jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Emiliano Martínez-Vollbert | Christian Philouze | Isabelle Gautier‐Luneau | Yohann Moreau | Pierre‐Henri Lanoë | Frédérique Loiseau
Source: Physical Chemistry Chemical Physics (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

Study of a phosphorescent cationic iridium(<scp>iii</scp>) complex displaying a blue-shift in crystals We report the synthesis and the characterization of a new cationic iridium(III) complex featuring two 1-(<i>p</i>-methoxyphenyl)-5-methoxybenzimidazole cyclometallating ligands and a dimethylbipyridine ancillary ligand. The complex has been fully characterized by 1D and 2D NMR (<sup>1</sup>H, <sup>13</sup>C, <sup>19</sup>F and <sup>31</sup>P), elemental analysis and high-resolution mass spectrometry (HRMS). The photoluminescence studies performed in a solution, on amorphous powder and on crystals revealed an unexpected behavior. Indeed, the emission spectra observed in both solution (CH<sub>2</sub>Cl<sub>2</sub>) and amorphous powder samples are centered at around 580 nm, whereas in crystals the emission displays a large hypsochromic shift of ∼800 cm<sup>-1</sup> (<i>λ</i><sub>em</sub> = 558 nm). X-ray diffraction experiments, photophysical studies and DFT calculations allow for rationalizing the hypsochromic shift. Iridium ; Cationic polymerization ; Phosphorescence ; Blueshift ; Chemistry ; Nanotechnology ; Crystallography ; Materials science ; Catalysis ; Organic chemistry ; Physics ; Optics ; Optoelectronics Luminescence and Fluorescent Materials ; Molecular Sensors and Ion Detection ; Photochromic and Fluorescence Chemistry
Authors: Maito Koga | Ryota Watanabe | Junji Mizukado | Hideyuki Shinzawa
Source: Analytical Sciences (article, 2021-02-18)
Field: Chemistry — Analytical Chemistry

Rheo-optical Near-infrared (NIR) Analysis of Binary Amorphous Polymer Blend Consisting of Polyvinyl Chloride (PVC) and Polymethyl Methacrylate (PMMA) Polyvinyl chloride ; Amorphous solid ; Polymer ; Ultimate tensile strength ; Composite material ; Deformation (meteorology) ; Polymethyl methacrylate ; Materials science ; Polymer chemistry ; Polymer blend ; Chemistry ; Organic chemistry ; Copolymer Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Cultural Heritage Materials Analysis
Authors: Daria S. Kutsybala | Alexander V. Shokurov | Andrey P. Kroitor | Alexander G. Martynov | S. L. Selektor | А. Yu. Tsivadze | Yulia G. Gorbunova
Source: Macroheterocycles (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

Unusual Cyan-Purple Electrochromism of Sandwich Phthalocyaninates Observed on the Example of µ-Carbido Diruthenium(IV) Complex Current research in sandwich phthalocyaninates enjoys increasing interest due to peculiar properties of these compounds, i.e. their intense coloration and ability to change and control it via redox processes. Obviously, such effect found its application in development of electrochromic devices. In the case of commonly studied rare earth element bisphthalocyaninates, the most dominant electrochromic palette observed for them lies within the red-green-blue gamut. In this work, we report that an unusual sandwich -covalently-bridged -carbido diruthenium(IV) bisphthalocyaninate -exhibits equally unusual color changes upon its reversible electrochemical oxidation/reduction, namely switching from cyan to purple. The study provides a detailed spectroelectrochemical characterization of this -carbido-dimeric complex in solution, in thin cast and ultrathin Langmuir-Schaefer films, which all demonstrate aforementioned variable vibrant coloration in action. Electrochromism ; Cyan ; Chemistry ; Optics ; Physics Porphyrin and Phthalocyanine Chemistry ; Photochromic and Fluorescence Chemistry ; Photochemistry and Electron Transfer Studies
Authors: Emil J. Zak | Andrey Yachmenev | Jochen Küpper
Source: Physical Review Research (article, 2021-06-07)
Field: Chemistry — Spectroscopy

Controlling rotation in the molecular frame with an optical centrifuge We computationally demonstrate a new method for coherently controlling the rotation-axis direction in asymmetric top molecules with an optical centrifuge. Appropriately chosen electric-field strengths and the centrifuge's acceleration rate allow to generate a nearly arbitrary rotational wave packet. For dihydrogen sulfide (D 2 S) and 2H-imidazole (C 3 H 4 N 2 ), we created wave packets at large values of the rotational quantum number J with the desired projections of the total angular momentum onto two of the molecules' principal axes of inertia. One application of the new method is three-dimensional alignment with a molecular axis aligned along the laser's wave vector, which is important for the three-dimensional imaging of molecules yet not accessible in standard approaches. The simultaneous orientation of the angular momentum in the laboratory frame and in the molecular frame could also be used in robust control of scattering experiments. Centrifuge ; Principal axis theorem ; Angular momentum ; Rotation (mathematics) ; Wave packet ; Scattering ; Orientation (vector space) ; Acceleration ; Total angular momentum quantum number ; Angular momentum operator Molecular spectroscopy and chirality ; Orbital Angular Momentum in Optics ; Laser-Matter Interactions and Applications
Authors: Eugen Rott | Werner Kofler
Source: Plant and Fungal Systematics (article, 2021-03-01)
Field: Materials Science — Biomaterials

Planktonic centric diatoms from the Eastern Alps: morphology, biogeography and ecology Centric diatoms from 26 lakes along the northern perialpine areas and lower altitude valleys and terraces along the W-E extension of the Eastern Alps (mostly in Austria) were studied with the intention to facilitate taxonomic identification and ecological studies in the context of water quality monitoring according to the Water Framework Directive of the European Community. Eight genera were found: Aulacoseira, Conticribra, Cyclostephanos, Cyclotella, Lindavia, Pantocsekiella, Stephanodiscus, Urosolenia. For more than 30 infrageneric taxa, morphology (light microscopy LM), ultrastructure of frustules (scanning electron microscopy SEM), distribution pattern and ecology were studied. Although most of the morphological and ultrastructural features correspond to earlier findings, some additional observations were recorded and some taxonomic views were critically assessed and commented based on our observations. The most important nomenclature-related updates in recent studies are adapted based on molecular findings in literature (mainly for the genera Lindavia and Pantocsekiella). The most widely distributed taxa found were small single celled centrics (mainly species of the genera Cyclotella s.str., Lindavia, Pantocsekiella and Stephanodiscus) dominating frequently in small enriched lakes. In contrast a few larger oligotraphentic single celled (e.g., Lindavia intermedia) and chain-forming taxa (e.g., Aulacoseira islandica) were found to be restricted to the large lakes. The evaluation of the taxa pattern by Canonical Correspondence Analysis (CCA) identified trophic status and lake size as the two primary influential environmental variables for taxa distribution within the dataset. The trophic ranking of species is set in relation to earlier studies and indicator lists. Biogeography ; Morphology (biology) ; Ecology ; Plankton ; Geography ; Biology ; Zoology Diatoms and Algae Research ; Aquatic Ecosystems and Phytoplankton Dynamics ; Marine and coastal ecosystems
Authors: Guangjun Li | Xianzhi Wang | Minxi Li
Source:  (review, 2021-10-29)
Field: Materials Science — Materials Chemistry

A Review of Recent Trends and Industry Prospects for Artificial Intelligence Technologies Recent years have witnessed various breakthroughs and advancements in research and application of Artificial Intelligence (AI) technologies. Notably, AI is leading the 4th global industrial revolution (Industry 4.0). In fact, deep learning rose to become one of the most highly researched aspects in various industrial sectors worldwide, making it especially important to timely review the recent progress and industry prospects of AI. In this paper, we briefly review the recent advances in AI technologies, discuss the various advantages and challenges they bring, and finally, offer a few points of thought regarding the future developments in the industry. Industrial Revolution ; Applications of artificial intelligence ; Emerging technologies ; Computer science ; Industry 4.0 ; Technology forecasting ; Engineering ; Data science ; Artificial intelligence ; Political science Machine Learning in Materials Science ; Industrial Vision Systems and Defect Detection ; Machine Learning and ELM
Authors: Lucas Foppa | Christopher Sutton | Luca M. Ghiringhelli | Sandip De | Patricia Löser | Stephan A. Schunk | Ansgar Schaefer | Matthias Scheffler
Source:  (preprint, 2021-10-21)
Field: Materials Science — Materials Chemistry

Learning design rules for selective oxidation catalysts from high-throughput experimentation and artificial intelligence The design of heterogeneous catalysts is challenged by the complexity of materials and processes that govern reactivity and by the fact that the number of good catalysts is very small compared to the number of possible materials. Here, we show how the subgroup-discovery (SGD) artificial-intelligence approach can be applied to an experimental plus theoretical data set to identify constraints on key physicochemical parameters, the so-called SG rules, which exclusively describe materials and reaction conditions with outstanding catalytic performance. By using high-hroughput experimentation, 120 SiO 2 -supported catalysts containing ruthenium, tungsten and phosphorus were synthesized and tested in the catalytic oxidation of propylene. As candidate descriptive parameters, the temperature and ten parameters related to the composition and chemical nature of the catalyst materials, derived from calculated free-atom properties, were offered. The temperature, the phosphorus content, and the composition-weighted electronegativity are identified as key parameters describing high yields towards the value-added oxygenate products acrolein and acrylic acid. The SG rules not only reflect the underlying processes particularly associated to high performance but also guide the design of more complex catalysts containing up to five elements in their composition. Catalysis ; Electronegativity ; Reactivity (psychology) ; Chemistry ; Computer science ; Chemical engineering ; Materials science ; Process engineering ; Organic chemistry ; Engineering Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Electrocatalysts for Energy Conversion
Authors: Annika Stuke | Patrick Rinke | Milica Todorović
Source: Machine Learning Science and Technology (preprint, 2021-03-12)
Field: Materials Science — Materials Chemistry

Efficient hyperparameter tuning for kernel ridge regression with Bayesian optimization Abstract Machine learning methods usually depend on internal parameters—so called hyperparameters—that need to be optimized for best performance. Such optimization poses a burden on machine learning practitioners, requiring expert knowledge, intuition or computationally demanding brute-force parameter searches. We here assess three different hyperparameter selection methods: grid search, random search and an efficient automated optimization technique based on Bayesian optimization (BO). We apply these methods to a machine learning problem based on kernel ridge regression in computational chemistry. Two different descriptors are employed to represent the atomic structure of organic molecules, one of which introduces its own set of hyperparameters to the method. We identify optimal hyperparameter configurations and infer entire prediction error landscapes in hyperparameter space that serve as visual guides for the hyperparameter performance. We further demonstrate that for an increasing number of hyperparameters, BO and random search become significantly more efficient in computational time than an exhaustive grid search, while delivering an equivalent or even better accuracy. Hyperparameter ; Hyperparameter optimization ; Machine learning ; Bayesian optimization ; Computer science ; Artificial intelligence ; Kernel (algebra) ; Bayesian probability ; Regression ; Support vector machine ; Mathematics ; Statistics Machine Learning in Materials Science ; Mass Spectrometry Techniques and Applications ; Analytical Chemistry and Chromatography
Authors: Gomaa Abdel‐Maksoud | Rehab Khattab
Source: Egyptian Journal of Chemistry (article, 2021-06-06)
Field: Materials Science — Biomaterials

Evaluation of traditional, starch nanoparticle and its hybrid composite for the Consolidation of Tracing Paper Tracing paper was used since the 12th century. The deterioration of tracing paper occurs as a result of external factors (inappropriate environmental conditions), or internal factors (such as manufacture process). The manufacturing process plays a significant role in the deterioration process since the preparation of tracing paper includes impregnating with oils or resins, heavy beating of the pulp, and treating the paper with chemicals such as sulfuric acid, and zinc chloride. The consolidation process is vital in the conservation of tracing paper to increase its strength and resistance against improper surrounding environmental conditions. Starch in its traditional form had been used as an adhesive for tracing paper since the last century. This paper aims to evaluate the use of starch in its traditional form and nanoscale form in improving the properties of 4 types of tracing paper. Mechanical properties, change of color, pH value, Fourier Transform Infrared spectroscopy (FTIR), and investigation of the surface morphology by scanning electron microscope (SEM) were used for the evaluation process. Thermal aging at 100 oC was used for the aging of treated and untreated tracing paper. The results revealed that the starch nanoparticles dissolved in water were the best consolidant for impregnated tracing paper. Starch nanoparticles with carboxymethyl cellulose dissolved in water were the best consolidant for the two types of genuine vegetable parchment paper. Starch nanoparticles dissolved in acetone with carboxymethyl cellulose were the best consolidant for modern tracing paper. Consolidation (business) ; Composite number ; Starch ; Tracing ; Nanoparticle ; Composite material ; Materials science ; Computer science ; Nanotechnology ; Business ; Chemistry ; Food science ; Accounting ; Programming language Advanced Cellulose Research Studies ; Cultural Heritage Materials Analysis ; Material Properties and Processing
Authors: Tariq Aziz
Source: Biomedical Journal of Scientific & Technical Research (article, 2021-02-18)
Field: Materials Science — Biomaterials

Improving Adhesive Properties of Cellulose Nanocrystals Modifying By 3-Glycidoxypropyltrimethoxy Silane (KH-560) Coupling Agents Cellulose nanocrystals (CNCs) is one of the outstanding nanomaterial. So far, much researcher nowadays has been developed their properties. These properties make CNCs a promising resource to replace fossil resources for the production of industrial materials and chemicals. Here, we firstly modified cellulose nanocrystals with 3-Glycidoxypropyltrimethoxy (KH-560) silane coupling agent. Chemical composition and surface morphologies of cellulose nanocrystals were analyzed and characterized by FT-IR, and SEM. The result indicated that polar hydroxyl groups were successfully introduced on the cellulose nanocrystals surface. The SEM results confirmed silane coupling agent was successfully grafted on cellulose nanocrystals. Modified CNCs demonstrated an excellent tensile strength and modulus exhibited by 1wt% modified cellulose nanocrystals composites. This novel procedure improves the dispersion of cellulose nanocrystals by modification and at the same time enhance the interfacial adhesion. Result shows increases more over than the native cellulose nanocrystals by modifying CNCs with KH-560 coupling agent. This study lays a foundation towards full polymers fabrication and consumptions most desirable in adhesive and mechanical industrial fields. Beijing ; China ; Chinese academy of sciences ; Engineering ; Library science ; Chemistry ; Engineering physics ; Political science ; Computer science ; Law Advanced Cellulose Research Studies ; Lignin and Wood Chemistry ; Electrospun Nanofibers in Biomedical Applications
Authors: Yanchun Zhang | Aimin Sun | Zhaxi Suonan
Source: Research Square (preprint, 2021-03-04)
Field: Materials Science — Materials Chemistry

The Structural, Magnetic and Optical Properties of Ni-Mg-Zn Ferrite Prepared With Different Complexing Agents via Sol-Gel Method Abstract Different complexing agents were used to prepare Ni-Mg-Zn ferrite with the composition formula Ni 0.2 Mg 0.2 Zn 0.6 Fe 2 O 4 via sol-gel method, which included citric acid, oxalic acid, egg white and EDTA. The Ni 0.2 Mg 0.2 Zn 0.6 Fe 2 O 4 ferrite with no complexing agent was also prepared as a comparison. The chemical phases of samples were analyzed by the X-ray diffraction (XRD), which indicated that samples had spinel phase structure. The lattice constants of samples are in the range of 8.3980 ~ 8.4089 Å. The composition and structure were further studied by fourier transform infrared spectroscopy (FTIR). There were two typical characteristic bands related to the stretching vibrations of spinel ferrite in FTIR spectra. Scanning electron microscope (SEM) micrographs and transmission electron microscope (TEM) images showed that the particles have the shape of spherical cube. Energy dispersive spectrometer (EDS) analyzed the elements and ingredients of samples, which included Ni, Mg, Zn, Fe and O. X-ray photoelectron spectroscopy (XPS) is used to examine further the elemental composition and chemical state of sample prepared with EDTA as complexing agent. The optical properties of samples were investigated by photoluminescence spectra and UV-Vis spectroscopy. Vibrating sample magnetometer (VSM) was used to characterize magnetic properties, hysteresis loops revealed the ferrimagnetism behavior of prepared samples. Sol-gel ; Ferrite (magnet) ; Materials science ; Chemical engineering ; Metallurgy ; Nanotechnology ; Composite material ; Engineering Magnetic Properties and Synthesis of Ferrites ; Magneto-Optical Properties and Applications ; Iron oxide chemistry and applications
Authors: Babacar Niang | Nicola Schiavone | Haroutioun Askanian | Diène Ndiaye | Vincent Verney
Source: Open Journal of Composite Materials (article, 2021-01-01)
Field: Materials Science — Biomaterials

Morphological, Rheological and Mechanical Properties of Pla-Typha Based Biocomposites Due to the demand from society for the consumption of ecological polymeric materials, one of the polymers that have satisfied this request is the poly (lactic acid) (PLA). This polymer is derived from renewable resources, it is recyclable and biodegradable. It presents a good understanding between the promising properties and the cost. However, a route to increase the mechanical properties and reduce the cost of PLA is the elaboration of PLA based biocomposites by using fillers from natural waste. In this work, The effect of Typha content on the morphological, rheological, thermal and mechanical properties of PLA matrix was studied. Four formulations were produced with different mass concentrations. The results showed an increase in the viscoelastic properties, as a function of the Typha stem powder concentration. The DSC analysis showed an increase in the crystallinity rate of the various composites confirming the nucleating effect provided by the filler. TGA analysis indicated a decrease in the decomposition temperature of the composites. Mechanical tensile tests have shown a significant improvement in the mechanical properties mainly for the samples containing 45% (w/w) of Typha powder. Materials science ; Ultimate tensile strength ; Crystallinity ; Rheology ; Composite material ; Polymer biodegradable polymer synthesis and properties ; Natural Fiber Reinforced Composites ; Polymer Nanocomposites and Properties
Authors: Esther Heid | William H. Green
Source:  (preprint, 2021-08-11)
Field: Materials Science — Materials Chemistry

Machine learning of reaction properties via learned representations of the condensed graph of reaction The estimation of chemical reaction properties such as activation energies, rates or yields is a central topic of computational chemistry. In contrast to molecular properties, where machine learning approaches such as graph convolutional neural networks (GCNNs) have excelled for a wide variety of tasks, no general and transferable adaptations of GCNNs for reactions have been developed yet. We therefore combined a popular cheminformatics reaction representation, the so-called condensed graph of reaction (CGR), with a recent GCNN architecture to arrive at a versatile, robust and compact deep learning model. The CGR is a superposition of the reactant and product graphs of a chemical reaction, and thus an ideal input for graph-based machine learning approaches. The model learns to create a data-driven, task dependent reaction embedding that does not rely on expert knowledge, similar to current molecular GCNNs. Our approach outperforms current state-of-the-art models in accuracy, is applicable even to imbalanced reactions and possesses excellent predictive capabilities for diverse target properties, such as activation energies, reaction enthalpies, rate constants, yields or reaction classes. We furthermore curated a large set of atom-mapped reactions along with their target properties, which can serve as benchmark datasets for future work. All datasets and the developed reaction GCNN model are available online, free of charge and open-source. Cheminformatics ; Computer science ; Benchmark (surveying) ; Convolutional neural network ; Graph ; Artificial intelligence ; Embedding ; Machine learning ; Molecular graph ; Theoretical computer science ; Biological system ; Chemistry ; Computational chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks