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The density of five 1-alcohols (1-hexanol, 1-heptanol, 1-octanol, 1-nonanol, and 1-decanol) was measured using a vibrating-tube densimeter at pressures of up to 120 MPa for temperatures between 298.15 and 423.15 K. The relative expanded uncertainty of the experimental data is below 0.2%. The new results significantly extend the available literature data. The density of the five alcohols was modeled using four molecular-based equations of state (EOSs), namely PC-SAFT, SAFT-VR Mie, soft SAFT, and CPA. New component-specific EOS models were parametrized for all considered EOSs and substances using vapor–liquid equilibrium data from the literature and the density data from this work. The resulting EOS models describe the density with deviations of about 0.2–1.5%. For the thermal second-order derivative properties, the relative deviations were 10–50% for the isothermal compressibility and 5–35% for the thermal expansion coefficient. Also, the extrapolation behavior of the EOS models was assessed. Overall, the best results were obtained from the SAFT-VR Mie EOS.

Poly(oxymethylene) dimethyl ethers (OME, CH3O(CH2O)nCH3) are new synthetic fuels that can be produced from renewable resources. An interesting application of OME fuels is the use of them in mixtures with hydrogenated vegetable oils (HVO), which mainly consist of alkanes. Data on diffusion coefficients of OME containing mixtures are lacking in the literature but are needed for modeling OME production processes and OME combustion. Therefore, in the present work, self-diffusion coefficients of binary mixtures of OME and alkanes were measured by pulsed field gradient nuclear magnetic resonance (PFG-NMR). OME with chain lengths n= 1...4 were studied; the alkanes were n-dodecane (C12) and n-hexadecane (C16). The measurements in the binary mixtures were carried out at high dilution of the diffusing components and extrapolated to obtain the self-diffusion coefficients at infinite dilution that are identical with the mutual diffusion coefficient. For completeness, the self-diffusion coefficients of the pure components were also measured. The experiments were carried out at temperatures between 298.15 and 353.15 K at ambient pressure. The experimental data for the diffusion coefficients at infinite dilution were compared with the results from established prediction methods (SEGWE and Wilke and Chang), revealing considerable discrepancies. Furthermore, entropy scaling (ES) was applied here for the first time for modeling diffusion coefficients at infinite dilution. By coupling the results from entropy scaling with the Vignes equation, mutual diffusion coefficients in mixtures of OME and alkanes can now be predicted as a function of temperature, pressure, and composition for a wide range of conditions.

Multiobjective optimization problems whose objectives have different evaluation costs are commonly seen in the real world. Such problems are now known as multiobjective optimization problems with heterogeneous objectives (HE-MOPs). So far, however, only a few studies have been reported on addressing HE-MOPs, and most of them focus on biobjective problems with one fast objective and one slow objective. In this work, we aim to deal with HE-MOPs having more than two black-box and heterogeneous objectives. To this end, we develop a multiobjective Bayesian evolutionary optimization (BEO) approach to HE-MOPs that can alleviate search biases resulting from the different numbers of function evaluations allowed for the cheap and expensive objectives, which is achieved by designing a new acquisition function that penalizes the search bias toward the fast objectives, thereby achieving a balance between convergence and diversity. In addition, to make the best use of the different amounts of training data while avoiding increasing the computational cost, an ensemble consisting of two Gaussian processes is constructed for each cheap objective, one trained on the data collected before the Bayesian optimization starts, and the other on those evaluated during the BEO. Empirical studies on widely used multi-/many-objective benchmark problems whose objectives are assumed to be heterogeneously expensive demonstrate that the proposed algorithm is able to find high-quality solutions for HE-MOPs compared with the state-of-the-art methods.

Lubricated contact processes are studied using classical molecular dynamics simulations for determining the entire range of the Stribeck curve. Therefore, the lateral movement of two solid bodies at different gap height are studied. In each simulation, a rigid asperity is moved at constant height above a flat iron surface in a lubricating fluid. Both methane and decane are considered as lubricants. The three main lubrication regimes of the Stribeck curve and their transition regions are covered by the study: Boundary lubrication (significant elastic and plastic deformation of the substrate), mixed lubrication (adsorbed fluid layer dominates the process), and hydrodynamic lubrication (shear flow is set up between the surface and the asperity). We find the formation of a tribofilm in which lubricant molecules are immersed into the metal surface—not only in the case of scratching, but also for boundary lubrication and mixed lubrication. The formation of a tribofilm is found to have important consequences for the contact process. Moreover, the two fluids are found to show distinctly different behavior in the three lubrication regimes: For hydrodynamic lubrication (large gap height), decane yields a better tribological performance; for boundary lubrication (small gap height), decane shows a larger friction coefficient than methane, which is due to the different mechanisms observed for the formation of the tribofilm; the mixed lubrication regime can be considered as a transition regime between the two other regimes. Moreover, it is found that the nature of the tribofilm depends on the lubricant: While methane particles substitute substrate atoms sustaining mostly the crystalline structure, the decane molecules distort the substrate surface and an amorphous tribofilm is formed.

The prediction of thermophysical properties at extreme conditions is an important application of molecular simulations. The quality of these predictions primarily depends on the quality of the employed force field. In this work, a systematic comparison of classical transferable force fields for the prediction of different thermophysical properties of alkanes at extreme conditions, as they are encountered in tribological applications, was carried out using molecular dynamics simulations. Nine transferable force fields from three different classes were considered (all-atom, united-atom, and coarse-grained force fields). Three linear alkanes (n-decane, n-icosane, and n-triacontane) and two branched alkanes (1-decene trimer and squalane) were studied. Simulations were carried out in a pressure range between 0.1 and 400 MPa at 373.15 K. For each state point, density, viscosity, and self-diffusion coefficient were sampled, and the results were compared to experimental data. The Potoff force field yielded the best results.

The degree of process quality during production ramp-up is critical to the success of a product’s life cycle. If the required process quality is not achieved, investments must be arranged in rework. This contributes to further costs and reduces the profit margin of the product. This research paper provides an artificial intelligence based decision model that gives recommendations for the configuration of the quality-relevant process parameters in order to ensure process quality in production ramp-up. Within a manufacturing simulation, the decision model is validated. It is shown that the required process quality is achieved by applying the decision model to accompany the production ramp-up.

In contrast to summer smog, the contribution of photochemistry to the formation of winter haze in northern mid-to-high latitude is generally assumed to be minor due to reduced solar UV and water vapor concentrations. Our comprehensive observations of atmospheric radicals and relevant parameters during several haze events in winter 2016 Beijing, however, reveal surprisingly high hydroxyl radical oxidation rates up to 15 ppbv/h, which is comparable to the high values reported in summer photochemical smog and is two to three times larger than those determined in previous observations during winter in Birmingham (Heard et al. Geophys. Res. Lett.2004, 31, (18)), Tokyo (Kanaya et al. J. Geophys. Res.: Atmos.2007, 112, (D21)), and New York (Ren et al. Atmos. Environ.2006, 40, 252–263). The active photochemistry facilitates the production of secondary pollutants. It is mainly initiated by the photolysis of nitrous acid and ozonolysis of olefins and maintained by an extremely efficiently radical cycling process driven by nitric oxide. This boosted radical recycling generates fast photochemical ozone production rates that are again comparable to those during summer photochemical smog. The formation of ozone, however, is currently masked by its efficient chemical removal by nitrogen oxides contributing to the high level of wintertime particles. The future emission regulations, such as the reduction of nitrogen oxide emissions, therefore are facing the challenge of reducing haze and avoiding an increase in ozone pollution at the same time. Efficient control strategies to mitigate winter haze in Beijing may require measures similar as implemented to avoid photochemical smog in summer.

Secondary organic aerosol contributes to the atmospheric particle burden with implications for air quality and climate. Biogenic volatile organic compounds such as terpenoids emitted from plants are important secondary organic aerosol precursors with isoprene dominating the emissions of biogenic volatile organic compounds globally. However, the particle mass from isoprene oxidation is generally modest compared to that of other terpenoids. Here we show that isoprene, carbon monoxide and methane can each suppress the instantaneous mass and the overall mass yield derived from monoterpenes in mixtures of atmospheric vapours. We find that isoprene ‘scavenges’ hydroxyl radicals, preventing their reaction with monoterpenes, and the resulting isoprene peroxy radicals scavenge highly oxygenated monoterpene products. These effects reduce the yield of low-volatility products that would otherwise form secondary organic aerosol. Global model calculations indicate that oxidant and product scavenging can operate effectively in the real atmosphere. Thus highly reactive compounds (such as isoprene) that produce a modest amount of aerosol are not necessarily net producers of secondary organic particle mass and their oxidation in mixtures of atmospheric vapours can suppress both particle number and mass of secondary organic aerosol. We suggest that formation mechanisms of secondary organic aerosol in the atmosphere need to be considered more realistically, accounting for mechanistic interactions between the products of oxidizing precursor molecules (as is recognized to be necessary when modelling ozone production).

Quantum two-level systems, i.e., qubits, form the basis for most quantum machine learning approaches that have been proposed throughout the years. However, higher dimensional quantum systems constitute a promising alternative and are increasingly explored in theory and practice. Here, we explore the capabilities of multi-level quantum systems, so-called qudits, for their use in a quantum machine learning context. We formulate classification and regression problems with the data re-uploading approach and demonstrate that a quantum circuit operating on a single qudit is able to successfully learn highly non-linear decision boundaries of classification problems such as the MNIST digit recognition problem. We demonstrate that the performance strongly depends on the relation between the qudit states representing the labels and the structure of labels in the training data set. Such a bias can lead to substantial performance improvement over qubit-based circuits in cases where the labels, the qudit states, and the operators employed to encode the data are well-aligned. Furthermore, we elucidate the influence of the choice of the elementary operators and show that a squeezing operator is necessary to achieve good performances. We also show that there exists a trade-off for qudit systems between the number of circuit-generating operators in each processing layer and the total number of layers needed to achieve a given accuracy. Finally, we compare classification results from numerically exact simulations and their equivalent implementation on actual IBM quantum hardware. The findings of our work support the notion that qudit-based algorithms exhibit attractive traits and constitute a promising route to increasing the computational capabilities of quantum machine learning approaches.

Anthropogenic nitrogen oxides may influence the cloud condensation nuclei (CCN) activity of biogenic secondary organic aerosols (SOA) in both daytime photooxidation and nighttime NO3oxidation, which has significant implications for the climatic impact of SOA. We investigated the influence of NOxon the CCN activity of monoterpene‐derived SOA in OH oxidation and in NO3oxidation. In OH oxidation, NOxhad little influence on the hygroscopic parameter κ of organic aerosol (κOrg), which was attributed to the minor fraction of organic nitrates (ON) in SOA (<24%), resulted from the low branching ratio of RO2+ NO to form ON. In contrast, in NO3oxidation κOrgwas much reduced compared to OH/O3oxidation due to a dominant fraction of ON. We report κof MT‐derived ON formed in photo‐oxidation and NO3oxidation (0.029–0.052) for the first time to our knowledge, which may be used to improve model simulations of CCN concentrations. Anthropogenic nitrogen oxides may influence the cloud formation ability of biogenic secondary organic aerosols (SOA) in both daytime and nighttime, which has implications to understand the climatic impact of SOA. However, the influence remains unclear. We found that for monoterpenes, a major class of precursors of biogenic SOA, NOxhad little influence on the cloud formation ability of SOA in the daytime oxidation. In contrast, in the nighttime oxidation of monoterpenes by NO3, an important oxidant formed from NOxat night‐time, SOA had much lower cloud formation ability than that in the photo‐oxidation. The difference was attributed to the different fractions of organic nitrates (ON) in SOA. We also determined the κ of monoterpene‐derived ON for the first time to our knowledge. In daytime OH oxidation NOxhad little influences on the cloud condensation nuclei (CCN) activity of MT‐SOAIn nighttime NO3oxidation MT‐SOA had much lower CCN activity compared with those formed via OH or O3oxidationWe report the κof monoterpene‐derived organic nitrates (0.029–0.052) for the first time to our knowledge In daytime OH oxidation NOxhad little influences on the cloud condensation nuclei (CCN) activity of MT‐SOA In nighttime NO3oxidation MT‐SOA had much lower CCN activity compared with those formed via OH or O3oxidation We report the κof monoterpene‐derived organic nitrates (0.029–0.052) for the first time to our knowledge

MolMod, a web-based database for classical force fields for molecular simulations of fluids [Mol. Sim. 45, 10 (2019), 806–814], was extended to transferable force fields. Eight transferable force fields, including all-atom and united-atom type force fields, were implemented in the MolModdatabase: OPLS-UA, OPLS-AA, COMPASS, CHARMM, GROMOS, TraPPE, Potoff, and TAMie. These transferable force fields cover a large variety of chemical substance classes. The system is designed such that new transferable force fields can be readily integrated. A graphical user interface was implemented that enables the construction of molecules. The MolModdatabase compiles the force field for the specified component and force field type and provides the corresponding data and meta data as well as ready-to-use input files for the molecule for different simulation engines. This helps the user to flexibly choose molecular models and integrate them swiftly in their individual workflows, reducing risks of input errors in molecular simulations.

Foot deformities are found in several neurologic conditions, most typically, but not exclusively, Charcot-Marie-Tooth disease. Posttraumatic deformities and undercorrection or overcorrection of congenital talipes equinovarus are also encountered. A severely deformed foot that cannot fit into normal shoes presents a significant day-to-day challenge to the young and active patient. This article presents some basic principles for evaluating the deformity and a toolkit of procedures to deal with these complex cases.

Brain transcriptome and connectome maps are being generated, but an equivalent effort on the proteome is currently lacking. We performed high-resolution mass spectrometry–based proteomics for in-depth analysis of the mouse brain and its major brain regions and cell types. Comparisons of the 12,934 identified proteins in oligodendrocytes, astrocytes, microglia and cortical neurons with deep sequencing data of the transcriptome indicated deep coverage of the proteome. Cell type–specific proteins defined as tenfold more abundant than average expression represented about a tenth of the proteome, with an overrepresentation of cell surface proteins. To demonstrate the utility of our resource, we focused on this class of proteins and identified Lsamp, an adhesion molecule of the IgLON family, as a negative regulator of myelination. Our findings provide a framework for a system-level understanding of cell-type diversity in the CNS and serves as a rich resource for analyses of brain development and function.

We report on the development of a novel nano-composite transparent electrode material to be used in various energy applications e.g. as contacts for solar cells, composed of a wet-chemically synthesized silver nanowire (AgNW) network encapsulated in a transparent conductive oxide (TCO) which was deposited with nano-scale precision by atomic layer deposition (ALD). The AgNWs form a random network on a substrate of choice when being drop casted. ALD encapsulation of AgNWs guarantees a conformal and thickness controlled coating of the wires e.g. by the selected aluminum doped zinc oxide (AZO). Annealing of the AgNWs prior to ALD coating, yield a local sintering of AgNWs at their points of intersection, which improves the conductivity of the composite electrodes by reducing their sheet resistance. To demonstrate the performance of these AgNW/AZO composite transparent electrodes, they were used as a top electrode on wafer-based silicon (Si) - solar cells. A novel combination of scanning electron microscopy and image processing is used to determine the degree of percolation of the AgNWs on large areas of the nano-composite AgNW/AZO electrodes. Our results show that the solar cell with percolated AgNW/AZO electrode show the highest short circuit current density (28mA/cm2) and a series resistance in the same order of magnitude compared to reference solar cells with a thermally evaporated silver grid electrode. The electrode example we chose reveals that the developed AgNW/AZO electrode is a technologically relevant and cheap alternative to conventional solar cell screen printed grid electrodes, which contain ~95% more Ag per device area, with a high potential to be further systematically optimized by the presented image processing method.

Single semiconducting nanowires with sub-wavelength diameters exhibit superior light absorption, and hence triggered a vivid discussion regarding the application of these nanostructures into future generations of high efficiency solar cells. We examine the transition from a single highly absorbing silicon wire into an array composed of such individuals in order to validate the application of these into solar harvesting devices. We use finite-difference time-domain simulations to show that the coupling of the Fabry–Perot oscillations with the waveguide resonances inside the wires has a significant effect on the array absorption. For example, the ultimate absorption efficiency of a square-tiled wire array under normal incidence (array period of 0.5µm, wire diameter of 0.4µm and wire height of 2) is 81% higher than a 2µm thin-film when the Fabry–Perot oscillations are considered and 37% higher when these oscillations are not considered. This coupling screens out the contribution of the waveguide modes to the array absorption and therefore, unlike previously published work, we eliminate the contribution of the Fabry–Perot oscillations. In this manner we demonstrate the absorption enhancement due to waveguide modes, and general correlations between the nanowire geometry and the overall array absorption are presented. First, we show that once an isolated wire with high absorption cross-section is nested inside an array its absorption decreases due to wire proximity effects. Secondly, the array absorption is maximized with relatively wide wires of low absorption cross-sections. We show that a 75nm wire inside an square-tiled array with 2µm period has an average absorption efficiency factor of 6.5 and the average relative absorption of the array is 0.5%, while the same wire nested inside an array of a 0.25µm period exhibits 2.3 average absorption efficiency factor and the array exhibits average relative absorption of 9.85%. Finally, there is an optimized wire diameter that once exceeded the array absorption converges to that of a continuous film. For example, the maximum absorption of 0.5µm array is obtained with wire diameter of 0.4µm where a decrease in relative absorption is recorded for arrays with wires exceeding 0.4µm.

During ramp-up the production system's behavior is not predictable as most of the conditions apply for the first time. Due to the unpredictable consequences of single actions, which widely affect the whole production system's structure, decision making and therewith controlling the ramp-up is challenging. Caused by a lack of control instabilities occur, which lead to reduced production effectiveness and delay achieving the target production volume. This research paper focuses on managing instabilities in ramp-up by facilitating decision making through reducing the root cause of instabilities which is the underlying complexity of the ramp-up. For this purpose four solution principles are provided.

In order to provide higher accuracy and an increased life time for bearings in kinematic drives of servo-driven stroke controlled presses this article presents a combination of a roller and a plain bearing. With an example of the 3D-Servo-Press, the applied design is illustrated and the advantages are discussed. A technological solution is presented which enables a specific pretension for each individual bearing according to its tribological requirements. Using a developed matlab-programme the behaviour of a conventional plain bearing is opposed to the new bearing combination by its illustrated displacement paths of the shaft in the bearing shell under certain conditions of a common balancer system. The feasibility of this initial tension system is shown by means of a bearing prototype. Finally measurement results of a prototype will be shown which proof the manufacturability of the presented bearing combination.

Despite the recent decrease in pollution events in Chinese urban areas, the World Health Organization air quality guideline values are still exceeded. Observations from monitoring networks show a stronger decrease of organic aerosol directly emitted to the atmosphere relative to secondary organic aerosol (SOA) generated from oxidation processes. Here, the uptake of water‐soluble gas‐phase oxidation products is reported as a major SOA contribution to particulate pollution in Beijing, triggered by the increase of aerosol liquid water. In pollution episodes, this pathway is enough to explain the increase in SOA mass, with formaldehyde, acetaldehyde, glycolaldehyde, formic acid, and acetic acid alone explaining 15%–25% of the SOA increase. Future mitigation strategies to reduce non‐methane volatile organic compound emissions should be considered to reduce organic particulate pollution in China. In the rapidly developing Chinese economy, air pollution from particulate matter (PM) is a major human health risk factor. We show that secondary organic aerosol (SOA) generated from oxidation processes represent 50%–80% of the organic PM in Beijing. We find that non‐equilibrium dissolution of C1−C2 carbonyl compounds to particles is a major pathway of SOA formation during pollution events. These compounds are ubiquitous products in the chemical oxidation of hydrocarbons; thus, the reduction of a single volatile organic compound precursor would not reduce the organic PM, but rather a broad reduction of the organic reactivity is required. Secondary organic aerosol generated from oxidation processes dominates organic particulate pollution in BeijingNon‐equilibrium dissolution of carbonyl compounds to particles is a major pathway of SOA formation during haze episodesA broad reduction of the gas‐phase organic reactivity is required to reduce secondary organic aerosol formation in haze events Secondary organic aerosol generated from oxidation processes dominates organic particulate pollution in Beijing Non‐equilibrium dissolution of carbonyl compounds to particles is a major pathway of SOA formation during haze episodes A broad reduction of the gas‐phase organic reactivity is required to reduce secondary organic aerosol formation in haze events

BCAS1 expression identifies newly formed and actively myelinating oligodendrocytes in development, adulthood, and disease.