jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Malte Koch | Hsin Tseng | Anton Weissbach | Benjamı́n Iñı́guez | Karl Leo | Alexander Kloes | Hans Kleemann | Ghader Darbandy
Source: IEEE Journal of the Electron Devices Society (article, 2023-01-01)
Field: Materials Science — Polymers and Plastics

Device Physics, Modeling and Simulation of Organic Electrochemical Transistors In this work, we investigate organic electrochemical transistors (OECTs) as a novel artificial electronic device for the realization of synaptic behavior, bioelectronics, and a variety of applications. A numerical method considering the Poisson-Boltzmann statistics is introduced to reproduce associated charge densities, electrostatics and switching properties of OECTs. We shed light on the working principle of OECTs by taking into account the ionic charge distribution in the electrolyte and incomplete ionization of the organic semiconductor describing the underlying electrochemical redox reaction. This enables analyzing the OECTs electrical performance as well as a simplified chemical properties via an electrical double layer, doping and de-doping of the OMIEC layer. We have fabricated, characterized, simulated and analyzed OECTs based on PEDOT:PSS, and we show that the proposed model reveals important properties of the device’s working mechanism. The model shows a good agreement with the experimental data of the fabricated devices. Bioelectronics ; Materials science ; Transistor ; Organic semiconductor ; Doping ; Electrochemistry ; Electrostatics ; PEDOT:PSS ; Semiconductor ; Electrolyte ; Microelectronics ; Optoelectronics ; Nanotechnology ; Layer (electronics) ; Biological system ; Electrode ; Voltage ; Physics ; Biosensor ; Quantum mechanics Conducting polymers and applications ; Organic Electronics and Photovoltaics ; Analytical Chemistry and Sensors
Authors: Albert Sabadell‐Rendón | Kamila Kaźmierczak | Santiago Morandi | Florian Euzenat | Daniel Curulla‐Ferré | Núria López
Source: Digital Discovery (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Automated MUltiscale simulation environment Multiscale techniques integrating detailed atomistic information on materials and reactions to predict the performance of heterogeneous catalytic full-scale reactors have been suggested but lack seamless implementation. The largest challenges in the multiscale modeling of reactors can be grouped into two main categories: catalytic complexity and the difference between time and length scales of chemical and transport phenomena. Here we introduce the Automated MUltiscale Simulation Environment AMUSE, a workflow that starts from Density Functional Theory (DFT) data, automates the analysis of the reaction networks through graph theory, prepares it for microkinetic modeling, and subsequently integrates the results into a standard open-source Computational Fluid Dynamics (CFD) code. We demonstrate the capabilities of AMUSE by applying it to the unimolecular iso-propanol dehydrogenation reaction and then, increasing the complexity, to the pre-commercial Pd/In<sub>2</sub>O<sub>3</sub> catalyst employed for the CO<sub>2</sub> hydrogenation to methanol. The results show that AMUSE allows the computational investigation of heterogeneous catalytic reactions in a comprehensive way, providing essential information for catalyst design from the atomistic to the reactor scale level. Computer science Machine Learning in Materials Science ; Zeolite Catalysis and Synthesis ; Catalysis and Oxidation Reactions
Authors: Walid Boultif | Charif Dehchar | Youghourta Belhocine | Emna Zouaoui | Seyfeddine Rahali | Salah Eddine Zouari | Najoua Sbei | Mahamadou Seydou
Source: Separations (article, 2023-08-31)
Field: Chemistry — Electrochemistry

Chitosan and Metal Oxide Functionalized Chitosan as Efficient Sensors for Lead (II) Detection in Wastewater The work presented in this paper describes the preparation and the electrochemical application of functionalized chitosan-entrapped carbon paste electrodes (CH/CPE) for lead ions (Pb2+) detection in industrial wastewater. The chitosan was first functionalized using TiO2 and CuO, which were both metal oxides that were obtained by extracting it from waste products derived from shrimp shells. The analytical performance of the as-prepared electrodes, CH/CPE, TiO2-CH/CPE, and NiO-CH/CPE, for the detection of lead (II) was examined using electrochemical impedance spectroscopy (EIS) technique in the 0.1 M KNO3 electrolyte solution. The effect of experimental conditions, including polarization potential, frequency, and pH, are optimized to maximize the sensitivity of the measurements. The developed impedimetric sensors provided a linear response over a concentration range of 10−6 to 10−4 M with a detection limit of 3.10−7 M based on S/N = 3. The DFT computational analysis demonstrated that chitosan biopolymer possesses the ability to adsorb Pb (II) ions that are present in wastewater. Chitosan and the derivatives of chitosan, have the potential to remove heavy metals from industrial effluent in a manner that is both economical and eco-friendly to the environment. Chitosan is a biopolymer that is abundantly renewable. Chitosan ; Biopolymer ; Detection limit ; Wastewater ; Dielectric spectroscopy ; Metal ions in aqueous solution ; Electrochemistry ; Adsorption ; Materials science ; Industrial wastewater treatment ; Electrode ; Nuclear chemistry ; Chemistry ; Metal ; Chemical engineering ; Chromatography ; Waste management ; Metallurgy ; Organic chemistry ; Polymer ; Composite material Electrochemical Analysis and Applications ; Analytical Chemistry and Sensors ; Advanced Chemical Sensor Technologies
Authors: Iulia Babutan | Otto Todor-Boer | Leonard Ionuț Atanase | Adriana Vulpoi | Ioan Botiz
Source: Polymers (article, 2023-04-15)
Field: Materials Science — Materials Chemistry

Self-Assembly of Block Copolymers in Thin Films Swollen-Rich in Solvent Vapors In this study we have employed a polymer processing method based on solvent vapor annealing in order to condense relatively large amounts of solvent vapors onto thin films of block copolymers and thus to promote their self-assembly into ordered nanostructures. As revealed by the atomic force microscopy, a periodic lamellar morphology of poly(2-vinylpyridine)-<i>b</i>-polybutadiene and an ordered morphology comprised of hexagonally-packed structures made of poly(2-vinylpyridine)-<i>b</i>-poly(cyclohexyl methacrylate) were both successfully generated on solid substrates for the first time. Copolymer ; Lamellar structure ; Polybutadiene ; Materials science ; Annealing (glass) ; Solvent ; Polymer chemistry ; Thin film ; Polymer ; Chemical engineering ; Self-assembly ; Morphology (biology) ; Nanostructure ; Methacrylate ; Nanotechnology ; Composite material ; Chemistry ; Organic chemistry Block Copolymer Self-Assembly ; Advanced Polymer Synthesis and Characterization ; Polymer Surface Interaction Studies
Authors: Nhat Khang Ngo | Truong Son Hy | Risi Kondor
Source: The Journal of Chemical Physics (article, 2023-07-19)
Field: Materials Science — Materials Chemistry

Multiresolution graph transformers and wavelet positional encoding for learning long-range and hierarchical structures Contemporary graph learning algorithms are not well-suited for large molecules since they do not consider the hierarchical interactions among the atoms, which are essential to determining the molecular properties of macromolecules. In this work, we propose Multiresolution Graph Transformers (MGT), the first graph transformer architecture that can learn to represent large molecules at multiple scales. MGT can learn to produce representations for the atoms and group them into meaningful functional groups or repeating units. We also introduce Wavelet Positional Encoding (WavePE), a new positional encoding method that can guarantee localization in both spectral and spatial domains. Our proposed model achieves competitive results on three macromolecule datasets consisting of polymers, peptides, and protein-ligand complexes, along with one drug-like molecule dataset. Significantly, our model outperforms other state-of-the-art methods and achieves chemical accuracy in estimating molecular properties (e.g., highest occupied molecular orbital, lowest unoccupied molecular orbital, and their gap) calculated by Density Functional Theory in the polymers dataset. Furthermore, the visualizations, including clustering results on macromolecules and low-dimensional spaces of their representations, demonstrate the capability of our methodology in learning to represent long-range and hierarchical structures. Our PyTorch implementation is publicly available at https://github.com/HySonLab/Multires-Graph-Transformer. Computer science ; Transformer ; Macromolecule ; Cluster analysis ; Graph ; Hierarchical clustering ; Artificial intelligence ; Theoretical computer science ; Pattern recognition (psychology) ; Biological system ; Algorithm ; Chemistry ; Physics ; Biology Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Abdel Kader | Mokhtar K. Mohamed | Yasmeen A. Hasanien | Eman M. Kandeel
Source: Journal of Cluster Science (article, 2023-06-13)
Field: Materials Science — Biomaterials

Experimental and Modeling Optimization of Strontium Adsorption on Microbial Nanocellulose, Eco-friendly Approach Abstract Green synthesized cellulose nanocrystals (CNCs) was prepared using Neurospora intermedia , characterized, and used to remove Strontium ions (Sr 2+ ) from an aqueous solution with high efficiency. The characterization of CNCs was performed using a UV-Vis Spectrophotometer, Dynamic Light Scattering (DLS), Zeta Potential (ZP), Fourier Transform Infrared Spectroscopy (FTIR), X-Ray Diffraction (XRD), and Scanning Electron Microscopy (SEM) mapping, EDX elemental analysis and BET surface analyzer. In this study, Response Surface Methodology (RSM) based on Box-Behnken Design (BBD) was successfully applied for the first time to optimize the dynamic adsorption conditions for the maximum removal of Sr 2+ ions from aqueous solutions using CNCs as adsorbent. The effects of parameters, such as initial concentration of Sr 2+ (50–500 ppm), adsorbent dosage (0.05–0.2 g/50ml), and contact time (15–120 min.) on removal efficiency were investigated. A mathematical model was studied to predict the removal performance. The significance and adequacy of the model were surveyed using the analysis of variance (ANOVA). The results showed that the second-order polynomial model is suitable for the prediction removal of Sr 2+ with regression coefficient (R 2 = 97.41%). The highest sorption capacity value of Sr 2+ was obtained (281.89 mg/g) at the adsorbent dosage of 0.05 g/50 ml, contact time of 120 min., and the pollutant (Sr 2+ ) concentration of 275 ppm. Response surface methodology ; Adsorption ; Zeta potential ; Box–Behnken design ; Fourier transform infrared spectroscopy ; Sorption ; Aqueous solution ; Materials science ; Nanocellulose ; Strontium ; Central composite design ; Nuclear chemistry ; Coefficient of determination ; Dynamic light scattering ; Analytical Chemistry (journal) ; Chemistry ; Cellulose ; Chemical engineering ; Chromatography ; Nanoparticle ; Nanotechnology ; Mathematics ; Organic chemistry Advanced Cellulose Research Studies ; Adsorption and biosorption for pollutant removal
Authors: Kang Wang | Du Cheng | Bi‐Cheng Zhou
Source: npj Computational Materials (article, 2023-05-19)
Field: Materials Science — Biomaterials

Generalization of the mixed-space cluster expansion method for arbitrary lattices Abstract Mixed-space cluster expansion (MSCE), a first-principles method to simultaneously model the configuration-dependent short-ranged chemical and long-ranged strain interactions in alloy thermodynamics, has been successfully applied to binary FCC and BCC alloys. However, the previously reported MSCE method is limited to binary alloys with cubic crystal symmetry on a single sublattice. In the current work, MSCE is generalized to systems with multiple sublattices by formulating compatible reciprocal space interactions and combined with a crystal-symmetry-agnostic algorithm for the calculation of constituent strain energy. This generalized approach is then demonstrated in a hypothetical HCP system and Mg-Zn alloys. The current MSCE can significantly improve the accuracy of the energy parameterization and account for all the fully relaxed structures regardless of lattice distortion. The generalized MSCE method makes it possible to simultaneously analyze the short- and long-ranged configuration-dependent interactions in crystalline materials with arbitrary lattices with the accuracy of typical first-principles methods. Reciprocal lattice ; Binary number ; Generalization ; Lattice (music) ; Cluster expansion ; Distortion (music) ; Cluster (spacecraft) ; Reciprocal ; Statistical physics ; Symmetry (geometry) ; Crystal system ; Space (punctuation) ; Work (physics) ; Crystal structure ; Materials science ; Mathematics ; Thermodynamics ; Physics ; Mathematical analysis ; Computer science ; Chemistry ; Crystallography ; Geometry ; Quantum mechanics ; Diffraction Magnesium Alloys: Properties and Applications ; Metal and Thin Film Mechanics ; Thermal Expansion and Ionic Conductivity
Authors: Charles Le Losq | Barbara Baldoni
Source: Journal of Non-Crystalline Solids (article, 2023-07-08)
Field: Materials Science — Ceramics and Composites

Machine learning modeling of the atomic structure and physical properties of alkali and alkaline-earth aluminosilicate glasses and melts Aluminosilicate ; Alkaline earth metal ; Alkali metal ; Earth (classical element) ; Mineralogy ; Materials science ; Inorganic chemistry ; Chemistry ; Physics ; Catalysis ; Organic chemistry Glass properties and applications ; Recycling and utilization of industrial and municipal waste in materials production ; Material Science and Thermodynamics
Authors: Masato Kato | Masashi Watanabe | Shun Hirooka | Romain Vauchy
Source: Frontiers in Nuclear Engineering (article, 2023-01-27)
Field: Materials Science — Materials Chemistry

Oxygen diffusion in the fluorite-type oxides CeO2, ThO2, UO2, PuO2, and (U, Pu)O2 This study evaluates the self-diffusion and chemical diffusion coefficients of oxygen in the fluorite-type oxides CeO 2 , ThO 2 , UO 2 , PuO 2 , and (U, Pu)O 2 using point defect chemistry (oxygen vacancies and interstitials). The self-diffusion coefficient changed in proportion to the 1/n power of oxygen partial pressure, similar to the defect concentration. All parameters used to represent the diffusion coefficients were determined, and the experimental data were accurately stated. The defect formation and migration energies of the oxides were compared, and the change in Frenkel defect concentration was found to affect the high-temperature heat capacities of CeO 2 and ThO 2 . The oxygen chemical diffusion was evaluated in the oxides, excluding the line compound ThO 2 , and the coefficients increased dramatically around the stoichiometric composition, i.e., the chemical diffusion coefficient was much higher at stoichiometric composition, with the oxygen-to-metal ratio equal to 2.00, than in low oxygen-to-metal oxides. This difference altered the mechanism of the reduction and oxidation processes. In the reduction process, the chemical diffusion control rate was dominant and a new phase with the oxygen-to-metal ratio equal to 2.00 was formed, which then expanded from the surface in the oxidation process from a low oxygen-to-metal ratio to the stoichiometric composition. Oxygen ; Fluorite ; Stoichiometry ; Diffusion ; Oxide ; Metal ; Chemistry ; Chemical composition ; Partial pressure ; Analytical Chemistry (journal) ; Materials science ; Inorganic chemistry ; Physical chemistry ; Thermodynamics ; Metallurgy Nuclear Materials and Properties ; Nuclear reactor physics and engineering ; Radioactive element chemistry and processing
Authors: Maximilian J. Werny | Florian Meirer | Bert M. Weckhuysen
Source: Angewandte Chemie International Edition (review, 2023-10-02)
Field: Materials Science — Surfaces, Coatings and Films

Visualizing the Structure, Composition and Activity of Single Catalyst Particles for Olefin Polymerization and Polyolefin Decomposition The structural and morphological characterization of individual catalyst particles for olefin polymerization, as well as for the reverse process of polyolefin decomposition, can provide an improved understanding for how these catalyst materials operate under relevant reaction conditions. In this review, we discuss an emerging analytical toolbox of 2D and 3D chemical imaging techniques that is suitable for investigating the chemistry and reactivity of related catalyst systems. While synchrotron-based X-ray microscopy still provides unparalleled spatial resolutions in 2D and 3D, a number of laboratory-based techniques, most notably focused ion beam-scanning electron microscopy, confocal fluorescence microscopy, infrared photoinduced force microscopy and laboratory-based X-ray nano-computed tomography, have helped to significantly expand the arsenal of analytical tools available to scientists in heterogeneous catalysis and polymer science. In terms of future research, the review outlines the role and impact of in situ and operando (spectro-)microscopy experiments, involving sophisticated reactors as well as online reactant and product analysis, to obtain real-time information on the formation, decomposition, and mobility of polymer phases within single catalyst particles. Furthermore, the potential of fluorescence microscopy, X-ray microscopy and optical microscopy is highlighted for the high-throughput characterization of olefin polymerization and polyolefin decomposition catalysts. By combining these chemical imaging techniques with, for example, chemical staining methodologies, selective probe molecules as well as particle sorting approaches, representative structure-activity relationships can be derived at the level of single catalyst particles. Polyolefin ; Microscopy ; Characterization (materials science) ; Polymerization ; Materials science ; Polymer ; Olefin fiber ; Decomposition ; Nanotechnology ; Chemical imaging ; Catalysis ; Chemical engineering ; Chemistry ; Organic chemistry ; Computer science ; Optics ; Composite material Electron and X-Ray Spectroscopy Techniques ; Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications
Authors: Jiahua Deng | Qiang Cui
Source: Journal of the American Chemical Society (article, 2023-05-12)
Field: Materials Science — Materials Chemistry

Second-Shell Residues Contribute to Catalysis by Predominately Preorganizing the Apo State in PafA Residues beyond the first coordination shell are often observed to make considerable cumulative contributions in enzymes. Due to typically indirect perturbations of multiple physicochemical properties of the active site, however, their individual and specific roles in enzyme catalysis and disease-causing mutations remain difficult to predict and understand at the molecular level. Here we analyze the contributions of several second-shell residues in phosphate-irrepressible alkaline phosphatase of flavobacterium (PafA), a representative system as one of the most efficient enzymes. By adopting a multifaceted approach that integrates quantum-mechanical/molecular-mechanical free energy computations, molecular-mechanical molecular dynamics simulations, and density functional theory cluster model calculations, we probe the rate-limiting phosphoryl transfer step and structural properties of all relevant enzyme states. In combination with available experimental data, our computational results show that mutations of the studied second-shell residues impact catalytic efficiency mainly by perturbation of the apo state and therefore substrate binding, while they do not affect the ground state or alter the nature of phosphoryl transfer transition state significantly. Several second-shell mutations also modulate the active site hydration level, which in turn influences the energetics of phosphoryl transfer. These mechanistic insights also help inform strategies that may improve the efficiency of enzyme design and engineering by going beyond the current focus on the first coordination shell. Chemistry ; Active site ; Molecular dynamics ; Enzyme catalysis ; Transition state ; Computational chemistry ; Enzyme ; Catalysis ; Biochemistry Enzyme Structure and Function ; Alkaline Phosphatase Research Studies ; Biochemical and Molecular Research
Authors: Khanh Dang | Jie Chen | Brian Rodgers | Saryu Fensin
Source: Computer Physics Communications (article, 2023-01-26)
Field: Materials Science — Materials Chemistry

LAVA 1.0: A general-purpose python toolkit for calculation of material properties with LAMMPS and VASP Python (programming language) ; Computational science ; Computer science ; Scripting language ; Molecular dynamics ; Lava ; Programming language ; Chemistry ; Computational chemistry ; Geology Machine Learning in Materials Science ; Microstructure and mechanical properties ; High-pressure geophysics and materials
Authors: Hadeer Abd El-Fattah | Ali Maged | Rasha M. Kamel | Sherif Kharbish
Source: Frontiers in Scientific Research and Technology (review, 2023-01-10)
Field: Chemistry — Spectroscopy

Recent Technologies for The Elimination of Pharmaceutical Compounds from aqueous solutions: A review Water pollution is a major environmental issue that has a wide range of impacts on ecosystems, human health, and the economy. During recent years, pharmaceutical compounds have been considered an emerging water micro-pollutant due to their potential eco-toxicity. Pharmaceuticals, personal care items, steroid hormones, and agrochemicals are synthetic and indigenous products that makeup micropollutants, also known as emerging contaminants. Due to the Corona pandemic (Covid-19), excessive use of antibiotics in addition to the painkillers used in the treatment protocol established by the World Health Organization. Thus, these compounds have become in water sources with higher concentrations. New techniques have been used to remove these environmental pollutants to achieve sustainable development goals (SDGs). This article provides a critical review of various methods presenting the potential to be applied for removing pharmaceuticals from water. Several processes: adsorption, advanced oxidation processes (photodegradation, photocatalysis, ozonation, Fenton reaction, Wet Air Oxidation, ultraviolet radiation, hydrogen peroxide oxidation), and membrane-based techniques (ultrafiltration, nanofiltration, reverse osmosis, forward osmosis, membrane distillation) are analyzed, and their performance during removal of pharmaceuticals from water is compared. Moreover, summaries presenting the efficiency of various materials applied during each process are provided. Finally, the advantages and disadvantages of each proposed method are summarised and comprehensively discussed. Aqueous solution ; Biochemical engineering ; Chemistry ; Combinatorial chemistry ; Process engineering ; Computer science ; Nanotechnology ; Materials science ; Organic chemistry ; Engineering Analytical Chemistry and Chromatography ; Electrochemical Analysis and Applications ; Chemical and Physical Properties in Aqueous Solutions