arXiv subjects

Larry Lüer

Publications and source records attributed to Larry Lüer.

9 recordsLinked to original sources

arXiv2026

Predicting Organic Solar Cell Performance and Stability from Fast, Morphology-aware Current-Voltage Modeling

Understanding the relationship between morphology and performance in organic solar cells is essential for developing devices that are both high performing and resilient to aging. This work introduces a unique method capable of calculating the current-voltage (JV) curve of complex heterojunction morphologies containing up to five phases (donor amorphous, donor crystalline, acceptor amorphous, acceptor crystalline, mixed amorphous) with a very low computation time using morphology-aware descriptors of light absorption, exciton dissociation, non-geminate recombination and free charge carrier mobilities. The method is validated against Monte Carlo and 3D drift-diffusion simulations and applied to P3HT:PCBM and PM6:Y6 systems, shedding light on the physical compromises encountered to optimize device performance and lifetime. Finally, we show that the morphology-performance relationship is dependent on the materials system studied.

cond-mat.mtrl-sci↗

arXiv2026

3D Electron Diffraction -- The Missing Slice for a Complete Nanoscale Analysis of Organic Solar Cells in TEM

Optimizing the performance of organic solar cells (OSCs) hinges on a comprehensive understanding of their nanostructures, yet traditional characterization methods often fall short, delivering incomplete structural snapshots. We introduce elastically filtered 3D Electron Diffraction (3D ED) as a ground-breaking technique bridging full reciprocal- and real-space structural analysis within a single transmission electron microscope (TEM). Using solvent-vapor annealed DRCN5T:PC71BM, 3D ED reproduces key structural parameters from GIWAXS including lattice spacings, coherence lengths, and mosaicity, while uniquely delivering true in-plane access and direct registration with high-resolution imaging, diffraction imaging and nano-spectroscopy on the same sample. A low-dose, distributed tilt strategy plus energy filtering yields high signal-to-background below damage thresholds. Extension to a second archetypal blend (P3HT:PC71BM; annealing evolution) demonstrates generality. Our findings underscore the transformative potential of 3D ED, particularly in analysing beam-sensitive organic thin films. This paves the way for new avenues in advanced correlative structural characterization of OSCs and holds potential for application to a multitude of other nanostructured materials.

cond-mat.mtrl-sci↗

arXiv2025

Understanding the effect of drying time in process-structure-performance relationships for PM6-Y6 organic solar cells

Making solution-cast organic solar cells industrially available generally comes at the cost of significant performance losses compared to device prototypes manufactured under laboratory conditions. Adjusting solvent evaporation kinetics is postulated to recover efficiency. Yet, a comprehensive characterization of their effect, independently of other property-defining parameters, is lacking. Thus, the present objective is to isolate the influence of the solvent drying rate on solution-deposited organic active layer nanomorphologies and performances. To this end, a specially designed gas quenching technique is employed to fabricate PM6:Y6 donor-acceptor films under systematic variations of evaporation conditions. Using an extensive investigation protocol that combines insights from numerical simulations and experimental measurements, process-structure-performance relationships are unraveled. It is found that higher drying rates imply finer and more dispersed nanomorphologies with increased fractions of amorphous material. This enhances electric charge generation, thereby improving short-circuit current density and overall cell performance. The open-circuit voltage is also boosted under accelerated evaporation due to changes in the aggregation mode of the Y6 small molecule that induce higher effective bandgaps. The results demonstrate that the developed gas-quenching technique is a valuable tool for optimizing the performance of upscaled organic photovoltaics, as it is readily compatible with high-throughput equipment, such as roll-to-roll coating machines.

cond-mat.mtrl-sci↗

arXiv2024

Discovering one molecule out of a million: inverse design of molecular hole transporting semiconductors tailored for perovskite solar cells

The inverse design of tailored organic molecules for specific optoelectronic devices of high complexity holds an enormous potential, but has not yet been realized1,2. The complexity and literally infinite diversity of conjugated molecular structures present both, an unprecedented opportunity for technological breakthroughs as well as an unseen optimization challenge. Current models rely on big data which do not exist for specialized research films. However, a hybrid computational and high throughput experimental screening workflow allowed us to train predictive models with as little as 149 molecules. We demonstrate a unique closed-loop workflow combining high throughput synthesis and Bayesian optimization that discovers new hole transporting materials with tailored properties for solar cell applications. A series of high-performance molecules were identified from minimal suggestions, achieving up to 26.23% (certified 25.88%) power conversion efficiency in perovskite solar cells. Our work paves the way for rapid, informed discovery in vast molecular libraries, revolutionizing material selection for complex devices. We believe that our approach can be generalized to other emerging fields and indeed accelerate the development of optoelectronic semiconductor devices in general.

cond-mat.mtrl-sci↗

arXiv2024

Precise Control of Process Parameters for >23% Efficiency Perovskite Solar Cells in Ambient Air Using an Automated Device Acceleration Platform

Achieving high-performance perovskite photovoltaics, especially in ambient air relies heavily on optimizing process parameters. However, traditional manual methods often struggle to effectively control the key variables. This inherent challenge requires a paradigm shift toward automated platforms capable of precise and reproducible experiments. Herein, we use a fully automated device acceleration platform (DAP) to optimize the process parameters for preparing full perovskite devices using a two-step method in ambient air. Eight process parameters that have the potential to significantly influence device performance are systematically optimized. Specifically, we delve into the impact of the dispense speed of organic ammonium halide, a parameter that is difficult to control manually, on both perovskite film and device performance. Through the targeted design of experiments, we reveal that the dispense speed significantly affects device performance primarily by adjusting the residual PbI2 content in the films. We find that moderate dispense speeds, e.g., 50 μl/s, contribute to top-performance devices. Conversely, too fast or too slow speeds result in devices with relatively poorer performance and lower reproducibility. The optimized parameter set enables us to establish a Standard Operation Procedure (SOP) for additive-free perovskite processing under ambient conditions, which yield devices with efficiencies surpassing 23%, satisfactory reproducibility, and state-of-the-art photo-thermal stability. This research underscores the importance of understanding the causality of process parameters in enhancing perovskite photovoltaic performance. Furthermore, our study highlights the pivotal role of automated platforms in discovering innovative workflows and accelerating the development of high-performing perovskite photovoltaic technologies.

physics.app-ph↗

arXiv2023

Diffusive and Unimolecular Nonradiative Decay of Excited States in Doped Carbon Nanotubes

Doping can profoundly affect the electronic- and optical-structure of semiconductors. Here we address the effect of surplus charges on non-radiative (NR) exciton and trion decay in doped semiconducting single-wall carbon nanotubes. The dependence of exciton photoluminescence quantum yields and exciton decay on the doping level, with its characteristically stretched-exponential kinetics, is attributed to diffusion-limited NR decay at charged impurity sites. By contrast, trion decay is unimolecular with a rate constant of $2.0\,\rm ps^{-1}$. Our experiments thus show that charged impurities not only trap trions and scavenge mobile excitons but that they also facilitate efficient NR energy dissipation for both.

cond-mat.mes-hall↗

arXiv2023

Bypassing the single junction limit with advanced photovoltaic architectures

In single-junction photovoltaic (PV) devices, the maximum achievable power conversion efficiency (PCE) is mainly limited by thermalization and transmission losses, because polychromatic solar irradiation cannot be matched to a single bandgap. Several concepts are being investigated to reduce these losses, such as the classical vertical multijunction cells, 'lateral' tandem cells, and multi-exciton generation in the form of photon up- and down-conversion. While in theory, efficiencies exceeding 90% are possible (Landsberg or thermodynamic limit), there are severe practical limitations in terms of processability, cost, and spectral sensitivity. Here, we present a simulation environment based on Bayesian Optimization that is able to predict and optimize the electrical performance of multi-junction architectures, both vertical and lateral, in combination with multi-exciton materials. With respect to vertical stacks, we show that by optimizing bandgap energies of multi-exciton generation (MEG) layers, double junctions can reach efficiencies beyond those of five-junction tandem devices (57%). Moreover, such combinations of MEG and double junction devices would be highly resilient against spectral changes of the incoming sunlight. We point out three main challenges for PV material science to realize such devices. With respect to lateral architectures, we show that MEG layers might allow reducing nonradiative voltage losses following the Energy Gap Law. Finally, we show that the simulation environment is able to use machine learned quantitative structure-property relationships obtained from high-throughput experiments to virtually optimise the active layer (such as, the film thickness and the donor-acceptor ratio) for a given architecture. The simulation environment thus represents an important building block towards a digital twin of PV materials.

physics.app-ph↗

arXiv2023

Cutting 'lab-to fab' short: High Throughput Optimization and Process Assessment in Roll-to-Roll Slot Die Coating of Printed Photovoltaics

Commercialization of printed photovoltaics requires knowledge of the optimal composition and microstructure of the single layers, and the ability to control these properties over large areas under industrial conditions. While microstructure optimization can be readily achieved by lab scale methods, the transfer from laboratory scale to a pilot production line ('lab to fab') is a slow and cumbersome process. Here, we show how we can optimize the performance of organic solar cells and at the same time assess process performance in a 2D combinatorial approach directly on an industrially relevant slot die coating line. This is enabled by a multi-nozzle slot die coating head allowing parameter variations along and across the web. This modification allows us to generate and analyze 3750 devices in a single coating run, varying the active layer donor:acceptor ratio and the thickness of the electron transport layer (ETL). We use Gaussian Process Regression (GPR) to exploit the whole dataset for precise determination of the optimal parameter combination. Performance-relevant features of the active layer morphology are inferred from UV-Vis absorption spectra. By mapping morphology in this way, small undesired gradients of process conditions (extrusion rates, annealing temperatures) are detected and their effect on device performance is quantified. The correlation between process parameters, morphology and performance obtained by GPR provides hints to the underlying physics, which are finally quantified by automated high-throughput drift-diffusion simulations. This leads to the conclusion that voltage losses which are observed for very thin ETL coatings are due to incomplete coverage of the electrode by the ETL, which cause enhanced surface recombination.

physics.app-ph↗

arXiv2023

A Digital Twin to overcome long-time challenges in Photovoltaics

The recent successes of emerging photovoltaics (PV) such as organic and perovskite solar cells are largely driven by innovations in material science. However, closing the gap to commercialization still requires significant innovation to match contradicting requirements such as performance, longevity and recyclability. The rate of innovation, as of today, is limited by a lack of design principles linking chemical motifs to functional microscopic structures, and by an incapacity to experimentally access microscopic structures from investigating macroscopic device properties. In this work, we envision a layout of a Digital Twin for PV materials aimed at removing both limitations. The layout combines machine learning approaches, as performed in materials acceleration platforms (MAPs), with mathematical models derived from the underlying physics and digital twin concepts from the engineering world. This layout will allow using high-throughput (HT) experimentation in MAPs to improve the parametrization of quantum chemical and solid-state models. In turn, the improved and generalized models can be used to obtain the crucial structural parameters from HT data. HT experimentation will thus yield a detailed understanding of generally valid structure-property relationships, enabling inverse molecular design, that is, predicting the optimal chemical structure and process conditions to build PV devices satisfying a multitude of requirements at the same time. After motivating our proposed layout of the digital twin with causal relationships in material science, we discuss the current state of the enabling technologies, already being able to yield insight from HT data today. We identify open challenges with respect to the multiscale nature of PV materials and the needed volume and diversity of data, and mention promising approaches to address these challenges.

cond-mat.mtrl-sci↗