jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Andrzej Sikora | Dariusz Czylkowski | Bartosz Hrycak | Magdalena Moczała-Dusanowska | Marcin Łapiński | M. Dors | M. Jasiński
Source: Scientific Reports (article, 2021-04-29)
Field: Materials Science — Materials Chemistry

Surface modification of PMMA polymer and its composites with PC61BM fullerene derivative using an atmospheric pressure microwave argon plasma sheet This paper presents the results of experimental investigations of the plasma surface modification of a poly(methyl methacrylate) (PMMA) polymer and PMMA composites with a [6,6]-phenyl-C61-butyric acid methyl ester fullerene derivative (PC<sub>61</sub>BM). An atmospheric pressure microwave (2.45 GHz) argon plasma sheet was used. The experimental parameters were: an argon (Ar) flow rate (up to 20 NL/min), microwave power (up to 530 W), number of plasma scans (up to 3) and, the kind of treated material. In order to assess the plasma effect, the possible changes in the wettability, roughness, chemical composition, and mechanical properties of the plasma-treated samples' surfaces were evaluated by water contact angle goniometry (WCA), atomic force microscopy (AFM), attenuated total reflectance Fourier transform infrared spectroscopy (ATR-FTIR) and X-ray photoelectron spectroscopy (XPS). The best result concerning the water contact angle reduction was from 83° to 29.7° for the PMMA material. The ageing studies of the PMMA plasma-modified surface showed long term (100 h) improved wettability. As a result of plasma treating, changes in the samples surface roughness parameters were observed, however their dependence on the number of plasma scans is irregular. The ATR-FTIR spectra of the PMMA plasma-treated surfaces showed only slight changes in comparison with the spectra of an untreated sample. The more significant differences were demonstrated by XPS measurements indicating the surface chemical composition changes after plasma treatment and revealing the oxygen to carbon ratio increase from 0.1 to 0.4. Contact angle ; X-ray photoelectron spectroscopy ; Materials science ; Wetting ; Fourier transform infrared spectroscopy ; Analytical Chemistry (journal) ; Argon ; Surface modification ; Atmospheric-pressure plasma ; Surface roughness ; Composite material ; Polymer ; Surface energy ; Atmospheric pressure ; Plasma ; Chemical engineering ; Chemistry ; Organic chemistry Diamond and Carbon-based Materials Research ; Surface Modification and Superhydrophobicity ; Metal and Thin Film Mechanics
Authors: Beibei Wang | Tan Liu | Hao Chen | Bangqi Yin | Zhao Zhang | Thomas P. Russell | Shaowei Shi
Source: Angewandte Chemie International Edition (article, 2021-06-29)
Field: Materials Science — Materials Chemistry

Molecular Brush Surfactants: Versatile Emulsifiers for Stabilizing and Structuring Liquids Abstract Using amphiphilic molecular brushes to stabilize emulsions usually requires the synthesis of specific side chains, which can be a time‐consuming and difficult challenge to meet. By taking advantage of the electrostatic interactions between water‐soluble molecular brushes and oil‐soluble oligomeric ligands, the in situ formation, assembly and jamming of molecular brush surfactants (MBSs) at the oil‐water interface is described. With MBSs, stable emulsions including o/w, w/o and o/w/o can be easily prepared by varying the molar ratios of the molecular brushes to the ligands. Moreover, when jammed, the binding energy of MBSs at the interface is sufficiently strong to allow the stabilization of liquids in nonequilibrium shapes, i.e., structuring liquids, producing an elastic film at the interface with exceptional mechanical properties. These structured liquids have numerous potential applications, including chemical biphasic reactions, liquid electronics, and all‐liquid biomimetic system. Amphiphile ; Materials science ; Molecular dynamics ; Brush ; Chemical engineering ; Molecule ; Nanotechnology ; Chemistry ; Organic chemistry ; Polymer ; Computational chemistry ; Composite material ; Copolymer Pickering emulsions and particle stabilization ; Surfactants and Colloidal Systems ; Polymer Surface Interaction Studies
Authors: Md Mizan Kabir Shuvo | Md Imran Hossain | Sydur Rahman | Sultan Mahmud | Sikder Sunbeam Islam | Mohammad Tariqul Islam
Source: IEEE Access (article, 2021-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

A Wide-Angle, Enhanced Oblique Incidence, Bend-Able Metamaterial Absorber Employed in Visible Region With a Sun Shape Resonator Solar spectrum is supposed to be a key source of renewable energy in the form of electromagnetic (EM) radiation. For efficiently harnessing this abundant energy, Metamaterial Absorber (MMA) emerges as a game-changing tool. Along with solar energy harvesting, MMA can be used in biochemical sensors, optical modulators, magnetic resonance imaging, photoelectric detectors, plasmonic sensors, etc. In this paper, a sun shape resonator-based MMA is designed with three layers of materials (W-SiO2-W) and analysed for broadband absorption encompassing the entire visible region (390-760nm). The unit cell that is presented in this study is polarization-insensitive and ultrathin with an average absorption of 96.43% with an optimum peak of 99.99% at 523.22nm. The proposed MMA exhibits satisfactory absorption under various oblique angles. The effect of mechanical loading is also investigated and the MMA is found to hold good broadband absorbance for some extents of mechanical bending. Finite Integration Technique (FIT) is used to numerically simulate the proposed MMA unit cell structure and validated with the Finite Element Method (FEM). The suggested MMA in this paper can be used in many optical applications like efficient nano solar cells, imaging applications, sensors, light detectors, biochemical applications, etc. Materials science ; Optoelectronics ; Optics ; Finite element method ; Absorption (acoustics) ; Resonator ; Energy harvesting ; Metamaterial ; Metamaterial absorber ; Polarization (electrochemistry) ; Plasmon ; Absorbance ; Solar cell ; Energy (signal processing) ; Physics ; Composite material ; Chemistry Metamaterials and Metasurfaces Applications ; Antenna Design and Analysis ; Advanced Antenna and Metasurface Technologies
Authors: Wei Li | Haibo Ma | Shuhua Li | Jing Ma
Source: Chemical Science (review, 2021-01-01)
Field: Materials Science — Materials Chemistry

Computational and data driven molecular material design assisted by low scaling quantum mechanics calculations and machine learning Electronic structure methods based on quantum mechanics (QM) are widely employed in the computational predictions of the molecular properties and optoelectronic properties of molecular materials. The computational costs of these QM methods, ranging from density functional theory (DFT) or time-dependent DFT (TDDFT) to wave-function theory (WFT), usually increase sharply with the system size, causing the curse of dimensionality and hindering the QM calculations for large sized systems such as long polymer oligomers and complex molecular aggregates. In such cases, in recent years low scaling QM methods and machine learning (ML) techniques have been adopted to reduce the computational costs and thus assist computational and data driven molecular material design. In this review, we illustrated low scaling ground-state and excited-state QM approaches and their applications to long oligomers, self-assembled supramolecular complexes, stimuli-responsive materials, mechanically interlocked molecules, and excited state processes in molecular aggregates. Variable electrostatic parameters were also introduced in the modified force fields with the polarization model. On the basis of QM computational or experimental datasets, several ML algorithms, including explainable models, deep learning, and on-line learning methods, have been employed to predict the molecular energies, forces, electronic structure properties, and optical or electrical properties of materials. It can be conceived that low scaling algorithms with periodic boundary conditions are expected to be further applicable to functional materials, perhaps in combination with machine learning to fast predict the lattice energy, crystal structures, and spectroscopic properties of periodic functional materials. Scaling ; Molecular mechanics ; Quantum ; Quantum chemical ; Statistical physics ; Computer science ; Molecular dynamics ; Physics ; Materials science ; Quantum mechanics ; Molecule ; Mathematics ; Geometry Machine Learning in Materials Science ; Metal-Organic Frameworks: Synthesis and Applications ; Protein Structure and Dynamics
Authors: Zhilong Wang | Qingxun Wang | Yanqiang Han | Ma Yan | Hua Zhao | Andrzej P. Nowak | Jinjin Li
Source: Energy storage materials (article, 2021-04-08)
Field: Materials Science — Materials Chemistry

Deep learning for ultra-fast and high precision screening of energy materials Band gap ; Materials science ; Semiconductor ; Hybrid functional ; Ab initio ; Optoelectronics ; Engineering physics ; Computational physics ; Density functional theory ; Computational chemistry ; Physics Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; X-ray Diffraction in Crystallography
Authors: Callum J. Court | Apoorv Jain | Jacqueline M. Cole
Source: Chemistry of Materials (article, 2021-09-16)
Field: Materials Science — Materials Chemistry

Inverse Design of Materials That Exhibit the Magnetocaloric Effect by Text-Mining of the Scientific Literature and Generative Deep Learning Magnetic materials play an important role in a wide variety of everyday applications, and they are critical components in many devices used for energy conversion. However, there are very few materials known to exhibit magnetism of any kind, and the slow process of experimentally driven magnetic-materials discovery has limited the development of devices for functional applications. In this work, a complete magnetic-materials discovery pipeline is presented that uses natural language processing (NLP), machine learning, and generative models to predict ferromagnetic compounds in the Heusler alloy family. Using the “chemistry-aware” NLP toolkit, ChemDataExtractor, a database of 2910 magnetocaloric compounds is autogenerated by sourcing from the scientific literature. These data are then used to train property-prediction models for key figures of merit that describe the magnetocaloric effect. The predictive models are applied to novel Heusler alloy material candidates that have been created using deep generative representation learning. Convex-hull meta-stability analysis and ab initio validation of these candidates identify six potential materials for solid-state refrigeration applications. Generative grammar ; Artificial intelligence ; Magnetic refrigeration ; Computer science ; Magnetism ; Machine learning ; Pipeline (software) ; Ferromagnetism ; Materials informatics ; Materials science ; Nanotechnology ; Physics ; Magnetization ; Condensed matter physics ; Programming language ; Magnetic field Machine Learning in Materials Science ; Heusler alloys: electronic and magnetic properties ; Inorganic Chemistry and Materials
Authors: Bipeng Wang | Weibin Chu | Alexandre Tkatchenko | Oleg V. Prezhdo
Source: The Journal of Physical Chemistry Letters (article, 2021-06-25)
Field: Materials Science — Materials Chemistry

Interpolating Nonadiabatic Molecular Dynamics Hamiltonian with Artificial Neural Networks Nonadiabatic (NA) molecular dynamics (MD) allows one to study far-from-equilibrium processes involving excited electronic states coupled to atomic motions. While NAMD involves expensive calculations of excitation energies and NA couplings (NACs), ground-state properties require much less effort and can be obtained with machine learning (ML) at a fraction of the ab initio cost. Application of ML to excited states and NACs is more challenging, due to costly reference methods, many states, and complex geometry dependence. We developed a NAMD methodology that avoids time extrapolation of excitation energies and NACs. Instead, under the classical path approximation that employs a precomputed ground-state trajectory, we use a small fraction (2%) of the geometries to train neural networks and obtain excited-state energies and NACs for the remaining 98% of the geometries by interpolation. Demonstrated with metal halide perovskites that exhibit complex MD, the method provides nearly two orders of computational savings while generating accurate NAMD results. Artificial neural network ; Statistical physics ; Hamiltonian (control theory) ; Classical mechanics ; Physics ; Molecular dynamics ; Computer science ; Quantum mechanics ; Artificial intelligence ; Mathematics ; Mathematical optimization Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Quantum, superfluid, helium dynamics
Authors: Eun Hyun Cho | Li‐Chiang Lin
Source: The Journal of Physical Chemistry Letters (article, 2021-03-01)
Field: Materials Science — Materials Chemistry

Nanoporous Material Recognition via 3D Convolutional Neural Networks: Prediction of Adsorption Properties Nanoporous materials can be effective adsorbents for various energy applications. Because of their abundant number, brute-force-based material discovery can, however, be challenging. Data-driven approaches can be advantageous for such purposes. In this study, we demonstrate for the first time the applicability of a 3D convolutional neural network (CNN) in material recognition for predicting adsorption properties. 2D CNNs have been widely applied to image recognition, where the CNN self-learns important features of images, without the need of handcrafting features that are subject to human bias. This study explores methane adsorption in zeolites as a case study, where ∼6500 hypothetical zeolites are utilized to train/validate our designed CNN model. The CNN model offers highly accurate predictions, and the self-learned features resemble the channel and pore-like geometry of structures. This study demonstrates the extension of computer vision to materials science and paves the way for future studies such as carbon capture. Nanoporous ; Convolutional neural network ; Computer science ; Artificial intelligence ; Adsorption ; Extension (predicate logic) ; Pattern recognition (psychology) ; Machine learning ; Materials science ; Nanotechnology ; Chemistry Machine Learning in Materials Science ; Metal-Organic Frameworks: Synthesis and Applications ; Hydrocarbon exploration and reservoir analysis
Authors: Sajad Sabzi | Razieh Pourdarbani | Mohammad Hossein Rohban | Ginés Garcı́a-Mateos | Juan Ignacio Arribas
Source: Chemometrics and Intelligent Laboratory Systems (article, 2021-08-17)
Field: Chemistry — Analytical Chemistry

Estimation of nitrogen content in cucumber plant (Cucumis sativus L.) leaves using hyperspectral imaging data with neural network and partial least squares regressions In recent years, farmers have often mistakenly resorted to overuse of chemical fertilizers to increase crop yield. However, excessive consumption of fertilizers might lead to severe food poisoning. If nutritional deficiencies are detected early, it can help farmers to design better fertigation practices before the problem becomes unsolvable. The aim of this study is to predict the amount of nitrogen (N) content (mg ​l−1) in cucumber (Cucumis sativus L., var. Super Arshiya-F1) plant leaves using hyperspectral imaging (HSI) techniques and three different regression methods: a hybrid artificial neural networks-particle swarm optimization (ANN-PSO); partial least squares regression (PLSR); and unidimensional deep learning convolutional neural networks (CNN). Cucumber plant seeds were planted in 20 different pots. After growing the plants, pots were categorized and three levels of nitrogen overdose were applied to each category: 30%, 60% and 90% excesses, called N30%, N60%, N90%, respectively. HSI images of plant leaves were captured before and after the application of nitrogen excess. A prediction regression model was developed for each individual category. Results showed that mean regression coefficients (R) for ANN-PSO were inside 0.937–0.965, PLSR 0.975–0.997, and CNN 0.965–0.985 ranges, test set. We conclude that regression models have a remarkable ability to accurately predict the amount of nitrogen content in cucumber plants from hyperspectral leaf images in a non-destructive way, being PLSR slightly ahead of CNN and ANN-PSO methods. Partial least squares regression ; Cucumis ; Hyperspectral imaging ; Mathematics ; Convolutional neural network ; Regression ; Pattern recognition (psychology) ; VNIR ; Artificial intelligence ; Statistics ; Horticulture ; Agronomy ; Computer science ; Biology Spectroscopy and Chemometric Analyses ; Leaf Properties and Growth Measurement ; Smart Agriculture and AI
Authors: Chun-Sheng Wang | Joshua Brinkerhoff
Source: International Journal of Hydrogen Energy (article, 2021-05-29)
Field: Materials Science — Materials Chemistry

Predicting hydrogen adsorption and desorption rates in cylindrical metal hydride beds: Empirical correlations and machine learning Hydride ; Desorption ; Adsorption ; Isothermal process ; Hydrogen storage ; Hydrogen ; Thermodynamics ; Materials science ; Thermal conductivity ; Diffusion ; Metal ; Chemistry ; Metallurgy ; Composite material ; Physical chemistry ; Organic chemistry ; Physics Hydrogen Storage and Materials ; Catalysis and Hydrodesulfurization Studies ; Ammonia Synthesis and Nitrogen Reduction
Authors: Wen‐Hao Su | Huidan Xue
Source: Foods (review, 2021-09-10)
Field: Chemistry — Analytical Chemistry

Imaging Spectroscopy and Machine Learning for Intelligent Determination of Potato and Sweet Potato Quality Imaging spectroscopy has emerged as a reliable analytical method for effectively characterizing and quantifying quality attributes of agricultural products. By providing spectral information relevant to food quality properties, imaging spectroscopy has been demonstrated to be a potential method for rapid and non-destructive classification, authentication, and prediction of quality parameters of various categories of tubers, including potato and sweet potato. The imaging technique has demonstrated great capacities for gaining rapid information about tuber physical properties (such as texture, water binding capacity, and specific gravity), chemical components (such as protein, starch, and total anthocyanin), varietal authentication, and defect aspects. This paper emphasizes how recent developments in spectral imaging with machine learning have enhanced overall capabilities to evaluate tubers. The machine learning algorithms coupled with feature variable identification approaches have obtained acceptable results. This review briefly introduces imaging spectroscopy and machine learning, then provides examples and discussions of these techniques in tuber quality determinations, and presents the challenges and future prospects of the technology. This review will be of great significance to the study of tubers using spectral imaging technology. Quality (philosophy) ; Artificial intelligence ; Computer science ; Machine learning ; Agricultural engineering ; Spectral imaging ; Food quality ; Chemistry ; Remote sensing ; Engineering ; Food science ; Physics ; Geography Spectroscopy and Chemometric Analyses ; Identification and Quantification in Food ; Advanced Chemical Sensor Technologies
Authors: Supriya Ghosal | Suman Chowdhury | Debnarayan Jana
Source: ACS Applied Materials & Interfaces (article, 2021-11-29)
Field: Materials Science — Materials Chemistry

Impressive Thermoelectric Figure of Merit in Two-Dimensional Tetragonal Pnictogens: a Combined First-Principles and Machine-Learning Approach Over the past decade, two-dimensional materials have gained a lot of interest due to their fascinating applications in the field of thermoelectricity. In this study, tetragonal monolayers of group-V elements (T-P, T-As, T-Sb, and T-Bi) are systematically analyzed in the framework of density functional theory in combination with the machine-learning approach. The phonon spectra, as well as the strain profile, dictate that these tetragonal structures are geometrically stable as well as they are potential candidates for experimental synthesis. Electronic analysis suggests that tetragonal pnictogens offer a band gap in the semiconducting regime. Thermal transport characteristics are investigated by solving the semiclassical Boltzmann transport equation. Exceptionally low lattice thermal conductivity has been observed as the atomic number increases in the group. The high Seebeck coefficient and electrical conductivity as well as the low thermal conductivity of T-As, T-Sb, and T-Bi lead to the generation of a very high thermoelectric figure of merit as compared to standard thermoelectric materials. Furthermore, the thermoelectric conversion efficiency of these materials has been observed to be much higher, which ensures their implications in thermoelectric device engineering. Thermoelectric effect ; Tetragonal crystal system ; Materials science ; Thermoelectric materials ; Thermal conductivity ; Figure of merit ; Seebeck coefficient ; Condensed matter physics ; Phonon ; Boltzmann equation ; Engineering physics ; Optoelectronics ; Thermodynamics ; Physics ; Phase (matter) ; Quantum mechanics ; Composite material Advanced Thermoelectric Materials and Devices ; Thermal properties of materials ; 2D Materials and Applications
Authors: Hang Chen | Angjian Wu | Stéphanie Mathieu | Peihan Gao | Xiaodong Li | Bo Xu | Jianhua Yan | Xin Tu
Source: Plasma Processes and Polymers (article, 2021-04-21)
Field: Materials Science — Materials Chemistry

Highly efficient nitrogen fixation enabled by an atmospheric pressure rotating gliding arc Abstract A rotating gliding arc is proposed as a promising alternative to enable direct nitrogen fixation from ubiquitous air under mild conditions. The effect of different process parameters on NO x generation and energy consumption has been investigated through a combination of experiments and artificial neural network (ANN) modeling. The optical emission spectroscopic diagnostics together with electrical diagnostics and high‐speed photography has been used to understand the variation of the discharge characteristics. The lowest energy consumption of NO x production (4.2 MJ/mol) is achieved at a gas flow rate of 12 L/min and an O 2 concentration of 20 vol%. The simulation results from the ANN model show a good agreement with the experimental data and the model enables us to evaluate the relative importance of the process parameters to the reaction performance. Nitrogen ; Artificial neural network ; Electric arc ; Atmospheric pressure ; Energy consumption ; Materials science ; Arc (geometry) ; Volumetric flow rate ; Analytical Chemistry (journal) ; Plasma ; Fixation (population genetics) ; Environmental science ; Mechanics ; Biological system ; Mechanical engineering ; Computer science ; Chemistry ; Electrode ; Engineering ; Meteorology ; Electrical engineering ; Artificial intelligence ; Physics ; Environmental chemistry ; Physical chemistry Catalytic Processes in Materials Science ; Ammonia Synthesis and Nitrogen Reduction ; Plasma Applications and Diagnostics