ABSTRACT
Heterojunction Si solar cells exhibit notable performance degradation. We modeled this degradation by electronic defects getting generated by thermal activation across energy barriers over time. To analyze the physics of this degradation, we developed the SolDeg platform to simulate the dynamics of electronic defect generation. First, femtosecond molecular dynamics simulations were performed to create a-Si/c-Si stacks, using the machine learning-based Gaussian approximation potential. Second, we created shocked clusters by a cluster blaster method. Third, the shocked clusters were analyzed to identify which of them supported electronic defects. Fourth, the distribution of energy barriers that control the generation of these electronic defects was determined. Fifth, an accelerated Monte Carlo method was developed to simulate the thermally activated time-dependent defect generation across the barriers. Our main conclusions are as follows. (1) The degradation of a-Si/c-Si heterojunction solar cells via defect generation is controlled by a broad distribution of energy barriers. (2) We developed the SolDeg platform to track the microscopic dynamics of defect generation across this wide barrier distribution and determined the time-dependent defect density N(t) from femtoseconds to gigaseconds, over 24 orders of magnitude in time. (3) We have shown that a stretched exponential analytical form can successfully describe the defect generation N(t) over at least 10 orders of magnitude in time. (4) We found that in relative terms, Voc degrades at a rate of 0.2%/year over the first year, slowing with advancing time. (5) We developed the time correspondence curve to calibrate and validate the accelerated testing of solar cells. We found a compellingly simple scaling relationship between accelerated and normal times tnormal â taccelT(accel)/T(normal). (6) We also carried out experimental studies of defect generation in a-Si:H/c-Si stacks. We found a relatively high degradation rate at early times that slowed considerably at longer time scales.
ABSTRACT
The efficiency of nanoparticle (NP) solar cells has grown impressively in recent years, exceeding 16%. However, the carrier mobility in NP solar cells, and in other optoelectronic applications remains low, thus critically limiting their performance. Therefore, carrier transport in NP solids needs to be better understood to further improve the overall efficiency of NP solar cell technology. However, it is technically challenging to simulate experimental scale samples, as physical processes from atomic to mesoscopic scales all crucially impact transport. To rise to this challenge, here we report the development of TRIDENS: the Transport in Defected Nanoparticle Solids Simulator, that adds three more hierarchical layers to our previously developed HINTS code for nanoparticle solar cells. In TRIDENS, we first introduced planar defects, such as twin planes and grain boundaries into individual NP SLs superlattices (SLs) that comprised the order of 103 NPs. Then we used HINTS to simulate the transport across tens of thousands of defected NP SLs, and constructed the distribution of the NP SL mobilities with planar defects. Second, the defected NP SLs were assembled into a resistor network with more than 104 NP SLs, thus representing about 107 individual NPs. Finally, the TRIDENS results were analyzed by finite size scaling to explore whether the percolation transition, separating the phase where the low mobility defected NP SLs percolate, from the phase where the high mobility undefected NP SLs percolate drives a low-mobility-to-highmobility transport crossover that can be extrapolated to genuinely macroscopic length scales. For the theoretical description, we adapted the Efros-Shklovskii bimodal mobility distribution percolation model. We demonstrated that the ES bimodal theory's two-variable scaling function is an effective tool to quantitatively characterize this low-mobility-to-high-mobility transport crossover.
ABSTRACT
We show that adapting the knowledge developed for the disordered Mott-Hubbard model to nanoparticle (NP) solids can deliver many very helpful new insights. We developed a hierarchical nanoparticle transport simulator (HINTS), which builds from localized states to describe the disorder-localized and Mott-localized phases of NP solids and the transitions out of these localized phases. We also studied the interplay between correlations and disorder in the corresponding multiorbital Hubbard model at and away from integer filling by dynamical mean field theory. This DMFT approach is complementary to HINTS, as it builds from the metallic phase of the NP solid. The mobility scenarios produced by the two methods are strikingly similar and account for the mobilities measured in NP solids. We conclude this work by constructing the comprehensive phase diagram of PbSe NP solids on the disorder-filling plane.
