2D Ionic Liquid-Like State of Charged Rare-Earth Clusters on a Metal Surface.

Rare-earth complexes are vital for separation chemistry and useful in many advanced applications including emission and energy upconversion. Here, 2D rare-earth clusters having net charges are formed on a metal surface, enabling investigations of their structural and electronic properties on a one-cluster-at-a-time basis using scanning tunneling microscopy. While these ionic complexes are highly mobile on the surface at ≈100 K, their mobility is greatly reduced at 5 K and reveals stable and self-limiting clusters. In each cluster, a pair of charged rare-earth complexes formed by electrostatic and dispersive interactions act as a basic unit, and the clusters are chiral. Unlike other non-ionic molecular clusters formed on the surfaces, these rare-earth clusters show mechanical stability. Moreover, their high mobility on the surface suggests that they are in a 2D liquid-like state.

Interfacial solvation-structure regulation for stable Li metal anode by a desolvation coating technique.

Rechargeable lithium (Li) metal batteries face challenges in achieving stable cycling due to the instability of the solid electrolyte interphase (SEI). The Li-ion solvation structure and its desolvation process are crucial for the formation of a stable SEI on Li metal anodes and improving Li plating/stripping kinetics. This research introduces an interfacial desolvation coating technique to actively modulate the Li-ion solvation structure at the Li metal interface and regulate the participation of the electrolyte solvent in SEI formation. Through experimental investigations conducted using a carbonate electrolyte with limited compatibility to Li metal, the optimized desolvation coating layer, composed of 12-crown-4 ether-modified silica materials, selectively displaces strongly coordinating solvents while simultaneously enriching weakly coordinating fluorinated solvents at the Li metal/electrolyte interface. This selective desolvation and enrichment effect reduce solvent participation to SEI and thus facilitate the formation of a LiF-dominant SEI with greatly reduced organic species on the Li metal surface, as conclusively verified through various characterization techniques including XPS, quantitative NMR, operando NMR, cryo-TEM, EELS, and EDS. The interfacial desolvation coating technique enables excellent rate cycling stability (i.e., 1C) of the Li metal anode and prolonged cycling life of the Li||LiCoO2 pouch cell in the conventional carbonate electrolyte (E/C 2.6 g/Ah), with 80% capacity retention after 333 cycles.

Light- and Chemical-Doping-Induced Magnetic Behavior of Eu Molecular Systems.

Variable temperature electron paramagnetic resonance (VT-EPR) was used to investigate the role of the environment and oxidation states of several coordinated Eu compounds. We find that while Eu(III) chelating complexes are diamagnetic, simple chemical reduction results in the formation of paramagnetic species. In agreement with the distorted D3h symmetry of Eu molecular complexes investigated in this study, the EPR spectrum of reduced complexes showed axially symmetric signals (g⊥ = 2.001 and g∥ = 1.994) that were successfully simulated with two Eu isotopes with nuclear spin 5/2 (151Eu and 153Eu with 48% and 52% natural abundance, respectively) and nuclear g-factors 151Eu/153Eu = 2.27. Illumination of water-soluble complex Eu(dipic)3 at 4 K led to the ligand-to-metal charge transfer (LMCT) that resulted in the formation of Eu(II) in a rhombic environment (gx = 2.006, gy = 1.995, gz = 1.988). The existence of LMCT affects the luminescence of Eu(dipic)3, and pre-reduction of the complex to Eu(II)(dipic)3 reversibly reduces red luminescence with the appearance of a weak CT blue luminescence. Furthermore, encapsulation of a large portion of the dipic ligand with Cucurbit[7]uril, a pumpkin-shaped macrocycle, inhibited ligand-to-metal charge transfer, preventing the formation of Eu(II) upon illumination.

Accurate Prediction of Adiabatic Ionization Potentials of Organic Molecules using Quantum Chemistry Assisted Machine Learning.

In previous work (Dandu et al., J. Phys. Chem. A, 2022, 126, 4528-4536), we were successful in predicting accurate atomization energies of organic molecules using machine learning (ML) models, obtaining an accuracy as low as 0.1 kcal/mol compared to the G4MP2 method. In this work, we extend the use of these ML models to adiabatic ionization potentials on data sets of energies generated using quantum chemical calculations. Atomic specific corrections that were found to improve atomization energies from quantum chemical calculations have also been used in this study to improve ionization potentials. The quantum chemical calculations were performed on 3405 molecules containing eight or fewer non-hydrogen atoms derived from the QM9 data set, using the B3LYP functional with the 6-31G(2df,p) basis set for optimization. Low-fidelity IPs for these structures were obtained using two density functional methods: B3LYP/6-31+G(2df,p) and ωB97XD/6-311+G(3df,2p). Highly accurate G4MP2 calculations were performed on these optimized structures to obtain high-fidelity IPs to use in ML models based on the low-fidelity IPs. Our best performing ML methods gave IPs of organic molecules within a mean absolute deviation of 0.035 eV from the G4MP2 IPs for the whole data set. This work demonstrates that ML predictions assisted by quantum chemical calculations can be used to successfully predict IPs of organic molecules for use in high throughput screening.

Characterization of just one atom using synchrotron X-rays.

Since the discovery of X-rays by Roentgen in 1895, its use has been ubiquitous, from medical and environmental applications to materials sciences1-5. X-ray characterization requires a large number of atoms and reducing the material quantity is a long-standing goal. Here we show that X-rays can be used to characterize the elemental and chemical state of just one atom. Using a specialized tip as a detector, X-ray-excited currents generated from an iron and a terbium atom coordinated to organic ligands are detected. The fingerprints of a single atom, the L2,3 and M4,5 absorption edge signals for iron and terbium, respectively, are clearly observed in the X-ray absorption spectra. The chemical states of these atoms are characterized by means of near-edge X-ray absorption signals, in which X-ray-excited resonance tunnelling (X-ERT) is dominant for the iron atom. The X-ray signal can be sensed only when the tip is located directly above the atom in extreme proximity, which confirms atomically localized detection in the tunnelling regime. Our work connects synchrotron X-rays with a quantum tunnelling process and opens future X-rays experiments for simultaneous characterizations of elemental and chemical properties of materials at the ultimate single-atom limit.

Microkinetic insights into the role of catalyst and water activity on the nucleation, growth, and dissolution during COF-5 synthesis.

The chemical pathway for synthesizing covalent organic frameworks (COFs) involves a complex medley of reaction sequences over a rippling energy landscape that cannot be adequately described using existing theories. Even with the development of state-of-the-art experimental and computational tools, identifying primary mechanisms of nucleation and growth of COFs remains elusive. Other than empirically, little is known about how the catalyst composition and water activity affect the kinetics of the reaction pathway. Here, for the first time, we employ time-resolved in situ Fourier transform infrared spectroscopy (FT-IR) coupled with a six-parameter microkinetic model consisting of ∼10 million reactions and over 20 000 species. The integrated approach elucidates previously unrecognized roles of catalyst pKa on COF yield and water on growth rate and size distribution. COF crystalline yield increases with decreasing pKa of the catalysts, whereas the effect of water is to reduce the growth rate of COF and broaden the size distribution. The microkinetic model reproduces the experimental data and quantitatively predicts the role of synthesis conditions such as temperature, catalyst, and precursor concentration on the nucleation and growth rates. Furthermore, the model also validates the second-order reaction mechanism of COF-5 and predicts the activation barriers for classical and non-classical growth of COF-5 crystals. The microkinetic model developed here is generalizable to different COFs and other multicomponent systems.

A KMnO4-Generated Colloidal Electrolyte for Redox Mediation and Anode Protection in a Li-Air Battery.

The rechargeable lithium-oxygen (Li-O2) battery has the highest theoretical specific energy density of any rechargeable batteries and could transform energy storage systems if a practical device could be attained. However, among numerous challenges, which are all interconnected, are polarization due to sluggish kinetics, low cycle life, small capacity, and slow rates. In this study, we report on use of KMnO4 to generate a colloidal electrolyte made up of MnO2 nanoparticles. The resulting electrolyte provides a redox mediator for reducing the charge potential and lithium anode protection to increase cycle life. This electrolyte in combination with a stable binary transition metal dichalcogenide alloy, Nb0.5Ta0.5S2, as the cathode enables the operation of a Li-O2 battery at a current density of 1 mA·cm-2 and specific capacity ranging from 1000 to 10 000 mA·h·g-1 (corresponding to 0.1-1 mA·h·cm-2) in a dry air environment with a cycle life of up to 150. This colloidal electrolyte provides a robust approach for advancing Li-air batteries.

Atomically precise control of rotational dynamics in charged rare-earth complexes on a metal surface.

Complexes containing rare-earth ions attract great attention for their technological applications ranging from spintronic devices to quantum information science. While charged rare-earth coordination complexes are ubiquitous in solution, they are challenging to form on materials surfaces that would allow investigations for potential solid-state applications. Here we report formation and atomically precise manipulation of rare-earth complexes on a gold surface. Although they are composed of multiple units held together by electrostatic interactions, the entire complex rotates as a single unit when electrical energy is supplied from a scanning tunneling microscope tip. Despite the hexagonal symmetry of the gold surface, a counterion at the side of the complex guides precise three-fold rotations and 100% control of their rotational directions is achieved using a negative electric field from the scanning probe tip. This work demonstrates that counterions can be used to control dynamics of rare-earth complexes on materials surfaces for quantum and nanomechanical applications.

Improving the Accuracy of Composite Methods: A G4MP2 Method with G4-like Accuracy and Implications for Machine Learning.

G4MP2 theory has proven to be a reliable and accurate quantum chemical composite method for the calculation of molecular energies using an approximation based on second-order perturbation theory to lower computational costs compared to G4 theory. However, it has been found to have significantly increased errors when applied to larger organic molecules with 10 or more nonhydrogen atoms. We report here on an investigation of the cause of the failure of G4MP2 theory for such larger molecules. One source of error is found to be the "higher-level correction (HLC)", which is meant to correct for deficiencies in correlation contributions to the calculated energies. This is because the HLC assumes that the contribution is independent of the element and the type of bonding involved, both of which become more important with larger molecules. We address this problem by adding an atom-specific correction, dependent on atom type but not bond type, to the higher-level correction. We find that a G4MP2 method that incorporates this modification of the higher-level correction, referred to as G4MP2A, becomes as accurate as G4 theory (for computing enthalpies of formation) for a test set of molecules with less than 10 nonhydrogen atoms as well as a set with 10-14 such atoms, the set of molecules considered here, with a much lower computational cost. The G4MP2A method is also found to significantly improve ionization potentials and electron affinities. Finally, we implemented the G4MP2A energies in a machine learning method to predict molecular energies.

Concentration-dependent ion correlations impact the electrochemical behavior of calcium battery electrolytes.

Ion interactions strongly determine the solvation environments of multivalent electrolytes even at concentrations below that required for practical battery-based energy storage. This statement is particularly true of electrolytes utilizing ethereal solvents due to their low dielectric constants. These solvents are among the most commonly used for multivalent batteries based on reactive metals (Mg, Ca) due to their reductive stability. Recent developments in multivalent electrolyte design have produced a variety of new salts for Mg2+ and Ca2+ that test the limits of weak coordination strength and oxidative stability. Such electrolytes have great potential for enabling full-cell cycling of batteries based on these working ions. However, the ion interactions in these electrolytes exhibit significant and non-intuitive concentration relationships. In this work, we investigate a promising exemplar, calcium tetrakis(hexafluoroisopropoxy)borate (Ca(BHFIP)2), in the ethereal solvents 1,2-dimethoxyethane (DME) and tetrahydrofuran (THF) across a concentration range of several orders of magnitude. Surprisingly, we find that effective salt dissociation is lower at relatively dilute concentrations (e.g. 0.01 M) than at higher concentrations (e.g. 0.2 M). Combined experimental and computational dielectric and X-ray spectroscopic analyses of the changes occurring in the Ca2+ solvation environment across these concentration regimes reveals a progressive transition from well-defined solvent-separated ion pairs to de-correlated free ions. This transition in ion correlation results in improvements in both conductivity and calcium cycling stability with increased salt concentration. Comparison with previous findings involving more strongly associating salts highlights the generality of this phenomenon, leading to important insight into controlling ion interactions in ether-based multivalent battery electrolytes.

Graph-Based Approaches for Predicting Solvation Energy in Multiple Solvents: Open Datasets and Machine Learning Models.

The solvation properties of molecules, often estimated using quantum chemical simulations, are important in the synthesis of energy storage materials, drugs, and industrial chemicals. Here, we develop machine learning models of solvation energies to replace expensive quantum chemistry calculations with inexpensive-to-compute message-passing neural network models that require only the molecular graph as inputs. Our models are trained on a new database of solvation energies for 130,258 molecules taken from the QM9 dataset computed in five solvents (acetone, ethanol, acetonitrile, dimethyl sulfoxide, and water) via an implicit solvent model. Our best model achieves a mean absolute error of 0.5 kcal/mol for molecules with nine or fewer non-hydrogen atoms and 1 kcal/mol for molecules with between 10 and 14 non-hydrogen atoms. We make the entire dataset of 651,290 computed entries openly available and provide simple web and programmatic interfaces to enable others to run our solvation energy model on new molecules. This model calculates the solvation energies for molecules using only the SMILES string and also provides an estimate of whether each molecule is within the domain of applicability of our model. We envision that the dataset and models will provide the functionality needed for the rapid screening of large chemical spaces to discover improved molecules for many applications.

High-Rate Long Cycle-Life Li-Air Battery Aided by Bifunctional InX3 (X = I and Br) Redox Mediators.

Redox mediators (RMs) are solution-based additives that have been extensively used to reduce the charge potential and increase the energy efficiency of Li-oxygen (Li-O2) batteries. However, in the presence of RMs, achieving a long cycle-life operation of Li-O2 batteries at a high current rate is still a major challenge. In this study, we discover a novel synergy among InX3 (X = I and Br) bifunctional RMs, molybdenum disulfide (MoS2) nanoflakes as the air electrode, dimethyl sulfoxide/ionic liquid hybrid electrolyte, and LiTFSI as a salt to achieve long cycle-life operations of Li-O2 batteries in a dry air environment at high charge-discharge rates. Our results indicate that batteries with InI3 operate up to 450 cycles with a current density of 0.5 A g-1 and 217 cycles with a current density of 1 A g-1 at a fixed capacity of 1 A h g-1. Batteries with InBr3 operate up to 600 cycles with a current density of 1 A g-1. These batteries can also operate at a higher charge rate of 2 A g-1 up to 200 cycles (for InBr3) and 160 cycles (for InI3). Our experimental and computational results reveal that while X3- is the source of the redox mediator, LiX at the MoS2 cathode, In3+ reacts on the lithium anode side to form a protective layer on the surface, thus acting as an effective bifunctional RM in a dry air environment. This evidence for a simultaneous improvement in the current rates and cycle life of a battery in a dry air atmosphere opens a new direction for research for advanced energy storage systems.

Passivating Nucleobases Bring Charge Transfer Character to Optically Active Transitions in Small Silver Nanoclusters.

DNA-wrapped silver nanoclusters (DNA-AgNCs) are known for their efficient luminescence. However, their emission is highly sensitive to the DNA sequence, the cluster size, and its charge state. To get better insights into photophysics of these hybrid systems, simulations based on density functional theory (DFT) are performed. Our calculations elucidate the effect of the structural conformations, charges, solvent polarity, and passivating bases on optical spectra of DNA-AgNCs containing five and six Ag atoms. It is found that inclusion of water in calculations as a polar solvent media results in stabilization of nonplanar conformations of base-passivated clusters, while their planar conformations are more stable in vacuum, similar to the bare Ag5 and Ag6 clusters. Cytosines and guanines interact with the cluster twice stronger than thymines, due to their larger dipole moments. In addition to the base-cluster interactions, hydrogen bonds between bases notably contribute to the structure stabilization. While the relative intensity, line width, and the energy of absorption peaks are slightly changing depending on the cluster charge, conformations, and base types, the overall spectral shape with five well-resolved bands at 2.5-5.5 eV is consistent for all structures. Independent of the passivating bases and the cluster size and charge, the low energy optical transitions at 2.5-3.5 eV exhibit a metal to ligand charge transfer (MLCT) character with the main contribution emerging from Ag-core to the bases. Cytosines facilitate the MLCT character to a larger degree comparing to the other bases. However, the doublet transitions in clusters with the open shell electronic structure (Ag5 and Ag6+) result in appearance of additional red-shifted (<2.5 eV) and optically weak band with negligible MLCT character. The passivated clusters with the closed shell electronic structure (Ag5+ and Ag6) exhibit higher optical intensity of their lowest transitions with much higher MLCT contribution, thus having better potential for emission, than their open shell counterparts.

Quantum-Chemically Informed Machine Learning: Prediction of Energies of Organic Molecules with 10 to 14 Non-hydrogen Atoms.

High-fidelity quantum-chemical calculations can provide accurate predictions of molecular energies, but their high computational costs limit their utility, especially for larger molecules. We have shown in previous work that machine learning models trained on high-level quantum-chemical calculations (G4MP2) for organic molecules with one to nine non-hydrogen atoms can provide accurate predictions for other molecules of comparable size at much lower costs. Here we demonstrate that such models can also be used to effectively predict energies of molecules larger than those in the training set. To implement this strategy, we first established a set of 191 molecules with 10-14 non-hydrogen atoms having reliable experimental enthalpies of formation. We then assessed the accuracy of computed G4MP2 enthalpies of formation for these 191 molecules. The error in the G4MP2 results was somewhat larger than that for smaller molecules, and the reason for this increase is discussed. Two density functional methods, B3LYP and ωB97X-D, were also used on this set of molecules, with ωB97X-D found to perform better than B3LYP at predicting energies. The G4MP2 energies for the 191 molecules were then predicted using these two functionals with two machine learning methods, the FCHL-Δ and SchNet-Δ models, with the learning done on calculated energies of the one to nine non-hydrogen atom molecules. The better-performing model, FCHL-Δ, gave atomization energies of the 191 organic molecules with 10-14 non-hydrogen atoms within 0.4 kcal/mol of their G4MP2 energies. Thus, this work demonstrates that quantum-chemically informed machine learning can be used to successfully predict the energies of large organic molecules whose size is beyond that in the training set.

Ground-state actinide chemistry with scalar-relativistic multiconfiguration pair-density functional theory.

We have examined the performance of Multiconfiguration Pair-Density Functional Theory (MC-PDFT) for computing the ground-state properties of actinide species. Specifically, we focused on the properties of UN2 and various actinyl species. The properties obtained with MC-PDFT at the scalar-relativistic level are compared to Kohn-Sham DFT (KS-DFT); complete active space self-consistent field theory, CASSCF; coupled-cluster theory, CCSD(T) and CCSDT; as well as multireference perturbation theory (CASPT2). We examine the degree to which MC-PDFT improves over KS-DFT and CASSCF while aligning with CASPT2, CCSD(T), and CCSDT. All properties that we considered were for the CASPT2 electronic ground states. For structural parameters, MC-PDFT confers very little advantage over KS-DFT, especially the B3LYP density functional. For NpO2 3+, MC-PDFT and local KS-DFT functionals excessively favor the bent structure, whereas CCSDT and CASPT2 predict the bent and linear structures as isoenergetic. For this special case, hybrid KS-DFT functionals like PBE0 and B3LYP provide results closer to CASPT2 and CCSDT than MC-PDFT. On a more positive note, MC-PDFT is very close to CASPT2 and CCSD(T) for the redox potentials, energetics of redox chemical reactions, as well as ligand-binding energies. These are encouraging results since MC-PDFT is more affordable. The best MC-PDFT functional is ft-PBE. Our findings suggest that MC-PDFT can be used to study systems and excited states with larger strong electron correlation effects than were considered here. However, for the systems and properties considered here, KS-DFT functionals do well, justifying their usage as the bulwark of computational actinyl chemistry over the last two to three decades.

Nitrogen Reduction by Multimetallic trans-Uranium Actinide Complexes: A Theoretical Comparison of Np and Pu to U.

There is recent interest in organometallic complexes of the trans-uranium elements. However, preparation and characterization of such complexes are hampered by radioactivity and chemotoxicity issues as well as the air-sensitive and poorly understood behavior of existing compounds. As such, there are no examples of small-molecule activation via redox reactivity of organometallic trans-uranium complexes. This contrasts with the situation for uranium. Indeed, a multimetallic uranium(III) nitride complex was recently synthesized, characterized, and shown to be able to capture and functionalize molecular nitrogen (N2) through a four-electron reduction process, N2 → N24-. The bis-uranium nitride, U-N-U core of this complex is held in a potassium siloxide framework. Importantly, the N24- product could be further functionalized to yield ammonia (NH3) and other desirable species. Using the U-N-U potassium siloxide complex, K3U-N-U, and its cesium analogue, Cs3U-N-U, as starting points, we use scalar-relativistic and spin-orbit coupled density functional theory calculations to shed light on the energetics and mechanism for N2 capture and functionalization. The N2 → N24- reactivity depends on the redox potentials of the U(III) centers and crucially on the stability of the starting complex with respect to decomposition into the mixed oxidation U(IV)/U(III) K2U-N-U or Cs2U-N-U species. For the trans-uranium, Np and Pu analogues of K3U-N-U, the N2 → N24- process is endoergic and would not occur. Interestingly, modification of the Np-O and Pu-O bonds between the actinide cores and the coordinated siloxide framework to Np-NH, Pu-NH, Np-CH2, and Pu-CH2 bonds drastically improves the reaction free energies. The Np-NH species are stable and can reductively capture and reduce N2 to N24-. This is supported by analysis of the spin densities, molecular structure, long-range dispersion effects, as well as spin-orbit coupling effects. These findings chart a path for achieving small-molecule activation with organometallic neptunium analogues of existing uranium complexes.

Performance of density functional theory for describing hetero-metallic active-site motifs for methane-to-methanol conversion in metal-exchanged zeolites.

Methane-to-methanol conversion (MMC) can be facilitated with high methanol selectivities by copper-exchanged zeolites. There are however two open questions regarding the use of these zeolites to facilitate the MMC process. The first concerns the possibility of operating the three cycles in the stepwise MMC process by these zeolites in an isothermal fashion. The second concerns the possibility of improving the methanol yields by systematic substitution of some copper centers in these active sites with other earth-abundant transition metals. Quantum-mechanical computations can be used to compare methane activation by copper oxide species and analogous mixed-metal systems. To carry out such screening, it is important that we use theoretical methods that are accurate and computationally affordable for describing the properties of the hetero-metallic catalytic species. We have examined the performance of 47 exchange-correlation density functionals for predicting the relative spin-state energies and chemical reactivities of six hetero-metallic [M-O-Cu]2+ and [M-O2 -Cu]2+ , (where MCo, Fe, and Ni), species by comparison with coupled cluster theory including iterative single, double excitations as well as perturbative treatment of triple excitations, CCSD(T). We also performed multireference calculations on some of these systems. We considered two types of reactions (hydrogen addition and oxygen addition) that are relevant to MMC. We recommend the use of τ-HCTH and OLYP to determine the spin-state energy splittings in the hetero-metallic motifs. ωB97, ωB97X, ωB97X-D3, and MN15 performed best for predicting the energies of the hydrogen and oxygen addition reactions. In contrast, local, and semilocal functionals do poorly for chemical reactivity. Using [Fe-O-Cu]2+ as a test, we see that the nonlocal functionals perform well for the methane CH activation barrier. In contrast, the semilocal functionals perform rather poorly. © 2018 Wiley Periodicals, Inc.

Mechanism of Charged, Neutral, Mono-, and Polyatomic Donor Ligand Coordination to Perchlorinated Cyclohexasilane (Si6Cl12).

We report the detailed computational study of several perchlorinated cyclohexasilane (Si6Cl12)-based inverse sandwich compounds. It was found that regardless of the donor ligand size and charge, for example, Cl- and CN- anions or neutral HCN and NCPh nitriles, their coordination to the puckered Si6Cl12 ring results in its flattening. The NBO and CDA studies of the complexes showed that coordination occurs due to hybridization of low-lying antibonding σ*(Si-Cl) and σ*(Si-Si) unoccupied molecular orbitals (UMOs) of Si6Cl12 and occupied molecular orbitals (OMOs) of donor molecules (predominantly lone-pair-related), resulting in donor-to-ring charge transfer accompanied by complex stabilization and ring flattening. It is known that the Si6 ring distortion results from vibronic coupling of OMO and UMO pairs (pseudo-Jahn-Teller effect, PJT). Consequently, the Si6 ring flattening most probably occurs due to suppression of the PJT effect in all of the studied compounds. In this paper, the stabilization energy E(2) associated with donor-acceptor charge transfer (delocalization) was estimated using NBO analysis for [Si6Cl12·2Cl]2-, [Si6Cl12·2(NC)]2-, Si6Cl12·2(NCH), and Si6Cl12·2(NCPh). It was found that the polarizability of the donor might significantly affect the stabilization energy value (Cl- > CN- > HCN). For the neutral complexes, the E(2) value is correlated with the charge on the nitrogen atoms. All of these factors, that is, specific donor E(2) value, charge transfer, complex MO energy diagrams, and so on, should be taken into account when choosing the ligands suitable for forming Si-based 1D compounds and other nanoscale materials.

Thickness-Controlled Quasi-Two-Dimensional Colloidal PbSe Nanoplatelets.

We demonstrate controlled synthesis of discrete two-dimensional (2D) PbSe nanoplatelets (NPLs), with measurable photoluminescence, via oriented attachment directed by quantum dot (QD) surface chemistry. Halide passivation is critical to the growth of these (100) face-dominated NPLs, as corroborated by density functional theory studies. PbCl2 moieties attached to the (111) and (110) of small nanocrystals form interparticle bridges, aligning the QDs and leading to attachment. We find that a 2D bridging network is energetically favored over a 3D network, driving the formation of NPLs. Although PbI2 does not support bridging, its presence destabilizes the large (100) faces of NPLs, providing means for tuning NPL thickness. Spectroscopic analysis confirms the predicted role of thickness-dependent quantum confinement on the NPL band gap.

Enhanced Luminescent Stability through Particle Interactions in Silicon Nanocrystal Aggregates.

Close-packed assemblies of ligand-passivated colloidal nanocrystals can exhibit enhanced photoluminescent stability, but the origin of this effect is unclear. Here, we use experiment, simulation, and ab initio computation to examine the influence of interparticle interactions on the photoluminescent stability of silicon nanocrystal aggregates. The time-dependent photoluminescence emitted by structures ranging in size from a single quantum dot to agglomerates of more than a thousand is compared with Monte Carlo simulations of noninteracting ensembles using measured single-particle blinking data as input. In contrast to the behavior typically exhibited by the metal chalcogenides, the measured photoluminescent stability shows an enhancement with respect to the noninteracting scenario with increasing aggregate size. We model this behavior using time-dependent density functional theory calculations of energy transfer between neighboring nanocrystals as a function of nanocrystal size, separation, and the presence of charge and/or surface-passivation defects. Our results suggest that rapid exciton transfer from "bright" nanocrystals to surface trap states in nearest-neighbors can efficiently fill such traps and enhance the stability of emission by promoting the radiative recombination of slowly diffusing excited electrons.