Energy harvesting of non-emissive triplet excitons in tetracene by emissive PbS nanocrystals.

Triplet excitons are ubiquitous in organic optoelectronics, but they are often an undesirable energy sink because they are spin-forbidden from emitting light and their high binding energy hinders the generation of free electron-hole pairs. Harvesting their energy is consequently an important technological challenge. Here, we demonstrate direct excitonic energy transfer from 'dark' triplets in the organic semiconductor tetracene to colloidal PbS nanocrystals, thereby successfully harnessing molecular triplet excitons in the near infrared. Steady-state excitation spectra, supported by transient photoluminescence studies, demonstrate that the transfer efficiency is at least (90 ± 13)%. The mechanism is a Dexter hopping process consisting of the simultaneous exchange of two electrons. Triplet exciton transfer to nanocrystals is expected to be broadly applicable in solar and near-infrared light-emitting applications, where effective molecular phosphors are lacking at present. In particular, this route to 'brighten' low-energy molecular triplet excitons may permit singlet exciton fission sensitization of conventional silicon solar cells.

Stacked Ensemble Machine Learning for Range-Separation Parameters.

Density functional theory-based high-throughput materials and drug discovery has achieved tremendous success in recent decades, but its power on organic semiconducting molecules suffered catastrophically from the self-interaction error until the nonempirical but expensive optimally tuned range-separated hybrid (OT-RSH) functionals were developed. An OT-RSH transitions from a short-range (semi)local functional to a long-range Hartree-Fock exchange at a distance characterized by a molecule-specific range-separation parameter (ω). Herein, we propose a stacked ensemble machine learning model that provides an accelerated alternative of OT-RSH based on system-dependent structural and electronic configurations. We trained ML-ωPBE, the first functional in our series, using a database of 1970 molecules with sufficient structural and functional diversity, and assessed its accuracy and efficiency using another 1956 molecules. Compared with nonempirical OT-ωPBE, ML-ωPBE reaches a mean absolute error of 0.00504a0-1 for optimal ω's, reduces the computational cost by 2.66 orders of magnitude, and achieves comparable predictive power in optical properties.

A Heterogeneous Kinetics Model for Triplet Exciton Transfer in Solid-State Upconversion.

High internal quantum efficiency semiconductor nanocrystal (NC)-based photon upconversion devices are currently based on a single monolayer of active NCs. Devices are therefore limited in their external quantum efficiency based on the low number of photons absorbed. Increasing the number of photons absorbed is expected to increase the upconversion efficiency, yet experimentally increasing the number of layers does not appreciably increase the upconverted light output. We unravel this mystery by combining kinetic modeling and transient photoluminescence spectroscopy. The inherent energetic disorder stemming from the polydispersity of the NCs means that the kinetics are governed by a stochastic transfer matrix. By drawing the rates from a probabilistic distribution and constructing a reaction network with realistic connectivity, we are able to fit complex photoluminescence traces with a very simple model. We use this model to explain the thickness-dependent performance of the upconversion devices and can attribute the reduced efficiencies to the low excitonic diffusivity of the exciton within the NC layers and increased back transfer of the created singlets from the organic annihilator rubrene. We suggest some avenues for overcoming these limitations in future devices.

Speed Limit for Triplet-Exciton Transfer in Solid-State PbS Nanocrystal-Sensitized Photon Upconversion.

Hybrid interfaces combining inorganic and organic materials underpin the operation of many optoelectronic and photocatalytic systems and allow for innovative approaches to photon up- and down-conversion. However, the mechanism of exchange-mediated energy transfer of spin-triplet excitons across these interfaces remains obscure, particularly when both the macroscopic donor and acceptor are composed of many separately interacting nanoscopic moieties. Here, we study the transfer of excitons from colloidal lead sulfide (PbS) nanocrystals to the spin-triplet state of rubrene molecules. By reducing the length of the carboxylic acid ligands on the nanocrystal surface from 18 to 4 carbon atoms, thinning the effective ligand shell from 13 to 6 Å, we are able to increase the characteristic transfer rate by an order of magnitude. However, we observe that the energy transfer rate asymptotes for shorter separation distances (≤10 Å) which we attribute to the reduced Dexter coupling brought on by the increased effective dielectric constant of these solid-state devices when the aliphatic ligands are short. This implies that the shortest ligands, which hinder long-term colloidal stability, offer little advantage for energy transfer. Indeed, we find that hexanoic acid ligands are already sufficient for near-unity transfer efficiency. Using nanocrystals with these optimal-length ligands in an improved solid-state device structure, we obtain an upconversion efficiency of (7 ± 1)% with excitation at λ = 808 nm.