jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Alejandra Rodríguez‐Contreras
Source: Bioengineering (review, 2019-09-12)
Field: Materials Science — Biomaterials

Recent Advances in the Use of Polyhydroyalkanoates in Biomedicine Polyhydroxyalkanoates (PHAs), a family of natural biopolyesters, are widely used in many applications, especially in biomedicine. Since they are produced by a variety of microorganisms, they possess special properties that synthetic polyesters do not have. Their biocompatibility, biodegradability, and non-toxicity are the crucial properties that make these biologically produced thermoplastics and elastomers suitable for their applications as biomaterials. Bacterial or archaeal fermentation by the combination of different carbohydrates or by the addition of specific inductors allows the bioproduction of a great variety of members from the PHAs family with diverse material properties. Poly(3-hydroxybutyrate) (PHB) and its copolymers, such as poly(3-hydroxybutyrate-co-3-hydroxyvalerate) (PHVB) or poly(3-hydroxybutyrate-co-4-hydroxybutyrate) (PHB4HB), are the most frequently used PHAs in the field of biomedicine. PHAs have been used in implantology as sutures and valves, in tissue engineering as bone graft substitutes, cartilage, stents for nerve repair, and cardiovascular patches. Due to their good biodegradability in the body and their breakdown products being unhazardous, they have also been remarkably applied as drug carriers for delivery systems. As lately there has been considerable and growing interest in the use of PHAs as biomaterials and their application in the field of medicine, this review provides an insight into the most recent scientific studies and advances in PHAs exploitation in biomedicine. Polyhydroxyalkanoates ; Biodegradation ; Biocompatibility ; Biomedicine ; Drug delivery ; Nanotechnology ; Materials science ; Polyester ; Chemistry ; Biology ; Bacteria ; Bioinformatics ; Organic chemistry biodegradable polymer synthesis and properties ; Electrospun Nanofibers in Biomedical Applications ; Carbon dioxide utilization in catalysis
Authors: Shoichi Ishida | Kei Terayama | Ryosuke Kojima | Kiyosei Takasu | Yasushi Okuno
Source: Journal of Chemical Information and Modeling (article, 2019-11-26)
Field: Materials Science — Materials Chemistry

Prediction and Interpretable Visualization of Retrosynthetic Reactions Using Graph Convolutional Networks Recently, many research groups have been addressing data-driven approaches for (retro)synthetic reaction prediction and retrosynthetic analysis. Although the performances of the data-driven approach have progressed because of recent advances of machine learning and deep learning techniques, problems such as improving capability of reaction prediction and the black-box problem of neural networks persist for practical use by chemists. To spread data-driven approaches to chemists, we focused on two challenges: improvement of retrosynthetic reaction prediction and interpretability of the prediction. In this paper, we propose an interpretable prediction framework using graph convolutional networks (GCN) for retrosynthetic reaction prediction and integrated gradients (IG) for visualization of contributions to the prediction to address these challenges. As a result, from the viewpoint of balanced accuracies, our model showed better performances than the approach using an extended-connectivity fingerprint. Furthermore, IG-based visualization of the GCN prediction successfully highlighted reaction-related atoms. Interpretability ; Retrosynthetic analysis ; Visualization ; Computer science ; Graph ; Artificial intelligence ; Machine learning ; Convolutional neural network ; Data mining ; Theoretical computer science ; Chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Badri Narayanan | Paul C. Redfern | Rajeev S. Assary | Larry A. Curtiss
Source: Chemical Science (article, 2019-01-01)
Field: Chemistry — Organic Chemistry

Accurate quantum chemical energies for 133 000 organic molecules The energies of the 133 000 molecules in the GDB-9 database have been calculated at the G4MP2 level of theory and then were used to calculate their enthalpies of formation. This database contains organic molecules having nine or less atoms of carbon, nitrogen, oxygen, and fluorine, as well as hydrogen atoms. The accuracy of the G4MP2 energies was investigated on a subset of 459 of the molecules having experimental enthalpies of formation with small uncertainties. On this subset the G4MP2 enthalpies of formation have an accuracy of 0.79 kcal mol<sup>-1</sup>, which is similar to its accuracy previously reported for the smaller G3/05 test set. An error analysis of the theoretical enthalpies of formation of the 459 molecules is presented in terms of the size and type of the molecules. Three different density functionals (B3LYP, ωB97X-D, M06-2X) were also assessed on 459 molecules of accurate enthalpy data for comparison with the G4MP2 results. The G4MP2 energies for the 133 K molecules provide a database that can be used to calculate accurate reaction energies as well as to assess new or existing experimental enthalpies of formation. Several examples are given of types of reactions that can be predicted using the G4MP2 database of energies. The G4MP2 energies of the GDB-9 molecules will also be useful in future investigations of applications of machine learning to quantum chemical data. Quantum chemical ; Molecule ; Organic molecules ; Standard enthalpy of formation ; Quantum ; Computational chemistry ; Chemistry ; Chemical physics ; Materials science ; Physics ; Physical chemistry ; Quantum mechanics Chemical Thermodynamics and Molecular Structure ; Advanced Chemical Physics Studies ; Atmospheric Ozone and Climate
Authors: Xin‐Ping Wu | Indrani Choudhuri | Donald G. Truhlar
Source: Energy & environment materials (article, 2019-10-24)
Field: Chemistry — Inorganic Chemistry

Computational Studies of Photocatalysis with Metal–Organic Frameworks Metal–organic frameworks (MOFs) as photocatalysts and photocatalyst supports combine several advantages of homogeneous and heterogeneous catalyses, including stability, post‐reaction separation, catalyst reusability, and tunability, and they have been intensively studied for photocatalytic applications. There are several reviews that focus mainly or even entirely on experimental work. The present review is intended to complement those reviews by focusing on computational work that can provide a further understanding of the photocatalytic properties of MOF photocatalysts. We first present a summary of computational methods, including density functional theory, combined quantum mechanical and molecular mechanical methods, and force fields for MOFs. Then, computational investigations on MOF‐based photocatalysis are briefly discussed. The discussions focus on the electronic structure, photoexcitation, charge mobility, and photoredox catalysis of MOFs, especially the widely studied UiO‐66‐based MOFs. Photocatalysis ; Reusability ; Metal-organic framework ; Photoexcitation ; Materials science ; Nanotechnology ; Homogeneous ; Catalysis ; Computer science ; Chemistry ; Physics ; Physical chemistry ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Advanced Photocatalysis Techniques ; Machine Learning in Materials Science
Authors: Anne Bech Risum | Rasmus Bro
Source: Talanta (article, 2019-05-21)
Field: Chemistry — Spectroscopy

Using deep learning to evaluate peaks in chromatographic data Analysis of untargeted gas-chromatographic data is time consuming. With the earlier introduction of the PARAFAC2 (PARAllel FACtor analysis 2) based PARADISe (PARAFAC2 based Deconvolution and Identification System) approach in 2017, this task was made considerably more time-efficient. However, there are still a number of manual steps in the analysis which require data analytical expertise. One of these is the need to define whether or not each PARAFAC2 resolved component represents a peak suitable for integration. As the peaks may change in both shape and location on the elution time-axis, this presents a problem which cannot be readily solved by applying a linear classifier, such as PLS-DA (Partial Least Squares regression for Discriminant Analysis). As part of our ongoing efforts to further automate analysis of Gas Chromatography with Mass Spectrometry (GC-MS), we therefore explore a convolutional neural network classifier, capable of handling these shifts and variations in shape. The theory of convolutional neural networks and application on vector samples is briefly explained, and the performance is tested against a PLS-DA classifier, a shallow artificial neural network and a locally weighted regression model. The models are built on a training set with PARAFAC2 resolved components from eight different aroma related GC-MS runs with a total of over 70,000 elution profile samples, and validated using another, independent, GC-MS dataset. Based on Receiver Operating Characteristic curves (ROC) and manual analysis of the misclassified cases, it is shown that the convolutional network consistently outperforms the competing models, yielding an Area Under the Curve (AUC) value of 0.95 for peak classification. Examples are given illustrating that this new approach provides convincing means to automatically assess and evaluate modelled elution profiles of chromatographic data and thereby remove this laborious manual step. Linear discriminant analysis ; Pattern recognition (psychology) ; Chemistry ; Artificial intelligence ; Artificial neural network ; Classifier (UML) ; Deconvolution ; Convolutional neural network ; Partial least squares regression ; Elution ; Chromatography ; Chemometrics ; Support vector machine ; Computer science ; Machine learning ; Algorithm Analytical Chemistry and Chromatography ; Advanced Chemical Sensor Technologies ; Metabolomics and Mass Spectrometry Studies
Authors: Mohammad Mahdi Moein | Abbi Abdel‐Rehim | Mohamed Abdel‐Rehim
Source: Molecules (review, 2019-08-09)
Field: Chemistry — Analytical Chemistry

Recent Applications of Molecularly Imprinted Sol-Gel Methodology in Sample Preparation Due to their selectivity and chemical stability, molecularly imprinted polymers have attracted great interest in sample preparation. Imprinted polymers have been applied for the extraction and the enrichment of different sorts of trace analytes in biological and environmental samples before their analysis. Additionally, MIPs are utilized in various sample preparation techniques such as SPE, SPME, SBSE and MEPS. Nevertheless, molecularly imprinted polymers suffer from thermal (stable only up to 150 °C) and mechanical stability issues, improper porosity and poor capacity. The sol-gel methodology as a promising alternative to address these limitations allowing the production of sorbents with controlled porosity and higher surface area. Thus the combination of molecularly imprinted technology and sol-gel technology can create influential materials with high selectivity, high capacity and high thermal stability. This work aims to present an overview of molecularly imprinted sol-gel polymerization methods and their applications in analytical and bioanalytical fields. Molecularly imprinted polymer ; Bioanalysis ; Sample preparation ; Materials science ; Molecular imprinting ; Polymer ; Nanotechnology ; Thermal stability ; Polymerization ; Chromatography ; Chemistry ; Selectivity ; Organic chemistry ; Catalysis Analytical chemistry methods development ; Analytical Chemistry and Chromatography ; Mass Spectrometry Techniques and Applications
Authors: Na Li | Ze Chang | Hongliang Huang | Rui Feng | Weiwei He | Ming Zhong | David G. Madden | Michael J. Zaworotko | Xian‐He Bu
Source: Small (article, 2019-04-12)
Field: Chemistry — Inorganic Chemistry

Specific K<sup>+</sup> Binding Sites as CO<sub>2</sub> Traps in a Porous MOF for Enhanced CO<sub>2</sub> Selective Sorption Metal-organic frameworks (MOFs) can be fine-tuned to boost sorbent-sorbate interactions in order to improve gas sorption and separation performance, but the design of MOFs with ideal structural features for gas separation applications remains a challenge. Herein it is reported that unsaturated alkali metal sites can be immobilized in MOFs through a tetrazole based motif and that gas affinity can thereby be boosted. In the prototypal MOF of this type-NKU-521 (NKU denotes Nankai University), K<sup>+</sup> cations are effectively embedded in a trinuclear Co<sup>2+</sup> -tetrazole coordination motif. The embedded K<sup>+</sup> sites are exposed to the pores of NKU-521 through water removal, and the isosteric heat (Q<sub>st</sub> ) for CO<sub>2</sub> is boosted to 41 kJ mol<sup>-1</sup> . The nature of the binding site is validated by molecular simulations and structural characterization. The K<sup>+</sup> cations in effect serve as gas traps and boost the CO<sub>2</sub> -framework affinity, as measured by the Q<sub>st</sub> , by 24%. In addition, the impact of unsaturated alkali metal sites upon the separation of hydrocarbons is evaluated for the first time in MOFs using ideal adsorbed solution theory (IAST) calculations and column breakthrough experiments. The results reveal that the presence of exposed K<sup>+</sup> sites benefits gas sorption and hydrocarbon separation performances of this MOF. Sorption ; Tetrazole ; Sorbent ; Metal-organic framework ; Alkali metal ; Adsorption ; Porosity ; Materials science ; Hydrocarbon ; Chemical engineering ; Chemistry ; Physical chemistry ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Covalent Organic Framework Applications ; Carbon Dioxide Capture Technologies
Authors: José Francisco Algorri | Dimitrios C. Zografopoulos | Antonio Ferraro | Braulio García‐Cámara | Romeo Beccherelli | José Manuel Sánchez‐Pena
Source: Optics Express (article, 2019-02-20)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Ultrahigh-quality factor resonant dielectric metasurfaces based on hollow nanocuboids In this work, a dielectric metasurface consisting of hollow dielectric nanocuboids, with ultrahigh quality factor, is theoretically proposed and demonstrated. The variation of the hole size of the cuboid allows for the tuning of the resonant anapole mode in the nanoparticles. The metasurface is designed to operate in two complementary modes, namely electromagnetically induced transparency and narrowband selective reflection. Thanks to the non-radiative nature of the anapole resonances, the minimal absorption losses of the dielectric materials, and the near-field coupling among the metasurface nanoparticles, a very high quality factor of 𝑄=2.5×106 is achieved. The resonators are characterized by a simple bulk geometry and the subwavelength dimensions of the metasurface permit operation in the non-diffractive regime. The high quality factors and strong energy confinement of the proposed devices open new avenues of research on light-matter interactions, which may find direct applications, e.g., in non-linear devices, biological sensors, laser cavities, and optical communications. Dielectric ; Q factor ; Resonator ; Cuboid ; Optics ; Materials science ; Electromagnetically induced transparency ; Nanophotonics ; Optoelectronics ; Plasmon ; Metamaterial ; Quality (philosophy) ; Narrowband ; Coupling (piping) ; Physics Metamaterials and Metasurfaces Applications ; Plasmonic and Surface Plasmon Research ; Advanced Antenna and Metasurface Technologies
Authors: Raimondas Galvelis | Stefan Doerr | João M. Damas | M J Harvey | Gianni De Fabritiis
Source: Journal of Chemical Information and Modeling (article, 2019-07-19)
Field: Materials Science — Materials Chemistry

A Scalable Molecular Force Field Parameterization Method Based on Density Functional Theory and Quantum-Level Machine Learning Fast and accurate molecular force field (FF) parameterization is still an unsolved problem. Accurate FF are not generally available for all molecules, like novel druglike molecules. While methods based on quantum mechanics (QM) exist to parameterize them with better accuracy, they are computationally expensive and slow, which limits applicability to a small number of molecules. Here, we present an automated FF parameterization method which can utilize either density functional theory (DFT) calculations or approximate QM energies produced by different neural network potentials (NNPs), to obtain improved parameters for molecules. We demonstrate that for the case of torchani-ANI-1x NNP, we can parameterize small molecules in a fraction of time compared with an equivalent parameterization using DFT QM calculations while producing more accurate parameters than FF (GAFF2). We expect our method to be of critical importance in computational structure-based drug discovery (SBDD). The current version is available at <i>PlayMolecule</i> ( www.playmolecule.org ) and implemented in HTMD, allowing to parameterize molecules with different QM and NNP options. Density functional theory ; Force field (fiction) ; Scalability ; Quantum ; Computer science ; Field (mathematics) ; Molecule ; Statistical physics ; Fraction (chemistry) ; Algorithm ; Physics ; Mathematics ; Quantum mechanics ; Chemistry ; Artificial intelligence Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemical Synthesis and Analysis
Authors: Nathaniel Raimbault | Andrea Grisafi | Michele Ceriotti | Mariana Rossi
Source: New Journal of Physics (article, 2019-09-18)
Field: Materials Science — Materials Chemistry

Using Gaussian process regression to simulate the vibrational Raman spectra of molecular crystals Abstract Vibrational properties of molecular crystals are constantly used as structural fingerprints, in order to identify both the chemical nature and the structural arrangement of molecules. The simulation of these properties is typically very costly, especially when dealing with response properties of materials to e.g. electric fields, which require a good description of the perturbed electronic density. In this work, we use Gaussian process regression (GPR) to predict the static polarizability and dielectric susceptibility of molecules and molecular crystals. We combine this framework with ab initio molecular dynamics to predict their anharmonic vibrational Raman spectra. We stress the importance of data representation, symmetry, and locality, by comparing the performance of different flavors of GPR. In particular, we show the advantages of using a recently developed symmetry-adapted version of GPR. As an examplary application, we choose Paracetamol as an isolated molecule and in different crystal forms. We obtain accurate vibrational Raman spectra in all cases with fewer than 1000 training points, and obtain improvements when using a GPR trained on the molecular monomer as a baseline for the crystal GPR models. Finally, we show that our methodology is transferable across polymorphic forms: we can train the model on data for one crystal structure, and still be able to accurately predict the spectrum for a second polymorph. This procedure provides an independent route to access electronic structure properties when performing force-evaluations on empirical force-fields or machine-learned potential energy surfaces. Polarizability ; Physics ; Raman spectroscopy ; Crystal (programming language) ; Statistical physics ; Molecular dynamics ; Gaussian ; Force field (fiction) ; Chemical physics ; Molecular physics ; Molecule ; Computational chemistry ; Computer science ; Chemistry ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Deniz Turan
Source: Food Engineering Reviews (article, 2019-11-19)
Field: Materials Science — Polymers and Plastics

Water Vapor Transport Properties of Polyurethane Films for Packaging of Respiring Foods Abstract Thermoplastic polyurethane (PU) polymers with different chemical compositions were synthesized and casted to films, and their water vapor barrier properties at different range of relative humidity (RH) were characterized. The water vapor permeability (WVP) of packaging films is one of their most important properties to identify their suitability for use as packaging materials and is rather a complicated phenomena if the polymer has polar nature. The WVPs of PU films are determined both by permeation measurements which are a steady-state method and water vapor sorption measurements which are a non-steady-state method. Effective permeability (P eff ), solubility (S eff ), and diffusion (D eff ) coefficients of PU films were determined at 23 °C within the RH range of 0–97%. It was found that P eff , S eff , and D eff increased with increasing RH gradient due to water vapor and polymer interactions. Microscopic images showed that 1,4-butanediol (BDO) helped to improve porous structure. Castor oil (CO) caused a decrease in the intensity of active absorption sites, namely, the C=O···H-N hydrogen bonds between chains. Results of two methods were yielded in the same magnitude of order. In most cases, the non-steady-state (sorption) method yields higher WVP values than steady state. At 0➔85% RH, the difference was up to 8-fold. Conditioning and equilibrating of films at 50% RH helped to reach sorption data approximate to permeation data. It was suitable to use sorption measurements to estimate the WVP which is a considerable simplification for polar polymers, e.g., developed PU film. Sorption ; Polymer ; Permeation ; Water vapor ; Relative humidity ; Polyurethane ; Materials science ; Chemical engineering ; Diffusion ; Humidity ; Analytical Chemistry (journal) ; Chemistry ; Polymer chemistry ; Organic chemistry ; Thermodynamics ; Membrane ; Adsorption Polymer composites and self-healing ; Advanced Sensor and Energy Harvesting Materials ; Polymer crystallization and properties
Authors: M. H. Jannat Abadi | Seyed Mohammad Mahdi Nouri | Rahele Zhiani | H. D. Heydarzadeh | Alireza Motavalizadehkakhky
Source: International Journal of Industrial Chemistry (article, 2019-07-16)
Field: Chemistry — Organic Chemistry

Removal of tetracycline from aqueous solution using Fe-doped zeolite Tetracycline is one of the most widely used antibiotics that causes contamination of aqueous environments and has raised serious concern during the past few years. In this work, adsorption of tetracycline on a modified zeolite was studied through a batch system. Synthetic zeolite 13X was modified using Fe(III). The results show that the removal efficiency of tetracycline by modified zeolite has considerably increased. Different experiments were carried out in order to analyze the effect of parameters such as pH, initial concentration of tetracycline, time, etc. The results indicate that tetracycline adsorption on the zeolite strongly depends on the pH of the solution due to amphoteric functional groups of tetracycline and maximum adsorption capacity of tetracycline by modified zeolite with a pH of approximately 6. The Langmuir isotherm shows good agreement with the experimental data suggesting monolayer adsorption. Maximum adsorption capacity of the modified zeolite reached at the experiments is almost 200 mg/g. XRD, XRF and FTIR results confirm the existence of the Fe phase in the zeolite texture. Amide groups of TC were responsible for the complexation with Fe3+. Also, tetracycline removal was studied in a continuous column to simulate an industrial waste water process. Zeolite ; Tetracycline ; Adsorption ; Aqueous solution ; Chemistry ; Langmuir adsorption model ; Tetracycline antibiotics ; Nuclear chemistry ; Langmuir ; Monolayer ; Fourier transform infrared spectroscopy ; Inorganic chemistry ; Chromatography ; Chemical engineering ; Organic chemistry ; Catalysis ; Antibiotics ; Biochemistry Nanomaterials for catalytic reactions ; Adsorption and biosorption for pollutant removal ; Environmental remediation with nanomaterials