jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Sergio Acuña | María C. Veloso | Pedro G. Toledo
Source: Journal of Nanomaterials (article, 2018-07-11)
Field: Materials Science — Biomaterials

Self-Assembly of Diphenylalanine-Based Nanostructures in Water and Electrolyte Solutions Diphenylalanine (FF) is a peptide that can form different nanostructures; this makes it particularly attractive for both biological and technological applications. However, any application using this type of nanostructures requires controlling their size and shape. Information is provided about the various structures formed through the peptide FF self-assembly in different salt solutions (NaCl, CaCl 2 , and AlCl 3 ), concentrations (50 mM, 100 mM, and 200 mM), and pH (3 to 10). Transmission electron microscopy (TEM), scanning electron microscopy (SEM), and Fourier-transform infrared (FTIR) spectroscopy were used to characterize the nanotubes. Results show that FF nanotube formation through self-assembly is a delicate balance between electrostatic, hydrogen bonding, and hydrophobic interactions; any imbalance in these can impede nanotube formation. Our results demonstrate that salts, such as NaCl and CaCl 2 , along with the studied concentrations promote the formation of very long nanotube agglomerates. This would be due to a combined screening effect and the fact that cations are structure-forming and promote hydrophobic interactions; therefore, nanotube agglomeration occurs and also benefits electrostatic interactions, hydrogen bonds, and longer nanotubes. The presence of AlCl 3 produces an imbalance in the abovementioned interactions because of excess Cl-, a structure-breaking anion that impedes the nanostructure formation. Nanotube ; Materials science ; Agglomerate ; Nanostructure ; Hydrogen bond ; Fourier transform infrared spectroscopy ; Chemical engineering ; Electrolyte ; Self-assembly ; Transmission electron microscopy ; Nanotechnology ; Scanning electron microscope ; Molecule ; Organic chemistry ; Chemistry ; Carbon nanotube ; Physical chemistry Supramolecular Self-Assembly in Materials ; Polydiacetylene-based materials and applications ; Antimicrobial Peptides and Activities
Authors: Razieh Beigmoradi | Abdolreza Samimi | Davod Mohebbi‐Kalhori
Source: Beilstein Journal of Nanotechnology (review, 2018-02-05)
Field: Materials Science — Materials Chemistry

Engineering of oriented carbon nanotubes in composite materials The orientation and arrangement engineering of carbon nanotubes (CNTs) in composite structures is considered a challenging issue. In this regard, two groups of in situ and ex situ techniques have been developed. In the first, the arrangement is achieved during CNT growth, while in the latter, the CNTs are initially grown in random orientation and the arrangement is then achieved during the device integration process. As the ex situ techniques are free from growth restrictions and more flexible in terms of controlling the alignment and sorting of the CNTs, they are considered by some as the preferred technique for engineering of oriented CNTs. This review focuses on recent progress in the improvement of the orientation and alignment of CNTs in composite materials. Moreover, the advantages and disadvantages of the processes are discussed as well as their future outlook. Carbon nanotube ; Materials science ; Composite number ; Nanotechnology ; Orientation (vector space) ; Composite material Carbon Nanotubes in Composites ; Graphene research and applications ; Conducting polymers and applications
Authors: Hossein Taghinejad | Ali A. Eftekhar | Philip M. Campbell | Brian R. Beatty | Mohammad Taghinejad | Yao Zhou | Christopher J. Perini | Hesam Moradinejad | Walter Henderson | Eric Woods | Xiang Zhang | Pulickel M. Ajayan | Evan J. Reed | Eric M. Vogel | Ali Adibi
Source: npj 2D Materials and Applications (article, 2018-04-25)
Field: Materials Science — Materials Chemistry

Strain relaxation via formation of cracks in compositionally modulated two-dimensional semiconductor alloys Abstract Composition modulation of two-dimensional transition-metal dichalcogenides (TMDs) has introduced an enticing prospect for the synthesis of Van der Waals alloys and lateral heterostructures with tunable optoelectronic properties. Phenomenologically, the optoelectronic properties of alloys are entangled to a strain that is intrinsic to synthesis processes. Here, we report an unprecedented biaxial strain that stems from the composition modulation of monolayer TMD alloys (e.g., MoS 2 x Se 2(1 - x ) ) and inflicts fracture on the crystals. We find that the starting crystal (MoSe 2 ) fails to adjust its lattice constant as the atoms of the host crystal (selenium) are replaced by foreign atoms (sulfur) during the alloying process. Thus, the resulting alloy forms a stretched lattice and experiences a large biaxial tensile strain. Our experiments show that the biaxial strain relaxes via formation of cracks in interior crystal domains or through less constraint bounds at the edge of the monolayer alloys. Griffith’s criterion suggests that defects combined with a sulfur-rich environment have the potential to significantly reduce the critical strain at which cracking occurs. Our calculations demonstrate a substantial reduction in fracture-inducing critical strain from 11% (in standard TMD crystals) to a range below 4% in as-synthesized alloys. Materials science ; Monolayer ; Heterojunction ; Semiconductor ; Condensed matter physics ; Crystal (programming language) ; Crystal structure ; Alloy ; Lattice constant ; Strain (injury) ; Relaxation (psychology) ; Lattice (music) ; Crystallography ; Composite material ; Nanotechnology ; Optoelectronics ; Chemistry ; Diffraction ; Optics 2D Materials and Applications ; MXene and MAX Phase Materials ; Perovskite Materials and Applications
Authors: Giuseppe M. Paternò | Nadia Barbero | Simone Galliano | Claudia Barolo | Guglielmo Lanzani | Francesco Scotognella | Raffaele Borrelli
Source: Journal of Materials Chemistry C (article, 2018-01-01)
Field: Chemistry — Physical and Theoretical Chemistry

Excited state photophysics of squaraine dyes for photovoltaic applications: an alternative deactivation scenario Squaraine dyes (SQs) represent a versatile class of functional molecules with strong absorption and emission features, widely used as near-infrared sensitizers in organic and hybrid photovoltaic devices. Photovoltaic system ; Materials science ; Excited state ; Photochemistry ; Molecule ; Absorption (acoustics) ; Infrared ; Optoelectronics ; Chemistry ; Organic chemistry ; Electrical engineering ; Atomic physics ; Optics ; Physics Photochemistry and Electron Transfer Studies ; Quantum Dots Synthesis And Properties ; Luminescence and Fluorescent Materials
Authors: Miryam Criado‐Gonzalez | Jennifer Rodon Fores | Déborah Wagner | A. Schröder | Alain Carvalho | Marc Schmutz | Eva Harth | Pierre Schaaf | Loı̈c Jierry | Fouzia Boulmedais
Source: Chemical Communications (article, 2018-12-19)
Field: Materials Science — Biomaterials

Enzyme-assisted self-assembly within a hydrogel induced by peptide diffusion The diffusion of adequate peptide through an enzyme-embedded host hydrogel leads to the in situ start-up and growth of an interpenetrated fibrous network. Based on the enzyme-assisted self-assembly concept, both chemistry and mechanical features of the hybrid hydrogel can be tuned. Self-healing hydrogels ; Peptide ; Diffusion ; Self-assembly ; Self-assembling peptide ; Enzyme ; Chemistry ; Nanotechnology ; Materials science ; In situ ; Chemical engineering ; Biophysics ; Polymer chemistry ; Biochemistry ; Organic chemistry Supramolecular Self-Assembly in Materials ; Polydiacetylene-based materials and applications ; Silk-based biomaterials and applications
Authors: Brian Szychowski | Matthew Pelton | Marie‐Christine Daniel
Source: Nanophotonics (article, 2018-06-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Preparation and properties of plasmonic‐excitonic nanoparticle assemblies Abstract The assembly of inorganic nanoparticles often leads to collective properties that are different from the combined properties of the individual components. In particular, coupling plasmonic and excitonic nanoparticles has been shown to modify their optical properties, including absorption, emission, and scattering. Because of this, these coupled assemblies have potential applications in a wide range of areas, including sensing, light harvesting, and photocatalysis. More recently, unique properties, including Fano interference and Rabi splitting, have been observed by increasing the coupling strength. However, the behavior of coupled nanoparticles is highly dependent on the exact organization of the components, including the number of particles coupled, the distance separating them, and their spatial orientation. This is especially true in the case of strongly coupled particles. Because of this, it is important to achieve synthetic techniques that not only can link particles together but also offer good control over how the particles are connected. In this review, assemblies of plasmonic and excitonic nanoparticles are reviewed, including the various methods that have been used for their construction, the properties that these systems have been predicted to possess as well as the ones that have been observed, and their current applications along with current challenges in the field and potential future applications. Plasmon ; Nanoparticle ; Materials science ; Nanotechnology ; Plasmonic nanoparticles ; Coupling (piping) ; Nanomaterials ; Scattering ; Chemical physics ; Exciton ; Absorption (acoustics) ; Optoelectronics ; Chemistry ; Physics ; Condensed matter physics ; Optics Gold and Silver Nanoparticles Synthesis and Applications ; Quantum Dots Synthesis And Properties ; Copper-based nanomaterials and applications
Authors: Fei Han | S. R. C. Vivekchand | Alexander H. Soeriyadi | Yuanhui Zheng | J. Justin Gooding
Source: Nanoscale (article, 2018-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Thermoresponsive plasmonic core–satellite nanostructures with reversible, temperature sensitive optical properties Herein is described a facile method for the assembly of plasmonic gold nanoparticles into smart plasmonic core-satellite nanostructures that allow for the dynamic and reversible tuning of the localised surface plasmon resonance using temperature. This smart system takes advantage of the thermoresponsive polymer linker that modulates the gap distance between the core and satellites in response to the temperature, resulting in the tuning of the surface plasmon coupling and resultant optical shift. It permits optical shifts over a wide wavelength range and reversible control of the optical properties by altering the temperature, which may allow these systems to become candidates for temperature sensitive nanosensors. Plasmon ; Surface plasmon resonance ; Materials science ; Nanostructure ; Core (optical fiber) ; Nanotechnology ; Nanoparticle ; Satellite ; Plasmonic nanoparticles ; Optoelectronics ; Physics Gold and Silver Nanoparticles Synthesis and Applications ; Plasmonic and Surface Plasmon Research ; Quantum Dots Synthesis And Properties
Authors: Yen‐Cheng Kung | Nahid Hosseini | Dumitru Dumcenco | Georg E. Fantner | András Kis
Source: Advanced Electronic Materials (article, 2018-11-08)
Field: Materials Science — Materials Chemistry

Air and Water‐Stable n‐Type Doping and Encapsulation of Flexible MoS<sub>2</sub> Devices with SU8 Abstract Favorable mechanical and electrical properties motivate the use of 2D semiconductors in flexible electronic devices. One of the main challenges here is the absence of a practical doping strategy which should provide air‐stable, tunable doping levels in a process with a low thermal budget. Here, it is shown that SU8, an epoxy‐based photoresist, can be used for nondegenerate n‐type doping of monolayer MoS 2 . The doping level can be finely tuned via low‐temperature annealing. The doping method exhibits good ambient stability. The high degree of mechanical flexibility and low processing temperature also allows the integration of SU8 coating with flexible MoS 2 FETs, where it can provide both controllable doping and act as an encapsulation layer. The demonstrated stability of the devices to bending and exposure to water confirms the attractiveness of using SU8 in flexible electronic devices based on 2D semiconductors in a simple, versatile and scalable approach. Materials science ; Doping ; Photoresist ; Monolayer ; Semiconductor ; Optoelectronics ; Nanotechnology ; Annealing (glass) ; Epoxy ; Thermal stability ; Electronics ; Encapsulation (networking) ; Coating ; Composite material ; Chemical engineering ; Layer (electronics) ; Electrical engineering ; Computer science 2D Materials and Applications ; Advanced Sensor and Energy Harvesting Materials ; MXene and MAX Phase Materials
Authors: Jikai Wang | J. M. Schwarz | Joseph D. Paulsen
Source: Nature Communications (article, 2018-07-13)
Field: Materials Science — Materials Chemistry

Hyperuniformity with no fine tuning in sheared sedimenting suspensions Particle suspensions, present in many natural and industrial settings, typically contain aggregates or other microstructures that can complicate macroscopic flow behaviors and damage processing equipment. Recent work found that applying uniform periodic shear near a critical transition can reduce fluctuations in the particle concentration across all length scales, leading to a hyperuniform state. However, this strategy for homogenization requires fine tuning of the strain amplitude. Here we show that in a model of sedimenting particles under periodic shear, there is a well-defined regime at low sedimentation speed where hyperuniform scaling automatically occurs. Our simulations and theoretical arguments show that the homogenization extends up to a finite length scale that diverges as the sedimentation speed approaches zero. Homogenization (climate) ; Scaling ; Fine-tuning ; Shear (geology) ; Work (physics) ; Length scale ; Sedimentation ; Shear flow Material Dynamics and Properties ; Advanced Mathematical Modeling in Engineering ; Composite Material Mechanics
Authors: Ruobing Wang
Source: The Journal of Physical Chemistry C (article, 2018-04-12)
Field: Materials Science — Materials Chemistry

Significantly Improving the Prediction of Molecular Atomization Energies by an Ensemble of Machine Learning Algorithms and Rescanning Input Space: A Stacked Generalization Approach Machine learning holds the promise of learning the energy functional via examples, bypassing the need to solve complicated quantum–chemical equations and realizing efficient computing of molecular electronic properties. However, a single machine learning model may reach an upper limit of prediction accuracy even with optimal parameters. Unlike the “bagging” and “boosting” approaches, which can only combine the machine learning algorithms of a same type, the stacking approach can combine several distinct types of algorithms through a meta-machine learning model to maximize the generalization accuracy. Here, we present a stacked generalization approach for predicting the ground state molecular atomization energies. The results suggest prediction error from stacked generalization frameworks are significantly reduced by 38%, compared to the best level 0 individual algorithm that construct the stacked generalization framework. Furthermore, compared to conventional stacked generalization framework, it shows that rescanning the original input space by level 1 meta model can extract more information, which can further improve the prediction accuracy. Generalization ; Space (punctuation) ; Computer science ; Algorithm ; Ensemble learning ; Artificial intelligence ; Mathematics ; Mathematical analysis Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Fuel Cells and Related Materials
Authors: Katherine Margulis | Zhenpeng Zhou | Qizhi Fang | Richard E. Sievers | Randall J. Lee | Richard N. Zare
Source: Analytical Chemistry (article, 2018-09-06)
Field: Chemistry — Spectroscopy

Combining Desorption Electrospray Ionization Mass Spectrometry Imaging and Machine Learning for Molecular Recognition of Myocardial Infarction Lipid profile changes in heart muscle have been previously linked to cardiac ischemia and myocardial infarction, but the spatial distribution of lipids and metabolites in ischemic heart remains to be fully investigated. We performed desorption electrospray ionization mass spectrometry imaging of hearts from in vivo myocardial infarction mouse models. In these mice, myocardial ischemia was induced by blood supply restriction via a permanent ligation of left anterior descending coronary artery. We showed that applying the machine learning algorithm of gradient boosting tree ensemble to the ambient mass spectrometry imaging data allows us to distinguish segments of infarcted myocardium from normally perfused hearts on a pixel by pixel basis. The machine learning algorithm selected 62 molecular ion peaks important for classification of each 200 μm-diameter pixel of the cardiac tissue map as normally perfused or ischemic. This approach achieved very high average accuracy (97.4%), recall (95.8%), and precision (96.8%) at a spatial resolution of ∼200 μm. In addition, we determined the chemical identity of 27 species, mostly small metabolites and lipids, selected by the algorithm as the most significant for cardiac pathology classification. This molecular signature of myocardial infarction may provide new mechanistic insights into cardiac ischemia, assist with infarct size assessment, and point toward novel therapeutic interventions. Myocardial infarction ; Chemistry ; Mass spectrometry imaging ; Mass spectrometry ; Ischemia ; Cardiology ; Electrospray ionization ; Desorption electrospray ionization ; Internal medicine ; Ionization ; Medicine ; Chromatography ; Ion ; Chemical ionization Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Ion-surface interactions and analysis
Authors: Ghanshyam Pilania | Xiang-Yang Liu
Source: Journal of Materials Science (article, 2018-01-12)
Field: Materials Science — Materials Chemistry

Machine learning properties of binary wurtzite superlattices Materials science ; Binary number ; Superlattice ; Wurtzite crystal structure ; Solid mechanics ; Condensed matter physics ; Nanotechnology ; Composite material ; Metallurgy ; Optoelectronics ; Mathematics ; Physics ; Zinc Machine Learning in Materials Science ; Neural Networks and Applications ; Machine Learning and ELM
Authors: R. Vaira Vignesh | R. Padmanaban
Source: Russian Journal of Non-Ferrous Metals (article, 2018-03-01)
Field: Materials Science — Biomaterials

Forecasting Tribological Properties of Wrought AZ91D Magnesium Alloy Using Soft Computing Model The wear characteristics of wrought magnesium alloy AZ91D is assessed by varying the wear test parameters namely sliding velocity, sliding distance and normal load in the pin-on-disc tribometer. The experimental results are used to develop a statistical model, and soft computing models based on artificial neural network and Sugeno–Fuzzy logic to predict the wear rate of AZ91D alloy. Sugeno–Fuzzy model had the highest accuracy in prediction and hence used to study the effect of wear test parameters on the wear rate of AZ91D alloy. It is observed that the wear rate increases with decrease in load, increase in sliding velocity, and increase in sliding distance. Tribometer ; Magnesium alloy ; Materials science ; Tribology ; Alloy ; Soft computing ; Fuzzy logic ; Metallurgy ; Computer science ; Artificial intelligence Magnesium Alloys: Properties and Applications ; Aluminum Alloys Composites Properties ; Aluminum Alloy Microstructure Properties
Authors: Robert M. Patton | Travis Johnston | Steven R. Young | Catherine D. Schuman | Don D. March | Thomas E. Potok | Derek Rose | Seung–Hwan Lim | Thomas P. Karnowski | Maxim Ziatdinov | Sergei V. Kalinin
Source:  (article, 2018-11-01)
Field: Materials Science — Materials Chemistry

167-PFlops Deep Learning for Electron Microscopy: From Learning Physics to Atomic Manipulation An artificial intelligence system called MENNDL, which used 25,200 NVIDIA Volta GPUs on Oak Ridge National Laboratory's Summit machine, automatically designed an optimal deep learning network in order to extract structural information from raw atomic-resolution microscopy data. In a few hours, MENNDL creates and evaluates millions of networks using a scalable, parallel, asynchronous genetic algorithm augmented with a support vector machine to automatically find a superior deep learning network topology and hyper-parameter set than a human expert can find in months. For the application of electron microscopy, the system furthers the goal of improving our understanding of the electron-beam-matter interactions and real-time image-based feedback, which enables a huge step beyond human capacity towards nanofabricating materials automatically. MENNDL has been scaled to the 4,200 available nodes of Summit achieving a measured 152.5 PFlops, with an estimated sustained performance of 167 PFlops when the entire machine is available. Computer science ; Artificial intelligence ; Scalability ; Deep learning ; Machine learning ; Summit ; Artificial neural network ; Asynchronous communication ; Computational science ; Computer engineering ; Operating system Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Advanced Electron Microscopy Techniques and Applications
Authors: Alberto Roldán
Source: Current Opinion in Electrochemistry (article, 2018-03-20)
Field: Chemistry — Electrochemistry

Frontiers in first principles modelling of electrochemical simulations Computer simulations are a useful tool to describe physical and chemical processes. However, the state-of-the-art techniques lack practicality to simulate the dynamical movement of species while accounting for the electron exchange. This is the case of electrochemical processes, i.e. species in solution interact with the surface of the electrode and the solvent molecules arrange at the interface according to the field created by the electrode potential. Here we present the latest innovative methods and frontiers for the treatment of electrochemical simulations including microsolvation protocols implemented in the density functional theory framework. We also suggest alternative electronic structure mixed-force calculations to pioneer a more realistic simulation framework of reactive processes. Electrochemistry ; Density functional theory ; Computer science ; Field (mathematics) ; Interface (matter) ; Nanotechnology ; Electrode ; Statistical physics ; Molecule ; Computational chemistry ; Materials science ; Chemistry ; Physics ; Physical chemistry ; Quantum mechanics ; Mathematics Electrochemical Analysis and Applications ; Electrocatalysts for Energy Conversion ; Molecular Junctions and Nanostructures