jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Salah M. Tawfik | Shavkatjon Azizov | Mohamed R. Elmasry | Mirkomil Sharipov | Yong‐Ill Lee
Source: Nanomaterials (review, 2020-12-30)
Field: Materials Science — Biomaterials

Recent Advances in Nanomicelles Delivery Systems The efficient and selective delivery of therapeutic drugs to the target site remains the main obstacle in the development of new drugs and therapeutic interventions. Up until today, nanomicelles have shown their prospective as nanocarriers for drug delivery owing to their small size, good biocompatibility, and capacity to effectively entrap lipophilic drugs in their core. Nanomicelles are formed via self-assembly in aqueous media of amphiphilic molecules into well-organized supramolecular structures. Molecular weights and structure of the core and corona forming blocks are important properties that will determine the size of nanomicelles and their shape. Selective delivery is achieved via novel design of various stimuli-responsive nanomicelles that release drugs based on endogenous or exogenous stimulations such as pH, temperature, ultrasound, light, redox potential, and others. This review summarizes the emerging micellar nanocarriers developed with various designs, their outstanding properties, and underlying principles that grant targeted and continuous drug delivery. Finally, future perspectives, and challenges for nanomicelles are discussed based on the current achievements and remaining issues. Nanocarriers ; Drug delivery ; Nanotechnology ; Amphiphile ; Materials science ; Nanomedicine ; Supramolecular chemistry ; Biocompatibility ; Micelle ; Homogeneous ; Combinatorial chemistry ; Nanoparticle ; Chemistry ; Aqueous solution ; Molecule ; Organic chemistry ; Copolymer Nanoparticle-Based Drug Delivery ; Supramolecular Self-Assembly in Materials ; Nanoplatforms for cancer theranostics
Authors: Sami Ullah | Qitao Shi | Junhua Zhou | Xiaoqin Yang | Huy Q. Ta | Maria Hasan | Nasir M. Ahmad | Lei Fu | Alicja Bachmatiuk | Mark H. Rümmeli
Source: Advanced Materials Interfaces (article, 2020-10-11)
Field: Materials Science — Materials Chemistry

Advances and Trends in Chemically Doped Graphene Abstract Chemically doped graphene materials are fascinating because these have different desirable attributes with possible synergy. The inert and gapless nature of graphene can be changed by adding a small number of heteroatoms to substitute carbon in the lattice. The doped material may display superior catalytic activities; durable, fast, and selective sensing; improved magnetic moments; photoresponses; and activity in chemical reactions. In the current review, recent advances are covered in chemically doped graphene. First, the different types of heteroatoms, their bonding configurations, and briefly their properties are discussed. This is followed by the description of various synthesis and analytical methods essential for assessing the characteristics of heterographene with specific focus on the selected graphene materials of different dopants (particularly, single dopants, including N, B, S, P, first three halogens, Ge, and Ga, and codopants, such as N/O), and more importantly, up‐to‐date applications enabled by the intentional doping. Finally, outlook and perspectives section review the existing challenges, future opportunities, and possible ways to improve the graphitic materials. The goal is to update and inspire the readers to establish novel doped graphene with valuable properties and for current and futuristic applications. Graphene ; Heteroatom ; Dopant ; Materials science ; Doping ; Nanotechnology ; Optoelectronics ; Organic chemistry ; Chemistry Graphene research and applications ; Supercapacitor Materials and Fabrication ; Advancements in Battery Materials
Authors: Yun Zhang | Xiaojie Xu
Source: Journal of Magnetism and Magnetic Materials (article, 2020-05-29)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Curie temperature modeling of magnetocaloric lanthanum manganites using Gaussian process regression Magnetic refrigeration ; Orthorhombic crystal system ; Curie temperature ; Materials science ; Condensed matter physics ; Thermodynamics ; Lattice (music) ; Refrigeration ; Lanthanum ; Crystal structure ; Magnetization ; Ferromagnetism ; Magnetic field ; Physics ; Chemistry ; Crystallography Magnetic and transport properties of perovskites and related materials ; Advanced Condensed Matter Physics ; Multiferroics and related materials
Authors: Theodore Hueckel | Glen M. Hocky | Jérémie Palacci | Stefano Sacanna
Source: Nature (article, 2020-04-22)
Field: Materials Science — Materials Chemistry

Ionic solids from common colloids Ionic bonding ; Crystallization ; Nucleation ; Materials science ; Colloid ; Colloidal crystal ; Chemical physics ; Nanotechnology ; Particle (ecology) ; Polymer ; Chemical engineering ; Isostructural ; Chemistry ; Crystallography ; Crystal structure ; Ion ; Organic chemistry ; Composite material Pickering emulsions and particle stabilization ; Polymer Surface Interaction Studies ; Electrostatics and Colloid Interactions
Authors: Shuaihua Lu | Qionghua Zhou | Yilv Guo | Yehui Zhang | Yilei Wu | Jinlan Wang
Source: Advanced Materials (article, 2020-06-15)
Field: Materials Science — Materials Chemistry

Coupling a Crystal Graph Multilayer Descriptor to Active Learning for Rapid Discovery of 2D Ferromagnetic Semiconductors/Half‐Metals/Metals 2D ferromagnetic (FM) semiconductors/half-metals/metals are the key materials toward next-generation spintronic devices. However, such materials are still rather rare and the material search space is too large to explore exhaustively. Here, an adaptive framework to accelerate the discovery of 2D intrinsic FM materials is developed, by combining advanced machine-learning (ML) techniques with high-throughput density functional theory calculations. Successfully, about 90 intrinsic FM materials with desirable bandgap and excellent thermodynamic stability are screened out and a database containing 1459 2D magnetic materials is set up. To improve the performance of ML models on small-scale datasets like diverse 2D materials, a crystal graph multilayer descriptor using the elemental property is proposed, with which ML models achieve prediction accuracy over 90% on thermodynamic stability, magnetism, and bandgap. This study not only provides dozens of compelling FM candidates for future spintronics, but also paves a feasible route for ML-based rapid screening of diverse structures and/or complex properties. Spintronics ; Materials science ; Ferromagnetism ; Magnetism ; Semiconductor ; Coupling (piping) ; Band gap ; Density functional theory ; Magnetic semiconductor ; Stability (learning theory) ; Chemical stability ; Nanotechnology ; Condensed matter physics ; Computer science ; Machine learning ; Optoelectronics ; Quantum mechanics ; Thermodynamics ; Metallurgy ; Physics Machine Learning in Materials Science ; 2D Materials and Applications ; Ferroelectric and Negative Capacitance Devices
Authors: Karo Sedighiani | Martin Diehl | Konstantina Traka | Franz Roters | Jilt Sietsma | Dierk Raabe
Source: International Journal of Plasticity (article, 2020-06-26)
Field: Materials Science — Materials Chemistry

An efficient and robust approach to determine material parameters of crystal plasticity constitutive laws from macro-scale stress–strain curves A severe obstacle for the routine use of crystal plasticity models is the effort associated with determining their constitutive parameters. Obtaining these parameters usually requires time-consuming micromechanical tests that allow probing of individual grains. In this study, a novel, computationally efficient, and fully automated approach is introduced which allows the identification of constitutive parameters from macroscopic tests. The approach presented here uses the response surface methodology together with a genetic algorithm to determine an optimal set of parameters. It is especially suited for complex models with a large number of parameters. The proposed approach also helps to develop a quantitative and thorough understanding of the relative influence of the different constitutive parameters and their interactions. Such general insights into parameter relations in complex models can be used to improve constitutive laws and reduce redundancy in parameter sets. The merits of the methodology are demonstrated on the examples of a dislocation-density-based crystal plasticity model for bcc steel, a phenomenological crystal plasticity model for fcc copper, and a phenomenological crystal plasticity model incorporating twinning deformation for hcp magnesium. The approach proposed is, however, model-independent and can be also used to identify parameters of, for instance, fatigue, creep and damage models. The method has been implemented into the Düsseldorf Advanced Material Simulation Kit (DAMASK) and is available as free and open-source software. The capability of translating complex material response into a micromechanical digital twin is an essential precondition for the ongoing digitalization of material property prediction, quality control of semi-finished parts, material response in manufacturing and the long-term behavior of products and materials when in service. Constitutive equation ; Plasticity ; Materials science ; Crystal twinning ; Mechanics ; Computer science ; Structural engineering ; Finite element method ; Microstructure ; Engineering ; Metallurgy ; Physics ; Composite material Microstructure and mechanical properties ; Metal Forming Simulation Techniques ; Metallurgy and Material Forming
Authors: Xuyang Yao | Jie Wang | Dejin Jiao | Zizhao Huang | Oumaima Mhirsi | Francisco Lossada | Lisa Chen | Bastian Haehnle | Alexander J. C. Kuehne | Xiang Ma | He Tian | Andreas Walther
Source: Advanced Materials (article, 2020-12-21)
Field: Materials Science — Materials Chemistry

Room‐Temperature Phosphorescence Enabled through Nacre‐Mimetic Nanocomposite Design A generic, facile, and waterborne strategy is introduced to fabricate flexible, low-cost nanocomposite films with room-temperature phosphorescence (RTP) by incorporating waterborne RTP polymers into self-assembled bioinspired polymer/nanoclay nanocomposites. The excellent oxygen barrier of the lamellar nanoclay structure suppresses the quenching effect from ambient oxygen (k<sub>q</sub> ) and broadens the choice of polymer matrices towards lower glass transition temperature (T<sub>g</sub> ), while providing better mechanical properties and processability. Moreover, the oxygen permeation and diffusion inside the films can be fine-tuned by varying the polymer/nanoclay ratio, enabling programmable retention times of the RTP signals, which is exploited for transient information storage and anti-counterfeiting materials. Additionally, anti-interception materials are showcased by tracing the interception-induced oxygen history that interferes with the preset self-erasing time. Merging bioinspired nanocomposite design with RTP materials contributes to overcoming the inherent limitations of molecular design of organic RTP compounds, and allows programmable temporal features to be added into RTP materials by controlled mesostructures. This will assist in paving the way for practical applications of RTP materials as novel anti-counterfeiting materials. Materials science ; Nanocomposite ; Lamellar structure ; Phosphorescence ; Polymer ; Quenching (fluorescence) ; Polymer nanocomposite ; Oxygen ; Nanotechnology ; Chemical engineering ; Composite material ; Organic chemistry ; Fluorescence Luminescence and Fluorescent Materials ; Polymer Surface Interaction Studies ; Analytical Chemistry and Sensors
Authors: Yuuya Nagata | Tadashi Mori
Source: Frontiers in Chemistry (review, 2020-06-09)
Field: Chemistry — Organic Chemistry

Irreverent Nature of Dissymmetry Factor and Quantum Yield in Circularly Polarized Luminescence of Small Organic Molecules Recently, a rational modification of small organic molecules has attracted considerable attention for designing advanced materials with enhanced circularly polarized luminescence (CPL) activity. A particular emphasis has been placed on fully allowed π-π<sup>*</sup> transition of rigid aromatic systems, due to their relatively superior emission properties or quantum yields of luminescence (Φ<sub>lum</sub>). However, their dissymmetry factors (<i>g</i> <sub>lum</sub>), differential left and right CPL intensities, are typically disappointingly low at least in one to two orders of magnitude. Truly useful organic CPL materials, rated by a circular polarization luminosity index (Λ<sub>CPL</sub>) per single molecule, possess both |<i>g</i> <sub>lum</sub>| and Φ<sub>lum</sub> values high. However, how to improve these two factors simultaneously with a proper molecular design is an open question. Here, we addressed this issue by theoretical and statistical inspection on a possible relation of the <i>g</i> <sub>lum</sub> and Φ<sub>lum</sub> values. According to the analysis, we propose simple, unpretentious, yet pertinent guidelines for designing superior organic CPL materials for the future with large Λ<sub>CPL</sub> values. Luminescence ; Chirality (physics) ; Organic molecules ; Molecule ; Polarization (electrochemistry) ; Circular polarization ; Quantum yield ; Quantum ; Materials science ; Chemistry ; Physics ; Optoelectronics ; Optics ; Organic chemistry ; Physical chemistry ; Quantum mechanics Synthesis and Properties of Aromatic Compounds ; Luminescence and Fluorescent Materials ; Synthesis and characterization of novel inorganic/organometallic compounds
Authors: Zhaxylyk A. Kudyshev | Alexander V. Kildishev | Vladimir M. Shalaev | Alexandra Boltasseva
Source: Nanophotonics (article, 2020-10-28)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Machine learning–assisted global optimization of photonic devices Abstract Over the past decade, artificially engineered optical materials and nanostructured thin films have revolutionized the area of photonics by employing novel concepts of metamaterials and metasurfaces where spatially varying structures yield tailorable “by design” effective electromagnetic properties. The current state‐of‐the‐art approach to designing and optimizing such structures relies heavily on simplistic, intuitive shapes for their unit cells or metaatoms. Such an approach cannot provide the global solution to a complex optimization problem where metaatom shape, in‐plane geometry, out‐of‐plane architecture, and constituent materials have to be properly chosen to yield the maximum performance. In this work, we present a novel machine learning–assisted global optimization framework for photonic metadevice design. We demonstrate that using an adversarial autoencoder (AAE) coupled with a metaheuristic optimization framework significantly enhances the optimization search efficiency of the metadevice configurations with complex topologies. We showcase the concept of physics‐driven compressed design space engineering that introduces advanced regularization into the compressed space of an AAE based on the optical responses of the devices. Beyond the significant advancement of the global optimization schemes, our approach can assist in gaining comprehensive design “intuition” by revealing the underlying physics of the optical performance of metadevices with complex topologies and material compositions. Photonics ; Topology optimization ; Metamaterial ; Computer science ; Network topology ; Global optimization ; Artificial intelligence ; Computer engineering ; Materials science ; Engineering ; Finite element method ; Algorithm Metamaterials and Metasurfaces Applications ; Photonic Crystals and Applications ; Thermal Radiation and Cooling Technologies
Authors: Bijun Tang | Yuhao Lu | Jiadong Zhou | Tushar Chouhan | Han Wang | Prafful Golani | Manzhang Xu | Quan Xu | Cuntai Guan | Zheng Liu
Source: Materials Today (article, 2020-10-27)
Field: Materials Science — Materials Chemistry

Machine learning-guided synthesis of advanced inorganic materials Process (computing) ; Property (philosophy) ; Work (physics) ; Acceleration ; Characterization (materials science) ; Outcome (game theory) ; Carbon fibers ; Experimental data Machine Learning in Materials Science ; 2D Materials and Applications ; Advanced Photocatalysis Techniques
Authors: Ajay Vikram Singh | Daniel Rosenkranz | Mohammad Hasan Dad Ansari | Rishabh Singh | Anurag Kanase | Shubham Singh | Blair D. Johnston | Jutta Tentschert | Peter Laux | Andreas Luch
Source: Advanced Intelligent Systems (article, 2020-10-07)
Field: Materials Science — Materials Chemistry

Artificial Intelligence and Machine Learning Empower Advanced Biomedical Material Design to Toxicity Prediction Materials at the nanoscale exhibit specific physicochemical interactions with their environment. Therefore, evaluating their toxic potential is a primary requirement for regulatory purposes and for the safer development of nanomedicines. In this review, to aid the understanding of nano–bio interactions from environmental and health and safety perspectives, the potential, reality, challenges, and future advances that artificial intelligence (AI) and machine learning (ML) present are described. Herein, AI and ML algorithms that assist in the reporting of the minimum information required for biomaterial characterization and aid in the development and establishment of standard operating procedures are focused. ML tools and ab initio simulations adopted to improve the reproducibility of data for robust quantitative comparisons and to facilitate in silico modeling and meta‐analyses leading to a substantial contribution to safe‐by‐design development in nanotoxicology/nanomedicine are mainly focused. In addition, future opportunities and challenges in the application of ML in nanoinformatics, which is particularly well‐suited for the clinical translation of nanotherapeutics, are highlighted. This comprehensive review is believed that it will promote an unprecedented involvement of AI research in improvements in the field of nanotoxicology and nanomedicine. Nanomedicine ; Computer science ; Artificial intelligence ; Engineering ; Nanotechnology ; Risk analysis (engineering) ; Medicine ; Materials science Nanoparticles: synthesis and applications ; Nanotechnology research and applications ; Computational Drug Discovery Methods
Authors: Zheng Li | Luke E. K. Achenie | Hongliang Xin
Source: ACS Catalysis (article, 2020-03-19)
Field: Materials Science — Materials Chemistry

An Adaptive Machine Learning Strategy for Accelerating Discovery of Perovskite Electrocatalysts We develop an adaptive machine learning strategy in search of high-performance ABO3-type cubic perovskites for catalyzing the oxygen evolution reaction (OER). The strategy has two essential components: a set of multifidelity features (e.g., composition and electronic structure) and probabilistic models with Gaussian processes trained with ab initio data for predicting activity descriptors (i.e., *O and *OH adsorption energies). By iteratively validating/refining the candidates which have theoretical overpotentials <0.5 V, albeit with large uncertainties, we attain machine learning models (RMSE < 0.5 eV) that can rapidly navigate through a chemical subspace of ∼4000 double perovskites (AA′B2O6) and single out stable structures with promising OER activity. Our approach successfully identified several known perovskites with improved catalytic performance over the benchmark LaCoO3 along with 10 other candidates that have not been reported. Importantly, by analyzing the feature distributions of better and worse catalysts than LaCoO3, we draw molecular orbital insights into physical factors governing the OER activity. Probabilistic logic ; Benchmark (surveying) ; Perovskite (structure) ; Computer science ; Catalysis ; Materials science ; Ab initio ; Chemistry ; Artificial intelligence Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Advanced Photocatalysis Techniques
Authors: Jian Song | Benjamin Winkeljann | Oliver Lieleg
Source: Advanced Materials Interfaces (article, 2020-07-14)
Field: Materials Science — Surfaces, Coatings and Films

Biopolymer‐Based Coatings: Promising Strategies to Improve the Biocompatibility and Functionality of Materials Used in Biomedical Engineering Abstract It was a physicist, Wolfgang Pauli, who recognized a century ago that “God made the bulk; the surface was invented by the devil.” And indeed, adjusting the surface properties of materials has kept engineers and chemists busy since—and it still does. In the context of biomedical engineering, the key challenge is ensuring the functionality of an artificial object, which is inserted into the human body—an environment that passively and actively rejects foreign materials. Here, recent advances in this area while focusing on those approaches that employ surface coating strategies with biopolymers are summarized. Nanotechnology ; Biocompatibility ; Materials science ; Biopolymer ; Context (archaeology) ; Surface engineering ; Coating ; Engineering ethics ; Engineering ; Composite material ; Polymer ; Metallurgy Polymer Surface Interaction Studies ; Electrospun Nanofibers in Biomedical Applications ; Bone Tissue Engineering Materials
Authors: Luke D. Elliott | Surajit Kayal | Michael W. George | Kevin I. Booker‐Milburn
Source: Journal of the American Chemical Society (article, 2020-08-04)
Field: Chemistry — Organic Chemistry

Rational Design of Triplet Sensitizers for the Transfer of Excited State Photochemistry from UV to Visible Time Dependent Density Functional Theory has been used to assist the design and synthesis of a series thioxanthone triplet sensitizers. Calculated energies of the triplet excited state (<i>E</i><sub>T</sub>) informed both the type and position of auxochromes placed on the thioxanthone core, enabling fine-tuning of the UV-vis absorptions and associated triplet energies. The calculated results were highly consistent with experimental observation in both the order of the λ<sub>max</sub> and <i>E</i><sub>T</sub> values. The synthesized compounds were then evaluated for their efficacies as triplet sensitizers in a variety of UV and visible light preparative photochemical reactions. The results of this study exceeded expectations; in particular [2 + 2] cycloaddition chemistry that had previously been sensitized in the UV was found to undergo cycloaddition at 455 nm (blue) with a 2- to 9-fold increase in productivity (g/h) relative to input power. This study demonstrates the ability of powerful modern computational methods to aid in the design of successful and productive triplet sensitized photochemical reactions. Chemistry ; Thioxanthone ; Photochemistry ; Triplet state ; Excited state ; Cycloaddition ; Density functional theory ; Visible spectrum ; Singlet state ; Computational chemistry ; Molecule ; Organic chemistry ; Optoelectronics ; Atomic physics ; Catalysis Radical Photochemical Reactions ; Oxidative Organic Chemistry Reactions ; Photochromic and Fluorescence Chemistry
Authors: Natasha Raura | Anirudh Garg | Arpit Arora | M Roma
Source: Biomaterials Research (review, 2020-03-20)
Field: Materials Science — Materials Chemistry

Nanoparticle technology and its implications in endodontics: a review The application of nanoparticles from natural and synthetic materials is rapidly evolving in dentistry. These biomaterials have helped in treatment of oral diseases, in eradication of smear layer and biofilms, have been incorporated in various dental materials for their antimicrobial effects. Combining all their beneficial aspects, these nanoparticles will provide new paradigm shift in dentistry. This review on nanoparticles will provide the reader with the latest knowledge of these materials, their mechanism of action and its implications in endodontics. Endodontics ; Nanoparticle ; Materials science ; Nanotechnology ; Root canal ; Dentistry ; Medicine Nanoparticles: synthesis and applications ; Endodontics and Root Canal Treatments ; Bone Tissue Engineering Materials