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Abstract For decades, researchers have elucidated essential enzymatic functions on the atomic length scale by tracing atomic positions in real-time. Our work builds on possibilities unleashed by mix-and-inject serial crystallography (MISC) at X-ray free electron laser facilities. In this approach, enzymatic reactions are triggered by mixing substrate or ligand solutions with enzyme microcrystals. Here, we report in atomic detail (between 2.2 and 2.7 Å resolution) by room-temperature, time-resolved crystallography with millisecond time-resolution (with timepoints between 3 ms and 700 ms) how the Mycobacterium tuberculosis enzyme BlaC is inhibited by sulbactam (SUB). Our results reveal ligand binding heterogeneity, ligand gating, cooperativity, induced fit, and conformational selection all from the same set of MISC data, detailing how SUB approaches the catalytic clefts and binds to the enzyme noncovalently before reacting to a trans-enamine. This was made possible in part by the application of singular value decomposition to the MISC data using a program that remains functional even if unit cell parameters change up to 3 Å during the reaction.

The need for a cleaner and more efficient transportation sector emphasizes the development of new technologies aimed at the integrated reduction of pollutant emissions and increases in efficiency. Among these, promising technologies such as low-temperature combustion (LTC) systems operate in the field of the combustion physics, combining the attributes of both spark-ignited (SI) and compression-ignited (CI) engines. In particular, in a gasoline compression ignition (GCI) engine, gasoline is injected in closely spaced multiple pulses near the top dead center (TDC), creating a highly stratified charge which locally auto-ignites based on the thermodynamic conditions. In this work, a sectorial mesh of the combustion chamber was built. Initial and boundary conditions were set according to a one-dimensional model of the engine from a GT-suite platform. Then, a dedicated Matlab R2023b code was used to capture the effect of the pressure wave propagation on the shape of the fuel mass rate in closely spaced multiple injection events. Finally, a 3D-CFD code was validated comparing pressure trace, rate of heat release (RoHR) and emissions with experimental data provided by the test bench. The results highlight the robustness of the tabulated combustion model, which is able to capture the auto-ignition delay with a considerably low amount of computational time compared to common detailed kinetic solvers.

Serial crystallography at conventional synchrotron light sources (SSX) offers the possibility to routinely collect data at room temperature using micrometre-sized crystals of biological macromolecules. However, SSX data collection is not yet as routine and currently takes significantly longer than the standard rotation series cryo-crystallography. Thus, its use for high-throughput approaches, such as fragment-based drug screening, where the possibility to measure at physiological temperatures would be a great benefit, is impaired. On the way to high-throughput SSX using a conveyor belt based sample delivery system – the CFEL TapeDrive – with three different proteins of biological relevance (Klebsiella pneumoniae CTX-M-14 β-lactamase, Nectria haematococca xylanase GH11 and Aspergillus flavus urate oxidase), it is shown here that complete datasets can be collected in less than a minute and only minimal amounts of sample are required.

Serial crystallography (SX) is a cutting-edge technique in structural biology, involving the systematic collection of X-ray diffraction data from numerous randomly oriented microcrystals. To extract comprehensive three-dimensional information about the studied system, SX utilises thousands of measured diffraction patterns. As such, SX takes advantages of the properties of modern X-ray sources, including Free Electron Lasers (FELs) and third and fourth generation synchrotrons, as well as contemporary high-repetition-rate detectors. Efficient analysis of the extensive datasets generated during SX experiments demands fast and effective algorithms. The FDIP library offers meticulously optimised functions tailored for preprocessing data obtained in SX experiments. This encompasses tasks such as background subtraction, identification and masking of parasitic streaks, elimination of unwanted powder diffraction (e.g., from ice or salt crystals), and pinpointing useful Bragg peaks in each diffraction pattern. The library is equipped with a user-friendly graphical interface for facile parameter adjustment tailored to specific datasets. Compatible with popular SX processing software like OnDA, Cheetah, CrystFEL, and Merge3D, the FDIP library enhances the capabilities of these tools for streamlined and precise serial crystallography analyses.

Research Octane Number (RON) is one of the primary indicators for the determination of the resistance of gasoline fuels to autoignition. This parameter is usually determined with a test procedure involving a standardized engine that requires expensive hardware and time-consuming tests. In this work, a set of different methods with which to determine the RON of gasoline fuel surrogates is presented, considering only computer simulations, which allows to reduce both cost and time for the evaluation. A palette of 11 chemical species has been chosen as the basis for the surrogates’ database, which will be investigated in the work, allowing the representation of the complex chemical formulation of fuels in an easier way. A simplified zero-dimensional engine model of the standard variable compression ratio is used to provide pressure and temperature, then employed to calculate RON. This is done first by means of existing methods, and then by introducing new processes concerning a simplified chemical reactor built on kinetic schemes. Finally, these different methodologies are tested against a molar weighted sum of RONs of each chemical specie, allowing to have a criterion for comparison and evaluating their real prediction capabilities.

Ayan et al. report two structures of the protein streptavidin - one at ambient temperature determined using serial femtosecond crystallography and a second one determined at cryogenic temperature. These results provide insights into the structural dynamics of apo streptavidin and reveal a cooperative allostery between monomers for binding to biotin, and the findings are supported by GNM analysis.

With advances in X-ray free-electron lasers (XFELs), serial femtosecond crystallography (SFX) has enabled the static and dynamic structure determination for challenging proteins such as membrane protein complexes. In SFX with XFELs, the crystals are typically destroyed after interacting with a single XFEL pulse. Therefore, thousands of new crystals must be sequentially introduced into the X-ray beam to collect full data sets. Because of the serial nature of any SFX experiment, up to 99% of the sample delivered to the X-ray beam during its “off-time” between X-ray pulses is wasted due to the intrinsic pulsed nature of all current XFELs. To solve this major problem of large and often limiting sample consumption, we report on improvements of a revolutionary sample-saving method that is compatible with all current XFELs. We previously reported 3D-printed injection devices coupled with gas dynamic virtual nozzles (GDVNs) capable of generating samples containing droplets segmented by an immiscible oil phase for jetting crystal-laden droplets into the path of an XFEL. Here, we have further improved the device design by including metal electrodes inducing electrowetting effects for improved control over droplet generation frequency to stimulate the droplet release to matching the XFEL repetition rate by employing an electrical feedback mechanism. We report the improvements in this electrically triggered segmented flow approach for sample conservation in comparison with a continuous GDVN injection using the microcrystals of lysozyme and 3-deoxy-D-manno-octulosonate 8-phosphate synthase and report the segmented flow approach for sample injection applied at the Macromolecular Femtosecond Crystallography instrument at the Linear Coherent Light Source for the first time.

Here, we illustrate what happens inside the catalytic cleft of an enzyme when substrate or ligand binds on single-millisecond timescales. The initial phase of the enzymatic cycle is observed with near-atomic resolution using the most advanced X-ray source currently available: the European XFEL (EuXFEL). The high repetition rate of the EuXFEL combined with our mix-and-inject technology enables the initial phase of ceftriaxone binding to the Mycobacterium tuberculosis β-lactamase to be followed using time-resolved crystallography in real time. It is shown how a diffusion coefficient in enzyme crystals can be derived directly from the X-ray data, enabling the determination of ligand and enzyme–ligand concentrations at any position in the crystal volume as a function of time. In addition, the structure of the irreversible inhibitor sulbactam bound to the enzyme at a 66 ms time delay after mixing is described. This demonstrates that the EuXFEL can be used as an important tool for biomedically relevant research.

Mini-drones can be used for a variety of tasks, ranging from weather monitoring to package delivery, search and rescue, and also recreation. In outdoor scenarios, they leverage Global Positioning Systems (GPS) and/or similar systems for localization in order to preserve safety and performance. In indoor scenarios, technologies such as Visual Simultaneous Localization and Mapping (V-SLAM) are used instead. However, more advancements are still required for mini-drone navigation applications, especially in the case of stricter safety requirements. In this research, a novel method for enhancing indoor mini-drone localization performance is proposed. By merging Oriented Rotated Brief SLAM (ORB-SLAM2) and Semi-Direct Monocular Visual Odometry (SVO) via an Adaptive Complementary Filter (ACF), the proposed strategy achieves better position estimates under various conditions (low light in low-surface-texture environments and high flying speed), showing an average percentage error of 18.1% and 25.9% smaller than that of ORB-SLAM and SVO against the ground-truth.

Time-resolved crystallography (TRX) is used for monitoring only small conformational changes of biomacromolecules within the same lattice. Here, the authors report the interplay between synchronous molecular rearrangements and lattice phase transitions in RNA crystals, providing the basis for the investigation of large conformational changes using TRX.

Hydrogen is the energy vector that will lead us toward a more sustainable future. It could be the fuel of both fuel cells and internal combustion engines. Internal combustion engines are today the only motors characterized by high reliability, duration and specific power, and low cost per power unit. The most immediate solution for the near future could be the application of hydrogen as a fuel in modern internal combustion engines. This solution has advantages and disadvantages: specific physical, chemical and operational properties of hydrogen require attention. Hydrogen is the only fuel that could potentially produce no carbon, carbon monoxide and carbon dioxide emissions. It also allows high engine efficiency and low nitrogen oxide emissions. Hydrogen has wide flammability limits and a high flame propagation rate, which provide a stable combustion process for lean and very lean mixtures. Near the stoichiometric air–fuel ratio, hydrogen-fueled engines exhibit abnormal combustions (backfire, pre-ignition, detonation), the suppression of which has proven to be quite challenging. Pre-ignition due to hot spots in or around the spark plug can be avoided by adopting a cooled or unconventional ignition system (such as corona discharge): the latter also ensures the ignition of highly diluted hydrogen–air mixtures. It is worth noting that to correctly reproduce the hydrogen ignition and combustion processes in an ICE with the risks related to abnormal combustion, 3D CFD simulations can be of great help. It is necessary to model the injection process correctly, and then the formation of the mixture, and therefore, the combustion process. It is very complex to model hydrogen gas injection due to the high velocity of the gas in such jets. Experimental tests on hydrogen gas injection are many but never conclusive. It is necessary to have a deep knowledge of the gas injection phenomenon to correctly design the right injector for a specific engine. Furthermore, correlations are needed in the CFD code to predict the laminar flame velocity of hydrogen–air mixtures and the autoignition time. In the literature, experimental data are scarce on air–hydrogen mixtures, particularly for engine-type conditions, because they are complicated by flame instability at pressures similar to those of an engine. The flame velocity exhibits a non-monotonous behavior with respect to the equivalence ratio, increases with a higher unburnt gas temperature and decreases at high pressures. This makes it difficult to develop the correlation required for robust and predictive CFD models. In this work, the authors briefly describe the research path and the main challenges listed above.

Due to the pulsed nature of X-ray free electron laser (XFEL) instruments the majority of protein crystals, which are injected using continuous jet injection techniques are wasted. Here, the authors present a microfluidic device to deliver aqueous protein crystal laden droplets segmented with an immiscible oil and demonstrate that with this device an approx. 60% reduction in sample waste was achieved for data collection of 3-deoxy-D-manno-octulosonate 8-phosphate synthase crystals at the EuXFEL.

Macromolecular crystallography (MX) is the dominant means of determining the three-dimensional structures of biological macromolecules. Over the last few decades, most MX data have been collected at synchrotron beamlines using a large number of different detectors produced by various manufacturers and taking advantage of various protocols and goniometries. These data came in their own formats: sometimes proprietary, sometimes open. The associated metadata rarely reached the degree of completeness required for data management according to Findability, Accessibility, Interoperability and Reusability (FAIR) principles. Efforts to reuse old data by other investigators or even by the original investigators some time later were often frustrated. In the culmination of an effort dating back more than two decades, a large portion of the research community concerned with high data-rate macromolecular crystallography (HDRMX) has now agreed to an updated specification of data and metadata for diffraction images produced at synchrotron light sources and X-ray free-electron lasers (XFELs). This `Gold Standard' will facilitate the processing of data sets independent of the facility at which they were collected and enable data archiving according to FAIR principles, with a particular focus on interoperability and reusability. This agreed standard builds on the NeXus/HDF5 NXmx application definition and the International Union of Crystallography (IUCr) imgCIF/CBF dictionary, and it is compatible with major data-processing programs and pipelines. Just as with the IUCr CBF/imgCIF standard from which it arose and to which it is tied, the NeXus/HDF5 NXmx Gold Standard application definition is intended to be applicable to all detectors used for crystallography, and all hardware and software developers in the field are encouraged to adopt and contribute to the standard.

Increasingly stringent pollutant emission limits and CO2 reduction policies are forcing the automotive industry toward cleaner and decarbonized mobility. The goal is to achieve carbon neutrality within 2050 and limit global warming to 2 °C (possibly 1.5 °C) with respect to pre-industrial levels as stated in both the European Green Deal and the Paris Agreement and further reiterated at the COP26. With the aim of simultaneously reducing both pollutants and CO2 emissions, a large amount of research is currently carried out on low-temperature highly efficient combustions (LTC). Among these advanced combustions, one of the most promising is Gasoline Compression Ignition (GCI), based on the spontaneous ignition of a gasoline-like fuel. Nevertheless, despite GCI proving to be effective in reducing both pollutants and CO2 emissions, GCI combustion controllability represents the main challenge that hinders the diffusion of this methodology for transportation. Several works in the literature demonstrated that to properly control GCI combustion, a multiple injections strategy is needed. The rise of pressure and temperature generated by the spontaneous ignition of small amounts of early-injected fuel reduces the ignition delay of the following main injection, responsible for the torque production of the engine. Since the combustion of the pre-injections is chemically driven, the ignition delay might be strongly affected by a slight variation in the engine control parameters and, consequently, lead to misfire or knocking. The goal of this work was to develop a control-oriented ignition delay model suitable to improve the GCI combustion stability through the proper management of the pilot injections. After a thorough analysis of the quantities affecting the ignition delay, this quantity was modeled as a function of both a thermodynamic and a chemical–physical index. The comparison between the measured and modeled ignition delay shows an accuracy compatible with the requirements for control purposes (the average root mean squared error between the measured and estimated start of combustion is close to 1.3 deg), over a wide range of operating conditions. As a result, the presented approach proved to be appropriate for the development of a model-based feed-forward contribution for a closed-loop combustion control strategy.

The European X-ray free-electron laser (EuXFEL) in Hamburg is the first XFEL with a megahertz repetition rate. Here the authors present the 2.9 Å structure of the large membrane protein complex Photosystem I from T. elongatus that was determined at the EuXFEL.

Efficient and reliable sample delivery has remained one of the bottlenecks for serial crystallography experiments. Compared with other methods, fixed-target sample delivery offers the advantage of significantly reduced sample consumption and shorter data collection times owing to higher hit rates. Here, a new method of on-chip crystallization is reported which allows the efficient and reproducible growth of large numbers of protein crystals directly on micro-patterned silicon chips for in-situ serial crystallography experiments. Crystals are grown by sitting-drop vapor diffusion and previously established crystallization conditions can be directly applied. By reducing the number of crystal-handling steps, the method is particularly well suited for sensitive crystal systems. Excessive mother liquor can be efficiently removed from the crystals by blotting, and no sealing of the fixed-target sample holders is required to prevent the crystals from dehydrating. As a consequence, `naked' crystals are obtained on the chip, resulting in very low background scattering levels and making the crystals highly accessible for external manipulation such as the application of ligand solutions. Serial diffraction experiments carried out at cryogenic temperatures at a synchrotron and at room temperature at an X-ray free-electron laser yielded high-quality X-ray structures of the human membrane protein aquaporin 2 and two new ligand-bound structures of thermolysin and the human kinase DRAK2. The results highlight the applicability of the method for future high-throughput on-chip screening of pharmaceutical compounds.

Smooth power injection is one of the possible services that modern wind farms could provide in the not-so-far future, for which energy storage is required. Indeed, this is one among the three possible operations identified by the International Energy Agency (IEA)-Hydrogen Implementing Agreement (HIA) within the Task 24 final report, that may promote their integration into the main grid, in particular when paired to hydrogen-based energy storages. In general, energy storage can mitigate the inherent unpredictability of wind generation, providing that they are deployed with appropriate control algorithms. On the contrary, in the case of no storage, wind farm operations would be strongly affected, as well as their economic performances since the penalty fees wind farm owners/operators incur in case of mismatches between the contracted power and that actually delivered. This paper proposes a Model Predictive Control (MPC) algorithm that operates a Hydrogen-based Energy Storage System (HESS), consisting of one electrolyzer, one fuel cell and one tank, paired to a wind farm committed to smooth power injection into the grid. The MPC relies on Mixed-Logic Dynamic (MLD) models of the electrolyzer and the fuel cell in order to leverage their advanced features and handles appropriate cost functions in order to account for the operating costs, the potential value of hydrogen as a fuel and the penalty fee mechanism that may negatively affect the expected profits generated by the injection of smooth power. Numerical simulations are conducted by considering wind generation profiles from a real wind farm in the center-south of Italy and spot prices according to the corresponding market zone. The results show the impact of each cost term on the performances of the controller and how they can be effectively combined in order to achieve some reasonable trade-off. In particular, it is highlighted that a static choice of the corresponding weights can lead to not very effective handling of the effects given by the combination of the system conditions with the various exogenous’, while a dynamic choice may suit the purpose instead. Moreover, the simulations show that the developed models and the set-up mathematical program can be fruitfully leveraged for inferring indications on the devices’ sizing.

This review focuses on the use of unmanned aerial vehicles (UAVs) in precision agriculture, and specifically, in precision viticulture (PV), and is intended to present a bibliometric analysis of their developments in the field. To this aim, a bibliometric analysis of research papers published in the last 15 years is presented based on the Scopus database. The analysis shows that the researchers from the United States, China, Italy and Spain lead the precision agriculture through UAV applications. In terms of employing UAVs in PV, researchers from Italy are fast extending their work followed by Spain and finally the United States. Additionally, the paper provides a comprehensive study on popular journals for academicians to submit their work, accessible funding organizations, popular nations, institutions, and authors conducting research on utilizing UAVs for precision agriculture. Finally, this study emphasizes the necessity of using UAVs in PV as well as future possibilities.

Reducing green-house gases emission from light-duty vehicles is compulsory in order to slow down the climate change. The application of High Frequency Ignition systems based on the Corona discharge effect has shown the potential to extend the dilution limit of engine operating conditions promoting lower temperatures and faster combustion events, thus, higher thermal and indicating efficiency. Furthermore, predicting the behavior of Corona ignition devices against new sustainable fuel blends, including renewable hydrogen and biogas, is crucial in order to deal with the short-intermediate term fleet electric transition. The numerical evaluation of Corona-induced discharge radius and radical species under those conditions can be helpful in order to capture local effects that could be reached only with complex and expensive optical investigations. Using an extended version of the Corona one-dimensional code previously published by the present authors, the simulation of pure methane and different methane–hydrogen blends, and biogas–hydrogen blends mixed with air was performed. Each mixture was simulated both for 10% recirculated exhaust gas dilution and for its corresponding dilute upper limit, which was estimated by means of chemical kinetics simulations integrated with a custom misfire detection criterion.

The new European X-Ray Free-Electron Laser (EuXFEL) is the first XFEL that generates X-ray pulses with a megahertz inter-pulse spacing. Here the authors demonstrate that high-quality and damage-free protein structures can be obtained with the currently available 1.1 MHz repetition rate pulses using lysozyme as a test case and furthermore present a β-lactamase structure.

Liquid microjets are a common means of delivering protein crystals to the focus of X-ray free-electron lasers (FELs) for serial femtosecond crystallography measurements. The high X-ray intensity in the focus initiates an explosion of the microjet and sample. With the advent of X-ray FELs with megahertz rates, the typical velocities of these jets must be increased significantly in order to replenish the damaged material in time for the subsequent measurement with the next X-ray pulse. This work reports the results of a megahertz serial diffraction experiment at the FLASH FEL facility using 4.3 nm radiation. The operation of gas-dynamic nozzles that produce liquid microjets with velocities greater than 80 m s−1 was demonstrated. Furthermore, this article provides optical images of X-ray-induced explosions together with Bragg diffraction from protein microcrystals exposed to trains of X-ray pulses repeating at rates of up to 4.5 MHz. The results indicate the feasibility for megahertz serial crystallography measurements with hard X-rays and give guidance for the design of such experiments.

Abstract Background Ever since the first atomic structure of an enzyme was solved, the discovery of the mechanism and dynamics of reactions catalyzed by biomolecules has been the key goal for the understanding of the molecular processes that drive life on earth. Despite a large number of successful methods for trapping reaction intermediates, the direct observation of an ongoing reaction has been possible only in rare and exceptional cases. Results Here, we demonstrate a general method for capturing enzyme catalysis “in action” by mix-and-inject serial crystallography (MISC). Specifically, we follow the catalytic reaction of the Mycobacterium tuberculosis β-lactamase with the third-generation antibiotic ceftriaxone by time-resolved serial femtosecond crystallography. The results reveal, in near atomic detail, antibiotic cleavage and inactivation from 30 ms to 2 s. Conclusions MISC is a versatile and generally applicable method to investigate reactions of biological macromolecules, some of which are of immense biological significance and might be, in addition, important targets for structure-based drug design. With megahertz X-ray pulse rates expected at the Linac Coherent Light Source II and the European X-ray free-electron laser, multiple, finely spaced time delays can be collected rapidly, allowing a comprehensive description of biomolecular reactions in terms of structure and kinetics from the same set of X-ray data.

Nowadays reducing green-house gas emissions and pushing the fossil fuel savings in the field of light-duty vehicles is compulsory to slow down climate change. To this aim, the use of new combustion modes and dilution strategies to increase the stability of operations rich in diluent is an effective technique to reduce combustion temperatures and heat losses in throttled operations. Since the combustion behavior in those solutions highly differs from that of typical market systems, fundamental analyses in optical engines are mandatory in order to gain a deep understanding of those and to tune new models for improving the mutual support between experiments and simulations. However, it is known that optical accessible engines suffer from significant blow-by collateral flow due to the installation of the optical measure line. Thus, a reliable custom blow-by model capable of being integrated with both mono-dimensional and three-dimensional simulations was developed and validated against experimental data. The model can work for two different configurations: (a) stand-alone, aiming at providing macroscopic data on the ignitable mixture mass loss/recover through the piston rings; (b) combined, in which it is integrated in CFD engine simulations for the local analysis of likely collateral heat release induced by blow-by. Furthermore, once the model was validated, the effect of the engine speed and charge dilution on the blow-by phenomenon in the optical engine were simulated and discussed in the stand-alone mode.

Regenerative braking can significantly improve the energy efficiency of hybrid and electric vehicles, and many studies have been carried out in order to improve and optimize the energy recovery of the braking energy. In the paper, the optimization of regenerative braking by means of braking force modulation is analysed, with specific application to the case of cars converted into Through-the-road (TTR) hybrid vehicles, and an optimal modulation strategy is also proposed. Car hybridization is an emerging topic since it may be a feasible, low-cost, intermediate step toward the green transition of the transport system with a potential positive impact in third-world countries. In this case, the presence of two in-wheel-motors installed on the rear axle and of the original mechanical braking system mounted on the vehicle can result in limited braking energy recovery in the absence of proper braking management strategies. A vehicle longitudinal model has been integrated with an algorithm of non-linear constrained optimization to maximize the energy recovery for various starting speed and stopping time, also considering the efficiency map and power limitations of the electric components. In the best conditions, the recovery can reach about 40% of the vehicle energy, selecting the best deceleration at each speed and proper modulation, and with a realistic estimate of the grip coefficient.

Many researchers in industry and academia are showing an increasing interest in the definition of fuel surrogates for Computational Fluid Dynamics simulation applications. This need is mainly driven by the necessity of the engine research community to anticipate the effects of new gasoline formulations and combustion modes (e.g., Homogeneous Charge Compression Ignition, Spark Assisted Compression Ignition) to meet future emission regulations. Since those solutions strongly rely on the tailored mixture distribution, the simulation and accurate prediction of the mixture formation will be mandatory. Focusing purely on the definition of surrogates to emulate liquid phase and liquid-vapor equilibrium of gasolines, the following target properties are considered in this work: density, Reid vapor pressure, chemical macro-composition and volatility. A set of robust algorithms has been developed for the prediction of volatility and Reid vapor pressure. A Bayesian optimization algorithm based on a customized merit function has been developed to allow for the efficient definition of surrogate formulations from a palette of 15 pure compounds. The developed methodology has been applied on different real gasolines from literature in order to identify their optima surrogates. Furthermore, the ‘unicity’ of the surrogate composition is discussed by comparing the optimum solution with the most different one available in the pool of equivalent-valuable solutions. The proposed methodology has proven the potential to formulate surrogates characterized by an overall good agreement with the target properties of the experimental gasolines (max relative error below 10%, average relative error around 3%). In particular, the shape and the end-tails of the distillation curve are well captured. Furthermore, an accurate prediction of key chemical macro-components such as ethanol and aromatics and their influence on evaporative behavior is achieved. The study of the ‘unicity’ of the surrogate composition has revealed that (i) the unicity is strongly correlated with the accuracy and that (ii) both ‘unicity’ and accuracy of the prediction are very sensitive to the high presence of aromatics.

Unravelling the interaction of biological macromolecules with ligands and substrates at high spatial and temporal resolution remains a major challenge in structural biology. The development of serial crystallography methods at X-ray free-electron lasers and subsequently at synchrotron light sources allows new approaches to tackle this challenge. Here, a new polyimide tape drive designed for mix-and-diffuse serial crystallography experiments is reported. The structure of lysozyme bound by the competitive inhibitor chitotriose was determined using this device in combination with microfluidic mixers. The electron densities obtained from mixing times of 2 and 50 s show clear binding of chitotriose to the enzyme at a high level of detail. The success of this approach shows the potential for high-throughput drug screening and even structural enzymology on short timescales at bright synchrotron light sources.

The new European X-ray Free-Electron Laser (European XFEL) is the first X-ray free-electron laser capable of delivering intense X-ray pulses with a megahertz interpulse spacing in a wavelength range suitable for atomic resolution structure determination. An outstanding but crucial question is whether the use of a pulse repetition rate nearly four orders of magnitude higher than previously possible results in unwanted structural changes due to either radiation damage or systematic effects on data quality. Here, separate structures from the first and subsequent pulses in the European XFEL pulse train were determined, showing that there is essentially no difference between structures determined from different pulses under currently available operating conditions at the European XFEL.

This paper presents a computationally efficient novel heuristic approach for solving the combined heat and power economic dispatch (CHP-ED) problem in residential buildings considering component interconnections. The proposed solution is meant as a substitute for the cutting-edge approaches, such as model predictive control, where the problem is a mixed-integer nonlinear program (MINLP), known to be computationally-intensive, and therefore requiring specialized hardware and sophisticated solvers, not suited for residential use. The proposed heuristic algorithm targets simple embedded hardware with limited computation and memory and, taking as inputs the hourly thermal and electrical demand estimated from daily load profiles, computes a dispatch of the energy vectors including the CHP. The main idea of the heuristic is to have a procedure that initially decomposes the three energy vectors’ requests: electrical, thermal, and hot water. Then, the latter are later combined and dispatched considering interconnection and operational constraints. The proposed algorithm is illustrated using series of simulations on a residential pilot with a nano-cogenerator unit and shows around 25–30% energy savings when compared with a meta-heuristic genetic algorithm approach.

A mono-dimensional code for the simulation of the effects of High Frequency Ignition systems (HFI) on the production of chemical radicals was developed and here presented. The simulations were carried out by considering the typical environmental thermodynamic conditions of a nowadays engine at full load. An electron transport model is linked with a Boltzmann solver coupled with a chemistry solver, affecting the Electron Energy Distribution Function (EEDF) in order to obtain the physical conditions leading to the production of radical components for a given fuel mixture. The transport equations for the electrons, the positive and the negative ions, and the Gauss’ law in a steady-state plasma region. Then the Boltzmann equation for the electrons, in a spatially homogeneous steady-state case, is solved in order to obtain the EEDF. Finally the chemical kinetics model is employed assuming a fuel-air mixture neglecting the fuel carbon atoms due to the assumption that electron-impact dissociation reactions, which initiate the combustion, exhibit a greater reaction rate compared to those based on hydrocarbon thermal dissociation and therefore can be neglected in this work. Results show the production of the hydrogen (H), nitrogen (N), and oxygen (O) radicals and the radius of the initial discharge under different simulated engine operating conditions characterizing the role of a plasma corona effect for the induced chemical ignition in gasoline-powered engines.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

The use of electric power by wind generation in actual grids is hampered by its inherent stochastic nature and the penalty deviations adopted in several electricity regulation markets with respect to power quality requirements. Coupling wind farms with advanced Energy Storage Systems (ESS) can help their integration within grids. In this direction, several studies have been conducted, but the problem is still open due to the constraints and limitations regarding the ESSs time autonomy, time response, degradation issues and overall costs. In order to take into account these relevant aspects, advanced control algorithms are needed. In this paper, a Model-Based Predictive Controller (MPC) is presented. Such a controller minimizes the degradation of the ESS and the load tracking error while fulfilling the operational constraints and dynamics. The ESS considered is hydrogen-based and the study has been developed within the EU-FCH 2 JU (European Union Fuel Cells and Hydrogen 2 Joint Undertaking) funded project HAEOLUS aiming at building and integrating advanced control strategies for a hydrogen-based ESS within a wind farm fence. Numerical simulations show the feasibility and the effectiveness of the proposed approach.