Publications

Carbon-based quantum dots (CQDs) have been around for a few decades. Low cell toxicity, good water solubility, excellent and tunable fluorescence properties, and the ability to dope and modify the surfaces of these CQDs make them an incredible choice for the visualization and treatment of various cancers. This Perspective analyzes some recent progress on size-color correlation, modification, and cancer treatment applications of CQDs. Synthesis and modification of CQDs to make them more efficient and biocompatible are essential to their bioapplications.

Optimization of parameters and hyperparameters is a general process for any data analysis. Because not all models are mathematically well-behaved, stochastic optimization can be useful in many analyses by randomly choosing parameters in each optimization iteration. Many such algorithms have been reported and applied in chemistry data analysis, but the one reported here is interesting to check out, where a naïve algorithm searches each parameter sequentially and randomly in its bounds. Then it picks the best for the next iteration. Thus, one can ignore irrational solution of the model itself or its gradient in parameter space and continue the optimization.

Many interactions between small molecules and particles occur in solutions. They are surrounded by other molecules that do not react, for example, biological processes in water, chemical reactions in gas or liquid solutions, and environmental reactions in air and water. However, predicting the rate of such diffusive interactions remains challenging, due to the random motion of molecules in solutions, as exampled by the famous Brownian motion of pollen particles. In this report, we experimentally confirmed that a disruptive rate equation we have published before can predict the association rate of typical adsorption at interfaces, which has a surprising fraction order of 4/3 that has not been considered before. This could be an important step toward a generalized method to predict the adsorption rate of many reactions.

The COVID-19 pandemic has passed. It gives us a real-world example of kinetic data analysis practice for our undergraduate physical chemistry laboratory class. It is a great example to connect this seemingly very different problem to the kinetic theories for chemical reactions that the students have learned in the lecture class. At the beginning of the spring 2023 semester, we obtained COVID-19 kinetic data from the “Our World in Data” database, which summarizes the World Health Organization (WHO) data reported from different countries. We analyzed the effective spreading kinetics based on the susceptible-infectious-recovered-vaccinated (SIR-V) model. We then compared the effective rate constants represented by the real-time reproduction numbers (R_t) underlining the reported data for these countries and discussed the results and the limitations of the model with the students.

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).