jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Torgom Yezekyan | Khachatur Nerkararyan | Sergey I. Bozhevolnyi
Source: Optics Letters (article, 2020-02-10)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Maximizing absorption and scattering by spherical nanoparticles The absorption and scattering resonances of metal nanostructures are often assumed to be defined by the same condition of localized surface plasmon resonance. Using an electrostatic approximation, we demonstrate that the absorption and scattering cross sections of spherical nanoparticles reach their maxima at different wavelengths, which in turn differ from that defined by the Fröhlich condition (FC). These deviations from the FC originate from and are proportional to the material absorption. Our results provide the design guidelines for maximizing absorption and scattering of spherical nanoparticles and are thus of special importance for applications where the efficiency of radiation absorption or scattering is crucial. Optics ; Scattering ; Absorption (acoustics) ; Mie scattering ; Materials science ; Light scattering ; Attenuation coefficient ; Nanoparticle ; Refractive index ; Physics ; Nanotechnology Gold and Silver Nanoparticles Synthesis and Applications ; Plasmonic and Surface Plasmon Research ; Metamaterials and Metasurfaces Applications
Authors: Andrew L. Ferguson | Johannes Hachmann | Thomas F. Miller | Jim Pfaendtner
Source: The Journal of Physical Chemistry B (article, 2020-11-05)
Field: Materials Science — Materials Chemistry

<i>The Journal of Physical Chemistry A</i>/<i>B</i>/<i>C</i> Virtual Special Issue on Machine Learning in Physical Chemistry Physical chemistry stands today at an exciting transition state where the integration of machine learning and data science tools into all corners of the field stands poised to do nothing short of revolutionizing the discipline. These powerful techniques—when appropriately combined with domain knowledge, tools, and expertise—have led to new physical insights, better understanding, accelerated discovery, rational design, and inverse engineering that transcend traditional approaches to materials, molecular, and chemical science and engineering. The primary driver of this trend has been the impressive advances enabled by machine learning, artificial intelligence, and data science tools, ranging from the discovery of novel electronic and optical materials by high-throughput virtual screening, to the massive acceleration of molecular simulations using learned classical force fields with quantum accuracy, to the powering of “self-driving laboratories” for automated chemical discovery. The 2011 White House Materials Genome Initiative (MGI), the 2017 NSF Data-Driven Discovery Science in Chemistry (D3SC) initiative, and the 2019 NSF Big Idea Harnessing the Data Revolution are some of the US federal programs that have provided incentive, attention, momentum, and support to power these advances and help drive the field forward. Necessity is also the mother of invention, and the prevalence of large data sets routinely generated by high-throughput virtual screening or automated experimentation have spurred the need for scalable data science and machine learning techniques to parse, explore, and harness the full power of these voluminous data streams. It bears remembering that physical chemistry is no stranger to machine learning, most visibly in the cheminformatics and quantitative structure property relation (QSPR) work that emerged in the 1980s. Some of the techniques being implemented today are, to some degree, reinventions of these ideas, but others are fundamentally new concepts that have been adopted and adapted from diverse fields including computer vision, manifold learning, and deep learning. This Virtual Special Issue on Machine Learning in Physical Chemistry covering all sections of The Journal of Physical Chemistry A/B/C pays tribute to this development, and the relevance and popularity of this topic is reflected in the depth and breadth of excellent articles in this exciting collection. Chemistry ; Engineering physics ; Nanotechnology ; Engineering ; Materials science Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Ruslan Kuliiev
Source: Journal of International Crisis and Risk Communication Research (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

Mechanical Properties of Boron Carbide (B4C) Boron carbide (B4C) is one of the most important opaque boride ceramics that has high hardness and Young's modulus that along with low density lead to a significant resistance to ballistic impact and, thus, B4C is broadly used as a protective material. B4C has also high neutron capturing cross section; therefore, it is used as control rods and neutron absorption shielding in nuclear reactors. In this work thermal, electrical and mechanical properties of dense B4C ceramics (99%) sintered using Spark Plasma Sintering (SPS) were investigated. The Young's modulus of B4C measured by three different techniques – IE, RUS, and nanoindentation showed a very good overlap in values, which ranges from 419.2 ± 47.3 GPa for nanoindentation to 458.7 GPa for RUS measurements at room temperature. The mean contact pressure-contact depth plots obtained from load-displacement nanoindentation data indicated pop-in events during loading and an "elbow" event during unloading, both of which are indicative of possible structural changes in B4C structure during nanoindentation. The appearance of "elbow" deviations in load-displacement nanoindentation curves of B4C was detected for the first time. The 4-point bending strength of the B4C ceramics was equal to 585 ± 70 MPa with Weibull parameter of 9.9 and scale parameter equal to 611 MPa. The biaxial strength of B4C was measured to be much lower and equal to 238.6 ± 122 MPa with Weibull parameters of 2.2 and scale parameter equal to 271 MPa. To the best of our knowledge the biaxial strength of B4C was also measured for the first time. In this work it was determined that failure of B4C occurred by fully transgranular fracture, with no intergranular failure present on fracture surface. B4C's fracture toughness Klc = 3 ± 0.19 MPa x m1/2 was measured using SEVNB technique, which is similar to previously reported values. Boron carbide ; Materials science ; Carbide ; Boron ; Metallurgy ; Chemistry Boron and Carbon Nanomaterials Research ; Aluminum Alloys Composites Properties ; Advanced ceramic materials synthesis
Authors: Shingo Sotoma | Chongxia Zhong | James Chen Yong Kah | Hayato Yamashita | Taras Plakhotnik | Yoshie Harada | Madoka Suzuki
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2020-06-04)
Field: Materials Science — Materials Chemistry

<i>In situ</i> measurement of intracellular thermal conductivity using heater-thermometer hybrid diamond nanosensor Abstract Understanding heat dissipation processes at nanoscale during cellular thermogenesis is essential to clarify the relationships between the heat and biological processes in cells and organisms. A key parameter determining the heat flux inside a cell is the local thermal conductivity, a factor poorly investigated both experimentally and theoretically. Here, using a nanoheater/nanothermometer hybrid based on a polydopamine shell encapsulating a fluorescent diamond nanocrystal, we measured the intracellular thermal conductivity of HeLa cell with a spatial resolution of about 200 nm. Its mean value of 0.11 Wm -1 K -1 determined for the first time is significantly smaller than that of water. Bayesian analysis of the data strongly supports the existence of variation of the intracellular thermal conductivity of about 40%. These results present a major milestone towards understanding the intracellular heat transfer phenomena at nanoscale. Thermal conductivity ; Materials science ; Diamond ; Nanoscopic scale ; Intracellular ; Heat transfer ; Heat flux ; Conductivity ; Nanotechnology ; Biophysics ; Chemistry ; Thermodynamics ; Composite material ; Physics ; Biology ; Physical chemistry Thermal properties of materials ; thermodynamics and calorimetric analyses ; Diamond and Carbon-based Materials Research
Authors: Alaa G. Al-Hashimi | NAJLA HUSSEN AL-GARORY | Sinan J. Abdul - Abbas
Source: Basrah Journal of veterinary Research (review, 2020-12-31)
Field: Materials Science — Biomaterials

PROTEIN-BASED EDIBLE FILMS A REVIEW The edible or bio-degradable films are materials which compose of suitable means toincrease the storage time of food and improve their condition externally conductiveto environmental contamination.The protein, lipid and polysaccharide are materials can form the edible film. Wereproteins based edible films are considered mainly appropriate in food coating.Collagen, gelatin films, corn zein, wheat gluten, soy protein, casein and mung beanprotein are the most types films used in coating food. The protein edible films haveremarkable gas block features equalled to films made from polysaccharides andlipids. These films have mechanical properties which make it best thanpolysaccharide and fat based film Although the water vapor permeability is low andthe tensile strength is less than the polymers used in the packaging, manymodifications have been used to improve the functional properties of the film viaphysical, chemical and enzymatic methods and bonding with hydrophobic materialsand some polymers Gelatin ; Ultimate tensile strength ; Casein ; Soy protein ; Polymer ; Coating ; Materials science ; Food packaging ; Polysaccharide ; Gluten ; Chemistry ; Chemical engineering ; Food science ; Composite material ; Organic chemistry Nanocomposite Films for Food Packaging
Authors: Jiachi Yao | Qingzhou Wu | Haozhe Cui | Guowei Zhang | Guanghao Qu | Zhonghua Zhao | Daomin Min
Source: Applied Sciences (article, 2020-08-30)
Field: Materials Science — Materials Chemistry

Effect of Aging under AC Superimposed Harmonic Voltage on Trap and Carrier Transport Properties of Silicone Rubber The rapid growth of power grid capacity and the widespread use of a large number of power electronics and non-linear loads have led to harmonics in the power system. Harmonics in the power system will cause safety hazards to the normal operation of power equipment, exacerbate the aging of insulation materials, and reducing the overall operation reliability of the system. In the present work, we used power frequency ac voltage superimposed harmonics to carry out ageing experiments on power cable terminals. Then, we tested the infrared spectra, dielectric spectra, electrical conductivity, and surface potential decay characteristics of silicone rubber insulation materials on the cable terminals aged for different times. The experimental results show that the dielectric constant and dielectric loss of silicone rubber gradually increase with the aging time. In particular, the dielectric loss of silicone rubber changed greatly at low frequencies. The effect of dc conductance of aged silicone rubber on dielectric loss is significantly enhanced at low frequencies, which causes the dielectric loss to increase as the frequency decreases following an inverse power law. The surface potential decay rates of silicone rubber insulation after positive and negative corona charging accelerate with increasing the aging time, which is consistent with the experimental results of electrical conductivity. By analyzing the distribution characteristics of electron and hole traps in silicone rubbers, it is found that the trap energy levels of electron and hole traps become shallower as the operating time increases. The calculation of the carrier hopping conduction model shows that the shallow trap formed with increasing the aging time will lead to increases in both carrier mobility and conductivity. When the conductivity rises to a certain value, the silicone rubber will lose its insulation performance, resulting in insulation failure. Materials science ; Silicone rubber ; Composite material ; Dielectric ; Dielectric loss ; Harmonics ; Natural rubber ; Conductivity ; Electrical engineering ; Accelerated aging ; Voltage ; Optoelectronics ; Chemistry ; Engineering High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Lightning and Electromagnetic Phenomena
Authors: Ahmed Thabet | Youssef Mobarak | Nourhan Salem | Ahmed Mohamed Elnoby
Source: International Journal of Electrical and Computer Engineering (IJECE) (article, 2020-03-08)
Field: Materials Science — Materials Chemistry

Performance comparison of selection nanoparticles for insulation of three core belted power cables This paper presents an investigation on the enhancement of electrical insulations of power cables materials using a new multi-nanoparticles technique. It has been studied the effect of adding specified types and concentrations of nanoparticles to polymeric materials such as PVC for controlling on electric and dielectric performance. Prediction of effective dielectric constant has been done for the new nanocomposites based on Interphase Power Law (IPL) model. The multi-nanoparticles technique has been succeeded for enhancing electric and dielectric performance of power cables insulation compared with adding individual nanoparticles. Finally, it has been investigated on electric field distribution in the new proposed modern insulations for three-phase core belted power cables. This research has focused on studying development of PVC nanocomposite materials performance with electric field distribution superior to the unfilled matrix, and has stressed particularly the effect of filler volume fraction on the electric field distribution. Materials science ; Dielectric ; Nanocomposite ; Nanoparticle ; Composite material ; Electric field ; Electric power ; Power (physics) ; Nanotechnology ; Optoelectronics High voltage insulation and dielectric phenomena ; Lightning and Electromagnetic Phenomena ; Dielectric materials and actuators
Authors: Zai-Qun Yu | Reginald B. H. Tan
Source: Crystals (article, 2020-08-26)
Field: Materials Science — Materials Chemistry

Anti-Solvent Crystallization n/a Crystallization ; Solvent ; Materials science ; Chemistry ; Organic chemistry Crystallization and Solubility Studies ; Analytical Chemistry and Chromatography ; Chemical and Physical Properties in Aqueous Solutions
Authors: Qi Liu | Hui Li | Jian Li
Source: Research and Application of Materials Science (article, 2020-06-24)
Field: Materials Science — Polymers and Plastics

Basic Properties of Pyrolysis carbon black of Waste Tyres and Application of Pyrolysis carbon black in Transition Layer Rubber of All Steel Radial Tire The main chemical composition of pyrolysis carbon black of waste tires is C, O, Cu, Zn and so on. The content of ash and fine powder in pyrolysis carbon black is high, and the 300% elongation stress is high. The difference between pyrolysis carbon black and furnace black N326, which is commonly used in rubber, is obvious compared with chemical property. The pyrolysis carbon black was used to replace furnace black N326 in the transition layer of all steel load Radial tire rubber through experimental study. It was found that the compression heat generation and dynamic loss (Tanδ) of the blend rubber before and after aging were obviously reduced, the elongation at break and resilience increased, while the tensile stress and tear strength decreased by 100% and 300% , but the hardness and tensile strength changed little before and after aging. According to the latest raw material price calculation, 15 used tire pyrolysis carbon black instead of furnace carbon black N326 used in all steel Radial tire transition layer rubber application, excluding labor costs, electricity and equipment depreciation, a ton of blended rubber saves about $22.86 in production costs. Carbon black ; Natural rubber ; Pyrolysis ; Materials science ; Ultimate tensile strength ; Composite material ; Carbon fibers ; Elongation ; Waste management ; Composite number Polymer Nanocomposites and Properties ; Fiber-reinforced polymer composites
Authors: Muammar El Khatib | Wibe A. de Jong
Source: arXiv (Cornell University) (preprint, 2020-03-02)
Field: Materials Science — Materials Chemistry

ML4Chem: A Machine Learning Package for Chemistry and Materials Science ML4Chem is an open-source machine learning library for chemistry and materials science. It provides an extendable platform to develop and deploy machine learning models and pipelines and is targeted to the non-expert and expert users. ML4Chem follows user-experience design and offers the needed tools to go from data preparation to inference. Here we introduce its atomistic module for the implementation, deployment, and reproducibility of atom-centered models. This module is composed of six core building blocks: data, featurization, models, model optimization, inference, and visualization. We present their functionality and easiness of use with demonstrations utilizing neural networks and kernel ridge regression algorithms. Computer science ; Software deployment ; Inference ; Machine learning ; Artificial intelligence ; Visualization ; Kernel (algebra) ; Software engineering ; Data science Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Electron and X-Ray Spectroscopy Techniques
Authors: Stefan Heinen | Guido Falk von Rudorff | O. Anatole von Lilienfeld
Source: arXiv (Cornell University) (preprint, 2020-09-28)
Field: Materials Science — Materials Chemistry

Quantum based machine learning of competing chemical reaction profiles Kinetic and thermodynamic effects govern the outcome of competing chemical reactions, and are key in organic synthesis. They are crucially influenced, if not dominated, by the chemical composition of the reactants. For two competing exemplary reactions, E2 and SN2, we show how to use quantum machine learning in chemical compound space to rapidly predict outcome and respective transition states for any new reactant. Machine learning model based predictions of reactions in the chemical compound space of reactant candidates affords numerical results suggesting that Hammond's postulate is valid for SN2, but not to E2. The predictions are demonstrated to enable the construction of decision trees for rational prospective experimental design efforts. SN2 reaction ; Chemical space ; Quantum chemical ; Outcome (game theory) ; Chemical reaction ; Space (punctuation) ; Computer science ; Chemistry ; Biochemical engineering ; Artificial intelligence ; Thermodynamics ; Physics ; Mathematics ; Mathematical economics ; Organic chemistry ; Molecule ; Engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Text Analysis Techniques
Authors: Flore Mekki‐Berrada | Zekun Ren | Tan Huang | Wai Kuan Wong | Zheng Fang | Jiaxun Xie | Siyu Tian | J. Senthilnath | Zackaria Mahfoud | Daniil Bash | Kedar Hippalgaonkar | Saif A. Khan | Tonio Buonassisi | Qianxiao Li | Xiaonan Wang
Source: ChemRxiv (preprint, 2020-07-21)
Field: Materials Science — Materials Chemistry

Two-Step Machine Learning Enables Optimized Nanoparticle Synthesis In materials science, the discovery of recipes that yield nanomaterials with defined optical properties is costly and time-consuming. In this study, we present a two-step framework for a machine learning driven high-throughput microfluidic platform to rapidly produce silver nanoparticles with a desired absorbance spectrum. Combining a Gaussian Process based Bayesian Optimization (BO) with a Deep Neural Network (DNN), the algorithmic framework is able to converge towards the target spectrum after sampling 120 conditions. Once the dataset is large enough to train the DNN with sufficient accuracy in the region of the target spectrum, the DNN is used to predict the colour palette accessible with the reaction synthesis. While remaining interpretable by humans, the proposed framework efficiently optimizes the nanomaterial synthesis, and can extract fundamental knowledge of the relationship between chemical composition and optical properties, such as the role of each reactant on the shape and amplitude of the absorbance spectrum. Bayesian optimization ; Computer science ; Artificial neural network ; Absorbance ; Gaussian process ; Deep learning ; Nanomaterials ; Process (computing) ; Throughput ; Artificial intelligence ; Microfluidics ; Kriging ; Deep neural networks ; Machine learning ; Biological system ; Gaussian ; Nanotechnology ; Materials science ; Chemistry ; Telecommunications Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Gold and Silver Nanoparticles Synthesis and Applications
Authors: Nagendra Somanath | Ryan Noraas | Michael Giering | Oshin Olesegun
Source: AIAA Scitech 2020 Forum (article, 2020-01-05)
Field: Materials Science — Materials Chemistry

Structural Material Property Tailoring of Dual Phase Titanium Alloy Microstructures Using Deep Neural Networks Machine intelligence can enable businesses to improve performance by reducing errors and improving sensitivity, quality, speed, and in some cases achieving outcomes that go beyond current resource capabilities. The explosive increase in the power of numerical analytics combined with tensor algebra manifold optimization has been driven by breakthroughs in computational algorithms, machine learning and, to a large degree, deep neural networks (a.k.a. deep learning), enabling us to automate and learn from ever more complex datasets and engineer efficient rapid response algorithms in areas of practical relevance. These areas may include new product architecture design, rapid material characterization, life-cycle management and can be tied with a digital strategy that will enable efficient development from cradle to grave. Collectively, these technologies have been used to address fundamental challenges in materials engineering e.g. automated classification and quantification of microstructure images. In this work, a total of 19 different Ti-6Al-4V microstructures were produced by varying heat treat parameters such as solution temperature, cooling rate, aging times and temperatures. Over 14,000 images were collected and used to train models for microstructure classification, quantification, and synthetic image generation. A framework is proposed to enable rapid material design space exploration, property prediction and optimization that takes into account real-time decision cycles and trade-offs between speed and accuracy. Computer science ; Artificial intelligence ; Artificial neural network ; Machine learning ; Deep learning Machine Learning in Materials Science ; Additive Manufacturing Materials and Processes ; Titanium Alloys Microstructure and Properties