Simple thermal treatment of guanine at temperatures ranging from 600 to 700 °C leads to C1N1 condensates with unprecedented CO2/N2 selectivity when compared to other carbonaceous solid sorbents. Increasing the surface area of the CN condensates in the presence of ZnCl2 salt melts enhances the amount of CO2 adsorbed while preserving the high selectivity values and C1N1 structure. Results indicate that these new materials show a sorption mechanism a step closer to that of natural CO2 caption proteins and based on metal free structural cryptopores.

Employing the ultrafast control of electronic states of a semiconductor quantum dot in a cavity, we introduce an approach to achieve on-demand emission of single photons with almost perfect indistinguishability and photon pairs with near ideal entanglement. Our scheme is based on optical excitation off resonant to a cavity mode followed by ultrafast control of the electronic states using the time-dependent quantum-confined Stark effect, which then allows for cavity-resonant emission. Our theoretical analysis considers cavity-loss mechanisms, the Stark effect, and phonon-induced dephasing, allowing realistic predictions for finite temperatures.

Abstract Developing resource-abundant and sustainable metal-free bifunctional oxygen electrocatalysts is essential for the practical application of zinc–air batteries (ZABs). 2D black phosphorus (BP) with fully exposed atoms and active lone pair electrons can be promising for oxygen electrocatalysts, which, however, suffers from low catalytic activity and poor electrochemical stability. Herein, guided by density functional theory (DFT) calculations, an efficient metal-free electrocatalyst is demonstrated via covalently bonding BP nanosheets with graphitic carbon nitride (denoted BP-CN-c). The polarized PN covalent bonds in BP-CN-c can efficiently regulate the electron transfer from BP to graphitic carbon nitride and significantly promote the OOH* adsorption on phosphorus atoms. Impressively, the oxygen evolution reaction performance of BP-CN-c (overpotential of 350 mV at 10 mA cm−2, 90\% retention after 10 h operation) represents the state-of-the-art among the reported BP-based metal-free catalysts. Additionally, BP-CN-c exhibits a small half-wave overpotential of 390 mV for oxygen reduction reaction, representing the first bifunctional BP-based metal-free oxygen catalyst. Moreover, ZABs are assembled incorporating BP-CN-c cathodes, delivering a substantially higher peak power density (168.3 mW cm−2) than the Pt/C+RuO2-based ZABs (101.3 mW cm−2). The acquired insights into interfacial covalent bonds pave the way for the rational design of new and affordable metal-free catalysts.

The recent surge of interest in explainability in artificial intelligence (XAI) is propelled by not only technological advancements in machine learning but also by regulatory initiatives to foster transparency in algorithmic decision making. In this article, we revise the current concept of explainability and identify three limitations: passive explainee, narrow view on the social process, and undifferentiated assessment of explainee’s understanding. In order to overcome these limitations, we present explanation as a social practice in which explainer and explainee co-construct understanding on the microlevel. We view the co-construction on a microlevel as embedded into a macrolevel, yielding expectations concerning, e.g., social roles or partner models: typically, the role of the explainer is to provide an explanation and to adapt it to the current level of explainee’s understanding; the explainee, in turn, is expected to provide cues that direct the explainer. Building on explanations being a social practice, we present a conceptual framework that aims to guide future research in XAI. The framework relies on the key concepts of monitoring and scaffolding to capture the development of interaction. We relate our conceptual framework and our new perspective on explaining to transparency and autonomy as objectives considered for XAI.

Model predictive control is a prominent approach to construct a feedback control loop for dynamical systems. Due to real-time constraints, the major challenge in MPC is to solve model-based optimal control problems in a very short amount of time. For linear-quadratic problems, Bemporad et al. have proposed an explicit formulation where the underlying optimization problems are solved a priori in an offline phase. In this article, we present an extension of this concept in two significant ways. We consider nonlinear problems and - more importantly - problems with multiple conflicting objective functions. In the offline phase, we build a library of Pareto optimal solutions from which we then obtain a valid compromise solution in the online phase according to a decision maker's preference. Since the standard multi-parametric programming approach is no longer valid in this situation, we instead use interpolation between different entries of the library. To reduce the number of problems that have to be solved in the offline phase, we exploit symmetries in the dynamical system and the corresponding multiobjective optimal control problem. The results are verified using two different examples from autonomous driving.

The machine recognition of speech spoken at a distance from the microphones, known as far-field automatic speech recognition (ASR), has received a significant increase of attention in science and industry, which caused or was caused by an equally significant improvement in recognition accuracy. Meanwhile it has entered the consumer market with digital home assistants with a spoken language interface being its most prominent application. Speech recorded at a distance is affected by various acoustic distortions and, consequently, quite different processing pipelines have emerged compared to ASR for close-talk speech. A signal enhancement front-end for dereverberation, source separation and acoustic beamforming is employed to clean up the speech, and the back-end ASR engine is robustified by multi-condition training and adaptation. We will also describe the so-called end-to-end approach to ASR, which is a new promising architecture that has recently been extended to the far-field scenario. This tutorial article gives an account of the algorithms used to enable accurate speech recognition from a distance, and it will be seen that, although deep learning has a significant share in the technological breakthroughs, a clever combination with traditional signal processing can lead to surprisingly effective solutions.

The control of complex systems is of critical importance in many branches of science, engineering, and industry, many of which are governed by nonlinear partial differential equations. Controlling an unsteady fluid flow is particularly important, as flow control is a key enabler for technologies in energy (e.g., wind, tidal, and combustion), transportation (e.g., planes, trains, and automobiles), security (e.g., tracking airborne contamination), and health (e.g., artificial hearts and artificial respiration). However, the high-dimensional, nonlinear, and multi-scale dynamics make real-time feedback control infeasible. Fortunately, these high- dimensional systems exhibit dominant, low-dimensional patterns of activity that can be exploited for effective control in the sense that knowledge of the entire state of a system is not required. Advances in machine learning have the potential to revolutionize flow control given its ability to extract principled, low-rank feature spaces characterizing such complex systems.We present a novel deep learning modelpredictive control framework that exploits low-rank features of the flow in order to achieve considerable improvements to control performance. Instead of predicting the entire fluid state, we use a recurrent neural network (RNN) to accurately predict the control relevant quantities of the system, which are then embedded into an MPC framework to construct a feedback loop. In order to lower the data requirements and to improve the prediction accuracy and thus the control performance, incoming sensor data are used to update the RNN online. The results are validated using varying fluid flow examples of increasing complexity.

<jats:p>In the present work, we provide an electronic structure based method for the “on-the-fly” determination of vibrational sum frequency generation (v-SFG) spectra. The predictive power of this scheme is demonstrated at the air-water interface. While the instantaneous fluctuations in dipole moment are obtained using the maximally localized Wannier functions, the fluctuations in polarizability are approximated to be proportional to the second moment of Wannier functions. The spectrum henceforth obtained captures the signatures of hydrogen bond stretching, bending, as well as low-frequency librational modes.</jats:p>

Photovoltaics is one of the most promising and fastest-growing renewable energy technologies. Although the price-performance ratio of solar cells has improved significantly over recent years{,} further systematic investigations are needed to achieve higher performance and lower cost for future solar cells. In conjunction with experiments{,} computer simulations are powerful tools to investigate the thermodynamics and kinetics of solar cells. Over the last few years{,} we have developed and employed advanced computational techniques to gain a better understanding of solar cells based on copper indium gallium selenide (Cu(In{,}Ga)Se2). Furthermore{,} we have utilized state-of-the-art data-driven science and machine learning for the development of photovoltaic materials. In this Perspective{,} we review our results along with a survey of the field.

<jats:p>The three-dimensional (3D) crystal structures of the GAF3 domain of cyanobacteriochrome Slr1393 (<jats:italic>Synechocystis</jats:italic> PCC6803) carrying a phycocyanobilin chromophore could be solved in both 15-<jats:italic>Z</jats:italic> dark-adapted state, Pr, λ<jats:sub>max</jats:sub> = 649 nm, and 15-<jats:italic>E</jats:italic> photoproduct, Pg, λ<jats:sub>max</jats:sub> = 536 nm (resolution, 1.6 and 1.86 Å, respectively). The structural data allowed identifying the large spectral shift of the Pr-to-Pg conversion as resulting from an out-of-plane rotation of the chromophore’s peripheral rings and an outward movement of a short helix formed from a formerly unstructured loop. In addition, a third structure (2.1-Å resolution) starting from the photoproduct crystals allowed identification of elements that regulate the absorption maxima. In this peculiar form, generated during X-ray exposition, protein and chromophore conformation still resemble the photoproduct state, except for the D-ring already in 15-<jats:italic>Z</jats:italic> configuration and tilted out of plane akin the dark state. Due to its formation from the photoproduct, it might be considered an early conformational change initiating the parental state-recovering photocycle. The high quality and the distinct features of the three forms allowed for applying quantum-chemical calculations in the framework of multiscale modeling to rationalize the absorption maxima changes. A systematic analysis of the PCB chromophore in the presence and absence of the protein environment showed that the direct electrostatic effect is negligible on the spectral tuning. However, the protein forces the outer pyrrole rings of the chromophore to deviate from coplanarity, which is identified as the dominating factor for the color regulation.</jats:p>

CP2K is an open source electronic structure and molecular dynamics software package to perform atomistic simulations of solid-state, liquid, molecular, and biological systems. It is especially aimed at massively parallel and linear-scaling electronic structure methods and state-of-theart ab initio molecular dynamics simulations. Excellent performance for electronic structure calculations is achieved using novel algorithms implemented for modern high-performance computing systems. This review revisits the main capabilities of CP2K to perform efficient and accurate electronic structure simulations. The emphasis is put on density functional theory and multiple post–Hartree–Fock methods using the Gaussian and plane wave approach and its augmented all-electron extension.

We numerically simulate multiple light scattering in discrete disordered media represented by large clusters of irregular non-absorbing particles. The packing density of clusters is 0.5. With such conditions diffuse scattering is significantly reduced and light transport follows propagation channels that are determined by the particle size and topology of the medium. This kind of localization produces coherent backscattering intensity surge and enhanced negative polarization branch if compared to lower density samples.

Individual grains of chalcopyrite solar cell absorbers can facet in different crystallographic directions at their surfaces. To gain a deeper understanding of the junction formation in these devices, we correlate variations in the surface facet orientation with the defect electronic properties. We use a combined analytical approach based on scanning tunneling spectroscopy (STS), scanning electron microscopy, and electron back scatter diffraction (EBSD), where we perform these experiments on identical surface areas as small as 2 × 2 µm2 with a lateral resolution well below 50 nm. The topography of the absorber surfaces indicates two main morphological features: micro-faceted, long basalt-like columns and their short nano-faceted terminations. Our STS results reveal that the long columns exhibit spectral signatures typical for the presence of pronounced oxidation-induced surface dipoles in conjunction with an increased density of electronic defect levels. In contrast, the nano-faceted terminations of the basalt-like columns are largely passivated in terms of electronic defect levels within the band gap region. Corresponding crystallographic data based on EBSD experiments show that the surface of the basalt-like columns can be assigned to intrinsically polar facet orientations, while the passivated terminations are assigned to non-polar planes. Ab-initio calculations suggest that the polar surfaces are more prone to oxidation and resulting O-induced defects, in comparison to non-polar planes. Our results emphasize the correlation between morphology, surface facet orientations and surface electronic properties. Furthermore, this work aids in gaining a fundamental understanding of oxidation induced lateral inhomogeneities in view of the p-n junction formation in chalcopyrite thin-film solar cells.

The electrochemical nitrogen reduction reaction (NRR) to ammonia (NH3) is a promising alternative route for an NH3 synthesis at ambient conditions to the conventional high temperature and pressure Haber--Bosch process without the need for hydrogen gas. Single metal ions or atoms are attractive candidates for the catalytic activation of non-reactive nitrogen (N2), and for future targeted improvement of NRR catalysts, it is of utmost importance to get detailed insights into structure-performance relationships and mechanisms of N2 activation in such structures. Here, we report density functional theory studies on the NRR catalyzed by single Au and Fe atoms supported in graphitic C2N materials. Our results show that the metal atoms present in the structure of C2N are the reactive sites, which catalyze the aforesaid reaction by strong adsorption and activation of N2. We further demonstrate that a lower onset electrode potential is required for Fe--C2N than for Au--C2N. Thus, Fe--C2N is theoretically predicted to be a potentially better NRR catalyst at ambient conditions than Au--C2N owing to the larger adsorption energy of N2 molecules. Furthermore, we have experimentally shown that single sites of Au and Fe supported on nitrogen-doped porous carbon are indeed active NRR catalysts. However, in contrast to our theoretical results, the Au-based catalyst performed slightly better with a Faradaic efficiency (FE) of 10.1{\%} than the Fe-based catalyst with an FE of 8.4{\%} at −0.2 V vs. RHE. The DFT calculations suggest that this difference is due to the competitive hydrogen evolution reaction and higher desorption energy of ammonia.