jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Tayengwa Christopher Maponga | Courtie Mahamadi
Source: Scientific Reports (article, 2019-03-04)
Field: Materials Science — Biomaterials

Efficient $${\boldsymbol{A}}{\boldsymbol{u}}{({\boldsymbol{C}}{\boldsymbol{N}})}_{{\bf{2}}}^{-{\bf{1}}}$$ adsorption using peach stone-derived granular activated carbon Agricultural wastes have attracted attention as alternative precursors for the production of granular activated carbon. Using batch sorption studies, we demonstrated that granular activated carbon prepared through chemical activation using zinc chloride followed by physical activation in nitrogen environment at high temperatures can effectively adsorb aurocyanide from solution. Furthermore, gold recovery process using the granular activated carbon was amenable to optimization of adsorption parameters such as carbon dosage, initial aurocyanide concentration and contact time. These results may open new fronts on the application of low-cost granular activated carbon, particularly in the carbon-in-pulp metallurgical process. Adsorption ; Sorption ; Activated carbon ; Carbon fibers ; Nitrogen ; Chemistry ; Materials science ; Nuclear chemistry ; Chemical engineering ; Physical chemistry ; Composite material ; Organic chemistry Clay minerals and soil interactions ; Adsorption and biosorption for pollutant removal ; Cassava research and cyanide
Authors: Yipeng Gao | Yongfeng Zhang | Daniel Schwen | Chao Jiang | Jian Gan
Source: Scientific Reports (article, 2019-05-24)
Field: Materials Science — Materials Chemistry

Bifurcation and Pattern Symmetry Selection in Reaction-Diffusion Systems with Kinetic Anisotropy Ordering and self-organization are critical in determining the dynamics of reaction-diffusion systems. Here we show a unique pattern formation mechanism, dictated by the coupling of thermodynamic instability and kinetic anisotropy. Intrinsically different from the physical origin of Turing instability and patterning, the ordered patterns we obtained are caused by the interplay of the instability from uphill diffusion, the symmetry breaking from anisotropic diffusion, and the reactions. To understand the formation of the void/gas bubble superlattices in crystals under irradiation, we establish a general theoretical framework to predict the symmetry selection of superlattice structures associated with anisotropic diffusion. Through analytical study and phase field simulations, we found that the symmetry of a superlattice is determined by the coupling of diffusion anisotropy and the reaction rate, which indicates a new type of bifurcation phenomenon. Our discovery suggests a means for designing target experiments to tailor different microstructural patterns. Anisotropy ; Instability ; Superlattice ; Pattern formation ; Condensed matter physics ; Symmetry breaking ; Symmetry (geometry) ; Diffusion ; Statistical physics ; Anisotropic diffusion ; Physics ; Kinetic energy ; Bifurcation ; Chemical physics ; Classical mechanics ; Thermodynamics ; Mechanics ; Mathematics ; Geometry ; Optics ; Quantum mechanics ; Nonlinear system Solidification and crystal growth phenomena ; Nonlinear Dynamics and Pattern Formation ; nanoparticles nucleation surface interactions
Authors: S. Sels | R. Ferrer | K. Dockx | C. Granados Buitrago | M. Huyse | Yu. A. Kudryavtsev | Sandro Kraemer | S. Raeder | P. Van den Bergh | P. Van Duppen | M. Verlinde | E. Verstraelen | A. Zadvornaya
Source: Nuclear Instruments and Methods in Physics Research Section B Beam Interactions with Materials and Atoms (article, 2019-06-14)
Field: Chemistry — Spectroscopy

Design and commissioning of an ion guide system for In-Gas Laser Ionization and Spectroscopy experiments Ion ; Thermal emittance ; Ion source ; Spectroscopy ; Atomic physics ; Ionization ; Transverse plane ; Laser ; Ion beam ; Chemistry ; Beam (structure) ; Optics ; Physics Mass Spectrometry Techniques and Applications ; Atomic and Molecular Physics ; Pesticide Residue Analysis and Safety
Authors: Yuxi Wang | Zhenhong Jia | Jie Yang
Source: IEEE Access (article, 2019-01-01)
Field: Chemistry — Analytical Chemistry

An Variable Selection Method of the Significance Multivariate Correlation Competitive Population Analysis for Near-Infrared Spectroscopy in Chemical Modeling The high dimensionality of spectral datasets makes it difficult to select the optimal subset of variables. This paper presents a new method for variable selection called the significant multivariate competitive population analysis (SMCPA), Which combines ideas of significant multivariate correlation (SMC) and model population analysis, and employs weighted bootstrap sampling (WBS) and exponential decline function (EDF) competition methods. In this study, the values of SMC distributions are used as an index for evaluating the importance of each wavelength. Then, based on the importance level of each wavelength. SMCPA sequentially selects N subsets of spectral wavelengths by N Monte Carlo sampling in an iterative and competitive procedure. In each sampling run, a fixed ratio of samples is used to build a calibrated partial least-squares model, and then SMC is performed to obtain the score and threshold values. Next, based on the significant multivariate correlation scores, the key variables are selected by two steps: the compulsory selection of exponential decline function and the competitive selection of adaptive weighted sampling. Finally, cross-validation(CV) is applied to select the optimal subset with the lowest root mean square error. This method is tested on three NIR spectral datasets and compared against three high-performance variable selection methods. The experimental results show that the proposed algorithm has the highest efficiency and the best selection effect, and can usually locate the optimal combination of key wavelength variables in a dataset. The evaluation result after PLS modeling is also the best. Feature selection ; Population ; Multivariate statistics ; Sampling (signal processing) ; Statistics ; Mathematics ; Selection (genetic algorithm) ; Curse of dimensionality ; Computer science ; Multivariate normal distribution ; Algorithm ; Pattern recognition (psychology) ; Artificial intelligence ; Filter (signal processing) Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Advanced Statistical Methods and Models
Authors: Klara Čebular | Bojan Božić | Stojan Stavber
Source: Molecules (article, 2019-07-17)
Field: Chemistry — Organic Chemistry

1,3-Dibromo-5,5-dimethylhydantoin as a Precatalyst for Activation of Carbonyl Functionality <b>Keywords:</b><b>&nbsp</b>. Chemistry ; Combinatorial chemistry Chemical Synthesis and Reactions ; Chemical Synthesis and Analysis ; Chemical Reactions and Mechanisms
Authors: Santosh Kurhade | Markella Konstantinidou | Fandi Sutanto | Katarzyna Kurpiewska | Justyna Kalinowska‐Tłuścik | Alexander Dömlingꝉ
Source: European Journal of Organic Chemistry (article, 2019-02-21)
Field: Chemistry — Organic Chemistry

Sequential Multicomponent Synthesis of 2‐(Imidazo[1,5‐α]pyridin‐1‐yl)‐1,3,4‐Oxadiazoles A 21 membered library of 2‐(imidazo[1,5‐α]pyridine‐1‐yl)‐1,3,4‐oxadiazoles is synthesized in an unprecedented short sequence starting from an Ugi tetrazole reaction with a cleavable isocyanide component. The intermediate tetrazole is subjected to an acetic anhydride‐mediated cyclization, followed by a Huisgen‐type rearrangement with acyl chlorides to afford the imidazopyridine‐oxadiazole bis‐heterocycles. The scope and limitations of the methodology were investigated with substitutions on both the oxadiazole and the imidazopyridine rings. The introduced enabling technology for imidazopyridine oxadiazole synthesis combines a short reaction sequence with high scaffold diversity, based on commercially available starting materials and high functional groups tolerance. Chemistry ; Imidazopyridine ; Tetrazole ; Oxadiazole ; Isocyanide ; Combinatorial chemistry ; Acetic anhydride ; Pyridine ; Sequence (biology) ; Organic chemistry ; Catalysis Multicomponent Synthesis of Heterocycles ; Synthesis and biological activity ; Synthesis and Characterization of Heterocyclic Compounds
Authors: Lena Quambusch | Ina Landel | Laura Depta | Jörn Weisner | Niklas Uhlenbrock | Matthias Müller | Franziska Glanemann | Kristina Althoff | Jens T. Siveke | Daniel Rauh
Source: Angewandte Chemie (article, 2019-10-04)
Field: Chemistry — Organic Chemistry

Covalent‐Allosteric Inhibitors to Achieve Akt Isoform‐Selectivity Abstract Isoforms of protein kinase Akt are involved in essential processes including cell proliferation, survival, and metabolism. However, their individual roles in health and disease have not been thoroughly evaluated. Thus, there is an urgent need for perturbation studies, preferably mediated by highly selective bioactive small molecules. Herein, we present a structure‐guided approach for the design of structurally diverse and pharmacologically beneficial covalent‐allosteric modifiers, which enabled an investigation of the isoform‐specific preferences and the important residues within the allosteric site of the different isoforms. The biochemical, cellular, and structural evaluations revealed interactions responsible for the selective binding profiles. The isoform‐selective covalent‐allosteric Akt inhibitors that emerged from this approach showed a conclusive structure–activity relationship and broke ground in the development of selective probes to delineate the isoform‐specific functions of Akt kinases. Allosteric regulation ; Gene isoform ; Protein kinase B ; Chemistry ; Biochemistry ; Small molecule ; Covalent bond ; Kinase ; Enzyme ; Cell biology ; Signal transduction ; Biology ; Gene Click Chemistry and Applications ; Quinazolinone synthesis and applications ; Protein Degradation and Inhibitors
Authors: Surya Subianto | Cheng Li | David Rubin | Santu Rana | Sunil Gupta | Rongliang He | Svetha Venkatesh | Alessandra Sutti
Source: ACS Omega (article, 2019-09-23)
Field: Materials Science — Surfaces, Coatings and Films

Optimizing a High-Entropy System: Software-Assisted Development of Highly Hydrophobic Surfaces using an Amphiphilic Polymer In materials science, the investigation of a large and complex experimental space is time-consuming and thus may induce bias to exclude potential solutions where little to no knowledge is available. This work presents the development of a highly hydrophobic material from an amphiphilic polymer through a novel, adaptive artificial intelligence approach. The hydrophobicity arises from the random packing of short polymer fibers into paper, a highly entropic, multistep process. Using Bayesian optimization, the algorithm is able to efficiently navigate the parameter space without bias, including areas which a human experimenter would not address. This resulted in additional knowledge gain, which can then be applied to the fabrication process, resulting in a highly hydrophobic material (static water contact angle 135°) from an amphiphilic polymer (contact angle of 90°) through a simple and scalable filtration-based method. This presents a potential pathway for surface modification using the short polymer fibers to create fluorine-free hydrophobic surfaces on a larger scale. Polymer ; Amphiphile ; Materials science ; Contact angle ; Nanotechnology ; Surface modification ; Scalability ; Hydrophobic effect ; Biological system ; Computer science ; Chemical engineering ; Chemistry ; Composite material ; Organic chemistry ; Engineering ; Copolymer Surface Modification and Superhydrophobicity ; Advanced Sensor and Energy Harvesting Materials ; Heat Transfer and Optimization
Authors: Carlos Henrique da Silva Santos | João Victor Ferreira de Morais | Felipe Bertelli | Amauri Garcia | Noé Cheung
Source: Separation and Purification Technology (article, 2019-09-14)
Field: Materials Science — Materials Chemistry

Purification of naphthalene by zone refining: Mathematical modelling and optimization by swarm intelligence-based techniques Refining (metallurgy) ; Naphthalene ; Biochemical engineering ; Swarm intelligence ; Process engineering ; Computer science ; Chemistry ; Engineering ; Particle swarm optimization ; Organic chemistry ; Machine learning Crystallization and Solubility Studies ; Process Optimization and Integration ; Analytical Chemistry and Chromatography
Authors: Takuya Kanazawa | Akinori Asahara | Hidekazu Morita
Source: Journal of Physics Materials (article, 2019-08-19)
Field: Materials Science — Materials Chemistry

Accelerating small-angle scattering experiments with simulation-based machine learning Abstract Making material experiments more efficient is a high priority for materials scientists who seek to discover new materials with desirable properties. In this paper, we investigate how to optimize the laborious sequential measurements of materials properties with data-driven methods, taking the small-angle neutron scattering (SANS) experiment as a test case. We propose two methods for optimizing sequential data sampling. These methods iteratively suggest the best target for the next measurement by performing a statistical analysis of the already acquired data, so that maximal information is gained at each step of an experiment. We conducted numerical simulations of SANS experiments for virtual materials and confirmed that the proposed methods significantly outperform baselines. Computer science ; Sampling (signal processing) ; Small-angle scattering ; Neutron scattering ; Experimental data ; Machine learning ; Scattering ; Artificial intelligence ; Statistics ; Mathematics ; Physics ; Optics ; Computer vision Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Nuclear Materials and Properties
Authors: Niels de Jonge | Kristian Mølhave | Damien Alloyeau
Source: Microscopy and Microanalysis (article, 2019-02-01)
Field: Materials Science — Surfaces, Coatings and Films

Introduction to the Proceedings of CISCEM 2018 - the 4th Conference on <i>In-Situ</i> and Correlative Electron Microscopy The Conference on In-Situ and Correlative Electron Microscopy (CISCEM) took place in Saarbrcken, Germany, for the fourth time [1], from October 10 to 12, 2018 (website: www.ciscem2018.de). CISCEM is about understanding nano-and atomic-scale processes under native and realistic conditions. In so-called in situ methods, observations and measurements are carried out in an environment similar to the real world, for example, in gases, in liquids, or at high temperatures. Correlative ; In situ ; Electron microscope ; Nanotechnology ; Atomic units ; Materials science ; Engineering physics ; Analytical Chemistry (journal) ; Chemistry ; Physics ; Optics ; Environmental chemistry ; Philosophy ; Organic chemistry Electron and X-Ray Spectroscopy Techniques ; Advanced Electron Microscopy Techniques and Applications ; Surface and Thin Film Phenomena
Authors: P. S. Lyubutin | С. В. Панин | Vladimir Titkov -Csc | А. В. Еремин | R. Sunder | Vladimir V. Titkov | R Sunder | J Barron | D Fleet | S Beauchemin | B Horn | B Schunck | B Lucas | T Kanade | C Fennema | W Thompson | A Jepson | M Jenkin | E Adelson | J Bergen | D Heeger | S Zhu | K Ma | W Li | E Salari | H Schreier | Q Wu | M Otte | H.-H Nagel | W Enkelmann | Wang C.-M Fan | K.-C Wang | C.-T | M Irani | W Gao | Q Kemao | J Marzat | Y Dumortier | A Ducrot | A Rav-Acha | S Peleg | T Ahn | Y Moon | J Kim | F Lopes | M Ghanbari | G Besnard | F Hild | S Roux | C Cofaru | W Philips | W Van Paepegem | K Kirschke | S Velinsky | L Sun | M Kamaliardakani | Y Zhang | R Lins | S Givigi | R Glantz | W Kropatsch | D Nowell | P De Matos | F Yusof | P Lopez-Crespo | P Withers | Z Lu | Y Liu | J Toribio | V Kharin | J Rice | S Mcneill | W Peters | M Sutton | Y Hos | M Vormwald | J Freire | P Matos | De
Source: PNRPU Mechanics Bulletin (article, 2019-01-01)
Field: Materials Science — Materials Chemistry

DEVELOPMENT OF THE DIGITAL IMAGE CORRELATION METHOD TO STUDY DEFORMATION AND FRACTURE PROCESSES OF STRUCTURAL MATERIALS РАЗВИТИЕ МЕТОДА КОРРЕЛЯЦИИ ЦИФРОВЫХ ИЗОБРАЖЕНИЙ ДЛЯ Digital image correlation ; Fracture (geology) ; Deformation (meteorology) ; Materials science ; Digital image ; Geology ; Image (mathematics) ; Computer science ; Composite material ; Artificial intelligence ; Image processing Material Properties and Failure Mechanisms ; Material Properties and Applications ; Engineering Diagnostics and Reliability
Authors: María Isabel Sánchez‐Rodríguez | Elena Sánchez‐López | Alberto Marinas | Francisco J. Urbano | José María Caridad y Ocerín
Source: Food Science & Nutrition (article, 2019-12-05)
Field: Chemistry — Analytical Chemistry

Functional approach and agro‐climatic information to improve the estimation of olive oil fatty acid content from near‐infrared data Extra virgin olive oil (EVOO) is very appreciated by its taste, flavor, and benefits for health, and so, it has a high price of commercialization. This fact makes it necessary to provide reliable and cost-effective analytical procedures, such as near-infrared (NIR) spectroscopy, to analyze its traceability and purity, in combination with chemometrics. Fatty acids profile of EVOO, considered as a quality parameter, is estimated, firstly, from NIR data and, secondly, by adding agro-climatic information. NIR and agro-climatic data sets are summarized by using principal component analysis (PCA) and treated by both scalar and functional approaches. The corresponding PCA and FPCA are progressively introduced in regression models, whose goodness of fit is evaluated by the dimensionless root-mean-square error. In general, SFAs, MUFAs, and PUFAs (and disaggregated fatty acids) estimations are improved by adding agro-climatic besides NIR information (mainly, temperature or evapotranspiration) and considering a functional point of view for both NIR and agro-climatic data. Principal component analysis ; Chemometrics ; Partial least squares regression ; Mathematics ; Mean squared error ; Food science ; Computer science ; Statistics ; Chemistry ; Machine learning Spectroscopy and Chemometric Analyses ; Edible Oils Quality and Analysis ; Advanced Chemical Sensor Technologies
Authors: Richard G. Brereton
Source: Journal of Chemometrics (article, 2019-05-17)
Field: Chemistry — Analytical Chemistry

Determining the significance of individual factors for orthogonal designs In previous articles, we have looked at how to determine the significance of a model. However, many people are also interested in whether we can determine the significance of individual factors. For example, if we are interested in how to improve the efficiency of an extraction or the yield of a reaction, what factors are likely to be significant? Can we determine p values for individual factors? Excluding factors one by one from a model, we can determine their significance. In this article, we will be primarily concerned with linear models that are those of the form where factors are orthogonal. For the enthusiasts, these are often studied using factorial designs, which will be discussed in future articles. The reader is invited to check these calculations in Excel, MATLAB, or any other environment. The full mechanics have been presented in previous articles; we will however summarise the main results. For the overall model, we can determine the p value and the best-fit coefficients as follows. As the p value is less than 0.01, we can conclude that there is a less than 1% chance that we would obtain at least this value if the null hypothesis was true, and we can conclude that there is indeed likely to be a relationship between the x and y values and that we have sufficient terms in the model. However, what we may be interested in is which of the terms in the model are significant. The null hypothesis for each term is that it is not significant. Just because the model including all three terms is significant does not imply each of x1, x2, and x3 are significant. There are many caveats to these observations. Second, of course, the p values are dependent on the underlying data being normal, and all other hidden significant factors being evened out. This is often possible in controlled laboratory experiments, such as chemical reactions but may be very difficult to control in metabolomics, especially when sampling populations where there are genetic, environmental, behavioural, and other hidden factors. However, p values can still provide an indication of significance, so long as they are not taken too seriously. Above, we have seen that by excluding factors one by one from a model, we can determine their contribution to the overall error, and so obtain an indicator of their significance. As we will show in later articles, this property is dependent on the factors in the design being orthogonal, and somewhat different conclusions are obtained if not. Null hypothesis ; Value (mathematics) ; Null (SQL) ; MATLAB ; Statistics ; Econometrics ; Contrast (vision) ; Factorial ; p-value ; Statistical significance ; Computer science ; Mathematics ; Statistical hypothesis testing ; Data mining ; Artificial intelligence Analytical Methods in Pharmaceuticals ; Pharmacy and Medical Practices
Authors: Hoheok Kim | Tatsuki Yamamoto | Yushi Sato | Junya Inoue
Source: Acta Materialia (article, 2019-07-08)
Field: Materials Science — Materials Chemistry

Establishment of structure-property linkages using a Bayesian model selection method: Application to a dual-phase metallic composite system Principal component analysis ; Materials science ; Bayesian inference ; Homogenization (climate) ; Bayesian probability ; Monte Carlo method ; Microstructure ; Biological system ; Feature selection ; Statistical physics ; Computer science ; Machine learning ; Artificial intelligence ; Statistics ; Mathematics ; Composite material Machine Learning in Materials Science ; Microstructure and mechanical properties ; Composite Material Mechanics
Authors: Yinkai Lei | Tianle Cheng | David S. Mebane | Youhai Wen
Source: Journal of Power Sources (article, 2019-02-01)
Field: Materials Science — Materials Chemistry

Reduced-order model for microstructure evolution prediction in the electrodes of solid oxide fuel cell with dynamic discrepancy reduced modeling Microstructure ; Solid oxide fuel cell ; Ostwald ripening ; Materials science ; Work (physics) ; Biological system ; Electrode ; Phase (matter) ; Oxide ; Thermodynamics ; Computer science ; Statistical physics ; Simulation ; Nanotechnology ; Chemistry ; Composite material ; Metallurgy ; Physics ; Physical chemistry Advancements in Solid Oxide Fuel Cells ; Nuclear Materials and Properties ; Solidification and crystal growth phenomena