Accurate prediction of solubility remains a central challenge across materials science and sustainable chemistry. In particular due to emerging technologies like organic and hybrid photovoltaics, batteries, and catalysis, solvent usage is expected to increase significantly within the coming years. Therefore, substituting solvents with greener alternatives is vital. This is where machine learning can have substantial impact. However, the limited data on critical parameters of solubility significantly constraints machine learning efficacy. In this work, we transfer a pre-trained foundational model on QM9 targets to our application with minimal data requirements. Additionally, the pipeline integrates uncertainty quantification, allowing the user to gauge the confidence of the predictions. As baseline, we succeed in predicting the Hansen solubility parameters and Dielectric Constant for which extensive databases exist. Importantly, we achieve high model performance on additional targets, such as Gutmann Donor and Acceptor numbers, where the available data is extremely limited. Overall, we augment data on solubility descriptors by orders of magnitude with high quality predictions. For effective dissemination, we deploy easy-to-use, easily integrateable with high throughput labs, customizable tool for ranking and screening possible solvent substitutes. Finally, we rediscovered known green solvent alternatives and proposed new candidates proving its relevance for finding eco-friendly solvents.

Rapid and consistent drying of thin solution films is a key enabler for high-throughput fabrication of emerging semiconductor materials. This work has the objective of achieving consistent drying rates of a solution film deposited on a 20 cm-wide substrate (approximate to silicon-wafer size) that is dried dynamically with a narrow air flow ejected by a slot nozzle (or "air knife"). The methodology is based on evaluating drying dynamics at multiple positions and investigating the impact of the relative movement trajectory between the air knife and the substrate on the resulting drying rates. It is assumed that the Biot number is sufficiently small and that the drying rate at a critical stage determines the quality of the deposited film. The main result is a novel optimization strategy overcoming the trade-off between homogeneity and drying speed: The dynamic movement of the air knife is tailored to the wet film thickness distribution following the "drying front". If the film thickness does not decrease along the movement direction of the air knife, there is a consistent solution of the optimization. This is showcased both for idealized and experimental thickness distributions. Generally, wet films cannot always be dried fully consistently by optimizing the air knife trajectory alone, but optimization improves drying results significantly in all investigated scenarios. Such trajectory optimization could yield substantial improvements in consistency and speed of thin film drying, potentially increasing throughput by an order of magnitude while maintaining drying rate control.

Perovskite solar cells (PSCs) have experienced a remarkable rise in power conversion efficiency (PCE) over the past 15 years, positioning them as a promising alternative or complement to silicon for large-scale photovoltaic deployment. However, beyond scalable fabrication, operational stability remains a major bottleneck for commercialization. Reliable and rapid methods to assess device health and degradation mechanisms - ideally compatible with field applications - are therefore essential. We present a deep-learning framework to estimate efficiency retention, R_PCE=PCE_t/PCE_0, directly from multimodal luminescence imaging acquired during device aging. Each training sample includes electroluminescence (EL), open-circuit photoluminescence (PLoc), and short-circuit photoluminescence (PLsc) images at an aged state, together with device-specific reference images at t=0. This design enables the model to learn spatially resolved degradation patterns relative to the pristine condition. The dataset was collected over 5-70 hours using an automated, in-house measurement platform. We introduce LumPerNet, a compact convolutional neural network that regresses R_PCE from stacked multimodal image tensors, and benchmark it against an intensity-only multilayer perceptron baseline. Using a leakage-aware protocol with device-level hold-out testing and four-fold cross-validation, restricted to R_PCE∈[0.8,1.2], LumPerNet achieves substantially improved and more robust performance (MAE -23.4

Within the last 20 years, hybrid perovskite solar cells (PSCs) have reached remarkable power conversion efficiencies. Further, scalability of hybrid perovskite deposition routines and stability of PSCs have been significantly improved. Yet, a critical roadblock remains: Poor reproducibility largely caused by inconsistent control and reporting of process parameters. Key aspects such as the handling of the perovskite solution, the air jet used for drying, or the process atmosphere are often incompletely specified. In response, this review systematically presents the empirical evidence linking process parameters to the film morphology and the device performance for solution-based one-step and two-step deposition routines of highly efficient PSCs as well as large-area perovskite modules. To maximize interdisciplinary understanding, the process parameters are standardized within the thin-film solar cell ontology (TFSCO), structured according to the internal logic of sequential deposition and classified by fundamental mass transfer mechanisms. In a final literature study, the state-of-the-art of parameter reporting is assessed—mirroring to the community where reporting standards can be improved. By using the here-presented parameter list as a template, perovskite workflows become fully and unambiguously specified—bridging the gap between manual and automated process optimization and fostering data-driven acceleration via digital twins of perovskite research.

Large area fabrication of high-quality polycrystalline perovskite thin films remains one of the key challenges for the commercial readiness of perovskite photovoltaic (PV). To enable high-throughput and high-yield processing, reliable and fast in-line characterization methods are required. The present work reports on a non-invasive characterization technique based on intensity-dependent photoluminescence (PL) imaging. The change in PL intensity as a function of excitation power density can be approximated by a power-law with exponent k, which is a useful quality indicator for the perovskite layer, providing information about the relative magnitudes of radiative and non-radiative recombination. By evaluating k-parameter maps instead of more established PL intensity images, 2D information is obtained that is robust to optically induced artifacts such as intensity variations in excitation and reflection. Application to various half stacks of a perovskite solar cell showcase its ability to determine the importance of the interface between the charge transporting and perovskite layers. In addition, the k-parameter correlates to the bulk passivation concentration, enabling rapid assessment of open-circuit voltage variations in the range of 20 mV. Considering expected improvements in data acquisition speed, the presented k-imaging method will possibly be obtained in real-time, providing large-area quality control in industrial-scale perovskite PV production. Perovskite photovoltaic (PV) show promise as an alternative to silicon solar cells. However, an obstacle that remains to be mastered is the transition from laboratoryscale to manufacturing-scale of PV devices without compromising power conversion efficiency. In this context, a fast, non-invasive, quantitative in-line mapping technique is presented based on intensity-dependent photoluminescence, offering insights into perovskite quality for large-scale production.image

Abstract Hybrid perovskite photovoltaics (PVs) promise cost‐effective fabrication with large‐scale solution‐based manufacturing processes as well as high power conversion efficiencies. Almost all of today's high‐performance solution‐processed perovskite absorber films rely on so‐called quenching techniques that rapidly increase supersaturation to induce a prompt crystallization. However, to date, there are no metrics for comparing results obtained with different quenching methods. In response, the first quantitative modeling framework for gas quenching, anti‐solvent quenching, and vacuum quenching is developed herein. Based on dynamic thickness measurements in a vacuum chamber, previous works on drying dynamics, and commonly known material properties, a detailed analysis of mass transfer dynamics is performed for each quenching technique. The derived models are delivered along with an open‐source software framework that is modular and extensible. Thereby, a deep understanding of the impact of each process parameter on mass transfer dynamics is provided. Moreover, the supersaturation rate at critical concentration is proposed as a decisive benchmark of quenching effectiveness, yielding ≈ 10−3 − 10−1s−1 for vacuum quenching, ≈ 10−5 − 10−3s−1 for static gas quenching, ≈ 10−2 − 100s−1 for dynamic gas quenching and ≈ 102s−1 for antisolvent quenching. This benchmark fosters transferability and scalability of hybrid perovskite fabrication, transforming the “art of device making” to well‐defined process engineering.

Deep-Learning enhanced literature analysis on parameter specification in solution processing of hybrid perovskite solar cellsSimon Ternes a, Alessio Gagliardi c, Aldo Di Carlo a, ba CHOSE, Centre for Hybrid and Organic Solar Energy, Department of Electronic Engineering, University of Rome "Tor Vergata", Rome, Italyb ISM-CNR, Institute of Structure of Matter, National Research Council, Rome, Italyc Technical University of Munich (TUM), Hans-Piloty-Str. 1, 85748 Garching b. München, GermanyInternational Conference on Hybrid and Organic PhotovoltaicsProceedings of International Conference on Hybrid and Organic Photovoltaics (HOPV24)València, Spain, 2024 May 12th - 15thOrganizer: Bruno EhrlerOral, Simon Ternes, presentation 063DOI: https://doi.org/10.29363/nanoge.hopv.2024.063Publication date: 6th February 2024Perovskite solar cells (PSCs) are one of the most promising candidates for next-generation photovoltaics. Due to their exceptional increase in power conversion efficiencies (PCEs) over the last 15 years and their excellent suitability for tandem device architectures, the emerging technology has reached the brink of commercialization. This achievement was enabled by immense efforts in fast, manual prototyping based on a culture of apprenticeships, where each team develops fabrication processes customized for their specific equipment. While this approach was very successful for improving the state-of-the-art PCEs, researchers should now consider focusing more on rigorousness of scientific reporting. The reason is that the field suffers from immense issues with reproducibility, leaving a huge potential for standardization and refining of routines. A simple indicator for the rigorousness of scientific reporting is the extent to which process parameters are controlled and provided in the description of experiments. Leveraging extensive experience with modelling of perovskite processing[1], we perform a meta survey of the process parameters provided in published literature on perovskite solution processing. For conducting the analysis, ways of sampling representative meta data are developed. Previously, meta data was collected in an extensive effort establishing the perovskite open source database[2][3]. However, we found that the database prioritizes frequently reported over rarely reported parameters. Additionally, the acquisition of these data took between 5.000 to 10.000 of volunteer work hours. To shortcut the process of data acquisition, we leverage recent progress from the field of deep machine learning to collect data on perovskite solution processing on a representative sample of around 1.500 publications with minimal need for human intervention. However, this approach comes with novel challenges. Common deep learning algorithms do not have an understanding of the technology, such that the potential for misinterpretation and error is high. Therefore, the algorithm must be carefully assessed in comparison to human reading performance. The exact formulation of instructions, the choice of the specific model and its hyperparameters, as well as pre-training are critical indicators for increasing model performance. We find that deep leaning models can be on par with human reading performance (that is not error-free either), when given simple instructions. It is however an open question, if these models can be scaled to advanced meta data extraction projects like the perovskite database. Furthermore, a strategy on how to publish these models must be developed as the trained models could potentially be exploited to access copyright-protected content. After conducting the analysis for each of the 10 process parameters, we identified as critically important for reproducing perovskite solution processing, we find that there is a great potential for improving the rigorousness of the description of experiments in perovskite solar cell fabrication. Furthermore, there are clear differences in the likelihood of certain parameters to be provided. For example, the spin speed (or the sheering velocity) of the perovskite solution deposition is provided in almost all cases, while the temperature of the atmosphere during processing is almost never reported. As consequence of our analysis, we propose that the specification of experiments in perovskite processing should be given more attention, in particular when reviewing and writing articles on perovskite solar cells (we do not exclude ourselves from this imperative). Because the technology is so successful that it is in the process of being passed on to industry, researchers now have the privilege of prioritizing rigorousness of reporting and process analysis over device optimization speed. This further opens up new opportunities for publishing studies on fundamental process physics and chemistry, differentiating technology development carried out at rapid pace in industry from research on fundamentals conduced by publicly funded research institutions. References:[1] S. Ternes, F. Laufer, U. W. Paetzold, Modeling and Fundamental Dynamics of Vacuum, Gas, and Antisolvent Quenching for Scalable Perovskite Processes. Adv. Sci. 2024, 2308901.[2] Jacobsson, T.J., Hultqvist, A., García-Fernández, A. et al. An open-access database and analysis tool for perovskite solar cells based on the FAIR data principles. Nat Energy 7, 107–115 (2022).[3] Eva Unger and T. Jesper Jacobsson ACS Energy Letters 2022 7 (3), 1240-1245Acknowledgements:This research was funded by the European Union's Horizon Europe MCSA "INT-PVK-PRINT" (n. 101107885) © FUNDACIO DE LA COMUNITAT VALENCIANA SCITOnanoGe is a prestigious brand of successful science conferences that are developed along the year in different areas of the world since 2009. Our worldwide conferences cover cutting-edge materials topics like perovskite solar cells, photovoltaics, optoelectronics, solar fuel conversion, surface science, catalysis and two-dimensional materials, among many others.MATSUSPreviously nanoGe Spring Meeting (NSM) and nanoGe Fall Meeting (NFM), MATSUS is a multiple symposia conference focused on a broad set of topics of advanced materials preparation, their fundamental properties, and their applications, in fields such as renewable energy, photovoltaics, lighting, semiconductor quantum dots, 2-D materials synthesis, charge carriers dynamics, microscopy and spectroscopy semiconductors fundamentals, etc.International Conference on Hybrid and Organic PhotovoltaicsInternational Conference on Hybrid and Organic Photovoltaics (HOPV) is celebrated yearly in May. The main topics are the development, function and modeling of materials and devices for hybrid and organic solar cells. The field is now dominated by perovskite solar cells but also other hybrid technologies, as organic solar cells, quantum dot solar cells, and dye-sensitized solar cells and their integration into devices for photoelectrochemical solar fuel production.Asia-Pacific International Conference on Perovskite, Organic Photovoltaics and OptoelectronicsThe main topics of the Asia-Pacific International Conference on Perovskite, Organic Photovoltaics and Optoelectronics (IPEROP) are discussed every year in Asia-Pacific for gathering the recent advances in the fields of material preparation, modeling and fabrication of perovskite and hybrid and organic materials. Photovoltaic devices are analyzed from fundamental physics and materials properties to a broad set of applications. The conference also covers the developments of perovskite optoelectronics, including light-emitting diodes, lasers, optical devices, nanophotonics, nonlinear optical properties, colloidal nanostructures, photophysics and light-matter coupling.International Conference on Perovskite Thin Film Photovoltaics Perovskite Photonics and OptoelectronicsThe International Conference on Perovskite Thin Film Photovoltaics Perovskite Photonics and Optoelectronics (NIPHO) is the best place to hear the latest developments in perovskite solar cells as well as on recent advances in the fields of perovskite light-emitting diodes, lasers, optical devices, nanophotonics, nonlinear optical properties, colloidal nanostructures, photophysics and light-matter coupling.

To address the challenge of upscaling single-junction perovskite photovoltaics (PV) toward market-relevant performance in a structured and efficient manner, a stage-gate approach that divides the process into stages according to technology readiness levels (TRLs) is proposed. Whereas the first stage contains only material research, the later stages are concerned with the development from lab-scale devices to large-area modules, and properties such as device size as well as processing methods are adapted step-by-step toward commercializable techniques. The stages are connected by gates that specify the criteria that must be met for a material or process to be transferred to the next stage. In addition, a literature survey for the keywords "perovskite" and "module" is performed. This analysis shows that most of the reported modules have an area between 10 cm2 and 20 cm2, corresponding to stage 3 or TRL 5 in the scheme, and operational stability is often incompletely reported. These findings analysis indicate a significant gap in the research focus on large-area modules and elevated stress and field tests, which are essential for transitioning to commercial applications. It is suggested to use the proposed stage-gate process as an efficient and structured guideline toward commercializing perovskite PV.

Transferring record power conversion efficiency (PCE) >25% of spin coated perovskite solar cells (PSCs) from the laboratory scale to large-area photovoltaic modules requires significant advances in scalable fabrication techniques. In this work, we demonstrate the fundamental interrelation between drying dynamics of slot-die coated precursor solution thin films and the quality of resulting slot-die coated gas-quenched polycrystalline perovskite thin films. Well-defined drying conditions are established using a temperature-stabilized, movable table and a flow-controlled, oblique impinging slot nozzle purged with nitrogen. The accurately deposited solution thin film on the substrate is recorded by a tilted CCD camera, allowing for in situ monitoring of the perovskite thin film formation. With the tracking of crystallization dynamics during the drying process, we identify the critical process parameters needed for the design of optimal drying and gas quenching systems. In addition, defining different drying regimes, we derive practical slot jet adjustments preventing gas backflow and demonstrate large-area, homogeneous, and pinhole-free slot-die coated perovskite thin films that result in solar cells with PCEs of up to 18.6%. Our study reveals key interrelations of process parameters, e.g., the gas flow and drying velocity, and the exact crystallization position with the morphology formation of fabricated thin films, resulting in a homogeneous performance of corresponding 50 × 50 mm2 solar minimodules (17.2%) with only minimal upscaling loss. In addition, we validate a previously developed model on the drying dynamics of perovskite thin films on small-area slot-die coated areas of ≥100 cm2. The study provides methodical guidelines for the design of future slot-die coating setups and establishes a step forward to a successful transfer of solution processes towards industrial-scale deposition systems beyond brute force optimization.

Remarkable progress in efficiency and stability has been demonstrated for the next generation photovoltaic (PV) technology of thin-film perovskite solar cells (PSCs) on the laboratory scale. Small-area PSCs already exceed 25 % of power conversion efficiency (PCE) since they were first investigated about one decade ago [1, 2]. However, besides the unquestionable potential of perovskite-absorber films for efficient thin-film PV, there are significant challenges when scaling the technology. The reason is that established, industrial deposition techniques such as slot-die coating are difficult to control during the entirety of the complex perovskite film formation - causing low operational stability when fabricating larger areas [3]. In response, this work leverages prior studies of our group on a quantitative model of the drying dynamics based on local heat transfer measurements [9], for controlling the crystallization of slot-die coated perovskite absorber layers dried via gas-quenching. More specifically, we apply and validate and this knowledge by systematically implementing and evaluating the upscaling strategy for fabricating slot-die coated modules on an area of 100 cm2 with an elongated slot nozzle. In situ monitoring with a CCD camera is used to critically evaluate the position of the crystallization front on the sample. This position depends directly on the drying dynamics and thus the applied process parameters. Remarkably, we succeed in demonstrating the accurate correlation of the position of the drying front with the evolving thin-film morphology as predicted by the drying models. We further show that these morphological differences have a direct impact on the achievable PCE of perovskite test devices. These methological findings denote a corner stone toward scaling perovskite fabrication preventing expensive brute force optimization. Thus, the transition from perovskite PV to large scale commercially viable manufacturing plants can be significantly facilitated.

In this work, we introduce a bilayer ETL composed of lithium (Li)-doped compact SnO2 (c-SnO2) and potassium-capped SnO2 nanoparticle layers (NP-SnO2) to enhance the electron extraction and charge transport properties in perovskite solar cells, resulting in an improved PCE and a strongly reduced J–V hysteresis.

Hybrid perovskite photovoltaics combine high performance with the ease of solution processing. However, to date, a poor understanding of morphology formation in coated perovskite precursor thin films casts doubt on the feasibility of scaling-up laboratory-scale solution processes. Oblique slot jet drying is a widely used scalable method to induce fast crystallization in perovskite thin films, but deep knowledge and explicit guidance on how to control this dynamic method are missing. In response, we present a quantitative model of the drying dynamics under oblique slot jets. Using this model, we identify a simple criterion for successful scaling of perovskite solution printing and predict coating windows in terms of air velocity and web speed for reproducible fabrication of perovskite solar cells of ∼15% in power conversion efficiency─in direct correlation with the morphology of fabricated thin films. These findings are a corner stone toward scaling perovskite fabrication from simple principles instead of trial and error optimization.

Given the outstanding progress in research over the past decade, perovskite photovoltaics (PV) is about to step up from laboratory prototypes to commercial products. For this to happen, realizing scalable processes to allow the technology to transition from solar cells to modules is pivotal. This work presents all-evaporated perovskite PV modules with all thin films coated by established vacuum deposition processes. A common 532-nm nanosecond laser source is employed to realize all three interconnection lines of the solar modules. The resulting module interconnections exhibit low series resistance and a small total lateral extension down to 160 mu m. In comparison with interconnection fabrication approaches utilizing multiple scribing tools, the process complexity is reduced while the obtained geometrical fill factor of 96% is comparable with established inorganic thin-film PV technologies. The all-evaporated perovskite minimodules demonstrate power conversion efficiencies of 18.0% and 16.6% on aperture areas of 4 and 51 cm(2), respectively. Most importantly, the all-evaporated minimodules exhibit only minimal upscaling losses as low as 3.1%(rel) per decade of upscaled area, at the same time being the most efficient perovskite PV minimodules based on an all-evaporated layer stack sequence.

To scale up production of perovskite photovoltaics, state‐of‐the‐art laboratory recipes and processes must be transferred to large‐area coating and drying systems. The development of in situ monitoring methods that provide real‐time feedback for process control is pivotal to overcome this challenge. Herein, correlative in situ multichannel imaging (IMI) obtaining reflectance, photoluminescence intensity, and central photoluminescence emission wavelength images on areas larger than 100 with subsecond temporal resolution using a simple, cost‐effective setup is demonstrated. Installed on top of a drying channel with controllable laminar air flow and substrate temperature, IMI is shown to consistently monitor solution film drying, perovskite nucleation, and perovskite crystallization. If the processing parameters differ, IMI reveals characteristic changes in large‐area perovskite formation dynamics already before the final annealing step. Moreover, when IMI is used to study >130 blade‐coated devices processed at the same parameters, about 90% of low‐performing devices contain coating inhomogeneities detected by IMI. The results demonstrate that IMI should be of value for real‐time 2D monitoring and feedback control in industrial‐scale, high‐throughput fabrication such as roll‐to‐roll printing.

For the upscaling of hybrid perovskite photovoltaics, the understanding of the intermediate steps from solution to the polycrystalline film is pivotal. In situ characterization techniques can be a powerful tool to investigate the perovskite formation process and obtain direct feedback on its quality. Here, we propose in situ photoluminescence intensity, photoluminescence emission wavelength and reflectance imaging combined in one optical system with a high spatial and sub-second temporal resolution as an in situ real-time, monitoring technique. It is capable of surveilling the perovskite processing on areas larger 100 cm 2 and resolving even tiny differences in the processing parameters.

Controlling the thickness and homogeneity of thin passivation layers on polycrystalline perovskite thin films is challenging. We report CVD polymerization of poly(p-xylylene) layers at controlled substrate temperatures for efficient surface passivation of perovskite films.