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Due to its outstanding properties, graphene has emerged as one of the most promising 2D materials in a large variety of research fields. Among the available fabrication protocols, chemical vapor deposition (CVD) enables the production of high quality single-layered large area graphene. To better understand the kinetics of CVD graphene growth, multiscale modeling approaches are sought after. Although a variety of models have been developed to study the growth mechanism, prior studies are either limited to very small systems, are forced to simplify the model to eliminate the fast process, or they simplify reactions. While it is possible to rationalize these approximations, it is important to note that they have non-trivial consequences on the overall growth of graphene. Therefore, a comprehensive understanding of the kinetics of graphene growth in CVD remains a challenge. Here, we introduce a kinetic Monte Carlo protocol that permits, for the first time, the representation of relevant reactions on the atomic scale, without additional approximations, while still reaching very long time and length scales of the simulation of graphene growth. The quantum-mechanics-based multiscale model, which links kinetic Monte Carlo growth processes with the rates of occurring chemical reactions, calculated from first principles makes it possible to investigate the contributions of the most important species in graphene growth. It permits the proper investigation of the role of carbon and its dimer in the growth process, thus indicating the carbon dimer to be the dominant species. The consideration of hydrogenation and dehydrogenation reactions enables us to correlate the quality of the material grown within the CVD control parameters and to demonstrate an important role of these reactions in the quality of the grown graphene in terms of its surface roughness, hydrogenation sites, and vacancy defects. The model developed is capable of providing additional insights to control the graphene growth mechanism on Cu(111), which may guide further experimental and theoretical developments.

The interaction of proteins and peptides with inorganic surfaces is relevant in a wide array of technological applications. A rational approach to design peptides for specific surfaces would build on amino-acid and surface specific interaction models, which are difficult to characterize experimentally or by modeling. Even with such a model at hand, the large number of possible sequences and the large conformation space of peptides make comparative simulations challenging. Here we present a computational protocol, the effective implicit surface model (EISM), for efficient in silico evaluation of the binding affinity trends of peptides on parameterized surface, with a specific application to the widely studied gold surface. In EISM the peptide surface interactions are modeled with an amino-acid and surface specific implicit solvent model, which permits rapid exploration of the peptide conformational degrees of freedom. We demonstrate the parametrization of the model and compare the results with all-atom simulations and experimental results for specific peptides.

While materials based on organic molecules usually have either superior optoelectronic or superior chiral properties, the combination of both is scarce. Here, a crystalline chiroptical film based on porphyrin with homochiral side groups is presented. While the dissolved molecule has a planar, thus, achiral porphyrin core, upon assembly in a metal–organic framework (MOF) film, the porphyrin core is twisted and chiral. The close packing and the crystalline order of the porphyrin cores in the MOF film also results in excellent optoelectronic properties. By exciting the Soret band of porphyrin, efficient photoconduction with a high On-Off-ratio is realized. More important, handedness-dependent circularly-polarized-light photoconduction with a dissymmetry factor g of 4.3×10$^{−4}$ is obtained. We foresee the combination of such assembly-induced chirality with the rich porphyrin chemistry will enable a plethora of organic materials with exceptional chiral and optoelectronic properties.

Multivalent batteries show promising prospects for next-generation sustainable energy storage applications. Herein, we report a polytriphenylamine (PTPAn) composite cathode capable of highly reversible storage of tetrakis(hexafluoroisopropyloxy) borate [B(hfip)$_4$] anions in both Magnesium (Mg) and calcium (Ca) battery systems. Spectroscopic and computational studies reveal the redox reaction mechanism of the PTPAn cathode material. The Mg and Ca cells exhibit a cell voltage >3 V, a high-power density of ~3000 W kg$^{−1}$ and a high-energy density of ~300 Wh kg$^{−1}$, respectively. Moreover, the combination of the PTPAn cathode with a calcium-tin (Ca-Sn) alloy anode could enable a long battery-life of 3000 cycles with a capacity retention of 60%. The anion storage chemistry associated with dual-ion electrochemical concept demonstrates a new feasible pathway towards high-performance divalent ion batteries.

Conductivity doping has emerged as an indispensable method to overcome the inherently low conductivity of amorphous organic semiconductors, which presents a great challenge in organic electronics applications. While tuning ionization potential and electron affinity of dopant and matrix is a common approach to control the doping efficiency, many other effects also play an important role. Here, we show that the quadrupole moment of the dopant anion in conjunction with the mutual near-field host-dopant orientation have a crucial impact on the conductivity. In particular, a large positive quadrupole moment of a dopant leads to an overscreening in host-dopant integer charge transfer complexes. Exploitation of this effect may enhance the conductivity by several orders of magnitude. This finding paves the way to a computer-aided systematic and efficient design of highly conducting amorphous small molecule doped organic semiconductors.

Layer-by-layer synthesis of surface-coordinated metal–organic frameworks (SURMOF) enables the assembly of asymmetric, dipolar linkers into non-centrosymmetric pillar-layered structures. Using appropriate substrate terminations can yield oriented growth with the dipoles aligned perpendicular to the surface. The aligned pillar linkers give rise to a built-in electrostatic field. In addition, the non-centrosymmetric structure of the SURMOF gives rise to intriguing nonlinear optical features, such as second harmonic generation. Previous research with methyl-functionalized bipyridine pillar linkers have demonstrated that this approach works in principle, but so far the total degree of alignment is only very small. Herein, a multiscale modelling approach is presented for in-silico SURMOF assembly to identify and overcome limitations in the growth of pillar-layered SURMOFs and to develop a strategy to maximize linker alignment. Using master equation models and kinetic Monte Carlo simulations, it is found that the formation of a highly ordered state corresponding to the thermodynamic equilibrium is often prevented by long-lasting transient effects. Based on ab initio binding energies for a wide selection of hypothetical pillar linkers, a fast-binding, slow-relaxation scheme is able to be identified during the SURMOF growth for a range of different pillar linkers. These observations allow them to derive a rational strategy for the design of novel linkers to yield SURMOF-based non-centrosymmetric materials with substantially improved properties.

Multivalente Batterien sind sehr vielversprechend für nachhaltige Energiespeicheranwendungen der nächsten Generation. Hier berichten wir über eine Polytriphenylamin (PTPAn)‐Verbundkathode, die in der Lage ist, Tetrakis(hexafluorisopropyloxy)borat [B(hfip)₄]⁻ Anionen in sowohl Magnesium‐ (Mg) als auch Calcium‐ (Ca) Batteriesystemen hochreversibel zu speichern. Spektroskopische und Computerstudien zeigen den Redoxreaktionsmechanismus des PTPAn‐Kathodenmaterials. Die Mg‐ und Ca‐Zellen weisen eine Zellspannung von ∼3 V, eine hohe Leistungsdichte von ∼3000 W kg⁻¹ bzw. eine hohe Energiedichte von ∼300 Wh kg⁻¹ auf. Darüber hinaus könnte die Kombination der PTPAn‐Kathode mit einer Anode aus einer Calcium‐Zinn‐Legierung (Ca−Sn) eine lange Batterielebensdauer von 3000 Zyklen bei einer Kapazitätserhaltung von 60 % ermöglichen. Die Anionenspeicherchemie in Verbindung mit dem elektrochemischen Doppelionenkonzept demonstriert einen neuen gangbaren Weg zu Hochleistungsbatterien mit zweiwertigen Ionen.

Adsorption and desorption of molecules are key processes in extraction and purification of biomolecules, engineering of drug carriers, and designing of surface-specific coatings. To understand the adsorption process on the atomic scale, state-of-the-art quantum mechanical and classical simulation methodologies are widely used. However, studying adsorption using a full quantum mechanical treatment is limited to picoseconds simulation timescales, while classical molecular dynamics simulations are limited by the accuracy of the existing force fields. To overcome these challenges, we propose a systematic way to generate flexible, application-specific highly accurate force fields by training artificial neural networks. As a proof of concept, we study the adsorption of the amino acid alanine on graphene and gold (111) surfaces and demonstrate the force field generation methodology in detail. We find that a molecule-specific force field with Lennard-Jones type two-body terms incorporating the 3rd and 7th power of the inverse distances between the atoms of the adsorbent and the surfaces yields optimal results, which is surprisingly different from typical Lennard-Jones potentials used in traditional force fields. Furthermore, we present an efficient and easy-to-train machine learning model that incorporates system-specific three-body (or higher order) interactions that are required, for example, for gold surfaces. Our final machine learning-based force field yields a mean absolute error of less than 4.2 kJ/mol at a speed-up of ∼105 times compared to quantum mechanical calculation, which will have a significant impact on the study of adsorption in different research areas.

Macromolecular self-assembly is at the basis of many phenomena in material and life sciences that find diverse applications in technology. One example is the formation of virus-like particles (VLPs) that act as stable empty capsids used for drug delivery or vaccine fabrication. Similarly to the capsid of a virus, VLPs are protein assemblies, but their structural formation, stability, and properties are not fully understood, especially as a function of the protein modifications. In this work, we present a data-driven modeling approach for capturing macromolecular self-assembly on scales beyond traditional molecular dynamics (MD), while preserving the chemical specificity. Each macromolecule is abstracted as an anisotropic object and high-dimensional models are formulated to describe interactions between molecules and with the solvent. For this, data-driven protein–protein interaction potentials are derived using a Kriging-based strategy, built on high-throughput MD simulations. Semi-automatic supervised learning is employed in a high performance computing environment and the resulting specialized force-fields enable a significant speed-up to the micrometer and millisecond scale, while maintaining high intermolecular detail. The reported generic framework is applied for the first time to capture the formation of hepatitis B VLPs from the smallest building unit, i.e., the dimer of the core protein HBcAg. Assembly pathways and kinetics are analyzed and compared to the available experimental observations. We demonstrate that VLP self-assembly phenomena and dependencies are now possible to be simulated. The method developed can be used for the parameterization of other macromolecules, enabling a molecular understanding of processes impossible to be attained with other theoretical models.

Macromolecular self-assembly is at the basis of many phenomena in material and life sciences that find diverse applications in technology. One example is the formation of virus-like particles (VLPs) that act as stable empty capsids used for drug delivery or vaccine fabrication. Similarly to the capsid of a virus, VLPs are protein assemblies, but their structural formation, stability, and properties are not fully understood, especially as a function of the protein modifications. In this work, we present a data-driven modeling approach for capturing macromolecular self-assembly on scales beyond traditional molecular dynamics (MD), while preserving the chemical specificity. Each macromolecule is abstracted as an anisotropic object and high-dimensional models are formulated to describe interactions between molecules and with the solvent. For this, data-driven protein-protein interaction potentials are derived using a Kriging-based strategy, built on high-throughput MD simulations. Semi-automatic supervised learning is employed in a high performance computing environment and the resulting specialized force-fields enable a significant speed-up to the micrometer and millisecond scale, while maintaining high intermolecular detail. The reported generic framework is applied for the first time to capture the formation of hepatitis B VLPs from the smallest building unit, i.e., the dimer of the core protein HBcAg. Assembly pathways and kinetics are analyzed and compared to the available experimental observations. We demonstrate that VLP self-assembly phenomena and dependencies are now possible to be simulated. The method developed can be used for the parameterization of other macromolecules, enabling a molecular understanding of processes impossible to be attained with other theoretical models.

Noncovalent interactions (NCIs) play an essential role in soft matter and biomolecular simulations. The ab initio method symmetry-adapted perturbation theory allows a precise quantitative analysis of NCIs and offers an inherent energy decomposition, enabling a deeper understanding of the nature of intermolecular interactions. However, this method is limited to small systems, for instance, dimers of molecules. Here, we present a scale-bridging approach to systematically derive an intermolecular force field from ab initio data while preserving the energy decomposition of the underlying method. We apply the model in molecular dynamics simulations of several solvents and compare two predicted thermodynamic observables-mass density and enthalpy of vaporization-to experiments and established force fields. For a data set limited to hydrocarbons, we investigate the extrapolation capabilities to molecules absent from the training set. Overall, despite the affordable moderate quality of the reference ab initio data, we find promising results. With the straightforward data set generation procedure and the lack of target data in the fitting process, we have developed a method that enables the rapid development of predictive force fields with an extra dimension of insights into the balance of NCIs.

The twin arginine translocase (Tat) exports folded proteins across bacterial membranes. The putative pore-forming or membrane-weakening component (TatA

The performance of a photoinitiator is key to control efficiency and resolution in 3D laser nanoprinting. Upon light absorption, a cascade of competing excited states and photoreactions leads to the radical formation that initiates free radical polymerization. Here we investigate 7-diethylamino-3-thenoylcoumarin (DETC), one of the most efficient photoinitiators for two-photon polymerization (TPP). Depending on the presence of a co-initiator, DETC causes radical generation either with two-photon or with a unique three-photon excitation, but the mechanism for these processes is not well understood. Here we show that the unique three-photon based radical formation of DETC in the absence of a co-initiator results from the excitation of special highly excited triplet states followed by multiple bond scission possibilities generating radicals. In contrast, photoinitiation in the presence of a co-initiator proceeds via intermolecular electron transfer or hydrogen atom transfer after the photosensitization of the photoinitiator to the lowest triplet excited state. Our quantum mechanical calculations explain the different pathways for the multiphoton activation mechanism of DETC and its radical formation, which enables the rational design of efficient photoinitiators to increase the speed and sensitivity of 3D laser nanoprinting.

Metal-organic framework (MOF)-based separators in Li-ion batteries (LIBs) have the potential to improve the battery performance. The mobility and conduction of lithium and organic ionic liquids (ILs) in these materials acting as (quasi) solid-state electrolytes are crucial for the battery power output. Here, we investigate the mobility of a Li-based IL in MOF nanopores and unveil the details of the conduction mechanism by molecular dynamics (MD) simulations. A complex conductivity depending on the Li-IL loading and on the IL composition is observed. Most importantly, the presence of Li prevents the collapse of the conductivity at high IL loadings. The fully atomistic MD simulations including guest-guest and guest-host interactions elucidate the competing mechanisms: Li follows a Grotthuss-like conduction mechanism with large mobility. While at small pore fillings, the Li conduction is limited by the large distance between the anions facilitating the Grotthuss-like conduction; the conduction at high pore fillings is governed by field-induced concentration inhomogeneities. Because of the small MOF pore windows, which hinders the simultaneous passage of the large IL cations and anions in opposite directions, the IL shows field-induced MOF pore blocking and ion bunching. The regions of low anion concentration and high cation concentration represent barriers for Li, decreasing its mobility. In comparison to Li-free IL, the IL bunching effect is attenuated by the formation of charge-neutral Li-anion complexes, resulting in a tremendously increased conductivity at maximum pore filling. The exploitation of this mechanism may enhance the development of advanced batteries based on IL and nanoporous separators.

Helical wrapping by conjugated polymer has been demonstrated as a powerful tool for the sorting of single-walled carbon nanotubes (SWCNTs) according to their electronic type, chiral index, and even handedness. However, a method of one-step extraction of left-handed (

The understanding of interactions between proteins with silica surface is crucial for a wide range of different applications: from medical devices, drug delivery and bioelectronics to biotechnology and downstream processing. We show the application of EISM (Effective Implicit Surface Model) for discovering the set of peptide interactions with silica surface. The EISM is employed for a high-speed computational screening of peptides to model the binding affinity of small peptides to silica surfaces. The simulations are complemented with experimental data of peptides with silica nanoparticles from microscale thermophoresis and from infrared spectroscopy. The experimental work shows excellent agreement with computational results and verifies the EISM model for the prediction of peptide-surface interactions. 57 peptides, with amino acids favorable for adsorption on Silica surface, are screened by EISM model for obtaining results, which are worth to be considered as a guidance for future experimental and theoretical works. This model can be used as a broad platform for multiple challenges at surfaces which can be applied for multiple surfaces and biomolecules beyond silica and peptides.

We demonstrate a facile approach to encapsulate ligand-stabilized Au55 clusters [Au55(PPh3)12Cl6] within highly oriented surface-supported metal–organic framework (SURMOF) thin films via liquid-pha...

Several photophysical properties of chromophores depend crucially on intermolecular interactions. Thermally‐activated delayed fluorescence (TADF) is often influenced by close packing of the chromophore assembly. In this context, the metal‐organic framework (MOF) approach has several advantages: it can be used to steer aggregation such that the orientation within aggregated structures can be predicted using rational approaches. We demonstrate this design concept for a DPA‐TPE (diphenylamine‐tetraphenylethylene) chromophore, which is non‐emissive in its solvated state due to vibrational quenching. Turning this DPA‐TPE into a ditopic linker makes it possible to grow oriented MOF thin films exhibiting pronounced green electroluminescence with low onset voltages. Measurements at different temperatures clearly demonstrate the presence of TADF. Finally, this work reports that the layer‐by‐layer process used for MOF thin film deposition allows the integration of the TADF‐DPA‐TPE in a functioning LED device., A crystalline, oriented thin film with green electroluminescence is illustrated. Enforced by SURMOF assembly, controlled aggregation of the DPA‐TPE linker chromophore turned‐on TADF process, boosting the luminance of the crystalline thin film.

Protein‐surface interactions are exploited in various processes in life sciences and biotechnology. Many of such processes are performed in presence of a buffer system, which is generally believed to have an influence on the protein‐surface interaction but is rarely investigated systematically. Combining experimental and theoretical methodologies, we herein demonstrate the strong influence of the buffer type on protein‐surface interactions. Using state of the art chromatographic experiments, we measure the interaction between individual amino acids and silica, as a reference to understand protein‐surface interactions. Among all the 20 proteinogenic amino acids studied, we found that arginine (R) and lysine (K) bind most strongly to silica, a finding validated by free energy calculations. We further measured the binding of R and K at different pH in presence of two different buffers, MOPS (3‐(N‐morpholino)propanesulfonic acid) and TRIS (tris(hydroxymethyl)aminomethane), and find dramatically different behavior. In presence of TRIS, the binding affinity of R/K increases with pH, whereas we observe an opposite trend for MOPS. These results can be understood using a multiscale modelling framework combining molecular dynamics simulation and Langmuir adsorption model. The modelling approach helps to optimize buffer conditions in various fields like biosensors, drug delivery or bio separation engineering prior to the experiment., How do buffers affect protein surface interactions? Experimental zonal elution chromatography helps to identify interactions of silica surfaces with amino acids. These interactions can be comprehended with a multiscale modelling framework combining molecular dynamics simulation and different flavors of the classical Langmuir adsorption model.

The automobile is a classic example of technology going full circle. Vehicle power sources, namely gasoline, electricity, and even hybrids, were first debated more than 120 years ago when more than 30% of vehicles operated on electricity. Pioneers like Thomas Edison and Ferdinand Porsche lauded the superiority of electrics in the late 1800s, with Porsche introducing the world's first hybrid around 1890. The debate has been dominated by the internal combustion engine since the early 20th century, when Karl Benz and Henry Ford both created methods to mass-produce more affordable petrol-based vehicles.

Precision polymerization techniques offer the exciting opportunity to manufacture single-chain nanoparticles (SCNPs) with intramolecular crosslinks placed in specific positions along the polymer chain. Earlier studies showed that synthetic polymer chains can fold into defined SCNP conformations through a reversible two-state process, similar to that observed for small peptides and proteins – yet far behind in its structural sophistication. While the natural structures of proteins arise from polypeptides of perfectly defined stereochemistry, the role of main-chain stereochemistry on SCNP folding remains largely unexplored. To investigate the effect of tacticity on SCNP architectures, the development of specific simulation strategies is critical to provide reliable data. Herein, we investigate the structural transitions of SCNPs of different stereochemistries, i.e. atactic, syndiotactic and isotactic of various lengths (L = 10 to L = 30) using all-atom Monte-Carlo simulations. The results indicate that structural transitions occur in syndiotactic polymers at lower temperature compared to atactic and isotactic polymer chains. The effect of main chain stereochemistry on the transition temperature was found to be especially pronounced for shorter polymer chains of length L = 10 to L = 20.

Conductivity doping of organic semiconductors is an essential prerequisite for many organic devices, but the specifics of dopant activation are still not well understood. Using many-body simulations that include Coulomb interactions and dopant ionization/de-ionization events explicitly we here show significant doping efficiency even before the electron affinity of the dopant exceeds the ionization potential of the organic matrix (p-doping), similar to organic salts. We explicitly demonstrate that the ionization of weak molecular dopants in organic semiconductors is a disorder-, rather than thermally induced process. Practical implications of this finding are a weak dependence of the ionized dopant fraction on the electron affinity of the dopant, and an enhanced ionization of the weak dopants upon increasing dopant molar fraction. As a result, strategies towards dopant optimization should aim for presently neglected goals, such as the binding energy in host-dopant charge-transfer states being responsible for the number of mobile charge carriers. Insights into reported effects are provided from the analysis of the density of states, where two novel features appear upon partial dopant ionization. The findings in this work can be used in the rational design of dopant molecules and devices.

Understanding the phenomena that lead to the formation of a specific helicity in helical polymers remains a challenge even today. Various polymers have been shown to assume different helical screw-senses depending on different stimuli. Acid-base chiral amines, for example, can induce helical conformations on

Organic light emitting diodes (OLED) play an important role in commercial displays and are promising candidates for energy-efficient lighting applications. Although they have been continuously developed since their discovery in 1987, some unresolved challenges remain. The performance of OLEDs is determined by a multifaceted interplay of materials and device architectures. A commonly used technique to overcome the charge injection barrier from the electrodes to the organic layers, are doped injection layers. The optimization of doped injection layers is critical for high-efficiency OLED devices, but has been driven mainly by chemical intuition and experimental experience, slowing down the progress in this field. Therefore, computer-aided methods for material and device modeling are promising tools to accelerate the device development process. In this work, we studied the effect of doped hole injection layers on the injection barrier in dependence on material and layer properties by using a parametric kinetic Monte Carlo model. We were able to quantitatively elucidate the influence of doping concentration, material properties, and layer thickness on the injection barrier and device conductivity, leading to the conclusion that our kMC model is suitable for virtual device design.

International audience; Driven by the continuous search for improving performances, understanding the phenomena at the electrode/electrolyte interfaces has become an overriding factor for the success of sustainable and efficient battery technologies for mobile and stationary applications. Toward this goal, rapid advances have been made regarding simulations/modeling techniques and characterization approaches, including high-throughput electrochemical measurements coupled with spectroscopies. Focusing on Li-ion batteries, current developments are analyzed in the field as well as future challenges in order to gain a full description of interfacial processes across multiple length/timescales; from charge transfer to migration/diffusion properties and interphases formation, up to and including their stability over the entire battery lifetime. For such complex and interrelated phenomena, developing a unified workflow intimately combining the ensemble of these techniques will be critical to unlocking their full investigative potential. For this paradigm shift in battery design to become reality, it necessitates the implementation of research standards and protocols, underlining the importance of a concerted approach across the community. With this in mind, major collaborative initiatives gathering complementary strengths and skills will be fundamental if societal and environmental imperatives in this domain are to be met. \textcopyright 2021 The Authors. Advanced Energy Materials published by Wiley-VCH GmbH

In recent years, modeling and simulation of materials have become indispensable to complement experiments in materials design. High-throughput simulations increasingly aid researchers in selecting the most promising materials for experimental studies or by providing insights inaccessible by experiment. However, this often requires multiple simulation tools to meet the modeling goal. As a result, methods and tools are needed to enable extensive-scale simulations with streamlined execution of all tasks within a complex simulation protocol, including the transfer and adaptation of data between calculations. These methods should allow rapid prototyping of new protocols and proper documentation of the process. Here an overview of the benefits and challenges of workflow engineering in virtual material design is presented. Furthermore, a selection of prominent scientific workflow frameworks used for the research in the BATTERY 2030+ project is presented. Their strengths and weaknesses as well as a selection of use cases in which workflow frameworks significantly contributed to the respective studies are discussed.

Ionic liquids (ILs) in nanoporous confinement are the core of many supercapacitors and batteries, where the mobility of the nanoconfined ILs is crucial. Here, by combining experiments based on impedance spectroscopy with molecular dynamics simulations, the mobility of a prototype IL in the nanopores of an isoreticular metal-organic framework (MOF)-series with different pore sizes is explored, where an external electric field is applied. It has been found that the conduction behavior changes tremendously depend on the pore size. For small-pore apertures, the IL cations and anions cannot pass the pore window simultaneously, causing the ions to mutually block the pores. This results in a strong concentration dependence of the ionic conduction, where the conduction drops by two orders of magnitude when filling the pores. For large-pore MOFs, the mutual hindrance of the ions in the pores is small, causing only a small concentration dependence. The cutoff between the large-pore and small-pore behavior is approximately the size of a cation-anion-dimer and increasing the pore diameter by only 0.2 nm changes the conduction behavior fundamentally. This study shows that the pore aperture size has a substantial effect on the mobility of ions in nanoporous confinement and has to be carefully optimized for realizing highly-mobile nanoconfined ILs.

4,4-Bis(carbazol-9-yl)-2,2-biphenyl (CBP) is widely used as a host material in phosphorescent organic light-emitting diodes (PhOLEDs). In the present study, we simulate the absorption spectra of CBP in gas and condensed phases, respectively, using the efficient time-dependent long-range corrected tight-binding density functional theory (TD-LC-DFTB). The accuracy of the condensed-phase absorption spectra computed using the structures obtained from classical molecular dynamics (MD) and quantum mechanical/molecular mechanical (QM/MM) simulations is examined by comparison with the experimental absorption spectrum. It is found that the TD-LC-DFTB gas-phase spectrum is in good agreement with the GW-BSE spectrum, indicating TD-LC-DFTB is an accurate and robust method in calculating the excitation energies of CBP. For the condensed-phase spectrum, we find that the electrostatic embedding has a minor influence on the excitation energy. Computing accurate absorption spectra is a particular challenge since static and dynamic disorders have to be taken into account. The static disorder results from the molecular packing in the material, which leads to molecule deformations. Since these structural changes sensitively impact the excitation energies of the individual molecules, a proper representation of this static disorder indicates that a reasonable structural model of the material has been generated. The good agreement between computed and experimental absorption spectra is therefore an indicator for the structural model developed. Concerning dynamic disorder, we find that molecular changes occur on long timescales in the ns-regime, which requires the use of fast computation approaches to reach convergence. The structural models derived in this work are the basis for future studies of charge and exciton transfer in CBP and related materials, also concerning the degradation mechanisms of CBP-based PhOLEDs.

Fully exploiting the potential of enzymes in cell-free biocatalysis requires stabilization of the catalytically active proteins and their integration into efficient reactor systems. Although in recent years initial steps towards the immobilization of such biomolecules in metal-organic frameworks (MOFs) have been taken, these demonstrations have been limited to batch experiments and to aqueous conditions. Here we demonstrate a MOF-based continuous flow enzyme reactor system, with high productivity and stability, which is also suitable for organic solvents. Under aqueous conditions, the stability of the enzyme was increased 30-fold, and the space-time yield exceeded that obtained with other enzyme immobilization strategies by an order of magnitude. Importantly, the infiltration of the proteins into the MOF did not require additional functionalization, thus allowing for time- and cost-efficient fabrication of the biocatalysts using label-free enzymes.

Manufacturing programmable materials, whose mechanical properties can be adapted on demand, is highly desired for their application in areas ranging from robotics, to biomedicine, or microfluidics. Herein, the inclusion of dynamic and living bonds, such as alkoxyamines, in a printable formulation suitable for two-photon 3D laser printing is exploited. On one hand, taking advantage of the dynamic covalent character of alkoxyamines, the nitroxide exchange reaction is investigated. As a consequence, a reduction of the Young´s Modulus by 50%, is measured by nanoindentation. On the other hand, due to its “living” characteristic, the chain extension becomes possible via nitroxide mediated polymerization. In particular, living nitroxide mediated polymerization of styrene results not only in a dramatic increase of the volume (≈8 times) of the 3D printed microstructure but also an increase of the Young's Modulus by two orders of magnitude (from 14 MPa to 2.7 GPa), while maintaining the shape including fine structural details. Thus, the approach introduces a new dimension by enabling to create microstructures with dynamically tunable size and mechanical properties.

International audience; This roadmap presents the transformational research ideas proposed by "BATTERY 2030+", the European large-scale research initiative for future battery chemistries. In this paper we outline a "chemistry-neutral" roadmap to advance battery research, particularly at low TRL, with a time horizon of about ten years. The roadmap is centered around six themes: 1) accelerated materials discovery platform, 2) battery interface genome, with the integration of smart functionalities such as 3) sensoring and 4) self-healing processes. Beyond chemistry related aspects we also include crosscutting research regarding 5) manufacturability and 6) recyclability. This roadmap should be seen as an important enabling complement to the many global battery roadmaps which focus on expected ultrahigh battery performance, especially for the future of transports. Batteries are used in many other applications and are considered to be one of Europe's key technologies necessary to reach the climate goals. Currently the market is dominated by lithium-ion batteries, which performs well in most applications, but despite new generations coming in near time, soon will approach their performance limits. Without major breakthroughs, battery performance and production requirements will not be sufficient to enable the building of a climate-neutral society. Through our "chemistry neutral" approach we aim to create a generic toolbox transforming the way we develop and design batteries, which later benefit into the development of specific battery chemistries and technologies. The goal is to integrate modeling and high-through-put experimental results in a closed integrated loop and manage the large amounts of data we generate to learn more about complex processes on different levels affecting the function of a battery cell or a battery system. Based on this we suggest concrete actions with the ambition to be part of and support the implementation of the European Green Deal, the UN Sustainable Development Goals, as well as the European Strategic Action plan on Batteries and the Strategic Energy Technology Plan.

Mn-based hexacyanoferrate (Mn-HCF) cathodes for Na-ion batteries usually suffer from poor reversibility and capacity decay resulting from unfavorable phase transitions and structural degradation during cycling. To address this issue, the high-entropy concept is here applied to Mn-HCF materials, significantly improving the sodium storage capabilities of this system via a solid-solution mechanism with minor crystallographic changes upon de-/sodiation. Complementary structural, electrochemical, and computational characterization methods are used to compare the behavior of high-, medium-, and low-entropy multicomponent Mn-HCFs resolving, to our knowledge for the first time, the link between configurational entropy/compositional disorder (entropy-mediated suppression of phase transitions, etc.) and cycling performance/stability in this promising class of next-generation cathode materials.

Organic semiconductors (OSC) are key components in applications such as organic photovoltaics, organic sensors, transistors and organic light emitting diodes (OLED). OSC devices, especially OLEDs, often consist of multiple layers comprising one or more species of organic molecules. The unique properties of each molecular species and their interaction determine charge transport in OSCs—a key factor for device performance. The small charge carrier mobility of OSCs compared to inorganic semiconductors remains a major limitation of OSC device performance. Virtual design can support experimental R&D towards accelerated R&D of OSC compounds with improved charge transport. Here we benchmark a de novo multiscale workflow to compute the charge carrier mobility solely on the basis of the molecular structure: We generate virtual models of OSC thin films with atomistic resolution, compute the electronic structure of molecules in the thin films using a quantum embedding procedure and simulate charge transport with kinetic Monte-Carlo protocol. We show that for 15 common amorphous OSC the computed zero-field and field-dependent mobility are in good agreement with experimental data, proving this approach to be an effective virtual design tool for OSC materials and devices.

FID-detected nutations of the antiferromagnetic crystal form of [Y(pc)

In amorphous organic semiconductor devices, electrons and holes are transported through layers of small organic molecules or polymers. The overall performance of the device depends both on the material and the device configuration. Measuring a single device configuration requires a large effort of synthesizing the molecules and fabricating the device, rendering the search for promising materials in the vast molecular space both nontrivial and time-consuming. This effort could be greatly reduced by computing the device characteristics from the first principles. Here, we compute transport characteristics of unipolar single-layer devices of prototypical hole- and electron-transporting materials, N,N'-di(1-naphthyl)-N,N'-diphenyl-(1,1'-biphenyl)-4,4'-diamine (α-NPD) and 2,2',2″-(1,3,5-benzinetriyl)-tris(1-phenyl-1-H-benzimidazole) (TPBi) using a first-principles multiscale approach that requires only the molecular constituents and the device geometry. This approach of generating a digital twin of the entire device can be extended to multilayer stacks and enables the computer design of materials and devices to facilitate systematic improvement of organic light-emitting diode (OLED) devices.