Negative diffusion of excitons in quasi-two-dimensional systems.

We show how two different mobile-immobile type models explain the observation of negative diffusion of excitons reported in experimental studies in quasi-two-dimensional semiconductor systems. The main reason for the effect is the initial trapping and a delayed release of free excitons in the area close to the original excitation spot. The density of trapped excitons is not registered experimentally. Hence, the signal from the free excitons alone includes the delayed release of not diffusing trapped particles. This is seen as the narrowing of the exciton density profile or decrease of mean-squared displacement which is then interpreted as a negative diffusion. The effect is enhanced with the increase of recombination intensity as well as the rate of the exciton-exciton binary interactions.

Non-Markovian diffusion of excitons in layered perovskites and transition metal dichalcogenides.

The diffusion of excitons in perovskites and transition metal dichalcogenides shows clear anomalous, subdiffusive behaviour in experiments. In this paper we develop a non-Markovian mobile-immobile model which provides an explanation of this behaviour through paired theoretical and simulation approaches. The simulation model is based on a random walk on a 2D lattice with randomly distributed deep traps such that the trapping time distribution involves slowly decaying power-law asymptotics. The theoretical model uses coupled diffusion and rate equations for free and trapped excitons, respectively, with an integral term responsible for trapping. The model provides a good fitting of the experimental data, thus, showing a way for quantifying the exciton diffusion dynamics.

Active learning-based framework for optimal reaction mechanism selection from microkinetic modeling: a case study of electrocatalytic oxygen reduction reaction on carbon nanotubes.

The elucidation of complex electrochemical reaction mechanisms requires advanced models with many intermediate reaction steps, which are governed by a large number of parameters like reaction rate constants and charge transfer coefficients. Overcomplicated models introduce high uncertainty in the choice of the parameters and cannot be used to obtain meaningful insights on the reaction pathway. We describe a new framework of optimal reaction mechanism selection based on the mean-field microkinetic modeling approach (MF-MKM) and adaptive sampling of model parameters. The optimal model is selected to provide both the accurate fitting of experimental data within the experimental error and low uncertainty of model parameters choice. Generally, this approach can be applied for any complex heterogeneous electrochemical reaction. We use the "2e-" electrocatalytic oxygen reduction reaction (ORR) on carbon nanotubes (CNTs) as a representative example of a sufficiently complex reaction. Rotating disk electrode (RDE) experimental data for both ORR in O2-saturated 0.1 M KOH solution and hydrogen peroxide oxidation/reduction reaction (HPRR/HPOR) in Ar-purged 0.1 M KOH solution with different HO2- concentrations were used to show the dependence of the model parameters uniqueness on the completeness of the experimental dataset. It is demonstrated that the optimal reaction mechanism for ORR on CNT and available experimental data consists of O2 adsorption step on the electrode surface and effective step of two-electron reduction to HO2- combined with its desorption from the electrode. The low uncertainty of estimated model parameters is provided only within the 2-step model being applied to the full available experimental dataset. The assessment of elementary step mechanisms on electro-catalytic materials including carbon-based electrodes requires more diverse experimental data and/or higher precision of experimental measurements to facilitate more precise microkinetic modeling of more complex reaction mechanisms.

Decoupling the roles of carbon and metal oxides on the electrocatalytic reduction of oxygen on La1-xSrxCoO3-δ perovskite composite electrodes.

Perovskite oxides are active room-temperature bifunctional oxygen electrocatalysts in alkaline media, capable of performing the oxygen reduction reaction (ORR) and oxygen evolution reaction (OER) with lower combined overpotentials relative to their precious metal counterparts. However, their semiconducting nature necessitates the use of activated carbons as conductive supports to generate applicably relevant current densities. In efforts to advance the performance and theory of oxide electrocatalysts, the chemical and physical properties of the oxide material often take precedence over contributions from the conductive additive. In this work, we find that carbon plays an important synergistic role in improving the performance of La1-xSrxCoO3-δ (0 ≤ x ≤ 1) electrocatalysts through the activation of O2 and spillover of radical oxygen intermediates, HO2- and O2-, which is further reduced through chemical decomposition of HO2- on the perovskite surface. Through a combination of thin-film rotating disk electrochemical characterization of the hydrogen peroxide intermediate reactions (hydrogen peroxide reduction reaction (HPRR), hydrogen peroxide oxidation reaction (HPOR)) and oxygen reduction reaction (ORR), surface chemical analysis, HR-TEM, and microkinetic modeling on La1-xSrxCoO3-δ (0 ≤ x ≤ 1)/carbon (with nitrogen and non-nitrogen doped carbons) composite electrocatalysts, we deconvolute the mechanistic aspects and contributions to reactivity of the oxide and carbon support.