Exploring Transport Behavior in Hybrid Perovskites Solar Cells via Machine Learning Analysis of Environmental-Dependent Impedance Spectroscopy

Synergistic Ion-Anchoring Passivation for Perovskite Solar Cells with Efficiency Exceeding 24% and Ultra-Ambient Stability

The high-density defect states existing at the grain boundaries and heterojunction interfaces induce nonradiative charge recombination and ion migration processes within perovskite film, which seriously impair the device efficiency and stability. Here, we propose a novel synergistic ion-anchoring passivation (SIP) strategy for high-performance perovskite solar cells, by designing a multifunctional molecule to heal the charged defects via electrostatic interactions. The anion and cation species of the multifunctional molecule are rationally screened via high-throughput DFT simulation and experimental verification, which act as efficient surface passivation agents to heal the lead- and iodine-related defects. As a result, the defect-less perovskite films deliver encouraging device power conversion efficiency >24% with negligible hysteresis. A remarkable open-circuit voltage (Voc) of 1.17 V was obtained with a Voc deficit of 370 mV, featuring the outstanding defect-passivation capability of the SIP strategy. Moreover, the SIP-treated devices show exceptional ambient stability and maintain 70% of the initial efficiency after 150 h of high humidity exposure (relative humidity 70%-80%). Our results highlight the importance of the rational design of passivation agents to realize high-performance perovskite electronics.

A Robust Neural Network for Extracting Dynamics from Electrostatic Force Microscopy Data

Advances in scanning probe microscopy (SPM) methods such as time-resolved electrostatic force microscopy (trEFM) now permit the mapping of fast local dynamic processes with high resolution in both space and time, but such methods can be time-consuming to analyze and calibrate. Here, we design and train a regression neural network (NN) that accelerates and simplifies the extraction of local dynamics from SPM data directly in a cantilever-independent manner, allowing the network to process data taken with different cantilevers. We validate the NN's ability to recover local dynamics with a fidelity equal to or surpassing conventional, more time-consuming, calibrations using both simulated and real microscopy data. We apply this method to extract accurate photoinduced carrier dynamics on n = 1 butylammonium lead iodide, a halide perovskite semiconductor film that is of interest for applications in both solar photovoltaics and quantum light sources. Finally, we use SHapley Additive exPlanations to evaluate the robustness of the trained model, confirm its cantilever-independence, and explore which parts of the trEFM signal are important to the network.

Operando Analysis of Losses in Commercial-Sized Solid Oxide Cells: Methodology Development and Validation

The development of decarbonised systems is being fostered by the increasing demand for technological solutions for the energy transition. Solid Oxide Cells are high-efficiency energy conversion systems that are foreseen for commercial development. They exhibit potential power generation and power-to-gas applications, including a reversible operation mode. Long-lasting high performance is essential for guaranteeing the success of the technology; therefore, it is fundamental to provide diagnosis tools at this early stage of development. In this context, operando analysis techniques help detect and identify incipient degradation phenomena to either counteract damage at its origin or correct operando protocols. Frequent switches from the fuel cell to the electrolyser mode add more challenges with respect to durable performance, and deep knowledge of reverse-operation-induced damage is lacking in the scientific and technical literature. Following on from preliminary experience with button cells, in this paper, the authors aim to transfer the methodology to commercial-sized Solid Oxide Cells. On the basis of the experimental evidence collected on planar square cells under dry and wet reactant feed gases, the main contributions to impedance are identified as being charge transfer (f = 103–104 Hz), oxygen surface exchanged and diffusion in bulk LSCF (f = 102–103 Hz), and gas diffusion in the fuel electrode (two peaks, f = 1–100 Hz). The results are validated using the ECM methodology, implementing an LRel(RctQ)GWFLW circuit.