jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Sybren K. Schoustra | Joshua A. Dijksman | Han Zuilhof | Maarten M. J. Smulders
Source: Chemical Science (article, 2020-11-03)
Field: Materials Science — Polymers and Plastics

Molecular control over vitrimer-like mechanics – tuneable dynamic motifs based on the Hammett equation in polyimine materials In this work, we demonstrate that fine-grained, quantitative control over macroscopic dynamic material properties can be achieved using the Hammett equation in tuneable dynamic covalent polyimine materials. <i>Via</i> this established physical-organic principle, operating on the molecular level, one can fine-tune and control the dynamic material properties on the macroscopic level, by systematic variation of dynamic covalent bond dynamics through selection of the appropriate substituent of the aromatic imine building blocks. Five tuneable, crosslinked polyimine network materials, derived from dianiline monomers with varying Hammett parameter (<i>σ</i>) were studied by rheology, revealing a distinct correlation between the <i>σ</i> value and a range of corresponding dynamic material properties. Firstly, the linear correlation of the kinetic activation energy (<i>E</i> <sub>a</sub>) for the imine exchange to the <i>σ</i> value, enabled us to tune the <i>E</i> <sub>a</sub> from 16 to 85 kJ mol<sup>-1</sup>. Furthermore, the creep behaviour (<i>γ</i>), glass transition (<i>T</i> <sub>g</sub>) and the topology freezing transition temperature (<i>T</i> <sub>v</sub>), all showed a strong, often linear, dependence on the <i>σ</i> value of the dianiline monomer. These combined results demonstrate for the first time how dynamic material properties can be directly tuned and designed in a quantitative - and therefore predictable - manner through correlations based on the Hammett equation. Moreover, the polyimine materials were found to be strong elastic rubbers (<i>G</i>' > 1 MPa at room temperature) that were stable up to 300 °C, as confirmed by TGA. Lastly, the dynamic nature of the imine bond enabled not only recycling, but also intrinsic self-healing of the materials over multiple cycles without the need for solvent, catalysts or addition of external chemicals. Substituent ; Rheology ; Monomer ; Imine ; Materials science ; Glass transition ; Covalent bond ; Molecular dynamics ; Thermodynamics ; Polymer ; Chemistry ; Computational chemistry ; Organic chemistry ; Composite material ; Catalysis ; Physics Polymer composites and self-healing ; Advanced Polymer Synthesis and Characterization ; Synthetic Organic Chemistry Methods
Authors: Corentin Pigot | Guillaume Noirbent | Dominique Brunel | Frédéric Dumur
Source: European Polymer Journal (article, 2020-05-24)
Field: Chemistry — Organic Chemistry

Recent advances on push–pull organic dyes as visible light photoinitiators of polymerization Photopolymer ; Visible spectrum ; Photochemistry ; Polymerization ; Electron acceptor ; Moiety ; Materials science ; Intramolecular force ; Electron transfer ; Chemistry ; Optoelectronics ; Polymer ; Organic chemistry ; Composite material Photopolymerization techniques and applications ; Photochromic and Fluorescence Chemistry ; Advanced Polymer Synthesis and Characterization
Authors: Malik Salman Haider | Michael M. Lübtow | Sebastian Endres | Stefan Forster | Vanessa J. Flegler | Bettina Böttcher | Vladimir Aseyev | Ann‐Christin Pöppler | Robert Luxenhofer
Source: ACS Applied Materials & Interfaces (article, 2020-05-07)
Field: Materials Science — Biomaterials

Think Beyond the Core: Impact of the Hydrophilic Corona on Drug Solubilization Using Polymer Micelles Polymeric micelles are typically characterized as core-shell structures. The hydrophobic core is considered as a depot for hydrophobic molecules, and the corona-forming block acts as a stabilizing and solubilizing interface between the core and aqueous milieu. Tremendous efforts have been made to tune the hydrophobic block to increase the drug loading and stability of micelles, whereas the role of hydrophilic blocks is rarely investigated in this context, with poly(ethylene glycol) (PEG) being the gold standard of hydrophilic polymers. To better understand the role of the hydrophilic corona, a small library of structurally similar A-B-A-type amphiphiles based on poly(2-oxazoline)s and poly(2-oxazine)s is investigated by varying the hydrophilic block A utilizing poly(2-methyl-2-oxazoline) (pMeOx; A) or poly(2-ethyl-2-oxazoline) (pEtOx; A*). In terms of hydrophilicity, both polymers closely resemble PEG. The more hydrophobic block B bears either a poly(2-oxazoline) and poly(2-oxazine) backbone with C3 (propyl) and C4 (butyl) side chains. Surprisingly, major differences in loading capacities from A-B-A > A*-B-A > A*-B-A* is observed for the formulation with two poorly water-soluble compounds, curcumin and paclitaxel, highlighting the importance of the hydrophilic corona of polymer micelles used for drug formulation. The formulations are also characterized by various nuclear magnetic resonance spectroscopy methods, dynamic light scattering, cryogenic transmission electron microscopy, and (micro) differential scanning calorimetry. Our findings suggest that the interaction between the hydrophilic block and the guest molecule should be considered an important, but previously largely ignored, factor for the rational design of polymeric micelles. Micelle ; Materials science ; Polymer ; Amphiphile ; Context (archaeology) ; Copolymer ; Ethylene glycol ; Chemical engineering ; Dynamic light scattering ; Polymer chemistry ; Nanoparticle ; Aqueous solution ; Organic chemistry ; Nanotechnology ; Chemistry Nanoparticle-Based Drug Delivery ; Dendrimers and Hyperbranched Polymers ; Surfactants and Colloidal Systems
Authors: Alex Casey | Steven F. Son | Ilias Bilionis | Brian C. Barnes
Source: Journal of Chemical Information and Modeling (article, 2020-10-15)
Field: Materials Science — Materials Chemistry

Prediction of Energetic Material Properties from Electronic Structure Using 3D Convolutional Neural Networks We develop a convolutional neural network capable of directly parsing the 3D electronic structure of a molecule described by spatial point data for charge density and electrostatic potential represented as a 4D tensor. This method effectively bypasses the need to construct complex representations, or descriptors, of a molecule. This is beneficial because the accuracy of a machine learned model depends on the input representation. Ideally, input descriptors encode the essential physics and chemistry that influence the target property. Thousands of molecular descriptors have been proposed, and proper selection of features requires considerable domain expertise or exhaustive and careful statistical downselection. In contrast, deep learning networks are capable of learning rich data representations. This provides a compelling motivation to use deep learning networks to learn molecular structure-property relations from "raw" data. The convolutional neural network model is jointly trained on over 20,000 molecules that are potentially energetic materials (explosives) to predict dipole moment, total electronic energy, Chapman-Jouguet (C-J) detonation velocity, C-J pressure, C-J temperature, crystal density, HOMO-LUMO gap, and solid phase heat of formation. This work demonstrates the first use of complete 3D electronic structure for machine learning of molecular properties. Convolutional neural network ; Artificial intelligence ; Computer science ; Artificial neural network ; Representation (politics) ; Deep learning ; Electronic structure ; Dipole ; Machine learning ; Pattern recognition (psychology) ; Physics ; Chemistry ; Computational chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Crystallography and molecular interactions
Authors: Conor D. Rankine | Marwah M. M. Madkhali | Thomas J. Penfold
Source: The Journal of Physical Chemistry A (article, 2020-05-05)
Field: Materials Science — Materials Chemistry

A Deep Neural Network for the Rapid Prediction of X-ray Absorption Spectra X-ray spectroscopy delivers strong impact across the physical and biological sciences by providing end users with highly detailed information about the electronic and geometric structure of matter. To decode this information in challenging cases, <i>e.g.</i>, <i>in operando</i> catalysts, batteries, and temporally evolving systems, advanced theoretical calculations are necessary. The complexity and resource requirements often render these out of reach for end users, and therefore, the data are often not interpreted exhaustively, leaving a wealth of valuable information unexploited. In this paper, we introduce supervised machine learning of X-ray absorption spectra through the development of a deep neural network (DNN) that is able to estimate Fe K-edge X-ray absorption near-edge structure spectra in less than a second with no input beyond geometric information about the local environment of the absorption site. We predict peak positions with sub-eV accuracy and peak intensities with errors over an order of magnitude smaller than the spectral variations that the model is engineered to capture. The performance of the DNN is promising, as illustrated by its application to the structural refinement of tris(bipyridine)iron(II) and nitrosylmyoglobin, but also highlights areas on which future developments should focus. Focus (optics) ; Artificial neural network ; Absorption (acoustics) ; Enhanced Data Rates for GSM Evolution ; Computer science ; Spectral line ; Absorption spectroscopy ; Artificial intelligence ; Deep learning ; X-ray absorption spectroscopy ; Physics ; Optics Machine Learning in Materials Science ; X-ray Spectroscopy and Fluorescence Analysis ; X-ray Diffraction in Crystallography
Authors: Yeimmy Peralta-Ruíz | Carlos David Grande‐Tovar | Angie Sinning-Mangonez | Edgar A. Coronell | Marcos F. Marino | Clemencia Chaves‐López
Source: Polymers (article, 2020-08-13)
Field: Materials Science — Biomaterials

Reduction of Postharvest Quality Loss and Microbiological Decay of Tomato “Chonto” (Solanum lycopersicum L.) Using Chitosan-E Essential Oil-Based Edible Coatings under Low-Temperature Storage The tomato (<i>Solanum lycopersicum</i> L.) is one of the many essential vegetables around the world due to its nutritive content and attractive flavor. However, its short shelf-life and postharvest losses affect its marketing. In this study, the effects of chitosan-<i>Ruta graveolens</i> (CS + RGEO) essential oil coatings on the postharvest quality of Tomato var. "chonto" stored at low temperature (4 °C) for 12 days are reported. The film-forming dispersions (FFD) were eco-friendly synthesized and presented low viscosities (between 0.126 and 0.029 Pa s), small particle sizes (between 1.29 and 1.56 μm), and low densities. The mature index (12.65% for uncoated fruits and 10.21% for F4 coated tomatoes), weight loss (29.8% for F1 and 16.7% for F5 coated tomatoes), and decay index (3.0 for uncoated and 1.0 for F5 coated tomatoes) were significantly different, indicating a preservative effect on the quality of the tomato. Moreover, aerobic mesophilic bacteria were significantly reduced (in five Log CFU/g compared to control) by using 15 μL/mL of RGEO. The coatings, including 10 and 15 μL/mL of RGEO, completely inhibited the mold and yeast growth on tomato surfaces without negatively affecting the consumer acceptation, as the sensorial analysis demonstrated. The results presented in this study show that CS + RGEO coatings are promising in the postharvest treatment of tomato var. "chonto". Postharvest ; Solanum ; Preservative ; Shelf life ; Horticulture ; Food science ; Mesophile ; Cherry tomato ; Chitosan ; Flavor ; Chemistry ; Biology ; Bacteria Nanocomposite Films for Food Packaging ; Postharvest Quality and Shelf Life Management ; Nanoparticles: synthesis and applications
Authors: Nasim Sanadgol | Judith Wackerlig
Source: Pharmaceutics (review, 2020-08-31)
Field: Chemistry — Analytical Chemistry

Developments of Smart Drug-Delivery Systems Based on Magnetic Molecularly Imprinted Polymers for Targeted Cancer Therapy: A Short Review Cancer therapy is still a huge challenge, as especially chemotherapy shows several drawbacks like low specificity to tumor cells, rapid elimination of drugs, high toxicity and lack of aqueous solubility. The combination of molecular imprinting technology with magnetic nanoparticles provides a new class of smart hybrids, i.e., magnetic molecularly imprinted polymers (MMIPs) to overcome limitations in current cancer therapy. The application of these complexes is gaining more interest in therapy, due to their favorable properties, namely, the ability to be guided and to generate slight hyperthermia with an appropriate external magnetic field, alongside the high selectivity and loading capacity of imprinted polymers toward a template molecule. In cancer therapy, using the MMIPs as smart-drug-delivery robots can be a promising alternative to conventional direct administered chemotherapy, aiming to enhance drug accumulation/penetration into the tumors while fewer side effects on the other organs. Overview: In this review, we state the necessity of further studies to translate the anticancer drug-delivery systems into clinical applications with high efficiency. This work relates to the latest state of MMIPs as smart-drug-delivery systems aiming to be used in chemotherapy. The application of computational modeling toward selecting the optimum imprinting interaction partners is stated. The preparation methods employed in these works are summarized and their attainment in drug-loading capacity, release behavior and cytotoxicity toward cancer cells in the manner of in vitro and in vivo studies are stated. As an essential issue toward the development of a body-friendly system, the biocompatibility and toxicity of the developed drug-delivery systems are discussed. We conclude with the promising perspectives in this emerging field. Areas covered: Last ten years of publications (till June 2020) in magnetic molecularly imprinted polymeric nanoparticles for application as smart-drug-delivery systems in chemotherapy. Drug delivery ; Drug ; Nanotechnology ; Targeted drug delivery ; Magnetic hyperthermia ; Biocompatibility ; Magnetic nanoparticles ; Pharmacology ; Materials science ; Medicine ; Nanoparticle Analytical chemistry methods development ; Nanoparticle-Based Drug Delivery ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Lei Gan | Arunraj Chidambaram | Pol Gimeno‐Fonquernie | Mark E. Light | Duane Choquesillo‐Lazarte | Hongliang Huang | Eduardo Solano | Julio Fraile | Clara Viñas | Francesç Teixidor | Jorge A. R. Navarro | Kyriakos C. Stylianou | José Giner Planas
Source: Journal of the American Chemical Society (article, 2020-04-27)
Field: Chemistry — Inorganic Chemistry

A Highly Water-Stable <i>meta</i>-Carborane-Based Copper Metal–Organic Framework for Efficient High-Temperature Butanol Separation Biofuels are considered sustainable and renewable alternatives to conventional fossil fuels. Biobutanol has recently emerged as an attractive option compared to bioethanol and biodiesel, but a significant challenge in its production lies in the separation stage. The current industrial process for the production of biobutanol includes the ABE (acetone-butanol-ethanol) fermentation process from biomass; the resulting fermentation broth has a butanol concentration of no more than 2 wt% (the rest is essentially water). Therefore, the development of a cost-effective process for separation of butanol from dilute aqueous solutions is highly desirable. The use of porous materials for the adsorptive separation of ABE mixtures is considered a highly promising route, as these materials can potentially have high affinities for alcohols and low affinities for water. To date, zeolites have been tested toward this separation, but their hydrophilic nature makes them highly incompetent for this application. The use of metal-organic frameworks (MOFs) is an apparent solution; however, their low hydrolytic stabilities hinder their implementation in this application. So far, a few nanoporous zeolitic imidazolate frameworks (ZIFs) have shown excellent potential for butanol separation due to their good hydrolytic and thermal stabilities. Herein, we present a novel, porous, and hydrophobic MOF based on copper ions and carborane-carboxylate ligands, <i>m</i><b>CB-MOF-1</b>, for butanol recovery. <i>m</i><b>CB-MOF-1</b> exhibits excellent stability when immersed in organic solvents, water at 90 °C for at least two months, and acidic and basic aqueous solutions. We found that, like ZIF-8, <i>m</i><b>CB-MOF-1</b> is non-porous to water (type II isotherm), but it has higher affinity for ethanol, butanol, and acetone compared to ZIF-8, as suggested by the shape of the vapor isotherms at the crucial low-pressure region. This is reflected in the separation of a realistic ABE mixture in which <i>m</i><b>CB-MOF-1</b> recovers butanol more efficiently compared to ZIF-8 at 333 K. Chemistry ; Butanol ; Aqueous solution ; Metal-organic framework ; Chemical engineering ; Biofuel ; Acetone ; Organic chemistry ; Adsorption ; Ethanol ; Waste management Metal-Organic Frameworks: Synthesis and Applications ; Covalent Organic Framework Applications ; Membrane Separation and Gas Transport
Authors: Soumya Mukherjee | Yonghe He | Douglas Franz | Shi‐Qiang Wang | Wan‐Ru Xian | Andrey A. Bezrukov | Brian Space | Zhengtao Xu | Jun He | Michael J. Zaworotko
Source: Chemistry - A European Journal (article, 2020-01-07)
Field: Chemistry — Inorganic Chemistry

Halogen–C<sub>2</sub>H<sub>2</sub> Binding in Ultramicroporous Metal–Organic Frameworks (MOFs) for Benchmark C<sub>2</sub>H<sub>2</sub>/CO<sub>2</sub> Separation Selectivity Acetylene (C<sub>2</sub> H<sub>2</sub> ) capture is a step in a number of industrial processes, but it comes with a high-energy footprint. Although physisorbents have the potential to reduce this energy footprint, they are handicapped by generally poor selectivity versus other relevant gases, such as CO<sub>2</sub> and C<sub>2</sub> H<sub>4</sub> . In the case of CO<sub>2</sub> , the respective physicochemical properties are so similar that traditional physisorbents, such as zeolites, silica, and activated carbons cannot differentiate well between CO<sub>2</sub> and C<sub>2</sub> H<sub>2</sub> . Herein, we report that a family of three isostructural, ultramicroporous (<7 Å) diamondoid metal-organic frameworks, [Cu(TMBP)X] (TMBP=3,3',5,5'-tetramethyl-4,4'-bipyrazole), TCuX (X=Cl, Br, I), offer new benchmark C<sub>2</sub> H<sub>2</sub> /CO<sub>2</sub> separation selectivity at ambient temperature and pressure. We attribute this performance to a new type of strong binding site for C<sub>2</sub> H<sub>2</sub> . Specifically, halogen⋅⋅⋅HC interactions coupled with other noncovalent in a tight binding site is C<sub>2</sub> H<sub>2</sub> specific versus CO<sub>2</sub> . The binding site is distinct from those found in previous benchmark sorbents, which are based on open metal sites or electrostatic interactions enabled by inorganic fluoro or oxo anions. Isostructural ; Chemistry ; Binding energy ; Selectivity ; Metal-organic framework ; Halogen ; Diamondoid ; Imidazolate ; Metal ; Inorganic chemistry ; Molecule ; Crystallography ; Physical chemistry ; Catalysis ; Adsorption ; Crystal structure ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Covalent Organic Framework Applications
Authors: Daniel G. A. Smith | Doaa Altarawy | Lori A. Burns | Matthew Welborn | Levi N. Naden | Logan Ward | Sam Ellis | Benjamin P. Pritchard | T. Daniel Crawford
Source: Wiley Interdisciplinary Reviews Computational Molecular Science (article, 2020-07-31)
Field: Materials Science — Materials Chemistry

The <scp>MolSSI</scp> QCA <scp>rchive</scp> project: An open‐source platform to compute, organize, and share quantum chemistry data Abstract The Molecular Sciences Software Institute's (MolSSI) Quantum Chemistry Archive (QCA rchive ) project is an umbrella name that covers both a central server hosted by MolSSI for community data and the Python‐based software infrastructure that powers automated computation and storage of quantum chemistry (QC) results. The MolSSI‐hosted central server provides the computational molecular sciences community a location to freely access tens of millions of QC computations for machine learning, methodology assessment, force‐field fitting, and more through a Python interface. Facile, user‐friendly mining of the centrally archived quantum chemical data also can be achieved through web applications found at https://qcarchive.molssi.org . The software infrastructure can be used as a standalone platform to compute, structure, and distribute hundreds of millions of QC computations for individuals or groups of researchers at any scale. The QCA rchive I nfrastructure is open‐source (BSD‐3C), code repositories can be found at https://github.com/MolSSI , and releases can be downloaded via PyPI and Conda. This article is categorized under: Electronic Structure Theory &gt; Ab Initio Electronic Structure Methods Software &gt; Quantum Chemistry Data Science &gt; Computer Algorithms and Programming Python (programming language) ; Computer science ; Software ; Computational science ; Computation ; Quantum computer ; Source code ; Interface (matter) ; Quantum ; World Wide Web ; Operating system ; Programming language ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics