SciPost Chem. 2, 001 (2023) ·
published 13 April 2023
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The high chemical stability of SU-8 makes it irreplaceable for a wide range of applications, most notably as a lithography photoresist for micro and nanotechnology. This advantage becomes a problem when there is a need to remove SU-8 from the fabricated devices. Researchers have been struggling for two decades with this problem, and although a number of partial solutions have been found, this difficulty has limited the applications of SU-8. Here we demonstrate a fast, reproducible, and comparatively gentle method to chemically remove SU-8 photoresist. An ether cleavage mechanism for the observed reaction is proposed, and the hypothesis is tested with ab initio quantum chemical calculations. We also describe a complementary removal method based on atomic hydrogen inductively coupled plasma.
Maria H. Rasmussen, Diana S. Christensen, Jan H. Jensen
SciPost Chem. 2, 002 (2023) ·
published 27 June 2023
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While there is a great deal of interest in methods aimed at explaining machine learning predictions of chemical properties, it is difficult to quantitatively benchmark such methods, especially for regression tasks. We show that the Crippen logP model (J. Chem. Inf. Comput. Sci. 1999, 39, 868) provides an excellent benchmark for atomic attribution/heatmap approaches, especially if the ground truth heatmaps can be adjusted to reflect the molecular representation. The "atom attribution from finger prints"-method developed by Riniker and Landrum (J. Chem. Inf. Comput. Sci. 2013, 5, 43) gives atomic attribution heatmaps that are in reasonable agreement with the atomic contribution heatmaps of the Crippen logP model for most molecules, with average heatmap overlaps of up to 0.54. The agreement is increased significantly (to 0.75) when the atomic contributions are adjusted to match the fact that the molecular representation is fragment-based rather than atom-based (the finger print-adapted (FPA) ground truth vector). Most heatmaps and the corresponding FPA overlaps are relatively insensitive to the training set size and the results are close to converged for a training set size of 1000 molecules, although for molecules with low overlap some heatmaps change significantly. Using the "remove an atom" approach for graph convolutional neural networks (GCNNs) suggested by Matveieva and Polishchuk (J. Cheminform. 2021, 13, 41) we find an average heatmap overlap of 0.47 for the atomic contribution heatmaps of the Crippen logP model. Like the simpler attribution benchmarks for classification tasks that have come before it, this work sets the bar for regression tasks.
