jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Iman Sajedian | Heon Lee | Junsuk Rho
Source: Scientific Reports (article, 2019-07-29)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Double-deep Q-learning to increase the efficiency of metasurface holograms We use a double deep Q-learning network (DDQN) to find the right material type and the optimal geometrical design for metasurface holograms to reach high efficiency. The DDQN acts like an intelligent sweep and could identify the optimal results in ~5.7 billion states after only 2169 steps. The optimal results were found between 23 different material types and various geometrical properties for a three-layer structure. The computed transmission efficiency was 32% for high-quality metasurface holograms; this is two times bigger than the previously reported results under the same conditions. The found structure is transmission-type and polarization-independent and works in the visible region. Holography ; Computer science ; Polarization (electrochemistry) ; Transmission (telecommunications) ; Deep learning ; Optics ; Type (biology) ; Quality (philosophy) ; Optoelectronics ; Materials science ; Artificial intelligence ; Physics ; Telecommunications ; Chemistry ; Geology Metamaterials and Metasurfaces Applications ; Advanced Optical Imaging Technologies ; Advanced Antenna and Metasurface Technologies
Authors: Xiaoyang Liu | Dean Zhao | Weikuan Jia | Wei Ji | Yueping Sun
Source: IEEE Access (article, 2019-01-01)
Field: Chemistry — Analytical Chemistry

A Detection Method for Apple Fruits Based on Color and Shape Features The skins of most mature apple fruits are incompletely red and also include green and pale yellow color, which increases the difficulty of fruit detection by machine vision. A detection method based on color and shape features is proposed for this kind of apple fruits. Simple linear iterative clustering (SLIC) is adapted to segment images taken in orchards into super-pixel blocks. The color feature extracted from blocks is used to determine candidate regions, which can filter a large proportion of non-fruit blocks and improve detection precision. Next, the histogram of oriented gradient (HOG) is adopted to describe the shape of fruits, which is applied to detect fruits in candidate regions and locate the position of fruits further. The proposed method was tested by images taken under different illuminations. The average values of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">recall</i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">precision</i> , and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$F_{1}$ </tex-math></inline-formula> reach 89.80%, 95.12%, and 92.38% respectively. The performance of detecting fruits covered at different levels is also tested. The values of the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">recall</i> are all more than 85%, which indicates that proposed method can detect a great part of covered fruits. Compared with pedestrian detection method and faster region-based convolutional neural network (RCNN), the proposed method has the best performance and higher than faster RCNN slightly. However, the proposed method is not robust to noise and its elapsed time of one image is 1.94 s and less than faster RCNN. Computer science ; Artificial intelligence ; Computer vision ; Pattern recognition (psychology) Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Remote Sensing and Land Use
Authors: Lifei Wei | Ziran Yuan | Yanfei Zhong | Lanfang Yang | Xin Hu | Yangxi Zhang
Source: Applied Sciences (article, 2019-05-12)
Field: Chemistry — Analytical Chemistry

An Improved Gradient Boosting Regression Tree Estimation Model for Soil Heavy Metal (Arsenic) Pollution Monitoring Using Hyperspectral Remote Sensing Hyperspectral remote sensing can be used to effectively identify contaminated elements in soil. However, in the field of monitoring soil heavy metal pollution, hyperspectral remote sensing has the characteristics of high dimensionality and high redundancy, which seriously affect the accuracy and stability of hyperspectral inversion models. To resolve the problem, a gradient boosting regression tree (GBRT) hyperspectral inversion algorithm for heavy metal (Arsenic (As)) content in soils based on Spearman’s rank correlation analysis (SCA) coupled with competitive adaptive reweighted sampling (CARS) is proposed in this paper. Firstly, the CARS algorithm is used to roughly select the original spectral data. Second derivative (SD), Gaussian filtering (GF), and min-max normalization (MMN) pretreatments are then used to improve the correlation between the spectra and As in the characteristic band enhancement stage. Finally, the low-correlation bands are removed using the SCA method, and a subset with absolute correlation values greater than 0.6 is retained as the optimal band subset after each pretreatment. For the modeling, the five most representative characteristic bands were selected in the Honghu area of China, and the nine most representative characteristic bands were selected in the Daye area of China. In order to verify the generalization ability of the proposed algorithm, 92 soil samples from the Honghu and Daye areas were selected as the research objects. With the use of support vector machine regression (SVMR), linear regression (LR), and random forest (RF) regression methods as comparative methods, all the models obtained a good prediction accuracy. However, among the different combinations, CARS-SCA-GBRT obtained the highest precision, which indicates that the proposed algorithm can select fewer characteristic bands to achieve a better inversion effect, and can thus provide accurate data support for the treatment and recovery of heavy metal pollution in soils. Hyperspectral imaging ; Support vector machine ; Random forest ; Remote sensing ; Normalization (sociology) ; Regression ; Environmental science ; Computer science ; Mathematics ; Statistics ; Artificial intelligence ; Geology Spectroscopy and Chemometric Analyses ; Remote-Sensing Image Classification ; Geochemistry and Geologic Mapping
Authors: Wei‐Jun Kong | Zhigao Shen | Lars H. Finger | Lutz Ackermann
Source: Angewandte Chemie International Edition (article, 2019-12-03)
Field: Chemistry — Organic Chemistry

Electrochemical Access to Aza‐Polycyclic Aromatic Hydrocarbons: Rhoda‐Electrocatalyzed Domino Alkyne Annulations Nitrogen-doped polycyclic aromatic hydrocarbons (aza-PAHs) have found broad applications in material sciences. Herein, a modular electrochemical synthesis of aza-PAHs was developed via a rhodium-catalyzed cascade C-H activation and alkyne annulation. A multifunctional O-methylamidoxime enabled the high chemo- and regioselectivity. The isolation of two key rhodacyclic intermediates made it possible to delineate the exact order of three C-H activation steps. In addition, the metalla-electrocatalyzed multiple C-H transformation is characterized by unique functional group tolerance, including highly reactive iodo and azido groups. Alkyne ; Regioselectivity ; Domino ; Chemistry ; Annulation ; Combinatorial chemistry ; Rhodium ; Functional group ; Cascade reaction ; Catalysis ; Organic chemistry Catalytic C–H Functionalization Methods ; Radical Photochemical Reactions ; CO2 Reduction Techniques and Catalysts
Authors: Martin Koller
Source: Bioengineering (review, 2019-04-16)
Field: Materials Science — Biomaterials

Polyhydroxyalkanoate Biosynthesis at the Edge of Water Activity-Haloarchaea as Biopolyester Factories Haloarchaea, the extremely halophilic branch of the Archaea domain, encompass a steadily increasing number of genera and associated species which accumulate polyhydroxyalkanoate biopolyesters in their cytoplasm. Such ancient organisms, which thrive in highly challenging, often hostile habitats characterized by salinities between 100 and 300 g/L NaCl, have the potential to outperform established polyhydroxyalkanoate production strains. As detailed in the review, this optimization presents due to multifarious reasons, including: cultivation setups at extreme salinities can be performed at minimized sterility precautions by excluding the growth of microbial contaminants; the high inner-osmotic pressure in haloarchaea cells facilitates the recovery of intracellular biopolyester granules by cell disintegration in hypo-osmotic media; many haloarchaea utilize carbon-rich waste streams as main substrates for growth and polyhydroxyalkanoate biosynthesis, which allows coupling polyhydroxyalkanoate production with bio-economic waste management; finally, in many cases, haloarchaea are reported to produce copolyesters from structurally unrelated inexpensive substrates, and polyhydroxyalkanoate biosynthesis often occurs in parallel to the production of additional marketable bio-products like pigments or polysaccharides. This review summarizes the current knowledge about polyhydroxyalkanoate production by diverse haloarchaea; this covers the detection of new haloarchaea producing polyhydroxyalkanoates, understanding the genetic and enzymatic particularities of such organisms, kinetic aspects, material characterization, upscaling and techno-economic and life cycle assessment. Polyhydroxyalkanoates ; Haloarchaea ; Halophile ; Archaea ; Bioplastic ; Biology ; Biotechnology ; Ecology ; Bacteria ; Genetics biodegradable polymer synthesis and properties ; Microplastics and Plastic Pollution ; Enzyme Catalysis and Immobilization
Authors: Jianxing Huang | Gábor Cśanyi | Jinbao Zhao | Jun Cheng | Volker L. Deringer
Source: Journal of Materials Chemistry A (article, 2019-01-01)
Field: Materials Science — Materials Chemistry

First-principles study of alkali-metal intercalation in disordered carbon anode materials The intercalation of alkali metals in disordered carbon anode materials is studied by a combination of first-principles and machine-learning methods. Intercalation (chemistry) ; Alkali metal ; Anode ; Materials science ; Carbon fibers ; Metal ; Inorganic chemistry ; Chemical engineering ; Metallurgy ; Chemistry ; Composite material ; Physical chemistry ; Composite number ; Electrode ; Organic chemistry ; Engineering Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Advancements in Battery Materials
Authors: Jing Wan | Jin-Wu Jiang | Harold S. Park
Source: Carbon (article, 2019-10-22)
Field: Materials Science — Materials Chemistry

Machine learning-based design of porous graphene with low thermal conductivity Thermal conductivity ; Graphene ; Materials science ; Phonon ; Porosity ; Porous medium ; Condensed matter physics ; Nanotechnology ; Composite material ; Physics Thermal properties of materials ; Graphene research and applications ; Advanced Thermoelectric Materials and Devices
Authors: Oğuz Uncu | Banu Özen
Source: Food Control (article, 2019-06-07)
Field: Chemistry — Analytical Chemistry

A comparative study of mid-infrared, UV–Visible and fluorescence spectroscopy in combination with chemometrics for the detection of adulteration of fresh olive oils with old olive oils Chemometrics ; Partial least squares regression ; Olive oil ; Chemistry ; Ultraviolet visible spectroscopy ; Fluorescence spectroscopy ; Chromatography ; Analytical Chemistry (journal) ; Fluorescence ; Fourier transform infrared spectroscopy ; Spectroscopy ; Food science ; Mathematics ; Organic chemistry Spectroscopy and Chemometric Analyses ; Edible Oils Quality and Analysis ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Lucas Faccioni Chanchetti | Daniel Rodrigo Leiva | Leandro Innocentini Lopes de Faria | Tomaz Toshimi Ishikawa
Source: International Journal of Hydrogen Energy (review, 2019-07-10)
Field: Materials Science — Materials Chemistry

A scientometric review of research in hydrogen storage materials Hydrogen storage ; Computer science ; Hydrogen ; Hydrogen fuel ; Ammonia borane ; Materials science ; Renewable energy ; Nanotechnology ; Process engineering ; Environmental science ; Chemistry ; Engineering Hydrogen Storage and Materials ; Hybrid Renewable Energy Systems ; Ammonia Synthesis and Nitrogen Reduction
Authors: Mengdan Zhao | Junqin Li | Fengying Chen | Wei Dong | Lijuan Wen | Weidong Fei | Xiao Zhang | Pei-Lei Yang | Xinmei Zhang | Caihong Zheng
Source: International Journal of Nanomedicine (article, 2019-12-01)
Field: Materials Science — Biomaterials

&lt;p&gt;Co-Delivery of Curcumin and Paclitaxel by “Core-Shell” Targeting Amphiphilic Copolymer to Reverse Resistance in the Treatment of Ovarian Cancer&lt;/p&gt; This special structure of targeting nanoparticles co-delivery with the curcumin and paclitaxel can increase the anti-tumor efficacy without increasing the adverse reactions as a promising strategy for therapy ovarian cancer. Paclitaxel ; Nanocarriers ; Ovarian cancer ; Pharmacology ; Curcumin ; Cancer research ; Drug delivery ; Cancer cell ; Cytotoxicity ; Ovarian tumor ; Cancer ; Chemistry ; Medicine ; Materials science ; In vitro ; Internal medicine ; Drug ; Biochemistry ; Nanotechnology Nanoparticle-Based Drug Delivery ; RNA Interference and Gene Delivery ; Dendrimers and Hyperbranched Polymers
Authors: Nurhasni Hasan | Jiafu Cao | Juho Lee | Shwe Phyu Hlaing | Murtada A. Oshi | Muhammad Naeem | Min-Hyo Ki | Bok Luel Lee | Yunjin Jung | Jin‐Wook Yoo
Source: Pharmaceutics (article, 2019-05-15)
Field: Chemistry — Organic Chemistry

Bacteria-Targeted Clindamycin Loaded Polymeric Nanoparticles: Effect of Surface Charge on Nanoparticle Adhesion to MRSA, Antibacterial Activity, and Wound Healing Adhesion of nanoparticles (NPs) to the bacterial cell wall by modifying their physicochemical properties can improve the antibacterial activity of antibiotic. In this study, we prepared positively charged clindamycin-loaded poly (lactic-co-glycolic acid)-polyethylenimine (PLGA-PEI) nanoparticles (Cly/PPNPs) and negatively charged clindamycin-loaded PLGA NPs (Cly/PNPs) and investigated the effect of NP adhesion to bacteria on the treatment of methicillin-resistant <i>Staphylococcus aureus</i> (MRSA)-infected wounds. The Cly/PPNPs and Cly/PNPs were characterized according to particle size, polydispersity index, surface charge, and drug loading. Both Cly/PPNPs and Cly/PNPs exhibited sustained drug release over 2 days. The Cly/PPNPs bind to the MRSA surface, thereby enhancing bactericidal efficacy against MRSA compared with the Cly/PNPs. Furthermore, compared with other groups, Cly/PPNPs significantly accelerated the healing and re-epithelialization of wounds in a mouse model of a MRSA-infected wounds. We also found that both NPs are harmless to healthy fibroblast cells. Therefore, our results suggest that the Cly/PPNPs developed in this study improve the efficacy of clindamycin for the treatment of MRSA-infected wounds. PLGA ; Adhesion ; Clindamycin ; Antibacterial activity ; Microbiology ; Chemistry ; Nanoparticle ; Antibiotics ; Bacteria ; Nanotechnology ; Materials science ; Organic chemistry ; Biochemistry Antimicrobial agents and applications ; Bacterial biofilms and quorum sensing ; Wound Healing and Treatments
Authors: Bowen Han | Yanjun Li | Ying Yu | Lei Gong
Source: Nature Communications (article, 2019-08-23)
Field: Chemistry — Organic Chemistry

Photocatalytic enantioselective α-aminoalkylation of acyclic imine derivatives by a chiral copper catalyst Copper-based asymmetric photocatalysis has great potential in the development of green synthetic approaches to chiral molecules. However, there are several formidable challenges associated with such a conception. These include the relatively weak visible light absorption, short excited-state lifetimes, incompatibility of different catalytic cycles, and the difficulty of the stereocontrol. We report here an effective strategy by means of single-electron-transfer (SET) initiated formation of radicals and photoactive intermediates to address the long-standing problems. Through elaborate selection of well-matched reaction partners, the chiral bisoxazoline copper catalyst is engaged in the SET process, photoredox catalysis, Lewis acid activation and asymmetric induction. Accordingly, a highly enantioselective photocatalytic α-aminoalkylation of acyclic imine derivatives has been accessed. This strategy sheds light on how to make use of diverse functions of a single transition metal catalyst in one reaction, and offers an economic and simplified approach to construction of highly valuable chiral vicinal diamines. Enantioselective synthesis ; Imine ; Photocatalysis ; Catalysis ; Lewis acids and bases ; Vicinal ; Combinatorial chemistry ; Radical ; Copper ; Photochemistry ; Chemistry ; Photoredox catalysis ; Organic chemistry Radical Photochemical Reactions ; Catalytic C–H Functionalization Methods ; Sulfur-Based Synthesis Techniques
Authors: Zhufeng Hou | Yoshiki Takagiwa | Yoshikazu Shinohara | Yibin Xu | Koji Tsuda
Source: ACS Applied Materials & Interfaces (article, 2019-03-18)
Field: Materials Science — Materials Chemistry

Machine-Learning-Assisted Development and Theoretical Consideration for the Al<sub>2</sub>Fe<sub>3</sub>Si<sub>3</sub> Thermoelectric Material Chemical composition alteration is a general strategy to optimize the thermoelectric properties of a thermoelectric material to achieve high-efficiency conversion of waste heat into electricity. Recent studies show that the Al<sub>2</sub>Fe<sub>3</sub>Si<sub>3</sub> intermetallic compound with a relatively high power factor of ∼700 μW m<sup>-1</sup> K<sup>-2</sup> at 400 K is promising for applications in low-cost and nontoxic thermoelectric devices. To accelerate the exploration of the thermoelectric properties of this material in a mid-temperature range and to enhance its power factor, a machine-learning method was employed herein to assist the synthesis of off-stoichiometric samples (namely, Al<sub>23.5+ x</sub>Fe<sub>36.5</sub>Si<sub>40- x</sub>) of the Al<sub>2</sub>Fe<sub>3</sub>Si<sub>3</sub> compound by tuning the Al/Si ratio. The optimal Al/Si ratio for a high power factor in the mid-temperature range was found rapidly and efficiently, and the optimal ratio of the sample at x = 0.9 was found to increase the power factor at ∼510 K by about 40% with respect to that of the initial sample at x = 0.0. The possible mechanism for the enhanced power factor is discussed in terms of the precipitations of the metallic secondary phases in the Al<sub>23.5+ x</sub>Fe<sub>36.5</sub>Si<sub>40- x</sub> samples. Furthermore, the maximum achievable thermal conductivity of Al<sub>2</sub>Fe<sub>3</sub>Si<sub>3</sub> estimated by the Slack model is ∼10 W m<sup>-1</sup> K<sup>-1</sup> at the Debye temperature. An avoided-crossing behavior of the acoustic and the low-lying optical modes along several crystallographic directions is found in the phonon dispersion of Al<sub>2</sub>Fe<sub>3</sub>Si<sub>3</sub> calculated by ab initio density functional theory method. These preliminary results suggest that Al<sub>2</sub>Fe<sub>3</sub>Si<sub>3</sub> can have a low thermal conductivity. The calculated formation energies of point defects suggest that the antisite defects between Al and Si are likely to cause the Al and Si off-stoichiometries in Al<sub>2</sub>Fe<sub>3</sub>Si<sub>3</sub>. The theoretically obtained insight provides additional information for the further understanding of Al<sub>2</sub>Fe<sub>3</sub>Si<sub>3</sub>. Materials science ; Thermoelectric effect ; Seebeck coefficient ; Thermoelectric materials ; Thermal conductivity ; Power factor ; Phonon ; Atmospheric temperature range ; Spark plasma sintering ; Intermetallic ; Thermodynamics ; Analytical Chemistry (journal) ; Condensed matter physics ; Power (physics) ; Composite material ; Microstructure ; Alloy Advanced Thermoelectric Materials and Devices ; Thermal properties of materials ; Machine Learning in Materials Science
Authors: Kang Xia | Xianxu Zheng | Jianan Yuan | Cong Liu | Hao Gao | Qiang Wu | Jian Sun
Source: The Journal of Physical Chemistry C (article, 2019-04-01)
Field: Materials Science — Materials Chemistry

Pressure-Stabilized High-Energy-Density Alkaline-Earth-Metal Pentazolate Salts Polynitrogen compounds especially pentazolate anion complexes recently have attracted substantial attention due to their promising potential as high-energy-density materials. Here, using a machine-learning-accelerated crystal structure search method and first-principles calculations, we predict a new hybrid compound by inserting a large fraction of nitrogen into alkaline-earth metals. It is a new stoichiometric type MN10 (M = Be, Mg), which possesses a metal-centering octahedral pentazolate framework with the space group Fdd2. This type of ionic-like molecular crystal is found to be energetically more favorable than the mixtures of M3N2 or MN4 compounds and pure nitrogen and is possibly synthesized at relatively low pressures (around 12 GPa for MgN10). The ab initio molecular dynamics simulations show that they are metastable and can be quenched to ambient conditions once synthesized at high pressure. Moreover, decomposition of this polymeric MN10 structure can release a large amount of energy and shows high performance in detonation. The detonation velocity and pressure of BeN10 are about twice and 4 times that of trinitrotoluene, respectively. Detonation velocity ; Detonation ; Alkaline earth metal ; Metastability ; Crystal structure prediction ; Ab initio ; Molecular dynamics ; Crystal structure ; Materials science ; Ionic bonding ; Metal ; Stoichiometry ; Alkali metal ; Inorganic chemistry ; Octahedron ; Decomposition ; Ionic radius ; Crystal (programming language) ; Chemistry ; Crystallography ; Physical chemistry ; Ion ; Computational chemistry ; Explosive material ; Organic chemistry ; Metallurgy Boron and Carbon Nanomaterials Research ; Energetic Materials and Combustion ; MXene and MAX Phase Materials
Authors: Abhirup Patra | Rohit Batra | Anand Chandrasekaran | Chiho Kim | Tran Doan Huan | Rampi Ramprasad
Source: Computational Materials Science (article, 2019-10-04)
Field: Materials Science — Materials Chemistry

A multi-fidelity information-fusion approach to machine learn and predict polymer bandgap Fidelity ; Kriging ; Computer science ; Benchmark (surveying) ; Gaussian process ; Categorical variable ; Machine learning ; Artificial intelligence ; Gaussian ; Regression ; Mathematics ; Statistics ; Physics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Fuel Cells and Related Materials
Authors: Jakub Kubečka | Vitus Besel | Theo Kurtén | Nanna Myllys | Hanna Vehkamäki
Source: The Journal of Physical Chemistry A (article, 2019-06-24)
Field: Chemistry — Spectroscopy

Configurational Sampling of Noncovalent (Atmospheric) Molecular Clusters: Sulfuric Acid and Guanidine We studied the configurational sampling of noncovalently bonded molecular clusters relevant to the atmosphere. In this article, we discuss possible approaches to searching for optimal configurations and present one alternative based on systematic configurational sampling, which seems able to overcome the typical problems associated with searching for global minima on multidimensional potential energy surfaces. Since atmospheric molecular clusters are usually held together by intermolecular bonds, we also present a cost-effective strategy for treating hydrogen bonding and proton transferring by using rigid molecules and ions in different protonation states and illustrate its performance on clusters containing guanidine and sulfuric acid. Protonation ; Guanidine ; Sulfuric acid ; Hydrogen bond ; Molecule ; Chemistry ; Computational chemistry ; Sampling (signal processing) ; Intermolecular force ; Chemical physics ; Maxima and minima ; Atmosphere (unit) ; Ion ; Computer science ; Physics ; Organic chemistry ; Mathematics ; Thermodynamics Molecular Spectroscopy and Structure ; Advanced Chemical Physics Studies ; Crystallography and molecular interactions