Population Balance Equations for Reactive Separation in Polymer Upcycling.

Many polymer upcycling efforts aim to convert plastic waste into high-value liquid hydrocarbons. However, the subsequent cleavage of middle distillates to light gases can be problematic. The reactor often contains a vapor phase (light gases and middle distillates) and a liquid phase (molten polymers and waxes with a suspended or dissolved catalyst). Because the catalyst resides in the liquid phase, middle distillates that partition into the vapor phase are protected against further cleavage into light gases. In this paper, we consider a simple reactive separation strategy, in which a gas outflow removes the volatile products as they form. We combine vapor-liquid equilibrium models and population balance equations (PBEs) to describe polymer upcycling in a two-phase semibatch reactor. The results suggest that the temperature, headspace volume, and flow rate of the reactor can be used to tune selectivity toward the middle distillates, in addition to the molecular mechanism of catalysis. We anticipate that two-phase reactor models will be important in many polymer upcycling processes and that reactive separation strategies will provide ways to boost the yield of the desired products in these cases.

Exchange controlled triplet fusion in metal-organic frameworks.

Triplet-fusion-based photon upconversion holds promise for a wide range of applications, from photovoltaics to bioimaging. The efficiency of triplet fusion, however, is fundamentally limited in conventional molecular and polymeric systems by its spin dependence. Here, we show that the inherent tailorability of metal-organic frameworks (MOFs), combined with their highly porous but ordered structure, minimizes intertriplet exchange coupling and engineers effective spin mixing between singlet and quintet triplet-triplet pair states. We demonstrate singlet-quintet coupling in a pyrene-based MOF, NU-1000. An anomalous magnetic field effect is observed from NU-1000 corresponding to an induced resonance between singlet and quintet states that yields an increased fusion rate at room temperature under a relatively low applied magnetic field of 0.14 T. Our results suggest that MOFs offer particular promise for engineering the spin dynamics of multiexcitonic processes and improving their upconversion performance.

Mechanism of Enhanced Triplet-Triplet Upconversion in Organic Molecules.

We use time-dependent density functional theory (TDDFT) to investigate the mechanism of efficient triplet-triplet upconversion (TTU) in certain organic materials. In particular, we focus on materials where some singlets are generated in a two-step spin-nonconserving process (T1 + T1 → T2 → S1). For this mechanism to contribute significantly, the intersystem crossing (ISC) from the high-lying triplet to the singlet (T2 → S1) must outcompete the internal conversion (IC) to the low-lying triplet (T2 → T1). By considering multiple families of materials, we show that the T2 → S1 ISC can be enhanced in a number of ways: the substitution of electron-donating (ED) and electron-withdrawing (EW) groups at appropriate positions; the substitution of bulky groups that distort the molecular geometry; and the substitution of heavy atoms that enhance the spin-orbit coupling (SOC). In the first two cases, the enhancements are consistent with El-Sayed's rule in that rapid T2 → S1 ISC requires significant differences in the characters of the S1 and the T2 wavefunctions. Together, these effects enable a wide tunability of T2 → S1 ISC rates over at least 5 orders of magnitude. Meanwhile, the T2 → T1 IC is inhibited in these systems due to the large T2 - T1 energy gap >0.5 eV, which entails a high energy barrier to the T2 → T1 IC and the prediction of a slow rate regardless of the substituents or the presence of heavy atoms. In this way, tuning the T2 → S1 ISC appears to provide an effective strategy to achieve systematic improvement of TTU materials.

Maximizing TADF via Conformational Optimization.

We investigate a new strategy to enhance thermally activated delayed fluorescence (TADF) in organic light-emitting diodes (OLEDs). Given that the TADF rate of a molecule depends on its conformation, we hypothesize that there exists a conformation that maximizes the TADF rate. To test this idea, we use time-dependent density functional theory (TDDFT) to simulate the TADF rates of several TADF emitters while varying their geometries in a select subspace of internal coordinates. We find that geometric changes in this subspace can increase the TADF rate up to 3 orders of magnitude with respect to the minimum energy conformation, and the simulated TADF rate can even be brought into the submicrosecond time scales under the right conditions. Furthermore, the TADF rate enhancement can be maintained with a conformational energy that might be within the reach of modern synthetic chemistry. Analyzing the maximum TADF conformation, we extract a number of structural motifs that might provide a useful handle on the TADF rate of a donor-acceptor (DA) system. The incorporation of conformational engineering into the TADF technology could usher in a new paradigm of OLEDs.

Machine learning dynamic correlation in chemical kinetics.

Lattice models are a useful tool to simulate the kinetics of surface reactions. Since it is expensive to propagate the probabilities of the entire lattice configurations, it is practical to consider the occupation probabilities of a typical site or a cluster of sites instead. This amounts to a moment closure approximation of the chemical master equation. Unfortunately, simple closures, such as the mean-field and the pair approximation (PA), exhibit weaknesses in systems with significant long-range correlation. In this paper, we show that machine learning (ML) can be used to construct accurate moment closures in chemical kinetics using the lattice Lotka-Volterra model as a model system. We trained feedforward neural networks on kinetic Monte Carlo (KMC) results at select values of rate constants and initial conditions. Given the same level of input as PA, the ML moment closure (MLMC) gave accurate predictions of the instantaneous three-site occupation probabilities. Solving the kinetic equations in conjunction with MLMC gave drastic improvements in the simulated dynamics and descriptions of the dynamical regimes throughout the parameter space. In this way, MLMC is a promising tool to interpolate KMC simulations or construct pretrained closures that would enable researchers to extract useful insight at a fraction of the computational cost.