Significant Experimental Effort Research Articles

Investigating the Primordial Universe through nuclear physics

Big Bang Nucleosynthesis (BBN) requires several nuclear physics inputs and nuclear reaction rates. An up-to-date compilation of direct cross sections of is given, being these ones among the most uncertain bare-nucleus cross sections. A particular attention is devoted to recently indirectly measured cross-section which give important hints for the nuclear astrophysics community.In reality, a significant experimental effort has been made over the past 10 years to explore reactions important to the BBN and determine their astrophysical S(E)-factor using the Trojan Horse Method (THM). Then, numerical calculations are made in the relevant temperature ranges for BBN (0.01<T9 <10) to determine the reaction rates and the relative error for the four reactions of interest. The effects of these values on the calculated primordial abundances and isotopical composition for H, He, and Li were then assessed by using them as input physics for computations of primordial nucleosynthesis. Additionally, recent findings regarding the 7Be(n,alpha)4He reaction rate were taken into consideration. These were put up against estimates of primordial abundance derived from observation at various astrophysical places. Additionally, perspectives on reactions will be examined.

Droop model identification via model-based design of experiments to describe microalgae nitrogen uptake in continuous photobioreactors

The comprehension of nutrients uptake and exploitation dynamics is a key aspect to increase the efficiency and the environmental sustainability of microalgal production at industrial scale. The Droop model is suitable for the description of the effect of nutrients on microalgal growth, but presents challenges in parameter identification due to the high correlation between parameters and the significant experimental effort required.Model-based design of experiments is employed to plan information-rich experiments for precise parameters estimation, based on dynamic variations of dilution rate and nitrogen concentration in a continuous photobioreactor growing microalga Tetradesmus obliquus. It is shown that only two optimal experiments of 8 days each are sufficient to attain a statistically satisfactory estimation of all model parameters, thus minimising time and resources. Validation experiments show that the model can capture nitrogen uptake dynamics effectively, and demonstrate that the design of optimal dynamic experiments allows calibrating the Droop model rapidly, making it a valuable tool for the study of nutrients dynamics in continuous microalgal cultivation systems.

Learning to Read and Write in the Language of Proteins

Learning to Read and Write in the Language of Proteins

Fast nonlinear mechanical features decoupling to identify and predict asphalt-based composites

Nonlinear matrices employed in asphalt-based composites exhibit a prominent nonlinear elastic-viscoplastic-viscodamageable mechanical response. The constitutive model for this type of matrices requires a significant experimental effort to identify the material constants. To alleviate the characterization effort without losing reliability in the mechanical prediction, this paper presents a fast procedure decoupling the elasto-viscoplastic response from the viscodamageable one and facilitating the identification of the material constants via efficient optimization. This procedure relies on a combined experimental–numerical also applicable to other rheologically complex materials like polyurea, thermoplastics or elasto-viscoplastic polycrystals. The experimental part is deliberately designed to only conduct cost- and time-effective monotonic loads at different strain rates in tension and compression. These pure monotonic results are used in the constitutive model to predict more complex loading conditions such as multiple load–unload–reload (LUR) cycles. To do this, an elasto-viscoplastic constitutive model suitable to describe creep-like phenomena and large irreversible deformations is proposed. This model incorporates pressure and strain rate sensitivity, which is essential to capture the compression–tension asymmetry in asphalt-based composites. A rate-dependent damage model is proposed to describe the degradation rate of the elasto-viscoplastic part. The stress and the consistent tangent modulus are derived. The model implementation for user-defined finite element subroutines are given for explicit or implicit solvers. The proposed decoupling facilitates an efficient identification of the constant parameters via an in-house Nelder–Mead-based optimization method. To accelerate the identification, all the stress–strain curves, either in tension or compression, are simultaneously used with applying physically-based constrains. The framework is validated with asphalt matrix at room temperature (24 °C) and verified under LUR conditions at different rates. The identified model predicts correctly the experimental observations, proving its applicability to investigate particle-based composites with highly nonlinear matrices.

Intramembrane serine protease catalysis in different physical environments

The cleavage of proteins inside cell membranes initiates pathological pathways and is a subject of a major interest. Thus, the nature of the coupling between the physical environment and the function of such proteins has recently attracted significant experimental and theoretical effort. However, it is difficult to determine the nature of this coupling in a unique way by experimental and theoretical studies without separating the chemical and the environmental factors. This work presents calculations of the activation barriers of the intramembrane rhomboid protease in neutral and charged lipid bilayers as well as in detergent micelle, trying to explore the environmental effect.

Computer-assisted catalyst development via automated modelling of conformationally complex molecules: application to diphosphinoamine ligands

Simulation of conformationally complicated molecules requires multiple levels of theory to obtain accurate thermodynamics, requiring significant researcher time to implement. We automate this workflow using all open-source code (XTBDFT) and apply it toward a practical challenge: diphosphinoamine (PNP) ligands used for ethylene tetramerization catalysis may isomerize (with deleterious effects) to iminobisphosphines (PPNs), and a computational method to evaluate PNP ligand candidates would save significant experimental effort. We use XTBDFT to calculate the thermodynamic stability of a wide range of conformationally complex PNP ligands against isomeriation to PPN (ΔGPPN), and establish a strong correlation between ΔGPPN and catalyst performance. Finally, we apply our method to screen novel PNP candidates, saving significant time by ruling out candidates with non-trivial synthetic routes and poor expected catalytic performance.

Center of mass velocity from mass polariton solution to Abraham-Minkowski controversy

A one-hundred-year old controversy, the Abraham-Minkowski controversy, concerns two widely disparate predictions for the momentum of light in glass. In the Abraham case the photon momentum is inversely proportional to the index of refraction while the photon momentum predicted by Minkowski formulation is proportional to the index of refraction. In spite of significant theoretical and experimental effort neither formulation could convincingly be shown to be correct. A new model, called the Mass Polariton model, MP, was recently introduced that resolves the contradictions in the Abraham and Minkowski formulations and experimental results. Both the MP and Minkowski model predict the same momentum change for a photon entering glass from a vacuum, yet the Minkowski model allows violation of the center of mass theorem. This paper shows that the MP model, in spite of predicting the same momentum change as the Minkowski model, obeys the center of mass theorem.

High Cycle Fatigue Data Transferability of MAR-M 247 Superalloy from Separately Cast Specimens to Real Gas Turbine Blade

Cast polycrystalline superalloys are widely used for critical components in aerospace and automotive industries, such as turbine blades or turbocharges. Therefore, their fatigue endurance belongs to one of the most essential mechanical characteristics. Full-scale testing of such components involves great technical difficulties and requires significant experimental effort. The present study evaluates the effects of microstructural parameters with respect to representative fatigue testing of a cast turbine blade by separately cast specimens. For that purpose, the cast polycrystalline MAR-M 247 Ni-based superalloy was investigated in the following conditions: (i) specimens extracted from a real gas turbine blade; specimens separately cast into the mould with (ii) top or (iii) bottom filling systems. Obtained diverse microstructures allowed us to assess the effect of grain size, porosity, and texture on fatigue performance. The tests were held at a symmetrical loading regime at temperature 800 °C in laboratory air. The results indicate that the level of porosity is a dominant structural parameter determining the fatigue endurance, while grain size and texture effects were of minor importance contributing mainly to fatigue life scatter.

Thermal desorption of cryopumped gases from laser treated copper

Recently, laser processing of copper samples has been demonstrated to produce rough surfaces whose nanostructuring ensures unquestionable advantages for electron cloud mitigation in future particle accelerators. The actual application of laser treated surfaces in accelerators implies that this new material is compliant with many issues, going from impedance vacuum properties to many others. A significant experimental effort is therefore ongoing to study and optimize their various properties of interest. Here we analyze their vacuum behavior versus temperature. To this end, we studied thermal programmed desorption from CO, ${\mathrm{CH}}_{4}$ and ${\mathrm{H}}_{2}$ once cryosorbed on laser treated copper substrate and on its flat counterpart. These molecules are typically present in the residual vacuum of any accelerator. The results show that the desorption of such gases from the laser treated substrates occurs in a much broader and higher temperature range with respect to what is observed from the flat substrate. We also show that, at equal doses, treated samples adsorb/desorb significantly more gas than their flat counterpart. These findings can be ascribed to their nanostructured porous morphology. A quantitative analysis is given, allowing to properly estimate fluctuations of the number of molecules during unavoidable temperature variations of the cryogenic vacuum system.

A machine learning approach to estimation of phase diagrams for three-component lipid mixtures

The plasma membrane of eukaryotic cells is commonly believed to contain ordered lipid domains. The interest in understanding the origin of such domains has led to extensive studies on the phase behavior of mixed lipid systems. Three-component phase diagrams, composed of a high melting temperature (Tm) lipid, cholesterol, and a low Tm lipid have been valuable in studying lipid phase behavior. However, developing phase diagrams over the entire composition space and with precise tie-lines requires significant experimental effort. In this study, a machine learning approach was used to predict the Tm of lipids and generate phase diagrams from lipid mixtures. First, artificial neural network (ANN) was used for the prediction of Tm. The network was trained using available Tm data and was able to generate Tm values that closely matched literature results for its testing dataset. This model was then used to predict the Tm for lipids that have not yet been experimentally tested. Then, random forests (RF) and support vector machines (SVM) were trained and tested for their ability to predict a test three-component phase diagram. The model from the RF algorithm was able to generate a diagram that closely matched published results. This model was then used to generate phase diagrams for lipid mixtures at various temperatures and various degrees of unsaturation. This machine learning approach to the generation of lipid phase diagrams has the potential to save significant time and resources in studies of lipid phase behavior.

Reaction: Molecular Spins as Qubits

Floriana Tuna is a Leverhulme Trust Senior Research Fellow at the University of Manchester (UoM). She holds MSc and PhD degrees in chemistry from the University of Bucharest (1989) and National Academy of Romania (1997), respectively. She worked as a visiting DAAD fellow at the University of Heidelberg (Germany), a CNRS postdoctoral fellow at ICMCB Bordeaux (France), and a Marie Curie fellow at the University of Warwick (UK) before joining UoM in 2003. She received the Romanian Academy “Ilie Murgulescu” Prize for excellence in research in 2005 and has published over 300 high-impact papers. Her major interests are in coordination chemistry, molecular magnetism, and quantum information science.

Molecular Dynamics in the Energy Sector: Experiment and Modeling of the CO2/CH4 Mixture

Knowledge of the thermodynamic properties of fluids and their mixtures is fundamental to the design of industrial processes. In the field of tertiary oil and gas production, significant experimental effort to identify properties of compressed gas mixtures has been made worldwide. Often expensive and dangerous high-pressure laboratory methods are used. Consequently, it is important to have a reliable simulation approach that allows exploratory and predictive modeling. In this work, experimental surface tension of a CO2 and CH4 mixture at elevated pressures is presented and discussed. The surface tension is an important quantity in several processes, (e.g., for describing multiphase flow). Molecular dynamics (MD) simulations are utilized to calculate the thermodynamic properties (i.e., surface tension, density, molar fractions, phase diagrams) under process-relevant conditions. MD has the potential to predict the values of these properties and provide insight into the principal behavior of the whole system....

Present and Future of Surface-Enhanced Raman Scattering.

The discovery of the enhancement of Raman scattering by molecules adsorbed on nanostructured metal surfaces is a landmark in the history of spectroscopic and analytical techniques. Significant experimental and theoretical effort has been directed toward understanding the surface-enhanced Raman scattering (SERS) effect and demonstrating its potential in various types of ultrasensitive sensing applications in a wide variety of fields. In the 45 years since its discovery, SERS has blossomed into a rich area of research and technology, but additional efforts are still needed before it can be routinely used analytically and in commercial products. In this Review, prominent authors from around the world joined together to summarize the state of the art in understanding and using SERS and to predict what can be expected in the near future in terms of research, applications, and technological development. This Review is dedicated to SERS pioneer and our coauthor, the late Prof. Richard Van Duyne, whom we lost during the preparation of this article.

Existence of internal domains in (Bi0.5La0.5FeO3)(1-x)–(Ba0.7Sr0.3TiO3)x solid solution and their effects on magnetic and dielectric behaviour

A significant experimental effort has been undertaken to evaluate the doping effect of La3+at Bi-site in solid solutions of BiFeO3 (BFO) and Ba0.7Sr0.3TiO3 (BST) through various techniques like x-ray diffraction, magnetic measurements, and frequency and/or temperature dependent impedance spectroscopy. The magnetic isotherm at room temperature in La doped BFO (BLFO)-BST solid solution indicates weak ferromagnetism due to improvement in the homogeneous canted spin structure. The elusive nano-sized domain structure inside the grains of 40% BST co-doped BLFO was observed by a scanning electron microscopy technique. The domains and domain boundaries are generally formed inside the grain due to strained/unstrained lattice defects and/or mismatch with the substitution of ions of different sizes at the A and B sites. An equivalent circuit model (internal barrier layer capacitor), based on domain, domain boundary, and grain boundary relaxations, has been established to justify the enhancement of the dielectric response with BST concentrations.

Quaternary isothermal vapor-liquid equilibrium of the model biofuel 2-butanone + n-heptane + tetrahydrofuran + cyclohexane using Raman spectroscopic characterization

Vapor-liquid equilibrium data of fuel mixtures are of major importance for both fuel production as well as combustion. However, the measurement of vapor-liquid equilibrium data usually requires significant experimental effort. The experimental effort is particularly high if multicomponent mixtures are of interest, as experimental effort rises strongly with a rising number of components. In this work, we efficiently characterize the vapor-liquid equilibrium of a quaternary model biofuel and its binary subsystems. For this purpose, we employ the recently developed milliliter-scale Raman Spectroscopic Phase Equilibrium Characterization (RAMSPEQU)-setup. Vapor pressures are collected at T = 283–333 K, and isothermal pTx-data for mixtures at T = 303.2 K resulting in a pressure range of p = 2.8–81.9 kPa. The PCP-SAFT equation of state is used for thermodynamic modeling. Our binary data agrees well with experimental data from literature. The quaternary phase behavior is predicted with very good accuracy using PCP-SAFT with parameters adjusted to pure substance and binary mixture data only. The milliliter-scale setup allows us to characterize the phase equilibria with just 22 ml (binary) and less than 105 ml (quaternary) of the respective mixtures. The agreement of predicted and experimental quaternary phase equilibrium data indicate the reliability of the employed method for multicomponent vapor-liquid equilibrium measurements.

Modeling of Bulk Kerogen Porosity: Methods for Control and Characterization

Shale gas is an unconventional source of energy, which has attracted a lot of attention during the last years. Kerogen is a prime constituent of shale formations and plays a crucial role in shale gas technology. Significant experimental effort in the study of shales and kerogen has produced a broad diversity of experimentally determined structural and thermodynamic properties even for samples of the same well. Moreover, proposed methods reported in the literature for constructing realistic bulk kerogen configurations have not been thoroughly investigated. One of the most important characteristics of kerogens is their porosity, due to its direct connection with their transport properties and its potential as discriminating and classifying metric between samples. In this study, molecular dynamics (MD) simulations are used to study the porosity of model kerogens. The porosity is controlled effectively with systematic variations of the number and the size of dummy LJ particles that are used during the constru...

A milliliter-scale setup for the efficient characterization of isothermal vapor-liquid equilibria using Raman spectroscopy

Experimental vapor-liquid equilibrium data are of major importance for the chemical industry. However, the measurement of vapor-liquid equilibrium data still requires significant experimental effort. Therefore, we present a novel setup and measurement procedure for the rapid determination of isothermal vapor-liquid equilibria using only milliliter samples. The compositions of both liquid and vapor phases are analyzed using Raman spectroscopy. The measurement setup is successfully validated by reproducing vapor pressures of four pure substances and binary vapor-liquid equilibrium data of methyl tert-butyl ether (MTBE) and 2,2,4-trimethylpentane (iso-octane) at T = 318.1 K as test system from literature. The vapor pressures of MTBE, ethanol, iso-octane and toluene agree with literature data within the measurement uncertainties. The measured binary vapor-liquid equilibrium data are modeled using the PCP-SAFT equation of state. Phase equilibrium data calculated from PCP-SAFT are compared to experimental data from literature. The data deviate by less than 1% in terms of pressure and vapor phase composition for given temperature and liquid phase composition demonstrating the reliability of the presented setup for vapor-liquid equilibrium measurements.

Material informatics driven design and experimental validation of lead titanate as an aqueous solar photocathode

Materials informatics is a rapidly emerging data- and knowledge-driven approach for the identification of novel materials for a range of applications, including solar energy conversion. Despite significant experimental effort, the development of highly efficient, stable, and cost-effective photovoltaic materials remains a challenging scientific problem. The quest for precisely defined semiconductor properties revolves around an immensely broad landscape of structural parameters. Here, we have resolved this challenge by applying material informatics to design a novel photocathode material for dye-sensitized solar cells (DSSCs). By conducting a virtual screening of 50,000 known inorganic compounds, we have identified lead titanate (PbTiO3), a perovskite, as the most promising photocathode material. Notably, lead titanate is significantly different from the traditional base elements or crystal structures used for photocathodes. The fabricated PbTiO3 DSSC devices exhibited the best performance in aqueous solution, showing remarkably high fill factors compared to typical photocathode systems. The results highlight the pivotal role materials informatics can play in streamlining the experimental development of materials with the desired properties.

Reconstruction of air shower muon densities using segmented counters with time resolution

Despite the significant experimental effort made in the last decades, the origin of the ultra-high energy cosmic rays is still largely unknown. Key astrophysical information to identify where these energetic particles come from is provided by their chemical composition. It is well known that a very sensitive tracer of the primary particle type is the muon content of the showers generated by the interaction of the cosmic rays with air molecules. We introduce a likelihood function to reconstruct particle densities using segmented detectors with time resolution. As an example of this general method, we fit the muon distribution at ground level using an array of counters like AMIGA, one of the Pierre Auger Observatory detectors. For this particular case we compare the reconstruction performance against a previous method. With the new technique, more events can be reconstructed than before. In addition the statistical uncertainty of the measured number of muons is reduced, allowing for a better discrimination of the cosmic ray primary mass.

Quantification of chloride in concrete samples using LA-ICP-MS

Inspection of the chloride content in concrete samples is an important factor for determination and planning of maintenance measures. The standard method given by European legislative for chloride determination is the titrimetric Volhard method. However, it requires significant experimental effort, and knowledge about the cement content in the concrete is required. In this work, laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) is proposed as an alternative method to the commonly used wet-chemical approach. LA-ICP-MS allows analysis of drilled core concrete samples with increased depth resolution, minimal workload for sample preparation, and fast analysis time. Furthermore, exploiting the multi-elemental capabilities of ICP-MS, an in-depth analysis of the sample composition, allowing a differentiation between cement phase and aggregates is feasible. Using this information, selective determination of chloride in the cement phase becomes possible, which helps to increase the reliability of the determined chloride concentrations.