Gasification of coal, municipal waste, or other organic materials is a potential hydrogen source that entails complex thermal decomposition and transport processes. This study provides a multiscale analysis of these processes for sub-bituminous (Usibelli, Healy, Alaska) and lignite (Center, North Dakota) coals and provides data useful for process design. The chemistry, mineralogy, and pore structures of pyrolyzed coal and their evolution with thermal decomposition are discussed. Samples pyrolyzed at 200-1000 degrees C were analyzed by small-angle neutron scattering; ultra-small, small-, and wide-angle X-ray scattering; and other complementary techniques. Scanning electron microscopy showed new pores in the high-temperature-pyrolyzed material. Upon heating, the coals became progressively denser, and the concentration of hydrogen decreased. Changes in pore volume fell into three temperature ranges: an initial, low-temperature range that, for the Usibelli coal, involved an increase in overall porosity; a mid-temperature range associated with pore volume loss; and a high-temperature range associated with significant porosity increase and char formation. This transformation was paralleled by changes in fractal dimension and correlation length. The higher the pyrolysis temperature the greater the small-porevolume fraction and overall surface area became. Pyrolysis increased the lateral size of coal crystallites, decreased the amorphous fraction, and increased the aromatics fraction and overall coal rank. Comparisons of neutron and X-ray scattering data and subsequent water uptake studies showed that pre-dried coals can rehydrate relatively rapidly upon exposure to air, which can significantly affect the porosity calculated from small-angle-scattering data. Fits to the cumulative porosity curves provide a method for modeling the physical and chemical transformation of hydrogen-containing feedstock during gasification.

Wastewater monitoring of SARS-CoV-2 presents a means of tracking COVID-19 community infection dynamics on a broader geographic scale. However, accounting for environmental and sample-processing losses may be necessary for wastewater measurements to readily inform our understanding of infection prevalence. Here, we present measurements of the SARS-CoV-2 N1 and N2 gene targets from weekly wastewater samples at three sites in Hamilton County, Ohio, during an increase and subsequent decline of COVID-19 infections. The concentration of N1 or N2 RNA in wastewater, measured over the course of six months, ranged from below the detection limit to over 104 gene copies/l, and correlated with case data at two wastewater treatment plants, but not at a sub-sewershed-level sampling site. We also evaluated the utility of a broader range of variables than has been reported consistently in previous work, in improving correlations of SARS-CoV-2 concentrations with case data. These include a spiked matrix recovery control (OC43), flow-normalization, and assessment of fecal loading using endogenous fecal markers (HF183, PMMoV, crAssphage). We found that adjusting for recovery, flow, and fecal indicators increased these correlations for samples from a larger sewershed (serving ~488,000 people) with greater industrial and stormwater inputs, but raw N1/N2 concentrations corresponded better with case data at a smaller, residential-oriented sewershed. Our results indicate that the optimal adjustment factors for correlating wastewater and clinical case data moving forward may not be generalizable to all sewersheds.

The pressure field of an impinging synthetic jet has been computed from time-resolved, three-dimensional, three-component (3D-3C) particle image velocimetry (PIV) velocity field data using a Poisson equationbased pressure solver. The pressure solver used in this work can take advantage of the temporal derivative of the pressure to enhance the temporal coherence of the calculated pressure field for time-resolved velocity data. The reconstructed pressure field shows sensitivity to the implementation of the boundary conditions, as well as to the spatial and temporal resolution of the PIV data. The pressure from a 3D Poisson solver that does not consider the temporal derivative of the pressure shows high random error. Invoking the temporal derivative of the pressure eliminates this high-frequency noise, however, the calculated pressure exhibits an unphysical temporal drift. This temporal drift is affected by both the temporal resolution of the PIV data and the spatial resolution of the PIV vector field, which was systematically evaluated by downsampling the instantaneous data and increasing the interrogation window size. It was observed that decreasing the temporal resolution increased the drift, while decreasing the spatial resolution decreased the drift.

The anti-soiling (AS) performance of solar mirrors coated with a highly transparent, superhydrophobic nanoparticle-textured coating has been characterized. The AS coatings were created on the mirror surface by depositing nano-textured silica nanoparticle layers of ∼250 nm thickness using a draw-down coating process, followed by fluorination of the nanoparticles in a molecular vapor deposition process. Highly uniform surface features of the AS-coated mirrors (20 × 30 cm2, no measurable loss in specular reflectance, and water contact angle >165°) provided an outstanding AS performance. A 4× reduction in the rate of dust accumulation as determined by gravimetric measurement of the accumulated dust on coated versus uncoated mirrors was observed. Additional evidence of a significant reduction in soiling rate was determined during measurements of specular reflectance in an outdoor environment test. The adhesion force between a model sand particle and nano-textured coatings in the hydrophobic to superhydrophobic range was also studied. A dramatic decrease in adhesive force acting on the particle was observed with increasing surface hydrophobicity. The results align well with the observed dust accumulation on the AS-coated mirrors. The AS-coated mirror maintains a high reflectivity by shedding dust and resisting dust accumulation, providing a potential benefit when applied to mirrors in the solar field of a concentrated solar power generation plant.

Soiling of optical surfaces due to sand and dust accumulation is the main cause for decreased efficiency of concentrating solar power and photovoltaic installations in desert areas. Nanostructured coatings with tailored surface roughness can reduce the rate of soil accumulation and maintain the high optical performance of the solar mirrors and cover glass. Here, we investigate the correlation between the size and structure of the surface features of the coating and its anti-soiling and optical properties. To control the morphology of the surface features we developed two types of coating: (1) based on small size (30-50 nm) silica particles with nanostructured surface and (2) based on unstructured nanoparticles with bimodal size distribution (80 and 35 nm). We tailored the surface features of the coatings to achieve synergistic improvements over different length-scales and thus decrease the adhesion force between the soil particles and the surface. Adhesion force measurements were performed using atomic force microscopy. The adhesion force and energy required to separate a silica particle from the surface of coated solar glass was significantly lower than the respective values from the surface of uncoated solar glass. A falling sand abrasion test, modeled after the procedure in ASTM D968, was performed. The optical properties of coated and uncoated solar glass were measured before and after the soiling test. Coated solar mirror samples were tested in the field. The results of the field test provided evidence that the anti-soiling coating is effective at reducing soiling and improving the specular reflectances of the coated mirrors. The use of the developed anti-soiling coating can be extended to other power-grid applications where reducing the soil accumulation is valuable.

Terry Johnson (Primary Contact), Robert Bowman (ORNL), Barton Smith (ORNL), Lawrence Anovitz (ORNL), Craig Jensen (HHC) Sandia National Laboratories 7011 East Avenue Livermore, CA 94551-0969 Phone: (925) 294-2512 Email: tajohns@sandia.gov DOE Managers (will be supplied – no input required) Subcontractors: Hawaii Hydrogen Carriers LLC, Honolulu, HI Project Start Date: October, 2016 Project End Date: September, 2019

Research on small team collaboration repeatedly shows that "distance matters". More recent work has refined this concept of distance to develop geographic dispersion measures to explain the negative effects that team configuration has on productivity and interaction. Dispersion measures explain why teams distributed across multiple time zones, or across multiple sites, have more coordination difficulties than collocated teams with a single remote member. Although larger online communities are increasingly used in enterprises, few studies have examined the effects of dispersion on community behavior. We studied 1206 online enterprise communities (OECs), each using a set of collaborative tools. We present new data showing counter-intuitively that OEC dispersion does not affect content generation or contribution inequality, even when restricting community size to those that most resemble small teams (with 3-12 members). We found that previously documented negative effects of geographic dispersion seem to be reduced in enterprise communities regardless of size. Our results provide additional support to prior case studies suggesting that online communities can mitigate geographic dispersion by providing resources that support coordination and resource sharing.

Recent research has developed analytics that threaten online self-presentation and privacy by automatically generating profiles of individuals' most personal traits-their personality, values, motivations, and so on. But we know little about people's reactions to personal traits profiles of themselves, or what influences their decisions to share such profiles. We present an early qualitative study of people's reactions to a working hyper-personal analytics system. The system lets them see their personality and values profile derived from their own social media text. Our results reveal a paradox. Participants found their personal traits profiles creepily accurate and did not like sharing them in many situations. However, they felt pressured by the social risks of not sharing and showed signs of learned helplessness, leading them to share despite their misgivings. Further, they felt unqualified to significantly modify their profile contents due to a surprising trust in the "expert" algorithm. We explore design implications for hyper-personal analytics systems that consider the needs and preferences of the people being profiled, suggesting ways to enhance the control they feel and the benefits they reap.

Optical surfaces such as mirrors and windows that are exposed to outdoor environmental conditions are susceptible to dust buildup and water condensation. The application of transparent superhydrophobic coatings on optical surfaces can improve outdoor performance via a 'self-cleaning' effect similar to the Lotus effect. The contact angle (CA) of water droplets on a typical hydrophobic flat surface varies from 100° to 120°. Adding roughness or microtexture to a hydrophobic surface leads to an enhancement of hydrophobicity and the CA can be increased to a value in the range of 160°–175°. This result is remarkable because such behavior cannot be explained using surface chemistry alone. When surface features are on the order of 100 nm or smaller, they exhibit superhydrophobic behavior and maintain their optical transparency. In this work we discuss our results on transparent superhydrophobic coatings that can be applied across large surface areas. We have used functionalized silica nanoparticles to coat various optical elements and have measured the CA and optical transmission between 190 and 1100 nm on these elements. The functionalized silica nanoparticles were dissolved in a solution of the solvents, while the binder used was a polyurethane clearcoat. This solution was spin-coated onto a variety of test glass substrates, and following a curing period of about 30 min, these coatings exhibited superhydrophobic behavior with a static CA ≥ 160°.

Online communities are successful only if they achieve their goals, but there has been little direct study of goals. We analyze novel data characterizing the goals of enterprise online communities, assessing the importance of goals for leaders, how goals influence member perceptions of community value, and how goals relate to success measures proposed in the literature. We find that most communities have multiple goals and common goals are learning, reuse of resources, collaboration, networking, influencing change, and innovation. Leaders and members agree that all of these goals are important, but their perceptions of success on goals do not align with each other, or with commonly used behavioral success measures. We conclude that simple behavioral measures and leader perceptions are not good success metrics, and propose alternatives based on specific goals members and leaders judge most important.

This paper presents a system, System U, which automatically derives people's personality traits from social media and recommends people for different tasks. The system leverages linguistic signals appearing in a person's social media activities to compute the personality portraits including Big Five personality, fundamental needs and basic human values. This system and technology can be used in a wide variety of personalized applications, such as recommending people to answer questions.

Most studies of social tools examine usage of each tool in isolation. Instead, we explore how online communities (a) combine multiple social tools, and (b) use social tools together with external tools. Based on interviews with community leaders and quantitative analysis of 128 online community spaces, we explored the combined use of six social software tools--wikis, blogs, forums, social bookmarks, social file repositories, and task-management tools. We contribute a detailed characterization of how enterprise online communities combine multiple social tools, adding to our understanding of community behaviors: Communities combine social tools to curate and organize complex information spaces. When combined, each tool is used for limited "core" functions; thus 'social' features are not always leveraged for every tool. Leaders and members divide labor by tool boundaries. Our results suggest that an important overlooked aspect of social media concerns how different tools can be effectively combined. While most prior work on communities emphasizes end user content, we identify additional important design activities where community participants curate and organize pre-existing content from multiple tools to serve their community needs.

An online community consists of a group of users who share a common interest, background, or experience and their collective goal is to contribute towards the welfare of the community members. Question answering is an important feature that enables community members to exchange knowledge within the community boundary. The overwhelming number of communities necessitates the need for a good question routing strategy so that new questions gets routed to the appropriately focused community and thus get resolved. In this paper, we consider the novel problem of routing questions to the right community and propose a framework to select the right set of communities for a question. We begin by using several prior proposed features for users and add some additional features, namely language attributes and inclination to respond, for community modeling. Then we introduce two k nearest neighbor based aggregation algorithms for computing community scores. We show how these scores can be combined to recommend communities and test the effectiveness of the recommendations over a large real world dataset.