jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Tian‐Ji Ji | Bei Feng | Jie Shen | Min Zhang | Yuqing Hu | Aixia Jiang | Diqi Zhu | Yiwei Chen | Wei Ji | Zhen Zhang | Hao Zhang | Fen Li
Source: Advanced Science (article, 2021-08-28)
Field: Materials Science — Biomaterials

An Avascular Niche Created by Axitinib‐Loaded PCL/Collagen Nanofibrous Membrane Stabilized Subcutaneous Chondrogenesis of Mesenchymal Stromal Cells Engineered cartilage derived from mesenchymal stromal cells (MSCs) always fails to maintain the cartilaginous phenotype in the subcutaneous environment due to the ossification tendency. Vascular invasion is a prerequisite for endochondral ossification during the development of long bone. As an oral antitumor medicine, Inlyta (axitinib) possesses pronounced antiangiogenic activity, owing to the inactivation of the vascular endothelial growth factor (VEGF) signaling pathway. In this study, axitinib-loaded poly(ε-caprolactone) (PCL)/collagen nanofibrous membranes are fabricated by electrospinning for the first time. Rabbit-derived MSCs-engineered cartilage is encapsulated in the axitinib-loaded nanofibrous membrane and subcutaneously implanted into nude mice. The sustained and localized release of axitinib successfully inhibits vascular invasion, stabilizes cartilaginous phenotype, and helps cartilage maturation. RNA sequence further reveals that axitinib creates an avascular, hypoxic, and low immune response niche. Timp1 is remarkably upregulated in this niche, which probably plays a functional role in inhibiting the activity of matrix metalloproteinases and stabilizing the engineered cartilage. This study provides a novel strategy for stable subcutaneous chondrogenesis of mesenchymal stromal cells, which is also suitable for other medical applications, such as arthritis treatment, local treatment of tumors, and regeneration of other avascular tissues (cornea and tendon). Mesenchymal stem cell ; Chondrogenesis ; Stromal cell ; Nanofiber ; Chemistry ; Cell biology ; Biomedical engineering ; Materials science ; Nanotechnology ; Medicine ; Pathology ; Biology Electrospun Nanofibers in Biomedical Applications ; Protease and Inhibitor Mechanisms ; Knee injuries and reconstruction techniques
Authors: Andrea Cosola | Marco Sangermano | Davide Terenziani | Riccardo Conti | Massimo Messori | Hansjörg Grützmacher | Candido Fabrizio Pirri | Annalisa Chiappone
Source: Applied Materials Today (article, 2021-05-23)
Field: Materials Science — Polymers and Plastics

DLP 3D – printing of shape memory polymers stabilized by thermoreversible hydrogen bonding interactions Materials science ; Methacrylate ; Shape-memory polymer ; Polymer ; Thermosetting polymer ; Photoinitiator ; Photopolymer ; Ethylene glycol ; Composite material ; Ethylene oxide ; Glass transition ; Smart material ; Hydrogen bond ; Chemical engineering ; Polymer chemistry ; Polymerization ; Molecule ; Monomer ; Organic chemistry ; Copolymer ; Chemistry Polymer composites and self-healing ; Photochromic and Fluorescence Chemistry ; Photopolymerization techniques and applications
Authors: Jacopo Vialetto | Natalie Nussbaum | Jotam Bergfreund | Peter Fischer | Lucio Isa
Source: Journal of Colloid and Interface Science (article, 2021-11-02)
Field: Materials Science — Materials Chemistry

Influence of the interfacial tension on the microstructural and mechanical properties of microgels at fluid interfaces Microgels are soft colloidal particles constituted by cross-linked polymer networks with a high potential for applications. In particular, after adsorption at a fluid interface, interfacial tension provides two-dimensional (2D) confinement for microgel monolayers and drives the reconfiguration of the particles, enabling their deployment in foam and emulsion stabilization and in surface patterning for lithography, sensing and optical materials. However, most studies focus on systems of fluids with a high interfacial tension, e.g. alkanes/ or air/water interfaces, which imparts similar properties to the assembled monolayers. Here, instead, we compare two organic fluid phases, hexane and methyl tert-butyl ether, which have markedly different interfacial tension (γ) values with water and thus tune the deformation of adsorbed microgels. We rationalize how γ controls the single-particle morphology, which consequently modulates the structural and mechanical response of the monolayers at varying interfacial compression. Specifically, when γ is low, the microgels are less deformed within the interface plane and their polymer networks can rearrange more easily upon lateral compression, leading to softer monolayers. Selecting interfaces with different surface energy offers an additional control to customize the 2D assembly of soft particles, from the fine-tuning of particle size and interparticle spacing to the tailoring of mechanical properties. Surface tension ; Monolayer ; Materials science ; Polymer ; Adsorption ; Particle (ecology) ; Pickering emulsion ; Nanotechnology ; Deformation (meteorology) ; Chemical engineering ; Emulsion ; Composite material ; Nanoparticle ; Chemistry ; Organic chemistry Pickering emulsions and particle stabilization ; Hydrogels: synthesis, properties, applications ; Material Dynamics and Properties
Authors: Xiaojun Zeng | Yang Zhao | Xudong Hu | Galen D. Stucky | Martin Moskovits
Source: Small Structures (article, 2021-01-15)
Field: Materials Science — Materials Chemistry

Rational Component and Structure Design of Noble‐Metal Composites for Optical and Catalytic Applications Great effort has recently been made to develop noble‐metal nanomaterials with good activity, high durability, and appropriate selectivity to improve their efficiency in functional applications, while maintaining low cost. The use of noble metals not only makes the last requirement hard to meet but also restricts stability at high temperatures and limits mass production. Recently, some promising strategies, such as optimizing the components and fine‐tuning the structure, have been explored, which promise to enable the fabrication of unique noble‐metal nanostructures with lower loading and higher stability. Herein, recent achievements in manufacturing noble‐metal composites are reviewed, particularly focusing on unique and key factors in rational design and control of structure and components. Two applications of noble‐metal composites in optical and catalytic applications are focused on to illustrate their rational synthesis and design. In addition, current challenges and prospects for future development in this rapidly blossoming field are discussed. Noble metal ; Rational design ; Nanotechnology ; Materials science ; Nanomaterials ; Durability ; Fabrication ; Catalysis ; Design strategy ; Metal ; Mechanical engineering ; Composite material ; Metallurgy ; Engineering ; Chemistry Copper-based nanomaterials and applications ; Gold and Silver Nanoparticles Synthesis and Applications ; Nanocluster Synthesis and Applications
Authors: Hamid Khalilpour | Parisa Shafiee | Amirhossein Darbandi | Mohammad Yusuf | Shirin Mahmoudi | Zahra Moazzami Goudarzi | Sadegh Mirzamohammadi
Source: Journal of Composites and Compounds (review, 2021-06-01)
Field: Materials Science — Materials Chemistry

Application of Polyoxometalate-based composites for sensor systems: A review Composites based on polyoxometalates (POMs) have been increasingly attracted by many researchers due to their multitudinous architectures and excellent redox activities as well as outstanding proton and electron transport capacities. Lately, much research has been done on POMs composited with well-porous framework materials (including ZIFs, MOFs) or conducting polymers, carbon quantum dot (CQD), graphene, carbon structures (e.g. carbon nanotubes (CNTs)), and metal nanoparticles (NPs). The results exhibited improved stability and enhanced electrochemical performances. Hence, developing POMs and POM-based composite materials (PCMs) has long been a topic of interest for chemical researchers. Herein, the properties and applications of pristine POMs, doped POMs, and composite-based POMs are reviewed in detail. The various compositions of POMs with sensing application such as POMs-nanocarbon composites (POMs-graphene composites and POMs-carbon nanotube composites), POMs-conductive polymer composites, and POMs-metal composites are also investigated in this review. Polyoxometalate ; Carbon nanotube ; Materials science ; Composite material ; Graphene ; Composite number ; Nanotechnology ; Chemistry ; Organic chemistry ; Catalysis Polyoxometalates: Synthesis and Applications ; Conducting polymers and applications ; Advanced Nanomaterials in Catalysis
Authors: Siyao Wang | Qingqing Zhou | Xiaoping Chen | Rong‐Hua Luo | Yunxue Li | Xinliang Liu | Liu‐Meng Yang | Yong‐Tang Zheng | Ping Wang
Source: Nature Communications (article, 2021-04-15)
Field: Chemistry — Organic Chemistry

Modification of N-terminal α-amine of proteins via biomimetic ortho-quinone-mediated oxidation Naturally abundant quinones are important molecules, which play essential roles in various biological processes due to their reduction potential. In contrast to their universality, the investigation of reactions between quinones and proteins remains sparse. Herein, we report the development of a convenient strategy to protein modification via a biomimetic quinone-mediated oxidation at the N-terminus. By exploiting unique reactivity of an ortho-quinone reagent, the α-amine of protein N-terminus is oxidized to generate aldo or keto handle for orthogonal conjugation. The applications have been demonstrated using a range of proteins, including myoglobin, ubiquitin and small ubiquitin-related modifier 2 (SUMO2). The effect of this method is further highlighted via the preparation of a series of 17 macrophage inflammatory protein 1β (MIP-1β) analogs, followed by preliminary anti-HIV activity and cell viability assays, respectively. This method offers an efficient and complementary approach to existing strategies for N-terminal modification of proteins. Quinone ; Chemistry ; Amine gas treating ; Combinatorial chemistry ; Ubiquitin ; Biochemistry ; Small molecule ; Reagent ; Organic chemistry Click Chemistry and Applications ; Peptidase Inhibition and Analysis ; Chemical Synthesis and Analysis
Authors: Łukasz Woźniak | Nicolai Cramer
Source: Angewandte Chemie International Edition (article, 2021-06-21)
Field: Chemistry — Organic Chemistry

Atropo‐Enantioselective Oxidation‐Enabled Iridium(III)‐Catalyzed C−H Arylations with Aryl Boronic Esters Atropo-enantioselective biaryl coupling through C-H bond functionalization is an emerging technology allowing direct construction of axially chiral molecules. This approach is largely limited to electrophilic coupling partners. We report a highly atropo-enantioselective C-H arylation of tetralone derivatives paired with aryl boronic esters as nucleophilic components. The transformation is catalyzed by chiral cyclopentadienyl (Cp<sup>x</sup> ) iridium(III) complexes and enabled by oxidatively enhanced reductive elimination from high-valent cyclometalated Ir-species. Enantioselective synthesis ; Iridium ; Aryl ; Electrophile ; Chemistry ; Cyclopentadienyl complex ; Nucleophile ; Combinatorial chemistry ; Reductive elimination ; Catalysis ; Medicinal chemistry ; Organic chemistry ; Alkyl Axial and Atropisomeric Chirality Synthesis ; Alkaloids: synthesis and pharmacology ; Molecular spectroscopy and chirality
Authors: Pierre Blanchet | Simon Pépin
Source: Coatings (review, 2021-12-09)
Field: Materials Science — Surfaces, Coatings and Films

Trends in Chemical Wood Surface Improvements and Modifications: A Review of the Last Five Years Increasing the use of wood in buildings is regarded by many as a key solution to tackle climate change. For this reason, a lot of research is carried out to develop new and innovative wood surface improvements and make wood more appealing through features such as increased durability, fire-retardancy, superhydrophobicity, and self-healing. However, in order to have a positive impact on the society, these surface improvements must be applied in real buildings. In this review, the last five years of research in the domain of wood surface improvements and modifications is first presented by sorting the latest innovations into different trends. Afterward, these trends are correlated to specifications representing different normative, ecologic and economic factors which must be considered when expecting to introduce a wood treatment to the market. With this review, the authors hope to help researchers to take into consideration the different factors influencing whether new innovations can leave the research laboratory or not, and thereby facilitate the introduction of new wood surface treatments in the society. Sorting ; Durability ; Order (exchange) ; Normative ; Architectural engineering ; Environmental science ; Engineering ; Computer science ; Business ; Political science Surface Modification and Superhydrophobicity ; Flame retardant materials and properties ; Polymer composites and self-healing
Authors: Debjyoti Bhattacharya | Tarak K. Patra
Source: Macromolecules (article, 2021-03-16)
Field: Materials Science — Materials Chemistry

dPOLY: Deep Learning of Polymer Phases and Phase Transition Machine learning (ML) and artificial intelligence (AI) have remarkable abilities to classify, recognize, and characterize complex patterns and trends in large data sets. Here, we adopt a subclass of ML methods, viz., deep learning, and develop a general purpose AI tool—dPOLY—for analyzing molecular dynamics (MD) trajectories and predicting phases and phase transitions in polymers. An unsupervised deep neural network (DNN) is used within this framework to map a MD trajectory undergoing thermophysical treatment such as cooling, heating, drying, and compression to a lower dimension. A supervised DNN is subsequently developed based on the lower dimensional data to characterize the phases and phase transitions. As a proof of concept, we employ this framework to study the coil to globule phase transition of a model polymer system. We conduct coarse-grained MD simulations to collect MD trajectories of a single polymer chain over a wide range of temperatures and use the dPOLY framework to predict polymer phases. The dPOLY framework accurately predicts the critical temperatures for the coil to globule transition for a wide range of polymer sizes. This method is generic and can be extended to capture various other phase transitions and dynamical crossovers in polymers and other soft materials. It can also significantly accelerate polymer phase prediction and characterization. Molecular dynamics ; Phase transition ; Polymer ; Artificial neural network ; Deep learning ; Artificial intelligence ; Computer science ; Trajectory ; Phase (matter) ; Materials science ; Statistical physics ; Biological system ; Chemical physics ; Thermodynamics ; Physics ; Chemistry ; Computational chemistry Machine Learning in Materials Science ; Protein Structure and Dynamics ; Phase Equilibria and Thermodynamics
Authors: Tatsuya HASEBE
Source: ACS Omega (article, 2021-10-14)
Field: Materials Science — Materials Chemistry

Knowledge-Embedded Message-Passing Neural Networks: Improving Molecular Property Prediction with Human Knowledge The graph neural network (GNN) has become a promising method to predict molecular properties with end-to-end supervision, as it can learn molecular features directly from chemical graphs in a black-box manner. However, to achieve high prediction accuracy, it is essential to supervise a huge amount of property data, which is often accompanied by a high property experiment cost. Prior to the deep learning method, descriptor-based quantitative structure-property relationships (QSPR) studies have investigated physical and chemical knowledge to manually design descriptors for effectively predicting properties. In this study, we extend a message-passing neural network (MPNN) to include a novel MPNN architecture called the knowledge-embedded MPNN (KEMPNN) that can be supervised together with nonquantitative knowledge annotations by human experts on a chemical graph that contains information on the important substructure of a molecule and its effect on the target property (e.g., positive or negative effect). We evaluated the performance of the KEMPNN in a small training data setting using a physical chemistry dataset in MoleculeNet (ESOL, FreeSolv, Lipophilicity) and a polymer property (glass-transition temperature) dataset with virtual knowledge annotations. The results demonstrate that the KEMPNN with knowledge supervision can improve the prediction accuracy obtained from the MPNN. The results also demonstrate that the accuracy of the KEMPNN is better than or comparable to those of descriptor-based methods even in the case of small training data. Computer science ; Property (philosophy) ; Artificial neural network ; Machine learning ; Graph ; Artificial intelligence ; Training set ; Quantitative structure–activity relationship ; Data mining ; Knowledge graph ; Lipophilicity ; Substructure ; Theoretical computer science ; Chemistry ; Engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Tomoki Yamashita | Shinichi Kanehira | Nobuya Sato | Hiori Kino | Kei Terayama | Hikaru Sawahata | Takumi Sato | Futoshi Utsuno | Koji Tsuda | Takashi Miyake | Tamio Oguchi
Source: Science and Technology of Advanced Materials Methods (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

CrySPY: a crystal structure prediction tool accelerated by machine learning We have developed an open-source software called CrySPY, which is a crystal structure prediction tool written in Python 3, and runs on Unix/Linux platforms. CrySPY enables anyone to easily perform crystal structure prediction simulations for materials discovery and design, and automates structure generation, structure optimization, energy evaluation, and efficiently selecting candidates using machine learning. Several searching algorithms are available such as random search, evolutionary algorithm, Bayesian optimization, and Look Ahead based on Quadratic Approximation. Machine learning is employed to efficiently select candidates for priority optimization. CrySPY does not require complex machine learning techniques for users. In the latest version of CrySPY, both atomic and molecular random structures can be generated. CrySPY supports VASP, QUANTUM ESPRESSO, OpenMX, soiap, and LAMMPS for local structure optimization and energy evaluation. CrySPY is distributed under the MIT license at https://github.com/Tomoki-YAMASHITA/CrySPY. Documentation of CrySPY is also available at https://Tomoki-YAMASHITA.github.io/CrySPY_doc. Computer science ; Python (programming language) ; Machine learning ; Unix ; MIT License ; Artificial intelligence ; Bayesian optimization ; Software ; Maintainability ; Crystal structure prediction ; Source code ; Documentation ; Naive Bayes classifier ; Algorithm ; Support vector machine ; Programming language ; Crystal structure ; Software engineering Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Crystallization and Solubility Studies
Authors: Guangshuai Han | Yixuan Sun | Yining Feng | Guang Lin | Na Lu
Source: ES Materials & Manufacturing (review, 2021-01-01)
Field: Materials Science — Materials Chemistry

Machine Learning Regression Guided Thermoelectric Materials Discovery – A Review This is the first comprehensive review to machine learning regression aided TE material discovery, which includes data generation, material descriptor selection, machine learning regression models, and model evaluation. Regression ; Regression analysis ; Machine learning ; Artificial intelligence ; Computer science ; Statistics ; Mathematics Machine Learning in Materials Science ; Advanced Thermoelectric Materials and Devices ; X-ray Diffraction in Crystallography
Authors: Manas Sajjan | Shree Hari Sureshbabu | Sabre Kais
Source: Journal of the American Chemical Society (article, 2021-10-27)
Field: Materials Science — Materials Chemistry

Quantum Machine-Learning for Eigenstate Filtration in Two-Dimensional Materials Quantum machine-learning algorithms have emerged to be a promising alternative to their classical counterparts as they leverage the power of quantum computers. Such algorithms have been developed to solve problems like electronic structure calculations of molecular systems and spin models in magnetic systems. However, the discussion in all these recipes focuses specifically on targeting the ground state. Herein we demonstrate a quantum algorithm that can filter any energy eigenstate of the system based on either symmetry properties or a predefined choice of the user. The workhorse of our technique is a shallow neural network encoding the desired state of the system with the amplitude computed by sampling the Gibbs-Boltzmann distribution using a quantum circuit and the phase information obtained classically from the nonlinear activation of a separate set of neurons. We show that the resource requirements of our algorithm are strictly quadratic. To demonstrate its efficacy, we use state filtration in monolayer transition metal dichalcogenides which are hitherto unexplored in any flavor of quantum simulations. We implement our algorithm not only on quantum simulators but also on actual IBM-Q quantum devices and show good agreement with the results procured from conventional electronic structure calculations. We thus expect our protocol to provide a new alternative in exploring the band structures of exquisite materials to usual electronic structure methods or machine-learning techniques that are implementable solely on a classical computer. Boltzmann machine ; Quantum computer ; Quantum ; Quantum machine learning ; Computer science ; Artificial neural network ; Statistical physics ; Quantum mechanics ; Topology (electrical circuits) ; Artificial intelligence ; Physics ; Mathematics Machine Learning in Materials Science ; Quantum many-body systems ; Quantum Computing Algorithms and Architecture
Authors: Wei Dai | Huamiao Wang | Qiang Guan | Dayong Li | Yinghong Peng | C.N. Tomé
Source: Acta Materialia (article, 2021-05-21)
Field: Materials Science — Materials Chemistry

Studying the micromechanical behaviors of a polycrystalline metal by artificial neural networks Artificial neural network ; Viscoplasticity ; Materials science ; Artificial intelligence ; Work (physics) ; Crystal plasticity ; Computational model ; Machine learning ; Computer science ; Plasticity ; Mechanical engineering ; Finite element method ; Engineering ; Structural engineering ; Constitutive equation Machine Learning in Materials Science ; Microstructure and mechanical properties ; Hydrogen embrittlement and corrosion behaviors in metals