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Interface engineering plays an important role in performance improvement for bulk-heterojunction organic solar cells (OSCs). The charge carrier dynamics and energetic landscape at the donor:accepto...

Dilute donor organic solar cells (OSCs) aim to circumvent the trade-off between open-circuit voltage Voc and short-circuit current density jsc by decreasing the donor/acceptor interface. The main c...

This work presents a novel theoretical description of the nonequilibrium thermodynamics of charge separation in organic solar cells (OSCs). Using stochastic thermodynamics, we take realistic state populations derived from the phonon-assisted dynamics of electron-hole pairs within photoexcited organic bilayers to connect the kinetics with the free energy profile of charge separation. Hereby, we quantify for the first time the difference between nonequilibrium and equilibrium free energy profile. We analyze the impact of energetic disorder and delocalization on free energy, average energy, and entropy. For a high disorder, the free energy profile is well-described as equilibrated. We observe significant deviations from equilibrium for delocalized electron-hole pairs at a small disorder, implying that charge separation in efficient OSCs proceeds via a cold but nonequilibrated pathway. Both a large Gibbs entropy and large initial electron-hole distance provide an efficient charge separation, while a decrease in the free energy barrier does not necessarily enhance charge separation.

One major factor limiting the efficiency in organic solar cells (OSCs) is the low open-circuit voltage (Voc). Existing theoretical studies link the Voc with the charge transfer (CT) state and nonra...

Organic-inorganic (hybrid) perovskite semiconductors offer a wide range of bandgaps, low-cost deposition, and wide optical absorption, making them an ideal candidate for new photovoltaic devices. All-perovskite two terminal (2T) tandem solar cells have the potential to achieve high efficiency and at the same time offer cost-effective fabrication. In a 2T tandem cell, it is needed to optimize various device parameters such as bandgaps and thicknesses of the subcells, in order to make the best use of the available solar spectrum. In this study, we propose a drift-diffusion (DD) simulation model to optimize the bandgaps and thicknesses of the top and bottom cells in all-perovskite 2T tandem solar cell. Using our simulation model, we investigated the effect of interface and bulk traps, mobility, doping of the charge transport layers and contact workfunctions to the power conversion efficiency. We calculated up to 36.6% efficiency for an ideal device. We found that the traps at the interfaces and in the bulk perovskite films are the most important factor hampering the tandem cell efficiency. We predicted up to 29.8% efficiency for a device with recombination losses. By changing the mobility in the active material of the bottom cell we found that, the mobility plays an important role in determining the optimum thicknesses of the top and the bottom cells. Optimizing cathode workfunctions leads to a 3–4% improvement in the efficiency. Our study will help to understand the role of various factors limiting tandem cell efficiency and ways to optimize the device parameters to ensure the best performing all-perovskite 2T tandem solar cell.

Dye-sensitized solar cells (DSCs) have gained great attention in recent years due to their low-cost fabrication, flexibility and high power conversion efficiency. In a DSC, due to interfaces between the dye and the charge transport materials, the interface electrostatics becomes a key factor determining the overall performance of the cell. Liquid electrolyte based DSCs suffer low stability, electrolyte leakage and in some cases electrode corrosion. Replacing liquid electrolyte with a solid semiconducting material leads to poor interfacial contacts, hence the interface electrostatics becomes one of the limiting factors. In this work, we present a drift-diffusion (DD) and density functional theory (DFT) study of solid-state DSCs to investigate the electrostatics at the TiO2/organic dye/Spiro OMeTAD interface, and its impact to the adsorbed dye energy levels, its absorption spectrum and the related charge injection. In our 3D drift-diffusion model, we solve a set of drift-diffusion equations coupled to Poisson equation for electrons, holes, doping impurities and the interface traps simultaneously. After that, we use first principles DFT modeling of dye-sensitized interfaces in the presence of the calculated electric fields. We find that interface traps located below the conduction band edge of mesoporous TiO2 influence the accumulation of photogenerated holes and built-in electric field near the interface. The built-in electric field leads to change the energetics at the dye/TiO2 interface leading to poor charge injection from excited dye into the TiO2. The simulations were carried out for different electronic trap density in TiO2 and different doping levels in the Spiro OMeTAD hole transport layer. This study helps to a better understanding of interface electrostatics and its role in the charge injection mechanism of solid-state DSCs.

The origin of the hysteresis in the current–voltage (J–V) characteristics in perovskite solar cells (PSCs) is one of the most debated topics of recent years. Hysteretic effects are connected with the slow redistribution of ionic defects during the voltage sweep. Existing literature focuses on the potential screening due to accumulated ions, solely, while neglecting the possibility of charge trapping and subsequent recombination via ions. We investigate the role of cation-mediated recombination of ions using time-dependent drift–diffusion simulations in MAPbI 3 PSCs. Slow-moving cations are considered as traps for the electrons. Trapped electrons can subsequently recombine non-radiatively with holes. We analyze the role of the cation-mediated trapping and its parameters (capture coefficient, cation energy, ion mobility) as well as the scan rate on the device performance. For shallow cation energies, a decrease in open-circuit voltage and slight enhancement in hysteresis is observed. Deep cation energies lead to a substantial deterioration of device performance and large hysteresis enhancement. The presented study emphasizes the importance of considering the interaction of ions with charge carriers beyond the simple electrostatic models to improve our understanding of PSCs.

Controlling the crystallization mechanism of metal halide perovskites is of utmost importance to grow defect-less perovskite layers for efficient solar cells and optoelectronic devices. Despite its relevance, there is a lack of microscopic understanding of the nucleation and crystallization processes during the formation of the perovskite phase from its precursors. To unveil the electronic and atomistic features of this process we carry outab initiomolecular dynamics simulations on a model system which consists of a stoichiometric layered lead iodide (PbI2)·methylammonium iodide (MAI) structure, characteristic of intermediate phases observed in sequential deposition methods. Our results show clear evidence of halide-driven chemistry: MAI iodine ions attack lead ions in the PbI2layers and cause a nucleophilic substitution of Pb–I bonds with a subsequent breaking of the PbI2layer. Undercoordinated [PbIn]2−ncomplexes are initially formed which create the 3D perovskite framework mediated by additional nucleophilic attacks. The relatively fast rearrangement of [PbIn]2−ncomplexes followed by motion of MA cations limits the perovskite growth. Our results provide insight into the key steps of the perovskite formation on a microscopic scale, providing hitherto inaccessible details on the factors limiting the perovskite growth and on the effect of different halides on the kinetics of crystal formation.

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Organic Solar Cells (OSCs) have been extensively studied regarding the impact of morphology and energetic disorder on power conversion efficiency (PCE). The impact of energetic disorder is well understood but the effect of disorder in molecular coupling (off-diagonal) and energetic disorder at the interface still needs investigation. We present a kinetic Monte Carlo (kMC) model to study the role of interface energetics and off-diagonal disorder on solar cell performance. We first analyze the impact of different energetic disorder at the acceptor(A)-donor(D) interface on the device performance. Our results reveal that the interface properties control the Voc and efficiency. For higher interface disorder, recombination increases and the Voc is reduced. We further demonstrate the impact of variable off-diagonal disorder at fixed interface disorder on the device. The results show that at higher off-diagonal disorder, recombination, as well as Voc, is reduced. The presented results allow a better understanding of the importance of the structural order at the interface for OSCs.

Organic-inorganic hybrid perovskite (OIHP) solar cells are one of the fastest-growing solar cell technologies ever. OIHPs offer a wide range of bandgaps, wide optical absorption and low-cost deposition, making them an ideal candidate for solar cells. Despite the amazing properties of these materials, the perovskite solar cell performance is hampered by grain boundaries, traps, and hysteresis behavior of current-voltage (JV). Though the exact mechanism behind the hysteresis is still unknown, ionic movements in the perovskite film are considered to be the primary reason behind it. We present a drift-diffusion (DD) model to study the role of cation-mediated non-radiative recombination in the JV hysteresis in CH3NH3PbI3 cells. We study how the mobile cations can trap electrons leading to the JV hysteresis and poor performance of the cell. Impact of cation-electron trapping constant and the JV scan rate is investigated. We also simulate how the accumulation of mobile ions at the grain boundaries and interfaces limits the solar cell output current. When the ions are distributed in the bulk grains, they hardly affect the cell performance. When they start accumulating at the grain boundaries, the Jsc drops rapidly. The presented results give an insight into the effect of grain boundaries, and cation-mediated recombination as a possible reason for the JV hysteresis in perovskite solar cells.

The ability to predict material properties without the need of resource consuming experimental efforts can immensely accelerate material and drug discovery. Although ab initio methods can be reliable and accurate in making such predictions, they are computationally too expensive at a large scale. The recent advancements in artificial intelligence and machine learning as well as availability of large quantum mechanics derived datasets enable us to train models on these datasets as benchmark and to make fast predictions on much larger datasets. The success of these machine learning models highly depends on the machine-readable fingerprints of the molecules that capture their chemical properties as well as topological information. In this work we propose a common deep learning based framework to combine different types of molecular fingerprints to enhance prediction accuracy. Graph Neural Network (GNN), Many Body Tensor Representation (MBTR) and a set of simple Molecular Descriptors (MD) were used to predict the total energies, Highest Occupied Molecular Orbital (HOMO) energies and Lowest Unoccupied Molecular Orbital (LUMO) energies of a dataset containing ~62k large organic molecules with complex aromatic rings and remarkably diverse functional groups. The results demonstrate that a combination of best performing molecular fingerprints can produce better results than the individual ones. The simple and flexible deep learning framework developed in this work can be easily adapted to incorporate other types of molecular fingerprints.

Core-shell nanocatalyst activities are chiefly controlled by bimetallic material composition, shell thickness, and nanoparticle size. We present a machine learning framework predicting strain with site-specific precision to rationalize how strain on Pt core-shell nanocatalysts can enhance oxygen reduction activities. Large compressive strain on Pt@Cu and Pt@Ni induces optimal mass activities at 1.9 nm nanoparticle size. It is predicted that bimetallic Pt@Au and Pt@Ag have the best mass activities at 2.8 nm, where active sites are exposed to weak compressive strain. We demonstrate that optimal strain depends on the nanoparticle size; for instance, strengthening compressive strain on 1.92 nm sized Pt@Cu and Pt@Ni, or weakening compressive strain on 2.83 nm sized Pt@Ag and Pt@Au, can lead to further enhanced mass activities.

This article may be downloaded for personal use only. Any other use requires prior permission of the author and AIP Publishing. This article appeared in Journal of Applied Physics (Vol.128, Issue 19) and may be found at https://doi.org/10.1063/5.0028029, Comment: This article may be downloaded for personal use only. Any other use requires prior permission of the author and AIP Publishing. This article appeared in Journal of Applied Physics (Vol.128, Issue 19) and may be found at https://doi.org/10.1063/5.0028029

Gaining insight into structure-property relations is a key factor for the development of organic electronics. We present a multiscale framework for charge carrier mobilities in organic thin films empowered by machine-learned charge transfer integrals. The choice of the molecular representation is crucial for accurate and sensitive predictions. Using pentacene thin films, we investigate kernel based algorithms and systematically compare representations ranging from system-specific geometric to Coulomb matrix features to predict absolute and logarithmic transfer integrals. We use the predicted transfer integrals to compute the mobility, including its anisotropy, and compare it to reference values. Best accuracies were obtained by models using the interaction part of the Coulomb matrix as a feature and the logarithm of the transfer integral as a target. We achieve R-2 values of 0.97 for transfer integrals within an extensive range of 20 orders of magnitude and less than 27% error in the mobility. We show the transferability of the CIP feature for tetracene and DNTT with excellent prediction accuracies. Furthermore, we demonstrate that the interaction part of the CM successfully encodes the molecular identity and provides a highly sensitive ML framework. The presented framework opens the possibility for highly accurate mesoscopic transport simulations saving orders of magnitude in computational cost.

Organic field effect transistors (OFETs) show considerable variation from device to device and batch to batch. For the basic understanding of the microscopic origins of these variations, it is crucial to develop both experimental and theoretical methods that allow to localize ineffective regions in a device. Raster techniques, which manipulate conductivity locally, combined with global current readout may provide this information, e.g. it has been suggested that diffraction limited illumination of OFETs by laser scanning photo-current microscopy provides trap density distribution maps. However, the question arises whether local variation of conductivity is indeed suited for localization of such defects given that the detected photo-current passes through the whole network of conductance in the thin film device. In this study, we present a simulation model based on resistor networks to investigate the effect of defective regions in organic thin films. We show that varying conductances locally allows indeed to reconstruct the spatial distributions of ineffective areas. We also demonstrate how such simulations can be applied to interpret photo-current microscopy maps in terms of trap densities.

Organic solar cells are an interesting and promising photovoltaic technology which has faced a strong development in the last decade. Still, there is plenty of room for device improvement. Many different parameters, not always easy to control, determine the final power conversion efficiency. In this work we analyze the impact of unbalance charge carrier mobilities. It is known that a different charge mobility for electrons and holes determines a reduction in performance. We have developed a numerical model that includes the 3D morphology of the blend in the simulator. With this model we are able to observe the charge and current density distribution across the blend at different working points. We have investigated the effect of charge unbalance to solar cell performance.

Tailored Pt nanoparticle catalysts are promising candidates to accelerate the oxygen reduction reaction (ORR) in fuel cells. However, the search for active nanoparticle catalysts is hindered by laborious effort of experimental synthesis and measurements. On the other hand, DFT-based approaches are still time consuming and often not efficient. In this study, we introduce a computational model which enables rapid catalytic activity calculation of unstrained pure Pt nanoparticle electrocatalysts. The generic setup of the computational model is based on DFT results and experimental data obtained worldwide over the past ca 20 years; whereas, importantly, the computational model dispenses with DFT calculations during runtime. This realizes feasible and sharply reduced computation effort in comparison to theoretical approaches where DFT calculations must be performed for each nanoparticle individually. Regarding particle size effects on Pt nanoparticles, experimental catalytic mass activities from previous studies are accurately reproduced by our computational model. Shedding light on the parameter space of particle size effects, this study enables predictions beyond available experiments: Our computational model identifies potential enhancement in mass activity up to 190% over the experimentally detected maximum. Importantly, the rapid activity calculation enabled by our computational model may pave the way for extensive nanoparticle screening to expedite the search for improved electrocatalysts., 21 pages, 6 figures

Fullerene-based organic solar cells with only a minute amount of donor show a substantial photocurrent while maintaining a large open-circuit voltage. At low concentrations the donor is fully dispe...

We present a quantum molecular model to calculate mobility of π-stacked P3HT polymer layers with electron acceptor dopants coupled next to side groups in random position with respect to the linear chain. The hole density, the acceptor LUMO energy and the hybridization transfer integral between the acceptor and polymer were found to be very critical factors to the final hole mobility. For a dopant LUMO energy close and high above the top of the polymer valence band we have found a significant mobility increase with the hole concentration and with the dopant LUMO energy approaching the top of the polymer valence band. Higher mobility was achieved for small values of hybridization transfer integral between polymer and the acceptor, corresponding to the case of weakly bound acceptor. Strong couplings between the polymer and the acceptor with Coulomb repulsion interactions induced from the electron localizations was found to suppress the hole mobility.

Low charge carrier mobility is one key factor limiting the performance and applicability of devices based on organic semiconductors. Theoretical studies on mobility using the kinetic Monte Carlo or master equation are mainly based on a Gaussian energetic disorder and regular cubic lattices. The dependence of mobility on the electric field, temperature and charge carrier density is well studied for the Gaussian disorder model. In this work, we investigate the influence of spatially correlated site energies and spatial disorder in the lattice sites on the mobility using kinetic Monte Carlo simulations. Our analysis is based on both a regular cubic and a non-cubic Voronoi lattice. The latter is used to include spatial disorder in order to study its influence on the mobility for amorphous organic materials. Our results show that charge carrier mobility is strongly influenced by correlations in the site energies. Strong correlations even invert the field dependence of the mobility as observed experimentally in semi-crystalline polymers such as P3HT. Evaluation of local currents between localized states reveals the formation of current filaments with increasing correlation. Furthermore, the influence of the electric field and the energy landscape on the transport energy is studied by evaluation of active sites. A strong correlation between the transport energy, filaments in the local currents and the charge carrier mobility is observed. Our studies on the spatial disorder model do not indicate an inversion of the field dependence as observed by other researchers. The negative field-dependence in semi-crystalline materials may be explained by a higher correlation in the site energies as shown in a strongly correlated energetic landscape.

In bulk-heterojunction organic solar cells the low permittivity in combination with the spatial and energetic disorder of the organic materials lead to a complex behavior of charge carriers within the active layer. Charges originate from exciton splitting at the heterojunction interface and the successive interplay between mutual Coulomb interactions and transport through the disordered organic can lead to insufficient separation from the interface, increased interface densities with respect to the bulk regions and, hence, affect recombination. To further understand the mechanisms of recombination, insight into the explicit spatial distribution of charge carriers within the blend is crucial. We performed kinetic Monte Carlo simulations on a bulk-heterojunction organic solar cell to assess the effect of Coulomb interactions and energetic disorder on the three-dimensional spatial distribution of charge carriers and highlight the correlation with both geminate and non-geminate recombination. We show that for materials with low permittivity and large energetic disorder the charge distribution is strongly inhomogeneous with accumulation along the heterojunction interface. In such cases recombination is not limited by recombination partners finding each other but rather an interface controlled process where geminate recombination dominates over nongeminate recombination.

This editorial aims to interest researchers and inspire novel research on the topic of non-equilibrium Thermodynamics and Monte Carlo for Electronic and Electrochemical Processes. We present a brief outline on recent progress and challenges in the study of non-equilibrium dynamics and thermodynamics using numerical Monte Carlo simulations. The aim of this special issue is to collect recent advances and novel techniques of Monte Carlo methods to study non-equilibrium electronic and electrochemical processes at the nanoscale.

Anatase titanium dioxide is a highly promising material for memristors and photocatalysis. Multiple electronic transport processes are known to be influenced by defects in nanoscale anatase. Hence, in this study, we examine charge transport due to defects with respect to the fabrication of nanometer-thin TiO2 films via kinetic Monte Carlo (kMC). A compact kMC model for metal–oxide–semiconductor (MOS) and metal–oxide–metal (MOM) structures comprising TiO2 was parametrized by the electronic properties of TiO2 in agreement with the literature, in particular, spectroscopic studies and DFT calculations on defects in anatase. kMC simulations of MOS structures were refined, for the first time, by separate drift-diffusion simulations on the band bending in p+-Si substrates as well as by barrier heights adjusted for the Fermi level pinning effect. Referring to the impact of specific TiO2 film growth methods and postgrowth treatments on the parameters for defect energies in particular, electrical jV characteristics...

By combining optical and drift-diffusion models, a comprehensive simulation of power conversion efficiency of tandem solar cells is presented. To obtain consistent current–voltage characteristics of polymer tandem solar cells, the model takes into account correct description of organic–metal interfaces and organic semiconductor physics, in order to include the effect of interfaces and energetic disorder. A generalized methodology is developed to obtain the current–voltage characteristics of polymer tandem solar cells, which fully accounts for the interplay between the two subcells. The model is applied to the tandem cell with different commercially available polymers and for different subcell thicknesses and interconnection architectures. Based on the results of this model, it will be possible to design and optimize tandem structures toward higher efficiencies. Finally, it is concluded that the parallel configuration shows the highest performance over all studied cell structures.

Photovoltaics is amongst the most important technologies for renewable energy sources, and plays a key role in the development of a society with a smaller environmental footprint. Key parameters for solar cells are their energy conversion efficiency, their operating lifetime, and the cost of the energy obtained from a photovoltaic system compared to other sources. The optimization of these aspects involves the exploitation of new materials and development of novel solar cell concepts and designs. Both theoretical modeling and characterization of such devices require a comprehensive view including all scales from the atomic to the macroscopic and industrial scale. The different length scales of the electronic and optical degrees of freedoms specifically lead to an intrinsic need for multiscale simulation, which is accentuated in many advanced photovoltaics concepts including nanostructured regions. Therefore, multiscale modeling has found particular interest in the photovoltaics community, as a tool to advance the field beyond its current limits. In this article, we review the field of multiscale techniques applied to photovoltaics, and we discuss opportunities and remaining challenges. © T. Abu Hamed et al., published by EDP Sciences, 2018., European Cooperation in Science and Technology: MP1406, The authors are grateful for the financial support by the COST Action MP1406 “MultiscaleSolar.”

The identification of suitable lead-free perovskites is crucial for their envisioned applications in photovoltaics. Efficient and accurate vetting of these compounds for a range of properties has recently been accomplished in high-throughput studies by use of statistical learning methods. Here we demonstrate how one such property, the fundamental bandgap, can be predicted for a family of inorganic mixed halide perovskites using fingerprints based solely on the atomic arrangement of the unit cell. Important trends and experimentally accessible factors controlling this property are thereby illuminated in a chemically intuitive manner.

Renewable energy sources are among the most important ingredients for the development of a human society with sustainable environmental footprint. Among these, photovoltaics (PV) plays a key role and is therefore a field of intense research. The key parameters of a solar cell technology includes not only the energy conversion efficiency but also the operating lifetime and the overall cost of the energy produced. The latter must also be compared with other energy sources. The optimization of all these different aspects involves research across the whole photovoltaics value chain, starting from material science up to system optimization. Development of new solar cell device concepts is as important as search for new materials with more suitable optoelectronic properties or improved approaches for PV module design and integration in power distribution systems. This requires a comprehensive view on PV technology across all scales, from the atomic to the macroscopic and industrial scale. An important aspect of PV research and of development of new devices and systems, is theoretical modeling as an indispensable tool for both basic understanding and device optimization. This involves modeling also on all scales, from the microscopic properties of materials and nanostructures up to the behavior of PV modules. During the last decade, multiscale approaches have seen increasing interest for application in numerical simulation of electronic devices. In particular, modeling and understanding of advanced photovoltaic devices are expected to benefit from multiscale modeling, which allows describing consistently both macroscopic device behavior and local microscopic processes governing light absorption, loss mechanisms, carrier transport, and extraction. In fact, many advanced PV concepts rely on effects or contain structural features that are insufficiently described by standard numerical simulation approaches or semianalytic models, both regarding electronic and optical properties. The different length scales of the electronic and optical degrees of freedoms specifically lead to an intrinsic need for multiscale simulation, which is accentuated in many advanced photovoltaics concepts including nanostructured regions. Moreover, the active layers in solar cells generally require to have a certain thickness and a large overall device area, in order to absorb a sufficient amount of light. This special issue is an attempt to collect articles on modeling of PV devices and systems on all scales. It includes two experimental articles, one shedding some light on photoreflectance measurements when probing above the pump beam energy and the other discussing morphology in hybrid lead halide perovskite solar cells. One article describes numerical modeling of Cu2O on Si tandem cells based on a semiempirical approach. A further contribution shows a physics-based model of a quantum dot solar cell, including a comparison with experimental data. The remaining two papers deal with system relevant aspects, namely, power point tracking and electrical inverters for connecting PV modules or power plants with appliances.

Trapping and de-trapping mechanisms play an important role on the performance of organic based devices, hence it is imperative to have a better understanding of such effects both from experimental and theoretical perspectives. We present a simulation model based on resistor networks to investigate the effect of these traps on the output currents in terms of their spatial distributions. Resistor networks are manipulated accordingly on a sub-micron scale to mimic spot illumination from a light source which triggers de-trapping of carriers leading to a change in the current evaluated.

Perovskite-based solar cells are emerging as a potential new leading photovoltaic technology. However, several fundamental aspects of the stability remain unclear. In this Letter, we combine experimental measurements and numerical simulations to show that a mesoporous interface between the perovskite and the electron collection layer mitigates the reversible performance loss associated with the ion migration. We argue that larger interfacial area dilutes the concentration of defects that accumulate as a result of the ion migration within the perovskite under working conditions. Our investigation provides a quantitative description of the mechanism, identifying a critical defect concentration that devices can tolerate without reporting reduced performance.

In this work we demonstrate how a full comprehensive model can be used to understand the electrical behavior of actual organic devices. We address all the aspects which need to be taken into account for realistic simulations of a wide range of device structures and configurations. In particular we stress the relevance of the correct modeling of contact/organic interfaces. The model is applied to perform predictive simulations of organic light-emitting diodes and to deduce how a full experimental characterization of an organic device should be performed in order to completely grasp its electrical behavior.

The directed assembly of ordered arrays of cubic silver nanoparticles featuring distinct electrical threshold‐switching characteristics is reported. Threshold selectors are key elements for nonvolatile resistive random‐access‐memory architectures, as they suppress sneak path currents in crosspoint arrays. Nanocubes are site‐selectively immobilized on a TiO2‐coated silicon surface via a complementary molecular surface functionalization of nanoparticles and substrate based on a Cu(I)‐catalyzed alkyne‐azide cycloaddition without any physical template. Electrical characterization of individual silver nanocubes by conductive‐probe atomic force microscopy reveals pronounced and reproducible threshold‐switching behavior, featuring ultralow OFF currents below 1 pA, steep turn‐on slopes of

Cross-conjugated molecules are known to exhibit destructive quantum interference, a property that has recently received considerable attention in single-molecule electronics. Destructive quantum interference can be understood as an antiresonance in the elastic transmission near the Fermi energy and leading to suppressed levels of elastic current. In most theoretical studies, only the elastic contributions to the current are taken into account. In this paper, we study the inelastic contributions to the current in cross-conjugated molecules and find that while the inelastic contribution to the current is larger than for molecules without interference, the overall behavior of the molecule is still dominated by the quantum interference feature. Second, an ongoing challenge for single molecule electronics is understanding and controlling the local geometry at the molecule-surface interface. With this in mind, we investigate a spectroscopic method capable of providing insight into these junctions for cross-conjugated molecules: inelastic electron tunneling spectroscopy (IETS). IETS has the advantage that the molecule interface is probed directly by the tunneling current. Previously, it has been thought that overtones are not observable in IETS. Here, overtones are predicted to be strong and, in some cases, the dominant spectroscopic features. We study the origin of the overtones and find that the interference features in these molecules are the key ingredient. The interference feature is a property of the transmission channels of the π system only, and consequently, in the vicinity of the interference feature, the transmission channels of the σ system and the π system become equally transmissive. This allows for scattering between the different transmission channels, which serves as a pathway to bypass the interference feature. A simple model calculation is able to reproduce the results obtained from atomistic calculations, and we use this to interpret these findings.

Following the experimental evidences of filament forming in organic thin film memories, we developed a semiclassical drift-diffusion model of electrical conductivity in the filament. We show that the global behavior of a memory device and the total current can be accounted for by fully-formed and well-connected filaments. We investigated and ruled out the eventual influence of coherent quantum tunneling in disconnected filaments. It is also shown how a heating model of the filament can be used to check if assumptions on the number of filaments and their radii are physically plausible.

Insight into the relation between morphology and transport properties of organic semiconductors can be gained using multiscale simulations. Since computing electronic properties, such as the intermolecular transfer integral, using quantum chemical (QC) methods requires a high computational cost, existing models assume several approximations. A machine learning (ML)-based multiscale approach is presented that allows to simulate charge transport in organic semiconductors considering the static disorder within disordered crystals. By mapping fingerprints of dimers to their respective transfer integral, a kernel ridge regression ML algorithm for the prediction of charge transfer integrals is trained and evaluated. Since QC calculations of the electronic structure must be performed only once, the use of ML reduces the computation time radically, while maintaining the prediction error small. Transfer integrals predicted by ML are utilized for the computation of charge carrier mobilities using off-lattice kinetic Monte Carlo (kMC) simulations. Benefiting from the rapid performance of ML, microscopic processes can be described accurately without the need for phenomenological approximations. The multiscale system is tested with the well-known molecular semiconductor pentacene. The presented methodology allows reproducing the experimentally observed anisotropy of the mobility and enables a fast estimation of the impact of disorder.

Phosphorescent sensitizers (PSs) are considered as a promising alternative for increasing the internal quantum efficiency (IQE) in organic solar cells (OSCs). By converting short‐lifetime singlet into long‐living triplet excitons, enhanced exciton diffusion and dissociation have been reported previously. However, only a limited increase in the OSC performance has been achieved. In this work, the interplay of the PS with both singlet and triplet excitons within organic blends is examined using kinetic Monte Carlo simulations including a comprehensive model of excitonic processes. Different morphologies of the conventional P3HT:PCBM solar cell are simulated, and the excitonic properties and their influence on the photovoltaic performance under doping are studied. The use of phosphorescent sensitization ensures high intersystem crossing and enlarges the diffusion length. An increase in the IQE of 34% is observed for a bilayer OSC. The increasing decay of triplets in proximity to the PS due to a strong spin‐orbit coupling limits the IQE. Unlike expected, triplet‐triplet annihilation does not provide a significant loss of excitons. A doped planar‐mixed molecular heterojunction outperforms an undoped bulk‐heterojunction OSC due to the enhanced exciton diffusion. A further study of optimal PS parameters predicts an increase in the IQE within bilayer solar cells by about 100%.

Charge pair separation in organic bulk‐heterojunction (BHJ) solar cells is a complex interplay between numerous factors, such as the spatial geometry of the blend, the distribution of energetic disorder, the electric field, thermal fluctuations, and the mutual electron–hole Coulomb attraction. Insufficient separation from the interface and concomitant charge pair recombination is a main limitation in improving the PCE of organic BHJ solar cells and requires an in‐depth understanding of the timescales involved. Here, a 3D kinetic Monte Carlo model of a BHJ organic solar cell is set up and the time‐dependent evolution of mutual electron–hole pair distances separating from the heterojunction interface is investigated. Large fluctuations in separation times are found, in particular in dependence of the energetic disorder and the permittivity of the organic materials. At commonly observed values of energetic disorder, slight modifications of the permittivity can drastically influence the charge separation time and even outweigh orders of magnitude of geminate recombination rates, hence help to suppress geminate recombination. Thus, the results strongly support the recent trend of developing high‐permittivity organic materials for solar cell applications.

We discuss three different models of switching between the high conductivity and low conductivity state in organic bistable devices (OBD) with embedded nanoparticles. All models assume the same basic mechanism: charge trapping and de-trapping in metal nanoparticles. We show trapped charges can both induce an increase or a reduction of the total current depending on device configurations. The influence of energy disorder is investigated.

Highly-ordered, sub-70 nm-MOS-junctions of Au/Ti/TiOx/p+-Si were efficiently and reliably fabricated by nanotransfer-printing (nTP) over large areas and their functionality was investigated with respect to their application as MOS-devices. First, we used a temperature-enhanced nTP process and integrated the plasma-oxidation of a nm-thin titanium film being e-beam evaporated directly on the stamp before the printing step without affecting the p+-Si substrate. Second, morphological investigations (scanning electron microscopy) of the nanostructures confirm the reliable transfer of Au/Ti/TiOx-pillars of 50 nm, 75 nm, and 100 nm size of superior quality on p+-Si by our transfer protocol. Third, the fabricated nanodevices are also characterized electrically by conductive AFM. Fourth, the results are compared to probe station measurements on identically processed, i.e., transfer-printed μm-MOS-structures including a systematic investigation of the oxide formation. The jV-characteristics of these MOS-junctions demonstrate the electrical functionality as plasma-grown tunneling oxides and the effectivity of the transfer-printing process for their large-scale fabrication. Next, our findings are supported by fits to the jV-curves of the plasma-grown titanium oxide by kinetic-Monte-Carlo simulations. These fits allowed us to determine the dominant conduction mechanisms, the material parameters of the oxides and, in particular, a calibration of the thickness depending on applied plasma time and power. Finally, also a relative dielectric permittivity of 12 was found for such plasma-grown TiOx-layers.

Electronic devices composed of single molecules constitute the ultimate limit in the continued downscaling of electronic components. A key challenge for single-molecule electronics is to control the temperature of these junctions. Controlling heating and cooling effects in individual vibrational modes, can in principle, be utilized to increase stability of single-molecule junctions under bias, to pump energy into particular vibrational modes to perform current-induced reactions or to increase the resolution in inelastic electron tunneling spectroscopy by controlling the life-times of phonons in a molecule by suppressing absorption and external dissipation processes. Under bias the current and the molecule exchange energy, which typically results in heating of the molecule. However, the opposite process is also possible, where energy is extracted from the molecule by the tunneling current. Designing a molecular 'heat sink' where a particular vibrational mode funnels heat out of the molecule and into the leads would be very desirable. It is even possible to imagine how the vibrational energy of the other vibrational modes could be funneled into the 'cooling mode', given the right molecular design. Previous efforts to understand heating and cooling mechanisms in single molecule junctions, have primarily been concerned with small models, where it is unclear which molecular systems they correspond to. In this paper, our focus is on suppressing heating and obtaining current-induced cooling in certain vibrational modes. Strategies for cooling vibrational modes in single-molecule junctions are presented, together with atomistic calculations based on those strategies. Cooling and reduced heating are observed for two different cooling schemes in calculations of atomistic single-molecule junctions., Comment: 18 pages, 6 figures