jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Narges Esmaile | Soroush Shabaneh | Shirin Mofavvaz | Mahmoud Reza Sohrabi | Behzad Torabi
Source: ChemistrySelect (article, 2020-05-18)
Field: Chemistry — Analytical Chemistry

Spectrophotometric Determination of Trace Amounts of Benzotriazole in Aqueous Solutions Using Gold Nanoparticles: Artificial Neural Network Modeling Abstract In this study, ultraviolet–visible (UV–Vis) spectrophotometry method was applied to determine benzotriazole (BTA) in aqueous solutions based on surface plasmon resonance (SPR) property of gold nanoparticles (AuNPs). The experimental variables such as pH, time, the volume of various buffers, the concentration of different electrolytes, and concentrations of AuNPs were investigated. Under optimum conditions, the proposed method represented a linear range between 10 to 150 μg L −1 (R 2 = 0.9941) with a detection limit (LOD) of 5 μg L −1 . This method was successfully used to measure trace amounts of BTA in Ahwaz drinking water and Ramin power plant cooling water samples. The proposed spectrophotometry assay is accurate, low‐cost, simple, and does not need any separation steps compared to the other methods. In the next step, an artificial neural network (ANN) using Levenberg‐Marquardt (LM) and Resilient Back propagation (RP) algorithms, was used to predict the absorbance of the mentioned factors. For each algorithm, 2 layers and for each layer 2,4,6,8, and 10 neurons were studied. According to mean square error (MSE) and coefficient of determination (R 2 ), the LM algorithm was better than the RP algorithm for the prediction of absorbance. Absorbance ; Spectrophotometry ; Benzotriazole ; Colloidal gold ; Aqueous solution ; Detection limit ; Mean squared error ; Chemistry ; Artificial neural network ; Analytical Chemistry (journal) ; Materials science ; Trace Amounts ; Chromatography ; Nanoparticle ; Biological system ; Mathematics ; Computer science ; Nanotechnology ; Artificial intelligence Analytical Methods in Pharmaceuticals ; Pharmacological Effects and Assays ; Analytical Chemistry and Sensors
Authors: May Ali Alsaffar | Alyaa K. Mageed | Mohamed Abdel Rahman Abdel Ghany | Bamidele Victor Ayodele | Siti Indati Mustapa
Source: IOP Conference Series Materials Science and Engineering (article, 2020-12-01)
Field: Materials Science — Materials Chemistry

Elucidating the non-linear effect of process parameters on hydrogen production by catalytic methane reforming: an artificial intelligence approach Abstract This study focuses on the non-linear effect of gas hourly space velocity (GHSV), oxygen (O 2 ) concentration in the feed, the reaction temperature, and the CH 4 /CO 2 ratio on hydrogen production by catalytic methane dry reforming using artificial neural networks (ANN). Ten different ANN models were configured by varying the hidden neurons from 1 to 10. The various ANN model architecture was tested using 30 datasets. The ANN model with the topology of 4-9-2 resulted in the best performance with the sum of square error (SSE) of 0.076 and coefficient of determination (R 2 ) greater than 0.9. The predicted hydrogen yield and the CH 4 conversions by the optimized ANN model were in close agreement with the observed values obtained from the experimental runs. The level of importance analysis revealed that all the parameters significantly influenced the hydrogen yield and the CH 4 conversion. However, the reaction temperature with the highest level of importance was adjudged the parameter with the highest level of influence on the methane dry reforming. The study demonstrated that ANN is a robust tool that can be employed to investigate predictive modeling and determine the level of importance of parameters on methane dry reforming. Methane ; Space velocity ; Hydrogen production ; Yield (engineering) ; Hydrogen ; Catalysis ; Carbon dioxide reforming ; Artificial neural network ; Biological system ; Production (economics) ; Thermodynamics ; Syngas ; Materials science ; Mathematics ; Chemistry ; Computer science ; Machine learning ; Physics ; Selectivity ; Organic chemistry Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions ; Catalysts for Methane Reforming
Authors: Kévin Abbasi | Hugh Smith | Matthew D. Hoffman | Elahe Farghadany | Laura S. Bruckman | Alp Sehirlioglu
Source: Advanced Materials Interfaces (article, 2020-12-10)
Field: Materials Science — Materials Chemistry

Dimensional Stacking for Machine Learning in ToF‐SIMS Analysis of Heterostructures Abstract Output from multidimensional datasets obtained from spectroscopic imaging techniques provides large data suitable for machine learning techniques to elucidate physical and chemical attributes that define the maximum variance in the specimens. Here, a recently proposed technique of dimensional stacking is applied to obtain a cumulative depth over several LaAlO 3 /SrTiO 3 heterostructures with varying thicknesses. Through dimensional reduction techniques via non‐negative matrix factorization (NMF) and principal component analysis (PCA), it is shown that dimensional stacking provides much more robust statistics and consensus while still being able to separate different specimens of varying parameters. The results of stacked and unstacked samples as well as the dimensional reduction techniques are compared. Applied to four LaAlO 3 /SrTiO 3 heterostructures with varying thicknesses, NMF is able to separate 1) surface and film termination; 2) film; 3) interface position; and 4) substrate attributes from each other with near perfect consensus. However, PCA results in the loss of data related to the substrate. Stacking ; Principal component analysis ; Heterojunction ; Materials science ; Non-negative matrix factorization ; Substrate (aquarium) ; Interface (matter) ; Position (finance) ; Artificial intelligence ; Matrix decomposition ; Pattern recognition (psychology) ; Computer science ; Optoelectronics ; Nuclear magnetic resonance ; Physics ; Composite material Electronic and Structural Properties of Oxides ; Ferroelectric and Piezoelectric Materials ; Ferroelectric and Negative Capacitance Devices
Authors: Roman Litvinov | Роман А. Дрокин | D. D Shamshina | M. Yu. Kalenova | L. E. Usmianova | E. A. Muraveva | Pavel M. Vasiliev | Егор К. Воинков | E. N. Ulomskiy | А. А. Спасов | В. Л. Русинов
Source: Russian Journal of Bioorganic Chemistry (article, 2020-11-01)
Field: Chemistry — Organic Chemistry

Prediction of Antiglycation Activity by Calculating the Energies of Frontier Molecular Orbitals for New 4-Hydroxy-1,4-Dihydroazolo[5,1-c]-1,2,4-Triazines Used as an Example Protein glycation and the formation of advanced glycation end products (AGEs) play an important role in the pathogenesis of diabetes mellitus (DM) complications, neurodegenerations, and age-related diseases. A model to predict antiglycation activity can reduce the costs and increase the productivity and quality of preclinical screening studies of new compounds. Azolo[5,1-c][1,2,4]triazines and azolo[1,5-a]pyrimidines are well known as biologically active compounds, which additionally have antiglycation properties. A number of 4-hydroxy-4H-azolo-1,4-dihydro[5.1-c]-1,2,4-triazines were selected for designing a prediction model. Azolotriazine derivatives were found to exert an antiglycation effect, inhibiting glycation of bovine serum albumin (BSA) with glucose and specific END fluorescence with equal or greater efficiency as compared with aminoguanidine. The activity range at 1000 μM was estimated at 23.0–71.6% for variously substituted derivatives (30.3 ± 1.2% for aminoguanidine). The highest activity was observed for 4-hydroxy-3-cyano-1,4-dihydro-1,2,4-triazolo[5.1-c]1,2,4-triazine. In all but one compound (aminoguanidine), antiglycation activity correlated with the energy difference ∆(HOMO – LUMO) between the highest occupied molecular orbital (HOMO) and lowest unoccupied molecular orbital (LUMO); the difference was established by a PM3 semiempirical method. Artificial neural network modeling was used to develop a mathematical model that describes the dependence of antiglycation activity on the calculated energies. The ELUMO and ∆(HOMO – LUMO) energies were found to make the largest contribution to the activity. The model can be used to predict antiglycation activity. HOMO/LUMO ; Glycation ; Chemistry ; Molecular orbital ; Atomic orbital ; Computational chemistry ; Stereochemistry ; Combinatorial chemistry ; Organic chemistry ; Biochemistry ; Molecule Synthesis and Characterization of Heterocyclic Compounds ; Advanced Glycation End Products research ; Macrophage Migration Inhibitory Factor
Authors: Olga Sarmanova | Kirill Laptinskiy | Sergey Burikov | Igor Isaev | Nikita Trifonov | Tatiana A. Dolenko | S. A. Dolenko
Source:  (article, 2020-04-01)
Field: Chemistry — Analytical Chemistry

Determination of heavy metal ions concentration in aqueous solutions using adaptive data analysis methods With the rapid development of industries such as mining activities, metal plating, pesticides and fertilizers manufacture, petroleum refining etc. heavy metal pollution has become a global threat. Unlike organic contaminants heavy metal ions are not biodegradable and tend to accumulate in soil, water and living organisms once being released in the environment. Heavy metal ions accumulated in an organism exceeding their critical level cause various diseases and disorders. That is why it is so vital to monitor the presence and concentrations of heavy metal ions in wastewaters. In this study we propose a new approach to solution of inverse multiparametric problem – determination of concentrations of 5 types of ions (Co<sup>2+</sup>, Ni<sup>2+</sup>, Cu<sup>2+</sup>, SO42-, NO3-) employing Raman and absorption spectroscopy, via adaptive data analysis methods. Such approach beneficially differs from conventional chemical analysis since it allows simultaneous and non-invasive monitoring of all the ions present in the aqueous solution compound in real mode. Aqueous solutions of CuSO<sub>4</sub>, NiSO<sub>4</sub>, CoSO<sub>4</sub>, Cu(NO<sub>3</sub>)<sub>2</sub>, Ni(NO<sub>3</sub>)<sub>2</sub>, Co(NO<sub>3</sub>)<sub>2</sub> salts were studied. The database consisting of 3854 absorption spectra and 3823 Raman spectra of these solutions was obtained. Adaptive data analysis methods comprise various data-driven methods which don’t require any apriori knowledge about the object of the study. This peculiarity beneficially distinguishes such methods from conventional modal-based approach especially concerning multicomponent aqueous solutions of heavy-metal ions where non-linear interactions take place. In this study artificial neural networks of various architectures as well as partial least squares regression were used in order to solve inverse multiparametric problem of optical spectroscopy. Results of the application of all the methods are discussed in the report. This study has been performed at the expense of the grant of Russian Science Foundation (project No. 19-11-00333, K.A.L., S.A.B., I.V.I, T.A.D., S.A.D. – conducting experiment, processing of spectra, discussion) and was supported by Foundation for the Advancement of Theoretical Physics and Mathematics “BASIS” (project No. 19-2-6-6-1, O.E.S. – application of artificial neural networks). Aqueous solution ; Metal ions in aqueous solution ; Chemistry ; Metal ; Ion ; Absorption (acoustics) ; Absorption spectroscopy ; Raman spectroscopy ; Analytical Chemistry (journal) ; Inorganic chemistry ; Environmental chemistry ; Materials science ; Physical chemistry ; Physics Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Karolina Gawęda | Anita Płazińska | Wojciech Płaziński
Source: Chemistry of Heterocyclic Compounds (article, 2020-12-01)
Field: Chemistry — Organic Chemistry

Conformations of saturated five-membered heterocycles evaluated by MP2 calculations Chemistry ; Computational chemistry Chemical Reaction Mechanisms ; Organic Chemistry Cycloaddition Reactions ; Asymmetric Synthesis and Catalysis
Authors: Andy S. Anker | Emil T. S. Kjær | Erik B. Dam | Simon J. L. Billinge | Kirsten M. Ø. Jensen | Raghavendra Selvan
Source: ChemRxiv (preprint, 2020-07-17)
Field: Materials Science — Materials Chemistry

Characterising the Atomic Structure of Mono-Metallic Nanoparticles from X-Ray Scattering Data Using Conditional Generative Models The development of new nanomaterials for energy technologies is dependent on understanding the intricate relation between material properties and atomic structure. It is, therefore, crucial to be able to routinely characterise the atomic structure in nanomaterials, and a promising method for this task is Pair Distribution Function (PDF) analysis. The PDF can be obtained through Fourier transformation of x-ray total scattering data, and represents a histogram of all interatomic distances in the sample. Going from the distance information in the PDF to a chemical structure is an unassigned distance geometry problem (uDGP), and solving this is often the bottleneck in nanostructure analysis. In this work, we propose to use a Conditional Variational Autoencoder (CVAE) to automatically solve the uDGP to obtain valid chemical structures from PDFs. We use a simple model system of hypothetical mono-metallic nanoparticles containing up to 100 atoms in the face centered cubic (FCC) structure as a proof of concept. The model is trained to predict the assigned distance matrix (aDM) from a simulated PDF of the structure as the conditional input. We introduce a novel representation of structures by projecting them inside a unit sphere and adding additional anchor points or satellites to help in the reconstruction of the chemical structure. The performance of the CVAE model is compared to a Deterministic Autoencoder (DAE) showing that both models are able to solve the uDGP reasonably well. We further show that the CVAE learns a structured and meaningful latent embedding space which can be used to predict new chemical structures. Conditional probability distribution ; Computer science ; Autoencoder ; Conditional probability ; Statistical physics ; Algorithm ; Biological system ; Physics ; Mathematics ; Artificial intelligence ; Artificial neural network Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Electron and X-Ray Spectroscopy Techniques
Authors: Juan M. Guerra | Carsten Mahr | Marcel Giar | Michael Czerner | Christian Heiliger
Source: Scientific Reports (article, 2020-10-01)
Field: Materials Science — Materials Chemistry

Ab initio calculations of conduction band effective mass parameters of thermoelectric $${\hbox {Mg}}_{2} {\hbox {X}}_{1{-}x} {\hbox {Y}}_x$$ (X, Y = Si, Ge, Sn) alloys Since there are still research interests in the physical properties of quasi-binary thermoelectric [Formula: see text] alloys, with X, Y = Si, Ge, Sn, we present an ab initio analysis that yields the relative formation energy and effective masses of the conduction bands, in the whole compositional range x. We base our calculations on the full-relativistic Korringa, Kohn and Rostocker (KKR) Green's functions formalism within the coherent potential approximation (CPA). Formation energies, measured relative to the end [Formula: see text] compounds, show no excess energy for the [Formula: see text] substitution thus indicating a complete solubility. In contrast, concave and asymmetric formation energies for intermediate compositions in the [Formula: see text] alloys manifest a miscibility gap. With this basis, we compute and discuss the crossing of the conduction bands observed in n-type [Formula: see text] materials. We present direction- and band-dependent effective masses using a generalized single parabolic band effective mass approximation to discuss anisotropic effects, to interpret available experimental and theoretical data, and to predict intermediate and not yet published transport parameters on these alloys. Materials science ; Crystallography ; Physics ; Chemistry Advanced Thermoelectric Materials and Devices ; Thermal Expansion and Ionic Conductivity ; Heusler alloys: electronic and magnetic properties
Authors: Houra Nekounam | Shayan Gholizadeh | Zahra Allahyari | Esmaeil Mirzaei | Mohammad Ali Shokrgozar | Reza Faridi‐Majidi
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2020-03-30)
Field: Materials Science — Biomaterials

Simple and Robust Fabrication and Characterization of Conductive Carbonized Nanofibers Loaded with Gold Nanoparticles for Bone Tissue Engineering Applications Abstract Bone tissue engineering is a new and applicable emerging approach to repair the bone defects. In this regard, designing and robust fabrication of tissue engineering scaffolds that could provide an appropriate environment for cell proliferation and differentiation is of high interest. Electrical conductive scaffolds which provide a substrate for stimulating cell growth and differentiation through a physiologically relevant physical signaling, electrical stimulation, has shown a highly promise in this approach. In this paper, we fabricated carbon nanofiber/gold nanoparticle (CNF/GNP) conductive scaffolds using two distinct methods; blending electrospinning in which gold nanoparticles were blended with electrospinning solution and electrospun, and electrospinning/electrospraying in which gold nanoparticle was electrosprayed simultaneously with electrospinning. The obtained electrospun mats underwent stabilization/carbonization process to prepare CNF/GNP scaffolds. The scaffolds were characterized by SEM, XRD, and Raman spectroscopy. SEM characterizations showed improved morphology and a slight decrease in the diameter of the spinned and sprayed nanofibers with moderate concentrations (from 178.66 ± 38.40 nm to 157.94 ± 24.14 nm and 120.81 ± 13.77 nm, respectively), In the electrosprayed form, better size distributions of nanofibers and less adhesion between individual fibers was observed, while XRD analysis confirmed the crystal structure of the nanofibers. Raman spectroscopy revealed enhancement in the graphitization of the structure, and the electrical conductivity of the structure improved by up to 29.2% and 81% in electrospraying and blending electrospinning modes, respectively. Indirect MTT and LDH toxicity assays directly were performed to assess MG63 cell toxicity, but no significant toxicity was observed and the scaffolds did not adversely affect cell proliferation. Overall, it can be concluded that in early tests, this structure have significant potential for bone tissue engineering applications. Abstract Figure Figure 1: Electrospinning ; Materials science ; Nanofiber ; Raman spectroscopy ; Tissue engineering ; Nanoparticle ; Chemical engineering ; Nanotechnology ; Carbonization ; Carbon nanofiber ; Fabrication ; Colloidal gold ; Carbon nanotube ; Composite material ; Scanning electron microscope ; Biomedical engineering ; Polymer Electrospun Nanofibers in Biomedical Applications ; Graphene and Nanomaterials Applications ; Bone Tissue Engineering Materials
Authors: Dure Najaf Iqbal | Erum Akbar Hussain | Ambreen Ghani | Faiza Hassan | Attaullah Bukhari | Shafia Iftikhar | Arif Nazir | Aftab Ahmad | Munawar Iqbal
Source: Polish Journal of Environmental Studies (article, 2020-03-16)
Field: Materials Science — Biomaterials

Green and Environmentally Friendly Techniquesfor Enhanced Physicochemical CharacteristicsAttributed to Polysaccharidesfor Industrial Applications Guar esters of long-chain aliphatic carboxylic acids (fatty acid) were prepared and examined. Esterification was done in two steps, with the first goal to prepare acid chloride by refluxing acid with slight excess of thionyl chloride, and the product was confirmed by elemental analysis. Ten different types of fatty acid (C 5 -C 18 ) were used for derivatization. The reaction mechanism for synthesis of fatty acid ester of guar was studied in detail. Physical properties of the esters were examined as well. The degree of substitution was calculated by titration method and structure elucidation was done using Fourier transform infrared spectroscopy (FTIR), proton-nuclear magnetic resonance ( 1 H-NMR) and scanning electron microscopy (SEM) techniques. Environmentally friendly ; Polysaccharide ; Chemistry ; Biochemical engineering ; Environmental science ; Environmental chemistry ; Engineering ; Organic chemistry ; Biology ; Ecology biodegradable polymer synthesis and properties
Authors: Takayoshi Ishimoto | Satoru Tsukada | Shin Wakitani | Kenji Sato | Daiki Saito | Yuki Nakanishi | Sakino Takase | Takashi Hamada | Joji Ohshita | Hiroyuki Kai
Source: RSC Advances (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

Model-based research toward design of innovative materials: molecular weight prediction of bridged polysilsesquioxanes Toward the design and manipulation of innovative materials, we propose a new concept called "model-based research (MBR)". In MBR, measurable physical and chemical properties of materials are mathematically modelled by explanatory parameters obtained by computer simulation from an atomistic point of view. To demonstrate the potential of MBR, we modelled the molecular weights of a series of polysilsesquioxanes with respect to the H<sub>2</sub>O/silane molar ratio employed for the polymerization of monomers bis(triethoxysilyl)methane, ethane, ethylene, and acetylene (BTES-M, -E1, -E2, and -E3), as an example. The equation <i>y</i> = <i>ax</i> <sup><i>n</i></sup> well reproduced the behaviour of the molecular weights of the BTES series, in which <i>a</i> and <i>n</i> were obtained using the calculated molecular parameters for monomers as the explanatory parameters. Detailed understanding and discussion were theoretically possible on the basis of the mathematical model. We predicted the molecular weights of polymers that would be obtained from monomers BTES-P and BTES-Ph with C<sub>3</sub>H<sub>6</sub> and C<sub>6</sub>H<sub>4</sub> as the spacer, respectively, using the mathematical model. Experimental validation of these polymers clearly showed the possibility of qualitative categorization. Our proposed concept, MBR, is a powerful tool to analyse materials science toward innovative materials design. Monomer ; Polymer ; Polymerization ; Molecular descriptor ; Thermodynamics ; Materials science ; Polymer science ; Methane ; Acetylene ; Silane ; Series (stratigraphy) ; Ethylene ; Computer science ; Polymer chemistry ; Chemistry ; Organic chemistry ; Physics ; Composite material ; Quantitative structure–activity relationship ; Geology Silicone and Siloxane Chemistry ; Catalysis and Oxidation Reactions ; Catalytic Processes in Materials Science