Energy transition: Stefania Cacovich maps photovoltaic materials for more efficient solar panels

What if the windows of the future were equipped with transparent photovoltaic cells to convert the light passing through them into electricity? This is not fiction but reality, thanks to a prototype developed as part of City Solar, a project supported by the European Union to which Stefania Cacovich, a CNRS researcher at the Île-de-France Photovoltaic Institute (IPVF*), contributed her expertise in advanced materials characterization.

Recently awarded a bronze medal by the CNRS, the scientist is dedicating her work to halogenated perovskite photovoltaic cells, a technology that has been studied for some fifteen years and is particularly promising in terms of efficiency, but which remains fragile.  “I examine the thin layers of materials that make up these cells in order to assess their stability over time and the efficiency of their electrical conduction,” explains the researcher. 

Let's take a closer look at these cells. When light strikes their absorbing surface, an electron (negative charge) is knocked out, leaving behind a hole (positive charge). The creation of this electron-hole pair generates an electric current that flows through the different layers of semiconductors before reaching the circuit to be powered. At the interface between these layers, certain irregularities or defects can hinder the transport of charges and promote their recombination. When holes and electrons meet, they produce energy that is dissipated in the form of light or heat instead of being converted into electricity. “Characterization allows us to understand the physical and chemical phenomena at play in this space. The goal is then to perform interfacial passivation, i.e., adding or modifying thin layers to facilitate the passage of electrons, avoid losses, and increase the efficiency of the cells,” explains Stefania Cacovich.

The researcher uses photoluminescence and electroluminescence to examine the different materials. With the first technique, a laser or LED light source excites the sample, which then re-emits light. “This provides information on the quality of the semiconductors used and their energy efficiency.” With the second technique, a voltage is applied to the material, which then behaves like an LED and provides information about its electrical conduction. At the same time, Stefania Cacovich places her samples in an aging chamber to test them under standardized conditions of humidity, temperature, and illumination, and to observe their evolution over time.

Going further and faster with AI 

“By correlating all this information, we obtain an image composed of millions of pixels, each of which reveals a spectrum containing a wealth of information: absorption, lifetime and mobility of charges, material stability, etc.,” says the researcher. This provides a highly detailed and precise map of the physical, chemical, and electronic characteristics of her samples, from the micrometric to the macroscopic scale. It then becomes possible to determine the areas of the photovoltaic cells where performance is optimal.

However, the amount of information that needs to be processed to achieve this result is considerable, measured in gigabytes of data. Stefania Cacovich is working with mathematicians from the IP Paris ecosystem to develop algorithms capable of exploiting this data. She integrates physical models that she develops and adjusts to the data measured at each pixel.

“The use of AI also allows us to reduce the time needed to acquire data,” the scientist points out. Indeed, obtaining measurements with a good signal-to-noise ratio requires an acquisition time of around ten minutes. However, during this time, depending on the environment in which the sample is tested (humidity, lighting, etc.), it can be completely destroyed. AI is used to effectively remove noise from the signals and save valuable time.

Energy transition projects

Using these methods, Stefania Cacovich has contributed to the optimization of stable, efficient, and transparent photovoltaic panels as part of the City Solar project. She also works with the SIRTA** platform at the Laboratory of Dynamic Meteorology (LMD)*** to test the stability of solar panels in real conditions (editor's note: among its activities, SIRTA studies the efficiency of photovoltaic panels depending on weather conditions). 

Finally, the researcher is contributing to an automated platform project selected as part of France 2030's PEPR DIADEM program for the development of innovative materials assisted by artificial intelligence. The goal is to design a tool capable of automatically manufacturing and characterizing thin layers of semiconductors. An AI-controlled robot will deposit materials and test several synthesis conditions and compositions. At the same time, it will be able to characterize samples using optical measurements and, based on this, automatically readjust certain synthesis parameters to optimize the efficiency of photovoltaic cells. 

About Stefania Cacovich

Stefania Cacovich is a materials scientist specializing in photovoltaics. A research scientist at the Centre national de la recherche scientifique (CNRS), she works at the Institut Photovoltaïque d’Île-de-France (IPVF) on the Saclay plateau. She obtained a PhD in materials science from the University of Cambridge in 2018, where she studied perovskite solar cells.

Her research focuses on understanding and improving next-generation photovoltaic cells, particularly through advanced imaging techniques and the analysis of the optoelectronic properties of materials. She combines experimental methods, physical modelling and artificial intelligence tools to better understand the mechanisms that limit the efficiency and stability of these devices. In 2025, she received the CNRS Bronze Medal, which recognizes young researchers for the excellence of their work.

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