jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Kristina Peranidze | Т. В. Сафронова | Н. Р. Кильдеева
Source: Polymers (review, 2021-12-28)
Field: Materials Science — Biomaterials

Fibrous Polymer-Based Composites Obtained by Electrospinning for Bone Tissue Engineering Currently, the significantly developing fields of tissue engineering related to the fabrication of polymer-based materials that possess microenvironments suitable to provide cell attachment and promote cell differentiation and proliferation involve various materials and approaches. Biomimicking approach in tissue engineering is aimed at the development of a highly biocompatible and bioactive material that would most accurately imitate the structural features of the native extracellular matrix consisting of specially arranged fibrous constructions. For this reason, the present research is devoted to the discussion of promising fibrous materials for bone tissue regeneration obtained by electrospinning techniques. In this brief review, we focus on the recently presented natural and synthetic polymers, as well as their combinations with each other and with bioactive inorganic incorporations in order to form composite electrospun scaffolds. The application of several electrospinning techniques in relation to a number of polymers is touched upon. Additionally, the efficiency of nanofibrous composite materials intended for use in bone tissue engineering is discussed based on biological activity and physiochemical characteristics. Electrospinning ; Tissue engineering ; Materials science ; Biocompatible material ; Polymer ; Composite number ; Bone tissue ; Extracellular matrix ; Composite material ; Nanotechnology ; Biomedical engineering ; Chemistry ; Engineering Electrospun Nanofibers in Biomedical Applications ; Bone Tissue Engineering Materials ; biodegradable polymer synthesis and properties
Authors: Francesca Reineri | Eleonora Cavallari | Carla Carrera | Silvio Aime
Source: Magnetic Resonance Materials in Physics Biology and Medicine (review, 2021-02-01)
Field: Chemistry — Spectroscopy

Hydrogenative-PHIP polarized metabolites for biological studies ParaHydrogen induced polarization (PHIP) is an efficient and cost-effective hyperpolarization method, but its application to biological investigations has been hampered, so far, due to chemical challenges. PHIP is obtained by means of the addition of hydrogen, enriched in the para-spin isomer, to an unsaturated substrate. Both hydrogen atoms must be transferred to the same substrate, in a pairwise manner, by a suitable hydrogenation catalyst; therefore, a de-hydrogenated precursor of the target molecule is necessary. This has strongly limited the number of parahydrogen polarized substrates. The non-hydrogenative approach brilliantly circumvents this central issue, but has not been translated to in-vivo yet. Recent advancements in hydrogenative PHIP (h-PHIP) considerably widened the possibility to hyperpolarize metabolites and, in this review, we will focus on substrates that have been obtained by means of this method and used in vivo. Attention will also be paid to the requirements that must be met and on the issues that have still to be tackled to obtain further improvements and to push PHIP substrates in biological applications. Spin isomers of hydrogen ; Hyperpolarization (physics) ; Chemistry ; Catalysis ; Combinatorial chemistry ; Induced polarization ; Hydrogen ; Polarization (electrochemistry) ; Substrate (aquarium) ; Molecule ; Stereochemistry ; Organic chemistry ; Nuclear magnetic resonance spectroscopy ; Physics ; Biology Advanced NMR Techniques and Applications ; Solid-state spectroscopy and crystallography ; Electron Spin Resonance Studies
Authors: Andrew Tarzia | James E. M. Lewis | Kim E. Jelfs
Source: Angewandte Chemie International Edition (article, 2021-07-13)
Field: Chemistry — Organic Chemistry

High‐Throughput Computational Evaluation of Low Symmetry Pd <sub>2</sub> L <sub>4</sub> Cages to Aid in System Design** Unsymmetrical ditopic ligands can self-assemble into reduced-symmetry Pd<sub>2</sub> L<sub>4</sub> metallo-cages with anisotropic cavities, with implications for high specificity and affinity guest-binding. Mixtures of cage isomers can form, however, resulting in undesirable system heterogeneity. It is paramount to be able to design components that preferentially form a single isomer. Previous data suggested that computational methods could predict with reasonable accuracy whether unsymmetrical ligands would preferentially self-assemble into single cage isomers under constraints of geometrical mismatch. We successfully apply a collaborative computational and experimental workflow to mitigate costly trial-and-error synthetic approaches. Our rapid computational workflow constructs unsymmetrical ligands and their Pd<sub>2</sub> L<sub>4</sub> cage isomers, ranking the likelihood for exclusively forming cis-Pd<sub>2</sub> L<sub>4</sub> assemblies. From this narrowed search space, we successfully synthesised four new, low-symmetry, cis-Pd<sub>2</sub> L<sub>4</sub> cages. Symmetry (geometry) ; Workflow ; Ranking (information retrieval) ; Chemistry ; Cage ; Space (punctuation) ; Computational complexity theory ; Computational chemistry ; Computer science ; Combinatorial chemistry ; Materials science ; Biological system ; Algorithm ; Mathematics ; Combinatorics ; Artificial intelligence ; Biology ; Geometry Supramolecular Chemistry and Complexes ; Supramolecular Self-Assembly in Materials ; Advanced biosensing and bioanalysis techniques
Authors: Marisa Thompson | Carmen Scholz
Source: Nanomaterials (review, 2021-04-26)
Field: Materials Science — Polymers and Plastics

Highly Branched Polymers Based on Poly(amino acid)s for Biomedical Application Polymers consisting of amino acid building blocks continue to receive consideration for biomedical applications. Since poly(amino acid)s are built from natural amino acids, the same building blocks proteins are made of, they are biocompatible, biodegradable and their degradation products are metabolizable. Some amino acids display a unique asymmetrical AB2 structure, which facilitates their ability to form branched structures. This review compares the three forms of highly branched polymeric structures: structurally highly organized dendrimers, dendrigrafts and the less organized, but readily synthesizable hyperbranched polymers. Their syntheses are reviewed and compared, methods of synthesis modulations are considered and variations on their traditional syntheses are shown. The potential use of highly branched polymers in the realm of biomedical applications is discussed, specifically their applications as delivery vehicles for genes and drugs and their use as antiviral compounds. Of the twenty essential amino acids, L-lysine, L-glutamic acid, and L-aspartic acid are asymmetrical AB2 molecules, but the bulk of the research into highly branched poly(amino acid)s has focused on the polycationic poly(L-lysine) with a lesser extent on poly(L-glutamic acid). Hence, the majority of potential applications lies in delivery systems for nucleic acids and this review examines and compares how these three types of highly branched polymers function as non-viral gene delivery vectors. When considering drug delivery systems, the small size of these highly branched polymers is advantageous for the delivery of inhalable drug. Even though highly branched polymers, in particular dendrimers, have been studied for more than 40 years for the delivery of genes and drugs, they have not translated in large scale into the clinic except for promising antiviral applications that have been commercialized. Amino acid ; Polymer ; Dendrimer ; Drug delivery ; Lysine ; Nucleic acid ; Combinatorial chemistry ; Gene delivery ; Glutamic acid ; Aspartic acid ; Chemistry ; Materials science ; Nanotechnology ; Organic chemistry ; Biochemistry ; Gene ; Transfection Dendrimers and Hyperbranched Polymers ; RNA Interference and Gene Delivery ; Polymer Surface Interaction Studies
Authors: Peng‐Ying Jiang | Kai-Fang Fan | Shaoyu Li | Shao‐Hua Xiang | Bin Tan
Source: Nature Communications (article, 2021-04-22)
Field: Chemistry — Organic Chemistry

Metal-free oxidative cross-coupling enabled practical synthesis of atropisomeric QUINOL and its derivatives Abstract As an important platform molecule, atropisomeric QUINOL plays a crucial role in the development of chiral ligands and catalysts in asymmetric catalysis. However, efficient approaches towards QUINOL remain scarce, and the resulting high production costs greatly impede the related academic research as well as downstream industrial applications. Here we report a direct oxidative cross-coupling reaction between isoquinolines and 2-naphthols, providing a straightforward and scalable route to acquire the privileged QUINOL scaffolds in a metal-free manner. Moreover, a NHC-catalyzed kinetic resolution of QUINOL N-oxides with high selectivity factor is established to access two types of promising axially chiral Lewis base catalysts in optically pure forms. The utility of this methodology is further illustrated by facile transformations of the products into QUINAP, an iconic ligand in asymmetric catalysis. Catalysis ; Kinetic resolution ; Combinatorial chemistry ; Ligand (biochemistry) ; Chemistry ; Enantioselective synthesis ; Oxidative coupling of methane ; Selectivity ; Metal ; Nanotechnology ; Materials science ; Organic chemistry ; Receptor Axial and Atropisomeric Chirality Synthesis ; Asymmetric Synthesis and Catalysis ; Synthetic Organic Chemistry Methods
Authors: Rama K. Vasudevan | Maxim Ziatdinov | Lukáš Vlček | Sergei V. Kalinin
Source: npj Computational Materials (article, 2021-01-27)
Field: Materials Science — Materials Chemistry

Off-the-shelf deep learning is not enough, and requires parsimony, Bayesianity, and causality Abstract Deep neural networks (‘deep learning’) have emerged as a technology of choice to tackle problems in speech recognition, computer vision, finance, etc. However, adoption of deep learning in physical domains brings substantial challenges stemming from the correlative nature of deep learning methods compared to the causal, hypothesis driven nature of modern science. We argue that the broad adoption of Bayesian methods incorporating prior knowledge, development of solutions with incorporated physical constraints and parsimonious structural descriptors and generative models, and ultimately adoption of causal models, offers a path forward for fundamental and applied research. Deep learning ; Artificial intelligence ; Generative grammar ; Computer science ; Causality (physics) ; Machine learning ; Deep neural networks ; Causal model ; Artificial neural network ; Bayesian probability ; Data science ; Cognitive science ; Psychology ; Mathematics Machine Learning in Materials Science ; Topic Modeling ; Domain Adaptation and Few-Shot Learning
Authors: Ziheng Lu
Source: Materials Reports Energy (review, 2021-06-28)
Field: Materials Science — Materials Chemistry

Computational discovery of energy materials in the era of big data and machine learning: A critical review The discovery of novel materials with desired properties is essential to the advancements of energy-related technologies. Despite the rapid development of computational infrastructures and theoretical approaches, progress so far has been limited by the empirical and serial nature of experimental work. Fortunately, the situation is changing thanks to the maturation of theoretical tools such as density functional theory, high-throughput screening, crystal structure prediction, and emerging approaches based on machine learning. Together these recent innovations in computational chemistry, data informatics, and machine learning have acted as catalysts for revolutionizing material design and hopefully will lead to faster kinetics in the development of energy-related industries. In this report, recent advances in material discovery methods are reviewed for energy devices. Three paradigms based on empiricism-driven experiments, database-driven high-throughput screening, and data informatics-driven machine learning are discussed critically. Key methodological advancements involved are reviewed including high-throughput screening, crystal structure prediction, and generative models for target material design. Their applications in energy-related devices such as batteries, catalysts, and photovoltaics are selectively showcased. Computer science ; Data science ; Materials informatics ; Informatics ; Machine learning ; Generative grammar ; Big data ; Artificial intelligence ; Throughput ; Health informatics ; Nanotechnology ; Data mining ; Engineering ; Engineering informatics ; Materials science ; Wireless Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; CO2 Reduction Techniques and Catalysts
Authors: Yilin Yang | Omar A. Jiménez-Negrón | John R. Kitchin
Source: The Journal of Chemical Physics (article, 2021-06-17)
Field: Materials Science — Materials Chemistry

Machine-learning accelerated geometry optimization in molecular simulation Geometry optimization is an important part of both computational materials and surface science because it is the path to finding ground state atomic structures and reaction pathways. These properties are used in the estimation of thermodynamic and kinetic properties of molecular and crystal structures. This process is slow at the quantum level of theory because it involves an iterative calculation of forces using quantum chemical codes such as density functional theory (DFT), which are computationally expensive and which limit the speed of the optimization algorithms. It would be highly advantageous to accelerate this process because then one could do either the same amount of work in less time or more work in the same time. In this work, we provide a neural network (NN) ensemble based active learning method to accelerate the local geometry optimization for multiple configurations simultaneously. We illustrate the acceleration on several case studies including bare metal surfaces, surfaces with adsorbates, and nudged elastic band for two reactions. In all cases, the accelerated method requires fewer DFT calculations than the standard method. In addition, we provide an Atomic Simulation Environment (ASE)-optimizer Python package to make the usage of the NN ensemble active learning for geometry optimization easier. Energy minimization ; Molecular dynamics ; Work (physics) ; Python (programming language) ; Density functional theory ; Quantum ; Process (computing) ; Surface (topology) Machine Learning in Materials Science ; Advanced Physical and Chemical Molecular Interactions ; Quantum Computing Algorithms and Architecture
Authors: Evan Komp | Nida Janulaitis | Stéphanie Valleau
Source: Physical Chemistry Chemical Physics (review, 2021-12-06)
Field: Materials Science — Materials Chemistry

Progress towards machine learning reaction rate constants Quantum and classical reaction rate constant calculations come at the cost of exploring potential energy surfaces. Due to the "curse of dimensionality", their evaluation quickly becomes unfeasible as the system size grows. Machine learning algorithms can accelerate the calculation of reaction rate constants by predicting them using low cost input features. In this perspective, we briefly introduce supervised machine learning algorithms in the context of reaction rate constant prediction. We discuss existing and recently created kinetic datasets and input feature representations as well as the use and design of machine learning algorithms to predict reaction rate constants or quantities required for their computation. Amongst these, we first describe the use of machine learning to predict activation, reaction, solvation and dissociation energies. We then look at the use of machine learning to predict reactive force field parameters, reaction rate constants as well as to help accelerate the search for minimum energy paths. Lastly, we provide an outlook on areas which have yet to be explored so as to improve and evaluate the use of machine learning algorithms for chemical reaction rate constants. Reaction rate constant ; Computer science ; Machine learning ; Curse of dimensionality ; Artificial intelligence ; Reaction rate ; Algorithm ; Chemistry ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Mass Spectrometry Techniques and Applications
Authors: Guoxiang Hu | Victor Fung | Jingsong Huang | Panchapakesan Ganesh
Source: The Journal of Physical Chemistry Letters (article, 2021-03-02)
Field: Materials Science — Materials Chemistry

Work Function Engineering of 2D Materials: The Role of Polar Edge Reconstructions 2D materials have attracted tremendous interest as functional materials because of their diverse and tunable properties, especially at their edges. A material's work function is a critical parameter in many electronic devices; however, a fundamental understanding and a path toward large alterations of the work function in 2D materials still remain elusive. Here, we report the first evidence for anisotropy of the work function in 2D MoS<sub>2</sub> from first-principles calculations. We also demonstrate large work-function tunability (in the range of 3.45-6.29 eV) choosing the 2H phase of MoS<sub>2</sub> as a model system by sampling various edge configurations. We furthermore reveal the origin of this work function anisotropy and tunability by extending the existing work function relation to the local dipole moment at surfaces of 3D materials to those at edges in 2D materials. We then use machine-learning approaches to correlate work function with edge structures. These results pave the way for intrinsic edge engineering for electronic and catalytic applications. Polar ; Function (biology) ; Work (physics) ; Enhanced Data Rates for GSM Evolution ; Materials science ; Geology ; Computer science ; Engineering ; Mechanical engineering ; Physics ; Artificial intelligence ; Cell biology ; Biology Graphene research and applications ; 2D Materials and Applications ; Thermal properties of materials
Authors: Xinxin Sun | Hongwei Li | Mei Zhan | Junyuan Zhou | Jian Zhang | Jia Gao
Source: International Journal of Plasticity (article, 2021-03-07)
Field: Materials Science — Materials Chemistry

Cross-scale prediction from RVE to component Representative elementary volume ; Materials science ; Microscale chemistry ; Microstructure ; Multiscale modeling ; Finite element method ; Deformation (meteorology) ; Component (thermodynamics) ; Dynamic recrystallization ; Constitutive equation ; Deformation mechanism ; Volume fraction ; Homogenization (climate) ; Structural engineering ; Composite material ; Thermodynamics ; Physics ; Mathematics Microstructure and mechanical properties ; Metallurgy and Material Forming ; Metal Forming Simulation Techniques
Authors: Patricia Lacchi da Silva | Ramiro Picoli Nippes | Paula Derksen Macruz | Fábio Luís Hegeto | Mara Heloísa Neves Olsen Scaliante
Source: Water Science & Technology (article, 2021-07-06)
Field: Materials Science — Materials Chemistry

Photocatalytic degradation of hydroxychloroquine using ZnO supported on clinoptilolite zeolite The objective of this work was to evaluate the photocatalytic activity of zinc oxide catalysts supported on natural zeolite clinoptilolite for photocatalytic degradation of the drug hydroxychloroquine, used in the treatment of malaria and which has been tested in the treatment of COVID-19. To synthesize 10%ZnOCP and 15%ZnOCP catalysts, the wet impregnation methodology was used. The raw and synthesized catalysts were characterized by XRD, SEM, XRF, BET, DRS, PCZ, FT-IR and PL. The degradation of hydroxychloroquine was calculated using UV-vis absorption from the samples before and after the photocatalytic process. The maximum percentage of degradation (96%) was obtained with the operational parameters of C<sub>0</sub> = 10 mg L<sup>-1</sup>; C<sub>cat</sub> = 2 g L<sup>-1</sup> of 15%ZnOCP; pH = 7.5; UV-A radiation. Ecotoxicological tests against the bioindicators Lactuca sativa and Artemia salina confirmed the reduction of effluent toxicity after treatment. Clinoptilolite ; Photocatalysis ; Nuclear chemistry ; Zeolite ; Catalysis ; Chemistry ; Zinc ; Environmental chemistry ; Organic chemistry Advanced Nanomaterials in Catalysis ; TiO2 Photocatalysis and Solar Cells ; Electrochemical sensors and biosensors
Authors: Yuming Wen | Ilman Nuran Zaini | Shule Wang | Wangzhong Mu | Pär G. Jönsson | Weihong Yang
Source: Energy (article, 2021-04-21)
Field: Materials Science — Polymers and Plastics

Synergistic effect of the co-pyrolysis of cardboard and polyethylene: A kinetic and thermodynamic study Pyrolysis of municipal solid waste (MSW) represents one of the most promising solutions to recycle materials and recover energy. Two of the main components of MSW are waste cardboard and plastic. In this study, the pyrolysis of cardboard and polyethylene (PE) and the co-pyrolysis of their mixtures were conducted to investigate the synergistic effect by using thermogravimetric analysis. The whole reaction process was divided into four pseudoreactions, namely, hemicellulose, lignin, cellulose, and PE, by using the Frazer-Suzuki deconvolution method. It was found that the co-pyrolysis of cardboard and PE could promote the decomposition degrees of cardboard from 70.28% to 75.31%, when the PE fraction increased from 0 to 75%. However, the presence of cardboard can hinder the heat adsorption of PE, which shifts the peak of the PE reaction to a higher temperature. This results in higher Ea and ΔH‡ values for PE pyrolysis with an increasing fraction of cardboard. On the other hand, the Ea and ΔH‡ values of cellulose pyrolysis have their lowest values when the mixing rate is around 50%. This research deepens the understanding of the synergistic effect of co-pyrolysis of cardboard and PE, which supports the potential application of pyrolysis of MSW. cardboard ; Pyrolysis ; Cellulose ; Polyethylene ; Hemicellulose ; Lignin ; Thermogravimetric analysis ; Decomposition ; Chemical engineering ; Waste management ; Chemistry ; Adsorption ; Materials science ; Pulp and paper industry ; Organic chemistry ; Composite material ; Engineering Natural Fiber Reinforced Composites ; Thermochemical Biomass Conversion Processes ; Lignin and Wood Chemistry
Authors: Philippe Ghosez | Javier Junquera
Source: Annual Review of Condensed Matter Physics (article, 2021-12-06)
Field: Materials Science — Materials Chemistry

Modeling of Ferroelectric Oxide Perovskites: From First to Second Principles Taking a historical perspective, we provide a brief overview of the first-principles modeling of ferroelectric perovskite oxides over the past 30 years. We emphasize how the work done by a relatively small community on the fundamental understanding of ferroelectricity and related phenomena has been at the origin of consecutive theoretical breakthroughs, with an impact going often well beyond the limit of the ferroelectric community. In this context, we first review key theoretical advances such as the modern theory of polarization, the computation of functional properties as energy derivatives, the explicit treatment of finite fields, or the advent of second-principles methods to extend the length and timescale of the simulations. We then discuss how these have revolutionized our understanding of ferroelectricity and related phenomena in this technologically important class of compounds. Ferroelectricity ; Context (archaeology) ; Perovskite (structure) ; Nanotechnology ; Engineering physics ; Materials science ; Computer science ; Physics ; Chemistry ; Optoelectronics Ferroelectric and Piezoelectric Materials ; Electronic and Structural Properties of Oxides ; Multiferroics and related materials