jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Dwi Raharjo | Muhammad Zukhrufuz Zaman | Danar Praseptiangga | Ahmad Yunus
Source: Open Agriculture (article, 2023-01-01)
Field: Chemistry — Inorganic Chemistry

Physicochemical and microbiological characteristics of various stem bark extracts of <i>Hopea beccariana</i> Burck potential as natural preservatives of coconut sap Abstract The bark of Hopea beccariana Burck is used to prevent damage to coconut sap. The purpose of this study was to determine the physicochemical and microbiological characteristics of various extracts of the bark of H. beccariana Burck as potential natural preservatives for coconut sap. The bark was extracted by maceration method for 24 h assisted by stirring using ethanol, methanol, n -hexane, and water at 60°C as solvent. The type of solvent used for extraction had a significant effect on the yield, total phenolic, total flavonoid, antioxidant activity, toxicity, and diameter of the inhibition zone against Lactobacillus plantarum . The highest yield was found in methanol extract at 22.34%, the highest total phenolic content was found in ethanol extract of 53.39 mg gallic acid equivalent/g extract, and the highest total flavonoid content was found in the aqueous extract at 60°C of 106.70 mg QE/g extract; all extracts have an IC 50 value of 80.28–91.80 ppm (very strong antioxidant) and ascorbic acid of 5.78 ppm. Methanol extract is classified as very toxic with an LC 50 of 38.21 ppm. The dominant compounds produced by gas chromatography–mass spectrometer were hexanedioic acid, bis(2-ethylhexyl) ester; ethyl oleate; 9-octadecenoic acid ( Z )-, ethyl ester; and hexadecanoic acid, ethyl ester. The diameter of the inhibition zone for Saccharomyces cerevisiae was 13.50–14.72 mm, L. plantarum was 10.31–17.72 mm, and Leuconostoc mesenteroides was 13.25–18.06 mm. All extracts had minimum inhibitory concentration values of 2.5 mg/mL and minimum bactericidal concentration values of 2.5 mg/mL for n -hexane extract and 60°C water, while ethanol and methanol extracts had MBC values &gt;50 mg/mL. Chemistry ; Gallic acid ; Preservative ; Maceration (sewage) ; Flavonoid ; Ascorbic acid ; Antioxidant ; Food science ; Ethanol ; Chromatography ; Biochemistry Coconut Research and Applications ; Essential Oils and Antimicrobial Activity ; Phytochemicals and Antioxidant Activities
Authors: Lei Bao | Jun Shi
Source: Crystals (article, 2023-03-22)
Field: Materials Science — Materials Chemistry

A Novel Approach to Grain Shape Factor in 3D Hexagonal Cellular Automaton Cellular automata (CA) modeling is a powerful and efficient tool for simulating the dynamic evolution of polycrystalline microstructures in modern materials and metallurgy studies, such as solidification, plastic deformation and recrystallization. We propose a novel model to calculate the shape factor of grains in three-dimensional hexagonal grid (3D-HEX) CA, which overcomes the disadvantages of 3D-HEX CA, such as complex algorithms and a long computation time. The shape factor is a quantitative measure of grain morphology based on the ratio of the surface area of the grain to its volume-equivalent-sphere and volume-equivalent-chain. It indicates how the shape of a grain or phase affects its mechanical properties, such as stiffness, deformation and fracture. Our model can easily calculate the shape factor for any grain by counting its surface cells and volume cells. We test our model on 1000 grains with different shapes (equiaxed, irregular and chain-like) by Monte Carlo (MC) methods. MC methods evaluate the validity of a calculation model by comparing the simulated outcomes with the observed or expected outcomes. The results show that our model can accurately describe the grain morphology and has a good comparability and generality. Materials science ; Shape factor ; Equiaxed crystals ; Monte Carlo method ; Nucleation ; Recrystallization (geology) ; Computation ; Mechanics ; Biological system ; Statistical physics ; Geometry ; Microstructure ; Composite material ; Algorithm ; Computer science ; Thermodynamics ; Mathematics ; Physics ; Geology Microstructure and mechanical properties ; Aluminum Alloy Microstructure Properties ; Solidification and crystal growth phenomena
Authors: Shraddha Kumar | Anuradha Purohit | Sunita Varma
Source: International Journal of Data Science (article, 2023-01-01)
Field: Chemistry — Spectroscopy

Review of spectral clustering algorithms used in proteomics Tandem mass spectrometry (MS/MS) generates a large number of spectra showing the signal intensity of detected ions as a function of mass-tocharge ratio.Spectral clustering in proteomics is a powerful but under-utilised technique.Based on the similarity of spectra, the spectral clustering algorithms systematically and unerringly classify large numbers of spectra, such that all spectra in a given cluster belong to the same peptide.The data points in the spectral clustering approach are connected and do not require having convex boundaries.Spectral clustering therefore reduces the running time and computation requirements of spectral library and database searches.It enhances peptide identification process and has fuelled the development of many new proteomics algorithms recently.The goal of this review is to provide a clear overview of the most popular spectral clustering algorithms used in proteomics.It describes a systematic analysis of these spectral clustering algorithms, evaluating the benefits and limitations of each approach. Cluster analysis ; Spectral clustering ; Computer science ; Algorithm ; Proteomics ; Pattern recognition (psychology) ; Data mining ; Artificial intelligence ; Chemistry Advanced Proteomics Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Mass Spectrometry Techniques and Applications
Authors: Ante Galić | Stjepan Pliestić | I. Kovačev | Krešimir Čopec | Ivan Brandić
Source: Krmiva (article, 2023-10-06)
Field: Chemistry — Analytical Chemistry

Primjena modela strojnog učenja u tehnologiji proizvodnje krmnih smjesa Umjetna inteligencija (AI) i strojno učenje (ML ) sve su prisutniji u poljoprivrednom sektoru pa tako i u proizvodnji krmnih smjesa, gdje omogućuju proizvođačima optimizaciju procesa proizvodnje, smanjenje troškova i poboljšanje učinkovitosti. Implementacija nelinearnih modela, poput umjetnih neuronskih mreža (UNM ), u ovoj domeni omogućava prepoznavanje uzoraka i rješavanje kompleksnih problema povezanih s krmnim smjesama. Primjena UNM u tehnologiji proizvodnje krmnih smjesa, uz ispravno odabranu strukturu, algoritme učenja i prijenosne funkcije, omogućava naprednu optimizaciju procesa, uključujući upravljanje proizvodnim postrojenjem, skladištenje i transport sirovina. Ovi modeli pružaju nove mogućnosti za poboljšanje kvalitete krajnjeg proizvoda, uzimajući u obzir sve faktore koji na nju utječu. Kroz ovu prilagodbu, AI pruža alate za brzo i učinkovito donošenje odluka, što rezultira poboljšanom automatizacijom i učinkovitosti u proizvodnji krmnih smjesa. Physics ; Humanities ; Mathematics ; Philosophy Spectroscopy and Chemometric Analyses ; Mineral Processing and Grinding ; Global Trade and Competitiveness
Authors: C. Richard A. Catlow | Nora H. de Leeuw | Angelos Michaelides | Scott M. Woodley
Source: Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences (article, 2023-05-21)
Field: Materials Science — Ceramics and Composites

Supercomputing modelling of advanced materials: preface Peer reviewed: True Supercomputer ; Computer science ; Computational science ; Parallel computing Advanced ceramic materials synthesis ; Composite Material Mechanics ; Advanced Mathematical Modeling in Engineering
Authors: Maximilian Dietlmeier | Aromal Somarajan Rajan | Nina Leiter | Maximilian Wohlschläger | Martin Versen
Source:  (article, 2023-03-03)
Field: Chemistry — Organic Chemistry

Klassifizierung von Altholz durch FD-FLIM Bilder mit neuronalen Netzen Holz ist eine der wichtigsten erneuerbaren Ressourcen und kann recycelt werden, wobei derzeit nur unbehandeltes Holz in mehreren Lebenszyklen verwendet werden kann. Allerdings werden in Altholzsortieranlagen meist behandeltes und unbehandeltes Altholz gemischt angeliefert und es gibt keine automatische Klassifizierung zur sortenreinen Sortierung. Frühere Studien zeigen, dass behandelte und unbehandelte Holzproben mit Hilfe von bildgebender Fluoreszenzabklingzeit Mikroskopie unterschieden werden können. Um den Prozess der Klassifizierung von Altholz anhand von Fluoreszenzabklingzeitbildern zu automatisieren, werden zwei neuronale Netzwerke (NN) evaluiert. Das erste NN ist ein MLP (Multi Layer Perceptron), das das Altholz anhand von Erwartungswert und Standardabweichung eines Fluoreszenzabklingzeitbildes klassifiziert. Das zweite untersuchte NN ist ein CNN (Convolutional Neural Network), das das Holz direkt anhand der Fluoreszenzabklingzeitbilder klassifiziert. Beide Netzwerke sind in Python implementiert, um die spätere Verwendung in einem automatischen Klassifizierungs- und Sortierprozess zu erleichtern. Die Evaluation der NN zeigt vielversprechende Ergebnisse zur automatisierten Klassifikation von Altholz. Physics ; Gynecology ; Medicine Wood and Agarwood Research ; Remote Sensing and LiDAR Applications ; Image Processing and 3D Reconstruction
Authors: Chin Kui Fern | Chai Chang Yii | Asfarina Abu Bakar | Ismail Saad | Herwansyah Lago | Pungut Ibrahim | Ahmad Razani Haron
Source: Journal of Engineering and Science Research (article, 2023-06-30)
Field: Materials Science — Materials Chemistry

Adaptive Wavelet De-noising Algorithm using Absolute Difference Optimization Technique for Partial Discharge Signal Discrete Wavelet Transform (DWT) de-noising method is widely used for one-dimension partial discharge (PD) signals measured from medium voltage underground cable. However, DWT de-noising has several drawbacks that prevent the DWT de-noising from improving its de-noising effectiveness In DWT de-noising, the two most important parameters are decomposition level and mother wavelet. The aforementioned parameters must be varied according to the noise level in the measured PD signal in order to effectively suppress the noise of the measured PD signal. In this paper, an adaptive DWT de-noising algorithm based on the Absolute Difference Optimizing (ADO) technique is presented to effectively suppress the varying noise levels in measured PD signal. First, the measured PD signal will be de-noised using a Daubechies 3 (db3) mother wavelet and a DWT decomposition level ranging from 1 to 10. Second, the de-noised PD signal will be subjected to the ADO technique. The sum of the absolute difference of local maxima in the de-noised PD signal will be used as an indicator to select the best decomposition level for the de-noised PD signal. Finally, the best-selected de-noised PD signal by using the ADO technique will be used to estimate the PD location on the underground cable. The results of PD location error using the ADO technique and normal DWT de-noising will be compared. The findings show that the ADO-based adaptive DWT de-noising algorithm significantly improved the de-noising process of the measured PD signal. Discrete wavelet transform ; SIGNAL (programming language) ; Wavelet ; Algorithm ; Noise (video) ; Daubechies wavelet ; Mathematics ; Computer science ; Wavelet transform ; Artificial intelligence ; Image (mathematics) High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Lightning and Electromagnetic Phenomena
Authors: V. Golovin | S Dobriyan | V Kasatkin | A Frumkin | G Neer | D Rajak | D Pagar | P Menezes | E Linul | M Zagho | E Hussein | A Elzatahry | V Golovin | Suryakanta Nayak | Tapan Kumar Rout | A Radhamani | H Lau | S Ramakrishna | N Karousis | N Tagmatarchis | D Tasis | F Brambilla | E Campazzi | D Sinolli | P-J Lathiere | D Ramesh | T Vasudevan | V Lukin | K Kolinenko | P Agarwal | M Orazem | L Garcia-Rubio | F Mansfeld
Source: International Journal of Corrosion and Scale Inhibition (article, 2023-12-24)
Field: Materials Science — Materials Chemistry

Evolution of electrochemical impedance spectra of adaptive polymer anticorrosive coatings modified with carbon nanotubes exposed to corrosive environment It is shown that modification of an adaptive epoxy coating material by carbon nanotubes (CNTs) can lead to a significant change in the structural and electro-physical properties of coatings, which manifests itself in a significant increase in thixotropic properties (by a factor of 5-13) and a sharp decrease (by 2-3 orders of magnitude) in the volume electric resistivity of the coating film.These properties determine the qualitative and quantitative changes in the electrochemical characteristics of coatings during exposure to aggressive media.It is shown that the evolution of the electrochemical properties of adaptive polymer coatings modified by CNT during exposure in an aggressive environment is primarily manifested in characteristic changes in the electrochemical impedance spectra.The changes are recorded from the initial moment of exposure in an aggressive environment and manifest themselves in the appearance of a new relaxation process and a significant change in the electrochemical impedance spectra during prolonged exposure of coatings.When modified by introduction of CNTs into the adaptive coating, a structuring effect is observed, which manifests itself in two ways: on the one hand, in the inhibition of the adaptive ability of the coating film at the initial stage of exposure on both Pt and St3 substrates, and on the other hand, it is expressed in the stabilization of the characteristics of the electrochemical impedance of modified coatings on carbon steel (St3) during prolonged exposure, even in hot chloride solutions.This is manifested in a slower drop in the absolute values of the impedance and a simultaneous increase in the phase angle. Carbon nanotube ; Materials science ; Corrosion ; Polymer ; Electrochemistry ; Dielectric spectroscopy ; Electrical impedance ; Carbon steel ; Chemical engineering ; Composite material ; Metallurgy ; Electrode ; Chemistry Corrosion Behavior and Inhibition ; Metal and Thin Film Mechanics
Authors: Patrick W. V. Butler | Roohollah Hafizi | Graeme M. Day
Source: ChemRxiv (preprint, 2023-10-27)
Field: Materials Science — Materials Chemistry

Machine Learned Potentials by Active Learning from Organic Crystal Structure Prediction Landscapes A primary challenge in organic molecular crystal structure prediction (CSP) is accurately ranking the energies of potential structures. While high-level solid-state density functional theory (DFT) methods allow for mostly reliable discrimination of the low energy structures, their high computational cost is problematic because of the need to evaluate tens to hundreds of thousands of trial crystal structures to fully explore typical crystal energy landscapes. Consequently, lower-cost but less accurate empirical force fields are often used, sometimes as the first stage of a hierarchical scheme involving multiple stages of increasingly accurate energy calculations. Machine learned potentials (MLPs), trained to reproduce the results of ab initio methods with computational cost close to that of force fields, can improve the efficiency of CSP by reducing or eliminating the need for costly DFT calculations at the final stages of CSP. Here, we investigate active learning methods for training MLPs with CSP datasets. The combination of active learning with the well-developed sampling methods from CSP yields potentials in a highly automated workflow that are relevant over a wide range of the crystal packing space. To demonstrate these potentials, we illustrate efficiently re-ranking large, diverse crystal structure landscapes to near-DFT accuracy from force field-based CSP, improving the reliability of the final energy ranking. Furthermore, we demonstrate how these potentials can be extended to more accurately model structures far from lattice energy minima through additional on-the-fly training within Monte Carlo simulations. Crystal structure prediction ; Force field (fiction) ; Ranking (information retrieval) ; Maxima and minima ; Computer science ; Machine learning ; Artificial intelligence ; Monte Carlo method ; Density functional theory ; Workflow ; Statistical physics ; Crystal structure ; Computational chemistry ; Physics ; Mathematics ; Chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Crystallography and molecular interactions
Authors: Zakari Ya’u Ibrahim | Adamu Uzairu | Gideon Adamu Shallangwa | Stephen Eyije Abechi | Sulaiman Isyaku
Source: Journal of Taibah University Medical Sciences (article, 2023-05-01)
Field: Chemistry — Organic Chemistry

Homology modeling, docking, and ADMET studies of benzoheterocyclic 4-aminoquinolines analogs as inhibitors of Plasmodium falciparum OBJECTIVES: The ongoing fight against endemic diseases is necessary due to the growing resistance of malarial parasites to widely accessible medications. Thus, there has been an ongoing search for antimalarial medications with improved efficacy. The goal of this study was to develop derivatives of benzoheterocyclic 4-aminoquinolines with enhanced activities and better binding affinities than the original compounds. METHODS: Thirty-four derivatives of benzoheterocyclic 4-aminoquinolines were docked (using a model of dihydrofolate reductase-thymidylate synthase [DRTS] protein) with Molegro software to identify the compound with the minimum docking score as a design template. The generated quantitative structure–activity model was employed to estimate the activity of the designed derivatives. The derivatives were also docked to determine the most stable derivatives. Furthermore, the designed derivatives were tested for their drug-likeness and pharmacokinetic properties using SwissADME software and pkCSM web application, respectively. RESULTS: Compound H-014, (N-(7-chloroquinolin-4-yl)-2-(4-methylpiperazin-1-yl)-1,3-benzoxazol-5-amine) with the lowest re-rank score of −115.423 was employed as the design template. Then 10 derivatives were further designed by substituting –OH, –OCH(3), –CHO, –F, and -Cl groups at various positions of the template. We found that the designed derivatives had improved activities compared to the template. The docking scores of the designed derivatives were lower than those of the original derivatives. Derivative h-06 (7-methoxy-4-((2-(4-methylpiperazin-1-yl)benzo[d]oxazol-5-yl)amino)quinolin-6-ol) with four hydrogen bonds was identified as the most stable due to its lowest re-rank score (−163.607). While all of the designed derivatives satisfied both the Lipinski and Verber rules, some derivatives such as h-10 (cytochrome P450 1A2 [CYP1A2]); h-05, h-08, h-09, and h-10 [CYP2C19]; and h-03, h-07, h-08, and h-10 [renal organic cation transporter 2 substrate]) showed poor absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties. CONCLUSION: Ten derivatives of benzoheterocyclic 4-aminoquinolines were designed with improved efficacies. Derivatives that follow Lipinski and Verber rules and are mostly non-toxic and non-sensitive to the skin can be utilized in the development of effective antimalarial medications. Docking (animal) ; Plasmodium falciparum ; Homology modeling ; Stereochemistry ; Combinatorial chemistry ; Chemistry ; Hydrogen bond ; Aminoquinolines ; Dihydrofolate reductase ; Computational biology ; Enzyme ; Biochemistry ; Biology ; Molecule ; Medicine ; Organic chemistry ; Malaria Synthesis and biological activity ; Computational Drug Discovery Methods ; Quinazolinone synthesis and applications
Authors: Pauline Bennet | A. Moreau | Denis Langevin | Olivier Teytaud | Peter R. Wiecha | Abdourahman Khaireh-Walieh
Source: arXiv (Cornell University) (preprint, 2023-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Illustrated tutorial on global optimization in nanophotonics Numerical optimization for the inverse design of photonic structures is a tool which is providing increasingly convincing results -- even though the wave nature of problems in photonics makes them particularly complex. In the meantime, the field of global optimization is rapidly evolving but is prone to reproducibility problems, making it harder to identify the right algorithms to use. This paper is thought as a tutorial on global optimization for photonic problems. We provide a general background on global optimization algorithms and a rigorous methodology for a physicist interested in using these tools -- especially in the context of inverse design. We suggest algorithms and provide explanations for their efficiency. We provide codes and examples as an illustration than can be run online, integrating quick simulation code and Nevergrad, a state-of-the-art benchmarking library. Finally, we show how physical intuition can be used to discuss optimization results and to determine whether the solutions are satisfactory or not. Computer science ; Benchmarking ; Photonics ; Global optimization ; Intuition ; Context (archaeology) ; Nanophotonics ; Optimization problem ; Mathematical optimization ; Theoretical computer science ; Algorithm ; Mathematics Metamaterials and Metasurfaces Applications ; Optical Coatings and Gratings ; Plasmonic and Surface Plasmon Research
Authors: Chi M. Phan
Source: ChemRxiv (preprint, 2023-07-12)
Field: Chemistry — Organic Chemistry

The affinity of amphiphilic molecules to air/water surface The affinity of amphiphiles to the water/air surface was modelled by extending Eberhart’s equation. The equation successfully describes surface tension for all amphiphilic structures, including alkanols, carboxylic acids, non-ionic, ionic and Gemini surfactants. The model is more effective than the conventional analysis for amphiphiles with multiple ionic states. The prediction was consistently validated at different temperatures and non-aqueous solvents. The modelling results show a linear correlation between surface affinity and hydrophobicity/hydrophilicity. For alkanols, the affinity increment is 2.84 kJ/mol per CH2 group, the same as the reported energy from monomer to aggregate for non-ionic surfactants. For carboxylic acids, the affinity increment per CH2 group is 3.18 kJ/mol, incorporating the degree of acid dissociation. The affinity-hydrophilicity correlation is approximately -0.22 kJ/mol per oxyethylene group. Amphiphile ; Surface tension ; Chemistry ; Ionic bonding ; Monomer ; Aqueous solution ; Molecule ; Dissociation (chemistry) ; Carboxylic acid ; Polymer chemistry ; Physical chemistry ; Thermodynamics ; Organic chemistry ; Copolymer ; Polymer ; Ion Surfactants and Colloidal Systems ; Carbon Dioxide Capture Technologies ; Phase Equilibria and Thermodynamics