jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Milad Norouzpour | Amin Azdarpour | Rafael M. Santos | Ali Esfandiarian | Moein Nabipour | Erfan Mohammadian | Abbas Khaksar Manshad | Alireza Keshavarz
Source: ACS Omega (article, 2023-07-06)
Field: Chemistry — Analytical Chemistry

Comparative Static and Dynamic Analyses of Solvents for Removal of Asphaltene and Wax Deposits above- and below-Surface at an Iranian Carbonate Oil Field During production from oil wells, the deposition of asphaltene and wax at surface facilities and porous media is one of the major operational challenges. The crude oil production rate is significantly reduced due to asphaltene deposition inside the reservoir. In addition, the deposition of these solids inside the surface facilities is costly to oil companies. In this study, the efficiency of different solvents in dissolving asphaltene and wax was investigated through static and dynamic tests. The analysis of solid deposits from the surface choke of one of the Iranian carbonate oil fields showed that they consisted of 41.3 wt % asphaltene, and the balance was predominantly wax. In addition, the asphaltenes obtained from the surface choke solid deposits had a more complex structure than that of asphaltenes extracted from the crude oil itself. The static tests showed that xylene, toluene, gasoline, kerosene, and gas condensate had the highest efficiencies in dissolving solid deposits; conversely, diesel had a negative impact on dissolving solid deposits. Static tests on pure asphaltene showed that, among the tested solvents, gas condensate and diesel had a negative effect on the solubility of asphaltene. The dynamic core flooding results showed that asphaltene deposition inside the cores reduced the permeability by 79-91%. Among the tested solvents, xylene, gasoline, and kerosene resulted in the highest efficacy in restoring the damaged permeability, and higher efficiency was obtained with an equivalent solvent injection rate of 1 bbl/min versus 3 bbl/min. Asphaltene ; Wax ; Dissolution ; Kerosene ; Diesel fuel ; Chemical engineering ; Xylene ; Solvent ; Petroleum ; Solubility ; Chemistry ; Materials science ; Toluene ; Chromatography ; Organic chemistry Petroleum Processing and Analysis ; Enhanced Oil Recovery Techniques ; Hydrocarbon exploration and reservoir analysis
Authors: Elham Tahsin Yasin | Murat Köklü
Source: Proceedings of the International Conference on New Trends in Applied Sciences (article, 2023-12-01)
Field: Chemistry — Analytical Chemistry

Utilizing Random Forests for the Classification of Pudina Leaves through Feature Extraction with InceptionV3 and VGG19 An analysis of the "Pudina Leaf Dataset: Freshness Analysis" reveals distinct classes of dried, fresh, and spoiled mint leaves. Convolutional neural networks, InceptionV3 and VGG19, were used to extract features from the dataset using advanced image processing techniques. The classification task was then performed using a Random Forest machine learning algorithm. In this study, notable results were obtained, proving the effectiveness of the selected methodologies. Mint (Pudina) leaves were classified accurately using InceptionV3-extracted features at 94.8%, demonstrating robust performance in distinguishing freshness states. This deep learning architecture was further shown to be able to capture meaningful patterns within the dataset by utilizing VGG19-extracted features, resulting in an improved accuracy of 96.8%. Random forest ; Artificial intelligence ; Computer science ; Pattern recognition (psychology) ; Convolutional neural network ; Feature extraction ; Feature (linguistics) ; Machine learning Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Leaf Properties and Growth Measurement
Authors: Amirhossein Fallah | Ahmad Kalhor | Leila Yousefi
Source: Scientific Reports (article, 2023-01-12)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Developing a carpet cloak operating for a wide range of incident angles using a deep neural network and PSO algorithm Designing invisibility cloaks has always been one of the most fascinating fields of research; in this regard, metasurface-based carpet cloaks have drawn researchers' attention due to their inherent tenuousness, resulting in a lower loss and easier fabrication. However, their performances are dependent on the incident angle of the coming wave; as a result, designing a carpet cloak capable of rendering objects under it invisible for a wide range of angles requires advanced methods. In this paper, using the Particle Swarm Optimization (PSO) algorithm, along with a trained neural network, a metasurface-based carpet cloak is developed capable to operate for a wide range of incident angles. The deep neural network is trained and used in order to accelerate the process of calculation of reflection phases provided by different unit cell designs. The resultant carpet cloak is numerically analyzed, and its response is presented and discussed. Both near-field and far-field results show that the designed carpet cloak operates very well for all incident angles in the range of 0 to 65 degrees. Cloak ; Particle swarm optimization ; Computer science ; Rendering (computer graphics) ; Artificial neural network ; Ray ; Optics ; Range (aeronautics) ; Algorithm ; Artificial intelligence ; Metamaterial ; Physics ; Materials science Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Antenna Design and Analysis
Authors: Stefan Höving | Laura Neuendorf | Timo Betting | Norbert Kockmann
Source: Materials (article, 2023-01-21)
Field: Materials Science — Materials Chemistry

Determination of Particle Size Distributions of Bulk Samples Using Micro-Computed Tomography and Artificial Intelligence The knowledge of product particle size distribution (PSD) in crystallization processes is of high interest for the pharmaceutical and fine chemical industries, as well as in research and development. Not only can the efficiency of crystallization/production processes and product quality be increased but also new equipment can be qualitatively characterized. A large variety of analytical methods for PSDs is available, most of which have underlying assumptions and corresponding errors affecting the measurement of the volume of individual particles. In this work we present a method for the determination of particle volumes in a bulk sample via micro-computed tomography and the application of artificial intelligence. The particle size of bulk samples of sucrose were measured with this method and compared to classical indirect measurement methods. Advantages of the workflow are presented. Crystallization ; Particle-size distribution ; Particle (ecology) ; Materials science ; Particle size ; Workflow ; Biological system ; Process engineering ; Statistical physics ; Computer science ; Physics ; Thermodynamics ; Engineering ; Chemical engineering Crystallization and Solubility Studies ; Field-Flow Fractionation Techniques ; Mineral Processing and Grinding
Authors: Penghui Wang | Hongxia Qiao | Cuizhen Xue | Qiong Feng
Source: Materials (article, 2023-04-25)
Field: Materials Science — Materials Chemistry

Prediction of Water Resistance of Magnesium Oxychloride Cement Concrete Based upon Hybrid-BP Neural Network To obtain the magnesium oxychloride cement concrete (MOCC) ratio with excellent water resistance quickly and accurately, a BP neural network (BPNN) model with a topology structure of 4-10-2 was designed, and the PSO (particle swarm optimization), GWO (gray wolf optimization), and WOA (whale optimization algorithm) algorithms were used to optimize the model. The input layer parameters of the model above were n(MgO/MgCl<sub>2</sub>), Grade I fly ash, phosphoric acid (PA), and phosphate fertilizer (PF) content, and the output layer was the MOCC's compressive strength and softening coefficient. The model had a dataset of 144 groups, including 100 training set data, 22 verification set data, and 22 test set data. The results showed that the PSO-BPNN model had the highest predictive accuracy among the four models, with a mean R<sup>2</sup> of 0.99, mean absolute error(MAE) of 0.52, mean absolute percentage error(MAPE) of 0.01, and root mean square error (RMSE) of 0.73 in predicting compressive strength, and a mean R<sup>2</sup> of 0.99, MAE of 0.44, MAPE of 0.01, and RMSE of 0.62 in predicting the softening coefficient. The results showed that using the PSO-BPNN to predict the compressive strength and softening coefficient of MOCC is feasible and can provide theoretical guidance for designing the MOCC mix. Mean squared error ; Compressive strength ; Mean absolute percentage error ; Particle swarm optimization ; Coefficient of determination ; Artificial neural network ; Materials science ; Approximation error ; Cement ; Mathematics ; Biological system ; Algorithm ; Composite material ; Statistics ; Computer science ; Machine learning Magnesium Oxide Properties and Applications ; Concrete and Cement Materials Research ; Innovative concrete reinforcement materials
Authors: Еlena N. Kalinichenko | Aliaksandr Faryna | Tatyana Bozhok | Anna Golyakovich | О. В. Панибрат
Source: Current Issues in Molecular Biology (review, 2023-02-22)
Field: Chemistry — Organic Chemistry

Novel Phthalic-Based Anticancer Tyrosine Kinase Inhibitors: Design, Synthesis and Biological Activity In this work, fragments of isophthalic and terephthalic acids are proposed as a structural scaffold to develop potential inhibitors of protein kinases. Novel isophthalic and terephthalic acid derivatives were designed as type-2 protein kinase inhibitors, synthesized and subjected to physicochemical characterization. The screening of their cytotoxic actions against a panel of cell lines derived from different types of tumors (liver, renal, breast and lung carcinomas, as well as chronic myelogenous and promyelocytic leukemia) and normal human B lymphocyte, for the sake of comparison, was performed. Compound <b>5</b> showed the highest inhibitory activity against four cancer cell lines, K562, HL-60, MCF-7 and HepG2 (IC<sub>50</sub> = 3.42, 7.04, 4.91 and 8.84 µM, respectively). Isophthalic derivative <b>9</b> revealed a high potency against EGFR and HER2, at the levels of 90% and 64%, respectively, being comparable to lapatinib at 10 µM. In general, tumor cell cultures were more sensitive to isophthalic acid derivatives than to terephthalic acid ones. In cell cycle studies, isophthalic analogue <b>5</b> showed a pronounced dose-dependent effect, and with the increase in its concentration up to 10.0 µM, the number of living cells decreased to 38.66%, while necrosis reached 16.38%. The considered isophthalic compounds had a similar docking performance to that of sorafenib against the VEGFR-2 (PDB id: 4asd, 3wze). The correct binding of compounds <b>11</b> and <b>14</b> with VEGFR-2 was validated using MD simulations and MM-GPSA calculations. Isophthalic acid ; Chemistry ; Lapatinib ; Cancer research ; Terephthalic acid ; Biochemistry ; Biology ; Cancer ; Medicine ; Breast cancer ; Internal medicine Synthesis and biological activity ; Quinazolinone synthesis and applications ; PI3K/AKT/mTOR signaling in cancer
Authors: Ting Chen | Tianyu Xiang | Tao Lei | Mingxing Xu
Source: IEEE photonics journal (article, 2023-03-13)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Deep Learning for the Design of Toroidal Metasurfaces In recent years, the toroidal dipoles have had a profound impact on several fields including electromagnetism. However, the on-demand design of toroidal metasurfaces is still a very time-consuming process. In this paper, a method of neural network simulating the nonlinear relationship between the structural parameters of metasurfaces and its multipole scattered powers is proposed based on a deep learning algorithm. The forward network can quickly predict the scattered powers from input structural parameters, which can achieve an accuracy comparable to the electromagnetic simulations. In addition, with the required scattering spectrum as input, the appropriate parameters of the structure could be automatically calculated and then output by the inverse network which can achieve a low mean square error of 0.074 in training set and 0.18 in the test set. Compared with the conventional design process, the proposed deep learning model can guide the design of the toroidal dipole metasurface faster and pave the way for the rapid development of toroidal metasurfaces. Toroid ; Multipole expansion ; Computer science ; Electromagnetism ; Dipole ; Artificial neural network ; Computational electromagnetics ; Electromagnetics ; Process (computing) ; Set (abstract data type) ; Inverse ; Scattering ; Deep learning ; Electronic engineering ; Topology (electrical circuits) ; Algorithm ; Physics ; Optics ; Artificial intelligence ; Electromagnetic field ; Mathematics ; Electrical engineering ; Engineering ; Geometry Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Antenna Design and Analysis
Authors: Esteban Lopez-Ramirez | Sandra Lopez‐Zamora | Salvador Escobedo | Hugo de Lasa
Source: Processes (article, 2023-07-06)
Field: Chemistry — Analytical Chemistry

Artificial Neural Networks (ANNs) for Vapour-Liquid-Liquid Equilibrium (VLLE) Predictions in N-Octane/Water Blends Blends of bitumen, clay, and quartz in water are obtained from the surface mining of the Athabasca Oil Sands. To facilitate its transportation through pipelines, this mixture is usually diluted with locally produced naphtha. As a result of this, naphtha has to be recovered later, in a naphtha recovery unit (NRU). The NRU process is a complex one and requires the knowledge of Vapour-Liquid-Liquid Equilibrium (VLLE) thermodynamics. The present study uses experimental data, obtained in a CREC-VL-Cell, and Artificial Intelligence (AI) for vapour-liquid-liquid equilibrium (VLLE) calculations. The proposed Artificial Neural Networks (ANNs) do not require prior knowledge of the number of vapour-liquid phases. These ANNs involve hyperparameters that are used to obtain the best ANN model architecture. To accomplish this, this study considers (a) R2 Coefficients of Determination and (b) ANN training requirements to avoid data underfitting and overfitting. Results demonstrate that temperature has a major influence on ANN vapour pressure predictions, while the concentration of octane, the naphtha surrogate having, in contrast, a lesser effect. Furthermore, the ANN data obtained allows the calculation of octane-in-water and water-in-octane maximum solubilities. Naphtha ; Octane ; Artificial neural network ; Chemistry ; Undecane ; Octane rating ; Thermodynamics ; Process engineering ; Biological system ; Artificial intelligence ; Organic chemistry ; Computer science ; Gasoline ; Engineering ; Physics Petroleum Processing and Analysis ; Enhanced Oil Recovery Techniques ; Hydrocarbon exploration and reservoir analysis
Authors: Md. Saddam Hussain | Khandokar Sadique Faisal | Andrew J. Clulow | Hugo Albrecht | Marta Krasowska | Anton Blencowe
Source: Polymers (article, 2023-04-21)
Field: Materials Science — Biomaterials

Influence of Lyophilization and Cryoprotection on the Stability and Morphology of Drug-Loaded Poly(ethylene glycol-b-ε-caprolactone) Micelles Polymeric micelles are promising carriers for the delivery of poorly water-soluble drugs, providing enhanced drug solubility, blood circulation times, and bioavailability. Nevertheless, the storage and long-term stability of micelles in solution present challenges requiring the lyophilization and storage of formulations in the solid state, with reconstitution immediately prior to application. Therefore, it is important to understand the effects of lyophilization/reconstitution on micelles, particularly their drug-loaded counterparts. Herein, we investigated the use of β-cyclodextrin (β-CD) as a cryoprotectant for the lyophilization/reconstitution of a library of poly(ethylene glycol-<i>b</i>-ε-caprolactone) (PEG-<i>b</i>-PCL) copolymer micelles and their drug-loaded counterparts, as well as the effect of the physiochemical properties of different drugs (phloretin and gossypol). The critical aggregation concentration (CAC) of the copolymers decreased with increasing weight fraction of the PCL block (<i>f<sub>PCL</sub></i>), plateauing at ~1 mg/L when the <i>f<sub>PCL</sub></i> was >0.45. The blank (empty) and drug-loaded micelles were lyophilized/reconstituted in the absence and presence of β-CD (9% <i>w</i>/<i>w</i>) and analyzed via dynamic light scattering (DLS) and synchrotron small-angle X-ray scattering (SAXS) to assess for changes in aggregate size (hydrodynamic diameter, <i>D<sub>h</sub></i>) and morphology, respectively. Regardless of the PEG-<i>b</i>-PCL copolymer or the use of β-CD, the blank micelles displayed poor redispersibility (<10% relative to the initial concentration), while the fraction that redispersed displayed similar <i>D<sub>h</sub></i> to the as-prepared micelles, increasing in <i>D<sub>h</sub></i> as the <i>f<sub>PCL</sub></i> of the PEG-<i>b</i>-PCL copolymer increased. While most blank micelles displayed discrete morphologies, the addition of β-CD or lyophilization/reconstitution generally resulted in the formation of poorly defined aggregates. Similar results were also obtained for drug-loaded micelles, with the exception of several that retained their primary morphology following lyophilization/reconstitution, although no obvious trends were noted between the microstructure of the copolymers or the physicochemical properties of the drugs and their successful redispersion. Caprolactone ; Ethylene glycol ; Micelle ; Chemical engineering ; Freeze-drying ; Morphology (biology) ; Materials science ; Poly ethylene ; Polymer chemistry ; Chemistry ; Ethylene ; Organic chemistry ; Chromatography ; Copolymer ; Catalysis ; Polymer ; Aqueous solution Nanoparticle-Based Drug Delivery ; Advanced Polymer Synthesis and Characterization ; Hydrogels: synthesis, properties, applications
Authors: Xiaoming Ma | Tingting Feng | Peng Zhang | Hui Zhang | Xuan Hu | Yuying Yang | Zhen Wang | Huifang Zhang | Dong Peng | Xun Li | Jianguo Xu
Source: Foods (article, 2023-01-08)
Field: Materials Science — Materials Chemistry

Downregulation of Peroxidase Activity of Platinum Cube Enables Minute–Time Scale Colorimetric Signaling of Hypoxanthine for Fish Freshness Monitoring Due to its unique biological composition, aquatic products, especially fish, are extremely perishable compared to other muscle products. Herein, we proposed an artificial nanozyme-based colorimetric detection of hypoxanthine (Hx), the indicator of fish freshness, in a minute-time scale without the assistance of a natural enzyme (hypoxanthine oxidase). The principle is based on the interaction between Hx and polyvinylpyrrolidone-modified platinum cubic nanomaterials (PVP-PtNC), in which the catalytic active sites of PVP-PtNC's surface were blocked by Hx. This causes the downregulation of PVP-PtNC's catalytic ability and weakened its ability to catalyze the oxidization of 3,3',5,5'-Tetramethylbenzidine (TMB) by H<sub>2</sub>O<sub>2</sub>. Accordingly, the decrease in the UV-vis absorption and the weakening of the colorimetric reaction color is proportional to the Hx concentration. On this basis, a target-triggered colorimetric method for detecting Hx is developed for fish freshness monitoring with a fast detection speed, low cost, high accuracy, and simplified operation. Experiments reveal that the correlation response of Hx is from 0.5 μM to 10 mM with a limit of detection of 0.16 μM. In particular, the Hx detected from real fish indicates that the method possesses a promising potential for practical application. All of these features are expected to promote the development of online detection tools for food safety monitoring. Hypoxanthine ; Chemistry ; Fish <Actinopterygii> ; Detection limit ; Catalysis ; Polyvinylpyrrolidone ; Platinum ; Combinatorial chemistry ; Nanotechnology ; Chromatography ; Enzyme ; Biochemistry ; Materials science ; Organic chemistry ; Fishery ; Biology Advanced Nanomaterials in Catalysis ; Advanced biosensing and bioanalysis techniques ; Biosensors and Analytical Detection
Authors: Ping Tao | Wei Zhou | Xinting Miao | Jian Peng | Wenming Liu
Source: Metals (article, 2023-10-16)
Field: Materials Science — Metals and Alloys

Review of Characterization on Hydrogen Embrittlement by Micro-Sample Testing Methods Conventional-sized specimens have been well and widely applied in research on hydrogen embrittlement. However, when the limited-size core components (nozzles and valves, etc.) of hydrogen energy equipment are evaluated for service damage, traditional testing with conventional-sized samples is no longer applicable and micro-sample testing methods are required. In this paper, recent progress in the characterization of hydrogen embrittlement achieved via a small-sized sample tensile test, small punch test and nanoindentation test is reviewed. The commonly used geometries and dimensions of various small-sized specimens are first described and the in situ hydrogen-containing environment testing cases equipped with small-sized specimens are presented, proving the advantages of direct observations of hydrogen influences on the mechanical property and microstructure evolution. Then, the quantitative analysis of hydrogen embrittlement sensitivity involving a small punch test is discussed, with a focus on the comparisons of the hydrogen embrittlement index calculated using different definition methods. Finally, the nanoindentation test of investigation on the interaction between hydrogen and dislocation in metals and the effect of indentation strain rate are summarized. Furthermore, the specific research directions and applications of micro-size specimens for further investigation on hydrogen embrittlement are identified. Hydrogen embrittlement ; Materials science ; Nanoindentation ; Embrittlement ; Hydrogen ; Tensile testing ; Characterization (materials science) ; Microstructure ; Metallurgy ; Indentation ; Ultimate tensile strength ; Composite material ; Nanotechnology ; Corrosion ; Chemistry Hydrogen embrittlement and corrosion behaviors in metals ; Nuclear Materials and Properties ; High Temperature Alloys and Creep
Authors: Vincenzo Barone | Lina Marcela Uribe Grajales | Silvia Di Grande | Federico Lazzari | Marco Mendolicchio
Source: The Journal of Physical Chemistry A (article, 2023-09-04)
Field: Chemistry — Spectroscopy

DFT Meets Wave-Function Methods for Accurate Structures and Rotational Constants of Histidine, Tryptophan, and Proline A new computational strategy has been applied to the conformational and spectroscopic properties in the gas phase of amino acids with very distinctive features, ranging from different tautomeric forms (histidine) to ring puckering (proline), and heteroaromatic structures with non-equivalent rings (tryptophan). The integration of modern double-hybrid functionals and wave-function composite methods has allowed us to obtain accurate results for a large panel of conformers with reasonable computer times. The remarkable agreement between computations and microwave experiments allows an unbiased interpretation of the latter in terms of stereoelectronic effects. Conformational isomerism ; Tautomer ; Histidine ; Tryptophan ; Computational chemistry ; Chemistry ; Interpretation (philosophy) ; Function (biology) ; Wave function ; Computation ; Microwave ; Proline ; Amino acid ; Gas phase ; Stereochemistry ; Molecule ; Physics ; Physical chemistry ; Quantum mechanics ; Mathematics ; Organic chemistry ; Algorithm ; Computer science Molecular Spectroscopy and Structure ; Advanced Chemical Physics Studies ; Spectroscopy and Laser Applications