Publications

Mixed halide perovskites (MHPs) are a group of semiconducting materials with promising applications in optoelectronics and photovoltaics, whose bandgap can be altered by adjusting the halide composition. However, the current challenge is to stabilize the light-induced halide separation, which undermines the device’s performance. Herein we track down the phase separation dynamics of CsPbBr_1.2I_1.8 MHP single cubic nanocrystals (NCs) and clusters as a function of time by in situ fluorescence spectromicroscopy. The particles were sorted into groups 1 and 2 using initial photoluminescence intensities. The phase separation followed by recovery kinetics under dark and photo blinking analysis suggests that group 1 behaved more like single NCs and group 2 behaved like clusters. Under the 0.64 W/cm² laser illumination, the phase shifts for single NCs are 3.4 ± 1.9 nm. The phase shifts are linearly correlated with the initial photoluminescence intensities of clusters, suggesting possible interparticle halide transportation.

Research output can be evaluated with productivity and impact, which are quantified by the numbers of publications (N and citations Nc, respectively. The h-index (H) unifies both factors. However, as an extensive variable, it grows with quantity of research output and favors senior researchers over juniors. In this report, by analyzing the data of the world top 2% scientists (n= 179,597) from an online database, we found that h-index follows power laws and proposes a different model from what Hirsch has originally proposed. We propose intensive indices (Q N and Q C) to measure quality research by comparing the actual h-index of a researcher with the power-law fitted h-indices from the top 2% scientists with the same numbers of publications and citations respectively. We further calculated a dynamic research quality (Q 1= Q N/Q C) and a reduced index (Q 2=(Q N Q C) 0.5) to evaluate research quality over time. We rationalized that the power law dependency of quality research is due to its heterogeneous production pathways that require extra effort with respect to “regular” research output. We found that research quality for the top 2% scientists is maximized with~ 100 citations/paper and with about~ 100 publications. A major advantage of these indices is that they are relative to the academic peers with similar accomplishments in publications and citations, and therefore, are independent of career stages. Since Q indices are positively correlated with H/N ratios, the research quality can also be quickly and conveniently estimated by the readily accessible values calculated using the equation H/(N)^(2/3) or H/(Nc)^(1/2).

Single-molecule DNA studies have improved our understanding of the DNAs' structure and their interactions with other molecules. A variety of DNA labeling dyes are available for single-molecule studies, among which the bis-intercalating dye YOYO-1 and mono-intercalating dye YO-PRO-1 are widely used. They have an extraordinarily strong affinity toward DNA and are bright with a high quantum yield (>0.5) when bound to DNAs. However, it is still not clear how these dyes behave in DNA molecules under higher ionic strength and strong buffer flow. Here, we have studied the effect of ionic strength and flow rate of buffer on their binding in single DNA molecules. The larger the flow rate and the higher the ionic strength, the faster the intercalated dyes are washed away from the DNAs. In the buffer with 1 M ionic strength, YOYO-1 and YO-PRO-1 are mostly washed away from DNA within 2 min of moderate buffer flow.

This report uses Monte Carlo simulations to connect stochastic single-molecule and ensemble surface adsorption of molecules from dilute solutions. Monte Carlo simulations often use a fundamental time resolution to simulate each discrete step for each molecule. The adsorption rate obtained from such a simulation surprisingly contains an error compared to the results obtained from the traditional method. Simulating adsorption kinetics is interesting in many processes, such as mass transportation within cells, the kinetics of drug-receptor interactions, membrane filtration, and other general reaction kinetics in diluted solutions. Thus, it is important to understand the origin of the disagreement and find a way to correct the results. This report reviews the traditional model, explains the single-molecule simulations, and introduces a method to correct the results of adsorption rate. For example, one can bin finer time steps into time steps of interest to simulate the fractal diffusion or simply introduce a correction factor for the simulations. Then two model systems, self-assembled monolayer (SAM) and biosensing on the patterned surface, are simulated to check the accuracy of the equations. It is found that the adsorption rate of SAM is highly dependent on the conditions and the uncertainty is large. However, the biosensing system is relatively accurate. This is because the concentration gradient near the interface varies significantly with reaction conditions for SAMs while relatively stable for the biosensing system.

Single-molecule FRET (smFRET) is a versatile technique to study the dynamics and function of biomolecules since it makes nanoscale movements detectable as fluorescence signals. The powerful ability to infer quantitative kinetic information from smFRET data is, however, complicated by experimental limitations. Diverse analysis tools have been developed to overcome these hurdles but a systematic comparison is lacking. Here, we report the results of a blind benchmark study assessing eleven analysis tools used to infer kinetic rate constants from smFRET trajectories. We test them against simulated and experimental data containing the most prominent difficulties encountered in analyzing smFRET experiments: different noise levels, varied model complexity, non-equilibrium dynamics, and kinetic heterogeneity. Our results highlight the current strengths and limitations in inferring kinetic information from smFRET trajectories. In addition, we formulate concrete recommendations and identify key targets for future developments, aimed to advance our understanding of biomolecular dynamics through quantitative experiment-derived models.

Predicting the reaction kinetics, that is, how fast a reaction can happen in a solution, is essential information for many processes, such as industrial chemical manufacturing, refining, synthesis and separation of petroleum products, environmental processes in air and water, biological reactions in cells, biosensing, and drug delivery. Collision theory was originally developed to explain the reaction kinetics of gas reactions with no dilution. For a reaction in a diluted inert gas solution or a diluted liquid solution, diffusion often dominates the collision process. Thus, it is necessary to include diffusion in such a calculation. Traditionally, the classical Smoluchowski rate is used as a starting point to predict the collision frequency of two molecules in a diluted solution. In this report, a different collision model is derived from the adsorption of molecules on a flat surface. A surprising result is obtained, showing that the reaction order for bimolecular reactions should be 2 and 1/3 instead of 2, following a fractal reaction kinetics.

A physical chemistry lab for undergraduate students described in this report is about applying kinetic models to analyze the spread of COVID-19 in the United States and obtain the reproduction numbers. The susceptible-infectious-recovery (SIR) model and the SIR-vaccinated (SIRV) model are explained to the students and are used to analyze the COVID-19 spread data from U.S. Centers for Disease Control and Prevention (CDC). The basic reproduction number R 0 and the real-time reproduction number R t of COVID-19 are extracted by fitting the data with the models, which explains the spreading kinetics and provides a prediction of the spreading trend in a given state. The procedure outlined here shows the differences between the SIR model and the SIRV model. The SIRV model considers the effect of vaccination which helps explain the later stages of the ongoing pandemic. The predictive power of the models is also shown giving the students some certainty in the predictions they made for the following months.

The COVID-19 pandemic has affected many in-person laboratory courses across the world. The viral spreading model is complicated but parameters, such as its reproduction number, R t, can be estimated with the susceptible, infectious, or recovered model. COVID-19 data for many states and countries are widely available online. This provides an opportunity for the students to analyze its spreading kinetics remotely. Here, we reported a laboratory set up online during the third week of the spring semester of 2021 to minimize social contacts. Due to the wide interest in developing online physical chemistry and analytical laboratories during the pandemic, we would like to share this laboratory design. The method, technique, procedure, and grading are described in this report. The student participants were able to apply the kinetic techniques learned in physical chemistry to successfully analyze an ongoing real-world problem through a remote learning environment and prepare this report.

Mixed-halide lead perovskites (MHLPs) are semiconductor materials with bandgaps that are tunable across the visible spectrum and have seen promising applications in photovoltaics and optoelectronics. However, their segregation into phases with enriched halide components, under resonant light illumination and/or electric field, have hindered their practical applications. Herein, we demonstrate the stabilization of the MHLP photoluminescence (PL) peak as a function of their excitation intensities. This effect is associated with the phase segregation of MHLPs and their subsequent remixing by photothermal heating. We conclude that the balance between these opposing processes dictates the equilibrium PL peak of the MHLPs. The findings in this work could serve as a potential approach to obtain MHLP with stable emission peaks under operating conditions.