Prof Henry Snaith FRS

Abstract:

Reducing heterointerface nonradiative recombination is a key challenge for realizing highly efficient perovskite solar cells (PSCs). Motivated by this, a facile strategy is developed via interfacial energetics reversal to functionalize perovskite heterointerface. A surfactant molecule, trichloro[3-(pentafluorophenyl)propyl]silane (TPFS) reverses perovskite surface energetics from intrinsic n-type to p-type, evidently demonstrated by ultraviolet and inverse photoelectron spectroscopies. The reconstructed perovskite surface energetics match well with the upper deposited hole transport layer, realizing an exquisite energy level alignment for accelerating hole extraction across the heterointerface. Meanwhile, TPFS further diminishes surface defect density. As a result, this cooperative strategy leads to greatly minimized nonradiative recombination. PSCs achieve an impressive power conversion efficiency of 25.9% with excellent reproducibility, and a nonradiative recombination-induced qVoc loss of only 57 meV, which is the smallest reported to date in n-i-p structured PSCs.

Abstract:

<jats:title>Abstract</jats:title><jats:p>2D lead halide perovskites (2DPs) offer chemical compatibility with 3D perovskites and enhanced stability, which are attractive for applications in photovoltaic and light‐emitting devices. However, such lowered structural dimensionality causes increased excitonic effects and highly anisotropic charge‐carrier transport. Determining the diffusivity of excitations, in particular for out‐of‐plane or inter‐layer transport, is therefore crucial, yet challenging to achieve. Here, an effective method is demonstrated for monitoring inter‐layer diffusion of photoexcitations in (PEA)<jats:sub>2</jats:sub>PbI<jats:sub>4</jats:sub> thin films by tracking time‐dependent changes in photoluminescence spectra induced by photon reabsorption effects. Selective photoexcitation from either substrate‐ or air‐side of the films reveals differences in diffusion dynamics encountered through the film profile. Time‐dependent diffusion coefficients are extracted from spectral dynamics through a 1D diffusion model coupled with an interference correction for refractive index variations arising from the strong excitonic resonance of 2DPs. Such analysis, together with structural probes, shows that minute misalignment of 2DPs planes occurs at distances far from the substrate, where efficient in‐plane transport consequently overshadows the less efficient out‐of‐plane transport in the direction perpendicular to the substrate. Through detailed analysis, a low out‐of‐plane excitation diffusion coefficient of (0.26 ± 0.03) ×10<jats:sup>−4</jats:sup> cm<jats:sup>2</jats:sup> s<jats:sup>−1</jats:sup> is determined, consistent with a diffusion anisotropy of ≈4 orders of magnitude.</jats:p>

Abstract:

<jats:p>In this work, we demonstrate that time-resolved photoluminescence data of metal halide perovskites can be effectively evaluated by combining Bayesian inference with a Markov-chain Monte-Carlo algorithm and a physical model. This approach enables us to infer a high number of parameters that govern the performance of metal halide perovskite-based devices, alongside the probability distributions of those parameters, as well as correlations among all parameters. Via studying a set of halfstacks, comprising electron- and hole-transport materials contacting perovskite thin films, we determine surface recombination velocities at these interfaces with high precision. From the probability distributions of all inferred parameters, we can simulate intensity-dependent photoluminescence quantum efficiency and compare it to experimental data. Finally, we estimate mobility values for vertical charge-carrier transport, which is perpendicular to the plane of the substrate, for all samples using our approach. Since this mobility estimation is derived from charge-carrier diffusion over the length scale of the film thickness and in the vertical direction, it is highly relevant for transport in photovoltaic and light-emitting devices. Our approach of coupling spectroscopic measurements with advanced computational analysis will help speed up scientific research in the field of optoelectronic materials and devices and exemplifies how carefully constructed computational algorithms can derive valuable plurality of information from simple datasets. We expect that our approach can be expanded to a variety of other analysis techniques and that our method will be applicable to other semiconductors.</jats:p> <jats:sec> <jats:title/> <jats:supplementary-material> <jats:permissions> <jats:copyright-statement>Published by the American Physical Society</jats:copyright-statement> <jats:copyright-year>2025</jats:copyright-year> </jats:permissions> </jats:supplementary-material> </jats:sec>

Abstract:

In this work we demonstrate that time-resolved photoluminescence data of metal halide perovskites can be effectively evaluated by combining Bayesian inference with a Markov-Chain Monte-Carlo algorithm and a physical model. This approach enables us to infer a high number of parameters which govern the performance of metal halide perovskite-based devices, alongside the probability distributions of those parameters, as well as correlations among all parameters. Via studying a set of "half-stacks’‘, comprising electron and hole transport materials contacting perovskite thin-films, we determine surface recombination velocities at these interfaces with high precision. From the probability distributions of all inferred parameters, we can simulate intensity-dependent photoluminescence quantum efficiency and compare it to the experimental data. Finally, we estimate mobility values for the "vertical’’ charge carrier transport, that perpendicular to the plane of the substrate, for all samples using our approach. Since this mobility estimation is derived from charge carrier diffusion over the length-scale of the film thickness and in the vertical direction, it is highly relevant to transport in photovoltaic and light emitting devices. Our approach of coupling spectroscopic measurements with advanced, computational analysis will help speed up scientific research in the field of optoelectronic materials and devices and exemplifies how carefully constructed computational algorithms can derive valuable plurality of information from simple datasets. We expect that our approach will be expandable to a variety of other analysis techniques and that our method will be applicable to other semiconductors.