A New Growth Strategy Boosts Efficiency and Stability in Perovskite Solar Cells

Published on Quantum Server Networks

Growth strategy for perovskite solar cells

Perovskite solar cells (PSCs) are at the forefront of next-generation photovoltaic research. Known for their low-cost fabrication potential and high efficiency, PSCs could one day replace or complement traditional silicon-based solar panels in powering the global clean energy transition. However, their development has faced a persistent challenge: improving efficiency often reduces stability, while enhancing stability tends to compromise efficiency.

A team of researchers at Nanyang Technological University (NTU), Singapore, has now reported a breakthrough strategy that addresses this trade-off head-on. As described in a recent TechXplore report and published in Nature Energy, their novel growth method introduces chemically-inert low-dimensional (CI LD) halogenometallate interfaces into perovskite devices, dramatically enhancing both performance and durability.

The Promise and Challenge of Perovskite Solar Cells

Since their introduction in 2009, PSCs have achieved rapid progress, reaching power conversion efficiencies of over 25% in laboratory settings—comparable to silicon solar cells. Unlike silicon wafers, perovskite thin films can be produced through low-cost solution processing, offering a potentially cheaper pathway to mass production.

However, perovskites are notoriously unstable. Exposure to heat, light, and moisture often degrades their performance over time, raising concerns about long-term reliability. This instability has been the biggest obstacle preventing PSCs from reaching large-scale commercialization.

A Selective Templating Growth Strategy

The NTU research team tackled the problem by engineering a **two-step selective templating process** to grow CI LD halogenometallate interfaces. These interfaces act as protective boundary layers that resist unwanted chemical reactions while allowing efficient charge transport.

Their method works as follows:

  • First, they grow a metastable low-dimensional (LD) interface, which forms easily but lacks long-term stability.
  • Next, they apply an organic cation exchange process, replacing weaker cations with bulkier, chemically inert ones. This transformation stabilizes the interface and prevents degradation of the perovskite layer beneath.

Record-Breaking Performance

Prototype solar cells built using this strategy achieved efficiencies of 25.1% over an active area of 1.235 cm², among the highest reported for devices of this size. More importantly, they demonstrated remarkable stability:

  • Maintaining over 93% of their efficiency after 1,000 hours of continuous operation.
  • Retaining 98% efficiency after 1,100 hours of thermal aging at 85 °C.

These results represent a major advance, as most perovskite cells degrade far more rapidly under such testing conditions.

Implications for Solar Energy

By overcoming the efficiency-stability trade-off, this templating growth method could accelerate the commercialization of perovskite photovoltaics. Beyond solar panels, stable perovskite materials are also being explored for applications in light-emitting diodes (LEDs), photodetectors, and even radiation sensors.

With continued refinement, researchers believe this strategy could be adapted to other perovskite architectures, bringing scalable, low-cost, and highly durable solar technologies closer to reality.

The Future of Clean Energy

Solar energy remains one of the most promising avenues for combating climate change, and innovations like this could tip the balance. By improving both performance and durability, perovskite solar cells are inching toward becoming a viable, large-scale solution for renewable power generation.

Source: Nanyang Technological University, Singapore. Original article available at TechXplore: https://techxplore.com/news/2025-08-growth-strategy-efficiency-stability-perovskite.html

This blog article was prepared with the assistance of AI technologies to support science communication.

Sponsored by PWmat (Lonxun Quantum) – a pioneer in GPU-accelerated materials simulation software for next-generation quantum, energy, and semiconductor research. Learn more at: https://www.pwmat.com/en

📘 Explore our advanced simulation features and case studies in the PWmat Company Brochure.

🎁 Interested in testing our software? Request a free trial and receive tailored insights for your R&D projects: Click here.

📞 Phone: +86 400-618-6006
📧 Email: support@pwmat.com

#SolarEnergy #PerovskiteSolarCells #CleanTech #RenewableEnergy #MaterialsScience #Nanotechnology #QuantumServerNetworks #GreenInnovation #NextGenEnergy

Comments

Post a Comment

Popular posts from this blog

Quantum Chemistry Meets AI: A New Era for Molecular Machine Learning

Image
In a powerful leap forward for computational chemistry and materials design, researchers from Carnegie Mellon University have developed a new kind of molecular machine learning model—one that goes beyond simplistic molecular graphs and integrates fundamental principles of quantum chemistry. Their work, published in Nature Machine Intelligence , could radically enhance our ability to predict chemical behavior, optimize catalysts, and discover new drugs, all while using less data and gaining more scientific insight. The Problem: Traditional Molecular Representations Fall Short Molecular machine learning (ML) models underpin key processes in drug discovery, materials science, and catalysis. However, most existing models use simplified representations such as SMILES strings, 2D graphs, or coordinate matrices. These techniques often lack the quantum-chemical information that governs how molecules truly behave—particularly electron interactions that define reactivity, stability, and...
Read more

Water Simulations Under Scrutiny: Researchers Confirm Methodological Errors

Image
Accurate water simulations are vital for research in biomolecular structures, drug discovery, and materials science. But a recent study from Oak Ridge National Laboratory (ORNL) has revealed a potential pitfall in long-standing computational methods, raising concerns about the reliability of countless molecular dynamics studies. The Time Step Problem in Simulations At the heart of the issue is the “time step” used in simulations—the small intervals at which calculations are performed. Since the 1970s, scientists have used time steps of 2 femtoseconds (quadrillionths of a second) to simulate molecular motion efficiently. However, the ORNL team has shown that using this standard can introduce significant errors when modeling liquid water, potentially leading to inaccurate predictions of properties such as density, pressure, and hydration energies. “Using longer time steps is tempting because it reduces computational cost,” explained senior scientist Dilip Asthagiri. “But a...
Read more

CrystalGPT: Redefining Crystal Design with AI-Driven Predictions

Image
July 18, 2025 In a major leap for materials chemistry, researchers in the UK have developed Molecular Crystal Representation from Transformers (MCRT) —an AI model likened to ChatGPT for its transformative impact. Dubbed "CrystalGPT" , this foundation model rapidly predicts the physical properties of molecular crystals, revolutionizing how chemists design materials in silico . The AI Revolution in Crystal Design Understanding and predicting the properties of molecular crystals is critical for designing new materials for applications ranging from pharmaceuticals to energy storage. Traditional computational approaches, though powerful, are often resource-intensive and slow. Machine learning methods such as Smooth Overlap of Atomic Positions (SOAP) and atom-centred symmetry functions (ACSFs) have offered improvements, but they struggle with large datasets and subtle structural nuances. CrystalGPT, developed by a team led by Andy Cooper at the University of Liverpool, o...
Read more