An imaging scheme to study the flow dynamics of co-flow regimes in microfluidics: implications for nanoprecipitation.

Co-flow microfluidics, in addition to its applications in droplet generation, has gained popularity for use with miscible solvent systems (continuous microfluidics). By leveraging the short diffusional distances in miniature devices, processes like nanomaterial synthesis can be precisely tailored for high-throughput production. In this context, the manipulation of flow regimes-from laminar to vortex formation, as well as the generation of turbulent and turbulent jet flows-plays a significant role in optimizing these processes.

Structural descriptors and information extraction from X-ray emission spectra: aqueous sulfuric acid.

Machine learning can reveal new insights into X-ray spectroscopy of liquids when the local atomistic environment is presented to the model in a suitable way. Many unique structural descriptor families have been developed for this purpose. We benchmark the performance of six different descriptor families using a computational data set of 24 200 sulfur Kβ X-ray emission spectra of aqueous sulfuric acid simulated at six different concentrations.

Photodissociation of bromine-substituted nitroimidazole radiosensitizers.

Heavy elements and some nitroimidazoles both exhibit radiosensitizing properties through different mechanisms. In an effort to see how the overall radiosensitivity might be affected when the two radiosensitizers are combined in the same molecule, we studied the gas-phase photodissociation of two brominated nitroimidazoles and a bromine-free reference sample. Synchrotron radiation was employed to initiate the photodynamics and energy-resolved multiparticle coincidence spectroscopy was used to study the ensuing dissociation.

Towards structural reconstruction from X-ray spectra.

We report a statistical analysis of Ge K-edge X-ray emission spectra simulated for amorphous GeO at elevated pressures. We find that employing machine learning approaches we can reliably predict the statistical moments of the K'' and K peaks in the spectrum from the Coulomb matrix descriptor with a training set of ∼ 10 samples. Spectral-significance-guided dimensionality reduction techniques allow us to construct an approximate inverse mapping from spectral moments to pseudo-Coulomb matrices.

Unsupervised classification of voltammetric data beyond principal component analysis.

In this study, we evaluate different apoproaches to unsupervised classification of cyclic voltammetric data, including Principal Component Analysis (PCA), t-distributed Stochastic Neighbour Embedding (t-SNE), Uniform Manifold Approximation and Projection (UMAP) as well as neural networks. To this end, we exploit a form of transfer learning, based on feature extraction in an image recognition network, VGG-16, in combination with PCA, t-SNE or UMAP. Overall, we find that t-SNE performs best when applied directly to numerical data (noise-free case) or to features (in the presence of noise), followed by UMAP and then PCA.