Dilute Rhenium Doping and its Impact on Defects in MoS2.

Substitutionally doped 2D transition metal dichalcogenides are primed for next-generation device applications such as field effect transistors (FET), sensors, and optoelectronic circuits. In this work, we demonstrate substitutional rhenium (Re) doping of MoS2 monolayers with controllable concentrations down to 500 ppm by metal-organic chemical vapor deposition (MOCVD). Surprisingly, we discover that even trace amounts of Re lead to a reduction in sulfur site defect density by 5-10×. Ab initio models indicate the origin of the reduction is an increase in the free-energy of sulfur-vacancy formation at the MoS2 growth-front when Re is introduced. Defect photoluminescence (PL) commonly seen in undoped MOCVD MoS2 is suppressed by 6× at 0.05 atomic percent (at. %) Re and completely quenched with 1 at. % Re. Furthermore, we find that Re-MoS2 transistors exhibit a 2× increase in drain current and carrier mobility compared to undoped MoS2, indicating that sulfur vacancy reduction improves carrier transport in the Re-MoS2. This work provides important insights on how dopants affect 2D semiconductor growth dynamics, which can lead to improved crystal quality and device performance.

Benchmarking First-Principles Reaction Equilibrium Composition Prediction.

The availability of thermochemical properties allows for the prediction of the equilibrium compositions of chemical reactions. The accurate prediction of these can be crucial for the design of new chemical synthesis routes. However, for new processes, these data are generally not completely available. A solution is the use of thermochemistry calculated from first-principles methods such as Density Functional Theory (DFT). Before this can be used reliably, it needs to be systematically benchmarked. Although various studies have examined the accuracy of DFT from an energetic point of view, few studies have considered its accuracy in predicting the temperature-dependent equilibrium composition. In this work, we collected 117 molecules for which experimental thermochemical data were available. From these, we constructed 2648 reactions. These experimentally constructed reactions were then benchmarked against DFT for 6 exchange-correlation functionals and 3 quality of basis sets. We show that, in reactions that do not show temperature dependence in the equilibrium composition below 1000 K, over 90% are predicted correctly. Temperature-dependent equilibrium compositions typically demonstrate correct qualitative behavior. Lastly, we show that the errors are equally caused by errors in the vibrational spectrum and the DFT electronic ground state energy.

Improving Accuracy and Transferability of Machine Learning Chemical Activation Energies by Adding Electronic Structure Information.

Predicting chemical activation energies is one of the longstanding and important challenges in computational chemistry. Recent advances have shown that machine learning can be used to create tools to predict them. Such tools can significantly decrease the computational cost for these predictions compared to traditional methods, which require an optimal path search along a high-dimensional potential energy surface. To enable this new route, we need both large and accurate datasets and a compact yet complete description of the reactions. Although data for chemical reactions is becoming increasingly available, the key step of encoding the reaction as an efficient descriptor remains a big challenge. In this paper, we demonstrate that including electronic energy levels in the description of the reaction significantly improves the prediction accuracy and transferability. Feature importance analysis further demonstrates that electronic energy levels have a higher importance than some structural information and typically require less space in the reaction encoding vector. In general, we observe that the results of the feature importance analysis relate well to the domain knowledge of fundamental chemical principles. This work can help to build better chemical reaction encodings for machine learning and thus improve the predictions of machine learning models for reaction activation energies. These models could ultimately be used to recognize reaction limiting steps in large reaction systems, allowing to account for bottlenecks at the design stage.

Elastic instabilities during the flow of hydrolyzed polyacrylamide solution in porous media: effect of pore-shape and salt.

We experimentally investigate the flow of hydrolyzed polyacrylamide (HPAM) solution with and without salt in model porous media at high Weissenberg numbers (Wi > 1.0). The effect of pore shapes on the flow pattern and pressure drop is explored by using periodic arrays of circular and square pillars in aligned and staggered layouts. In the apparent shear-thinning regime, we observe stationary dead zones upstream of the pillars. In addition, we confirm that the size of stationary dead zones correlates with the level of shear-thinning, by varying the amount of salt in HPAM solution. At higher shear rates (or Wi), these dead zones are periodically washed away. We present the mechanism of this elastic instability and characterize it based on the pressure drop fluctuation spectral density.