jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Piotr Cysewski | Tomasz Jeliński | Dominika Procek | Aleksandra Dratwa
Source: Fluid Phase Equilibria (article, 2020-10-27)
Field: Materials Science — Materials Chemistry

Solubility of Sulfanilamide and Sulfacetamide in neat solvents: Measurements and interpretation using theoretical predictive models, first principle approach and artificial neural networks Non-random two-liquid model ; Akaike information criterion ; Chemistry ; Extrapolation ; Solubility ; Thermodynamics ; Intermolecular force ; Applied mathematics ; Statistics ; Mathematics ; Activity coefficient ; Physical chemistry ; Organic chemistry ; Aqueous solution ; Molecule Crystallization and Solubility Studies ; Analytical Chemistry and Chromatography ; Computational Drug Discovery Methods
Authors: Amir Hossein Saeedi Dehaghani | Reza Soleimani
Source: Chemical Engineering & Technology (article, 2020-03-05)
Field: Chemistry — Analytical Chemistry

Prediction of CO<sub>2</sub>‐Oil Minimum Miscibility Pressure Using Soft Computing Methods Abstract Searching for computational approaches for determination of the minimum miscibility pressure ( MMP ) is highly requested during the miscible gas injection process. New models, namely, the stochastic gradient boosting (SGB) algorithm and two distinct hybrid artificial neural network (ANN) models were used to predict CO 2 MMP as a function of reservoir temperature, mole percent of volatile oil components, mole percent of intermediate oil components, molecular weight of pentane‐plus fraction in the oil phase, mole percentage of CO 2 in injected gas, volatile components, and intermediate components in the injected gas based on 144 published data points. The SGB model was found to provide the better performance. The reservoir temperature turned out to be the most important factor. Miscibility ; Mole fraction ; Artificial neural network ; Petroleum engineering ; Fraction (chemistry) ; Mass fraction ; Soft computing ; Component (thermodynamics) ; Pentane ; Thermodynamics ; Chemistry ; Biological system ; Materials science ; Chromatography ; Computer science ; Engineering ; Organic chemistry ; Polymer ; Machine learning ; Physics Petroleum Processing and Analysis ; Phase Equilibria and Thermodynamics ; Enhanced Oil Recovery Techniques
Authors: Pavlo Golub | Sergei Manzhos
Source: Computer Physics Communications (article, 2020-05-19)
Field: Materials Science — Materials Chemistry

CONUNDrum: A program for orbital-free density functional theory calculations Density functional theory ; Orbital-free density functional theory ; Statistical physics ; Computer science ; Theoretical physics ; Physics ; Time-dependent density functional theory ; Quantum mechanics Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; X-ray Diffraction in Crystallography
Authors: D. Gnanasangeetha | M. Suresh
Source: Nature Environment and Pollution Technology (review, 2020-12-27)
Field: Materials Science — Materials Chemistry

A Review on Green Synthesis of Metal and Metal Oxide Nanoparticles Metal oxide nanoparticles have captivated scrupulous research interest because of its major relevance in the field of medicine, catalysis, pigment, electronics, biotechnology, sensors, optical devices, adsorption, DNA labelling, drug delivery, kinetics, spintronics and piezoelectricity. Nanoparticles (NPs) became more significant for its reasonable property as a heterogeneous non-toxic catalyst with environmental reimbursement. The biogenic innovation of metal oxide NPs is an enhanced alternative owing to eco-friendliness. In the biological field, the probable efficacy of NPs has been reported by scores of scholars in the treatment of cancer. Owed to munificent returns, NPs explored as a powerful catalyst for several organic transformations. This section unlocks with a short course on to synthesize metal oxide NPs on a natural scale. Nanotechnology ; Nanoparticle ; Catalysis ; Oxide ; Materials science ; Metal ; Chemistry ; Organic chemistry ; Metallurgy Nanoparticles: synthesis and applications ; Chemistry and Chemical Engineering
Authors: Oleg Usoltsev | Anna Yu. Pnevskaya | Elizaveta G. Kamyshova | Andrei A. Tereshchenko | Alina Skorynina | Wei Zhang | Tao Yao | Aram L. Bugaev | А. В. Солдатов
Source: Nanomaterials (article, 2020-08-21)
Field: Materials Science — Materials Chemistry

Dehydrogenation of Ethylene on Supported Palladium Nanoparticles: A Double View from Metal and Hydrocarbon Sides Adsorption of ethylene on palladium, a key step in various catalytic reactions, may result in a variety of surface-adsorbed species and formation of palladium carbides, especially under industrially relevant pressures and temperatures. Therefore, the application of both surface and bulk sensitive techniques under reaction conditions is important for a comprehensive understanding of ethylene interaction with Pd-catalyst. In this work, we apply in situ X-ray absorption spectroscopy, X-ray diffraction and infrared spectroscopy to follow the evolution of the bulk and surface structure of an industrial catalysts consisting of 2.6 nm supported palladium nanoparticles upon exposure to ethylene under atmospheric pressure at 50 °C. Experimental results were complemented by ab initio simulations of atomic structure, X-ray absorption spectra and vibrational spectra. The adsorbed ethylene was shown to dehydrogenate to C<sub>2</sub>H<sub>3</sub>, C<sub>2</sub>H<sub>2</sub> and C<sub>2</sub>H species, and to finally decompose to palladium carbide. Thus, this study reveals the evolution pathway of ethylene on industrial Pd-catalyst under atmospheric pressure at moderate temperatures, and provides a conceptual framework for the experimental and theoretical investigation of palladium-based systems, in which both surface and bulk structures exhibit a dynamic nature under reaction conditions. Palladium ; Catalysis ; Ethylene ; Dehydrogenation ; Materials science ; Adsorption ; Infrared spectroscopy ; Carbide ; Absorption spectroscopy ; Absorption (acoustics) ; Metal ; Chemical engineering ; Inorganic chemistry ; Physical chemistry ; Photochemistry ; Chemistry ; Organic chemistry ; Metallurgy ; Composite material Catalytic Processes in Materials Science ; Machine Learning in Materials Science ; Catalysis and Oxidation Reactions
Authors: Ketao Yin | Pengyue Gao | Xuecheng Shao | Bo Gao | Hanyu Liu | Jian Lv | John S. Tse | Yanchao Wang | Yanming Ma
Source: npj Computational Materials (article, 2020-03-12)
Field: Materials Science — Materials Chemistry

An automated predictor for identifying transition states in solids Abstract The minimum energy path (MEP) and transition state are two key parameters in the investigation of the mechanisms of chemical reactions and structural phase transformations. However, determination of transition paths in solids is challenging. Here, we present an evolutionary method to search for the lowest energy path and the transition state for pressure-induced structural transformations in solids without any user input or prior knowledge of possible paths. Instead, the initial paths are chosen stochastically by connecting randomly selected atoms from the initial to final structure. The MEP of these trials paths were computed and ranked in order of their energies. The matrix particle swarm optimization algorithm is then used to generate improved transition paths. The procedure is repeated until the lowest energy MEP is found. This method is validated by reproducing results of several known systems. The new method also successfully located the MEP for the direct low-temperature pressure induced transformation of face centered-cubic (FCC) silicon to the simple hexagonal(sh) phase and FCC lithium to a complex body centered-cubic cI16 high-pressure phase. The proposed method provides a convenient, robust, and reliable approach to identify the MEP of phase transformations. The method is general and applicable to a variety of problems requiring the location of the transition state. Phase transition ; Path (computing) ; Phase (matter) ; Statistical physics ; Transformation (genetics) ; Computer science ; Energy (signal processing) ; Crystal structure prediction ; Particle swarm optimization ; Algorithm ; Materials science ; Mathematical optimization ; Thermodynamics ; Mathematics ; Chemistry ; Physics ; Crystal structure ; Crystallography ; Statistics ; Quantum mechanics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Crystallography and molecular interactions
Authors: Sharad Ambardar | Dang Nguyen | Grace Binder | Zachary W. Withers | Dmitri V. Voronine
Source: Applied Sciences (article, 2020-06-19)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Quantum Leap from Gold and Silver to Aluminum Nanoplasmonics for Enhanced Biomedical Applications Nanotechnology has been used in many biosensing and medical applications, in the form of noble metal (gold and silver) nanoparticles and nanostructured substrates. However, the translational clinical and industrial applications still need improvements of the efficiency, selectivity, cost, toxicity, reproducibility, and morphological control at the nanoscale level. In this review, we highlight the recent progress that has been made in the replacement of expensive gold and silver metals with the less expensive aluminum. In addition to low cost, other advantages of the aluminum plasmonic nanostructures include a broad spectral range from deep UV to near IR, providing additional signal enhancement and treatment mechanisms. New synergistic treatments of bacterial infections, cancer, and coronaviruses are envisioned. Coupling with gain media and quantum optical effects improve the performance of the aluminum nanostructures beyond gold and silver. Nanotechnology ; Materials science ; Plasmon ; Noble metal ; Nanostructure ; Colloidal gold ; Nanoparticle ; Metal ; Optoelectronics ; Metallurgy Gold and Silver Nanoparticles Synthesis and Applications ; Nanoplatforms for cancer theranostics ; Biosensors and Analytical Detection
Authors: Gabriel Bernardo | Manuel Melle‐Franco | A. L. Washington | Robert M. Dalgliesh | Fankang Li | Adélio Mendes | Steven R. Parnell
Source: RSC Advances (article, 2020-01-01)
Field: Chemistry — Organic Chemistry

Different agglomeration properties of PC<sub>61</sub>BM and PC<sub>71</sub>BM in photovoltaic inks – a spin-echo SANS study Fullerene derivatives are used in a wide range of applications including as electron acceptors in solution-processable organic photovoltaics. We report agglomeration of fullerene derivatives in optically opaque solutions of PC<sub>61</sub>BM and PC<sub>71</sub>BM, with concentrations ranging from 30 mg mL<sup>-1</sup> up to 90 mg mL<sup>-1</sup>, in different solvents with relevance to organic photovoltaics, using a novel neutron scattering technique, Spin-Echo Small Angle Neutron Scattering (SESANS). From SESANS, agglomerates with correlation lengths larger than 1 μm are found in some PC<sub>61</sub>BM solutions, in contrast no agglomerates are seen in PC<sub>71</sub>BM solutions. These results clearly show that PC<sub>71</sub>BM is fundamentally more soluble than PC<sub>61</sub>BM in the solvents commonly used in photovoltaic inks and corroborating similar observations previously achieved using other experimental techniques. Computer models are presented to study the energetics of solution and agglomeration of both species, ascribing the difference to a kinetic effect probably related to the larger anisotropy of PC<sub>71</sub>BM. Also, this work showcases the power of SESANS to probe agglomerates of fullerene derivatives in completely opaque solutions for agglomerates of the order of one to several microns. Fullerene ; Agglomerate ; Opacity ; Materials science ; Organic solar cell ; Chemical physics ; Photovoltaics ; Neutron scattering ; Scattering ; Photovoltaic system ; Chemical engineering ; Nanotechnology ; Optics ; Chemistry ; Organic chemistry ; Polymer ; Physics Fullerene Chemistry and Applications ; Carbon Nanotubes in Composites ; Ion-surface interactions and analysis
Authors: Fanny Castéran | Rubén Ibáñez | Clara Argerich | Karim Delage | Francisco Chinesta | Philippe Cassagnau
Source: Macromolecular Materials and Engineering (article, 2020-11-25)
Field: Materials Science — Polymers and Plastics

Application of Machine Learning Tools for the Improvement of Reactive Extrusion Simulation Abstract The purpose of this paper is to combine a classical 1D twin‐screw extrusion model with machine learning techniques to obtain accurate predictions of a complex system despite few data. Systems involving reactive polyethylene oligomer dispersed in situ in a polypropylene matrix by reactive twin‐screw extrusion are studied for this purpose. The twin‐screw extrusion simulation software LUDOVIC is used and machine learning techniques dealing with low data limit are used as a correction of the simulation. Reactive extrusion ; Extrusion ; Materials science ; Polypropylene ; Software ; Composite material ; Process engineering ; Mechanical engineering ; Engineering drawing ; Computer science ; Engineering Polymer crystallization and properties ; Rheology and Fluid Dynamics Studies ; Polymer Nanocomposites and Properties
Authors: Nathan A. Mahynski | Runfang Mao | Evan Pretti | Vincent K. Shen | Jeetain Mittal
Source: Soft Matter (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

Grand canonical inverse design of multicomponent colloidal crystals Inverse design methods are powerful computational approaches for creating colloidal systems which self-assemble into a target morphology by reverse engineering the Hamiltonian of the system. Despite this, these optimization procedures tend to yield Hamiltonians which are too complex to be experimentally realized. An alternative route to complex structures involves the use of several different components, however, conventional inverse design methods do not explicitly account for the possibility of phase separation into compositionally distinct structures. Here, we present an inverse design scheme for multicomponent colloidal systems by combining active learning with a method to directly compute their ground state phase diagrams. This explicitly accounts for phase separation and can locate stable regions of Hamiltonian parameter space which grid-based surveys are prone to miss. Using this we design low-density, binary structures with Lennard-Jones-like pairwise interactions that are simpler than in the single component case and potentially realizable in an experimental setting. This reinforces the concept that ground states of simple, multicomponent systems might be rich with previously unappreciated diversity, enabling the assembly of non-trivial structures with only few simple components instead of a single complex one. Inverse ; Colloid ; Non canonical ; Colloidal crystal ; Grand canonical ensemble ; Materials science ; Nanotechnology ; Chemical physics ; Theoretical physics ; Chemistry ; Physics ; Mathematics ; Physical chemistry ; Geometry ; Statistics ; Biology Pickering emulsions and particle stabilization ; Surfactants and Colloidal Systems ; Material Dynamics and Properties
Authors: Muftau Jide Akanbi | Suwan N. Jayasinghe | Adam Wojcik
Source: Polymer (article, 2020-10-10)
Field: Materials Science — Biomaterials

Characterisation of electrospun PS/PU polymer blend fibre mat for oil sorption Sorption ; Materials science ; Differential scanning calorimetry ; Miscibility ; Ultimate tensile strength ; Polystyrene ; Polymer ; Thermogravimetric analysis ; Composite material ; Fourier transform infrared spectroscopy ; Chemical engineering ; Polymer blend ; Sorbent ; Chemistry ; Adsorption ; Copolymer ; Organic chemistry Electrospun Nanofibers in Biomedical Applications ; Surface Modification and Superhydrophobicity ; Advanced Sensor and Energy Harvesting Materials
Authors: Xiangyue Liu | Gerard Meijer | Jesús Pérez-Ríos
Source: Physical Chemistry Chemical Physics (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

A data-driven approach to determine dipole moments of diatomic molecules We present a data-driven approach for the prediction of the electric dipole moment of diatomic molecules, which is one of the most relevant molecular properties. In particular, we apply Gaussian process regression to a novel dataset to show that dipole moments of diatomic molecules can be learned, and hence predicted, with a relative error ⪅5%. The dataset contains the dipole moment of 162 diatomic molecules, the most exhaustive and unbiased dataset of dipole moments up to date. Our findings show that the dipole moment of diatomic molecules depends on atomic properties of the constituents atoms: electron affinity and ionization potential, as well as on (a feature related to) the first derivative of the electronic kinetic energy at the equilibrium distance. Diatomic molecule ; Dipole ; Bond dipole moment ; Moment (physics) ; Electric dipole moment ; Transition dipole moment ; Gaussian ; Electric dipole transition Machine Learning in Materials Science ; Gaussian Processes and Bayesian Inference ; Molecular spectroscopy and chirality
Authors: Xiaoxuan Ge | Michael P. Thorgersen | Farris L. Poole | Adam M. Deutschbauer | John‐Marc Chandonia | Pavel S. Novichkov | Sara Gushgari-Doyle | Lauren Michelle Lui | Torben Nielsen | Romy Chakraborty | Paul D. Adams | Adam P. Arkin | Terry C. Hazen | Michael W. W. Adams
Source: Frontiers in Microbiology (article, 2020-10-19)
Field: Chemistry — Inorganic Chemistry

Characterization of a Metal-Resistant Bacillus Strain With a High Molybdate Affinity ModA From Contaminated Sediments at the Oak Ridge Reservation A nitrate- and metal-contaminated site at the Oak Ridge Reservation (ORR) was previously shown to contain the metal molybdenum (Mo) at picomolar concentrations. This potentially limits microbial nitrate reduction, as Mo is required by the enzyme nitrate reductase, which catalyzes the first step of nitrate removal. Enrichment for anaerobic nitrate-reducing microbes from contaminated sediment at the ORR yielded <i>Bacillus</i> strain EB106-08-02-XG196. This bacterium grows in the presence of multiple metals (Cd, Ni, Cu, Co, Mn, and U) but also exhibits better growth compared to control strains, including <i>Pseudomonas fluorescens</i> N2E2 isolated from a pristine ORR environment under low molybdate concentrations (<1 nM). Molybdate is taken up by the molybdate binding protein, ModA, of the molybdate ATP-binding cassette transporter. ModA of XG196 is phylogenetically distinct from those of other characterized ModA proteins. The genes encoding ModA from XG196, <i>P. fluorescens</i> N2E2 and <i>Escherichia coli</i> K12 were expressed in <i>E. coli</i> and the recombinant proteins were purified. Isothermal titration calorimetry analysis showed that XG196 ModA has a higher affinity for molybdate than other ModA proteins with a molybdate binding constant (K <sub><i>D</i></sub> ) of 2.2 nM, about one order of magnitude lower than those of <i>P. fluorescens</i> N2E2 (27.0 nM) and <i>E. coli</i> K12 (25.0 nM). XG196 ModA also showed a fivefold higher affinity for molybdate than for tungstate (11 nM), whereas the ModA proteins from <i>P. fluorescens</i> N2E2 [K <sub><i>D</i></sub> (Mo) 27.0 nM, K <sub><i>D</i></sub> (W) 26.7 nM] and <i>E. coli</i> K12[(K <sub><i>D</i></sub> (Mo) 25.0 nM, K <sub><i>D</i></sub> (W) 23.8 nM] had similar affinities for the two oxyanions. We propose that high molybdate affinity coupled with resistance to multiple metals gives strain XG196 a competitive advantage in Mo-limited environments contaminated with high concentrations of metals and nitrate, as found at ORR. Strain (injury) ; Ridge ; Molybdate ; Contamination ; Reservation ; Environmental science ; Environmental chemistry ; Chemistry ; Biology ; Metallurgy ; Materials science ; Ecology ; Paleontology ; Computer science Radioactive element chemistry and processing ; Chromium effects and bioremediation ; Microbial Community Ecology and Physiology
Authors: Mónica González Linares | Yu Jia | Geoffrey I. Sunahara | Joann K. Whalen
Source: Canadian Journal of Soil Science (article, 2020-02-25)
Field: Materials Science — Materials Chemistry

Barley (<i>Hordeum vulgare</i>) seedling growth declines with increasing exposure to silver nanoparticles in biosolid-amended soils Silver nanoparticles (AgNPs), a component of many consumer products, are considered an environmental risk due to the broad-spectrum toxicity of Ag + to non-target organisms. Most AgNPs released from consumer products will end up as biosolids in wastewater treatment plants, which are often applied as a fertilizer to agriculture. Land application of biosolids may add AgNPs to the soil–plant system, with unknown consequences. This study investigated the growth of Hordeum vulgare seedlings, Ag bioconcentration and distribution in shoot and root tissues of barley exposed to biosolid-amended Delacour and Organization for Economic Co-operation and Development (OECD) soils spiked with AgNPs (up to 366 mg Ag kg −1 dry soil). In both soils, root and shoot growth declined linearly as the concentration of AgNPs increased. Barley had higher Ag bioconcentration values when grown in the OECD soil than in the Delacour soil. Silver bioavailability was greater in the OECD soil due to its physicochemical properties, such as low calcium concentration and acidic pH, relative to the Delacour soil. Barley seedlings exhibited morphological changes, including smaller shoots and shorter, thick roots after 14 d exposure to AgNPs. We conclude that plant structural responses, particularly changes in root biomass, could be an early diagnostic of seedling exposure to AgNPs in biosolid-amended soils. Biosolids ; Shoot ; Seedling ; Soil water ; Bioconcentration ; Hordeum vulgare ; Agronomy ; Chemistry ; Fertilizer ; Horticulture ; Environmental chemistry ; Bioaccumulation ; Environmental science ; Poaceae ; Biology ; Environmental engineering ; Soil science Nanoparticles: synthesis and applications ; Heavy metals in environment
Authors: Shiva Hayati Raad | Zahra Atlasbaf | Carlos J. Zapata-Rodrı́guez
Source: Scientific Reports (article, 2020-11-04)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Broadband absorption using all-graphene grating-coupled nanoparticles on a reflector In this paper, the hybridized localized surface plasmon resonances (LSPRs) of a periodic assembly of graphene-wrapped nanoparticles are used to design a nanoparticle assisted optical absorber. Bandwidth enhancement of this structure via providing multiple types of plasmonic resonances in the associated unit cell using two densely packed crossly stacked graphene strips is proposed. The designed graphene strips support fundamental propagating surface plasmons on the ribbons, and gap plasmons in the cavity constructed by the adjacent sections. Graphene strips exhibit a hyperbolic dispersion region in the operating spectrum and assist in the bandwidth enhancement. Moreover, since the nanoparticles are deposited on the top strips, real-time biasing of them can be easily conducted by exciting the surface plasmons of the strip without the necessity to electrically connect the adjacent nanoparticles. The overall dynamic bandwidth of the structure, using a two-state biasing scheme, covers the frequencies of 18.16-40.47 THz with 90% efficiency. Due to the symmetry of the structure, the device performs similarly for both transverse electric (TE) and transverse magnetic (TM) waves and it has a high broadband absorption rate regarding different incident angles up to 40°. Due to the presence of 2D graphene material and also using hollow spherical particles, our proposed absorber is also lightweight and it is suitable for novel compact optoelectronic devices due to its sub-wavelength dimensions. Graphene ; Materials science ; STRIPS ; Optoelectronics ; Surface plasmon ; Biasing ; Plasmon ; Nanoparticle ; Grating ; Optics ; Nanotechnology ; Voltage ; Physics Metamaterials and Metasurfaces Applications ; Plasmonic and Surface Plasmon Research ; Advanced Antenna and Metasurface Technologies