jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Weixin Zhang | Johan Lub | Albertus P. H. J. Schenning | Guofu Zhou | Laurens T. de Haan
Source: International Journal of Molecular Sciences (article, 2020-03-06)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Polymer Stabilized Cholesteric Liquid Crystal Siloxane for Temperature-Responsive Photonic Coatings Temperature-responsive photonic coatings are appealing for a variety of applications, including smart windows. However, the fabrication of such reflective polymer coatings remains a challenge. In this work, we report the development of a temperature-responsive, infrared-reflective coating consisting of a polymer-stabilized cholesteric liquid crystal siloxane, applied by a simple bar coating method. First, a side-chain liquid crystal oligosiloxane containing acrylate, chiral and mesogenic moieties was successfully synthesized via multiple steps, including preparing precursors, hydrosilylation, deprotection, and esterification reactions. Products of all the steps were fully characterized revealing a chain extension during the deprotection step. Subsequently, the photonic coating was fabricated by bar-coating the cholesteric liquid crystal oligomer on glass, using a mediator liquid crystalline molecule. After the UV-curing and removal of the mediator, a transparent IR reflective polymer-stabilized cholesteric liquid crystal coating was obtained. Notably, this fully cured, partially crosslinked transparent polymer coating retained temperature responsiveness due to the presence of non-reactive liquid-crystal oligosiloxanes. Upon increasing the temperature from room temperature, the polymer-stabilized cholesteric liquid crystal coating showed a continuous blue-shift of the reflection band from 1400 nm to 800 nm, and the shift was fully reversible. Materials science ; Siloxane ; Coating ; Liquid crystal ; Polymer ; Mesogen ; Cholesteric liquid crystal ; Curing (chemistry) ; Chemical engineering ; Acrylate ; Side chain ; Polymer chemistry ; Composite material ; Optoelectronics ; Liquid crystalline Liquid Crystal Research Advancements ; Advanced Materials and Mechanics ; Silicone and Siloxane Chemistry
Authors: Yuki Arakawa | Kenta Komatsu | Jun Feng | Chenhui Zhu | Hideto Tsuji
Source: Materials Advances (article, 2020-11-08)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Distinct twist-bend nematic phase behaviors associated with the ester-linkage direction of thioether-linked liquid crystal dimers Two homologous series of thioether-linked liquid crystal dimers with oppositely directed esters,<italic>viz.</italic>CBCOO<italic>n</italic>SCB and CBOCO<italic>n</italic>SCB, exhibit largely different helical pitches in the N<sub>TB</sub>phase, which are ascribed to their molecular bend or biaxiality. Thioether ; Liquid crystal ; Homologous series ; Twist ; Phase (matter) ; Crystallography ; Stereochemistry ; Chemistry ; Materials science ; Organic chemistry ; Geometry Liquid Crystal Research Advancements ; Molecular spectroscopy and chirality ; Nonlinear Dynamics and Pattern Formation
Authors: Megan J. Neufeld | Alec Lutzke | Guillem Pratx | Conroy Sun
Source: Chemistry - A European Journal (review, 2020-09-25)
Field: Chemistry — Inorganic Chemistry

High‐<i>Z</i> Metal–Organic Frameworks for X‐ray Radiation‐Based Cancer Theranostics X-ray radiation is commonly employed in clinical practice for diagnostic and therapeutic applications. Over the past decade, developments in nanotechnology have led to the use of high-Z elements as the basis for innovative new treatment platforms that enhance the clinical efficacy of X-ray radiation. Nanoscale metal-frameworks (nMOFs) are coordination networks containing organic ligands that have attracted attention as therapeutic platforms in oncology and other areas of medicine. In cancer therapy, X-ray activated, high-Z nMOFs have demonstrated potential as radiosensitizers that increase local radiation dose deposition and generation of reactive oxygen species (ROS). This minireview summarizes current research on high-Z nMOFs in cancer theranostics and discusses factors that may influence future clinical application. Radiation therapy ; Cancer ; Clinical Practice ; Medicine ; Cancer research ; Radiation oncology ; Metal-organic framework ; Nanotechnology ; Medical physics ; Materials science ; Chemistry ; Internal medicine Metal-Organic Frameworks: Synthesis and Applications ; Nanoplatforms for cancer theranostics ; Boron Compounds in Chemistry
Authors: Yifan Chen | Siu Fai Hung | Wing Ki Lo | Yang Chen | Yang Shen | Kim Kafenda | Jia Su | Kangwei Xia | Sen Yang
Source: Nature Communications (article, 2020-10-21)
Field: Materials Science — Surfaces, Coatings and Films

A universal method for depositing patterned materials in situ Current techniques of patterned material deposition require separate steps for patterning and material deposition. The complexity and harsh working conditions post serious limitations for fabrication. Here, we introduce a single-step and easy-to-adapt method that can deposit materials in-situ. Its methodology is based on the semiconductor nanoparticle assisted photon-induced chemical reduction and optical trapping. This universal mechanism can be used for depositing a large selection of materials including metals, insulators and magnets, with quality on par with current technologies. Patterning with several materials together with optical-diffraction-limited resolution and accuracy can be achieved from macroscopic to microscopic scale. Furthermore, the setup is naturally compatible with optical microscopy based measurements, thus sample characterisation and material deposition can be realised in-situ. Various devices fabricated with this method in 2D or 3D show it is ready for deployment in practical applications. This method will provide a distinct tool in material technology. Deposition (geology) ; Characterization (materials science) ; In situ ; Semiconductor ; Nanoparticle ; Material selection ; High resolution ; Semiconductor materials Optical Coatings and Gratings ; Nanofabrication and Lithography Techniques ; Near-Field Optical Microscopy
Authors: Vladislav V. Klepov | Christian A. Juillerat | Kristen A. Pace | Gregory Morrison | Hans‐Conrad zur Loye
Source: Frontiers in Chemistry (review, 2020-06-26)
Field: Materials Science — Electronic, Optical and Magnetic Materials

“Soft” Alkali Bromide and Iodide Fluxes for Crystal Growth In this review we discuss general trends in the use of alkali bromide and iodide (ABI) fluxes for exploratory crystal growth. The ABI fluxes are ionic solution fluxes at moderate to high temperatures, 207 to ~1,300°C, which offer a good degree of flexibility in the selection of the temperature profile and solubility. Although their main use is to dissolve and recrystallize "soft" species such as chalcogenides, many compositions with "hard" anions, including oxides and nitrides, have been obtained from the ABI fluxes, highlighting their unique versatility. ABI fluxes can serve to provide a reaction and crystallization medium for different types of starting materials, mostly the elemental and binary compounds. As the use of alkali halide fluxes creates an excess of the alkali cations, these fluxes are often reactive, incorporating one of its components to the final compositions, although some examples of non-reactive ABI fluxes are known. Alkali metal ; Halide ; Iodide ; Bromide ; Solubility ; Inorganic chemistry ; Chemistry ; Crystal growth ; Nucleation ; Chemical engineering ; Materials science ; Mineralogy ; Crystallography ; Organic chemistry Crystal Structures and Properties ; Mineralogy and Gemology Studies ; X-ray Diffraction in Crystallography
Authors: Huijong Han | Ekaterina Round | Robin Schubert | Yasmin Gül | Jana Makroczyová | Domingo Meza | Philipp Heuser | Martin Aepfelbacher | Imrich Barák | Christian Betzel | Petra Fromme | Inari Kursula | Poul Nissen | E. Yu. Tereschenko | Joachim Schulz | Charlotte Uetrecht | Jozef Uličný | Matthias Wilmanns | János Hajdu | Victor S. Lamzin | Kristina Lorenzen
Source: Journal of Applied Crystallography (article, 2020-12-02)
Field: Materials Science — Materials Chemistry

The XBI BioLab for life science experiments at the European XFEL The science of X-ray free-electron lasers (XFELs) critically depends on the performance of the X-ray laser and on the quality of the samples placed into the X-ray beam. The stability of biological samples is limited and key biomolecular transformations occur on short timescales. Experiments in biology require a support laboratory in the immediate vicinity of the beamlines. The XBI BioLab of the European XFEL (XBI denotes XFEL Biology Infrastructure) is an integrated user facility connected to the beamlines for supporting a wide range of biological experiments. The laboratory was financed and built by a collaboration between the European XFEL and the XBI User Consortium, whose members come from Finland, Germany, the Slovak Republic, Sweden and the USA, with observers from Denmark and the Russian Federation. Arranged around a central wet laboratory, the XBI BioLab provides facilities for sample preparation and scoring, laboratories for growing prokaryotic and eukaryotic cells, a Bio Safety Level 2 laboratory, sample purification and characterization facilities, a crystallization laboratory, an anaerobic laboratory, an aerosol laboratory, a vacuum laboratory for injector tests, and laboratories for optical microscopy, atomic force microscopy and electron microscopy. Here, an overview of the XBI facility is given and some of the results of the first user experiments are highlighted. Sample (material) ; Characterization (materials science) ; Materials science ; Physics ; Nuclear engineering ; Optics ; Engineering Enzyme Structure and Function ; Advanced X-ray Imaging Techniques ; Biochemical and Molecular Research
Authors: Haining Yang | Daping Chu
Source: Journal of Physics Photonics (article, 2020-04-17)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Phase flicker in liquid crystal on silicon devices This paper reviews the phase flicker associated with the phase-only liquid crystal on silicon (LCOS) technology. The phase response of the LCOS device often fluctuates with the driving waveforms. Such phase flicker can cause detrimental effect in applications operating on either holographic or non-holographic principles. The origin of the phase flicker and its impact on the performances of various LCOS-based optical systems are explained. Methods to characterise the temporal response of the LCOS device will also be presented. The efforts to reduce the phase flicker in LCOS devices were reviewed. Perspectives on the future directions are also given. Liquid crystal on silicon ; Flicker ; Holography ; Phase (matter) ; Waveform ; Materials science ; Silicon ; Liquid crystal ; Computer science ; Optics ; Optoelectronics ; Electronic engineering ; Electrical engineering ; Engineering ; Physics ; Voltage ; Computer graphics (images) Liquid Crystal Research Advancements ; Photonic and Optical Devices ; Advanced Optical Imaging Technologies
Authors: Dániel Zámbó | Anja Schlosser | Pascal Rusch | Franziska Lübkemann | Julian Koch | H. Pfnür | Nadja C. Bigall
Source: Small (article, 2020-03-12)
Field: Materials Science — Materials Chemistry

A Versatile Route to Assemble Semiconductor Nanoparticles into Functional Aerogels by Means of Trivalent Cations 3D nanoparticle assemblies offer a unique platform to enhance and extend the functionality and optical/electrical properties of individual nanoparticles. Especially, a self-supported, voluminous, and porous macroscopic material built up from interconnected semiconductor nanoparticles provides new possibilities in the field of sensing, optoelectronics, and photovoltaics. Herein, a method is demonstrated for assembling semiconductor nanoparticle systems containing building blocks possessing different composition, size, shape, and surface ligands. The method is based on the controlled destabilization of the particles triggered by trivalent cations (Y<sup>3+</sup> , Yb<sup>3+</sup> , and Al<sup>3+</sup> ). The effect of the cations is investigated via X-ray photoelectron spectroscopy. The macroscopic, self-supported aerogels consist of the hyperbranched network of interconnected CdSe/CdS dot-in-rods, or CdSe/CdS as well as CdSe/CdTe core-crown nanoplatelets is used to demonstrate the versatility of the procedure. The non-oxidative assembly method takes place at room temperature without thermal activation in several hours and preserves the shape and the fluorescence of the building blocks. The assembled nanoparticle network provides longer exciton lifetimes with retained photoluminescence quantum yields, that make these nanostructured materials a perfect platform for novel multifunctional 3D networks in sensing. Various sets of photoelectrochemical measurements on the interconnected semiconductor nanorod structures also reveal the enhanced charge carrier separation. Nanoparticle ; Materials science ; Semiconductor ; Nanotechnology ; Nanorod ; Photoluminescence ; Photovoltaics ; X-ray photoelectron spectroscopy ; Quantum dot ; Cadmium telluride photovoltaics ; Charge carrier ; Optoelectronics ; Chemical engineering ; Photovoltaic system Quantum Dots Synthesis And Properties ; Advanced Photocatalysis Techniques ; ZnO doping and properties
Authors: C. Di Giorgio | Elena Blundo | Giorgio Pettinari | Marco Felici | Yuerui Lu | A. M. Cucolo | A. Polimeni | F. Bobba
Source: Advanced Materials Interfaces (article, 2020-10-07)
Field: Materials Science — Materials Chemistry

Nanoscale Measurements of Elastic Properties and Hydrostatic Pressure in H<sub>2</sub>‐Bulged MoS<sub>2</sub> Membranes Abstract The combination of extremely high stiffness and bending flexibility with tunable electrical and optical properties makes van der Waals transition metal dichalcogenides appealing both for fundamental science and applied research. By taking advantage of localized H 2 ‐bulged MoS 2 membranes, an innovative approach, based on atomic force microscopy nanoindentation, is demostrated and discussed here, aiming at measuring elastic and thermodynamic properties of nanoblisters made of 2D materials. The results, interpreted in the membrane limit of the Föppl–von Karman equation, lead to the quantification of the internal pressure and mole number of the trapped H 2 gas, as well as of the stretching modulus and adhesion energy of the MoS 2 membrane. The latter is discussed in the limit of strong (clamped and fully bonded interlayer interface) shear, as experimentally achieved in the investigated H 2 ‐bulged 2D blisters. Moreover, this approach allows to quantify the stress, and consequently the strain, locally imposed to the MoS 2 membrane by the bulging of the domes. Materials science ; van der Waals force ; Membrane ; Nanoindentation ; Hydrostatic pressure ; Hydrostatic equilibrium ; Nanoscopic scale ; Bending ; Shear modulus ; Elastic modulus ; Modulus ; Composite material ; Nanotechnology ; Thermodynamics ; Molecule ; Chemistry 2D Materials and Applications ; Graphene research and applications ; MXene and MAX Phase Materials
Authors: Johnatan M. Rodríguez‐Serna | Ricardo Albarracín | Ming Dong | Ming Ren
Source: Polymers (review, 2020-01-02)
Field: Materials Science — Materials Chemistry

Computer Simulation of Partial Discharges in Voids inside Epoxy Resins Using Three-Capacitance and Analytical Models Epoxy resin is one of the most common polymers used as part of the insulation system in key electrical assets such as power transformers and hydrogenerators. Thus, it is necessary to know their main characteristics and to evaluate their condition when subjected to High Voltage (HV). A brief review of epoxy resins' applications as insulating materials is made, their main characteristics as insulating media are given, the improvements with nano-fillers are summarized and the main electric properties required for Partial Discharges (PD) modelling are listed. In addition, the theoretical background and state-of-the-art of the three-capacitance and analytical models for simulating PD in solid dielectrics, such as epoxy resins, are reviewed in detail. Besides, their main advantages and disadvantages are presented, some critical arguments to the modelling procedure and assumptions are made and some improvements are proposed, taking into account conclusions made from other authors using models related to the PD development process. Finally, a case study was simulated using a modified three-capacitance model and the analytical model. The PD rate, q-φ-n diagrams and the minimum, mean and maximum PD electric charge are compared with measurements reported in the literature. Simulation results are in reasonable agreement with measured values. Capacitance models can be implemented in general purpose electric circuit simulation packages; however, its simulation is computationally expensive. Additional to this, although the modified three-capacitance model is not as accurate as finite elements or analytical models, results are also in agreement with real data. Capacitance ; Epoxy ; Transformer ; Materials science ; Dielectric ; Voltage ; Partial discharge ; Mechanical engineering ; Computer science ; Composite material ; Electrical engineering ; Engineering ; Physics ; Electrode ; Optoelectronics High voltage insulation and dielectric phenomena ; Dielectric materials and actuators ; Power Transformer Diagnostics and Insulation
Authors: Michael McCarthy | Kin Long Kelvin Lee
Source: The Journal of Physical Chemistry A (article, 2020-03-26)
Field: Chemistry — Spectroscopy

Molecule Identification with Rotational Spectroscopy and Probabilistic Deep Learning A proof-of-concept framework for identifying molecules of unknown elemental composition and structure using experimental rotational data and probabilistic deep learning is presented. Using a minimal set of input data determined experimentally, we describe four neural network architectures that yield information to assist in the identification of an unknown molecule. The first architecture translates spectroscopic parameters into Coulomb matrix eigenspectra as a method of recovering chemical and structural information encoded in the rotational spectrum. The eigenspectrum is subsequently used by three deep learning networks to constrain the range of stoichiometries, generate SMILES strings, and predict the most likely functional groups present in the molecule. In each model, we utilize dropout layers as an approximation to Bayesian sampling, which subsequently generates probabilistic predictions from otherwise deterministic models. These models are trained on a modestly sized theoretical dataset comprising ∼83 000 unique organic molecules (between 18 and 180 amu) optimized at the ωB97X-D/6-31+G(d) level of theory, where the theoretical uncertainties of the spectoscopic constants are well-understood and used to further augment training. Since chemical and structural properties depend strongly on molecular composition, we divided the dataset into four groups corresponding to pure hydrocarbons, oxygen-bearing species, nitrogen-bearing species, and both oxygen- and nitrogen-bearing species, training each type of network with one of these categories, thus creating "experts" within each domain of molecules. We demonstrate how these models can then be used for practical inference on four molecules and discuss both the strengths and shortcomings of our approach and the future directions these architectures can take. Probabilistic logic ; Computer science ; Artificial intelligence ; Molecule ; Artificial neural network ; Inference ; Set (abstract data type) ; Biological system ; Machine learning ; Chemistry Mass Spectrometry Techniques and Applications ; Molecular spectroscopy and chirality ; Analytical Chemistry and Chromatography
Authors: Daniel J. Cole | Letif Mones | Gábor Cśanyi
Source: Faraday Discussions (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

A machine learning based intramolecular potential for a flexible organic molecule Quantum mechanical predictive modelling in chemistry and biology is often hindered by the long time scales and large system sizes required of the computational model. Here, we employ the kernel regression machine learning technique to construct an analytical potential, using the Gaussian Approximation Potential software and framework, that reproduces the quantum mechanical potential energy surface of a small, flexible, drug-like molecule, 3-(benzyloxy)pyridin-2-amine. Challenges linked to the high dimensionality of the configurational space of the molecule are overcome by developing an iterative training protocol and employing a representation that separates short and long range interactions. The analytical model is connected to the MCPRO simulation software, which allows us to perform Monte Carlo simulations of the small molecule bound to two proteins, p38 MAP kinase and leukotriene A4 hydrolase, as well as in water. We demonstrate that our machine learning based intramolecular model is transferable to the condensed phase, and demonstrate that the use of a faithful representation of the quantum mechanical potential energy surface can result in corrections to absolute protein-ligand binding free energies of up to 2 kcal mol-1 in the example studied here. Intramolecular force ; Organic molecules ; Molecule ; Computer science ; Chemistry ; Combinatorial chemistry ; Stereochemistry ; Organic chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Stefan Palkovits
Source: ChemCatChem (article, 2020-03-18)
Field: Materials Science — Materials Chemistry

A Primer about Machine Learning in Catalysis – A Tutorial with Code Abstract Based on a well‐edited dataset from literature by Schmack et al. [1] this manuscript provides a tutorial‐like introduction to Machine Learning (ML) and Data Science (DS) based on the actual programming code in the Python programming language. The study will not only try to illustrate a ML workflow, but will also show important tasks like hyperparameter tuning and data pre‐processing which often cover much of the time of an actual study. Moreover, the study spans from classical ML methods to Deep Learning with Neural Networks. Python (programming language) ; Computer science ; Workflow ; Artificial intelligence ; Hyperparameter ; Code (set theory) ; Programming language ; Artificial neural network ; Machine learning ; Database Machine Learning in Materials Science ; Topic Modeling ; Ferroelectric and Negative Capacitance Devices