The Impact of Sequential Fluorination of π‐Conjugated Polymers on Charge Generation in All‐Polymer Solar Cells

The performance of all‐polymer solar cells (all‐PSCs) is often limited by the poor exciton dissociation process. Here, the design of a series of polymer donors ( P1 – P3 ) with different numbers of fluorine atoms on their backbone is presented and the influence of fluorination on charge generation in all‐PSCs is investigated. Sequential fluorination of the polymer backbones increases the dipole moment difference between the ground and excited states (Δµ_ge) from P1 (18.40 D) to P2 (25.11 D) and to P3 (28.47 D). The large Δµ_ge of P3 leads to efficient exciton dissociation with greatly suppressed charge recombination in P3 ‐based all‐PSCs. Additionally, the fluorination lowers the highest occupied molecular orbital energy level of P3 and P2 , leading to higher open‐circuit voltage (V_OC). The power conversion efficiency of the P3 ‐based all‐PSCs (6.42%) outperforms those of the P2 and P1 (5.00% and 2.65%)‐based devices. The reduced charge recombination and the enhanced polymer exciton lifetime in P3 ‐based all‐PSCs are confirmed by the measurements of light‐intensity dependent short‐circuit current density (J_SC) and V_OC, and time‐resolved photoluminescence. The results provide reciprocal understanding of the charge generation process associated with Δµ_ge in all‐PSCs and suggest an effective strategy for designing π‐conjugated polymers for high performance all‐PSCs.     

Experiment-Oriented Machine Learning of Polymer:Non-Fullerene Organic Solar Cells

Despite the capacity of conjugated materials for enhanced power conversion efficiency (PCE) of organic photovoltaics (OPV), a comprehensive survey of unexplored materials is beyond the reach of most researchers’ resources. In such instances, a data-driven approach using machine learning (ML) is an efficient alternative; however, bridging the gap between experimental observations and data science requires a number of refinements. In this investigation, using a random forest model based on an experimental dataset, a high correlation coefficient of 0.85 is achieved for the ML of polymer and non-fullerene small molecule acceptor OPVs and performed virtual screening of 200,932 conjugated polymers generated by the combinatorial coupling of donor and acceptor units. Further, to evaluate the effectiveness of the ML model, a series of conjugated polymers (based on benzodithiophene and thiazolothiazole) were designed, synthesized, and characterized with different alkyl chains. Among these, PBDTTzEH:IT-4F showed a PCE of 10.10%, which is in good correspondence with ML predictions with respect to the choice of alkyl chains. Thus, the current study demonstrates how ML can be utilized for developing OPVs using a relatively small number of experimental data points (566) and screening numerous molecular structures.     

High‐Performance Long‐Term‐Stable Dopant‐Free Perovskite Solar Cells and Additive‐Free Organic Solar Cells by Employing Newly Designed Multirole π‐Conjugated Polymers

Perovskite solar cells (PSCs) and organic solar cells (OSCs) are promising renewable light‐harvesting technologies with high performance, but the utilization of hazardous dopants and high boiling additives is harmful to all forms of life and the environment. Herein, new multirole π‐conjugated polymers (P1–P3) are developed via a rational design approach through theoretical hindsight, further successfully subjecting them into dopant‐free PSCs as hole‐transporting materials and additive‐free OSCs as photoactive donors, respectively. Especially, P3‐based PSCs and OSCs not only show high power conversion efficiencies of 17.28% and 8.26%, but also display an excellent ambient stability up to 30 d (for PSCs only), owing to their inherent superior optoelectronic properties in their pristine form. Overall, the rational approach promises to support the development of environmentally and economically sustainable PSCs and OSCs.