Improved Catalyst Performance for the Oxygen Evolution Reaction under a Chiral Bias
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Engineering topics
Publications and source records attributed to Dunlap-Shohl, Wiley A..
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Abstract Chiral perovskite nanoparticles and films are promising for integration in emerging spintronic and optoelectronic technologies, yet few design rules exist to guide the development of chiral material properties. The chemical space of potential building blocks for these nanostructures is vast, and the mechanisms through which organic ligands can impart chirality to the inorganic perovskite lattice are not well understood. In this work, we investigate how the properties of chiral ammonium ligands, the most common organic ligand type used with perovskites, affect the circular dichroism of strongly quantum confined CsPbBr 3 nanocrystals. We show that aromatic ammonium ligands with stronger electron-donating groups lead to higher-intensity circular dichroism associated with the lowest-energy excitonic transition of the perovskite nanocrystal. We argue that this behavior is best explained by a modulation of the exciton wavefunction overlap between the nanocrystal and the organic ligand, as the functional groups on the ligand can shift electron density toward the organic species-perovskite lattice interface to increase the imprinting.
Optical absorbance is used to study the kinetics of methylammonium lead iodide (MAPbI 3 ) thin film degradation in response to combinations of moisture, oxygen, and illumination over a range of temperatures. 105 degradations were conducted over 41 unique environmental conditions. We discover that water acts synergistically with oxygen in a water-accelerated photo-oxidation (WPO) pathway. This pathway is the dominant pathway at 25 °C and is 10, 100, 1000, and >1000 times faster than dry photooxidation (DPO), degradation via hydrate formation, thermal degradation, and blue light degradation, respectively. We find that the rate determining step for DPO is proton abstraction from methylammonium while for WPO it is proton abstraction from water, which occurs at a faster rate and results in water acting as an accelerant for photooxidation of MAPbI 3 . A full kinetic rate equation is derived and fitted to the data to determine activation energies and rate constants.
PV technologies based on hybrid perovskites offer the potential for reducing solar cell costs, but they are particularly vulnerable to degradation by environmental factors such as moisture, oxygen, and illumination. Commercialization will require not only stable materials and device architectures but also accelerated testing protocols and models that can predict degradation from the accelerated testing data. Here, we report results from in situ photoluminescence (PL) and photoconductivity (PC) measurements during perovskite degradation with simultaneous optical transmittance (Tr) measurements or reflected dark field (DF) imaging. From PL, PC, Tr, and DF, we determine (respectively) the steady-state quasi-Fermi level splitting, the mean effective carrier diffusion length, the extent of conversion of perovskite to higher bandgap degradation products, and the extent of scattering from domains with different orientation or composition, all as a function of time during degradation. Simultaneous measurement of PL-PC-Tr or PL-PC-DF on perovskite absorbers in an environmental chamber over a wide range of humidity, oxygen, temperature, and illumination levels yields a rich data set. We use machine learning to develop a model that accurately predicts (within 10%) the time for the diffusion length to decrease to 85% of its initial value. The model takes the environmental conditions and the first few measurements from the PL-PC-Tr or PL-PC-DF experiment as input. Thus, the model provides a framework to interpret the results of accelerated testing of absorber materials. One of the dominant features in the model of degradation for CH 3 NH 3 PbI 3 is the initial rate at which transmittance increase. For devices with opaque contacts, the pixel-averaged rate of change of the intensity in dark-field images can be used in place of transmittance. Results are also presented on full PV devices with in-situ current-voltage (JV) measurements. Here, the data from simultaneous PL-DF-JV under environmental stresses reveal intimate connection between degradation and shunts and provide a framework to extend accelerated testing to devices. Further, the presentation reveals underlying universal behavior in degradation pathways over a broad range of environmental stresses and perovskite compositions.
The practicality and economic viability of hybrid perovskite solar cells hinge on their operational lifetime, and methods for forecasting the performance of perovskites under different operational stresses are urgently needed. Here, we explore the evolution of material-level optoelectronic properties as MAPbI 3 degrades and discover universal behaviors where the carrier diffusion length (L D ) decays before quasi-Fermi-level splitting (ΔE F ), regardless of the specific stress protocol (oxygen, humidity, thermal stress, or a combination). We employ a machine learning greedy feature selection model that uses initially measured properties to predict the time it takes L D to decrease to 85% of its initial value with a prediction accuracy of 12.8%. This model reveals a strong correlation between the initial rate of transmittance change and the time until loss of transport. We translate this material-level finding to photovoltaic device-level forecasting by demonstrating that the rate of change of transmittance is equivalent to the rate of change of the spatial standard deviation of dark-field image intensity (i.e., scattered light intensity) collected in reflection mode (and thus applicable to devices with opaque contacts). Furthermore, this work demonstrates that transmittance and scattering methods are highly effective for accelerated material (and device) stability evaluation and forecasting.