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Polymer networks crosslinked with spring-like ortho -phenylene ( o P) foldamers were developed. NMR analysis indicated the o P crosslinkers were well-folded. Polymer networks with o P-based crosslinkers showed enhanced energy dissipation and elasticity compared to divinylbenzene crosslinked networks. The energy dissipation was attributed to the strain-induced reversible unfolding of the o P units. Energy dissipation increased with the number of helical turns in the foldamer. 

Online professional development (PD) can support broader accessibility than traditional face-to- face PD. However online delivery presents challenges for characteristics of high-quality PD, such as collaborative knowledge building and community development, that have proven positive outcomes in face-to- face modes. A few comparative studies have demonstrated equivalent outcomes when PD activities have been translated from a successful face-to-face implementation to an online format. This study investigates whether an online version of PD for high school biology teachers on using computer-supported complex systems curriculum and instruction can achieve the same high impact as the face-to-face version. We describe changes in design decisions to accommodate the online mode and measure impact on teachers’ perceptions of their experiences and student outcomes. The results show positive teacher perceptions in both PD formats and roughly equal student outcomes. However, teachers articulated other benefits to online activities that indicate opportunities for improved access to high-quality PD. 

Many researchers have identified the need for a more holistic understanding of the role of feedback in supporting learning in online environments. This study explores how our design, development, and implementation of an online feedback facilitation system influenced high school science teachers’ learning in an asynchronous teacher professional development online course. We then describe teachers’ and facilitators’, i.e., feedback providers’, perceptions of the effectiveness of the system’s features for supporting participants’ learning and engagement. Our work also responds to recent calls for developing a more nuanced understanding of how the complexity of feedback influences learning and the need for more qualitative research on online facilitators’ and learners’ experiences working with new technologies. Results demonstrated that, despite the difficulty of analyzing the complex variables influencing learners’ interactions and perceptions of the feedback system, designing adaptive feedback systems that draw on the principles of design- based implementation research (DBIR) offer promise for enhancing the systems’ contributions to teacher learning. 

Abstract The MicroBooNE liquid argon time projection chamber (LArTPC) maintains a high level of liquid argon purity through the use of a filtration system that removes electronegative contaminants in continuously-circulated liquid, recondensed boil off, and externally supplied argon gas. We use the MicroBooNE LArTPC to reconstruct MeV-scale radiological decays. Using this technique we measure the liquid argon filtration system's efficacy at removing radon. This is studied by placing a 500 kBq 222 Rn source upstream of the filters and searching for a time-dependent increase in the number of radiological decays in the LArTPC. In the context of two models for radon mitigation via a liquid argon filtration system, a slowing mechanism and a trapping mechanism, MicroBooNE data supports a radon reduction factor of greater than 97% or 99.999%, respectively. Furthermore, a radiological survey of the filters found that the copper-based filter material was the primary medium that removed the 222 Rn. This is the first observation of radon mitigation in liquid argon with a large-scale copper-based filter and could offer a radon mitigation solution for future large LArTPCs. 

Abstract In this article, we describe a modified implementation of Mask Region-based Convolutional Neural Networks (Mask-RCNN) for cosmic ray muon clustering in a liquid argon TPC and applied to MicroBooNE neutrino data. Our implementation of this network, called sMask-RCNN, uses sparse submanifold convolutions to increase processing speed on sparse datasets, and is compared to the original dense version in several metrics. The networks are trained to use wire readout images from the MicroBooNE liquid argon time projection chamber as input and produce individually labeled particle interactions within the image. These outputs are identified as either cosmic ray muon or electron neutrino interactions. We find that sMask-RCNN has an average pixel clustering efficiency of 85.9% compared to the dense network's average pixel clustering efficiency of 89.1%. We demonstrate the ability of sMask-RCNN used in conjunction with MicroBooNE's state-of-the-art Wire-Cell cosmic tagger to veto events containing only cosmic ray muons. The addition of sMask-RCNN to the Wire-Cell cosmic tagger removes 70% of the remaining cosmic ray muon background events at the same electron neutrino event signal efficiency. This event veto can provide 99.7% rejection of cosmic ray-only background events while maintaining an electron neutrino event-level signal efficiency of 80.1%. In addition to cosmic ray muon identification, sMask-RCNN could be used to extract features and identify different particle interaction types in other 3D-tracking detectors.