jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Xiaohan Liu | Miao Tian | Yunyi Wang | Yun Su | Jun Li
Source: Textile Research Journal (article, 2021-05-13)
Field: Materials Science — Polymers and Plastics

Modeling to predict thermal aging for flame-retardant fabrics considering thermal stability under fire exposure The performance of firefighters’ clothing will deteriorate due to various exposures. Predicting its service life before decommissioning is essential to guide the use and maintenance of the uniform. The aim of this study is to introduce a model to predict the tensile strength of flame-retardant fabrics under fire exposure. The thermal degradation and microstructure of Kevlar/polybenzimidazole and polyimide/Kevlar fabrics were investigated. The decrease of tensile strength was attributed to the chemical changes and the development of microstructure cracks and charring of the fibers. Multiple linear regression (MLR) and artificial neural network (ANN) models were established to predict the tensile strength after thermal aging. The ANN model presented a better prediction result ( R 2 = 0.88, root mean square error (RMSE) = 96.91) than the MLR method ( R 2 = 0.76, RMSE = 138.61). The addition of fabric backside temperature ( T), glass transition temperature ( T g ), and degradation temperature ( T d ) further increased the R 2 (4%) and decreased the RMSE (14.99) of the ANN model, which was recommended as a prediction approach with better accuracy. The findings of this study will contribute to estimating the continuous performance of firefighting clothing. Fire retardant ; Materials science ; Ultimate tensile strength ; Composite material ; Mean squared error ; Kevlar ; Composite number ; Mathematics ; Statistics Textile materials and evaluations ; Occupational Health and Performance ; Mechanical Behavior of Composites
Authors: Farzaneh Marahel | Bijan Mombeni Goodajdar | Neda Basri | Leila Niknam | Amir Abbas Ghazali
Source: SHILAP Revista de lepidopterología (article, 2021-09-28)
Field: Chemistry — Organic Chemistry

Applying Neural Network Model for Adsorption Methyl Paraben (MP) dye Using Ricinus Communis-capeed Fe3O4 NPs Synthesized from Aqueous Solution. The applicability of the synthesized Ricinus Communis-capeed Fe3O4NPs as a novel adsorbent for eliminating Methyl Paraben (MP) from aqueous media was investigated. Various techniques including Brunauer Emmett Teller theory (BET), Fourier Transform InfraRed (FT-IR) spectroscopy, X-Ray Diffraction (XRD), Scanning Electron Microscopy (SEM), and Energy Dispersive X-ray (EDX) were used to characterize this novel adsorbent. The maximum adsorption efficiency of (MP) dye onto Ricinus Communis-capeed Fe3O4 NPs was 98.6% at an optimum pH value of 7.0, the adsorbent dosage of 0.01 g, (MP) dye concentration of 15 mg/L, and contact time of 12 min were considered as the ideal values for (MP) dye. The adsorption data fitted well with the Langmuir isotherm model with a correlation coefficient (R2 > 0.97), whereas the adsorption kinetics followed the pseudo-second-order kinetics. The use of an artificial neural network model in predicting data with the Levenberg–Marquardt algorithm, purlin, or a linear transfer function at the output layer, and training was helpful. ANN model as a tool (mean square error) MSEANN = 0.0034, MSEFL = 0.023, and MSEANFIS = 0.0020 for removal of the (MP) dye onto Ricinus Communis-capeed Fe3O4 NPs synthesis. Thermodynamic parameters of free energy (ΔG0), enthalpy (ΔH0), and entropy (ΔS0) of adsorption were determined using isotherms. ∆H0=59.58 kJ/mol, ∆G0= -2.8324 kJ/mol and ∆S0=221.15 kJ/mol. K. The value of (ΔGo, ΔHo, and ΔSo) confirmed the sorption process was endothermic reflecting the affinity of Ricinus Communis-capeed Fe3O4NPs for removing (MP) dye onto Ricinus Communis-capeed Fe3O4NPs process requires heat. The maximum monolayer capacity (qmax) was observed to be 195.0 mg/g for (MP) dye at desired conditions. Adsorption ; Langmuir adsorption model ; Aqueous solution ; Chemistry ; Langmuir ; Fourier transform infrared spectroscopy ; Ricinus ; Nuclear chemistry ; Enthalpy ; Activated carbon ; Analytical Chemistry (journal) ; Materials science ; Chromatography ; Organic chemistry ; Chemical engineering ; Thermodynamics ; Physics Synthesis of Tetrazole Derivatives
Authors: Boran Li | Song Wang | Ziqi Tian | Ge Yao | Hui Li | Liang Chen
Source: Advanced Theory and Simulations (article, 2021-11-19)
Field: Materials Science — Materials Chemistry

Understanding the CO<sub>2</sub>/CH<sub>4</sub>/N<sub>2</sub> Separation Performance of Nanoporous Amorphous N‐Doped Carbon Combined Hybrid Monte Carlo with Machine Learning Abstract Amorphous carbon (aC) is widely used as the adsorbent in the purification of industrial gas. Introducing nitrogen dopant can regulate the morphology and improve the adsorption capacity of specific species. Due to the amorphous structure, it is difficult to understand the relationship between structural features and adsorption performance through atom‐based simulation. Here, a series of nitrogen‐doped amorphous carbon (N‐aC) models is built through reverse Monte Carlo method. The uptakes of three common gases, i.e., CO 2 , CH 4 , and N 2 are estimated in each constructed framework by using grand canonical Monte Carlo (GCMC). Deep neural network is trained based on the simulated adsorption capacity with nitrogen content, surface area, pore size, atomic charge, and other factors. Through the data‐driven approaches, the adsorption capacity and the selectivity of three gases are predicted. The simulation in this study shows that the nitrogen content has less influence on the capacity and selectivity than the structural parameters, while nitrogen doping may improve CO 2 loading and separation selectivity in the nanopores with pore size close to gas molecules. This work is helpful in constructing amorphous carbon structures for further simulation and understanding the influence of various features on gas separation. Amorphous solid ; Adsorption ; Amorphous carbon ; Nanoporous ; Materials science ; Carbon fibers ; Selectivity ; Monte Carlo method ; Gas separation ; Dopant ; Nitrogen ; Doping ; Chemical engineering ; Nanotechnology ; Chemistry ; Physical chemistry ; Organic chemistry ; Composite material Catalytic Processes in Materials Science ; Membrane Separation and Gas Transport ; Metal-Organic Frameworks: Synthesis and Applications
Authors: Yuqian Liu | Peiran Zhao | Chunying Duan | Cheng He
Source: RSC Advances (article, 2021-01-01)
Field: Chemistry — Inorganic Chemistry

A novel 3D terbium metal–organic framework as a heterogeneous Lewis acid catalyst for the cyanosilylation of aldehyde A novel 3D lanthanide(iii) metal-organic framework (MOF) (namely Tb-MOF), was synthesized by self-assembly from Tb(iii) ion nitrate and the rigid organic ligand H<sub>2</sub>sbdc (H<sub>2</sub>sbdc = 5,5-dioxo-5<i>H</i>-dibenzo[<i>b</i>,<i>d</i>]thiophene-3,7-dicarboxylic acid), and could work as an efficient heterogeneous catalyst for the cyanosilylation of aromatic aldehydes at room temperature. The obtained Tb-MOF has been characterized and analysed in detail by single crystal X-ray diffraction, powder X-ray diffraction, thermogravimetric analysis and so on. The pores of Tb-MOF provided a microenvironment that was beneficial for the substrates to be close to the Lewis acid catalytic sites. The IR spectrogram and the fluorescence titration proved that the substrates could be activated inside the channel of Tb-MOF. The heterogeneous Tb-MOF catalyst with fine catalytic efficiency exhibited a high TON (TON = 460), and could be recycled at least three times without significantly reducing its activity. Catalysis ; Metal-organic framework ; Terbium ; Lewis acids and bases ; Lanthanide ; Thermogravimetric analysis ; Chemistry ; Aldehyde ; Ligand (biochemistry) ; Heterogeneous catalysis ; Inorganic chemistry ; Materials science ; Organic chemistry ; Ion Metal-Organic Frameworks: Synthesis and Applications ; Chemical Synthesis and Reactions ; Covalent Organic Framework Applications
Authors: Marco Govoni | Jonathan K. Whitmer | Juan Pablo | François Gygi | Giulia Galli
Source: npj Computational Materials (article, 2021-02-19)
Field: Materials Science — Materials Chemistry

Code interoperability extends the scope of quantum simulations Abstract The functionality of many materials is critically dependent on the integration of dissimilar components and on the interfaces that arise between them. The description of such heterogeneous components requires the development and deployment of first principles methods, coupled to appropriate dynamical descriptions of matter and advanced sampling techniques, in order to capture all the relevant length and time scales of importance to the materials’ performance. It is thus essential to build simple, streamlined computational schemes for the prediction and design of multiple properties of broad classes of materials, by developing interoperable codes which can be efficiently coupled to each other to perform complex tasks. We discuss the use of interoperable codes to simulate the structural and spectroscopic characterization of materials, including chemical reactions for catalysis, the description of defects for quantum information science, and heat and charge transport. Interoperability ; Scope (computer science) ; Computer science ; Software deployment ; Code (set theory) ; Simple (philosophy) ; Quantum ; Characterization (materials science) ; Systems engineering ; Distributed computing ; Software engineering ; Nanotechnology ; Programming language ; Physics ; Materials science ; Engineering Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Advanced Chemical Physics Studies
Authors: Evgeniy O. Pentsak | Alexey S. Galushko | Vera A. Cherepanova | Valentine P. Ananikov
Source: Nanomaterials (article, 2021-10-02)
Field: Materials Science — Materials Chemistry

How to Make a Cocktail of Palladium Catalysts with Cola and Alcohol: Heteroatom Doping vs. Nanoscale Morphology of Carbon Supports Sparkling drinks such as cola can be considered an affordable and inexpensive starting material consisting of carbohydrates and sulfur- and nitrogen-containing organic substances in phosphoric acid, which makes them an excellent precursor for the production of heteroatom-doped carbon materials. In this study, heteroatom-doped carbon materials were successfully prepared in a quick and simple manner using direct carbonization of regular cola and diet cola. The low content of carbon in diet cola allowed reaching a higher level of phosphorus in the prepared carbon material, as well as obtaining additional doping with nitrogen and sulfur due to the presence of sweeteners and caffeine. Effects of carbon support doping with phosphorus, nitrogen and sulfur, as well as of changes in textural properties by ball milling, on the catalytic activity of palladium catalysts were investigated in the Suzuki-Miyaura and Mizoroki-Heck reactions. Contributions of the heteroatom doping and specific surface area of the carbon supports to the increased activity of supported catalysts were discussed. Additionally, the possibility of these reactions to proceed in 40% potable ethanol was studied. Moreover, transformation of various palladium particles (complexes and nanoparticles) in the reaction medium was detected by mass spectrometry and transmission electron microscopy, which evidenced the formation of a cocktail of catalysts in a commercial 40% ethanol/water solution. Palladium ; Heteroatom ; Catalysis ; Nanoscopic scale ; Doping ; Materials science ; Morphology (biology) ; Carbon fibers ; Alcohol ; Nanotechnology ; Chemical engineering ; Inorganic chemistry ; Organic chemistry ; Chemistry ; Ring (chemistry) ; Composite material ; Composite number Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions ; Mesoporous Materials and Catalysis
Authors: Luc Dewulf | Mauro Chiacchia | Aaron S. Yeardley | Robert A. Milton | Solomon Brown | Siddharth V. Patwardhan
Source: Molecular Systems Design & Engineering (article, 2021-01-01)
Field: Chemistry — Physical and Theoretical Chemistry

Designing bioinspired green nanosilicas using statistical and machine learning approaches This is a first comparison of the sequential design of experiments strategy and global sensitivity analysis for nanomaterials, thus enabling sustainable product and process design in future. Computer science ; Sensitivity (control systems) ; Process (computing) ; Sustainable design ; Product (mathematics) ; Artificial intelligence ; Machine learning ; Systems engineering ; Nanotechnology ; Engineering ; Sustainability ; Mathematics ; Materials science ; Electronic engineering Electrostatics and Colloid Interactions ; Nanotechnology research and applications ; Nanoparticles: synthesis and applications
Authors: Joseph S. Erlichman | James C. Leiter
Source: Antioxidants (review, 2021-04-01)
Field: Materials Science — Materials Chemistry

Complexity of the Nano-Bio Interface and the Tortuous Path of Metal Oxides in Biological Systems Metal oxide nanoparticles (NPs) have received a great deal of attention as potential theranostic agents. Despite extensive work on a wide variety of metal oxide NPs, few chemically active metal oxide NPs have received Food and Drug Administration (FDA) clearance. The clinical translation of metal oxide NP activity, which often looks so promising in preclinical studies, has not progressed as rapidly as one might expect. The lack of FDA approval for metal oxide NPs appears to be a consequence of the complex transformation of NP chemistry as any given NP passes through multiple extra- and intracellular environments and interacts with a variety of proteins and transport processes that may degrade or transform the chemical properties of the metal oxide NP. Moreover, the translational models frequently used to study these materials do not represent the final therapeutic environment well, and studies in reduced preparations have, all too frequently, predicted fundamentally different physico-chemical properties from the biological activity observed in intact organisms. Understanding the evolving pharmacology of metal oxide NPs as they interact with biological systems is critical to establish translational test systems that effectively predict future theranostic activity. Oxide ; Nanotechnology ; Metal ; Nanoparticle ; Chemistry ; Intracellular ; Biophysics ; Materials science ; Biochemistry ; Biology ; Organic chemistry Nanoparticles: synthesis and applications ; Advanced Nanomaterials in Catalysis ; Nanoparticle-Based Drug Delivery
Authors: Junxu Li | Sabre Kais
Source: Materials Theory (article, 2021-10-14)
Field: Materials Science — Materials Chemistry

Quantum cluster algorithm for data classification Abstract We present a quantum algorithm for data classification based on the nearest-neighbor learning algorithm. The classification algorithm is divided into two steps: Firstly, data in the same class is divided into smaller groups with sublabels assisting building boundaries between data with different labels. Secondly we construct a quantum circuit for classification that contains multi control gates. The algorithm is easy to implement and efficient in predicting the labels of test data. To illustrate the power and efficiency of this approach, we construct the phase transition diagram for the metal-insulator transition of VO 2 , using limited trained experimental data, where VO 2 is a typical strongly correlated electron materials, and the metallic-insulating phase transition has drawn much attention in condensed matter physics. Moreover, we demonstrate our algorithm on the classification of randomly generated data and the classification of entanglement for various Werner states, where the training sets can not be divided by a single curve, instead, more than one curves are required to separate them apart perfectly. Our preliminary result shows considerable potential for various classification problems, particularly for constructing different phases in materials. Construct (python library) ; Algorithm ; Computer science ; Cluster (spacecraft) ; Class (philosophy) ; Quantum entanglement ; Test data ; k-nearest neighbors algorithm ; Quantum ; Artificial intelligence ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Quantum Computing Algorithms and Architecture ; Electronic and Structural Properties of Oxides
Authors: Shengquan Huang | Ying Liu | Xuyuan Sun | Jinwei Li
Source: Molecules (article, 2021-11-06)
Field: Chemistry — Organic Chemistry

Application of Artificial Neural Network Based on Traditional Detection and GC-MS in Prediction of Free Radicals in Thermal Oxidation of Vegetable Oil In this study, electron paramagnetic resonance (EPR) and gas chromatography-mass spectrometry (GC-MS) techniques were applied to reveal the variation of lipid free radicals and oxidized volatile products of four oils in the thermal process. The EPR results showed the signal intensities of linseed oil (LO) were the highest, followed by sunflower oil (SO), rapeseed oil (RO), and palm oil (PO). Moreover, the signal intensities of the four oils increased with heating time. GC-MS results showed that (<i>E</i>)-2-decenal, (<i>E</i>,<i>E</i>)-2,4-decadienal, and 2-undecenal were the main volatile compounds of oxidized oil. Besides, the oxidized PO and LO contained the highest and lowest contents of volatiles, respectively. According to the oil characteristics, an artificial neural network (ANN) intelligent evaluation model of free radicals was established. The coefficients of determination (R2) of ANN models were more than 0.97, and the difference between the true and predicted values was small, which indicated that oil profiles combined with chemometrics can accurately predict the free radical of thermal oxidized oil. Chemistry ; Electron paramagnetic resonance ; Radical ; Rapeseed ; Vegetable oil ; Mass spectrometry ; Linseed oil ; Gas chromatography–mass spectrometry ; Sunflower oil ; Gas chromatography ; Chemometrics ; Chromatography ; Analytical Chemistry (journal) ; Organic chemistry ; Food science ; Nuclear magnetic resonance Edible Oils Quality and Analysis ; Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies
Authors: Shunji Yamada | Eisuke Chikayama | Jun Kikuchi
Source: International Journal of Molecular Sciences (article, 2021-01-22)
Field: Chemistry — Spectroscopy

Signal Deconvolution and Generative Topographic Mapping Regression for Solid-State NMR of Multi-Component Materials Solid-state nuclear magnetic resonance (ssNMR) spectroscopy provides information on native structures and the dynamics for predicting and designing the physical properties of multi-component solid materials. However, such an analysis is difficult because of the broad and overlapping spectra of these materials. Therefore, signal deconvolution and prediction are great challenges for their ssNMR analysis. We examined signal deconvolution methods using a short-time Fourier transform (STFT) and a non-negative tensor/matrix factorization (NTF, NMF), and methods for predicting NMR signals and physical properties using generative topographic mapping regression (GTMR). We demonstrated the applications for macromolecular samples involved in cellulose degradation, plastics, and microalgae such as <i>Euglena gracilis</i>. During cellulose degradation, <sup>13</sup>C cross-polarization (CP)-magic angle spinning spectra were separated into signals of cellulose, proteins, and lipids by STFT and NTF. GTMR accurately predicted cellulose degradation for catabolic products such as acetate and CO<sub>2</sub>. Using these methods, the <sup>1</sup>H anisotropic spectrum of poly-ε-caprolactone was separated into the signals of crystalline and amorphous solids. Forward prediction and inverse prediction of GTMR were used to compute STFT-processed NMR signals from the physical properties of polylactic acid. These signal deconvolution and prediction methods for ssNMR spectra of macromolecules can resolve the problem of overlapping spectra and support macromolecular characterization and material design. Deconvolution ; Biological system ; Fourier transform ; Solid-state nuclear magnetic resonance ; Materials science ; Magic angle spinning ; Two-dimensional nuclear magnetic resonance spectroscopy ; Short-time Fourier transform ; Nuclear magnetic resonance ; Chemistry ; Nuclear magnetic resonance spectroscopy ; Analytical Chemistry (journal) ; Fourier analysis ; Computer science ; Physics ; Algorithm ; Chromatography Advanced NMR Techniques and Applications ; NMR spectroscopy and applications ; Fiber-reinforced polymer composites
Authors: Nadimul Haque Faisal | Nazmi Sellami | Federico Venturi | Tanvir Hussain | Tapas K. Mallick | Firdaus Muhammad‐Sukki | Alex Bishop | Hari M. Upadhyaya | Nirmal Kumar Katiyar | Saurav Goel
Source: Emergent Materials (article, 2021-07-04)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Large-scale manufacturing route to metamaterial coatings using thermal spray techniques and their response to solar radiation Abstract Metamaterials, an artificial periodic two- or three-dimensional configuration, can change propagation characteristics of electromagnetic waves (i.e., reflection, transmission, absorption). The current challenges in the field of metamaterial coatings are their manufacturing in a large-scale and large-length scale. There is a clear need to enhance process technologies and scalability of these. Thermal spraying is a method used to deposit small- to large-scale coatings where the sprayed layer is typically formed by the successive impact of fully or partially molten particles of a material exposed to various process conditions. This work aims to investigate the feasibility to manufacture large scale metamaterial coatings using the thermal spray technique and examine their response to solar radiation. Two types of coatings namely, Cr 2 O 3 and TiO 2 , were deposited onto various substrates (e.g., steel, aluminium, glass, indium tin oxide (ITO)–coated glass) with a fine wire mesh (143 µm and 1 mm aperture sizes) as the masking sheet to manipulate the surface pattern using suspension high-velocity oxy-fuel thermal spraying (S-HVOF) and atmospheric plasma-sprayed (APS) methods, respectively. Post deposition, their responses subjected to electromagnetic wave (between 250 and 2500 nm or ultraviolet (UV)-visible (Vis)-infrared (IR) region) were characterised. The additional microstructural characterisation was performed using scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDS), X-ray diffraction (XRD), three-dimensional profilometry, and optical spectroscopy. It is demonstrated that through novel application of thermal spray techniques, large-scale manufacturing of metamaterial coating is possible, and such material can affect electromagnetic wave propagation. Comparison between Cr 2 O 3 and TiO 2 coatings on aluminium substrates showed reduced three orders of reduced reflectance for Cr 2 O 3 coatings (for 1-mm aperture size) throughout the spectrum. It was concluded that for a similar bandgap, Cr 2 O 3 coatings on aluminium substrate will yield improved optical performance than TiO 2 coating, and hence more useful to fabricate opto-electronic devices. Graphical abstract Materials science ; Thermal spraying ; Coating ; Metamaterial ; Optics ; Optoelectronics ; Composite material Electromagnetic wave absorption materials ; Glass properties and applications ; Metamaterials and Metasurfaces Applications