Variation in structure and properties of poly(glycerol adipate) via control of chain branching during enzymatic synthesis

Poly (glycerol adipate) (PGA) can be produced from divinyl adipate and unprotected glycerol by an enzymatic route to generate a polymer with relatively low molar mass (12 kDa). PGA bears a pendant hydroxyl group which imparts a hydrophilic character to this water insoluble polymer. We have examined the effect of synthesis temperature on polymer characteristics through various techniques including FT-IR, 1H and 13C NMR, surface and thermal analysis, both to expand the data already present in the literature about this material and to understand better its properties for potential pharmaceutical applications. The use of a lipase (Novozym 435) as a catalyst suppresses cross-linking at the pendant glyceryl hydroxyl through steric hindrance at the active site, thus producing polymers with low degrees of branching (5–30%), and removes the need for any pre- or post-polymerization protection/deprotection reactions. Careful temperature control during synthesis can give polymers with reproducible molecular weights and reduced amounts of polymer branching compared to synthesis at higher temperatures. Due to the ability of the synthetic route to produce a range of structures, PGA generated by enzymatic routes may emerge as a useful biodegradable polymer platform to engineer solid dispersions or nanoparticles for healthcare applications

Graph Neural Networks for low-energy event classification & reconstruction in IceCube

IceCube, a cubic-kilometer array of optical sensors built to detect atmospheric and astrophysical neutrinos between 1 GeV and 1 PeV, is deployed 1.45 km to 2.45 km below the surface of the ice sheet at the South Pole. The classification and reconstruction of events from the in-ice detectors play a central role in the analysis of data from IceCube. Reconstructing and classifying events is a challenge due to the irregular detector geometry, inhomogeneous scattering and absorption of light in the ice and, below 100 GeV, the relatively low number of signal photons produced per event. To address this challenge, it is possible to represent IceCube events as point cloud graphs and use a Graph Neural Network (GNN) as the classification and reconstruction method. The GNN is capable of distinguishing neutrino events from cosmic-ray backgrounds, classifying different neutrino event types, and reconstructing the deposited energy, direction and interaction vertex. Based on simulation, we provide a comparison in the 1 GeV–100 GeV energy range to the current state-of-the-art maximum likelihood techniques used in current IceCube analyses, including the effects of known systematic uncertainties. For neutrino event classification, the GNN increases the signal efficiency by 18% at a fixed background rate, compared to current IceCube methods. Alternatively, the GNN offers a reduction of the background (i.e. false positive) rate by over a factor 8 (to below half a percent) at a fixed signal efficiency. For the reconstruction of energy, direction, and interaction vertex, the resolution improves by an average of 13%–20% compared to current maximum likelihood techniques in the energy range of 1 GeV–30 GeV. The GNN, when run on a GPU, is capable of processing IceCube events at a rate nearly double of the median IceCube trigger rate of 2.7 kHz, which opens the possibility of using low energy neutrinos in online searches for transient events.Peer Reviewe