concepts that have not yet been investigated.

Since their discovery in 2015, high‐entropy oxides have introduced a paradigm shift in materials science, unveiling a class of compounds with exceptional structural and functional versatility. These high‐entropy materials (HEMs) offer exciting opportunities as next‐generation alternatives to conventional materials, owing to the synergistic interplay of multiple principal elements that results in enhanced stability, tunability, and multifunctionality. Their unique atomic configurations enable the design of materials with high surface areas and abundant active sites for catalysis, mechanically robust structures for energy storage, or tunable band gaps for electronic and optoelectronic devices. However, the vast compositional space of HEMs presents both a challenge and an opportunity. Meaningful property design requires a deep understanding of how synthesis routes influence structure–property relationships. In this review, a comprehensive overview of established and emerging synthesis strategies for HEMs, focusing on how each method affects resulting structural, electronic, electrochemical, and optical characteristics, is provided. Key process parameters that can be tailored to optimize material performance are highlighted. Additionally, the accelerating role of high‐throughput synthesis and characterization in navigating the design space of high‐entropy systems is discussed. By systematically connecting synthesis, structure, and function, this review aims to guide the rational design of HEMs for energy applications and beyond.

This study reports the novel photovoltaic compound Cu _{3 x} Bi _{1− x} I ₃ , yielding power conversion efficiencies of up to 0.65%

Light‐harvesting layers in organic bulk‐heterojunction solar cells are commonly fabricated from aromatic or even chlorinated solvents, which are often toxic or otherwise hazardous. In this work, nanoparticle dispersions of a blend of poly(3‐hexylthiophene) and [6,6]‐phenyl C ₇₁ ‐butric acid methyl ester (P3HT:PC ₇₁ BM) are synthesized in water, omitting any stabilizing surfactants which would remain in the layer and hamper the device performance. To overcome wetting issues and to master the deposition of thin films from aqueous dispersions, co‐solvents are employed. The solar cells exhibit power conversion efficiencies of 2.7% and show excellent long‐term stability, paving a promising way towards the all‐eco‐friendly production of organic solar cells in the future.

Hydrogen generation in electrostatically stabilized, aqueous organic nanoparticle dispersions is investigated. For this purpose, organic nanoparticle dispersions are synthesized in water by nanoprecipitation from tetrahydrofuran and stabilized by charging through strong molecular electron acceptors. The dispersions are stable for more than 10 weeks on the shelf and during the photocatalytic process, despite the continuous transfer of charges between the reactants. The hydrogen generation in the electrostatically stabilized dispersions outperforms the hydrogen generation in organic nanoparticle dispersions which contain the common stabilizer sodium dodecyl sulfate.

Decentralized consensus on the state of the Bitcoin blockchain is ensured by proofs of work. It relies on digital one-way functions and is associated with an enormous environmental impact. This paper conceptualizes a physical one-way function that aims to transform a digital, electricity-consuming consensus mechanism into a physical process. Boundary conditions for the security requirements are established and discussed as well as experimentally investigated for a specific setup based on printing and optical analysis of pigment-carrier composites. In the context of the applied methods, this setup promises to be mathematically unclonable, steady, reproducible, collision resistant and non-invertible and illustrates the feasibility of a physical one-way function. Based on this, a framework for a proof of physical work is conceptualized, which has the potential of a drastically lower CO2 footprint. This work initiates a progressive, interdisciplinary field of research and demands further investigations with regards to alternative setups, security definitions and strategies of challenging them.

Because of its excellent hole conductivity, p‐doped 2,2′7,7′‐tetrakis‐(N,N‐di‐p‐methoxyphenyl‐amine)‐9,9′‐spiro‐bifluorene (spiro‐MeOTAD) is commonly deployed for hole transport in organic metal halide perovskite solar cells, but its rather expensive synthesis prompts the research for alternatives. In this work, tetrasubstituted [2.2]paracyclophanes (PCPs) are synthesized and investigated for replacing spiro‐MeOTAD. To enhance their conductivity, different doping strategies are followed. Best conductivities are achieved by doping PCP thin films with tris(2‐(1H‐pyrazol‐1‐yl)‐4‐tert‐butylpyridine)cobalt(III) tris(bis(trifluoromethylsulfonyl)imide) (FK209), matching the conductivity of state‐of‐the‐art p‐doped spiro‐MeOTAD. Best performance in solar cells is leveraged by doping PCPs with the co‐dopants lithium bis(trifluoromethanesulfonyl)imide (LiTFSI) and 4‐tert‐butylpyridine (tBP) which are also used to p‐dope spiro‐MeOTAD thin films in solar cells. Yet, the thermal device stability is maximized upon doping PCPs with FK209 and 2,3,5,6‐tetrafluoro‐7,7,8,8‐tetracyanoquinodimethane (F₄TCNQ).