jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jun Li | Meilin Zhu | Chang Geng | Yingjie Yuan | Zewei Fu | Shu Yan | Rou Feng | Yingxu Wang | Ying Zhou | Liangliang Meng | Hui Zhang | Hongcun Bai
Source: Inorganics (article, 2023-05-27)
Field: Materials Science — Polymers and Plastics

A Molecular Understanding of the Flame Retardant Mechanism of Zinc Stannate/Polypropylene Composites via ReaxFF Simulations As an important new flame retardant, zinc stannate (ZS) shows wide application prospects due to its many advantages. However, the flame retardant mechanism of composites made with polymer combined with ZS is still unclear. In particular, there is a lack of molecular level description of the micro-scale flame retardant mechanism. The combustion mechanism through molecular simulation technology has become an important research paradigm in the field of fire, which can provide new insights for the development of new materials. This work studied the flame retardant mechanism of composites consistent with polypropylene (PP) and ZS using reactive force field molecular dynamics (ReaxFF MD) simulations. A new force field incorporating Sn/Zn/C/H/O components for ZS/PP composites combustion reactions was developed. Twenty different ZS/PP composites were analyzed for their combustion reactions at various temperatures. To investigate the flame retarding mechanism of ZS in composites, the evolutions of reactants, products, and reaction intermediates at the molecular scale were collected. It was revealed that the combustion temperature controlled the degree of oxidation by regulating the consumption of molecular oxygen during PP cracking. An increased combustion temperature reduced the oxygen consumption rate and overall oxygen consumption. As the PP component of composites exceeded 56%, oxygen consumption increased. Evolutions for carbon-containing intermediates and the products in combustions of PP/ZS composites were analyzed. The small carbon-based fragments were more likely to be produced for composites with low PP contents at high temperatures. These results are beneficial to design ZS/PP composites as flame retardant materials. Fire retardant ; ReaxFF ; Polypropylene ; Combustion ; Materials science ; Composite material ; Oxygen ; Stannate ; Zinc ; Molecular dynamics ; Chemistry ; Organic chemistry ; Computational chemistry Flame retardant materials and properties ; Fire dynamics and safety research ; Energetic Materials and Combustion
Authors: А. V. Suzdaltsev | O. R. Rakhmanova
Source: Applied Sciences (article, 2023-04-11)
Field: Materials Science — Materials Chemistry

Special Issue on Metal-Based Composite Materials: Preparation, Structure, Properties and Applications The Special Issue is aimed at analyzing modern trends and recent advances in the synthesis of new metal-based composite materials [...] Materials science ; Composite material MXene and MAX Phase Materials ; Boron and Carbon Nanomaterials Research ; Aluminum Alloys Composites Properties
Authors: Harald Fitzek | Martin Sterrer | Daniel Knez | Horst Schranger | Angelina Sarapulova | Sonia Dsoke | Hartmuth Schroettner | Gerald Kothleitner | Bernhard Gollas | Qamar Abbas
Source: Nanomaterials (article, 2023-05-04)
Field: Materials Science — Materials Chemistry

Impact of Iodine Electrodeposition on Nanoporous Carbon Electrode Determined by EQCM, XPS and In Situ Raman Spectroscopy The charging of nanoporous carbon via electrodeposition of solid iodine from iodide-based electrolyte is an efficient and ecofriendly method to produce battery cathodes. Here, the interactions at the carbon/iodine interface from first contact with the aqueous electrolyte to the electrochemical polarization conditions in a hybrid cell are investigated by a combination of in situ and ex situ methods. EQCM investigations confirm the flushing out of water from the pores during iodine formation at the positive electrode. XPS of the carbon surface shows irreversible oxidation at the initial electrolyte immersion and to a larger extent during the first few charge/discharge cycles. This leads to the creation of functional groups at the surface while further reactive sites are consumed by iodine, causing a kind of passivation during a stable cycling regime. Two sources of carbon electrode structural modifications during iodine formation in the nanopores have been revealed by in situ Raman spectroscopy, (i) charge transfer and (ii) mechanical strain, both causing reversible changes and thus preventing performance deterioration during the long-term cycling of energy storage devices that use iodine-charged carbon electrodes. Electrolyte ; X-ray photoelectron spectroscopy ; Materials science ; Raman spectroscopy ; Chemical engineering ; Electrochemistry ; Electrode ; Iodine ; Inorganic chemistry ; Carbon fibers ; Passivation ; Iodide ; Dielectric spectroscopy ; Nanoporous ; Chemistry ; Nanotechnology ; Composite material ; Physical chemistry ; Layer (electronics) Covalent Organic Framework Applications ; Transition Metal Oxide Nanomaterials ; Electrocatalysts for Energy Conversion
Authors: Timothy J. Giese | Darrin M. York
Source: The Journal of Chemical Physics (article, 2023-05-01)
Field: Chemistry — Spectroscopy

Estimation of frequency factors for the calculation of kinetic isotope effects from classical and path integral free energy simulations We use the modified Bigeleisen-Mayer equation to compute kinetic isotope effect values for non-enzymatic phosphoryl transfer reactions from classical and path integral molecular dynamics umbrella sampling. The modified form of the Bigeleisen-Mayer equation consists of a ratio of imaginary mode vibrational frequencies and a contribution arising from the isotopic substitution's effect on the activation free energy, which can be computed from path integral simulation. In the present study, we describe a practical method for estimating the frequency ratio correction directly from umbrella sampling in a manner that does not require normal mode analysis of many geometry optimized structures. Instead, the method relates the frequency ratio to the change in the mass weighted coordinate representation of the minimum free energy path at the transition state induced by isotopic substitution. The method is applied to the calculation of 16/18O and 32/34S primary kinetic isotope effect values for six non-enzymatic phosphoryl transfer reactions. We demonstrate that the results are consistent with the analysis of geometry optimized transition state ensembles using the traditional Bigeleisen-Mayer equation. The method thus presents a new practical tool to enable facile calculation of kinetic isotope effect values for complex chemical reactions in the condensed phase. Kinetic isotope effect ; Kinetic energy ; Path integral formulation ; Chemistry ; Reaction coordinate ; Path (computing) ; Statistical physics ; Thermodynamics ; Computational chemistry ; Physics ; Atomic physics ; Classical mechanics ; Quantum mechanics ; Computer science ; Deuterium Advanced NMR Techniques and Applications ; DNA and Nucleic Acid Chemistry ; Chemical Reaction Mechanisms
Authors: Mohammednoor Altarawneh
Source: Chemistry Teacher International (article, 2023-09-01)
Field: Chemistry — Physical and Theoretical Chemistry

Virtual undergraduate chemical engineering labs based on density functional theory calculations Abstract Offering virtual laboratories to chemical engineering students is likely to extend beyond the COVID-19 era. Thus, there is a need to develop more simulation-based tasks in a wide blend of subjects, spanning thermodynamics, chemical reactions, and unit operations. Molecular and material modeling based on density functional theory (DFT) calculations provides limitless opportunities to train students on how to obtain thermokinetic values that are typically measured experimentally. Through a series of illustrative cases, this contribution provides detailed procedures pertinent to calculating reaction rate constants, standard enthalpies of reactions, redox potentials, equilibrium reaction constants, and surface-assisted mechanisms. We hope that DFT-operated virtual labs will assist students in attaining learning outcomes and promote their self-learning in the above-mentioned subjects. From a broader prospective, the presented case studies are expected to encourage students to appreciate the practical applications of DFT, not only in the academic but also in the industrial domains. Density functional theory ; Virtual Laboratory ; Computer science ; Chemical equation ; Constant (computer programming) ; Chemistry ; Physical chemistry ; Computational chemistry ; Multimedia Various Chemistry Research Topics ; Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Partha Kumbhakar | Mizaj Shabil Sha | Chandra Sekhar Tiwary | Asan G. A. Muthalif | Somaya Al-Máadeed | Kishor Kumar Sadasivuni
Source: 3 Biotech (article, 2023-03-01)
Field: Chemistry — Spectroscopy

An efficient transition metal chalcogenide sensor for monitoring respiratory alkalosis The online version contains supplementary material available at 10.1007/s13205-023-03497-z. Chalcogenide ; Respiratory monitoring ; Respiratory alkalosis ; Respiratory system ; Medicine ; Intensive care medicine ; Materials science ; Internal medicine ; Metallurgy Molecular Sensors and Ion Detection ; Sulfur Compounds in Biology ; Gas Sensing Nanomaterials and Sensors
Authors: Rachel Woods‐Robinson | Matthew K. Horton | Kristin A. Persson
Source: Patterns (article, 2023-04-07)
Field: Materials Science — Materials Chemistry

A method to computationally screen for tunable properties of crystalline alloys Conventionally, high-throughput computational materials searches start from an input set of bulk compounds extracted from material databases, but, in contrast, many real functional materials are heavily engineered mixtures of compounds rather than single bulk compounds. We present a framework and open-source code to automatically construct and analyze possible alloys and solid solutions from a set of existing experimental or calculated ordered compounds, without requiring additional metadata except crystal structure. As a demonstration, we apply this framework to all compounds in the Materials Project to create a new, publicly available database of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mo>></mml:mo></mml:mrow></mml:math>600,000 unique "alloy pair" entries that can be used to search for materials with tunable properties. We exemplify this approach by searching for transparent conductors and reveal candidates that might have been excluded in a traditional screening. This work lays a foundation from which materials databases can go beyond stoichiometric compounds and approach a more realistic description of compositionally tunable materials. Metadata ; Set (abstract data type) ; Computer science ; Construct (python library) ; Code (set theory) ; Materials science ; Alloy ; Stoichiometry ; Chemistry ; Programming language ; Metallurgy Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; X-ray Diffraction in Crystallography
Authors: Kalyani Kharangate | Pravin Shende
Source: Particle & Particle Systems Characterization (article, 2023-02-07)
Field: Materials Science — Polymers and Plastics

Integration of Hybrid Dendrimers and Their Generations for Modulated Spatio‐Temporal Action Abstract Hybrid dendrimers constitute a unique class of well‐defined complex architecture featured with a central domain and bifurcated branches with two different dendritic sequences for their atomic configuration and functional groups at the periphery. This review article pioneers the field of new hybrid dendrimers using different generations by nanoconjugation with metals, carbohydrates, nucleotides, proteins/peptides, carbosilane, urea, silica, stem cells, guanidine, etc. The smart dendrimers contain desirable electrical, magnetic, optical, and biological attributes to increase surface area, monodisperse behavior, dose reduction, dissolution, permeability, long‐term stability, and significant decrement in nanotoxicity studies. The higher encapsulation of lipid soluble and insoluble moieties explores an excellent platform for the delivery of drugs (ibuprofen, indomethacin, etc.), nucleic acids (oligonucleotide, siRNA, and aptamer), genetic materials , and chemical diagnostic agents (gadolinium chelates and superparamagnetic iron oxide particles) for imaging. Owing to their flexibility in structural adaptability, different health conditions like glaucoma, inflammation, microbial infection, neurodegenerative problems (Alzheimer's disease and Parkinsonism), and cancer are benefited using such long‐lasting drug delivery. Advancements in molecular engineering techniques, 3D printing, artificial intelligence, robotic, green synthesis, and microwave‐assisted methods aid in the development of economically reliable and personalized pharmaceutical hybrid dendritic systems resembling antibodies, globular proteins, stem cells, enzymes, and genetic materials. Dendrimer ; Drug delivery ; Nanotechnology ; Combinatorial chemistry ; Dispersity ; Chemistry ; Materials science ; Biochemistry ; Organic chemistry Dendrimers and Hyperbranched Polymers ; RNA Interference and Gene Delivery ; Advanced biosensing and bioanalysis techniques
Authors: Santosh Kumar B | Kilaru Aswini | A. Kakoli Rao | Khursheed Alam | Muthana Saleh Mashkour | Alok Pal Jain
Source: E3S Web of Conferences (article, 2023-01-01)
Field: Chemistry — Inorganic Chemistry

RETRACTED: Fabrication and Characterization of Nanoscale Metal-Organic Frameworks (MOFs) for Gas Storage and Separation The Publisher has been made aware of ethical breaches affecting this proceeding published in E3S Web of Conferences, Volume 430 (2023) . These instances involve a specific author, K.K. Saxena who used citation manipulation and inappropriate references in 47 articles, for a total of 310 citations. We are extremely concerned by such malpractice which considerably impacts the image of our title and our Publisher’s reputation. See our publishing ethics policies . The Guest Editor of the proceedings volume endorsed the Publisher's decision to retract these articles. Web of Conferences is extremely grateful to the whistleblower for bringing this case to our attention. See the retraction notice E3S Web of Conferences 430 , 00002 (2023), https://doi.org/10.1051/e3sconf/202443000002 Fabrication ; Metal-organic framework ; Gas separation ; Materials science ; Hydrogen storage ; Nanotechnology ; Characterization (materials science) ; Nanoscopic scale ; Adsorption ; Energy storage ; Chemical engineering ; Methane ; Chemistry ; Organic chemistry ; Membrane Metal-Organic Frameworks: Synthesis and Applications ; Membrane Separation and Gas Transport ; Boron and Carbon Nanomaterials Research
Authors: Nico Unglert | Jesús Carrete | Lívia B. Pártay | Georg K. H. Madsen
Source: Physical Review Materials (article, 2023-12-20)
Field: Materials Science — Materials Chemistry

Neural-network force field backed nested sampling: Study of the silicon <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>p</mml:mi><mml:mtext>−</mml:mtext><mml:mi>T</mml:mi></mml:mrow></mml:math> phase diagram Phase diagrams map out the thermodynamically stable conditions for different phases. Their predictive atomistic simulations demands integration of statistical mechanics and quantum mechanics, making computational cost a challenge. Our work successfully combines a neural-network model for silicon's potential energy with nested-sampling to predict its low-pressure phase diagram accurately. Trained on diverse silicon structures, the model aligns remarkably well with experiments, accurately reproducing the melting line and identifying stable structures. The fusion of neural networks and nested sampling opens the door to not only predict phase diagrams but also assess the accuracy of the underlying exchange-correlation functionals. Artificial neural network ; Phase diagram ; Sampling (signal processing) ; Materials science ; Silicon ; Field (mathematics) ; Statistical physics ; Phase (matter) ; Algorithm ; Artificial intelligence ; Thermodynamics ; Computer science ; Mathematics ; Physics ; Quantum mechanics ; Pure mathematics Machine Learning in Materials Science ; Theoretical and Computational Physics ; Advanced Electron Microscopy Techniques and Applications
Authors: Mohammed Alyami | Satam Alotibi
Source: Nanomaterials (article, 2023-06-29)
Field: Materials Science — Polymers and Plastics

Physical Properties of E143 Food Dye as a New Organic Semiconductor Nanomaterial Organic semiconductors (OSCs) have attracted considerable attention for many promising applications, such as organic light-emitting diodes (OLEDs), organic field-effect transistors (OFETs), and organic photovoltaics (OPVs). The present work introduced E143 food dye as a new nanostructured organic semiconductor that has several advantages, such as low cost, easy fabrication, biocompatibility, and unique physical properties. The material was characterized using a transmission electron microscope (TEM), Fourier transform infrared (FT-IR) spectroscopy, thermogravimetric analysis (TGA), and optical absorption spectroscopy. The study of X-ray diffraction (XRD) showed that E143 dye has a monoclinic polycrystalline structure. Electrical and dielectric properties were performed by impedance spectroscopy at frequencies (20 Hz-1 MHz) in the temperature range (303-473 K). The values of interband transitions and activation energy recommended the application of E143 dye as a new organic semiconductor material with promising stability, especially in the range of hot climates such as KSA. Materials science ; Organic semiconductor ; Nanomaterials ; Organic solar cell ; Semiconductor ; Dielectric spectroscopy ; OLED ; Optoelectronics ; Nanotechnology ; Polymer ; Chemistry ; Composite material Conducting polymers and applications ; Luminescence and Fluorescent Materials ; Polydiacetylene-based materials and applications
Authors: Zhi‐Hang Lee | Po Chuan Lin | Tzuhsiung Yang
Source: Journal of the Chinese Chemical Society (article, 2023-03-26)
Field: Materials Science — Materials Chemistry

Inverse design of ligands using a deep generative model semi‐supervised by a data‐driven ligand field strength metric Abstract Transition metal (TM) complexes exhibit diverse structural and electronic properties. The properties of a TM complex can be tuned by modulating the ligand field strength (LFS) inflicted by its ligands. Current quantification of the LFS of a ligand is mainly derived from experimental measurements on a subset of highly symmetrical TM complexes and is limited in ligand scope. Herein, we report a data‐driven method to quantify the LFS of ligands assigned from experimental crystal structures of TM complexes. We first show that the experimental metal–ligand bond lengths of over 4,000 mononuclear Fe, Co, and Mn complexes form bimodal distributions. Using Gaussian fits on the bimodal distributions, each TM complex is assigned a spin state (SS) label. These SS labels can then be used to calculate the LFS of the ligands of the complexes. Using the obtained data‐driven LFS metric, we establish that a semi‐supervised deep generative model, junction tree variational autoencoder (JTVAE), can be employed to predict LFS values. Our model exhibits a mean absolute error (MAE) of 0.047 and root mean squared error of 0.072 on the training set. The model also allows the generation of novel ligands with desirable LFS values. Chemistry ; Ligand (biochemistry) ; Metric (unit) ; Gaussian ; Inverse ; Biological system ; Algorithm ; Computational chemistry ; Mathematics ; Geometry Machine Learning in Materials Science ; Organometallic Complex Synthesis and Catalysis ; Asymmetric Hydrogenation and Catalysis
Authors: Hossein Jalaei Salmani | Hadi Kalani | Mohammad Reza Moradi | Elahe Mansouri | Horng‐Jang Liaw | Hamed Karkhanechi
Source: Petroleum Science and Technology (article, 2023-08-18)
Field: Chemistry — Spectroscopy

A practical hybrid machine learning method for predicting the flash point of complex ternary alcohol-based mixtures The flash point (FP) modeling of ternary mixtures containing partially miscible regions, even by known models, is still challenging. This work proposes a practical and easy-to-use hybrid machine learning method, which combines artificial neural networks (ANNs) with the genetic algorithm (GA), to calculate the FPs of complex alcohol-based mixtures, particularly aqueous solutions of low-carbon alcohols. For this purpose, multilayer perceptron (MLP), radial basis function (RBF), and group method of data handling (GMDH) ANNs, were trained using the FPs of totally miscible region while the FPs of the partially miscible region were predicted by the trained ANN. By applying this approach, the FPs of partially miscible region of water-ethanol-1-butanol, water-ethanol-2-butanol, water-1-butanol-2-butanol, and methanol + toluene + 2,2,4-trimethylpentane were predicted. Another method was implemented by which the FPs of water-1-butanol-2-butanol mixture were predicted by only those of other mixtures, i.e., water-ethanol-1-butanol and water-ethanol-2-butanol. To the best of our knowledge, two applied approaches have not been previously used. Moreover, the FPs of two totally miscible aqueous solutions, namely water-methanol-ethanol and water-ethanol-isopropanol, were evaluated by different proposed ANNs in a usual way. The highest and lowest absolute average errors obtained by the applied methods for testing data were 5.09 and 0.45 °C, respectively. Ternary operation ; Butanol ; Flash point ; Alcohol ; Aqueous solution ; Toluene ; Methanol ; n-Butanol ; Artificial neural network ; Ethanol ; Chemistry ; Multilayer perceptron ; Radial basis function ; Computer science ; Artificial intelligence ; Organic chemistry Analytical Chemistry and Chromatography ; Process Optimization and Integration ; Thermodynamic properties of mixtures
Authors: Paulina Bednarczyk | Paula Ossowicz‐Rupniewska | Joanna Klebeko | Joanna Rokicka | Yongping Bai | Zbigniew Czech
Source: Coatings (article, 2023-12-05)
Field: Materials Science — Polymers and Plastics

Self-Healing UV-Curable Urethane (Meth)acrylates with Various Soft Segment Chemistry This study explores the synthesis and evaluation of UV-curable urethane (meth)acrylates (UA) incorporating a Diels–Alder adduct (HODA), diisocyanate, poly(ethylene glycol), and hydroxy (meth)acrylate. Six UAs, distinguished by the soft segment of polymer chains, underwent comprehensive characterization using FTIR and NMR spectroscopy. Real-time monitoring of the UV-curing process and analysis of self-healing properties were performed. The research investigates the influence of various molecular weights of PEGs on the self-healing process, revealing dependencies on photopolymerization kinetics, microstructure, thermal properties, and thermoreversibility of urethane (meth)acrylates. This work provides valuable insights into the development of UV-curable coatings with tailored properties for potential applications in advanced materials. Meth- ; Photopolymer ; Acrylate ; Fourier transform infrared spectroscopy ; Materials science ; Curing (chemistry) ; Polymer chemistry ; Ethylene glycol ; Polymer ; Chemical engineering ; Polymerization ; Chemistry ; Organic chemistry ; Composite material ; Copolymer Polymer composites and self-healing ; Photopolymerization techniques and applications ; Synthesis and properties of polymers