Integrated Bulk–Surface Engineering Stabilizes MA‐Free Wide‐Bandgap Perovskites for Tandem Photovoltaics

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Authors: Yu Tong, Biao Li, Youming Zhu, Yehui Wen, Tianchi Zhang, Weihua Ning, Yong Wang, Xuegong Yu, Deren Yang
Cited by: 2
References: 29

Abstract

ABSTRACT Optimal wide‐bandgap perovskites are essential for perovskite/silicon tandem solar cells. Conventional wide‐bandgap perovskites, typically FA 1‐ x ‐ y Cs x MA y PbI 1‐ z Br z , contain volatile methylammonium (MA) components and mixed halides that compromise device stability and performance. Removing MA to form FA 1‐ x Cs x PbI 1‐ z Br z eliminates volatile organic components; however, the absence of MA and high Br content required for bandgap widening inevitably accelerates crystallization, increases defect density, and induces severe voltage losses. Here, we present a coupled bulk–surface regulation strategy that fundamentally overcomes these intrinsic bottlenecks. Incorporation of homopiperidinic acid hydroiodide into the precursor heals bulk lattice defects via ─COOH─Pb 2+ coordination and suppresses halide migration through N─H…I– hydrogen bonding, while subsequent treatment with trimethylenediamine dihydroiodide salts neutralizes surface unsaturated Pb 2+ and halide vacancies through amino‐Pb 2+ coordination, hydrogen bonding, and electrostatic interactions. Their coupling effect precisely suppresses defect formation, minimizes non‐radiative recombination, and critically stabilizes halide distribution. As a result, the wide‐bandgap perovskite solar cells achieve an efficiency of 23.71% and enhanced operational stability with T 92 exceeding 1000 h. Integrated into silicon tandem devices, they deliver 32.26% with long‐term durability. This work establishes molecular coupling consolidation as a new paradigm for constructing stable, high‐efficiency, MA‐free wide‐bandgap perovskites, advancing the practical realization of reliable tandem photovoltaics.

Fields of Study & Subjects

Perovskite Materials and Applications TiO2 Photocatalysis and Solar Cells Machine Learning in Materials Science

Publication details

Journal / source
Advanced Science
Publisher
Wiley
Volume
13
Issue
16
Pages
e20950–e20950
Publication date
5 January 2026
Language
EN