Quantum dots are used in the research laboratory and in commercial applications for their bright, size-tunable luminescence. While empirical synthesis and processing optimization have led to many quantum dot systems with photoluminescence quantum yields at or approaching 100%, our understanding of the chemical principles that underlie this performance and our ability to access such materials on demand have lagged. In this Perspective, we present the status of our understanding of the connections between surface chemistry and quantum dot luminescence. We follow the historical arc that began with shell growth, which then led to an atomistic description of surface-derived charge trapping, and finally has brought us to a more nuanced picture of the role of surface chemistry in luminescence properties, including emerging concepts like surface dipoles and vibronic coupling.

As the commercial display market grows, the demand for low-toxicity, highly emissive, and size-tunable semiconducting nanoparticles has increased. Indium phosphide quantum dots represent a promising solution to these challenges; unfortunately, they typically suffer from low inherent emissivity resulting from charge carrier trapping. Strategies to improve the emissive characteristics of indium phosphide often involve zinc incorporation into or onto the core itself and the fabrication of core/shell heterostructures. InP clusters are high fidelity platforms for studying processes such as cation exchange and surface doping with exogenous ions since these clusters are used as single-source precursors for quantum dot synthesis. Here, we examined the incorporation of zinc and gallium ions in InP clusters and the use of the resultant doped clusters as single-source precursors to emissive heterostructured nanoparticles. Zinc ions were observed to readily react with InP clusters, resulting in partial cation exchange, whereas gallium resisted cluster incorporation. Zinc-doped clusters effectively converted to emissive nanoparticles, with quantum yields strongly correlated with zinc content. On the other hand, gallium-doped clusters failed to demonstrate improvements in quantum dot emission. These results indicate stark differences in the mechanisms associated with aliovalent and isovalent doping and provide insight into the use of doped clusters to make emissive quantum dots.

Indium phosphide quantum dots (InP QD) are a promising alternative to traditional QD materials that contain toxic heavy elements such as lead and cadmium. However, InP QD obtained from colloidal synthesis are often plagued by poor photoluminescence quantum yields (PL-QYs). In order to improve the PL-QY of InP QD, a number of post-synthetic treatments have been devised. Recently, it has been shown that InP post-synthetically treated with Lewis acid metal divalent cations (M-InP) exhibit enhanced PL-QY; however, the molecular structure and mechanism behind the improved PL-QY are not fully understood. To determine the surface structure of M-InP QD, dynamic nuclear polarization surface-enhanced nuclear magnetic resonance spectroscopy (DNP SENS) experiments were employed on a series of InP magic size clusters treated with Cd ions, InP QD, cadmium phosphide (Cd3P2) QD, and Cd-treated InP QD (Cd-InP QD). With the use of DNP SENS, we were able to obtain the 1D 31P and 113Cd NMR spectra, 113Cd{31P} rotational-echo double-resonance (REDOR) NMR spectra, and 31P{113Cd} dipolar heteronuclear multiple quantum correlation (D-HMQC) sequence. Changes in the phosphide 31P chemical shifts after Cd treatment provide indirect evidence that some Cd alloys into the sub-surface regions of the particle. DNPenhanced 113Cd solid-state NMR spectra suggest that most Cd ions are coordinated by oxygen atoms from either carboxylate ligands or surface phosphate groups. 113Cd{31P} REDOR and 31P{113Cd} D-HMQC experiments confirm that a subset of Cd ions are located on the surface of Cd-InP QD and coordinated with phosphate groups.

The prediction of chemical reaction outcomes using machine learning (ML) has emerged as a powerful tool for advancing materials synthesis. However, this approach requires large and diverse datasets which are extremely limited in the field of nanomaterials synthesis, due to inconsistent and nonstandardized reporting in the literature, and a lack of understanding of synthetic mechanisms. In this study, we extracted parameters of InP quantum dot (QD) syntheses as our inputs, and resultant properties (absorption, emission, diameter) as our outputs from 72 publications. We "filled in" missing outputs using a data imputation method to prepare a complete dataset containing 216 entries for training and testing predictive ML models. We defined the descriptor space in two ways (condensed and extended) based on the chemical identity or role of reagents to explore the best approach for categorizing input features. We achieved mean absolute errors (MAEs) as low as 20.29, 11.46, and 0.33 nm for absorption, emission, and diameter respectively with our best ML model. We used these models to deploy an accessible and interactive webapp for designing syntheses of InP (https://share.streamlit.io/cossairt-lab/indiumphosphide/Hot_injection/hot_injection_prediction.py). Using this webapp, we investigated the power of ML to uncover chemical trends in InP syntheses, such as the effects of common additives. We also designed and conducted new experiments based on extensions of literature procedures and compared our experimentally measured properties to predictions, thus evaluating the "real-life" accuracy of our models. Conversely, we designed an experiment to obtain InP QDs with specific properties. Finally, we applied the same approach to train, test, and launch predictive models for CdSe QDs by expanding a previously published dataset. Altogether, our data pre-processing method and ML implementations in this study show the ability to design materials with targeted properties and explore underlying reaction mechanisms despite limited data resources.

We demonstrate fine-tuning of the atomic composition of InP/ZnSe QDs at the core/shell interface. Specifically, we control the stoichiometry of both anions (P, As, S, and Se) and cations (In, Zn) at the InP/ZnSe core/shell interface and correlate these changes with the resultant steadystate and time-resolved optical properties of the nanocrystals. The use of reactive trimethylsilyl reagents results in surface-limited reactions that shift the nanocrystal stoichiometry to anion-rich and improve epitaxial growth of the shell layer. In general, anion deposition on the InP QD surface results in a redshift in the absorption, quenching of the excitonic photoluminescence, and a relative increase in the intensity of the broad trap-based photoluminescence, consistent with delocalization of the exciton wavefunction and relaxation of exciton confinement. Time-resolved photoluminescence data for the resulting InP/ZnSe QDs show an overall small change in the decay dynamics on the ns timescale, suggesting the relatively low photoluminescence quantum yields may be attributed to the creation of new thermally activated charge trap states and likely a dark population that is inseparable from the emissive QDs. Cluster-model density functional theory calculations show that the presence of core/shell interface anions give rise to electronic defects contributing to the redshift in the absorption. These results highlight a general strategy to