jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Chuanbao Liu | Yang Bai | Ji Zhou | Qian Zhao | Yihao Yang | Hongsheng Chen | Lijie Qiao
Source: npj Computational Materials (article, 2019-09-17)
Field: Materials Science — Electronic, Optical and Magnetic Materials

High-performance bifunctional polarization switch chiral metamaterials by inverse design method Abstract Multifunctional polarization controlling plays an important role in modern photonics, but their designs toward broad bandwidths and high efficiencies are still rather challenging. Here, by applying the inverse design method of model-based theoretical paradigm, we design cascaded chiral metamaterials for different polarization controls in oppositely propagating directions and demonstrate their broadband and high-efficiency performance theoretically and experimentally. Started with the derivation of scattering matrix towards specified polarization control, a chiral metamaterial is designed as a meta-quarter-wave plate for the forward propagating linearly polarized wave, which converts the x - or y -polarized wave into a nearly perfect left- or right-handed circularly polarized wave; intriguingly, it also serves as a 45° polarization rotator for the backward propagating linearly polarized waves. This bifunctional metamaterial shows a high transmission as well as a broad bandwidth due to the Fabry–Perot-like interference effect. Using the similar approach, an abnormal broadband meta-quarter-wave plate is achieved to convert the forward x - and y -polarized or the backward y - and x -polarized waves into left- and right-handed circularly polarized waves with high transmission efficiencies. The integration of multiple functions in a single structure endows the cascaded chiral metamaterials with great interests for the high-efficiency polarization-controlled applications. Metamaterial ; Broadband ; Polarization (electrochemistry) ; Physics ; Circular polarization ; Optics ; Waveplate ; Polarization rotator ; Elliptical polarization ; Linear polarization ; Birefringence ; Laser ; Microstrip Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Liquid Crystal Research Advancements
Authors: Chang Guo | Sarah Robertson | Ralf J. M. Weber | Alison Buckley | James Warren | A. Hodgson | Joshua Z. Rappoport | Konstantin Ignatyev | Kirsty Meldrum | Isabella Römer | Sameirah Macchiarulo | James Kevin Chipman | Tim Marczylo | Martin O. Leonard | Timothy W. Gant | Mark R. Viant | Rachel Smith
Source: Nanotoxicology (article, 2019-02-01)
Field: Materials Science — Materials Chemistry

Pulmonary toxicity of inhaled nano-sized cerium oxide aerosols in Sprague–Dawley rats Cerium oxide nanoparticles (CeO<sub>2</sub>NPs), used in some diesel fuel additives to improve fuel combustion efficiency and exhaust filter operation, have been detected in ambient air and concerns have been raised about their potential human health impact. The majority of CeO<sub>2</sub>NP inhalation studies undertaken to date have used aerosol particles of larger sizes than the evidence suggests are emitted from vehicles using such fuel additives. Hence, the objective of this study was to investigate the effects of inhaled CeO<sub>2</sub>NP aerosols of a more environmentally relevant size, utilizing a combination of methods, including untargeted multi-omics to enable the broadest possible survey of molecular responses and synchrotron X-ray spectroscopy to investigate cerium speciation. Male Sprague-Dawley rats were exposed by nose-only inhalation to aerosolized CeO<sub>2</sub>NPs (mass concentration 1.8<b> </b>mg/m<sup>3</sup>, aerosol count median diameter 40<b> </b>nm) for 3<b> </b>h/d for 4 d/week, for 1 or 2 weeks and sacrificed at 3 and 7<b> </b>d post-exposure. Markers of inflammation changed significantly in a dose- and time-dependent manner, which, combined with results from lung histopathology and gene expression analyses suggest an inflammatory response greater than that seen in studies using micron-sized ceria aerosols. Lipidomics of lung tissue revealed changes to minor lipid species, implying specific rather than general cellular effects. Cerium speciation analysis indicated a change in Ce<sup>3+</sup>/Ce<sup>4+</sup> ratio within lung tissue. Collectively, these results in conjunction with earlier studies emphasize the importance of aerosol particle size on toxicity determination. Furthermore, the limited effect resolution within 7<b> </b>d suggested the possibility of longer-term effects. Aerosol ; Inhalation ; Cerium oxide ; Cerium ; Inhalation exposure ; Environmental chemistry ; Toxicity ; Diesel exhaust ; Chemistry ; Materials science ; Combustion ; Oxide ; Medicine ; Metallurgy ; Organic chemistry ; Anesthesia Advanced Nanomaterials in Catalysis ; Air Quality and Health Impacts ; Nanoparticles: synthesis and applications
Authors: Mario Leclerc | Samuel Brassard | Serge Beaupré
Source: Polymer Journal (article, 2019-08-08)
Field: Materials Science — Polymers and Plastics

Direct (hetero)arylation polymerization: toward defect-free conjugated polymers Polymerization ; Stille reaction ; Nanotechnology ; Chemistry ; Materials science ; Polymer ; Combinatorial chemistry ; Organic chemistry Conducting polymers and applications ; Perovskite Materials and Applications ; Organoboron and organosilicon chemistry
Authors: Yao‐Hui Huang | Gislaine Kuminek | L. P. Roy | Katie L. Cavanagh | Qiuxiang Yin | Naír Rodríguez‐Hornedo
Source: Molecular Pharmaceutics (article, 2019-07-18)
Field: Materials Science — Materials Chemistry

Cocrystal Solubility Advantage Diagrams as a Means to Control Dissolution, Supersaturation, and Precipitation Cocrystals are often more soluble than needed and pose unnecessary risks for precipitation of less soluble forms of the drug during processing and dissolution. Such conversions lead to erratic cocrystal behavior and nullify the cocrystal solubility advantage over parent drug (SA = <i>S</i><sub>cocrystal</sub>/<i>S</i><sub>drug</sub>). This work demonstrates a quantitative method for additive selection to control cocrystal disproportionation based on cocrystal solubility advantage (SA) diagrams. The tunability of cocrystal SA is dependent on the selective drug-solubilizing power of surfactants (SP<sub>drug</sub> = (<i>S</i><sub>T</sub>/<i>S</i><sub>aq</sub>)<sub>drug</sub>). This cocrystal property is used to generate SA-SP diagrams that facilitate surfactant selection and provide a framework for evaluating how SA influences drug concentration-time profiles associated with cocrystal dissolution, drug supersaturation, and precipitation (DSP). Experimental results with indomethacin-saccharin cocrystal and surfactants (sodium lauryl sulfate, Brij, and Myrj) demonstrate the log-linear relationship characteristic of SA-SP diagrams and the dependence of σ<sub>max</sub> and dissolution area under the curve (AUC) on SA with characteristic maxima at a threshold supersaturation where drug nucleation occurs. This approach is expected to streamline cocrystal formulation as it facilitates additive selection by considering the interplay between thermodynamic (SA) and kinetic (DSP) processes. Cocrystal ; Supersaturation ; Solubility ; Dissolution ; Precipitation ; Chemistry ; Chemical engineering ; Thermodynamics ; Crystallography ; Physical chemistry ; Organic chemistry ; Molecule ; Physics ; Engineering ; Meteorology ; Hydrogen bond Crystallization and Solubility Studies ; Crystallography and molecular interactions ; nanoparticles nucleation surface interactions
Authors: Franck D’Agosto | Jutta Rieger | Muriel Lansalot
Source: Angewandte Chemie (article, 2019-10-04)
Field: Chemistry — Organic Chemistry

RAFT‐vermittelte polymerisationsinduzierte Selbstorganisation (PISA) Abstract Nach einem kurzen historischen Überblick zur polymerisationsvermittelten Selbstorganisation (polymerization‐induced self assembly, PISA) im Bereich der Polymerchemie werden in diesem Aufsatz die Grundlagen des PISA‐Mechanismus erklärt. Darüber hinaus behandelt der Aufsatz einige Eigenschaften und Einschränkungen von RAFT‐vermittelten PISA‐Systemen im Hinblick auf die Auswahl der beteiligten Komponenten, die Art und Steuerung der synthetisierbaren Nanoobjekte und Morphologien sowie mögliche Anwendungen. Raft ; Humanities ; Philosophy ; Chemistry ; Polymerization Advanced Polymer Synthesis and Characterization ; biodegradable polymer synthesis and properties ; Polymer Surface Interaction Studies
Authors: Francis Perton | Mariana Tasso | Guillermo Arturo Muñoz Medina | Mathilde Ménard | Cristina Blanco-Andujar | Enrique Leo Portiansky | M. B. Fernández van Raap | Dominique Bégin | Florent Meyer | Sylvie Bégin‐Colin | Damien Mertz
Source: Applied Materials Today (article, 2019-07-12)
Field: Materials Science — Biomaterials

Fluorescent and magnetic stellate mesoporous silica for bimodal imaging and magnetic hyperthermia Materials science ; Magnetic hyperthermia ; Nanotechnology ; Coating ; Nanocomposite ; Biocompatibility ; Mesoporous silica ; Nanomedicine ; Magnetic nanoparticles ; Fluorescence ; Nanoparticle ; Nanomaterials ; Biomedical engineering ; Mesoporous material ; Chemistry ; Medicine Nanoparticle-Based Drug Delivery ; Advanced Nanomaterials in Catalysis ; Gold and Silver Nanoparticles Synthesis and Applications
Authors: Keisuke Takahashi | Lauren Takahashi
Source: The Journal of Physical Chemistry Letters (article, 2019-01-04)
Field: Materials Science — Materials Chemistry

Creating Machine Learning-Driven Material Recipes Based on Crystal Structure Determining the manner in which crystal structures are formed is considered a great mystery within materials science. Potential solutions have the possibility to be uncovered by revealing hidden patterns within the material data. Data science is therefore implemented in order to link the material data to the crystal structure. In particular, unsupervised and supervised machine learning techniques are used where the Gaussian mixture model is employed in order to understand the data structure of the materials database while random forest classification is used to predict the crystal structure. As a result, the unsupervised and supervised machine learning techniques reveal descriptors for determining the crystal structure via the materials database. In addition, predicting atomic combinations from the crystal structure is also achieved using a trained machine where the first-principles calculations confirm the stability of predicted materials. Thus, one can consider that the estimation of the crystal structure can be achieved in principle via the combination of material data and machine learning, thereby leading to the advancement of crystal structure prediction. Crystal structure prediction ; Artificial intelligence ; Machine learning ; Unsupervised learning ; Stability (learning theory) ; Crystal structure ; Computer science ; Crystal (programming language) ; Random forest ; Algorithm ; Pattern recognition (psychology) ; Crystallography ; Chemistry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: Zongrui Pei | Junqi Yin
Source: Materials & Design (article, 2019-03-27)
Field: Materials Science — Materials Chemistry

Machine learning as a contributor to physics: Understanding Mg alloys Machine learning (ML) methods have played an increasingly important role in materials design. Take Mg alloys as an example, we show the ML methods not only supply mathematical solutions but more importantly also contribute to understand the physics in the problem. Hitherto, the role of ML methods is widely applied in high-throughput predictions, while their contribution to understand the physical mechanisms has been rarely explored. In this study, we firstly demonstrate that the Gaussian Process Classification algorithm reliably and efficiently predicts promising solutes for ductile Mg alloys, and then use these results to evaluate the correlation between two recently proposed mechanisms. Our results help clarify the controversy regarding the ductility mechanisms that can be used as the guide for materials design. Keywords: Mg alloys, Machine learning, Gaussian process classification Materials science ; Ductility (Earth science) ; Gaussian process ; Throughput ; Machine learning ; Process (computing) ; Artificial intelligence ; Nanotechnology ; Gaussian ; Computer science ; Metallurgy ; Computational chemistry ; Chemistry Machine Learning in Materials Science ; Nuclear Materials and Properties ; Hydrogen Storage and Materials
Authors: Xiangyu Zhang | Jing Cui | Kexin Zhang | Jiasheng Wu | Yongjin Lee
Source: Journal of Chemical Information and Modeling (article, 2019-10-29)
Field: Materials Science — Materials Chemistry

Machine Learning Prediction on Properties of Nanoporous Materials Utilizing Pore Geometry Barcodes In this work, we propose a computational framework for machine learning prediction on structural and performance properties of nanoporous materials for methane storage application. For our machine learning prediction, two descriptors based on pore geometry barcodes were developed; one descriptor is a set of distances from a structure to the most diverse set in barcode space, and the second descriptor extracts and uses the most important features from the barcodes. First, to identify the optimal condition for machine learning prediction, the effects of training set preparation method, training set size, and machine learning models were investigated. Our analysis showed that kernel ridge regression provides the highest prediction accuracy, and randomly selected 5% structures of the entire set would work well as a training set. Our results showed that both descriptors accurately predicted performance and even structural properties of zeolites. Furthermore, we demonstrated that our approach predicts accurately properties of metal-organic frameworks, which might indicate the possibility of this approach to be easily applied to predict the properties of other types of nanoporous materials. Nanoporous ; Geometry ; Computer science ; Materials science ; Artificial intelligence ; Nanotechnology ; Mathematics Machine Learning in Materials Science ; Metal-Organic Frameworks: Synthesis and Applications ; Advanced Polymer Synthesis and Characterization
Authors: Jonathan P. Mailoa | Mordechai Kornbluth | Simon Batzner | Georgy Samsonidze | Stephen T. Lam | Jonathan Vandermause | Chris Ablitt | Nicola Molinari | Boris Kozinsky
Source: Nature Machine Intelligence (article, 2019-09-30)
Field: Materials Science — Materials Chemistry

A fast neural network approach for direct covariant forces prediction in complex multi-element extended systems Covariant transformation ; Artificial neural network ; Exploit ; Quantum ; Computation ; Python (programming language) ; Ab initio ; Architecture Machine Learning in Materials Science ; Force Microscopy Techniques and Applications ; Advanced Physical and Chemical Molecular Interactions
Authors: Alireza Moradzadeh | N. R. Aluru
Source: The Journal of Physical Chemistry Letters (article, 2019-02-28)
Field: Materials Science — Materials Chemistry

Transfer-Learning-Based Coarse-Graining Method for Simple Fluids: Toward Deep Inverse Liquid-State Theory Machine learning is an attractive paradigm to circumvent difficulties associated with the development and optimization of force-field parameters. In this study, a deep neural network (DNN) is used to study the inverse problem of the liquid-state theory, in particular, to obtain the relation between the radial distribution function (RDF) and the Lennard-Jones (LJ) potential parameters at various thermodynamic states. Using molecular dynamics (MD), many observables, including RDF, are determined once the interatomic potential is specified. However, the inverse problem (parametrization of the potential for a specific RDF) is not straightforward. Here we present a framework integrating DNN with big data from 1.5 TB of MD trajectories with a cumulative simulation time of 52 μs for 26 000 distinct systems to predict LJ potential parameters. Our results show that DNN is successful not only in the parametrization of the atomic LJ liquids but also in parametrizing the LJ potential for coarse-grained models of simple multiatom molecules. Parametrization (atmospheric modeling) ; Observable ; Statistical physics ; Inverse ; Inverse problem ; Granularity ; Simple (philosophy) ; Radial distribution function ; Computer science ; Function (biology) ; Molecular dynamics ; Physics ; Mathematics ; Quantum mechanics ; Mathematical analysis Machine Learning in Materials Science ; Protein Structure and Dynamics ; Spectroscopy and Quantum Chemical Studies
Authors: Chunlin Li | Bangzheng Zong | Haowei Guo | Zhou Luo | Puming He | Shuying Gong | Fangyuan Fan
Source: Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy (article, 2019-10-25)
Field: Chemistry — Analytical Chemistry

Discrimination of white teas produced from fresh leaves with different maturity by near-infrared spectroscopy Chemistry ; Food science ; Ripeness ; Harvest time ; Polyphenol ; Horticulture ; Biochemistry ; Antioxidant ; Biology Spectroscopy and Chemometric Analyses ; Tea Polyphenols and Effects ; Advanced Chemical Sensor Technologies
Authors: Fatima Mousli | Ahcène Chaouchi | Mohamed Jouini | François Maurel | A. Kadri | Mohamed M. Chehimi
Source: Catalysts (article, 2019-06-30)
Field: Materials Science — Polymers and Plastics

Polyaniline-Grafted RuO2-TiO2 Heterostructure for the Catalysed Degradation of Methyl Orange in Darkness Massive industrial and agricultural developments have led to adverse effects of environmental pollution resisting conventional treatment processes. The issue can be addressed via heterogeneous photocatalysis as witnessed recently. Herein, we have developed novel metal/semi-conductor/polymer nanocomposite for the catalyzed degradation and mineralization of a model organic dye pollutant in darkness. RuO2-TiO2 mixed oxide nanoparticles (NPs) were modified with diphenyl amino (DPA) groups from the 4-diphenylamine diazonium salt precursor. The latter was reduced with ascorbic acid to provide radicals that modified the NPs and further served for in situ synthesis of polyaniline (PANI) that resulted in RuO2/TiO2-DPA-PANI nanocomposite catalyst. Excellent adhesion of PANI to RuO2/TiO2-DPA was noted but not in the case of the bare mixed oxide. This stresses the central role of diazonium compounds to tether PANI to the underlying mixed oxide. RuO2-TiO2/DPA/PANI nanocomposite revealed superior catalytic properties in the degradation of Methyl Orange (MO) compared to RuO2-TiO2/PANI and RuO2-TiO2. Interestingly, it is active even in the darkness due to high PANI mass loading. In addition, PANI constitutes a protective layer of RuO2-TiO2 NPs that permitted us to reuse the RuO2-TiO2/DPA/PANI nanocomposite nine times, whereas RuO2-TiO2/PANI and RuO2-TiO2 were reused seven and five times only, respectively. The electronic displacements at the interface of the heterojunction metal/semi-conductor under visible light and the synergistic effects between PANI and RuO2 result in the separation of electron-hole pairs and a reduction of its recombination rate as well as a significant catalytic activity of RuO2-TiO2/DPA/PANI under simulated sunlight and in the dark, respectively. Polyaniline ; Nanocomposite ; Methyl orange ; Materials science ; Oxide ; Chemical engineering ; Catalysis ; Photocatalysis ; Polymer ; Chemistry ; Organic chemistry ; Nanotechnology ; Polymerization ; Composite material Conducting polymers and applications ; Advanced Photocatalysis Techniques ; Electrochemical sensors and biosensors
Authors: Bohayra Mortazavi | Masoud Shahrokhi | Gianaurelio Cuniberti | Xiaoying Zhuang
Source: Coatings (article, 2019-08-16)
Field: Materials Science — Materials Chemistry

Two-Dimensional SiP, SiAs, GeP and GeAs as Promising Candidates for Photocatalytic Applications Group IV–V-type layered materials, such as SiP, SiAs, GeP and GeAs, are among the most attractive two-dimensional (2D) materials that exhibit anisotropic mechanical, optical and transport properties. In this short communication, we conducted density functional theory simulations to explore the prospect of SiP, SiAs, GeP and GeAs nanosheets for the water-splitting application. The semiconducting gaps of stress-free SiP, SiAs, GeP and GeAs monolayers were estimated to be 2.59, 2.34, 2.30 and 2.07 eV, respectively, which are within the desirable ranges for the water splitting. Moreover, all the considered nanomaterials were found to yield optical absorption in the visible spectrum, which is a critical feature for the employment in the solar water splitting systems. Our results furthermore confirm that the valence and conduction band edge positions in SiP, SiAs, GeP and GeAs monolayers also satisfy the requirements for the water splitting. Our results highlight the promising photocatalytic characteristics of SiP, SiAs, GeP and GeAs nanosheets for the application in solar water splitting and design of advanced hydrogen fuel cells. Photocatalysis ; Water splitting ; Materials science ; Monolayer ; Valence (chemistry) ; Conduction band ; Optoelectronics ; Chemistry ; Nanotechnology ; Physics 2D Materials and Applications ; Nanowire Synthesis and Applications ; Graphene research and applications
Authors: Paolo Lotti | Davide Comboni | Lara Gigli | Lucia Carlucci | Eros Mossini | Elena Macerata | Mario Mariani | G. Diego Gatta
Source: Construction and Building Materials (article, 2019-02-01)
Field: Materials Science — Materials Chemistry

Thermal stability and high-temperature behavior of the natural borate colemanite: An aggregate in radiation-shielding concretes Materials science ; Aggregate (composite) ; Boron ; Electromagnetic shielding ; Thermal expansion ; Thermal stability ; Composite material ; Thermal ; Radiation shielding ; Volume (thermodynamics) ; Thermodynamics ; Chemical engineering ; Chemistry Radiation Shielding Materials Analysis ; Nuclear materials and radiation effects ; Concrete and Cement Materials Research
Authors: Michael P. Howard | Ryan B. Jadrich | Beth A. Lindquist | Fardin Khabaz | Roger T. Bonnecaze | Delia J. Milliron | Thomas M. Truskett
Source: The Journal of Chemical Physics (article, 2019-09-23)
Field: Materials Science — Materials Chemistry

Structure and phase behavior of polymer-linked colloidal gels Low-density "equilibrium" gels that consist of a percolated, kinetically arrested network of colloidal particles and are resilient to aging can be fabricated by restricting the number of effective bonds that form between the colloids. Valence-restricted patchy particles have long served as one archetypal example of such materials, but equilibrium gels can also be realized through a synthetically simpler and scalable strategy that introduces a secondary linker, such as a small ditopic molecule, to mediate the bonds between the colloids. Here, we consider the case where the ditopic linker molecules are low-molecular-weight polymers and demonstrate using a model colloid-polymer mixture how macroscopic properties such as the phase behavior as well as the microstructure of the gel can be designed through the polymer molecular weight and concentration. The low-density window for equilibrium gel formation is favorably expanded using longer linkers while necessarily increasing the spacing between all colloids. However, we show that blends of linkers with different sizes enable wider variation in microstructure for a given target phase behavior. Our computational study suggests a robust and tunable strategy for the experimental realization of equilibrium colloidal gels. Colloid ; Polymer ; Microstructure ; Chemical physics ; Phase (matter) ; Materials science ; Linker ; Valence (chemistry) ; Molecule ; Nanotechnology ; Chemical engineering ; Chemistry ; Composite material ; Physical chemistry ; Organic chemistry ; Computer science Pickering emulsions and particle stabilization ; Material Dynamics and Properties ; Proteins in Food Systems