jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Magdalena Jarosz | Leszek Zaraska | Marcin Kozieł | Wojciech Simka | Grzegorz D. Sulka
Source: Nanomaterials (article, 2020-12-30)
Field: Materials Science — Materials Chemistry

Electrochemical Oxidation of Ti15Mo Alloy—The Impact of Anodization Parameters on Surface Morphology of Nanostructured Oxide Layers It is well-known that the structure and composition of the material plays an important role in the processes occurring at the surface. In this paper, a surface morphology of nanostructured oxide layers electrochemically grown on Ti15Mo, tuned by applying different anodization parameters, was investigated in detail. The one-step anodization of Ti15Mo alloy was performed at room temperature in an ethylene glycol-based electrolyte containing 0.11 M NH<sub>4</sub>F and 1.11 M H<sub>2</sub>O. Different anodization times (ranging from 5 to 60 min) and applied potentials (40-100 V) were tested, and the surface morphology, elemental content, and crystalline structure were monitored by scanning electron microscopy (SEM), energy dispersive X-ray spectrometry (EDS), and X-ray diffractometry (XRD), respectively. The results showed that contrary to the multistep anodization of titanium foil, the surface morphology of anodic oxide obtained via the one-step process contains the nanoporous outer layer covering the nanotubular structure. What is more, the pore diameter (D<sub>p</sub>) and interpore distance (D<sub>int</sub>) of such layers exhibit different trends than those observed for anodization of pure titanium. In particular, at a certain potential range, a decrease in both D<sub>p</sub> and D<sub>int</sub> with increasing potential was observed. However, independently on the used anodization conditions, the elemental content of oxide layers remained similar, showing the amount of molybdenum at c.a. 15 wt.%. Finally, the amorphous nature of as-anodized layers was confirmed, and their optical band-gap was determined from the diffuse reflectance UV-Vis spectra. It was found that E<sub>g</sub> is tunable to some extent by changing the anodizing potential. However, further thermal treatment in air at 400 °C resulted in the anatase phase formation that was accompanied by a significant E<sub>g</sub> reduction. Therefore, we believe that the presented results will greatly contribute to the understanding of anodic formation of nanostructured functional oxide layers with tunable properties that can be applied in various fields. Anodizing ; Materials science ; Nanoporous ; Oxide ; Chemical engineering ; Titanium ; Scanning electron microscope ; Amorphous solid ; Morphology (biology) ; Alloy ; Plasma electrolytic oxidation ; Electrolyte ; Analytical Chemistry (journal) ; Metallurgy ; Nanotechnology ; Composite material ; Aluminium ; Crystallography ; Chemistry ; Electrode Anodic Oxide Films and Nanostructures ; Bone Tissue Engineering Materials ; Electrocatalysts for Energy Conversion
Authors: Ludwig L. Bengel | Benjamin Aberle | Alexander‐N. Egler‐Kemmerer | Samuel Kienzle | Bernhard Hauer | Stephan C. Hammer
Source: Angewandte Chemie (article, 2020-12-11)
Field: Chemistry — Organic Chemistry

Modifizierte Enzyme ermöglichen die selektive <i>N</i>‐Alkylierung von Pyrazolen unter Verwendung einfacher Halogenalkane Abstract Die selektive Alkylierung von Pyrazolen ist eine Herausforderung in der Chemie und könnte die Synthese wichtiger Moleküle vereinfachen. In dieser Arbeit berichten wir über eine katalysatorgesteuerte Alkylierung von Pyrazolen durch eine cyclische Kaskadenreaktion mit zwei Enzymen. In diesem enzymatischen System nutzt ein promiskuitives Enzym Halogenalkane als Ausgangsstoffe, um nicht‐natürliche Analoga des Cosubstrats S‐Adenosyl‐ l ‐Methionin zu synthetisieren. Ein zweites engineertes Enzym überträgt die Alkylgruppen in einer hochselektiven C−N‐Bindungsknüpfung auf das Pyrazol‐Substrat. Das Cosubstrat wird regeneriert und nur in katalytischen Mengen eingesetzt. Für das Enzym‐Engineering wurde eine computerbasierte Methode verwendet, um eine Mutantenbibliothek in silico zu entwickeln. In einer Runde von Mutagenese und Screening wurde somit eine promiskuitive Methyltransferase in eine kleine Pyrazol‐alkylierende Enzymfamilie umgewandelt. Mit diesem bienzymatischen System konnte die Alkylierung von Pyrazolen (Methylierung, Ethylierung, Propylierung) mit bislang unerreichter Regioselektivität (&gt;99 %), Regiodivergenz und in einem ersten Beispiel in präparativem Maßstab gezeigt werden. Chemistry ; Molecular biology ; Medicinal chemistry Click Chemistry and Applications ; Sulfur-Based Synthesis Techniques ; Chemical Synthesis and Analysis
Authors: Bao-Xin Xue | Mario Barbatti | Pavlo O. Dral
Source: ChemRxiv (preprint, 2020-07-22)
Field: Materials Science — Materials Chemistry

Machine Learning for Absorption Cross Sections We present a machine learning (ML) method to accelerate the nuclear ensemble approach (NEA) for computing absorption cross sections. ML-NEA is used to calculate cross sections on vast ensembles of nuclear geometries to reduce the error due to insufficient statistical sampling. The electronic properties — excitation energies and oscillator strengths — are calculated with a reference electronic structure method only for relatively few points in the ensemble. Kernel-ridge-regression-based ML combined with the RE descriptor as implemented in MLatom is used to predict these properties for the remaining tens of thousands of points in the ensemble without incurring much of additional computational cost. We demonstrate for two examples, benzene and a 9-dicyanomethylene derivative of acridine, that ML-NEA can produce statistically converged cross sections even for very challenging cases and even with as few as several hundreds of training points. Computer science ; Kernel (algebra) ; Excitation ; Absorption (acoustics) ; Algorithm ; Artificial intelligence ; Machine learning ; Computational physics ; Statistical physics ; Physics ; Mathematics ; Optics ; Quantum mechanics ; Discrete mathematics Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Solid-state spectroscopy and crystallography
Authors: Julia Sordyl | Bartosz Puzio | Maciej Manecki | Olaf J. Borkiewicz | Justyna Topolska | Sylwia Zelek-Pogudz
Source: Minerals (article, 2020-05-29)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Structural Assessment of Fluorine, Chlorine, Bromine, Iodine, and Hydroxide Substitutions in Lead Arsenate Apatites (Mimetites)–Pb5(AsO4)3X Five lead-arsenate apatites (mimetites)-Pb5(AsO4)3X—where X denotes fluorine (F), chlorine (Cl), bromine (Br), iodine (I), and hydroxide (OH)—were synthesized via precipitation from aqueous solutions. The crystal structures were determined through Rietveld refinement of powder synchrotron X-ray data. All the compounds crystallized in the hexagonal class symmetry (space group P63/m). The Rietveld refinement indicated that mimetite-Cl, -Br, -I, and -OH had an anion deficiency at position X. Substitution of halogens in a mimetite structure brought about systematic changes in unit-cell parameters, interatomic distances, and metaprism twist angles φ, proportional to the substituted halogen’s ionic radius. Mimetite-OH did not follow the linear correlations determined within the series. Twist angle φ, a useful device for monitoring changes in apatite topology, ranged from 20.34° for mimetite-F to 11.42° for mimetite-I. The geometric method has been proposed for determining the diameter of hexagonal channels hosting halogens in apatites. A comparison of the results with halogenated pyromorphites showed similar systematic trends: the substitutions in mimetites have comparable effect on the interatomic distances as in their phosphorous analogues. Halogen ; Crystallography ; Bromine ; Rietveld refinement ; Chemistry ; Fluorine ; Chlorine ; Crystal structure ; Arsenate ; Hydroxide ; Ionic radius ; Inorganic chemistry ; Ion ; Arsenic Crystal Structures and Properties ; X-ray Diffraction in Crystallography ; Mineralogy and Gemology Studies
Authors: Chenglong You | Corey T. Matyas | Yin Huang | Jonathan P. Dowling | Georgios Veronis
Source: IEEE photonics journal (article, 2020-05-15)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Optimized Multilayer Structures With Ultrabroadband Near-Perfect Absorption We present aperiodic multilayer structures with ultrabroadband near-perfect absorption in the visible and near-infrared wavelength range. We use a hybrid optimization algorithm coupled with the transfer-matrix method, to optimize both the material composition and the layer thicknesses of the aperiodic multilayer structures that are composed of infinite slabs of material above a semi-infinite substrate. In order to achieve ultrabroadband near-perfect absorption, we consider a broad range of materials including dielectrics, metals, and semiconductors. The optimization algorithms previously used to design ultrabroadband near-perfect absorbers only optimized the layer thicknesses of structures with fixed material composition. In contrast, we find that our approach of simultaneously optimizing the material composition as well as the layer thicknesses leads to structures with broader near-perfect absorption. For an optimized eleven-layer structure the lower and upper absorption band edges are 400 nm and ~3800 nm, respectively. In addition, we find that, even though the structures are optimized for normally incident light, the absorption is high in a broad angular range within the wavelength range of interest. We also explain the physical origin of ultrabroadband absorption in these structures. Our results will contribute to the development of a new generation of devices for solar photovoltaics, imaging, and photodetection. Absorption (acoustics) ; Optics ; Materials science ; Optoelectronics ; Computer science ; Physics Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Thermal Radiation and Cooling Technologies
Authors: Henry P. Wood | F. Aaron Cruz-Navarrete | Nicola J. Baxter | Clare R. Trevitt | Angus J. Robertson | Samuel R. Dix | Andrea M. Hounslow | Matthew J. Cliff | Jonathan P. Waltho
Source: Nature Communications (article, 2020-11-02)
Field: Materials Science — Materials Chemistry

Allomorphy as a mechanism of post-translational control of enzyme activity Enzyme regulation is vital for metabolic adaptability in living systems. Fine control of enzyme activity is often delivered through post-translational mechanisms, such as allostery or allokairy. β-phosphoglucomutase (βPGM) from Lactococcus lactis is a phosphoryl transfer enzyme required for complete catabolism of trehalose and maltose, through the isomerisation of β-glucose 1-phosphate to glucose 6-phosphate via β-glucose 1,6-bisphosphate. Surprisingly for a gatekeeper of glycolysis, no fine control mechanism of βPGM has yet been reported. Herein, we describe allomorphy, a post-translational control mechanism of enzyme activity. In βPGM, isomerisation of the K145-P146 peptide bond results in the population of two conformers that have different activities owing to repositioning of the K145 sidechain. In vivo phosphorylating agents, such as fructose 1,6-bisphosphate, generate phosphorylated forms of both conformers, leading to a lag phase in activity until the more active phosphorylated conformer dominates. In contrast, the reaction intermediate β-glucose 1,6-bisphosphate, whose concentration depends on the β-glucose 1-phosphate concentration, couples the conformational switch and the phosphorylation step, resulting in the rapid generation of the more active phosphorylated conformer. In enabling different behaviours for different allomorphic activators, allomorphy allows an organism to maximise its responsiveness to environmental changes while minimising the diversion of valuable metabolites. Biochemistry ; Chemistry ; Phosphorylation ; Enzyme ; Allosteric regulation ; Biology Enzyme Structure and Function ; Amino Acid Enzymes and Metabolism ; Protein Structure and Dynamics
Authors: Karol Szałowski
Source: Materials (article, 2020-10-02)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Low-Temperature Magnetocaloric Properties of V12 Polyoxovanadate Molecular Magnet: A Theoretical Study The paper presents a computational study of the magnetocaloric properties of the V12 polyoxovanadate molecular magnet. The description is restricted to low-temperature range (below approximately 100 K), where the magnetic properties of the system in question can be sufficiently modelled by considering a tetramer that consists of four vanadium ions with spins S=1/2. The discussion is focused on the magnetocaloric effect in the cryogenic range. The exact and numerical diagonalization of the corresponding Hamiltonian is used in order to construct the thermodynamic description within a version of the canonical ensemble. The thermodynamic quantities of interest, such as magnetic entropy, specific heat, entropy change under isothermal magnetization/demagnetization, temperature change under adiabatic magnetization/demagnetization, refrigerant capacity, and magnetic Grüneisen ratio, are calculated and discussed extensively. The importance of two quantum level crossings for the described properties is emphasized. The significant ranges of direct and inverse magnetocaloric effect are predicted. In particular, the maximized inverse magnetocaloric response is found for cryogenic temperatures. Magnetic refrigeration ; Adiabatic process ; Magnetization ; Condensed matter physics ; Magnet ; Materials science ; Spins ; Thermodynamics ; Atmospheric temperature range ; Isothermal process ; Physics ; Magnetic field ; Quantum mechanics Magnetism in coordination complexes ; Magnetic and transport properties of perovskites and related materials ; Advanced Condensed Matter Physics
Authors: Sergei V. Kalinin | Mani Valleti | Rama K. Vasudevan | Maxim Ziatdinov
Source: Journal of Applied Physics (article, 2020-10-27)
Field: Materials Science — Materials Chemistry

Exploration of lattice Hamiltonians for functional and structural discovery via Gaussian process-based exploration–exploitation Statistical physics models ranging from simple lattice to complex quantum Hamiltonians are one of the mainstays of modern physics that have allowed both decades of scientific discovery and provided a universal framework to understand a broad range of phenomena from alloying to frustrated and phase separated materials to quantum systems. Traditionally, exploration of the phase diagrams corresponding to multidimensional parameter spaces of Hamiltonians was performed using a combination of basic physical principles, analytical approximations, and extensive numerical modeling. However, exploration of complex multidimensional parameter spaces is subject to the classic dimensionality problem, and the behaviors of interest concentrated on low dimensional manifolds remain undiscovered. Here, we demonstrate that a combination of exploration and exploration–exploitation with Gaussian process modeling and Bayesian optimization allows effective exploration of the parameter space for lattice Hamiltonians and effectively maps the regions at which specific macroscopic functionalities or local structures are maximized. We argue that this approach is general and can be further extended well beyond the lattice Hamiltonians to effectively explore the parameter space of more complex off-lattice and dynamic models. Curse of dimensionality ; Lattice (music) ; Gaussian ; Parameter space ; Bayesian optimization ; Quantum ; Phase diagram ; Gaussian process Machine Learning in Materials Science ; Quantum many-body systems ; Gaussian Processes and Bayesian Inference
Authors: Peter P. Maksimyak | C. Yu. Zenkova | V. M. Tkachuk
Source: Physics and Chemistry of Solid State (article, 2020-03-28)
Field: Chemistry — Spectroscopy

Carbon Nanoparticles. Production, properties, perspectives of use The paper is devoted to the study of the optical properties of carbon nanoparticles synthesized by the method developed during our experimental studies. The optimal conditions for the creation of carbon nanostructures with predetermined properties are defined. Nanoparticles of the size of about 100 nm were obtained, the maximum of absorption of which is localized at wavelengths in the violet-blue region of the spectrum, while the maximum of luminescence – in the green region of the spectrum. The assumption is made about the possibility of using the obtained particles for correlation diagnostics of optical speckle fields. Nanoparticle ; Luminescence ; Materials science ; Carbon fibers ; Absorption (acoustics) ; Nanostructure ; Wavelength ; Speckle pattern ; Carbon Nanoparticles ; Absorption spectroscopy ; Nanotechnology ; Optoelectronics ; Chemical engineering ; Optics ; Physics ; Composite material Adsorption, diffusion, and thermodynamic properties of materials ; Carbon Nanotubes in Composites
Authors: Jiřı́ Czernek | Jiřı́ Brus
Source: International Journal of Molecular Sciences (article, 2020-07-11)
Field: Chemistry — Physical and Theoretical Chemistry

Polymorphic Forms of Valinomycin Investigated by NMR Crystallography A dodecadepsipeptide valinomycin (VLM) has been most recently reported to be a potential anti-coronavirus drug that could be efficiently produced on a large scale. It is thus of importance to study solid-phase forms of VLM in order to be able to ensure its polymorphic purity in drug formulations. The previously available solid-state NMR (SSNMR) data are combined with the plane-wave DFT computations in the NMR crystallography framework. Structural/spectroscopical predictions (the PBE functional/GIPAW method) are obtained to characterize four polymorphs of VLM. Interactions which confer a conformational stability to VLM molecules in these crystalline forms are described in detail. The way how various structural factors affect the values of SSNMR parameters is thoroughly analyzed, and several SSNMR markers of the respective VLM polymorphs are identified. The markers are connected to hydrogen bonding effects upon the corresponding (<sup>13</sup>C/<sup>15</sup>N/<sup>1</sup>H) isotropic chemical shifts of (C<sub>O</sub>, N<sub>amid</sub>, H<sub>amid</sub>, H<sub>α</sub>) VLM backbone nuclei. These results are expected to be crucial for polymorph control of VLM and in probing its interactions in dosage forms. Valinomycin ; Crystallography ; Chemistry ; Biochemistry ; Membrane Crystallography and molecular interactions ; Chemical Synthesis and Analysis ; Cancer therapeutics and mechanisms
Authors: Moritz Glatt | Hendrik Hotz | Patrick Kölsch | Avik Mukherjee | Benjamin Kirsch | Jan C. Aurich
Source: The International Journal of Advanced Manufacturing Technology (article, 2020-10-10)
Field: Materials Science — Materials Chemistry

Predicting the martensite content of metastable austenitic steels after cryogenic turning using machine learning Abstract During cryogenic turning of metastable austenitic stainless steels, a deformation-induced phase transformation from γ-austenite to α’-martensite can be realized in the workpiece subsurface, which results in a higher microhardness as well as in improved fatigue strength and wear resistance. The α’-martensite content and resulting workpiece properties strongly depend on the process parameters and the resulting thermomechanical load during cryogenic turning. In order to achieve specific workpiece properties, extensive knowledge about this correlation is required. Parametric models, based on physical correlations, are only partly able to predict the resulting properties due to limited knowledge on the complex interactions between stress, strain, temperature, and the resulting kinematics of deformation-induced phase transformation. Machine learning algorithms can be used to detect this kind of knowledge in data sets. Therefore, the goal of this paper is to evaluate and compare the applicability of three machine learning methods (support vector regression, random forest regression, and artificial neural network) to derive models that support the prediction of workpiece properties based on thermomechanical loads. For this purpose, workpiece property data and respective process forces and temperatures are used as training and testing data. After training the models with 55 data samples, the support vector regression model showed the highest prediction accuracy. Austenite ; Materials science ; Artificial neural network ; Martensite ; Support vector machine ; Metastability ; Deformation (meteorology) ; Artificial intelligence ; Machine learning ; Austenitic stainless steel ; Phase (matter) ; Indentation hardness ; Parametric statistics ; Mechanical engineering ; Metallurgy ; Computer science ; Composite material ; Engineering ; Microstructure ; Mathematics ; Statistics Metal Alloys Wear and Properties ; Microstructure and Mechanical Properties of Steels ; Advanced machining processes and optimization
Authors: Zakari Ya’u Ibrahim | Adamu Uzairu | Gideon Adamu Shallangwa | Stephen Eyije Abechi
Source: Chemistry Africa (article, 2020-11-10)
Field: Chemistry — Organic Chemistry

In-silico Design of Aryl and Aralkyl Amine-Based Triazolopyrimidine Derivatives with Enhanced Activity Against Resistant Plasmodium falciparum Abstract A blend of genetic algorithm with multiple linear regression (GA-MLR) method was utilized in generating a quantitative structure–activity relationship (QSAR) model on the antimalarial activity of aryl and aralkyl amine-based triazolopyrimidine derivatives. The structures of derivatives were optimized using density functional theory (DFT) DFT/B3LYP/6–31 + G* basis set to generate their molecular descriptors, where two (2) predictive models were developed with the aid of these descriptors. The model with an excellent statistical parameters; high coefficient of determination (R 2 ) = 0.8884, cross-validated R 2 (Q 2 cv) = 0.8317 and highest external validated R 2 (R 2 pred ) = 0.7019 was selected as the best model. The model generated was validated through internal (leave-one-out (LOO) cross-validation), external test set, and Y-randomization test. These parameters are indicators of robustness, excellent prediction, and validity of the selected model. The most relevant descriptor to the antimalarial activity in the model was found to be GATS6p (Geary autocorrelation—lag 6/weighted by polarizabilities), in the model due to its highest mean effect. The descriptor (GATS6p) was significant in the in-silico design of sixteen (16) derivatives of aryl and aralkyl amine-based triazolopyrimidine adopting compound DSM191 with the highest activity (pEC 50 = 7.1805) as the design template. The design compound D8 was found to be the most active compound due to its superior hypothetical activity (pEC 50 = 8.9545). Quantitative structure–activity relationship ; Linear regression ; Aryl ; In silico ; Mathematics ; Biological system ; Chemistry ; Amine gas treating ; Stereochemistry ; Statistics ; Organic chemistry ; Biology Synthesis and biological activity ; Computational Drug Discovery Methods ; Synthesis and Biological Evaluation