jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Nicolae Goga | Leonhard Mayrhofer | Ionut Trancă | Silvia Nedea | Koen Heijmans | Veerapandian Ponnuchamy | Andrei Vasilăţeanu
Source: Catalysts (review, 2021-06-30)
Field: Materials Science — Materials Chemistry

A Review of Recent Developments in Molecular Dynamics Simulations of the Photoelectrochemical Water Splitting Process In this review, we provide a short overview of the Molecular Dynamics (MD) method and how it can be used to model the water splitting process in photoelectrochemical hydrogen production. We cover classical non-reactive and reactive MD techniques as well as multiscale extensions combining classical MD with quantum chemical and continuum methods. Selected examples of MD investigations of various aqueous semiconductor interfaces with a special focus on TiO2 are discussed. Finally, we identify gaps in the current state-of-the-art where further developments will be needed for better utilization of MD techniques in the field of water splitting. Water splitting ; Molecular dynamics ; Photoelectrochemistry ; Semiconductor ; Nanotechnology ; Process (computing) ; Computer science ; Chemical physics ; Engineering physics ; Materials science ; Physics ; Chemistry ; Physical chemistry ; Computational chemistry ; Photocatalysis ; Optoelectronics ; Electrochemistry Copper-based nanomaterials and applications ; Advanced Photocatalysis Techniques ; Iron oxide chemistry and applications
Authors: Maria Cazacu | Carmen Racleş | Mirela‐Fernanda Zaltariov | Mihaela Dascălu | Adrian Bele | Codrin Tugui | Alexandra Bargan | George Ştiubianu
Source: Polymers (review, 2021-05-16)
Field: Materials Science — Polymers and Plastics

From Amorphous Silicones to Si-Containing Highly Ordered Polymers: Some Romanian Contributions in the Field Polydimethylsiloxane (PDMS), in spite of its well-defined helical structure, is an amorphous fluid even at extremely high molecular weights. The cause of this behavior is the high flexibility of the siloxane backbone and the lack of intermolecular interactions attributed to the presence of methyl groups. These make PDMS incompatible with almost any organic or inorganic component leading to phase separation in siloxane-siloxane copolymers containing blocks with polar organic groups and in siloxane-organic copolymers, where dimethylsiloxane segments co-exist with organic ones. Self-assembly at the micro- or nanometric scale is common in certain mixed structures, including micelles, vesicles, et cetera, manifesting reversibly in response to an external stimulus. Polymers with a very high degree of ordering in the form of high-quality crystals were obtained when siloxane/silane segments co-exist with coordinated metal blocks in the polymer chain. While in the case of coordination of secondary building units (SBUs) with siloxane ligands 1D chains are formed; when coordination is achieved in the presence of a mixture of ligands, siloxane and organic, 2D structures are formed in most cases. The Romanian research group's results regarding these aspects are reviewed: from the synthesis of classic, amorphous silicone products, to their adaptation for use in emerging fields and to new self-assembled or highly ordered structures with properties that create perspectives for the use of silicones in hitherto unexpected areas. Polymer ; Romanian ; Materials science ; Amorphous solid ; Polymer science ; Field (mathematics) ; Chemical engineering ; Polymer chemistry ; Composite material ; Organic chemistry ; Chemistry ; Mathematics ; Engineering ; Philosophy Polymer Nanocomposites and Properties ; Silicone and Siloxane Chemistry ; Dielectric materials and actuators
Authors: Katsumi Hagita | Takeshi Aoyagi | Yuto Abe | Shinya Genda | T. Honda
Source: Scientific Reports (article, 2021-06-10)
Field: Materials Science — Materials Chemistry

Deep learning-based estimation of Flory–Huggins parameter of A–B block copolymers from cross-sectional images of phase-separated structures Abstract In this study, deep learning (DL)-based estimation of the Flory–Huggins χ parameter of A-B diblock copolymers from two-dimensional cross-sectional images of three-dimensional (3D) phase-separated structures were investigated. 3D structures with random networks of phase-separated domains were generated from real-space self-consistent field simulations in the 25–40 χ N range for chain lengths ( N ) of 20 and 40. To confirm that the prepared data can be discriminated using DL, image classification was performed using the VGG-16 network. We comprehensively investigated the performances of the learned networks in the regression problem. The generalization ability was evaluated from independent images with the unlearned χ N . We found that, except for large χ N values, the standard deviation values were approximately 0.1 and 0.5 for A-component fractions of 0.2 and 0.35, respectively. The images for larger χ N values were more difficult to distinguish. In addition, the learning performances for the 4-class problem were comparable to those for the 8-class problem, except when the χ N values were large. This information is useful for the analysis of real experimental image data, where the variation of samples is limited. Generalization ; Phase (matter) ; Block (permutation group theory) ; Copolymer ; Image (mathematics) ; Flory–Huggins solution theory ; Range (aeronautics) ; Artificial intelligence ; Mathematics ; Component (thermodynamics) ; Standard deviation ; Computer science ; Pattern recognition (psychology) ; Materials science ; Statistics ; Combinatorics ; Physics ; Thermodynamics ; Mathematical analysis ; Polymer ; Nuclear magnetic resonance Block Copolymer Self-Assembly ; Machine Learning in Materials Science ; Advancements in Photolithography Techniques
Authors: Zhen Luo | Yujuan Gao | Zhongyu Duan | Yu Yi | Hao Wang
Source: Frontiers in Bioengineering and Biotechnology (review, 2021-11-26)
Field: Materials Science — Biomaterials

Mitochondria-Targeted Self-Assembly of Peptide-Based Nanomaterials Mitochondria are well known to serve as the powerhouse for cells and also the initiator for some vital signaling pathways. A variety of diseases are discovered to be associated with the abnormalities of mitochondria, including cancers. Thus, targeting mitochondria and their metabolisms are recognized to be promising for cancer therapy. In recent years, great efforts have been devoted to developing mitochondria-targeted pharmaceuticals, including small molecular drugs, peptides, proteins, and genes, with several molecular drugs and peptides enrolled in clinical trials. Along with the advances of nanotechnology, self-assembled peptide-nanomaterials that integrate the biomarker-targeting, stimuli-response, self-assembly, and therapeutic effect, have been attracted increasing interest in the fields of biotechnology and nanomedicine. Particularly, <i>in situ</i> mitochondria-targeted self-assembling peptides that can assemble on the surface or inside mitochondria have opened another dimension for the mitochondria-targeted cancer therapy. Here, we highlight the recent progress of mitochondria-targeted peptide-nanomaterials, especially those <i>in situ</i> self-assembly systems in mitochondria, and their applications in cancer treatments. Mitochondrion ; Nanomedicine ; Cancer therapy ; Peptide ; Nanotechnology ; Chemistry ; Cancer ; Computational biology ; Biology ; Cell biology ; Medicine ; Biochemistry ; Materials science ; Nanoparticle ; Internal medicine Supramolecular Self-Assembly in Materials ; Advanced biosensing and bioanalysis techniques ; Supramolecular Chemistry and Complexes
Authors: Esmail Doustkhah | Rafat Tahawy | Ulla Simon | Nao Tsunoji | Yusuke Ide | Dorian Hanaor | M. Hussein N. Assadi
Source: Chemosphere (article, 2021-03-05)
Field: Chemistry — Inorganic Chemistry

Bispropylurea bridged polysilsesquioxane: A microporous MOF-like material for molecular recognition Microporous material ; Acetonitrile ; Adsorption ; Toluene ; Silsesquioxane ; Chemical engineering ; Urea ; Chemistry ; Materials science ; Organic chemistry ; Polymer Metal-Organic Frameworks: Synthesis and Applications ; Chemical Synthesis and Characterization ; Gas Sensing Nanomaterials and Sensors
Authors: Anuja Tripathi | Kenneth D. Harris | Anastasia Elias
Source: PLoS ONE (article, 2021-10-12)
Field: Materials Science — Materials Chemistry

High surface area nitrogen-functionalized Ni nanozymes for efficient peroxidase-like catalytic activity Nitrogen-functionalization is an effective means of improving the catalytic performances of nanozymes. In the present work, plasma-assisted nitrogen modification of nanocolumnar Ni GLAD films was performed using an ammonia plasma, resulting in an improvement in the peroxidase-like catalytic performance of the porous, nanostructured Ni films. The plasma-treated nanozymes were characterized by TEM, SEM, XRD, and XPS, revealing a nitrogen-rich surface composition. Increased surface wettability was observed after ammonia plasma treatment, and the resulting nitrogen-functionalized Ni GLAD films presented dramatically enhanced peroxidase-like catalytic activity. The optimal time for plasma treatment was determined to be 120 s; when used to catalyze the oxidation of the colorimetric substrate TMB in the presence of H2O2, Ni films subjected to 120 s of plasma treatment yielded a much higher maximum reaction velocity (3.7⊆10-8 M/s vs. 2.3⊆10-8 M/s) and lower Michaelis-Menten coefficient (0.17 mM vs. 0.23 mM) than pristine Ni films with the same morphology. Additionally, we demonstrate the application of the nanozyme in a gravity-driven, continuous catalytic reaction device. Such a controllable plasma treatment strategy may open a new door toward surface-functionalized nanozymes with improved catalytic performance and potential applications in flow-driven point-of-care devices. Catalysis ; Surface modification ; Wetting ; X-ray photoelectron spectroscopy ; Chemical engineering ; Substrate (aquarium) ; Ammonia ; Nitrogen ; Materials science ; Inductively coupled plasma ; Plasma ; Peroxidase ; Nanotechnology ; Chemistry ; Organic chemistry ; Enzyme Advanced Nanomaterials in Catalysis ; Advanced biosensing and bioanalysis techniques ; Nanocluster Synthesis and Applications
Authors: Nicolai Ree | Mads Koerstz | Kurt V. Mikkelsen | Jan H. Jensen
Source: The Journal of Chemical Physics (article, 2021-10-20)
Field: Chemistry — Physical and Theoretical Chemistry

Virtual screening of norbornadiene-based molecular solar thermal energy storage systems using a genetic algorithm We present a computational methodology for the screening of a chemical space of 10<sup>25</sup> substituted norbornadiene molecules for promising kinetically stable molecular solar thermal (MOST) energy storage systems with high energy densities that absorb in the visible part of the solar spectrum. We use semiempirical tight-binding methods to construct a dataset of nearly 34 000 molecules and train graph convolutional networks to predict energy densities, kinetic stability, and absorption spectra and then use the models together with a genetic algorithm to search the chemical space for promising MOST energy storage systems. We identify 15 kinetically stable molecules, five of which have energy densities greater than 0.45 MJ/kg, and the main conclusion of this study is that the largest energy density that can be obtained for a single norbornadiene moiety with the substituents considered here, while maintaining a long half-life and absorption in the visible spectrum, is around 0.55 MJ/kg. Norbornadiene ; Molecule ; Kinetic energy ; Absorption spectroscopy ; Thermal ; Absorption (acoustics) ; Solar energy ; Materials science ; Chemical physics ; Chemistry ; Biological system ; Physics ; Optics ; Thermodynamics ; Quantum mechanics ; Organic chemistry Photochemistry and Electron Transfer Studies ; Perovskite Materials and Applications ; Organic Electronics and Photovoltaics
Authors: Abraham Mansouri | Alreem Essa Binali | Najeeb Khan | Mehrooz Zamanzadeh | Peyman Taheri
Source: Scientific Reports (article, 2021-01-21)
Field: Materials Science — Materials Chemistry

Three-dimensional modeling of in-ground cathodic protection systems with deforming anodes The design of sacrificial cathodic protection (CP) systems conventionally involves steady-state assumptions, which means design parameters are considered constant during the in-service life of CP systems. In contrast, it is evident by experimental observations (including field measurements) that cathodic protection is a transient process due to variations in electrolyte properties such as seasonal changes in electrical conductivity of soil, depletion of anodes, and formation of corrosion deposits on anode material surface, to name a few. The lack of practical time-dependent models on this critical issue is apparent in the literature; accordingly, in this study, a pseudo transient electrochemical model is adopted to highlight the transient behavior of cathodic protection systems and investigate key differences with steady-state behavior. For the sake of demonstration, the developed model is used to simulate the time-dependent performance of a sacrificial anode bed for cathodic protection of screw-pile foundations. The methodology proposed in this study can be used by corrosion engineers to improve and optimize the design of CP systems and numerically estimate the performance of sacrificial anodes and the level of protection over time. Cathodic protection ; Anode ; Transient (computer programming) ; Corrosion ; Electrolyte ; Materials science ; Galvanic anode ; Electrochemistry ; Nuclear engineering ; Computer science ; Metallurgy ; Chemistry ; Engineering ; Electrode Corrosion Behavior and Inhibition ; Concrete Corrosion and Durability ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Vagish Channa Basappa | Sowmya Ramaiah | Sudeep Penubolu | K. Ajay Kumar | H Rashid | Y Xu | N Ahmad | Y Muhammad | L Wang | C Vagish | A Kumar | K Kumara | H Vivek | N Renuka | N Lokanath | K Kumar | L Rout | L Achary | S Mohanty | P Nayak | B Barik | P Dash | D Adnan | B Singh | S Mehta | V Kumar | R Kataria | S Burmaoglu | A Yilmaz | M Polat | R Kaya | I Gulcin | O Algul | D Lokeshwari | G Pavithra | S Naveen | K Ajay Kumar | V Sunitha | H Manjunath | A Dileep Kumar | I Warad | V Manivannan | M Prabhudeva | K Raghavendra | Ajay Kumar | K | L Martelli | L Viera | M Paixao | J Zukerman-Schpector | J De Souza | A Aguiar | G Oliva | R Guido | . Correa | D Nair | P Pavashelrishi | I Namboothiri | H Jayadevappa | N Agrawal
Source: Biointerface Research in Applied Chemistry (article, 2021-04-19)
Field: Chemistry — Organic Chemistry

Recent Developments on the Synthetic and Biological Applications of Chalcones-A Review Chalcones are precursors of the biosynthesis of flavonoids present in plants. These motifs serve a wide range of applications, from synthetic to pharmacological to physical spheres. Chalcone derivatives attracted the scientific community all over the world in recent times due to their diversified applications. The presence of reactive α,β-unsaturated carbonyl moiety in chalcones makes them a versatile intermediate in synthesizing various classes of compounds of biological and physical interest. More importantly, the chalcones themselves have been known to possess enormous biological activities and physical properties like semiconductor, non-linear optical, fluorescence, and electronic properties. In this context, the present review summarises the overall developments in the synthetic, pharmacological, and physical applications of chalcones in recent fast. The critical discussion was attempted on the synthetic applications and biological potencies as anti-cancer, antidiabetic, antimicrobial, antioxidant, and anti-inflammatory. Chalcone ; Context (archaeology) ; Nanotechnology ; Chemistry ; Moiety ; Combinatorial chemistry ; Biochemical engineering ; Organic chemistry ; Materials science ; Biology ; Engineering Synthesis and biological activity ; Computational Drug Discovery Methods ; Multicomponent Synthesis of Heterocycles
Authors: Jolene P. Reid
Source: Communications Chemistry (article, 2021-12-09)
Field: Chemistry — Organic Chemistry

Open questions on the transfer of chirality The transfer of chiral information from optically pure reaction components to products can generate enantiomerically-enriched molecules, but the control of stereochemistry often proves challenging. Here, the author highlights how our fundamental understanding of stereocontrol has evolved and discusses possible approaches for the rational development of enantioselective catalysts. Enantioselective synthesis ; Chirality (physics) ; Rational design ; Development (topology) ; Catalysis ; Chemistry ; Combinatorial chemistry ; Nanotechnology ; Stereochemistry ; Computer science ; Materials science ; Organic chemistry ; Physics ; Mathematics ; Quantum mechanics Asymmetric Synthesis and Catalysis ; Molecular spectroscopy and chirality ; Surface Chemistry and Catalysis
Authors: Shingo Takada | T. Suzuki | Yoshihiro Takebayashi | Takumi Ono | Satoshi Yoda
Source: Scientific Reports (article, 2021-12-15)
Field: Materials Science — Materials Chemistry

Machine learning assisted optimization of blending process of polyphenylene sulfide with elastomer using high speed twin screw extruder Abstract Random forest regression was applied to optimize the melt-blending process of polyphenylene sulfide (PPS) with poly(ethylene-glycidyl methacrylate-methyl acrylate) (E-GMA-MA) elastomer to improve the Charpy impact strength. A training dataset was constructed using four elastomers with different GMA and MA contents by varying the elastomer content up to 20 wt% and the screw rotation speed of the extruder up to 5000 rpm at a fixed barrel temperature of 300 °C. Besides the controlled parameters, the following measured parameters were incorporated into the descriptors for the regression: motor torque, polymer pressure, and polymer temperatures monitored by infrared-ray thermometers installed at four positions (T1 to T4) as well as the melt viscosity and elastomer particle diameter of the product. The regression without prior knowledge revealed that the polymer temperature T1 just after the first kneading block is an important parameter next to the elastomer content. High impact strength required high elastomer content and T1 below 320 °C. The polymer temperature T1 was much higher than the barrel temperature and increased with the screw speed due to the heat of shear. The overheating caused thermal degradation, leading to a decrease in the melt viscosity and an increase in the particle diameter at high screw speed. We thus reduced the barrel temperature to keep T1 around 310 °C. This increased the impact strength from 58.6 kJ m −2 as the maximum in the training dataset to 65.3 and 69.0 kJ m −2 at elastomer contents of 20 and 30 wt%, respectively. Materials science ; Elastomer ; Composite material ; Plastics extrusion ; Polymer ; Extrusion Machine Learning in Materials Science ; Injection Molding Process and Properties ; Advanced machining processes and optimization
Authors: Abdelfattah Faouzi | Alexandre Arnaud | Alexandre Bancet | Caroline Barette | Jordane Preto | Cong Viet | Lars Petter Jordheim | Zineb Bousfiha | Thị Thanh Bình Nguyễn | Marion Verrière | Amaury Farce | Marie‐Odile Fauvarque | Roland Barret | Thierry Lomberget
Source: European Journal of Medicinal Chemistry (article, 2021-02-09)
Field: Chemistry — Organic Chemistry

Combretastatin A-4 sulfur-containing heterocyclic derivatives: Synthesis, antiproliferative activities and molecular docking studies Chemistry ; Combretastatin ; Docking (animal) ; Tubulin ; Stereochemistry ; Colchicine ; Microtubule ; Cell cycle ; Cell growth ; Cell culture ; Polymerization ; Apoptosis ; Combinatorial chemistry ; Biochemistry ; Organic chemistry ; Cell biology Synthesis and biological activity ; Click Chemistry and Applications ; Fungal Plant Pathogen Control
Authors: Tengfei Ma | Jie Chen
Source: Proceedings of the AAAI Conference on Artificial Intelligence (article, 2021-05-18)
Field: Materials Science — Materials Chemistry

Unsupervised Learning of Graph Hierarchical Abstractions with Differentiable Coarsening and Optimal Transport Hierarchical abstractions are a methodology for solving large-scale graph problems in various disciplines. Coarsening is one such approach: it generates a pyramid of graphs whereby the one in the next level is a structural summary of the prior one. With a long history in scientific computing, many coarsening strategies were developed based on mathematically driven heuristics. Recently, resurgent interests exist in deep learning to design hierarchical methods learnable through differentiable parameterization. These approaches are paired with downstream tasks for supervised learning. In practice, however, supervised signals (e.g., labels) are scarce and are often laborious to obtain. In this work, we propose an unsupervised approach, coined OTCoarsening, with the use of optimal transport. Both the coarsening matrix and the transport cost matrix are parameterized, so that an optimal coarsening strategy can be learned and tailored for a given set of graphs. We demonstrate that the proposed approach produces meaningful coarse graphs and yields competitive performance compared with supervised methods for graph classification and regression. Computer science ; Heuristics ; Differentiable function ; Graph ; Artificial intelligence ; Parameterized complexity ; Machine learning ; Supervised learning ; Theoretical computer science ; Algorithm ; Artificial neural network ; Mathematics Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Machine Learning and ELM
Authors: Simon Crase | Benjamin Hall | Suresh N. Thennadil
Source: Computers, materials & continua/Computers, materials & continua (Print) (article, 2021-01-01)
Field: Chemistry — Analytical Chemistry

Cluster Analysis for IR and NIR Spectroscopy: Current Practices to Future Perspectives Supervised machine learning techniques have become well established in the study of spectroscopy data. However, the unsupervised learning technique of cluster analysis hasn’t reached the same level maturity in chemometric analysis. This paper surveys recent studies which apply cluster analysis to NIR and IR spectroscopy data. In addition, we summarize the current practices in cluster analysis of spectroscopy and contrast these with cluster analysis literature from the machine learning and pattern recognition domain. This includes practices in data pre-processing, feature extraction, clustering distance metrics, clustering algorithms and validation techniques. Special consideration is given to the specific characteristics of IR and NIR spectroscopy data which typically includes high dimensionality and relatively low sample size. The findings highlighted a lack of quantitative analysis and evaluation in current practices for cluster analysis of IR and NIR spectroscopy data. With this in mind, we propose an analysis model or workflow with techniques specifically suited for cluster analysis of IR and NIR spectroscopy data along with a pragmatic application strategy. Cluster analysis ; Spectroscopy ; Cluster (spacecraft) ; Computer science ; Curse of dimensionality ; Pattern recognition (psychology) ; Artificial intelligence ; Data mining ; Near-infrared spectroscopy ; Machine learning ; Physics Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Advanced Chemical Sensor Technologies