jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Andreas Goldmann | Waldemar Sauter | Marcel Oettinger | Tim Kluge | Uwe Schröder | Joerg R. Seume | Jens Friedrichs | Friedrich Dinkelacker
Source: Energies (article, 2018-02-08)
Field: Materials Science — Materials Chemistry

A Study on Electrofuels in Aviation With the growth of aviation traffic and the demand for emission reduction, alternative fuels like the so-called electrofuels could comprise a sustainable solution. Electrofuels are understood as those that use renewable energy for fuel synthesis and that are carbon-neutral with respect to greenhouse gas emission. In this study, five potential electrofuels are discussed with respect to the potential application as aviation fuels, being n-octane, methanol, methane, hydrogen and ammonia, and compared to conventional Jet A-1 fuel. Three important aspects are illuminated. Firstly, the synthesis process of the electrofuel is described with its technological paths, its energy efficiency and the maturity or research need of the production. Secondly, the physico-chemical properties are compared with respect to specific energy, energy density, as well as those properties relevant to the combustion of the fuels, i.e., autoignition delay time, adiabatic flame temperature, laminar flame speed and extinction strain rate. Results show that the physical and combustion properties significantly differ from jet fuel, except for n-octane. The results describe how the different electrofuels perform with respect to important aspects such as fuel and air mass flow rates. In addition, the results help determine mixture properties of the exhaust gas for each electrofuel. Thirdly, a turbine configuration is investigated at a constant operating point to further analyze the drop-in potential of electrofuels in aircraft engines. It is found that electrofuels can generally substitute conventional kerosene-based fuels, but have some downsides in the form of higher structural loads and potentially lower efficiencies. Finally, a preliminary comparative evaluation matrix is developed. It contains specifically those fields for the different proposed electrofuels where special challenges and problematic points are seen that need more research for potential application. Synthetically-produced n-octane is seen as a potential candidate for a future electrofuel where even a drop-in capability is given. For the other fuels, more issues need further research to allow the application as electrofuels in aviation. Specifically interesting could be the combination of hydrogen with ammonia in the far future; however, the research is just at the beginning stage. Combustion ; Jet fuel ; Diesel fuel ; Gasoline ; Kerosene ; Environmental science ; Octane rating ; Renewable energy ; Autoignition temperature ; Hydrogen fuel ; Nuclear engineering ; Process engineering ; Waste management ; Chemistry ; Thermodynamics ; Hydrogen ; Engineering ; Physics Catalytic Processes in Materials Science ; Advanced Combustion Engine Technologies ; Advanced Aircraft Design and Technologies
Authors: Corey J. Wilson | Andreas S. Bommarius | Julie A. Champion | Yury O. Chernoff | David G. Lynn | Anant K. Paravastu | Liang Chen | Ming-Chien Hsieh | Jennifer M. Heemstra
Source: Chemical Reviews (review, 2018-10-03)
Field: Materials Science — Biomaterials

Biomolecular Assemblies: Moving from Observation to Predictive Design Biomolecular assembly is a key driving force in nearly all life processes, providing structure, information storage, and communication within cells and at the whole organism level. These assembly processes rely on precise interactions between functional groups on nucleic acids, proteins, carbohydrates, and small molecules, and can be fine-tuned to span a range of time, length, and complexity scales. Recognizing the power of these motifs, researchers have sought to emulate and engineer biomolecular assemblies in the laboratory, with goals ranging from modulating cellular function to the creation of new polymeric materials. In most cases, engineering efforts are inspired or informed by understanding the structure and properties of naturally occurring assemblies, which has in turn fueled the development of predictive models that enable computational design of novel assemblies. This Review will focus on selected examples of protein assemblies, highlighting the story arc from initial discovery of an assembly, through initial engineering attempts, toward the ultimate goal of predictive design. The aim of this Review is to highlight areas where significant progress has been made, as well as to outline remaining challenges, as solving these challenges will be the key that unlocks the full power of biomolecules for advances in technology and medicine. Nanotechnology ; Function (biology) ; Biochemical engineering ; Key (lock) ; Computer science ; Chemistry ; Data science ; Engineering Supramolecular Self-Assembly in Materials ; Bacteriophages and microbial interactions ; RNA Research and Splicing
Authors: Sang Seok Lee | Jong Bin Kim | Yun Ho Kim | Shin‐Hyun Kim
Source: Science Advances (article, 2018-06-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Wavelength-tunable and shape-reconfigurable photonic capsule resonators containing cholesteric liquid crystals Cholesteric liquid crystals (CLCs) have a photonic bandgap due to the periodic change of refractive index along their helical axes. The CLCs containing optical gain have served as band-edge lasing resonators. In particular, CLCs in a granular format provide omnidirectional lasing, which are promising as a point light source. However, there is no platform that simultaneously achieves high stability in air and wavelength tunability. We encapsulate CLCs with double shells to design a capsule-type laser resonator. The fluidic CLCs are fully enclosed by an aqueous inner shell that promotes the planar alignment of LC molecules along the interface. The outer shell made of silicone elastomer protects the CLC core and the inner shell from the surroundings. Therefore, the helical axes of the CLCs are radially oriented within the capsules, which provide a stable omnidirectional lasing in the air. At the same time, the fluidic CLCs enable the fine-tuning of lasing wavelength with temperature. The capsules retain their double-shell structure during the dynamic deformation. Therefore, the CLCs in the core maintain the planar alignment along the deformed interface, and a lasing direction can be varied from omnidirectional to bi- or multidirectional, depending on the shape of deformed capsules. Resonator ; Materials science ; Wavelength ; Capsule ; Photonic crystal ; Optoelectronics ; Photonics ; Optics ; Cholesteric liquid crystal ; Liquid crystal ; Physics Liquid Crystal Research Advancements ; Photonic Crystals and Applications ; Photonic and Optical Devices
Authors: Martin Koller | Gerhart Braunegg
Source: The EuroBiotech Journal (article, 2018-04-01)
Field: Materials Science — Biomaterials

Advanced approaches to produce polyhydroxyalkanoate (PHA) biopolyesters in a sustainable and economic fashion Abstract Polyhydroxyalkanoates (PHA), the only group of “bioplastics” sensu stricto , are accumulated by various prokaryotes as intracellular “carbonosomes”. When exposed to exogenous stress or starvation, presence of these microbial polyoxoesters of hydroxyalkanoates assists microbes to survive. “Bioplastics” such as PHA must be competitive with petrochemically manufactured plastics both in terms of material quality and manufacturing economics. Cost-effectiveness calculations clearly show that PHA production costs, in addition to bioreactor equipment and downstream technology, are mainly due to raw material costs. The reason for this is PHA production on an industrial scale currently relying on expensive, nutritionally relevant “1 st -generation feedstocks”, such as like glucose, starch or edible oils. As a way out, carbon-rich industrial waste streams (“2 nd -generation feedstocks”) can be used that are not in competition with the supply of food; this strategy not only reduces PHA production costs, but can also make a significant contribution to safeguarding food supplies in various disadvantaged parts of the world. This approach increases the economics of PHA production, improves the sustainability of the entire lifecycle of these materials, and makes them unassailable from an ethical perspective. In this context, our EU-funded projects ANIMPOL and WHEYPOL, carried out by collaborative consortia of academic and industrial partners, successfully developed PHA production processes, which resort to waste streams amply available in Europe. As real 2 nd -generation feedstocks”, waste lipids and crude glycerol from animal-processing and biodiesel industry, and surplus whey from dairy and cheese making industry were used in these processes. Cost estimations made by our project partners determine PHA production prices below 3 € (WHEYPOL) and even less than 2 € (ANIMPOL), respectively, per kg; these values already reach the benchmark of economic feasibility. The presented studies clearly show that the use of selected high-carbon waste streams of (agro)industrial origin contributes significantly to the cost-effectiveness and sustainability of PHA biopolyester production on an industrial scale. Bioplastic ; Polyhydroxyalkanoates ; Context (archaeology) ; Raw material ; Supply chain ; Business ; Biorefinery ; Production (economics) ; Pulp and paper industry ; Biotechnology ; Waste management ; Commerce ; Economics ; Chemistry ; Engineering ; Biology biodegradable polymer synthesis and properties ; Microplastics and Plastic Pollution ; Recycling and Waste Management Techniques
Authors: Anna Tomberg | Magnus J. Johansson | Per‐Ola Norrby
Source: The Journal of Organic Chemistry (article, 2018-10-18)
Field: Chemistry — Organic Chemistry

A Predictive Tool for Electrophilic Aromatic Substitutions Using Machine Learning At the early stages of the drug development process, thousands of compounds are synthesized in order to attain the best possible potency and pharmacokinetic properties. Once successful scaffolds are identified, large libraries of analogues are made, which is a challenging and time-consuming task. Recently, late stage functionalization (LSF) has become increasingly prominent since these reactions selectively functionalize C-H bonds, allowing to quickly produce analogues. Classical electrophilic aromatic halogenations are a powerful type of reaction in the LSF toolkit. However, the introduction of an electrophile in a regioselective manner on a drug-like molecule is a challenging task. Herein we present a machine learning model able to predict the reactive site of an electrophilic aromatic substitution with an accuracy of 93% (internal validation set). The model takes as input a SMILES of a compound and uses six quantum mechanics descriptors to identify its reactive site(s). On an external validation set, 90% of all molecules were correctly predicted. Electrophile ; Chemistry ; Regioselectivity ; Electrophilic aromatic substitution ; Electrophilic substitution ; Drug discovery ; Combinatorial chemistry ; Chemical space ; Set (abstract data type) ; Computational chemistry ; Computer science ; Organic chemistry ; Programming language Catalytic C–H Functionalization Methods ; Synthesis and Catalytic Reactions ; Asymmetric Hydrogenation and Catalysis
Authors: Iago González-Fernández | Manuel Á. Iglesias-Otero | Mahnaz Esteki | O. Moldes | Juan C. Mejuto | Jesús Simal‐Gándara
Source: Critical Reviews in Food Science and Nutrition (review, 2018-01-30)
Field: Chemistry — Organic Chemistry

A critical review on the use of artificial neural networks in olive oil production, characterization and authentication Artificial neural networks (ANN) are computationally based mathematical tools inspired by the fundamental cell of the nervous system, the neuron. ANN constitute a simplified artificial replica of the human brain consisting of parallel processing neural elements similar to neurons in living beings. ANN is able to store large amounts of experimental information to be used for generalization with the aid of an appropriate prediction model. ANN has proved useful for a variety of biological, medical, economic and meteorological purposes, and in agro-food science and technology. The olive oil industry has a substantial weight in Mediterranean's economy. The different steps of the olive oil production process, which include olive tree and fruit care, fruit harvest, mechanical and chemical processing, and oil packaging have been examined in depth with a view to their optimization, and so have the authenticity, sensory properties and other quality-related properties of olive oil. This paper reviews existing literature on the use of bioinformatics predictive methods based on ANN in connection with the production, processing and characterization of olive oil. It examines the state of the art in bioinformatics tools for optimizing or predicting its quality with a view to identifying potential deficiencies or aspects for improvement. Artificial neural network ; Olive oil ; Computer science ; Production (economics) ; Artificial intelligence ; Biochemical engineering ; Quality (philosophy) ; Process (computing) ; Authentication (law) ; Characterization (materials science) ; Biotechnology ; Machine learning ; Engineering ; Food science ; Biology ; Nanotechnology Edible Oils Quality and Analysis ; Spectroscopy and Chemometric Analyses ; Meat and Animal Product Quality
Authors: Anna I. Krylov | Theresa L. Windus | Taylor Barnes | Eliseo Marin‐Rimoldi | Jessica A. Nash | Benjamin P. Pritchard | Daniel G. A. Smith | Doaa Altarawy | Paul Saxe | Cecilia Clementi | T. Daniel Crawford | Robert J. Harrison | Shantenu Jha | Vijay S. Pande | Teresa Head‐Gordon
Source: The Journal of Chemical Physics (article, 2018-11-08)
Field: Materials Science — Materials Chemistry

Perspective: Computational chemistry software and its advancement as illustrated through three grand challenge cases for molecular science The field of computational molecular sciences (CMSs) has made innumerable contributions to the understanding of the molecular phenomena that underlie and control chemical processes, which is manifested in a large number of community software projects and codes. The CMS community is now poised to take the next transformative steps of better training in modern software design and engineering methods and tools, increasing interoperability through more systematic adoption of agreed upon standards and accepted best-practices, overcoming unnecessary redundancy in software effort along with greater reproducibility, and increasing the deployment of new software onto hardware platforms from in-house clusters to mid-range computing systems through to modern supercomputers. This in turn will have future impact on the software that will be created to address grand challenge science that we illustrate here: the formulation of diverse catalysts, descriptions of long-range charge and excitation transfer, and development of structural ensembles for intrinsically disordered proteins. Software deployment ; Software ; Interoperability ; Computer science ; Transformative learning ; Software engineering ; Data science ; Nanotechnology ; Engineering ethics ; Engineering ; World Wide Web ; Sociology ; Materials science Machine Learning in Materials Science ; Protein Structure and Dynamics ; Scientific Computing and Data Management
Authors: Ganna Gryn’ova | Kun‐Han Lin | Clémence Corminbœuf
Source: Journal of the American Chemical Society (article, 2018-11-05)
Field: Materials Science — Materials Chemistry

Read between the Molecules: Computational Insights into Organic Semiconductors The performance and key electronic properties of molecular organic semiconductors are dictated by the interplay between the chemistry of the molecular core and the intermolecular factors of which manipulation has inspired both experimentalists and theorists. This Perspective presents major computational challenges and modern methodological strategies to advance the field. The discussion ranges from insights and design principles at the quantum chemical level, in-depth atomistic modeling based on multiscale protocols, morphological prediction and characterization as well as energy-property maps involving data-driven analysis. A personal overview of the past achievements and future direction is also provided. Chemistry ; Characterization (materials science) ; Key (lock) ; Field (mathematics) ; Perspective (graphical) ; Nanotechnology ; Intermolecular force ; Property (philosophy) ; Data science ; Management science ; Molecule ; Computer science ; Epistemology ; Artificial intelligence ; Organic chemistry ; Materials science Machine Learning in Materials Science ; Crystallography and molecular interactions ; Computational Drug Discovery Methods
Authors: Xian Tang | Hong Chen | Joice Sophia Ponraj | Sathish Chander Dhanabalan | Quanlan Xiao | Dianyuan Fan | Han Zhang
Source: Advanced Science (article, 2018-06-13)
Field: Materials Science — Materials Chemistry

Fluorination‐Enhanced Ambient Stability and Electronic Tolerance of Black Phosphorus Quantum Dots The environmental instability and uneliminable electronic trap states in black phosphorus quantum dots (BPQDs) limit the optoelectronics and related applications of BPQDs. Here, fluorinated BPQDs (F-BPQDs) are successfully synthesized by using a facile electrochemical exfoliation and synchronous fluorination method. The F-BPQDs exhibit robust ambient stability and limited fluorination capability, showing a nonstoichiometric fluorination degree (<i>D</i><sub>F</sub>) maximum of ≈0.68. Density functional theory calculations confirm that due to the edge etching effect of fluorine adatoms, the simulated F-BPQDs become structurally unstable when <i>D</i><sub>F</sub> surpasses the limit. Furthermore, the trap states of BPQDs can be effectively eliminated via fluorination to obtain a coordination number of 3 or 5 for fluorinated and unfluorinated phosphorus atoms. The results reveal that the air-stable F-BPQDs exhibit fluorine defect-enhanced electronic tolerance, which is crucial for nanophotonics and nanoelectronics applications. Quantum dot ; Exfoliation joint ; Black phosphorus ; Materials science ; Fluorine ; Density functional theory ; Etching (microfabrication) ; Nanotechnology ; Degradation (telecommunications) ; Chemical engineering ; Chemical physics ; Optoelectronics ; Computational chemistry ; Chemistry ; Metallurgy ; Layer (electronics) ; Electronic engineering ; Graphene 2D Materials and Applications ; Perovskite Materials and Applications ; Quantum Dots Synthesis And Properties
Authors: Henri Salmenjoki | Mikko J. Alava | Lasse Laurson
Source: Nature Communications (article, 2018-12-07)
Field: Materials Science — Materials Chemistry

Machine learning plastic deformation of crystals Plastic deformation of micron-scale crystalline solids exhibits stress-strain curves with significant sample-to-sample variations. It is a pertinent question if this variability is purely random or to some extent predictable. Here we show, by employing machine learning techniques such as regression neural networks and support vector machines that deformation predictability evolves with strain and crystal size. Using data from discrete dislocations dynamics simulations, the machine learning models are trained to infer the mapping from features of the pre-existing dislocation configuration to the stress-strain curves. The predictability vs strain relation is non-monotonic and exhibits a system size effect: larger systems are more predictable. Stochastic deformation avalanches give rise to fundamental limits of deformation predictability for intermediate strains. However, the large-strain deformation dynamics of the samples can be predicted surprisingly well. Predictability ; Deformation (meteorology) ; Dislocation ; Materials science ; Stress (linguistics) ; Statistical physics ; Computer science ; Artificial intelligence ; Strain (injury) ; Monotonic function ; Mathematics ; Physics ; Composite material ; Mathematical analysis ; Statistics Machine Learning in Materials Science ; Microstructure and mechanical properties ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Ankita Mangal | Elizabeth A. Holm
Source: International Journal of Plasticity (article, 2018-07-25)
Field: Materials Science — Materials Chemistry

Applied machine learning to predict stress hotspots I: Face centered cubic materials Equiaxed crystals ; Materials science ; Cubic crystal system ; Microstructure ; Artificial intelligence ; Crystallite ; Deformation (meteorology) ; Stress (linguistics) ; Machine learning ; Composite material ; Metallurgy ; Computer science ; Crystallography Microstructure and mechanical properties ; Machine Learning in Materials Science ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Lisa R. Savagian | Anna M. Österholm | Dwanleen Eric Shen | Dylan T. Christiansen | Michael Kuepfert | John R. Reynolds
Source: Advanced Optical Materials (article, 2018-07-01)
Field: Materials Science — Polymers and Plastics

Conjugated Polymer Blends for High Contrast Black‐to‐Transmissive Electrochromism Abstract Next‐generation electrochromic technologies, such as dimmable fenestration, eyewear integrated displays, and optical shutters require materials that reversibly transition between highly transmissive and broadly absorbing achromatic states, often with minimal intermediate coloration. In this work, it is shown how the properties of dioxythiophene‐based electrochromic polymers (ECPs) can be leveraged through straightforward color mixing to yield high‐contrast, black‐to‐transmissive materials with low driving voltages (&lt;1 V), extended functional lifetimes, and minimal transient chromaticity. Drawing from a family of five soluble colored‐to‐colorless switching polymers (including ECP‐cyan, ‐green, ‐magenta, ‐yellow, and a newly reported ECP‐orange), three unique cathodically coloring polymer blends are reported, with achromatic neutral states ( C* ab &lt; 5) and integrated optical contrast exceeding 40% across the entire visible spectrum (380–780 nm). By incorporating different high‐gap and dual‐band absorbing ECPs, subtle control is demonstrated over both the extreme and intermediate coloration of the blends. This work showcases how solution coprocessing of ECPs can be used to access highly targeted achromatic hues while also enabling fine‐tuned neutral optical transition between black and transmissive states. Electrochromism ; Materials science ; Achromatic lens ; Cyan ; Magenta ; Optoelectronics ; Polymer ; Optics ; Chromaticity ; Contrast ratio ; Femtosecond ; Visible spectrum ; Electrochromic devices ; Composite material ; Electrode ; Inkwell Conducting polymers and applications ; Photochromic and Fluorescence Chemistry ; Polydiacetylene-based materials and applications