Friday, February 06, 2026

 

A molecular shield against light: Stabilizing perovskite solar cells at record efficiency



KeAi Communications Co., Ltd.
Hindered amine stabilization suppresses light-induced degradation while enabling record-efficiency perovskite solar cells 

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Hindered amine stabilization suppresses light-induced degradation while enabling record-efficiency perovskite solar cells. Schematic illustration of the hindered amine stabilization strategy (HASS) and its impact on device performance. Under illumination and oxygen exposure, photoexcited electrons in the perovskite generate superoxide radicals (O₂•⁻), which trigger decomposition into MA/FA species, I₂, and PbI₂. Incorporation of a multifunctional hindered amine scavenges these reactive radicals while simultaneously coordinating with Pb²⁺ and iodine vacancy defects, thereby blocking degradation pathways and passivating trap states. The resulting inverted perovskite solar cell achieves a champion power conversion efficiency of 26.74% (certified 26.56%), demonstrating the synergistic role of radical suppression and defect passivation in enhancing both efficiency and light stability.

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Credit: Cong Chen




Perovskite solar cells have reached power conversion efficiencies comparable to established photovoltaic technologies, yet their vulnerability to light-induced degradation continues to hinder practical deployment. This study introduces a chemical stabilization strategy that suppresses the radical reactions responsible for photo-driven decomposition in perovskite materials. By integrating a multifunctional hindered amine into the perovskite layer, the approach simultaneously scavenges reactive oxygen species and passivates electronic defects. As a result, the solar cells achieve both exceptionally high efficiency and markedly improved operational durability under illumination. The work demonstrates that controlling light-activated chemical pathways at the molecular level can reconcile efficiency and stability—two long-standing, competing challenges in perovskite photovoltaics.

Metal-halide perovskite solar cells are attractive for next-generation photovoltaics due to their low fabrication cost and rapidly rising efficiencies. However, exposure to light and oxygen generates superoxide radicals that attack organic cations and disrupt the perovskite lattice, leading to rapid performance loss. While encapsulation and optical filtering can mitigate environmental damage, they do not address degradation originating inside the perovskite crystal or at defect-rich interfaces. Moreover, trap states at grain boundaries often accelerate radical formation and non-radiative recombination. Based on these challenges, there is a pressing need to develop strategies that directly suppress light-induced chemical degradation while simultaneously reducing defect density within perovskite films.

Researchers from Hebei University of Technology, Kunming University of Science and Technology, Macau University of Science and Technology, and Chimie ParisTech report a new stabilization approach for perovskite solar cells in eSciencepublished (DOI: 10.1016/j.esci.2025.100451) in January 2026. The team demonstrates that incorporating a hindered amine light stabilizer into inverted perovskite solar cells effectively blocks photo-induced decomposition pathways. The resulting devices deliver a certified power conversion efficiency above 26% while maintaining performance under prolonged light exposure, offering a promising route toward durable, high-performance perovskite photovoltaics.

The proposed hindered amine stabilization strategy operates through a dual mechanism. Under illumination, the hindered amine absorbs light energy and forms nitroxyl radicals that catalytically neutralize superoxide species generated within the perovskite layer. By removing these highly reactive radicals before they can attack organic cations or Pb–I bonds, the strategy suppresses the primary chemical trigger of light-induced degradation. Importantly, the radical-scavenging process is regenerative, allowing continuous protection during device operation.

In parallel, functional groups within the hindered amine molecule coordinate with under-coordinated lead ions and iodine vacancies at grain boundaries and surfaces. This chemical interaction passivates electronic trap states, enlarges perovskite grain size, smooths film morphology, and reduces non-radiative recombination. Spectroscopic and electrical analyses confirm lower trap densities, longer carrier lifetimes, and improved energy-level alignment at device interfaces.

Together, these effects enable inverted perovskite solar cells fabricated under ambient conditions to reach a champion efficiency of 26.74%. Unencapsulated devices retain over 95% of their initial efficiency after more than 1,000 hours of continuous light aging, demonstrating a rare combination of record efficiency and operational stability.

“This work shows that light instability in perovskite solar cells is not an unavoidable materials problem, but a chemically addressable one,” the researchers note. By targeting both reactive radicals and interfacial defects, the hindered amine approach offers a unified solution rather than a collection of incremental fixes. The authors emphasize that the strategy is compatible with existing device architectures and scalable fabrication methods, making it particularly relevant for translating laboratory advances into commercially viable photovoltaic technologies.

The demonstrated stabilization strategy could significantly accelerate the commercialization of perovskite solar cells, especially for applications requiring long-term exposure to sunlight, such as building-integrated photovoltaics and tandem solar modules. Beyond perovskites, the concept of combining radical scavenging with defect passivation may be applicable to other light-sensitive optoelectronic materials. By reframing stability as a controllable chemical process rather than a structural limitation, this work opens new pathways for designing durable, high-efficiency solar technologies that bridge the gap between laboratory performance and real-world deployment.

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Media contact

Name: Editorial Office of eScience

Email: eScience@nankai.edu.cn

eScience – a Diamond Open Access journal cooperated with KeAi and published online at ScienceDirect. eScience is founded by Nankai University (China) in 2021 and aims to publish high quality academic papers on the latest and finest scientific and technological research in interdisciplinary fields related to energy, electrochemistry, electronics, and environment. eScience provides insights, innovation and imagination for these fields by built consecutive discovery and invention. Now eScience has been indexed by SCIECASScopus and DOAJ. Its  impact factor is 36.6, which is ranked first in the field of electrochemistry.

 

AI meets electrocatalysis: Lessons from three decades and a roadmap ahead




KeAi Communications Co., Ltd.
From Bottlenecks to Breakthroughs: How AI Is Reshaping Electrocatalysis 

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This schematic illustrates the major bottlenecks that have long constrained electrocatalysis research—including the scale gap of atomistic simulations, limited inverse catalyst design, poor physical consistency of data-driven models, inefficient human experimentation, and scarce high-quality datasets—and how recent AI advances are helping to overcome them. Emerging approaches such as machine-learning interatomic potentials (MLIPs), diffusion-based generative models, physics-informed machine learning (PIML), autonomous robotic electrochemists, and FAIR-compliant data infrastructures are converging to transform electrocatalysis from a trial-and-error discipline into a predictive, data-centric discovery pipeline.

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Credit: Richard G. Compton





Electrocatalysis sits at the heart of clean hydrogen production, fuel cells, and carbon dioxide conversion, yet progress toward scalable, high-performance catalysts has remained frustratingly slow. A growing body of research now suggests that artificial intelligence (AI) may be key to breaking this bottleneck—but only if it is used wisely. By reviewing three decades of AI applications in electrocatalysis, researchers reveal how the field has shifted from isolated data analysis toward end-to-end, data-driven discovery. The work highlights a critical turning point: AI is no longer just accelerating experiments, but beginning to reshape how electrocatalysts are designed, evaluated, and understood at a fundamental level.

Despite intense global investment in clean energy technologies, electrocatalyst development still struggles with deep-rooted challenges. Atomistic simulations rarely translate into device-scale performance, experimental workflows remain labor-intensive and difficult to reproduce, and most machine-learning models lack physical interpretability. At the same time, the demand for efficient catalysts in hydrogen production and carbon-neutral chemical manufacturing continues to rise. The rapid expansion of artificial intelligence (AI) capabilities—ranging from physics-informed models to autonomous experimentation—has opened new possibilities, but also exposed systemic weaknesses in data quality and integration. Based on these challenges, a comprehensive reassessment of how AI should be deployed in electrocatalysis has become urgently needed.

Addressing this need, a review published (DOI: 10.1016/j.esci.2025.100515) in December 2025 in eScience by an international team of researchers from the University of Michigan, Xiamen University, and the University of Oxford examines 30 years of AI-driven electrocatalysis research. rather than cataloging past successes, the authors identify why many AI approaches have failed to deliver real-world impact—and how recent advances in machine learning, data infrastructure, and laboratory automation may finally change that trajectory. the review positions the field at a decisive moment, where strategic choices could determine whether AI delivers genuine breakthroughs or remains largely incremental.

The authors identify five structural bottlenecks that have limited the effectiveness of AI in electrocatalysis: mismatches between atomic-scale models and macroscopic performance, the immaturity of inverse catalyst design, poor physical consistency of black-box algorithms, inefficient manual experimentation, and a shortage of reliable experimental data. To overcome these barriers, recent work has introduced machine-learning interatomic potentials capable of simulating dynamic catalyst restructuring at unprecedented scales, alongside generative AI models that propose new materials rather than merely screening known ones.

Equally transformative is the rise of physics-informed machine learning, which embeds electrochemical laws directly into neural networks, enabling models that are both predictive and interpretable. The review also highlights the emergence of autonomous “robotic electrochemists” that integrate AI decision-making with high-throughput synthesis and testing. Together, these developments suggest that electrocatalysis is shifting from a trial-and-error discipline toward a closed-loop, self-improving discovery system—provided that data quality and model transparency are treated as core scientific priorities.

Importantly, the authors caution against viewing AI as a universal solution. They emphasize that poorly curated data and physically inconsistent models risk amplifying errors rather than accelerating discovery. Instead, they argue that the most impactful advances will come from combining AI with electrochemical theory, standardized data practices, and interdisciplinary collaboration. In their view, the true value of AI lies not in replacing human expertise, but in enabling scientists to ask deeper questions and explore chemical spaces that were previously inaccessible.

If these challenges are addressed, AI-driven electrocatalysis could significantly accelerate the deployment of clean energy technologies, from large-scale hydrogen production to carbon-neutral fuel synthesis. The review suggests that the next breakthroughs will likely emerge where automated laboratories, physics-informed models, and open data infrastructures converge. Beyond energy applications, the lessons outlined may influence how AI is applied across chemistry and materials science more broadly. By reframing AI as an integrated scientific partner rather than a standalone tool, the work points toward a future in which catalyst discovery becomes faster, more reliable, and more directly connected to real-world impact.

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Media contact

Name: Editorial Office of eScience

Email: eScience@nankai.edu.cn

eScience – a Diamond Open Access journal cooperated with KeAi and published online at ScienceDirect. eScience is founded by Nankai University (China) in 2021 and aims to publish high quality academic papers on the latest and finest scientific and technological research in interdisciplinary fields related to energy, electrochemistry, electronics, and environment. eScience provides insights, innovation and imagination for these fields by built consecutive discovery and invention. Now eScience has been indexed by SCIECASScopus and DOAJ. Its first impact factor is 36.6, which is ranked first in the field of electrochemistry.