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  1. We report here extracting SiO2 as spirosiloxane [(CH3)2C(O)CH2CH(O)CH3]2Si from rice hull ash (RHA) to carefully control the SiO2 : C mole ratios, allowing direct carbothermal reduction to SiC, Si3N4, or Si2N2O without the need to add extra carbon and as a mechanism to preserve the original nanocomposite structure. We can adjust SiO2 : C ratios from 2 : 15 to 13 : 35 simply by reacting RHA with hexylene glycol (HG) with catalytic base to distillatively extract SiO2 to produce silica depleted RHA (SDRHA) with SiO2 contents of 40–65 wt% and corresponding carbon contents of 60–35 wt% with specific surface areas (SSAs) of >400 m2 g−1. On heating SDRHA40–65 at 1400–1500 °C in an Ar, N2, or N2–H2 atmosphere, XRD patterns reveal formation of SiC, Si3N4, or Si2N2O as the major phase with some residual hard carbon. SEM studies reveal mixtures of particles and whiskers in the products, which show BET specific surface areas >40 m2 g−1 after oxidative removal of excess carbon. Dilute acid and boiling water prewashing of RHA with milling eliminates typical product impurities compared to those found using conventional carbothermal reduction of agricultural wastes, which qualifies the resulting composites as components for electrochemical energy storage devicesmore »among other applications, to be reported elsewhere.« less
    Free, publicly-accessible full text available September 23, 2023
  2. Biomass-derived materials offer low carbon approaches to energy storage. High surface area SiC w/wo 13 wt% hard carbon (SiC/HC, SiC/O), derived from carbothermal reduction of silica depleted rice hull ash (SDRHA), can function as Li+ battery anodes. Galvanostatic cycling of SiC/HC and SiC/O shows capacity increases eventually to >950 mA h g−1 (Li1.2–1.4SiC) and >740 mA h g−1 (Li1.1SiC), respectively, after 600 cycles. Post-mortem investigation via XRD and 29Si MAS NMR reveals partial phase transformation from 3C- to 6H-SiC, with no significant changes in unit cell size. SEMs show cycled electrodes maintain their integrity, implying almost no volume expansion on lithiation/delithiation, contrasting with >300% volume changes in Si anodes on lithiation. Significant void space is needed to compensate for these volume changes with Si in contrast to SiC anodes suggesting nearly competitive capacities. 6Li MAS NMR and XPS show no evidence of LixSi, with Li preferring all-C environments supported by computational modeling. Modeling also supports deviation from the 3C phase at high Li contents with minimal volume changes.
    Free, publicly-accessible full text available April 20, 2023
  3. Abstract: A volume-penalization immersed boundary (VPIB) method was developed to study flow interactions with aquatic vegetation. The model has been validated with data from laboratory experiments and previous high-fidelity models with satisfactory results. Sensitivity analyzes on both penalty parameter and thickness parameter were conducted, and optimal values for these parameters are recommended. The validated model has been applied to study the effects of swaying motion of vegetation stems on the flow dynamics at both vegetate-stem scale and patch scale. The swaying motion of the vegetation stem is prescribed following a cubic law that peaks at the top and decreases to zero at the bottom. At stem-scale, the hydrodynamics depend on the Keulegan Carpenter number (KC), which is defined as the maximum excursion of the vegetation stem to the diameter of the stem. Simulations with three KC values were carried out. For KC≥1, the flow turbulence is significantly enhanced by the swaying motion of the stem, and turbulence becomes more isotropic in the wake. The swaying motion of vegetation stems caused a 5% increase of the bottom shear stress at the shoulders of the stem, and the effect is negligible in the wake. At patch-scale, the hydrodynamics depend on the effectivemore »Keulegan Carpenter number based on the patch size of the vegetation patch, and the solid volume fraction for dense vegetation canopy. Solid volume fraction was varied while maintaining the same effective Keulegan Carpenter in the simulations. When the effective Keulgen Carpenter number is small (KC<1), effects of the swaying motion of vegetation stems on the large patch-scale dynamics are not significant, including both the turbulence statistics and the bottom stress.« less
  4. Metal nitrides are intensely investigated because they can offer high melting points, excellent corrosion resistance, high hardness, electronic and magnetic properties superior to the corresponding metals/metal oxides. Thus, they are used in diverse applications including refractory materials, semiconductors, elec- tronic devices, and energy storage/conversion systems. Here, we present a sim- ple, novel, scalable and general route to metal nitride precursors by reactions of metal chlorides with hexamethyldisilazane [HMDS, (Me3 Si)2 NH] in tetrahydro- furan or acetonitrile at low temperatures (ambient to 60◦C/N2). Such reactions have received scant attention in the literature. The work reported here focuses primarily on the Al-HMDS precursor pro- duced from the reaction of AlCl3 with HMDS (mole ratio = 1:3) characterized by matrix-assisted laser desorption/ionization-time of flight, Fourier-transform infrared spectroscopy, thermogravimetric analysis-differential thermal analysis, and multinuclear nuclear magnetic resonance spectroscopy (NMRs) for chemi- cal and structural analyses. The Al-HMDS precursor heated to 1600◦C/4 h/N2 produces aluminum nitride, characterized by X-ray powder diffraction, X-ray photoelectron spectroscopy, scanning electron microscopy/energy-dispersive X- ray spectroscopy, and magic-angle spinning NMR. On heating to 800–1200◦C/4 h/N2, the precursor transforms to an amorphous, oxygen-sensitive powder with very high surface areas (>200 m2/g) indicating nanosized particles, which can be used as additives to polymermore »matrices to modify their thermal stabilities. Al2O3 is also presented in the final product after heating, due to its high susceptibility to oxidation. This approach was extended via proof-of-concept studies to other metal chloride systems, including Zn-HMDS, Cu-HMDS, Fe-HMDS, and Bi-HMDS. The formed precursors are volatile, offering the potential utility as gas-phase deposition pre- cursors for their corresponding metal nitrides.« less
  5. Speakers build rapport in the process of aligning conversational behaviors with each other. Rapport engendered with a teachable agent while instructing domain material has been shown to promote learning. Past work on lexical alignment in the field of education suffers from limitations in both the measures used to quantify alignment and the types of interactions in which alignment with agents has been studied. In this paper, we apply alignment measures based on a data-driven notion of shared expressions (possibly composed of multiple words) and compare alignment in one-on-one human-robot (H-R) interactions with the H-R portions of collaborative human-human-robot (H-H-R) interactions. We find that students in the H-R setting align with a teachable robot more than in the H-H-R setting and that the relationship between lexical alignment and rapport is more complex than what is predicted by previous theoretical and empirical work.
  6. Mitrovic, A. ; & Bosch, N. (Ed.)
    Working collaboratively in groups can positively impact performance and student engagement. Intelligent social agents can provide a source of personalized support for students, and their benefits likely extend to collaborative settings, but it is difficult to determine how these agents should interact with students. Reinforcement learning (RL) offers an opportunity for adapting the interactions between the social agent and the students to better support collaboration and learning. However, using RL in education with social agents typically involves training using real students. In this work, we train an RL agent in a high-quality simulated environment to learn how to improve students’ collaboration. Data was collected during a pilot study with dyads of students who worked together to tutor an intelligent teachable robot. We explore the process of building an environment from the data, training a policy, and the impact of the policy on different students, compared to various baselines.