jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Emanuele Boattini | Susana Marín-Aguilar | Saheli Mitra | Giuseppe Foffi | Frank Smallenburg | Laura Filion
Source: Nature Communications (article, 2020-10-30)
Field: Materials Science — Materials Chemistry

Autonomously revealing hidden local structures in supercooled liquids Few questions in condensed matter science have proven as difficult to unravel as the interplay between structure and dynamics in supercooled liquids. To explore this link, much research has been devoted to pinpointing local structures and order parameters that correlate strongly with dynamics. Here we use an unsupervised machine learning algorithm to identify structural heterogeneities in three archetypical glass formers-without using any dynamical information. In each system, the unsupervised machine learning approach autonomously designs a purely structural order parameter within a single snapshot. Comparing the structural order parameter with the dynamics, we find strong correlations with the dynamical heterogeneities. Moreover, the structural characteristics linked to slow particles disappear further away from the glass transition. Our results demonstrate the power of machine learning techniques to detect structural patterns even in disordered systems, and provide a new way forward for unraveling the structural origins of the slow dynamics of glassy materials. Supercooling ; Local structure ; Computer science ; Statistical physics ; Snapshot (computer storage) ; Artificial intelligence ; Dynamics (music) ; Molecular dynamics ; Physics ; Chemical physics Material Dynamics and Properties ; Theoretical and Computational Physics
Authors: Abdelazeem S. Eltaweil | Eman M. Abd El-Monaem | Gehan M. El‐Subruiti | Mona M. Abd El‐Latif | Ahmed M. Omer
Source: RSC Advances (article, 2020-01-01)
Field: Chemistry — Inorganic Chemistry

Fabrication of UiO-66/MIL-101(Fe) binary MOF/carboxylated-GO composite for adsorptive removal of methylene blue dye from aqueous solutions This study provides a novel composite as an efficient adsorbent of cationic methylene blue dye. UiO-66/MIL-101(Fe) binary metal organic framework (MOF) was fabricated using a solvothermal technique. Additionally, the developed binary MOF was modified with carboxylated graphene oxide (GOCOOH) using a post-synthetic technique. The as-fabricated UiO-66/MIL-101(Fe)-GOCOOH composite was analyzed by FTIR, XRD, SEM, BET, TGA, XPS and zeta potential analysis. The adsorption performance of UiO-66/MIL-101(Fe)-GOCOOH composite was examined for its aptitude to adsorb cationic MB dye using a batch technique. The obtained data revealed that, the developed UiO-66/MIL-101(Fe)-GOCOOH composite exhibited higher adsorption capacity compared to UiO-66/MIL-101(Fe) binary MOF. Adsorption isotherms and kinetic studies revealed that MB dye adsorption onto UiO-66/MIL-101(Fe)-GOCOOH composite fitted a Langmuir isotherm model (<i>q</i> <sub>m</sub> = 448.71 mg g<sup>-1</sup>) and both pseudo 1<sup>st</sup> order and pseudo 2<sup>nd</sup> order kinetic models. An intra-particle diffusion model showed that the adsorption process occurs through three steps. Besides, thermodynamic data reflected that the adsorption of MB onto UiO-66/MIL-101(Fe)-GOCOOH composite is an endothermic and spontaneous process and the adsorption involves both physisorption and chemisorption interactions. The as-fabricated UiO-66/MIL-101(Fe)-GOCOOH composite exhibited good reusability and can be considered as a promising reusable adsorbent for the treatment of dye-containing industrial effluents with high efficiency. Adsorption ; Physisorption ; Langmuir adsorption model ; Composite number ; Aqueous solution ; Chemical engineering ; Methylene blue ; Materials science ; Endothermic process ; Metal-organic framework ; Nuclear chemistry ; Chemistry ; Organic chemistry ; Composite material ; Catalysis ; Photocatalysis Metal-Organic Frameworks: Synthesis and Applications ; Adsorption and biosorption for pollutant removal
Authors: Zhixue Liu | Xianyin Dai | Yonghui Sun | Yu Liu
Source: Aggregate (article, 2020-12-01)
Field: Chemistry — Organic Chemistry

Organic supramolecular aggregates based on water‐soluble cyclodextrins and calixarenes Abstract Macrocycle‐induced formation of pure organic supramolecular aggregates is a challenge that has attracted considerable attention from researchers in the fields of chemistry, biology, and materials science. In particular, aggregation induced by water‐soluble cyclodextrins and calixarenes, which are two classic of macrocycles with a hydrophobic cavity and a hydrophilic external surface, has attracted interest because these host molecules can form aggregates with guest molecules via various noncovalent interactions, including hydrophobic interactions, van der Waals forces, hydrogen bonds, and electrostatic interactions. In this review, we focus mainly on some impressive recent progress, both by our group and other groups, on the construction of cyclodextrin‐ and calixarene‐based organic supramolecular aggregates, control of their topological morphology, and their use for biological applications such as molecular recognition and bioimaging, photodynamic therapy, light‐harvesting energy transfer, and targeted drug delivery. We also discuss shortcomings of the current reported results and future prospects for the development of multifunctional organic supramolecular aggregates for use in various fields. Supramolecular chemistry ; Calixarene ; van der Waals force ; Chemistry ; Non-covalent interactions ; Nanotechnology ; Hydrogen bond ; Molecule ; Cyclodextrin ; Organic molecules ; Hydrophobic effect ; Host–guest chemistry ; Combinatorial chemistry ; Organic chemistry ; Materials science Supramolecular Chemistry and Complexes ; Supramolecular Self-Assembly in Materials ; Luminescence and Fluorescent Materials
Authors: Mariela P. Mezzina | Maria‐Tsampika Manoli | M. Auxiliadora Prieto | Pablo I. Nikel
Source: Biotechnology Journal (review, 2020-10-21)
Field: Materials Science — Biomaterials

Engineering Native and Synthetic Pathways in <i>Pseudomonas putida</i> for the Production of Tailored Polyhydroxyalkanoates Growing environmental concern sparks renewed interest in the sustainable production of (bio)materials that can replace oil-derived goods. Polyhydroxyalkanoates (PHAs) are isotactic polymers that play a critical role in the central metabolism of producer bacteria, as they act as dynamic reservoirs of carbon and reducing equivalents. PHAs continue to attract industrial attention as a starting point toward renewable, biodegradable, biocompatible, and versatile thermoplastic and elastomeric materials. Pseudomonas species have been known for long as efficient biopolymer producers, especially for medium-chain-length PHAs. The surge of synthetic biology and metabolic engineering approaches in recent years offers the possibility of exploiting the untapped potential of Pseudomonas cell factories for the production of tailored PHAs. In this article, an overview of the metabolic and regulatory circuits that rule PHA accumulation in Pseudomonas putida is provided, and approaches leading to the biosynthesis of novel polymers (e.g., PHAs including nonbiological chemical elements in their structures) are discussed. The potential of novel PHAs to disrupt existing and future market segments is closer to realization than ever before. The review is concluded by pinpointing challenges that currently hinder the wide adoption of bio-based PHAs, and strategies toward programmable polymer biosynthesis from alternative substrates in engineered P. putida strains are proposed. Polyhydroxyalkanoates ; Pseudomonas putida ; Biochemical engineering ; Synthetic biology ; Metabolic engineering ; Biocompatible material ; Biopolymer ; Bioplastic ; Biotechnology ; Chemistry ; Computational biology ; Polymer ; Bacteria ; Biology ; Biochemistry ; Engineering ; Organic chemistry biodegradable polymer synthesis and properties ; Microplastics and Plastic Pollution ; Enzyme Catalysis and Immobilization
Authors: Sukriti Singh | Monika Pareek | Avtar Changotra | Sayan Banerjee | Bangaru Bhaskararao | P. Balamurugan | Raghavan B. Sunoj
Source: Proceedings of the National Academy of Sciences (article, 2020-01-08)
Field: Materials Science — Materials Chemistry

A unified machine-learning protocol for asymmetric catalysis as a proof of concept demonstration using asymmetric hydrogenation Design of asymmetric catalysts generally involves time- and resource-intensive heuristic endeavors. In view of the steady increase in interest toward efficient catalytic asymmetric reactions and the rapid growth in the field of machine learning (ML) in recent years, we envisaged dovetailing these two important domains. We selected a set of quantum chemically derived molecular descriptors from five different asymmetric binaphthyl-derived catalyst families with the propensity to impact the enantioselectivity of asymmetric hydrogenation of alkenes and imines. The predictive power of the random forest (RF) built using the molecular parameters of a set of 368 substrate-catalyst combinations is found to be impressive, with a root-mean-square error (rmse) in the predicted enantiomeric excess (%<i>ee</i>) of about 8.4 ± 1.8 compared to the experimentally known values. The accuracy of RF is found to be superior to other ML methods such as convolutional neural network, decision tree, and eXtreme gradient boosting as well as stepwise linear regression. The proposed method is expected to provide a leap forward in the design of catalysts for asymmetric transformations. Enantiomeric excess ; Enantioselective synthesis ; Asymmetric hydrogenation ; Catalysis ; Heuristic ; Substrate (aquarium) ; Proof of concept ; Computer science ; Set (abstract data type) ; Chemistry ; Mathematics ; Artificial intelligence ; Organic chemistry Machine Learning in Materials Science ; Asymmetric Hydrogenation and Catalysis ; Surface Chemistry and Catalysis
Authors: Yue Liu | Junming Wu | Maxim Avdeev | Siqi Shi
Source: Advanced Theory and Simulations (article, 2020-01-15)
Field: Materials Science — Materials Chemistry

Multi‐Layer Feature Selection Incorporating Weighted Score‐Based Expert Knowledge toward Modeling Materials with Targeted Properties Abstract Selecting proper descriptors or features is one of the central problems in exploring structure–activity relationships of materials using machine learning models. The current feature selection algorithms usually require tedious hyperparameter tuning and do not actively consider the prior knowledge of domain experts about the features. Here, this work proposes a data‐driven multi‐layer feature selection method incorporating domain expert knowledge named DML‐FS dek , which is automated, with users entering training data without manual tuning of the hyperparameters. The domain expert knowledge is quantified by means of weighted scoring and integrated into the selection process to eliminate the risk of crucial features being removed. The test studies on ten material properties datasets demonstrate the potential of the approach to automatically search for a reduced feature set with lower root mean square errors than those for the initial feature set. Essentially, the most relevant material features, the number of which is much smaller than that in the original feature set, are automatically selected to establish a closer and more accurate structure–activity relationship for the materials of interest. As a result, the method represents the targeted properties of materials with a smaller and more interpretable set of features while ensuring equal or better prediction accuracy. Hyperparameter ; Feature selection ; Computer science ; Feature (linguistics) ; Domain knowledge ; Domain (mathematical analysis) ; Artificial intelligence ; Data mining ; Set (abstract data type) ; Machine learning ; Process (computing) ; Selection (genetic algorithm) ; Test set ; Pattern recognition (psychology) ; Mathematics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: Jomon Joy | Elssa George | Prakashan Haritha | Sabu Thomas | Saithalavi Anas
Source: Journal of Polymer Science (article, 2020-09-28)
Field: Materials Science — Polymers and Plastics

An overview of boron nitride based polymer nanocomposites Abstract Recently, boron nitride (BN) based materials have received significant attention in both academic and industrial sectors due to its interesting properties like large energy band gap, good resistance to oxidation, excellent thermal conductivity, thermal stability, chemical inertness, significant mechanical property and widespread applications. This review article deals with the preparation and properties of boron nitride and its nanocomposites with various polymers. Diverse polymers have been explored for the preparation of boron nitride filled polymer nanocomposites by adopting different mixing methods. Properties of the resulting polymer nanocomposites mainly depend up on filler size and dispersion, mixing conditions and type of interaction between polymer matrix and the filler. Herein, the structure, preparation and properties of various boron nitride based polymer nanocomposites are reviewed in detail along with a brief overview of different classes of BN nanomaterials. Boron nitride ; Nanocomposite ; Materials science ; Polymer ; Dispersion (optics) ; Polymer nanocomposite ; Thermal stability ; Filler (materials) ; Composite material ; Mixing (physics) ; Nitride ; Nanomaterials ; Boron ; Chemical engineering ; Nanotechnology ; Chemistry ; Organic chemistry Polymer Nanocomposite Synthesis and Irradiation ; Thermal properties of materials ; Graphene research and applications
Authors: Zahra El-Schich | Yuecheng Zhang | Marek Feith | Sarah Beyer | Louise Sternbæk | Lars Ohlsson | Maria M. Stollenwerk | Anette Gjörloff Wingren
Source: BioTechniques (review, 2020-10-01)
Field: Chemistry — Analytical Chemistry

Molecularly Imprinted Polymers in Biological Applications Molecularly imprinted polymers (MIPs) are currently widely used and further developed for biological applications. The MIP synthesis procedure is a key process, and a wide variety of protocols exist. The templates that are used for imprinting vary from the smallest glycosylated glycan structures or even amino acids to whole proteins or bacteria. The low cost, quick preparation, stability and reproducibility have been highlighted as advantages of MIPs. The biological applications utilizing MIPs discussed here include enzyme-linked assays, sensors, <i>in vivo</i> applications, drug delivery, cancer diagnostics and more. Indeed, there are numerous examples of how MIPs can be used as recognition elements similar to natural antibodies. Molecularly imprinted polymer ; Glycan ; Molecular imprinting ; Nanotechnology ; Template ; Computational biology ; Chemistry ; Biology ; Materials science ; Biochemistry Analytical chemistry methods development ; Mass Spectrometry Techniques and Applications ; Analytical Chemistry and Chromatography
Authors: Bruno Linclau | Ana Ardá | Niels‐Christian Reichardt | Matthieu Sollogoub | Luca Unione | Stéphane P. Vincent | Jesús Jiménez‐Barbero
Source: Chemical Society Reviews (review, 2020-01-01)
Field: Chemistry — Organic Chemistry

Fluorinated carbohydrates as chemical probes for molecular recognition studies. Current status and perspectives This review provides an extensive summary of the effects of carbohydrate fluorination with regard to changes in physical, chemical and biological properties with respect to regular saccharides. The specific structural, conformational, stability, reactivity and interaction features of fluorinated sugars are described, as well as their applications as probes and in chemical biology. Chemistry ; Fluorine ; Nanotechnology ; Biochemical engineering ; Organic chemistry ; Materials science ; Engineering Carbohydrate Chemistry and Synthesis ; Fluorine in Organic Chemistry ; Glycosylation and Glycoproteins Research
Authors: Pei‐Lin Kang | Cheng Shang | Zhi‐Pan Liu
Source: Accounts of Chemical Research (article, 2020-09-17)
Field: Materials Science — Materials Chemistry

Large-Scale Atomic Simulation via Machine Learning Potentials Constructed by Global Potential Energy Surface Exploration Atomic simulations based on quantum mechanics (QM) calculations have entered into the tool box of chemists over the past few decades, facilitating an understanding of a wide range of chemistry problems, from structure characterization to reactivity determination. Due to the poor scaling and high computational cost intrinsic to QM calculations, one has to either sacrifice accuracy or time when performing large-scale atomic simulations. The battle to find a better compromise between accuracy and speed has been central to the development of new theoretical methods.The recent advances of machine-learning (ML)-based large-scale atomic simulations has shown great promise to the benefit of many branches of chemistry. Instead of solving the Schrödinger equation directly, ML-based simulations rely on a large data set of accurate potential energy surfaces (PESs) and complex numerical models to predict the total energy. These simulations feature both a high speed and a high accuracy for computing large systems. Due to the lack of a physical foundation in numerical models, ML models are often frustrated in their predictivity and robustness, which are key to applications. Focusing on these concerns, here we overview the recent advances in ML methodologies for atomic simulations on three key aspects. Namely, the generation of a representative data set, the extensity of ML models, and the continuity of data representation. While global optimization methods are the natural choice for building a representative data set, the stochastic surface walking method is shown to provide the desired PES sampling for both minima and transition regions on the PES. The current ML models generally utilize local geometrical descriptors as an input and consider the total energy as the sum of atomic energies. There are many flavors of data descriptors and ML models, but the applications for material and reaction predictions are still limited, not least because of the difficulty to train the associated vast global data sets. We show that our recently designed power-type structure descriptors together with a feed-forward neural network (NN) model are compatible with highly complex global PES data, which has led to a large family of global NN (G-NN) potentials.Two recent applications of G-NN potentials in material and reaction simulations are selected to illustrate how ML-based atomic simulations can help the discovery of new materials and reactions. Robustness (evolution) ; Computer science ; Maxima and minima ; Potential energy surface ; Scaling ; Uncertainty quantification ; Range (aeronautics) ; Statistical physics ; Algorithm ; Machine learning ; Chemistry ; Mathematics ; Physics ; Aerospace engineering ; Quantum mechanics ; Ab initio ; Engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Topic Modeling
Authors: Badr M. Thamer | Ali Aldalbahi | Meera Moydeen Abdulhameed | Mostafizur Rahaman | Mohamed H. El‐Newehy
Source: Polymers (review, 2020-12-23)
Field: Materials Science — Biomaterials

Modified Electrospun Polymeric Nanofibers and Their Nanocomposites as Nanoadsorbents for Toxic Dye Removal from Contaminated Waters: A Review Electrospun polymer nanofibers (EPNFs) as one-dimensional nanostructures are characterized by a high surface area-to-volume ratio, high porosity, large number of adsorption sites and high adsorption capacity. These properties nominate them to be used as an effective adsorbent for the removal of water pollutants such as heavy metals, dyes and other pollutants. Organic dyes are considered one of the most hazardous water pollutants due to their toxic effects even at very low concentrations. To overcome this problem, the adsorption technique has proven its high effectiveness towards the removal of such pollutants from aqueous systems. The use of the adsorption technique depends mainly on the properties, efficacy, cost and reusability of the adsorbent. So, the use of EPNFs as adsorbents for dye removal has received increasing attention due to their unique properties, adsorption efficiency and reusability. Moreover, the adsorption efficiency and stability of EPNFs in aqueous media can be improved via their surface modification. This review provides a relevant literature survey over the last two decades on the fabrication and surface modification of EPNFs by an electrospinning technique and their use of adsorbents for the removal of various toxic dyes from contaminated water. Factors affecting the adsorption capacity of EPNFs, the best adsorption conditions and adsorption mechanism of dyes onto the surface of various types of modified EPNFs are also discussed. Finally, the adsorption capacity, isotherm and kinetic models for describing the adsorption of dyes using modified and composite EPNFs are discussed. Adsorption ; Chemical engineering ; Aqueous solution ; Reusability ; Nanocomposite ; Materials science ; Electrospinning ; Nanofiber ; Pollutant ; Water treatment ; Portable water purification ; Polymer ; Nanotechnology ; Chemistry ; Organic chemistry ; Composite material ; Environmental engineering ; Environmental science ; Computer science Electrospun Nanofibers in Biomedical Applications ; Membrane Separation Technologies ; Adsorption and biosorption for pollutant removal
Authors: Brian D. Hosterman
Source: Digital Scholarship - UNLV (University of Nevada Reno) (article, 2020-04-29)
Field: Materials Science — Materials Chemistry

Raman Spectroscopic Study of Solid Solution Spinel Oxides Solid solution spinel oxides of composition MgxNi1?xCr2O4, NiFexCr2?xO4, and FexCr3?xO4 were synthesized and characterized using x-ray diffraction and Raman spectroscopy. Frequencies of the Raman-active modes are tracked as the metal cations within the spinel lattice are exchanged. This gives information about the dependence of the lattice vibrations on the tetrahedral and octahedral cations. The highest-frequency Raman-active mode, A1g, is unaffected by substitution of the divalent tetrahedral cation, whereas the lower frequency vibrations are more strongly affected by substitution of the tetrahedral cation. The change in wavenumber of many phonons is nonlinear upon cation exchange. All detected modes of MgxNi1?xCr2O4 and FexCr3?xO4 exhibit one-mode behavior. Additional modes are detected in the NiFexCr2?xO4 series due to cation inversion of the spinel lattice. Results from the FexCr3?xO4 spinels are then applied to identifying the corrosion layers of three stainless steel samples exposed to lead-bismuth eutectic in a high temperature, oxygen-controlled environment. The Raman spectrum of the outer corrosion layer in all steels is identified as Fe3O4. The wavenumber of the A1g mode for the inner corrosion layer indicates an iron chromium spinel oxide. Micro-Raman spectroscopy proves capable of determining structural and compositional differences between complex corrosion layers of stainless steels. Raman spectroscopy ; Spinel ; Solid solution ; Materials science ; Mineralogy ; Chemistry ; Metallurgy ; Optics ; Physics Catalytic Processes in Materials Science ; Pigment Synthesis and Properties ; X-ray Diffraction in Crystallography