arXiv subjects

Alfred Ludwig

Publications and source records attributed to Alfred Ludwig.

35 records · Page 2Linked to original sources

arXiv2023

High-throughput study of the phase constitution of the thin film system Mg-Mn-Al-O in relation to Li recovery from slags

The increasing importance of recycling makes the recovery of valuable elements from slags interesting, e.g., by the concept of engineered artificial minerals (EnAMs). In this concept, it is aimed for the formation of EnAMs, meaning phase(s) with a high content of the to-be-recovered element(s) from slags of pyrometallurgical recycling processes. For this, understanding the phase constitution of the slag systems is of high importance. The system Mg-Mn-Al-O is a metal oxide slag subsystem from Li-ion battery recycling, that is critical for the formation of spinel phases, which are competing phases to the possible Li-containing EnAM phase LiAlO2. Here, the phase constitution was investigated using a thin film materials library that covers the composition space (Mg14-69Mn11-38Al14-74)Ox. By means of high-throughput energy-dispersive X-ray spectroscopy and X-ray diffraction, the formation of the spinel solid solution phase was confirmed for a wide composition space. Increasing preferential orientation of the spinel solid solution along (400) with increasing Mg content was identified. X-ray photoelectron spectroscopy was used to measure the near-surface composition of selected areas of the materials library, and detailed peak fitting of the Mn 2p3/2 region revealed the Mn oxidation state to be a mixture of Mn2+ and Mn3+. For one measurement area of the materials library containing equal atomic amounts of Mg, Mn and Al, transmission electron microscopy showed that the approximately 420 nm-thick film consists of columnar spinel grains with Mg, Mn and Al being evenly distributed. Based on these results, we suggest that the shown high likelihood of spinel formation in slags might be influenced by controlling the Mn oxidation state to enable the formation of desirable EnAM phases.

cond-mat.mtrl-sci↗

arXiv2023

Prediction of Ambient Pressure Conventional Superconductivity above 80K in Thermodynamically Stable Hydride Compounds

The primary challenge in the field of high-temperature superconductivity in hydrides is to achieve a superconducting state at ambient pressure rather than the extreme pressures that have been required in experiments so far. Here, we propose a family of compounds, of composition Mg$_2$XH$_6$ with X$=$Rh, Ir, Pd, or Pt, that achieves this goal. These materials were identified by scrutinizing more than a million compounds using a machine-learning accelerated high-throughput workflow. They are thermodynamically stable, indicating that they are serious candidates for experimental synthesis. We predict that their superconducting transition temperatures are in the range of 45-80K, or even above 100K with appropriate electron doping of the Pt compound. These results indicate that, although very rare, high-temperature superconductivity in thermodynamically stable hydrides is achievable at room pressure.

cond-mat.supr-con↗

arXiv2023

Speeding up high-throughput characterization of materials libraries by active learning: autonomous electrical resistance measurements

High-throughput experimentation enables efficient search space exploration for the discovery and optimization of new materials. However, large search spaces of, e.g., compositionally complex materials, require decreasing characterization times significantly. Here, an autonomous measurement algorithm was developed, which leverages active learning based on a Gaussian process model capable of iteratively scanning a materials library based on the highest uncertainty. The algorithm is applied to a four-point probe electrical resistance measurement device, frequently used to obtain indications for regions of interest in materials libraries. Ten materials libraries with different complexities of composition and property trends are analyzed to validate the model. By stopping the process before the entire library is characterized and predicting the remaining measurement areas, the measurement efficiency can be improved drastically. As robustness is essential for autonomous measurements, intrinsic outlier handling is built into the model and a dynamic stopping criterion based on the mean predicted covariance is proposed. A measurement time reduction of about 70-90% was observed while still ensuring an accuracy of above 90%.

cond-mat.mtrl-sci↗

arXiv2023

Combinatorial sputter synthesis of single-phase La(XYZ)O$_{3\pmσ}$ perovskite thin film libraries: a new platform for materials discovery

Compositionally complex perovskites provide the opportunity to develop stable and active catalysts for electrochemical applications. The challenge lies in the identification of single-phase perovskites with optimized composition for high electrical conductivity. Leveraging a recently discovered effect of self-organized thin film growth during reactive sputtering, La-Co-Mn-O and La-Co-Mn-Fe-O perovskite (ABO3) thin film materials libraries are synthesized. These show phase-pure La-perovskites over a wide range of chemical composition variation for the B-site elements for deposition temperatures equal to or higher than 300$^\circ$C. It is demonstrated that this approach enables the discovery and tailoring of chemical compositions for desired optical bandgap and electrical conductivity, and thereby opens the path for the targeted development of e.g. new high-performance electrocatalysts.

cond-mat.mtrl-sci↗

arXiv2022

Combinatorial sputter deposition of CrMnFeCoNi high entropy alloy thin films on agitated particles

A method for combinatorial sputter deposition of thin films on microparticles is presented. The method is developed for a laboratory-scale magnetron sputter system and uses a piezoelectric actuator to agitate the microparticles through oscillation. Custom-made components enable to agitate up to nine separate batches of particles simultaneously. Due to the agitation, the whole surface of the particles can be exposed to the sputter flux and thus can be completely covered with a thin film. By sputtering a CrMnFeCoNi high entropy alloy target, separate batches of polystyrene microspheres (500 um monodisperse diameter), Fe alloy particles (300 um mean size) and NaCl salt particles (350 um mean size) were simultaneously coated with a homogeneous thin film. In contrast, a CrMnFeCoNi thin film that was deposited on agglomerating Al particles (5 um mean size) only partially covers the surface of the particles. By co-sputtering a CrMn, an FeCo and a Ni target, nine separate batches of Al particles (25 um mean size) were coated with a CrMnFeCoNi thin film with a composition gradient. These depositions demonstrate the ability to coat different types of particles with uniform films (from elemental to multinary compositions) and to deposit films with composition gradients on uniform particles.

cond-mat.mtrl-sci↗

arXiv2022

Elemental (im-)miscibility determines phase formation of multinary nanoparticles co-sputtered in ionic liquids

Non-equilibrium synthesis methods allow to alloy bulk-immiscible elements into multinary nanoparticles, which broadens the design space for new materials. Whereas sputtering onto solid substrates can combine immiscible elements into thin film solid solutions, this is not clear for sputtering of nanoparticles in ionic liquids. Thus, the suitability of sputtering in ionic liquids for producing nanoparticles of immiscible elements is investigated by co-sputtering the systems Au-Cu (miscible), Au-Ru and Cu-Ru (both immiscible), and Au-Cu-Ru on the surface of the ionic liquid 1-butyl-3-methylimidazolium bis-trifluoromethylsulfonyl)imide [Bmim][(Tf)2N]. The sputtered nanoparticles were analyzed to obtain (i) knowledge concerning the general formation process of nanoparticles when sputtering onto ionic liquid surfaces and (ii) information, if alloy nanoparticles of immiscible elements can be synthesized as well as (iii) evidence if the Hume-Rothery rules for solid solubility are valid for sputtered nanoparticles. Accompanying atomistic simulations using density-functional theory for clusters of different size and ordering confirm that the miscibility of Au-Cu and the immiscibility of Au-Ru and Cu-Ru govern the thermodynamic stability of the nanoparticles. Based on the matching experimental and theoretical results for the NP/IL-systems concerning NP stability, a formation model of multinary NPs in ILs was developed.

cond-mat.mtrl-sci↗

arXiv2022

Unusual phase formation in reactively sputtered La-Co-O perovskite thin films

La-based perovskites are a versatile class of materials that are of interest for solid oxide fuel cells and electrocatalytic water splitting. During fabrication of composition spread materials libraries of La-Co-based oxide systems for the discovery of new catalytic materials, an unusual phase formation phenomenon was observed: instead of the expected continuous composition gradient, regions with homogeneous composition and single-phase structure (La2O3 or stoichiometric La-perovskite) form. This phenomenon occurs during reactive co-sputtering and is dependent on O2-flux and substrate temperature, investigated from room temperature up to 700 C and is independent of the used substrate. It can be described as a self-organized growth, where excess transition metal cannot be incorporated into the growing film and the forming single-phase regions. It is hypothesized that due to the high reactivity of La and the significantly low formation energies of La2O3 and La-perovskites, the reactive sputter deposition of La-based oxide films can turn, regarding film growth, into a partial CVD-like process which results in the unusual self-organized growth of single-phase regions. This phenomenon can be leveraged for the exploration of multinary perovskite thin film libraries, where the B-site atoms of La-perovskites are systematically substituted.

cond-mat.mtrl-sci↗

arXiv2021

Copper and silver thin film systems display differences in antiviral and antibacterial properties -- implications for the prevention of SARS-CoV-2 infections

The current Coronavirus Disease 19 (COVID-19) pandemic has exemplified the need for simple and efficient prevention strategies that can be rapidly implemented to mitigate infection risks. Various surfaces have a long history of antimicrobial properties and are well described for the prevention of bacterial infections. However, their effect on many viruses has not been studied in depth. In the context of COVID-19, several surfaces, including copper (Cu) and silver (Ag) coatings have been described as efficient antiviral measures that can easily be implemented to slow viral transmission. In this study, we detected antiviral properties against Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) on surfaces, which were coated with Cu by magnetron sputtering. However, no effect of Ag on viral titers was observed, in clear contrast to its well-known antibacterial properties. Further enhancement of Ag ion release kinetics based on an electrochemical sacrificial anode mechanism did not increase antiviral activity. These results clearly demonstrate that Cu and Ag thin film systems display significant differences in antiviral and antibacterial properties which need to be considered upon implementation.

physics.med-ph↗

arXiv2021

Combinatorial materials discovery strategy for high entropy alloy electrocatalysts using deposition source permutations

High entropy alloys offer a huge search space for new electrocatalysts. Searching for a global property maximum in one quinary system could require, depending on compositional resolution, the synthesis of up to 10E6 samples which is impossible using conventional approaches. Co-sputtered materials libraries address this challenge by synthesis of controlled composition gradients of each element. However, even such a materials library covers less than 1% of the composition space of a quinary system. We present a new strategy using deposition source permutations optimized for highest improvement of the covered new compositions. Using this approach, the composition space can be sampled in different subsections allowing identification of the contribution of individual elements and their combinations on electrochemical activity. Unsupervised machine learning reveals that electrochemical activity is governed by the complex interplay of chemical and structural factors. Out of 2394 measured compositions, a new highly active composition for the oxygen reduction reaction around Ru17Rh5Pd19Ir29Pt30 was identified.

cond-mat.mtrl-sci↗

arXiv2021

Bayesian Optimization of High-Entropy Alloy Compositions for Electrocatalytic Oxygen Reduction

Active, selective and stable catalysts are imperative for sustainable energy conversion, and engineering materials with such properties are highly desired. High-entropy alloys (HEAs) offer a vast compositional space for tuning such properties. Too vast, however, to traverse without the proper tools. Here, we report the use of Bayesian optimization on a model based on density functional theory (DFT) to predict the most active compositions for the electrochemical oxygen reduction reaction (ORR) with the least possible number of sampled compositions for the two HEAs Ag-Ir-Pd-Pt-Ru and Ir-Pd-Pt-Rh-Ru. The discovered optima are then scrutinized with DFT and subjected to experimental validation where optimal catalytic activities are verified for Ag-Pd, Ir-Pt, and Pd-Ru binary systems. This study offers insight into the number of experiments needed for exploring the vast compositional space of multimetallic alloys which has been determined to be on the order of 50 for ORR on these HEAs.

physics.chem-ph↗

arXiv2021

Deep learning for visualization and novelty detection in large X-ray diffraction datasets

We apply variational autoencoders (VAE) to X-ray diffraction (XRD) data analysis on both simulated and experimental thin-film data. We show that crystal structure representations learned by a VAE reveal latent information, such as the structural similarity of textured diffraction patterns. While other artificial intelligence (AI) agents are effective at classifying XRD data into known phases, a similarly conditioned VAE is uniquely effective at knowing what it does not know, rapidly identifying novel phases and mixtures. These capabilities demonstrate that a VAE is a valuable AI agent for materials discovery and understanding XRD measurements both on-the-fly and during post hoc analysis.

cond-mat.mtrl-sci↗

arXiv2021

Crystallography companion agent for high-throughput materials discovery

The discovery of new structural and functional materials is driven by phase identification, often using X-ray diffraction (XRD). Automation has accelerated the rate of XRD measurements, greatly outpacing XRD analysis techniques that remain manual, time-consuming, error-prone, and impossible to scale. With the advent of autonomous robotic scientists or self-driving labs, contemporary techniques prohibit the integration of XRD. Here, we describe a computer program for the autonomous characterization of XRD data, driven by artificial intelligence (AI), for the discovery of new materials. Starting from structural databases, we train an ensemble model using a physically accurate synthetic dataset, which output probabilistic classifications -- rather than absolutes -- to overcome the overconfidence in traditional neural networks. This AI agent behaves as a companion to the researcher, improving accuracy and offering significant time savings. It was demonstrated on a diverse set of organic and inorganic materials characterization challenges. This innovation is directly applicable to inverse design approaches, robotic discovery systems, and can be immediately considered for other forms of characterization such as spectroscopy and the pair distribution function.

cond-mat.mtrl-sci↗

arXiv2021

Nanocrystalline equiatomic CoCrFeNi alloy thin films: Are they single phase fcc?

The bulk quaternary equiatomic CoCrFeNi alloy is studied extensively in literature. Under experimental conditions, it shows a single-phase fcc structure and its physical and mechanical properties are similar to those of the quinary equiatomic CoCrFeMnNi alloy. Many studies in literature have focused on the mechanical properties of bulk nanocrystalline high entropy alloys or compositionally complex alloys, and their microstructure evolution upon annealing. The thin film processing route offers an excellent alternative to form nanocrystalline alloys. Due to the high nucleation rate and high density of defects in thin films synthesized by sputtering, the kinetics of microstructure evolution is often accelerated compared to those taking place in the bulk. Here, thin films are used to study the phase evolution in nanocrystalline CoCrFeNi deposited on Si/SiO 2 and c-sapphire substrates by magnetron cosputtering from elemental sources. The phases and microstructure of the films are discussed in comparison to the bulk alloy. The main conclusion is that second phases can form even at room temperature provided there are sufficient nucleation sites.

cond-mat.mtrl-sci↗

arXiv2020

Complex solid solution electrocatalyst discovery by prediction and high-throughput experimentation

Efficient discovery of electrocatalysts for electrochemical energy conversion reactions is of utmost importance to combat climate change. With the example of the oxygen reduction reaction we show that by utilising a data-driven discovery cycle, the multidimensionality challenge offered by compositionally complex solid solution (high entropy alloy) electrocatalysts can be mastered. Iteratively refined computational models predict activity trends for quinary target compositions, around which continuous composition spread thin-film libraries are synthesized. High-throughput characterisation datasets are then input for refinement of the model. The refined model correctly predicts activity maxima of the exemplary model system Ag-Ir-Pd-Pt-Ru for the oxygen reduction reaction. The method can identify optimal complex solid solutions for electrochemical reactions in an unprecedented manner.

cond-mat.mtrl-sci↗

arXiv2020

Microstructure evolution and thermal stability of equiatomic CoCrFeNi films on (0001)alpha-Al2O3

Homogeneous face-centered cubic (fcc) polycrystalline CoCrFeNi films were deposited at room temperature on (0001)alpha-Al2O3 (c-sapphire). Phase and morphological stability of 200 to 670 nm thick films were investigated between 973 K and 1423 K. The fcc-phase persists while the original <111> texture of 30-100 nm wide columnar grains evolves into ~10 or ~1000 micron wide grains. Only the grains having specific orientation relationships to the sapphire grow; as in the case of pure fcc metal (M) films 4 orientation relationships (OR) are found: OR1 (M(111)[1-10]//alpha-Al2O3 (0001)[1-100]) and OR2 (M(111)[1-10]//alpha-Al2O3 (0001)[11-20]) and their twin-related variants (OR1t and OR2t). Below 1000 K, the film microstructure stabilizes into 10 micron wide OR1 and OR1t twin grains independent of film thickness. Above 1000 K, the OR2 and OR2t grains expand to sizes exceeding more than a 1000 times the film thickness. Upon annealing, the films either retain their integrity or break-up depending on the competing kinetics of grain growth and grain boundary grooving. Triple junctions of the grain boundaries, the major actors in film stability, were tracked. Thinner films and higher temperatures favor film break-up by dewetting from the holes grooved at the triple junctions down to the substrate. The grain boundaries of the OR2 and OR2t grains migrate fast enough to overcome the nucleation of holes from which break-up could initiate. The growth of the OR2 and OR2t grains in this complex alloy is faster than in pure fcc metals at equivalent homologous annealing temperatures.

cond-mat.mtrl-sci↗

arXiv2019

Mastering processing-microstructure complexity through the prediction of thin film structure zone diagrams by generative machine learning models

Thin films are ubiquitous in modern technology and highly useful in materials discovery and design. For achieving optimal extrinsic properties their microstructure needs to be controlled in a multi-parameter space, which usually requires a too-high number of experiments to map. We propose to master thin film processing microstructure complexity and to reduce the cost of microstructure design by joining combinatorial experimentation with generative deep learning models to extract synthesis-composition-microstructure relations. A generative machine learning approach comprising a variational autoencoder and a conditional generative adversarial network predicts structure zone diagrams. We demonstrate that generative models provide a so far unseen level of quality of generated structure zone diagrams comprising chemical and processing complexity for the optimization of chemical composition and processing parameters to achieve a desired microstructure.

physics.app-ph↗

arXiv2012

Enhancing magnetocrystalline anisotropy of the Fe70Pd30 magnetic shape memory alloy by adding Cu

Strained epitaxial growth provides the opportunity to understand the dependence of intrinsic and extrinsic properties of functional materials at frozen intermediate stages of a phase transformation. In this study, a combination of thin film experiments and first-principles calculations yields the binding energy and magnetic properties of tetragonal Fe70Pd30-xCux ferromagnetic shape memory thin films with x = 0, 3, 7 and structures ranging from bcc to beyond fcc (1.07 1.41 the samples undergo structural relaxations through adaptive nanotwinning. For all tetragonal structures, we observe a significant increase of the magnetocrystalline anisotropy constant K1, which reaches a maximum of K1=-2.4*10^5 Jm^-3 at room temperature around c/a_bct=1.33 and is thus even larger than for binary Fe70Pd30 and the prototype Ni-Mn-Ga magnetic shape memory system. Since K1 represents the driving force for variant reorientation in magnetic shape memory systems, we conclude that Fe-Pd-Cu alloys offer a promising route towards microactuators applications with significantly improved work output.

cond-mat.mtrl-sci↗