Fluids by design using chaotic surface waves to create a metafluid that is Newtonian, thermal, and entirely tunable.

In conventional fluids, viscosity depends on temperature according to a strict relationship. To change this relationship, one must change the molecular nature of the fluid. Here, we create a metafluid whose properties are derived not from the properties of molecules but rather from chaotic waves excited on the surface of vertically agitated water. By making direct rheological measurements of the flow properties of our metafluid, we show that it has independently tunable viscosity and temperature, a quality that no conventional fluid possesses. We go on to show that the metafluid obeys the Einstein relation, which relates many-body response (viscosity) to single-particle dynamics (diffusion) and is a fundamental result in equilibrium thermal systems. Thus, our metafluid is wholly consistent with equilibrium thermal physics, despite being markedly nonequilibrium. Taken together, our results demonstrate a type of material that retains equilibrium physics while simultaneously allowing for direct programmatic control over material properties.

Double helical structure of the twist-bend nematic phase investigated by resonant X-ray scattering at the carbon and sulfur K-edges.

The mesogenic dimer displaying nematic and NTB phases was investigated by resonant X-ray scattering at both C and S absorption K-edges and supported by single X-ray crystallography. In the crystal resonant studies revealed the forbidden reflection in non-resonant diffraction similar to that found in the NTB phase. The lack of a second harmonic in both C and S resonant X-ray scattering supports the double helical structure of the twist-bend nematic phase.

The Turing Teacher: Identifying core attributes for AI learning in K-12.

Introduction: Artificial intelligence in the educational domain has many uses; however, using AI specifically to enhance education and teaching in a K-12 environment poses the most significant challenges to its use. Beyond usage and application, the quality of the education is made even more arduous due to the dynamics of teaching primary and secondary school children, whose needs far exceed mere fact recollection. Utilizing prior research using AI in education and online education in the K-12 space, we explore some of the hurdles that AI applications face in K-12 teaching and provide core attributes for a "Turing Teacher," i.e., an AI powered technology for learning, specifically targeting the K-12 space. Methods: Using a survey, which included qualitative responses during the implementation of online learning during the Covid Pandemic, we analyze the results using univariate and multivariate tests and analyzed the qualitative responses to create core attributes needed for AI powered teaching technology. Results: The results present the challenges faced by any technology in an education setting and show that AI technology must help overcome negative feelings about technology in education. Further, the core attributes identified in the research must be addressed from the three stakeholder perspectives of teachers, parents and students. Discussion: We present our findings and lay the groundwork for future research in the area of AI powered education. The Turing Teacher must be able to adapt and collaborate with real teachers and address the varying needs of students. In addition, we explore the use of AI technology as a means to close the digital divide in traditionally disadvantaged communities.

Feeling Positive About Reopening? New Normal Scenarios From COVID-19 US Reopen Sentiment Analytics.

The Coronavirus pandemic has created complex challenges and adverse circumstances. This research identifies public sentiment amidst problematic socioeconomic consequences of the lockdown, and explores ensuing four potential public sentiment associated scenarios. The severity and brutality of COVID-19 have led to the development of extreme feelings, and emotional and mental healthcare challenges. This research focuses on emotional consequences - the presence of extreme fear, confusion and volatile sentiments, mixed along with trust and anticipation. It is necessary to gauge dominant public sentiment trends for effective decisions and policies. This study analyzes public sentiment using Twitter Data, time-aligned to the COVID-19 reopening debate, to identify dominant sentiment trends associated with the push to reopen the economy. Present research uses textual analytics methodologies to analyze public sentiment support for two potential divergent scenarios - an early opening and a delayed opening, and consequences of each. Present research concludes on the basis of textual data analytics, including textual data visualization and statistical validation, that tweets data from American Twitter users shows more positive sentiment support, than negative, for reopening the US economy. This research develops a novel sentiment polarity based public sentiment scenarios (PSS) framework, which will remain useful for future crises analysis, well beyond COVID-19. With additional validation, this research stream could present valuable time sensitive opportunities for state governments, the federal government, corporations and societal leaders to guide local and regional communities, and the nation into a successful new normal future.

Preferred crystallographic orientation of cellulose in plant primary cell walls.

Cellulose, the most abundant biopolymer on earth, is a versatile, energy rich material found in the cell walls of plants, bacteria, algae, and tunicates. It is well established that cellulose is crystalline, although the orientational order of cellulose crystallites normal to the plane of the cell wall has not been characterized. A preferred orientational alignment of cellulose crystals could be an important determinant of the mechanical properties of the cell wall and of cellulose-cellulose and cellulose-matrix interactions. Here, the crystalline structures of cellulose in primary cell walls of onion (Allium cepa), the model eudicot Arabidopsis (Arabidopsis thaliana), and moss (Physcomitrella patens) were examined through grazing incidence wide angle X-ray scattering (GIWAXS). We find that GIWAXS can decouple diffraction from cellulose and epicuticular wax crystals in cell walls. Pole figures constructed from a combination of GIWAXS and X-ray rocking scans reveal that cellulose crystals have a preferred crystallographic orientation with the (200) and (110)/([Formula: see text]) planes preferentially stacked parallel to the cell wall. This orientational ordering of cellulose crystals, termed texturing in materials science, represents a previously unreported measure of cellulose organization and contradicts the predominant hypothesis of twisting of microfibrils in plant primary cell walls.

Molecular engineering of organic-inorganic hybrid perovskites quantum wells.

Semiconductor quantum-well structures and superlattices are key building blocks in modern optoelectronics, but it is difficult to simultaneously realize defect-free epitaxial growth while fine tuning the chemical composition, layer thickness and band structure of each layer to achieve the desired performance. Here we demonstrate the modulation of the electronic structure-and consequently the optical properties-of organic semiconducting building blocks that are incorporated between the layers of perovskites through a facile solution processing step. Self-aggregation of the conjugated organic molecules is suppressed by functionalization with sterically demanding groups and single crystalline organic-perovskite hybrid quantum wells (down to one-unit-cell thick) are obtained. The energy and charge transfers between adjacent organic and inorganic layers are shown to be fast and efficient, owing to the atomically flat interface and ultrasmall interlayer distance of the perovskite materials. The resulting two-dimensional hybrid perovskites are very stable due to protection given by the bulky hydrophobic organic groups.