Engineering topics
Juelsholt, Mikkel
Publications and source records attributed to Juelsholt, Mikkel.
Extracting structural motifs from pair distribution function data of nanostructures using explainable machine learning
Characterization of material structure with X-ray or neutron scattering using e.g. Pair Distribution Function (PDF) analysis most often rely on refining a structure model against an experimental dataset. However, identifying a suitable model is often a bottleneck. Recently, automated approaches have made it possible to test thousands of models for each dataset, but these methods are computationally expensive and analysing the output, i.e. extracting structural information from the resulting fits in a meaningful way, is challenging. Our Machine Learning based Motif Extractor (ML-MotEx) trains an ML algorithm on thousands of fits, and uses SHAP (SHapley Additive exPlanation) values to identify which model features are important for the fit quality. We use the method for 4 different chemical systems, including disordered nanomaterials and clusters. ML-MotEx opens for a type of modelling where each feature in a model is assigned an importance value for the fit quality based on explainable ML.
Breaking with the Principles of Coreduction to Form Stoichiometric Intermetallic PdCu Nanoparticles
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Towards a mechanistic understanding of the sol–gel syntheses of ternary carbides
Sol–gel chemistry, while being extremely established, is to this day not fully understood, and much of the underlying chemistry and mechanisms are yet to be unraveled. Here, in this work, we elaborate on the sol–gel chemistry of Cr 2 GaC, the first layered ternary carbide belonging to the MAX phase family to ever be synthesized using this wet chemical approach. Leveraging a variety of both in- and ex situ characterization techniques, including X-ray and neutron powder diffraction, X-ray absorption fine structure analyses, total scattering analyses, and differential scanning calorimetry coupled with mass spectrometry, in-depth analyses of the local structures and reaction pathways are elucidated. While the metals first form tetrahedrally and octahedrally coordinated oxidic structures, that subsequently grow and crystallize into oxides, the carbon source citric acid sits on a separate reaction pathway, that does not merge with the metals until the very end. In fact, after decomposing it remains nanostructured and disordered graphite until the temperature allows for the reduction of the metal oxides into the layered carbide. Based on this, we hypothesize that the method is mostly applicable to systems where the needed metals are reducible by graphite around the formation temperature of the target phase.
Surfactant-free syntheses and pair distribution function analysis of osmium nanoparticles
A surfactant-free synthesis of precious metal nanoparticles (NPs) performed in alkaline low-boiling-point solvents has been recently reported. Monoalcohols are here investigated as solvents and reducing agents to obtain colloidal Os nanoparticles by using low-temperature (<100 °C) surfactant-free syntheses. The effect of the precursor (OsCl 3 or H 2 OsCl 6 ), precursor concentration (up to 100 mM), solvent (methanol or ethanol), presence or absence of a base (NaOH), and addition of water (0 to 100 vol %) on the resulting nanomaterials is discussed. It is found that no base is required to obtain Os nanoparticles as opposed to the case of Pt or Ir NPs. The robustness of the synthesis for a precursor concentration up to 100 mM allows for the performance of X-ray total scattering with pair distribution function (PDF) analysis, which shows that 1–2 nm hexagonal close packed (hcp) NPs are formed from chain-like [OsO x Cl y ] complexes.