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Abstract To optimize CO2 EOR operations, such as Huff and Puff (HnP), it is necessary to have a good understanding of oil- CO2 transport both at nanopore and at reservoir scales. In this study, experiments were performed to investigate how pore adsorbed CO2 can mediate oil flow in analog nanopore arrays. These experiments quantified how much interfacial CO2 contributed to improving permeability to oil in nanopores, in addition to increasing mobility by viscosity reduction. The experimental procedure involved flowing C10 (decane) with and without CO2 through an Anodic Aluminum Oxide (AAO) membrane at a defined differential pressure and recording flow rate. Viscosity obtained from correlations was then used to calculate membrane pore permeability. Inlet pump pressure was lower than the oil-CO2 miscibility pressure at the test conditions. Pore permeability improvement due to pore wall adsorbed CO2 was computed by isolating the effect of viscosity reduction of the bulk fluid. An overall pore-permeability increase of 15% was observed in the CO2 and C10 mixture experiments compared to the C10-only experiments, due to interfacial CO2. These results lend support to the previous molecular dynamics simulations, which predicted that interfacial CO2 can significantly modulate C10 flow in nanopores up to 10 nm diameter (Moh et al. 2020). Some differences from the molecular dynamics simulations of Moh et al. (2020) observed in the experimental study also verify the potential contribution of other phenomena to the permeability enhancement of the nanoporous membrane in the presence of CO2. Therefore, this study provides further impetus for exploring the unique nanofluidic physics of oil and CO2 transport arising from CO2 at oil-wall interfaces. The demonstrated significance of the unique nanopore phenomena, which have not been observed and incorporated into large-scale flow models, emphasizes the importance of identifying and incorporating nanofluidic physics into commercial reservoir simulators' transport models for better representation of CO2 and oil flow in unconventional reservoirs. 

Reforming instruction is challenging. In this comparative case study of 12 school districts, we investigated the dilemmas that emerged for system leaders as they engaged in system building for elementary science and the approaches leaders took in managing them. We found that system leaders’ efforts to man- age their environments contributed to the preferential treatment of literacy and mathematics relative to science. Leaders managed this dilemma using three strategies: (a) integration of science with other subjects, (b) specialization of teachers, and (c) adopting curriculum materials. This study contrib- utes to literature on dilemma management by showing that dilemmas in education system building are school-subject sensitive, emerge in relation to system building for other subjects, and are embedded in school and education systems’ structural/organizational arrangements. 

Brain-inspired HyperDimensional (HD) computing emulates cognitive tasks by computing with long binary vectors–aka hypervectors–as opposed to computing with numbers. However, we observed that in order to provide acceptable classification accuracy on practical applications, HD algorithms need to be trained and tested on non-binary hypervectors. In this paper, we propose SearcHD, a fully binarized HD computing algorithm with a fully binary training. SearcHD maps every data points to a high-dimensional space with binary elements. Instead of training an HD model with non-binary elements, SearcHD implements a full binary training method which generates multiple binary hypervectors for each class. We also use the analog characteristic of non-volatile memories (NVMs) to perform all encoding, training, and inference computations in memory. We evaluate the efficiency and accuracy of SearcHD on a wide range of classification applications. Our evaluation shows that SearcHD can provide on average 31.1× higher energy efficiency and 12.8× faster training as compared to the state-of-the-art HD computing algorithms. 

Microbial production of the neurotoxin, methylmercury (MeHg), is a significant health and environmental concern as it can bioaccumulate and biomagnify in the food web. A chalkophore or a copper-binding compound, termed methanobactin (MB), has been shown to form strong complexes with mercury [as Hg(II)] and also enables some methanotrophs to degrade MeHg. It is unknown, however, if Hg(II) binding with MB can also impede Hg(II) methylation by other microbes. Contrary to expectations, MB produced by the methanotroph Methylosinus trichosporium OB3b (OB3b-MB) enhanced the rate and efficiency of Hg(II) methylation more than that observed with thiol compounds (such as cysteine) by the mercury-methylating bacteria, D. desulfuricans ND132 and G. sulfurreducens PCA. Compared to no-MB controls, OB3b-MB decreased the rates of Hg(II) sorption and internalization, but increased methylation by 5–7 fold, suggesting that Hg(II) complexation with OB3b-MB facilitated exchange and internal transfer of Hg(II) to the HgcAB proteins required for methylation. Conversely, addition of excess amounts of OB3b-MB or a different form of MB from Methylocystis strain SB2 (SB2-MB) inhibited Hg(II) methylation, likely due to greater binding of Hg(II). Collectively our results underscore complex roles of exogenous metal-scavenging compounds produced by microbes in controlling net production and bioaccumulation of MeHg in the environment.