Search

The Lieb lattice is one of the simplest lattices that exhibits both linear Dirac-like and flat topological electronic bands. We propose to further tailor its electronic properties through periodic 1D electrostatic superlattices (SLs), which, in the long wavelength limit, were predicted to give rise to novel transport signatures, such as the omnidirectional super-Klein tunnelling (SKT). By numerically modelling the electronic structure at tight-binding level, we uncover the evolution of the Lieb SL band structure from the discrete all the way to the continuum regime and build a comprehensive picture of the Lieb lattice under 1D potentials. This approach allows us to also take into consideration the discrete lattice symmetry-breaking that occurs at the well/barrier interfaces created by the 1D SL, whose consequences cannot be explored using the previous low energy and long wavelength approaches. We find novel features in the band structure, among which are intersections of quadratic and flat bands, tilted Dirac cones, or series of additional anisotropic Dirac cones at energies where the SKT is predicted. Such features are relevant to experimental realizations of electronic transport in Lieb 1D SL realized in artificial lattices or in real material systems like 2D covalent organic/metal-organic frameworks and inorganic 2D solids.

The influence of human activity own the earth's atmospheric composition has never been more pronounced. Anthropogenic pollution is in fact the largest effector of the observed evolving atmospheric composition (Wallace, 2006). PM2.5 is a class of particulate matter pollutants of notable interest due to their significant driving of chemical, atmospheric change, their wide-scale, global circulations, and their malignant effects on human health; with a diameter of less than 2.5 microns; PM2.5s derive from combustion of organic materials, including fossil fuel combustion (Wallace, 2006) and forest fires (Newman, 2007). The gases released in these combustion reactions then condense in the atmosphere, undergoing gas to particle conversion, resulting in the atmospheric presence of PM2.5s. Particulate matter (PM) pollutants are harmful to human health in all diameter scales; increasing in recent years global morbidity and mortality (Araujo, 2011). The health risks of PM2.5 in particular are troubling due to their small size, which facilities their permeability in the respiratory system and ready diffusion into the bloodstream, inducing pathologies like ischaemic heart disease, respiratory infections, and lung cancers to name a few (Araujo, 2011). Once PM2.5 manifest in the atmosphere, they circulate on a larger scale due to atmospheric circulation patterns. Though government-enacted air quality measures have reduced the average PM2.5 levels in North America, pollution episodes still cause localized, acute PM2.5 exposure. The purpose of this project was to analyze PM2.5 mean concentration across America to identify and quantify any pollution episodes, as well as try to explain their dynamics using large scale, meteorological processes., Comment: Withdrawn because it was submitted without consent of the co-author

Chemical transport models (CTMs) have been widely used to provide instructions for the control of ozone (O3) pollution. However, we find large discrepancies between observation- and model-based urban O3 chemical regimes: volatile organic compound (VOC)-limited regimes over N. China and weak nitrogen oxides (NOx)-limited regimes over S. China in observations, in contrast to simulations with widespread distributions of strong NOx-limited regimes. The conflicting O3 evolutions are caused by underestimated urban NOx concentrations and the possible overestimation of biogenic VOC emissions. Reductions in NOx emissions, in response to regulations, have thus led to an unintended deterioration of O3 pollution over N. China provinces, for example, an increase in surface O3 by approximately 7 ppb over the Sichuan Basin (SCB) in 2014-2020. The NOx-induced urban O3 changes resulted in an increase in premature mortality by approximately 3000 cases in 2015-2020.

With the advent of ALMA, it is now possible to observationally constrain how disks form around deeply embedded protostars. In particular, the recent ALMA C3H2 line observations of the nearby protostar L1527 have been interpreted as evidence for the so-called "centrifugal barrier," where the protostellar envelope infall is gradually decelerated to a stop by the centrifugal force in a region of super-Keplerian rotation. To test the concept of centrifugal barrier, which was originally based on angular momentum conserving-collapse of a rotating test particle around a fixed point mass, we carry out simple axisymmetric hydrodynamic simulations of protostellar disk formation including a minimum set of ingredients: self-gravity, rotation, and a prescribed viscosity that enables the disk to accrete. We find that a super-Keplerian region can indeed exist when the viscosity is relatively large but, unlike the classic picture of centrifugal barrier, the infalling envelope material is not decelerated solely by the centrifugal force. The region has more specific angular momentum than its surrounding envelope material, which points to an origin in outward angular momentum transport in the disk (subject to the constraint of disk expansion by the infalling envelope), rather than the spin-up of the envelope material envisioned in the classic picture as it falls closer to the center in order to conserve angular momentum. For smaller viscosities, the super-Keplerian rotation is weaker or non-existing. We conclude that, despite the existence of super-Keplerian rotation in some parameter regime, the classic picture of centrifugal barrier is not supported by our simulations.

A new, more comprehensive model of gas-grain chemistry in hot molecular cores is presented, in which nondiffusive reaction processes on dust-grain surfaces and in ice mantles are implemented alongside traditional diffusive surface/bulk-ice chemistry. We build on our nondiffusive treatments used for chemistry in cold sources, adopting a standard collapse/warm-up physical model for hot cores. A number of other new chemical model inputs and treatments are also explored in depth, culminating in a final model that demonstrates excellent agreement with gas-phase observational abundances for many molecules, including some (e.g. methoxymethanol) that could not be reproduced by conventional diffusive mechanisms. Observed ratios of structural isomers methyl formate, glycolaldehyde and acetic acid are well reproduced by the models. The main temperature regimes are identified in which various complex organic molecules (COMs) are formed. Nondiffusive chemistry advances the production of many COMs to much earlier times and lower temperatures than in previous model implementations. Those species may form either as by-products of simple-ice production, or via early photochemistry within the ices while external UV photons can still penetrate. Cosmic ray-induced photochemistry is less important than in past models, although it affects some species strongly over long timescales. Another production regime occurs during the high-temperature desorption of solid water, whereby radicals trapped in the ice are released onto the grain/ice surface, where they rapidly react. Several recently-proposed gas-phase COM-production mechanisms are also introduced, but they rarely dominate. New surface/ice reactions involving CH and CH$_2$ are found to contribute substantially to the formation of certain COMs., Comment: 92 pages, 21 figures, 21 tables. Accepted for publication in Astrophysical Journal Supplements

Tropospheric nitrogen dioxide (NO$_2$) concentrations are strongly affected by anthropogenic activities. Using space-based measurements of tropospheric NO$_2$, here we investigate the responses of tropospheric NO$_2$ to the 2019 novel coronavirus (COVID-19) over China, South Korea, and Italy. We find noticeable reductions of tropospheric NO$_2$ columns due to the COVID-19 controls by more than 40% over E. China, South Korea, and N. Italy. The 40% reductions of tropospheric NO$_2$ are coincident with intensive lockdown events as well as up to 20% reductions in anthropogenic nitrogen oxides (NO$_x$) emissions. The perturbations in tropospheric NO$_2$ diminished accompanied with the mitigation of COVID-19 pandemic, and finally disappeared within around 50-70 days after the starts of control measures over all three nations, providing indications for the start, maximum, and mitigation of intensive controls. This work exhibits significant influences of lockdown measures on atmospheric environment, highlighting the importance of satellite observations to monitor anthropogenic activity changes.

We use a hybrid deep learning model to predict June-July-August (JJA) daily maximum 8-h average (MDA8) surface ozone concentrations in the US. A set of meteorological fields from the ERA-Interim reanalysis as well as monthly mean NO$_x$ emissions from the Community Emissions Data System (CEDS) inventory are selected as predictors. Ozone measurements from the US Environmental Protection Agency (EPA) Air Quality System (AQS) from 1980 to 2009 are used to train the model, whereas data from 2010 to 2014 are used to evaluate the performance of the model. The model captures well daily, seasonal and interannual variability in MDA8 ozone across the US. Feature maps show that the model captures teleconnections between MDA8 ozone and the meteorological fields, which are responsible for driving the ozone dynamics. We used the model to evaluate recent trends in NO$_x$ emissions in the US and found that the trend in the EPA emission inventory produced the largest negative bias in MDA8 ozone between 2010-2016. The top-down emission trends from the Tropospheric Chemistry Reanalysis (TCR-2), which is based on satellite observations, produced predictions in best agreement with observations. In urban regions, the trend in AQS NO$_2$ observations provided ozone predictions in agreement with observations, whereas in rural regions the satellite-derived trends produced the best agreement. In both rural and urban regions the EPA trend resulted in the largest negative bias in predicted ozone. Our results suggest that the EPA inventory is overestimating the reductions in NO$_x$ emissions and that the satellite-derived trend reflects the influence of reductions in NO$_x$ emissions as well as changes in background NO$_x$. Our results demonstrate the significantly greater predictive capability that the deep learning model provides over conventional atmospheric chemical transport models for air quality analyses.