jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Nekane Guarrotxena | Olga Garcı́a | Isabel Quijada‐Garrido
Source: Scientific Reports (article, 2018-04-10)
Field: Materials Science — Materials Chemistry

Synthesis of Au@polymer nanohybrids with transited core-shell morphology from concentric to eccentric Emoji-N or Janus nanoparticles The combination of multifunctionality and synergestic effect displayed by hybrid nanoparticles (NPs) has been revealed as an effective stratagem in the development of advanced nanostructures with unique biotechnology and optoelectronic applications. Although important work has been devoted, the demand of facile, versatile and efficient synthetic approach remains still challenging. Herein, we report a feasible and innovative way for polymer-shell assembling onto gold nanoparticles in competitive conditions of hydrophobic/hydrophilic feature and interfacial energy of components to generate core-shell nanohybrids with singular morphologies. The fine control of reaction parameters allows a modulated transformation from concentric to eccentric nanostructure-geometries. In this regard, a rational selection of the components and solvent ratio guarantee the reproducibility and efficiency on hybrid-nanoassembly. Furthermore, the simplicity of the synthetic approach offers the possibility to obtain asymmetric Janus NPs and new morphologies (quizzical-aspheric polymer-shell, named Emoji-N-hybrids) with adjustable surface-coating, leading to new properties and applications that are unavailable to their symmetrical or single components. Materials science ; Nanotechnology ; Janus ; Nanoparticle ; Nanostructure ; Coating ; Polymer ; Shell (structure) ; Janus particles ; Composite material Pickering emulsions and particle stabilization ; Gold and Silver Nanoparticles Synthesis and Applications ; Nanocluster Synthesis and Applications
Authors: Amanda Manchini Dias | Marcia Perez dos Santos Cabrera | Aline Margarete Furuyama Lima | Sebastião Roberto Taboga | Patrícia Simone Leite Vilamaior | Márcio José Tiera | Vera Aparecida de Oliveira Tiera
Source: Carbohydrate Polymers (article, 2018-05-20)
Field: Materials Science — Biomaterials

Insights on the antifungal activity of amphiphilic derivatives of diethylaminoethyl chitosan against Aspergillus flavus Chemistry ; Phosphatidylcholine ; Aspergillus flavus ; Chitosan ; Amphiphile ; Hydrophobic effect ; Antifungal ; Membrane ; Biophysics ; Chitin ; Confocal microscopy ; Lipid bilayer ; Biochemistry ; Organic chemistry ; Phospholipid ; Biology ; Food science Nanocomposite Films for Food Packaging ; Antimicrobial agents and applications ; Polydiacetylene-based materials and applications
Authors: Charchit Kumar | Vincent Le Houérou | Thomas Speck | Holger F. Bohn
Source: Royal Society Open Science (article, 2018-04-01)
Field: Materials Science — Surfaces, Coatings and Films

Straightforward and precise approach to replicate complex hierarchical structures from plant surfaces onto soft matter polymer The surfaces of plant leaves are rarely smooth and often possess a species-specific micro- and/or nano-structuring. These structures usually influence the surface functionality of the leaves such as wettability, optical properties, friction and adhesion in insect–plant interactions. This work presents a simple, convenient, inexpensive and precise two-step micro-replication technique to transfer surface microstructures of plant leaves onto highly transparent soft polymer material. Leaves of three different plants with variable size (0.5–100 µm), shape and complexity (hierarchical levels) of their surface microstructures were selected as model bio-templates. A thermoset epoxy resin was used at ambient conditions to produce negative moulds directly from fresh plant leaves. An alkaline chemical treatment was established to remove the entirety of the leaf material from the cured negative epoxy mould when necessary, i.e. for highly complex hierarchical structures. Obtained moulds were filled up afterwards with low viscosity silicone elastomer (PDMS) to obtain positive surface replicas. Comparative scanning electron microscopy investigations (original plant leaves and replicated polymeric surfaces) reveal the high precision and versatility of this replication technique. This technique has promising future application for the development of bioinspired functional surfaces. Additionally, the fabricated polymer replicas provide a model to systematically investigate the structural key points of surface functionalities. Materials science ; Polymer ; Replication (statistics) ; Wetting ; Epoxy ; Elastomer ; Soft matter ; Nanotechnology ; Micrometer ; Silicone ; Microstructure ; Template ; Adhesion ; Thermosetting polymer ; Composite material ; Biological system ; Chemical engineering ; Colloid ; Optics Surface Modification and Superhydrophobicity ; Adhesion, Friction, and Surface Interactions ; Polymer Surface Interaction Studies
Authors: Brian Chacko | Palanisamy Sivanandy | N. L. Gowrishankar | J Honeypriya | A Sumathy Dr. A Sumathy
Source: Indian Journal of Pharmaceutical Sciences (review, 2018-01-01)
Field: Materials Science — Biomaterials

Effect of Surfactant Coating on Brain Targeting Polymeric Nanoparticles; a Review Targeting of drugs to brain is one of the most challenging issues for pharmaceutical research as blood-brain barrier acts as an insurmountable obstacle for the passage of systemically delivered therapeutics and the brain extracellular matrix attributes to poor distribution of locally delivered drugs. Amongst various invasive or non-invasive methods to warrant blood-brain barrier, nanoparticle is one of promising ways to administer central nervous system drugs. The concept of nanoparticle-based drug targeting make a tremendous progress and gigantic era to overcome the above limitations with improved drug efficacy and reduced drug toxicity. In recent years, new strategies of surfactant coating of biodegradable polymeric nanoparticles, which differ from conventional methodologies of brain targeting, have emerged at the forefront of medical science. The non-ionic surfactant, polysorbate 80 as a coating material promises an unparalleled opportunity for enhancement of brain targeting of colloidal particles. The aim of this review is to evaluate the potential application of surfactant coated nanoparticles as drug carrier system for various central nervous system diseases. Pulmonary surfactant ; Drug ; Nanotechnology ; Nanoparticle ; Blood–brain barrier ; Coating ; Drug delivery ; Medicine ; Pharmacology ; Central nervous system ; Chemistry ; Materials science ; Internal medicine Nanoparticle-Based Drug Delivery ; Advanced Drug Delivery Systems ; Advancements in Transdermal Drug Delivery
Authors: Juzeng An | Marco Bandini
Source: CHIMIA International Journal for Chemistry (article, 2018-09-01)
Field: Chemistry — Organic Chemistry

Gold-catalyzed Dearomatization Reactions The advent of homogenous gold catalysis has revolutionized the way of approaching organic synthesis providing new and desirable solutions for challenging chemical manipulations of unactivated unsaturated hydrocarbons. In the realm of aromatic compounds, dearomatization protocols have been emerging as valuable synthetic tools to densely functionalized polycyclic 3D-molecular scaffolds, starting from readily available 2D-aromatic congeners. Gold catalysis contributed substantially in expanding this scenario enabling the condensation of numerous electron-rich arenes with hydrocarbons, resulting in the partial or total dearomatization of the aryl fragment. An overview of the most recent findings in the field, organized by type of electrophilic partners, is hereby presented. Catalysis ; Chemistry ; Aryl ; Electrophile ; Nanotechnology ; Combinatorial chemistry ; Organic chemistry ; Materials science ; Alkyl Catalytic Alkyne Reactions ; Asymmetric Hydrogenation and Catalysis ; Catalytic Cross-Coupling Reactions
Authors: Shouqiang Li | Dandan Yu | Huan Ji | Baocun Zhao | Lili Ji | Xiaoping Leng
Source: BioMedical Engineering OnLine (article, 2018-06-20)
Field: Materials Science — Biomaterials

In vivo degradation and neovascularization of silk fibroin implants monitored by multiple modes ultrasound for surgical applications This study suggests that multiple modes ultrasound imaging could be an ideal method to evaluate the degradation and neovascularization of biomaterial implants in vivo for surgical applications. Fibroin ; SILK ; Neovascularization ; Biomedical engineering ; Hindlimb ; In vivo ; Ultrasound ; Biomaterial ; Implant ; Materials science ; Anatomy ; Surgery ; Medicine ; Angiogenesis ; Radiology ; Biology ; Composite material ; Internal medicine Silk-based biomaterials and applications ; Electrospun Nanofibers in Biomedical Applications ; Tendon Structure and Treatment
Authors: Songkil Kim | Ondrej Dyck | Anton V. Ievlev | Ivan Vlassiouk | Sergei V. Kalinin | Alex Belianinov | Stephen Jesse | Olga S. Ovchinnikova
Source: Carbon (article, 2018-06-14)
Field: Materials Science — Materials Chemistry

Graphene milling dynamics during helium ion beam irradiation Graphene ; Materials science ; Helium ; Focused ion beam ; Coalescence (physics) ; Nanopore ; Irradiation ; Ion ; Nanotechnology ; Ion beam ; Graphene nanoribbons ; Field ion microscope ; Ion milling machine ; Chemical physics ; Atomic physics ; Chemistry ; Nuclear physics Graphene research and applications ; Ion-surface interactions and analysis ; Nanopore and Nanochannel Transport Studies
Authors: T. Dudziak | Paweł Gajewski | Bartłomiej Śnieżyński | Vinay Deodeshmukh | M. Witkowska | W. Ratuszek | K. Chruściel
Source: Corrosion Science (article, 2018-02-02)
Field: Materials Science — Metals and Alloys

Neural network modelling studies of steam oxidised kinetic behaviour of advanced steels and Ni-based alloys at 800 °C for 3000 h Corrosion ; Materials science ; Alloy ; Artificial neural network ; Metallurgy ; Kinetic energy ; Bar (unit) ; Computer science ; Artificial intelligence ; Physics Hydrogen embrittlement and corrosion behaviors in metals ; High-Temperature Coating Behaviors ; Semiconductor materials and devices
Authors: Matthew K. Matlock | Na Le Dang | S. Joshua Swamidass
Source: ACS Central Science (article, 2018-01-03)
Field: Materials Science — Materials Chemistry

Learning a Local-Variable Model of Aromatic and Conjugated Systems A collection of new approaches to building and training neural networks, collectively referred to as deep learning, are attracting attention in theoretical chemistry. Several groups aim to replace computationally expensive <i>ab initio</i> quantum mechanics calculations with learned estimators. This raises questions about the representability of complex quantum chemical systems with neural networks. Can local-variable models efficiently approximate nonlocal quantum chemical features? Here, we find that convolutional architectures, those that only aggregate information locally, cannot efficiently represent aromaticity and conjugation in large systems. They cannot represent long-range nonlocality known to be important in quantum chemistry. This study uses aromatic and conjugated systems computed from molecule graphs, though reproducing quantum simulations is the ultimate goal. This task, by definition, is both computable and known to be important to chemistry. The failure of convolutional architectures on this focused task calls into question their use in modeling quantum mechanics. To remedy this heretofore unrecognized deficiency, we introduce a new architecture that propagates information back and forth in waves of nonlinear computation. This architecture is still a local-variable model, and it is both computationally and representationally efficient, processing molecules in sublinear time with far fewer parameters than convolutional networks. Wave-like propagation models aromatic and conjugated systems with high accuracy, and even models the impact of small structural changes on large molecules. This new architecture demonstrates that some nonlocal features of quantum chemistry can be efficiently represented in local variable models. Computer science ; Quantum nonlocality ; Quantum ; Theoretical computer science ; Convolutional neural network ; Variable (mathematics) ; Aromaticity ; Statistical physics ; Artificial intelligence ; Molecule ; Quantum mechanics ; Mathematics ; Physics ; Quantum entanglement Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Hasnain Hafiz | Adnan Khair | Hongchul Choi | Abdullah Mueen | Arun Bansil | Stephan Eidenbenz | J. M. Wills | Jian‐Xin Zhu | Alexander V. Balatsky | Towfiq Ahmed
Source: npj Computational Materials (article, 2018-11-16)
Field: Materials Science — Materials Chemistry

A high-throughput data analysis and materials discovery tool for strongly correlated materials Abstract Modeling of f -electron systems is challenging due to the complex interplay of the effects of spin–orbit coupling, electron–electron interactions, and the hybridization of the localized f -electrons with itinerant conduction electrons. This complexity drives not only the richness of electronic properties but also makes these materials suitable for diverse technological applications. In this context, we propose and implement a data-driven approach to aid the materials discovery process. By deploying state-of-the-art algorithms and query tools, we train our learning models using a large, simulated dataset based on existing actinide and lanthanide compounds. The machine-learned models so obtained can then be used to search for new classes of stable materials with desired electronic and physical properties. We discuss the basic structure of our f -electron database, and our approach towards cleaning and correcting the structure data files. Illustrative examples of the applications of our database include successful prediction of stable superstructures of double perovskites and identification of a number of physically-relevant trends in strongly correlated features of f -electron based materials. Context (archaeology) ; Computer science ; Electron ; Throughput ; Coupling (piping) ; Process (computing) ; Identification (biology) ; Materials science ; Physics ; Nuclear physics Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Advanced Condensed Matter Physics
Authors: Dhiren K. Pradhan | Shalini Kumari | Evgheni Strelcov | Dillip K. Pradhan | Ram S. Katiyar | Sergei V. Kalinin | Nouamane Laanait | Rama K. Vasudevan
Source: npj Computational Materials (article, 2018-04-18)
Field: Materials Science — Materials Chemistry

Reconstructing phase diagrams from local measurements via Gaussian processes: mapping the temperature-composition space to confidence Abstract We show the ability to map the phase diagram of a relaxor-ferroelectric system as a function of temperature and composition through local hysteresis curve acquisition, with the voltage spectroscopy data being used as a proxy for the (unknown) microscopic state or thermodynamic parameters of materials. Given the discrete nature of the measurement points, we use Gaussian processes to reconstruct hysteresis loops in temperature and voltage space, and compare the results with the raw data and bulk dielectric spectroscopy measurements. The results indicate that the surface transition temperature is similar for all but one composition with respect to the bulk. Through clustering algorithms, we recreate the main features of the bulk diagram, and provide statistical confidence estimates for the reconstructed phase transition temperatures. We validate the method by using Gaussian processes to predict hysteresis loops for a given temperature for a composition unseen by the algorithm, and compare with measurements. These techniques can be used to map phase diagrams from functional materials in an automated fashion, and provide a method for uncertainty quantification and model selection. Phase diagram ; Gaussian ; Hysteresis ; Gaussian function ; Cluster analysis ; Phase transition ; Materials science ; Phase space ; Algorithm ; Phase (matter) ; Statistical physics ; Computer science ; Thermodynamics ; Physics ; Artificial intelligence ; Condensed matter physics Machine Learning in Materials Science ; Neural Networks and Applications ; Statistical and numerical algorithms
Authors: Afroditi Alexandra Tamouridou | Xanthoula Eirini Pantazi | Thomas Alexandridis | Anastasia L. Lagopodi | Giorgos Kontouris | Dimitrios Moshou
Source: Sensors (article, 2018-08-23)
Field: Chemistry — Analytical Chemistry

Spectral Identification of Disease in Weeds Using Multilayer Perceptron with Automatic Relevance Determination Microbotryum silybum, a smut fungus, is studied as an agent for the biological control of Silybum marianum (milk thistle) weed. Confirmation of the systemic infection is essential in order to assess the effectiveness of the biological control application and assist decision-making. Nonetheless, in situ diagnosis is challenging. The presently demonstrated research illustrates the identification process of systemically infected S. marianum plants by means of field spectroscopy and the multilayer perceptron/automatic relevance determination (MLP-ARD) network. Leaf spectral signatures were obtained from both healthy and infected S. marianum plants using a portable visible and near-infrared spectrometer (310–1100 nm). The MLP-ARD algorithm was applied for the recognition of the infected S. marianum plants. Pre-processed spectral signatures served as input features. The spectra pre-processing consisted of normalization, and second derivative and principal component extraction. MLP-ARD reached a high overall accuracy (90.32%) in the identification process. The research results establish the capacity of MLP-ARD to precisely identify systemically infected S. marianum weeds during their vegetative growth stage. Silybum marianum ; Milk Thistle ; Weed ; Artificial intelligence ; Computer science ; Pattern recognition (psychology) ; Biology ; Botany Spectroscopy and Chemometric Analyses ; Remote Sensing in Agriculture ; Leaf Properties and Growth Measurement
Authors: Khubab Shaker | Muhammad Umair | Madeha Jabbar | Danish Mahmood Baitab | Yasir Nawab | Ali Afzal | Sheraz Ahmad
Source: Thermal Science (article, 2018-01-31)
Field: Materials Science — Polymers and Plastics

Effect of fabric structural design on the thermal properties of woven fabrics The thermal properties of a certain fabric govern its end usage. The enhanced thermal resistance can help to use light weight fabric for cold conditions. The aim of this study was the development fabric with a particular structural design having enhanced thermal resistance, without any change in the constituent materials or any extra process. Fabric samples were produced using cotton and core spun elastane yarns along weft, in a specific sequence. The fabrics had either a flat or puckered appearance, depending on the arrangement of weft yarns. It was observed that the percentage of core spun yarns and fabric thickness had a significant effect on the thermal resistance of fabrics. A valuable difference in the thermal resistance of flat and seersucker (puckered) fabrics, having same construction was observed. It was found to be the effect of the characteristic puckered effect of the seersucker fabric. Statistical models were developed to predict the thermal resistance of flat fabrics using core spun yarns percentage and fabric thickness. Materials science ; Composite material ; Core (optical fiber) ; Thermal resistance ; Fabric structure ; Thermal ; Woven fabric Textile materials and evaluations ; Dyeing and Modifying Textile Fibers ; Thermoregulation and physiological responses
Authors: Melisa J. Hidalgo | María T. Pozzi | Octavio J. Furlong | Eduardo J. Marchevsky | Roberto G. Pellerano
Source: Microchemical Journal (article, 2018-06-05)
Field: Chemistry — Analytical Chemistry

Classification of organic olives based on chemometric analysis of elemental data Linear discriminant analysis ; Chemometrics ; Partial least squares regression ; Inductively coupled plasma atomic emission spectroscopy ; Chemistry ; Analytical Chemistry (journal) ; Pattern recognition (psychology) ; Artificial intelligence ; Chromatography ; Mathematics ; Inductively coupled plasma ; Statistics ; Computer science Spectroscopy and Chemometric Analyses ; Isotope Analysis in Ecology ; Edible Oils Quality and Analysis
Authors: Pietro Romano | Thomas Hammarström | Tord Bengtsson | Antonino Imburgia | Antonino Madonia | Fabio Viola | Stanislaw Gubanski
Source: IEEE Transactions on Dielectrics and Electrical Insulation (article, 2018-04-01)
Field: Materials Science — Materials Chemistry

Partial discharges at different voltage waveshapes: Comparison between two different acquisition systems In modern HV apparatuses the wide use of electronic converters, increase the stress on the involved insulation systems and thus affect the reliability of the whole power grid. Additionally, such non-sinusoidal voltage shapes contain high gradient flanks that create problems in the detection of partial discharge (PD) activity. The aim of this paper is to discuss the methodology on how to suitably approach PD detection in insulation systems exposed to various voltage waveshapes in general by comparing two different measuring systems. The first one, equipped with a resonant PD decoupler, designed specifically for detection at typical power electronic waveshapes and the other one, based on an antenna sensor with flat frequency response, a general-purpose AC system. It was found that not so much the acquisition principles as the features are important for effective detection of PD signals appearing at rapidly changing test voltages from power electronic devices. Partial discharge ; Voltage ; Reliability (semiconductor) ; Electrical engineering ; Power (physics) ; Converters ; Electronic engineering ; Antenna (radio) ; Computer science ; Engineering ; Physics High voltage insulation and dielectric phenomena ; Lightning and Electromagnetic Phenomena ; Electrostatic Discharge in Electronics
Authors: Christian Heinrich | Jean Rehbinder | André Nazac | Benjamin Teig | Angelo Pierangelo | Jihad Zallat
Source: Journal of the Optical Society of America A (article, 2018-11-15)
Field: Chemistry — Analytical Chemistry

Mueller polarimetric imaging of biological tissues: classification in a decision-theoretic framework Mueller polarimetry is increasingly recognized as a powerful modality in biomedical imaging. Nevertheless, principled statistical analysis procedures are still lacking in this field. This paper presents a complete pipeline for polarimetric bioimages, with an application to ex vivo cervical precancer detection. In the preprocessing stage, we evaluate the replacement of pixels by superpixels. In the analysis stage, we resort to decision theory to select and tune a classifier. Performances of the retained classifier are evaluated. Decision theory provides a rigorous and versatile framework, allowing generalization to other pathologies, to other imaging procedures, and to classification problems involving more than two classes. Computer science ; Polarimetry ; Preprocessor ; Artificial intelligence ; Classifier (UML) ; Decision theory ; Pattern recognition (psychology) ; Pixel ; Machine learning ; Mathematics ; Optics Spectroscopy and Chemometric Analyses ; Optical Polarization and Ellipsometry ; Spectroscopy Techniques in Biomedical and Chemical Research