jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Muhammad Umar Aslam Khan | Saiful Izwan Abd Razak | Wafa Shamsan Al Arjan | Samina Nazir | T. Joseph Sahaya Anand | Hassan Mehboob | Rashid Amin
Source: Molecules (review, 2021-01-25)
Field: Materials Science — Biomaterials

Recent Advances in Biopolymeric Composite Materials for Tissue Engineering and Regenerative Medicines: A Review The polymeric composite material with desirable features can be gained by selecting suitable biopolymers with selected additives to get polymer-filler interaction. Several parameters can be modified according to the design requirements, such as chemical structure, degradation kinetics, and biopolymer composites' mechanical properties. The interfacial interactions between the biopolymer and the nanofiller have substantial control over biopolymer composites' mechanical characteristics. This review focuses on different applications of biopolymeric composites in controlled drug release, tissue engineering, and wound healing with considerable properties. The biopolymeric composite materials are required with advanced and multifunctional properties in the biomedical field and regenerative medicines with a complete analysis of routine biomaterials with enhanced biomedical engineering characteristics. Several studies in the literature on tissue engineering, drug delivery, and wound dressing have been mentioned. These results need to be reviewed for possible development and analysis, which makes an essential study. Biopolymer ; Tissue engineering ; Composite number ; Materials science ; Drug delivery ; Regenerative medicine ; Polymer ; Nanotechnology ; Biomedical engineering ; Composite material ; Chemistry ; Engineering Electrospun Nanofibers in Biomedical Applications ; Bone Tissue Engineering Materials ; biodegradable polymer synthesis and properties
Authors: Matěj Baláž | E.V. Boldyreva | Д. С. Рыбин | Stefan Pavlović | Daily Rodríguez‐Padrón | Tihana Mudrinić | Rafael Luque
Source: Frontiers in Bioengineering and Biotechnology (review, 2021-01-27)
Field: Materials Science — Materials Chemistry

State-of-the-Art of Eggshell Waste in Materials Science: Recent Advances in Catalysis, Pharmaceutical Applications, and Mechanochemistry Eggshell waste is among the most abundant waste materials coming from food processing technologies. Despite the unique properties that both its components (eggshell, ES, and eggshell membrane, ESM) possess, it is very often discarded without further use. This review article aims to summarize the recent reports utilizing eggshell waste for very diverse purposes, stressing the need to use a mechanochemical approach to broaden its applications. The most studied field with regards to the potential use of eggshell waste is catalysis. Upon proper treatment, it can be used for turning waste oils into biodiesel and moreover, the catalytic effect of eggshell-based material in organic synthesis is also very beneficial. In inorganic chemistry, the eggshell membrane is very often used as a templating agent for nanoparticles production. Such composites are suitable for application in photocatalysis. These bionanocomposites are also capable of heavy metal ions reduction and can be also used for the ozonation process. The eggshell and its membrane are applicable in electrochemistry as well. Due to the high protein content and the presence of functional groups on the surface, ESM can be easily converted to a high-performance electrode material. Finally, both ES and ESM are suitable for medical applications, as the former can be used as an inexpensive Ca<sup>2+</sup> source for the development of medications, particles for drug delivery, organic matrix/mineral nanocomposites as potential tissue scaffolds, food supplements and the latter for the treatment of joint diseases, in reparative medicine and vascular graft producing. For the majority of the above-mentioned applications, the pretreatment of the eggshell waste is necessary. Among other options, the mechanochemical pretreatment has found an inevitable place. Since the publication of the last review paper devoted to the mechanochemical treatment of eggshell waste, a few new works have appeared, which are reviewed here to underline the sustainable character of the proposed methodology. The mechanochemical treatment of eggshell is capable of producing the nanoscale material which can be further used for bioceramics synthesis, dehalogenation processes, wastewater treatment, preparation of hydrophobic filters, lithium-ion batteries, dental materials, and in the building industry as cement. Eggshell membrane ; Eggshell ; Nanocomposite ; Materials science ; Nanotechnology ; Catalysis ; Chemical engineering ; Waste management ; Chemistry ; Organic chemistry ; Ecology ; Engineering ; Biology Nanoparticles: synthesis and applications ; Proteins in Food Systems ; Trace Elements in Health
Authors: Zach Jensen | Soonhyoung Kwon | Daniel Schwalbe‐Koda | Cecilia Paris | Rafael Gómez‐Bombarelli | Yuriy Román‐Leshkov | Avelino Corma | Manuel Moliner | Elsa Olivetti
Source: ACS Central Science (article, 2021-04-16)
Field: Chemistry — Inorganic Chemistry

Discovering Relationships between OSDAs and Zeolites through Data Mining and Generative Neural Networks Organic structure directing agents (OSDAs) play a crucial role in the synthesis of micro- and mesoporous materials especially in the case of zeolites. Despite the wide use of OSDAs, their interaction with zeolite frameworks is poorly understood, with researchers relying on synthesis heuristics or computationally expensive techniques to predict whether an organic molecule can act as an OSDA for a certain zeolite. In this paper, we undertake a data-driven approach to unearth generalized OSDA-zeolite relationships using a comprehensive database comprising of 5,663 synthesis routes for porous materials. To generate this comprehensive database, we use natural language processing and text mining techniques to extract OSDAs, zeolite phases, and gel chemistry from the scientific literature published between 1966 and 2020. Through structural featurization of the OSDAs using weighted holistic invariant molecular (WHIM) descriptors, we relate OSDAs described in the literature to different types of cage-based, small-pore zeolites. Lastly, we adapt a generative neural network capable of suggesting new molecules as potential OSDAs for a given zeolite structure and gel chemistry. We apply this model to CHA and SFW zeolites generating several alternative OSDA candidates to those currently used in practice. These molecules are further vetted with molecular mechanics simulations to show the model generates physically meaningful predictions. Our model can automatically explore the OSDA space, reducing the amount of simulation or experimentation needed to find new OSDA candidates. Zeolite ; Heuristics ; Artificial neural network ; Generative grammar ; Generative model ; Computer science ; Mesoporous material ; Chemical space ; Organic molecules ; Artificial intelligence ; Materials science ; Nanotechnology ; Molecule ; Chemistry ; Drug discovery ; Organic chemistry ; Catalysis Zeolite Catalysis and Synthesis ; Catalysis and Oxidation Reactions ; Machine Learning in Materials Science
Authors: Alejandro Rodriguez | Stephen Lam | Ming Hu
Source: ACS Applied Materials & Interfaces (article, 2021-11-12)
Field: Materials Science — Materials Chemistry

Thermodynamic and Transport Properties of LiF and FLiBe Molten Salts with Deep Learning Potentials Molten salts have attracted interest as potential heat carriers and/or fuel solvents in the development of new Gen IV nuclear reactor designs, high-temperature batteries, and thermal energy storage. In nuclear engineering, salts containing lithium fluoride-based compounds are of particular interest due to their ability to lower the melting points of mixtures and their compatibility with alloys. A machine learning potential (MLP) combined with a molecular dynamics study is performed on two popular molten salts, namely, LiF (50% Li) and FLiBe (66% LiF and 33% BeF<sub>2</sub>), to predict the thermodynamic and transport properties, such as density, diffusion coefficients, thermal conductivity, electrical conductivity, and shear viscosity. Due to the large possibilities of atomic environments, we employ training using Deep Potential Smooth Edition (DPSE) neural networks to learn from large datasets of 141,278 structures with 70 atoms for LiF and 238,610 structures with 91 atoms for FLiBe molten salts. These networks are then deployed in fast molecular dynamics to predict the thermodynamic and transport properties that are only accessible at longer time scales and are otherwise difficult to calculate with classical potentials, <i>ab initio</i> molecular dynamics, or experiments. The prospect of this work is to provide guidance for future works to develop general MLPs for high-throughput thermophysical database generation for a wide spectrum of molten salts. Materials science ; Molecular dynamics ; Molten salt ; Thermal conductivity ; Thermodynamics ; Chemical physics ; Computational chemistry ; Chemistry Machine Learning in Materials Science ; Molten salt chemistry and electrochemical processes ; Nuclear Materials and Properties
Authors: Vincent Rigolot | Christophe Biot | Cédric Lion
Source: Angewandte Chemie International Edition (review, 2021-06-07)
Field: Chemistry — Organic Chemistry

To View Your Biomolecule, Click inside the Cell The surging development of bioorthogonal chemistry has profoundly transformed chemical biology over the last two decades. Involving chemical partners that specifically react together in highly complex biological fluids, this branch of chemistry now allows researchers to probe biomolecules in their natural habitat through metabolic labelling technologies. Chemical reporter strategies include metabolic glycan labelling, site-specific incorporation of unnatural amino acids in proteins, and post-synthetic labelling of nucleic acids. While a majority of literature reports mark cell-surface exposed targets, implementing bioorthogonal ligations in the interior of cells constitutes a more challenging task. Owing to limiting factors such as membrane permeability of reagents, fluorescence background due to hydrophobic interactions and off-target covalent binding, and suboptimal balance between reactivity and stability of the designed molecular reporters and probes, these strategies need mindful planning to achieve success. In this review, we discuss the hurdles encountered when targeting biomolecules localized in cell organelles and give an easily accessible summary of the strategies at hand for imaging intracellular targets. Bioorthogonal chemistry ; Biomolecule ; Click chemistry ; Chemistry ; Glycan ; Cell permeability ; Chemical biology ; Nucleic acid ; Labelling ; Nanotechnology ; Covalent bond ; Biochemistry ; Combinatorial chemistry ; Computational biology ; Biophysics ; Biology ; Organic chemistry ; Materials science ; Glycoprotein Click Chemistry and Applications ; Monoclonal and Polyclonal Antibodies Research ; Advanced Biosensing Techniques and Applications
Authors: Hai‐Chen Wang | Silvana Botti | Miguel A. L. Marques
Source: npj Computational Materials (article, 2021-01-26)
Field: Materials Science — Materials Chemistry

Predicting stable crystalline compounds using chemical similarity Abstract We propose an efficient high-throughput scheme for the discovery of stable crystalline phases. Our approach is based on the transmutation of known compounds, through the substitution of atoms in the crystal structure with chemically similar ones. The concept of similarity is defined quantitatively using a measure of chemical replaceability, extracted by data-mining experimental databases. In this way we build 189,981 possible crystal phases, including 18,479 that are on the convex hull of stability. The resulting success rate of 9.72% is at least one order of magnitude better than the usual success rate of systematic high-throughput calculations for a specific family of materials, and comparable with speed-up factors of machine learning filtering procedures. As a characterization of the set of 18,479 stable compounds, we calculate their electronic band gaps, magnetic moments, and hardness. Our approach, that can be used as a filter on top of any high-throughput scheme, enables us to efficiently extract stable compounds from tremendously large initial sets, without any initial assumption on their crystal structures or chemical compositions. Similarity (geometry) ; Convex hull ; Stability (learning theory) ; Substitution (logic) ; Throughput ; Computer science ; Chemical stability ; Crystal structure prediction ; Set (abstract data type) ; Filter (signal processing) ; Crystal (programming language) ; Crystal structure ; Materials science ; Data mining ; Algorithm ; Biological system ; Chemistry ; Mathematics ; Regular polygon ; Physics ; Thermodynamics ; Crystallography ; Artificial intelligence ; Machine learning Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Catalysis and Oxidation Reactions
Authors: Bhupalee Kalita | Li Li | Ryan J. McCarty | Kieron Burke
Source: Accounts of Chemical Research (article, 2021-02-03)
Field: Materials Science — Materials Chemistry

Learning to Approximate Density Functionals Density functional theory (DFT) calculations are used in over 40,000 scientific papers each year, in chemistry, materials science, and far beyond. DFT is extremely useful because it is computationally much less expensive than <i>ab initio</i> electronic structure methods and allows systems of considerably larger size to be treated. However, the accuracy of any Kohn-Sham DFT calculation is limited by the approximation chosen for the exchange-correlation (XC) energy. For more than half a century, humans have developed the art of such approximations, using general principles, empirical data, or a combination of both, typically yielding useful results, but with errors well above the chemical accuracy limit (1 kcal/mol). Over the last 15 years, machine learning (ML) has made major breakthroughs in many applications and is now being applied to electronic structure calculations. This recent rise of ML begs the question: Can ML propose or improve density functional approximations? Success could greatly enhance the accuracy and usefulness of DFT calculations without increasing the cost.In this work, we detail efforts in this direction, beginning with an elementary proof of principle from 2012, namely, finding the kinetic energy of several Fermions in a box using kernel ridge regression. This is an example of orbital-free DFT, for which a successful general-purpose scheme could make even DFT calculations run much faster. We trace the development of that work to state-of-the-art molecular dynamics simulations of resorcinol with chemical accuracy. By training on <i>ab initio</i> examples, one bypasses the need to find the XC functional explicitly. We also discuss how the exchange-correlation energy itself can be modeled with such methods, especially for strongly correlated materials. Finally, we show how deep neural networks with differentiable programming can be used to construct accurate density functionals from very few data points by using the Kohn-Sham equations themselves as a regularizer. All these cases show that ML can create approximations of greater accuracy than humans, and is capable of finding approximations that can deal with difficult cases such as strong correlation. However, such ML-designed functionals have not been implemented in standard codes because of one last great challenge: generalization. We discuss how effortlessly human-designed functionals can be applied to a wide range of situations, and how difficult that is for ML. Density functional theory ; Ab initio ; Computer science ; Electronic structure ; Statistical physics ; Kernel (algebra) ; Limit (mathematics) ; Work (physics) ; Computational chemistry ; Applied mathematics ; Chemistry ; Quantum mechanics ; Physics ; Mathematics ; Mathematical analysis Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Taishan Zhu | Ran He | Sheng Gong | Tian Xie | Prashun Gorai | Kornelius Nielsch | Jeffrey C. Grossman
Source: Energy & Environmental Science (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

Charting lattice thermal conductivity for inorganic crystals and discovering rare earth chalcogenides for thermoelectrics Charting and understanding lattice thermal conductivity of inorganic materials and discovery of REX materials for thermoelectrics. Thermoelectric materials ; Thermal conductivity ; Rare earth ; Materials science ; Lattice (music) ; Engineering physics ; Nanotechnology ; Metallurgy ; Physics ; Composite material Advanced Thermoelectric Materials and Devices ; Thermal properties of materials ; Machine Learning in Materials Science
Authors: Anna Gonzàlez‐Rosell | Cecilia Cerretani | Peter Mastracco | Tom Vosch | Stacy M. Copp
Source: Nanoscale Advances (review, 2021-01-01)
Field: Materials Science — Materials Chemistry

Structure and luminescence of DNA-templated silver clusters DNA serves as a versatile template for few-atom silver clusters and their organized self-assembly. These clusters possess unique structural and photophysical properties that are programmed into the DNA template sequence, resulting in a rich palette of fluorophores which hold promise as chemical and biomolecular sensors, biolabels, and nanophotonic elements. Here, we review recent advances in the fundamental understanding of DNA-templated silver clusters (Ag <sub><i>N</i></sub> -DNAs), including the role played by the silver-mediated DNA complexes which are synthetic precursors to Ag <sub><i>N</i></sub> -DNAs, structure-property relations of Ag <sub><i>N</i></sub> -DNAs, and the excited state dynamics leading to fluorescence in these clusters. We also summarize the current understanding of how DNA sequence selects the properties of Ag <sub><i>N</i></sub> -DNAs and how sequence can be harnessed for informed design and for ordered multi-cluster assembly. To catalyze future research, we end with a discussion of several opportunities and challenges, both fundamental and applied, for the Ag <sub><i>N</i></sub> -DNA research community. A comprehensive fundamental understanding of this class of metal cluster fluorophores can provide the basis for rational design and for advancement of their applications in fluorescence-based sensing, biosciences, nanophotonics, and catalysis. Luminescence ; DNA ; Nanotechnology ; Sequence (biology) ; Rational design ; Materials science ; Fluorescence ; DNA sequencing ; Chemistry ; Optoelectronics ; Physics ; Biochemistry Nanocluster Synthesis and Applications ; Gold and Silver Nanoparticles Synthesis and Applications ; Metal complexes synthesis and properties
Authors: Tetsuo Asakura
Source: Molecules (review, 2021-06-17)
Field: Materials Science — Biomaterials

Structure of Silk I (Bombyx mori Silk Fibroin before Spinning) -Type II β-Turn, Not α-Helix- Recently, considerable attention has been paid to <i>Bombyx mori</i> silk fibroin by a range of scientists from polymer chemists to biomaterial researchers because it has excellent physical properties, such as strength, toughness, and biocompatibility. These appealing physical properties originate from the silk fibroin structure, and therefore, structural determinations of silk fibroin before (silk I) and after (silk II) spinning are a key to make wider applications of silk. There are discrepancies about the silk I structural model, i.e., one is type II β-turn structure determined using many solid-state and solution NMR spectroscopies together with selectively stable isotope-labeled model peptides, but another is α-helix or partially α-helix structure speculated using IR and Raman methods. In this review, firstly, the process that led to type II β-turn structure by the authors was introduced in detail. Then the problems in speculating silk I structure by IR and Raman methods were pointed out together with the problem in the assignment of the amide I band in the spectra. It has been emphasized that the conformational analyses of proteins and peptides from IR and Raman studies are not straightforward and should be very careful when the proteins contain β-turn structure using many experimental data by Vass et al. In conclusion, the author emphasized here that silk I structure should be type II β-turn, not α-helix. Fibroin ; SILK ; Bombyx mori ; Polymer science ; Helix (gastropod) ; Raman spectroscopy ; Chemistry ; Materials science ; Protein secondary structure ; Physics ; Composite material ; Biology ; Biochemistry Silk-based biomaterials and applications ; Neurobiology and Insect Physiology Research ; Antimicrobial Peptides and Activities
Authors: Sehyeong Lim | Jin Hyung Kim | Hyunsu Park | Chaesu Kwak | Jeewon Yang | Ji‐Eun Kim | Seoung Young Ryu | Joohyung Lee
Source: RSC Advances (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

Role of electrostatic interactions in the adsorption of dye molecules by Ti<sub>3</sub>C<sub>2</sub>-MXenes MXenes, a new class of 2D materials, have recently attracted increasing attention as promising adsorbents for environmental remediation. It has been previously demonstrated that MXenes can successfully capture selected organic dyes from aqueous media; however, to date, the adsorption performance of MXenes for a wide variety of dyes in simulated real-life aquatic environments other than clean laboratory deionized (DI) water has not been systematically investigated. In this study, we systematically investigated the adsorption performance of delaminated Ti<sub>3</sub>C<sub>2</sub>-MXenes for six different organic dyes in aquatic media at different pH levels and ionic strengths. Our results strongly suggest the importance of the electrostatic interactions between the ionizable functional groups of MXenes and dyes for removal efficiency. The electrostatic repulsions between negatively charged MXenes and certain anionic dyes reduced the removal efficiencies of MXenes for these dyes in DI water; however, the presence of divalent cations significantly improved the removal efficiencies, possibly owing to the charge screening effects and like-charge attractions mediated by cation binding to the functionalities of dyes and MXenes. These results provide a rational strategy for optimizing the conditions for efficient removal of different types of organic dyes using MXenes. MXenes ; Adsorption ; Molecule ; Chemical physics ; Chemistry ; Focus (optics) ; Electrostatics ; Organic molecules ; Nanotechnology ; Materials science ; Physical chemistry ; Physics ; Organic chemistry ; Optics MXene and MAX Phase Materials ; Graphene research and applications ; Advanced Photocatalysis Techniques