jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Elise Guégain | Jean‐Philippe Michel | Tanguy Boissenot | Julien Nicolas
Source: Macromolecules (article, 2018-02-02)
Field: Chemistry — Organic Chemistry

Tunable Degradation of Copolymers Prepared by Nitroxide-Mediated Radical Ring-Opening Polymerization and Point-by-Point Comparison with Traditional Polyesters Two libraries of well-defined, degradable vinyl copolymers of opposite solubility, based either on methyl methacrylate (MMA) or on oligo(ethylene glycol) methyl ether methacrylate (OEGMA), and containing various amount of 2-methylene-4-phenyl-1,3-dioxolane (MPDL), were synthesized by nitroxide-mediated radical ring-opening polymerization. A comprehensive degradation study (long-term hydrolytic degradation, degradation of thick and thin films, water uptake, enzymatic degradation) was then performed, and results were compared with those from traditional aliphatic polyesters (PLGA, PLA, and PCL). It appeared that P(MMA-co-MPDL) copolymers slowly degraded in PBS with degradation kinetics slower than that of PCL whereas P(OEGMA-co-MPDL) copolymers led to significant degradation, in between that of PLA and PCL, depending on the amount of MPDL, but without leading to a dramatic drop of pH as for PLGA and PLA. Whereas P(MMA-co-MPDL) copolymers might be well-suited for biomaterials intended for long-term use (e.g., devices and implants), faster degrading P(OEGMA-co-MPDL) copolymers might be envisioned for short- or midterm applications such as nanoscale drug-delivery systems, meeting a need for hydrophilic degradable materials with tunable degradation kinetics. Copolymer ; Polymer chemistry ; Polyester ; Polymerization ; Chemistry ; Ethylene glycol ; Nitroxide mediated radical polymerization ; Ring-opening polymerization ; Degradation (telecommunications) ; Materials science ; Radical polymerization ; Organic chemistry ; Polymer Advanced Polymer Synthesis and Characterization ; biodegradable polymer synthesis and properties ; Antimicrobial agents and applications
Authors: Yin-Jia Zhang | Alireza Khorshidi | Georg Kastlunger | Andrew A. Peterson
Source: The Journal of Chemical Physics (article, 2018-06-13)
Field: Materials Science — Materials Chemistry

The potential for machine learning in hybrid QM/MM calculations Hybrid quantum-mechanics/molecular-mechanics (QM/MM) simulations are popular tools for the simulation of extended atomistic systems, in which the atoms in a core region of interest are treated with a QM calculator and the surrounding atoms are treated with an empirical potential. Recently, a number of atomistic machine-learning (ML) tools have emerged that provide functional forms capable of reproducing the output of more expensive electronic-structure calculations; such ML tools are intriguing candidates for the MM calculator in QM/MM schemes. Here, we suggest that these ML potentials provide several natural advantages when employed in such a scheme. In particular, they may allow for newer, simpler QM/MM frameworks while also avoiding the need for extensive training sets to produce the ML potential. The drawbacks of employing ML potentials in QM/MM schemes are also outlined, which are primarily based on the added complexity to the algorithm of training and re-training ML models. Finally, two simple illustrative examples are provided which show the power of adding a retraining step to such "QM/ML" algorithms. QM/MM ; Computer science ; Artificial intelligence ; Machine learning ; Chemistry ; Computational chemistry ; Molecular dynamics Machine Learning in Materials Science ; Nuclear Physics and Applications ; Electron and X-Ray Spectroscopy Techniques
Authors: James S. Peerless | Nina J. B. Milliken | Thomas J. Oweida | Matthew D. Manning | Yaroslava G. Yingling
Source: Advanced Theory and Simulations (article, 2018-11-11)
Field: Materials Science — Materials Chemistry

Soft Matter Informatics: Current Progress and Challenges Abstract The number of applications of informatics or data‐driven discovery is growing in many fields, including materials science. The large amount of data that is readily available, combined with high‐level statistical algorithms, is proving to be extremely useful in developing complex predictive models with little to no human supervision or bias. However, in the field of soft matter, which includes complex materials such as polymers, liquids, emulsions, colloids, and gels, there is a slower adoption of informatics strategies than in adjacent fields. Here, the current state of soft matter informatics is discussed. Challenges specific to soft materials, including data classification, various degrees of organization at multiple length scales, and process‐dependent properties require unique approaches by researchers in order to develop robust informatics approaches in soft matter. The current ability to extract and analyze the information from the PoLyInfo database is demonstrated by the fitting of the Flory–Fox equation for glass transition temperature for several polymers. This Progress Report serves to introduce and excite the scientific community about the remarkable potential of informatics for exploring the properties of soft materials. Informatics ; Materials informatics ; Soft matter ; Data science ; Computer science ; Engineering informatics ; Field (mathematics) ; Current (fluid) ; Process (computing) ; Health informatics ; Management science ; Engineering ; Mathematics ; Political science ; Electrical engineering ; Colloid Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Catalysis and Oxidation Reactions
Authors: Xiaolin Li | Zijiang Yang | L. Catherine Brinson | Alok Choudhary | Ankit Agrawal | Wei Chen
Source:  (article, 2018-08-26)
Field: Materials Science — Materials Chemistry

A Deep Adversarial Learning Methodology for Designing Microstructural Material Systems In Computational Materials Design (CMD), it is well recognized that identifying key microstructure characteristics is crucial for determining material design variables. However, existing microstructure characterization and reconstruction (MCR) techniques have limitations to be applied for materials design. Some MCR approaches are not applicable for material microstructural design because no parameters are available to serve as design variables, while others introduce significant information loss in either microstructure representation and/or dimensionality reduction. In this work, we present a deep adversarial learning methodology that overcomes the limitations of existing MCR techniques. In the proposed methodology, generative adversarial networks (GAN) are trained to learn the mapping between latent variables and microstructures. Thereafter, the low-dimensional latent variables serve as design variables, and a Bayesian optimization framework is applied to obtain microstructures with desired material property. Due to the special design of the network architecture, the proposed methodology is able to identify the latent (design) variables with desired dimensionality, as well as capturing complex material microstructural characteristics. The validity of the proposed methodology is tested numerically on a synthetic microstructure dataset and its effectiveness for materials design is evaluated through a case study of optimizing optical performance for energy absorption. Additional features, such as scalability and transferability, are also demonstrated in this work. In essence, the proposed methodology provides an end-to-end solution for microstructural design, in which GAN reduces information loss and preserves more microstructural characteristics, and the GP-Hedge optimization improves the efficiency of design exploration. Computer science ; Curse of dimensionality ; Scalability ; Generative Design ; Dimensionality reduction ; Latent variable ; Benchmark (surveying) ; Machine learning ; Artificial intelligence ; Materials science Machine Learning in Materials Science ; Injection Molding Process and Properties ; Industrial Vision Systems and Defect Detection
Authors: Deepali Rahangdale | Anupama Kumar
Source: Carbohydrate Polymers (article, 2018-09-03)
Field: Chemistry — Analytical Chemistry

Chitosan as a substrate for simultaneous surface imprinting of salicylic acid and cadmium Epichlorohydrin ; Chitosan ; Adsorption ; Chemistry ; Nuclear chemistry ; Molecular imprinting ; Acrylamide ; Langmuir ; Langmuir adsorption model ; Polymer chemistry ; Molecularly imprinted polymer ; Polymer ; Monomer ; Chemical engineering ; Organic chemistry ; Selectivity ; Catalysis Analytical chemistry methods development ; Adsorption and biosorption for pollutant removal ; Electrochemical Analysis and Applications
Authors: Yingli Yang | Xinlong Yan | Xiao Hu | Rui Feng | Min Zhou | Wenlong Cui
Source: Colloids and Surfaces A Physicochemical and Engineering Aspects (article, 2018-05-06)
Field: Chemistry — Inorganic Chemistry

Development of zeolitic imidazolate framework-67 functionalized Co-Al LDH for CO 2 adsorption Zeolitic imidazolate framework ; Adsorption ; Imidazolate ; Chemistry ; Chemical engineering ; Metal-organic framework ; Inorganic chemistry ; Materials science ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Layered Double Hydroxides Synthesis and Applications ; Membrane Separation and Gas Transport
Authors: Jingze Sun | L. V. Semenchenko | Woo Taik Lim | María Ballesteros‐Rivas | Víctor Varela-Guerrero | Hae‐Kwon Jeong
Source: Microporous and Mesoporous Materials (article, 2018-01-08)
Field: Chemistry — Inorganic Chemistry

Facile synthesis of Cd-substituted zeolitic-imidazolate framework Cd-ZIF-8 and mixed-metal CdZn-ZIF-8 Zeolitic imidazolate framework ; Propane ; Imidazolate ; Materials science ; Chemical engineering ; Metal-organic framework ; Phase (matter) ; Metal ; Adsorption ; Chemistry ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; MXene and MAX Phase Materials ; Covalent Organic Framework Applications
Authors: Sizhao Zhang | Junzong Feng | Jian Feng | Yonggang Jiang | Liangjun Li
Source: Materials & Design (article, 2018-07-05)
Field: Chemistry — Spectroscopy

Ultra-low shrinkage chitosan aerogels trussed with polyvinyl alcohol Aerogel ; Materials science ; Shrinkage ; Polyvinyl alcohol ; Chitosan ; Composite material ; Chemical engineering Aerogels and thermal insulation ; Advanced Sensor and Energy Harvesting Materials ; Surface Modification and Superhydrophobicity
Authors: Cheryl Suwen Law | Siew Yee Lim | Andrew D. Abell | Nicolas H. Voelcker | Abel Santos
Source: Nanomaterials (review, 2018-10-04)
Field: Materials Science — Materials Chemistry

Nanoporous Anodic Alumina Photonic Crystals for Optical Chemo- and Biosensing: Fundamentals, Advances, and Perspectives Optical sensors are a class of devices that enable the identification and/or quantification of analyte molecules across multiple fields and disciplines such as environmental protection, medical diagnosis, security, food technology, biotechnology, and animal welfare. Nanoporous photonic crystal (PC) structures provide excellent platforms to develop such systems for a plethora of applications since these engineered materials enable precise and versatile control of light⁻matter interactions at the nanoscale. Nanoporous PCs provide both high sensitivity to monitor in real-time molecular binding events and a nanoporous matrix for selective immobilization of molecules of interest over increased surface areas. Nanoporous anodic alumina (NAA), a nanomaterial long envisaged as a PC, is an outstanding platform material to develop optical sensing systems in combination with multiple photonic technologies. Nanoporous anodic alumina photonic crystals (NAA-PCs) provide a versatile nanoporous structure that can be engineered in a multidimensional fashion to create unique PC sensing platforms such as Fabry⁻Pérot interferometers, distributed Bragg reflectors, gradient-index filters, optical microcavities, and others. The effective medium of NAA-PCs undergoes changes upon interactions with analyte molecules. These changes modify the NAA-PCs' spectral fingerprints, which can be readily quantified to develop different sensing systems. This review introduces the fundamental development of NAA-PCs, compiling the most significant advances in the use of these optical materials for chemo- and biosensing applications, with a final prospective outlook about this exciting and dynamic field. Nanoporous ; Biosensor ; Nanotechnology ; Materials science ; Nanomaterials ; Photonics ; Photonic crystal ; Analyte ; Optoelectronics ; Chemistry Anodic Oxide Films and Nanostructures ; Photonic Crystals and Applications ; Optical Coatings and Gratings
Authors: Muhammad Raisul Abedin | Siddesh Umapathi | Harika Mahendrakar | Tunyaboon Laemthong | Holly Coleman | Denise Muchangi | Santimukul Santra | Manashi Nath | Sutapa Barua
Source: Journal of Nanobiotechnology (article, 2018-10-13)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Polymer coated gold-ferric oxide superparamagnetic nanoparticles for theranostic applications The PLL coating increased the colloidal stability and robustness of Au-Fe<sub>3</sub>O<sub>4</sub> NPs (PLL-Au-Fe<sub>3</sub>O<sub>4</sub>) in biological media including cell culture medium, PBS and PBS with 10% fetal bovine serum. It is revealed that no significant (< 10%) cytotoxicity was induced by PLL-Au-Fe<sub>3</sub>O<sub>4</sub> NPs itself in BT-474 and MDA-MB-231 cells at concentrations up to 100 μg/ml. Brightfield microscopy, fluorescence microscopy and TEM showed significant uptake of PLL-Au-Fe<sub>3</sub>O<sub>4</sub> NPs by BT-474 and MDA-MB-231 cells. The cells exhibited 40 and 60% inhibition in BT-474 and MDA-MB-231 cell growth, respectively following the internalized NPs were triggered by a photothermal laser using 100 μg/ml PLL-Au-Fe<sub>3</sub>O<sub>4</sub> NPs. The control cells treated with NPs but without laser showed < 10% cell death compared to no laser treatment control CONCLUSION: Combined together, the results demonstrate a new polymer gold superparamagnetic nanostructure that integrates both diagnostics function and photothermal ablation of tumors into a single multimodal nanoplatform exhibiting a significant cancer cell death. Superparamagnetism ; Materials science ; Photothermal therapy ; Surface modification ; Nanoparticle ; Nanotechnology ; Dynamic light scattering ; Surface plasmon resonance ; Polymer ; Nanomedicine ; Chemical engineering ; Magnetic nanoparticles ; Iron oxide nanoparticles ; Magnetization Gold and Silver Nanoparticles Synthesis and Applications ; Nanoparticle-Based Drug Delivery ; Nanoparticles: synthesis and applications
Authors: Sara Fateixa | M. Manuela M. Raposo | Helena I. S. Nogueira | Tito Trindade
Source: Talanta (article, 2018-02-06)
Field: Materials Science — Electronic, Optical and Magnetic Materials

A general strategy to prepare SERS active filter membranes for extraction and detection of pesticides in water Thiram ; Chemistry ; Detection limit ; Analyte ; Extraction (chemistry) ; Dichlorvos ; Membrane ; Nanotechnology ; Chromatography ; Pesticide ; Materials science Gold and Silver Nanoparticles Synthesis and Applications ; Biosensors and Analytical Detection
Authors: Ali Alsuraifi | A. D. M. Curtis | Dimitrios A. Lamprou | Clare Hoskins
Source: Pharmaceutics (review, 2018-08-22)
Field: Materials Science — Biomaterials

Stimuli Responsive Polymeric Systems for Cancer Therapy Nanoscale polymers systems have dominated the revolution of drug delivery advancement. Their potential in the fight against cancer is unrivalled with other technologies. Their functionality increase, targeting ability and stimuli responsive nature have led to a major boom in research focus. This review article concentrates on the use of these smart polymers in cancer therapy. Nanotechnologies have shown potential as drug carriers leading to increased drug efficacy and penetration. Multifunctional smart carriers which can release their payload upon an external or internal trigger such as pH or temperature are proving to be major frontrunners in the development of effective strategies to overcome this disease with minimal patient side effects. Nanotechnology ; Drug delivery ; Cancer therapy ; Payload (computing) ; Smart polymer ; Drug carrier ; Targeted drug delivery ; Boom ; Cancer ; Medicine ; Materials science ; Polymer ; Computer science ; Engineering ; Computer security Nanoparticle-Based Drug Delivery ; Hydrogels: synthesis, properties, applications ; Graphene and Nanomaterials Applications
Authors: Gleiston G. Dias | Torben Rogge | Rositha Kuniyil | Claus Jacob | Rubem F. S. Menna‐Barreto | Eufrânio N. da Silva Júnior | Lutz Ackermann
Source: Chemical Communications (article, 2018-01-01)
Field: Chemistry — Organic Chemistry

Ruthenium-catalyzed C–H oxygenation of quinones by weak O-coordination for potent trypanocidal agents Ruthenium-catalysis enabled the C-5 selective C-H oxygenation of naphthoquinones, and also sets the stage for the site-selective introduction of a hydroxyl group into anthraquinones. A-ring modified naphthoquinoidal compounds represent an important class of bioactive quinones for which the present study encompasses the first C-H oxygenation strategy by weak O-coordination. Ruthenium ; Catalysis ; Chemistry ; Oxygenation ; Substrate (aquarium) ; Combinatorial chemistry ; Scope (computer science) ; Medicinal chemistry ; Stereochemistry ; Organic chemistry ; Medicine ; Biology Synthesis and Catalytic Reactions ; Catalytic C–H Functionalization Methods ; Oxidative Organic Chemistry Reactions
Authors: Wiktor Pronobis | Kristof T. Schütt | Alexandre Tkatchenko | Klaus‐Robert Müller
Source: The European Physical Journal B (article, 2018-08-01)
Field: Materials Science — Materials Chemistry

Capturing intensive and extensive DFT/TDDFT molecular properties with machine learning Machine learning has been successfully applied to the prediction of chemical properties of small organic molecules such as energies or polarizabilities. Compared to these properties, the electronic excitation energies pose a much more challenging learning problem. Here, we examine the applicability of two existing machine learning methodologies to the prediction of excitation energies from time-dependent density functional theory. To this end, we systematically study the performance of various 2- and 3-body descriptors as well as the deep neural network SchNet to predict extensive as well as intensive properties such as the transition energies from the ground state to the first and second excited state. As perhaps expected current state-of-the-art machine learning techniques are more suited to predict extensive as opposed to intensive quantities. We speculate on the need to develop global descriptors that can describe both extensive and intensive properties on equal footing. Time-dependent density functional theory ; Artificial intelligence ; Artificial neural network ; Excitation ; Machine learning ; Excited state ; Density functional theory ; Computer science ; Ground state ; Physics ; Chemistry ; Computational chemistry ; Atomic physics ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Jason Hattrick‐Simpers | Kamal Choudhary | Claudio Corgnale
Source: Molecular Systems Design & Engineering (article, 2018-01-01)
Field: Materials Science — Materials Chemistry

A simple constrained machine learning model for predicting high-pressure-hydrogen-compressor materials Here we present the results of using techno-economic analysis as constraints for machine learning guided studies of new metal hydride materials. Gas compressor ; Simple (philosophy) ; Computer science ; Machine learning ; Artificial intelligence ; Mechanical engineering ; Engineering Machine Learning in Materials Science ; Nuclear Materials and Properties ; Nuclear Physics and Applications
Authors: Marlon M. Reis | Robbe Van Beers | Mahmoud Al-Sarayreh | Paul R. Shorten | Wei Qi Yan | Wouter Saeys | Reinhard Klette | C. Craigie
Source: Meat Science (review, 2018-05-30)
Field: Chemistry — Analytical Chemistry

Chemometrics and hyperspectral imaging applied to assessment of chemical, textural and structural characteristics of meat Hyperspectral imaging ; Chemometrics ; Imaging spectroscopy ; Chemical imaging ; Texture (cosmology) ; Near-infrared spectroscopy ; Meat packing industry ; Environmental science ; Pattern recognition (psychology) ; Food science ; Computer science ; Artificial intelligence ; Chemistry ; Biology ; Machine learning Spectroscopy and Chemometric Analyses ; Meat and Animal Product Quality ; Advanced Chemical Sensor Technologies
Authors: Jangwoon Sung | Gun‐Yeal Lee | Byoungho Lee
Source: Nanophotonics (article, 2018-06-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Progresses in the practical metasurface for holography and lens Abstract Metasurfaces have received enormous attention thanks to their unique ability to modulate electromagnetic properties of light in various frequency regimes. Recently, exploiting its fabrication ease and modulation strength, unprecedented and unique controlling of light that surpasses conventional optical devices has been suggested and studied a lot. Here, in this paper, we discuss some parts of this trend including holography, imaging application, dispersion control, and multiplexing, mostly operating for optical frequency regime. Finally, we will outlook the future of the devices with recent applications of these metasurfaces. Holography ; Multiplexing ; Lens (geology) ; Modulation (music) ; Computer science ; Optics ; Fabrication ; Materials science ; Optoelectronics ; Physics ; Telecommunications ; Acoustics Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Antenna Design and Analysis
Authors: Xi Wang | Zhenzong Zhang | Yuhao Zhao | Kai Xia | Yongfu Guo | Zan Qu | Renbi Bai
Source: Nanomaterials (article, 2018-08-29)
Field: Chemistry — Organic Chemistry

A Mild and Facile Synthesis of Amino Functionalized CoFe2O4@SiO2 for Hg(II) Removal To avoid the dangerous operational conditions, shorten the preparation time, and improve the adsorption performance of amino-functionalized nanomagnetic materials with a core⁻shell structure, a magnetic nanocomposite of CoFe₂O₄@SiO₂ was successfully functionalized with amino group (-NH₂) through a mild and facile hydrothermal method without the use of any toxic or harmful solvents at a relatively low temperature. The preparation time of the key steps of amino functionalization was shortened from 30 h to about 10 h. The core-shell structure and successful grafting were confirmed by various means. The amino-functionalized CoFe₂O₄@SiO₂ was used for the removal mercury (Hg(II)), a heavy metal, and exhibited excellent magnetic properties and a high Langmuir adsorption capacity of 149.3 mg Hg(II)/g. The adsorption of Hg(II) onto CoFe₂O₄@SiO₂⁻NH₂ followed the pseudo-second-order kinetic equation and Langmuir model. The thermodynamic data showed that the Hg(II) adsorption process was achieved through spontaneous exothermic and monolayer adsorption with electrostatic adsorption and chemisorption. In addition, the as-prepared CoFe₂O₄@SiO₂⁻NH₂ nanoparticles had a good reusable value, good application performance and stability, and can provide a mild and facile way to remove heavy metals from aqueous solution. Adsorption ; Langmuir adsorption model ; Aqueous solution ; Chemisorption ; Monolayer ; Surface modification ; Nanocomposite ; Materials science ; Langmuir ; Metal ; Metal ions in aqueous solution ; Inorganic chemistry ; Chemical engineering ; Chemistry ; Nuclear chemistry ; Organic chemistry ; Nanotechnology ; Physical chemistry ; Metallurgy Nanomaterials for catalytic reactions ; Adsorption and biosorption for pollutant removal ; Graphene research and applications