jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Christoph K. Hofer | Viera Skákalová | Tobias Görlich | Mukesh Tripathi | Andreas Mittelberger | Clemens Mangler | Mohammad Reza Ahmadpour Monazam | Toma Susi | Jani Kotakoski | Jannik C. Meyer
Source: Nature Communications (article, 2019-10-08)
Field: Materials Science — Materials Chemistry

Direct imaging of light-element impurities in graphene revealstriple-coordinated oxygen Along with hydrogen, carbon, nitrogen and oxygen are the arguably most important elements for organic chemistry. Due to their rich variety of possible bonding configurations, they can form a staggering number of compounds. Here, we present a detailed analysis of nitrogen and oxygen bonding configurations in a defective carbon (graphene) lattice. Using aberration-corrected scanning transmission electron microscopy and single-atom electron energy loss spectroscopy, we directly imaged oxygen atoms in graphene oxide, as well as nitrogen atoms implanted into graphene. The collected data allows us to compare nitrogen and oxygen bonding configurations, showing clear differences between the two elements. As expected, nitrogen forms either two or three bonds with neighboring carbon atoms, with three bonds being the preferred configuration. Oxygen, by contrast, tends to bind with only two carbon atoms. Remarkably, however, triple-coordinated oxygen with three carbon neighbors is also observed, a configuration that is exceedingly rare in organic compounds. Graphene ; Impurity ; Oxygen ; Nanotechnology ; Element (criminal law) ; Materials science ; Chemistry ; Political science Graphene research and applications ; Diamond and Carbon-based Materials Research ; Advanced Electron Microscopy Techniques and Applications
Authors: Da Huo | Zhenming Cao | Jun Li | Minghao Xie | Jing Tao | Younan Xia
Source: Nano Letters (article, 2019-03-29)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Seed-Mediated Growth of Au Nanospheres into Hexagonal Stars and the Emergence of a Hexagonal Close-Packed Phase Gold (Au) typically crystallizes in a cubic close-packed ( ccp) structure to present a face-centered cubic ( fcc) lattice or crystal phase. Herein, we demonstrate that Au nanoscale hexagonal stars featuring a hexagonal close-packed ( hcp) structure can be synthesized in an aqueous system in the presence of fcc-Au nanospheres as the seeds. The success of this synthesis critically relies on the use of ethylenediaminetetraacetic acid to complex with Au<sup>3+</sup> ions (the precursor) and the introduction of 2-phospho-l-ascorbic acid trisodium salt (Asc-2P) as a novel reducing agent to maneuver the reduction kinetics. The use of Asc-2P favorably promotes the formation of hexagonal stars with uneven surfaces at the top and bottom faces, together with concave side faces around the edges. By varying the amount of Asc-2P to fine-tune the reduction kinetics, we can adjust the concaveness of the side faces, with a faster reduction rate favoring greater concaveness and a red shift of the plasmon resonance peak to the near-infrared. For the first time, our results suggest that the phosphate and hydroxyl groups can act synergistically in controlling the morphology of Au nanocrystals. Most significantly, the newly deposited Au atoms can also crystallize in an hcp structure, leading to the observation of a phase transition from fcc to hcp along the growth direction. This new protocol based upon kinetic control can be potentially extended to other noble metals for the facile synthesis of nanocrystals featuring unprecedented structures or phases. Hexagonal crystal system ; Close-packing of equal spheres ; Materials science ; Hexagonal phase ; Phase (matter) ; Stars ; Crystallography ; Nanotechnology ; Chemical physics ; Condensed matter physics ; Chemistry ; Physics ; Astrophysics Gold and Silver Nanoparticles Synthesis and Applications ; Quantum Dots Synthesis And Properties ; Nanocluster Synthesis and Applications
Authors: Caio G. Otoni | Juliana S. L. Figueiredo | Larissa Brentano Capeletti | Mateus Borba Cardoso | Juliana S. Bernardes | Watson Loh
Source: ACS Applied Bio Materials (article, 2019-04-12)
Field: Materials Science — Biomaterials

Tailoring the Antimicrobial Response of Cationic Nanocellulose-Based Foams through Cryo-Templating To shed light on novel sustainable materials with antimicrobial functionality, in this contribution, we describe the use of cationic nanocellulose to produce foams featuring antibacterial activity against the powerful human pathogen <i>Escherichia coli</i>. Dialdehyde cellulose was cationized with Girard's reagent T (GRT), mechanically disintegrated into nanofibrillated cellulose (NFC), and shaped into foams through different protocols. All steps were carried out in aqueous media and in the absence of hazardous chemicals. While evaporative drying led to compact films (density of 1.3 g cm<sup>-3</sup>), freeze-casting (i.e., freezing and freeze-drying) produced monolithic cryogels with low densities (<50 mg cm<sup>-3</sup>) and porosities of ca. 98%. Although highly porous, the cryogels obtained through rapid freezing remarkably presented smaller pores than those that were previously frozen in a slow fashion. The quaternary ammonium groups of GRT-cationized NFC removed <i>E. coli</i> to different extents depending upon sample morphology. We demonstrated in an innovative manner that porosity, which is directly associated with surface area, and pore size play an essential role on the antimicrobial performance. This outcome arises from the inaccessibility of bacterial cells to cationic surfaces inside monoliths composed of small pores. We herein present an uncomplicated, environmentally friendly protocol for fine-tuning the porosity and pore size of all-cellulose materials through cryo-templating. Controlling these morphometric parameters allowed us to achieve a ca. 85% higher anti-<i>E. coli</i> activity when comparing samples made up of the very same material (i.e., the same NFC concentration and degree of substitution) but presented as dense films. These findings bear clear implications for the pursuit of sustainable materials presenting multifunctionality. Nanocellulose ; Cellulose ; Cationic polymerization ; Porosity ; Chemical engineering ; Materials science ; Bacterial cellulose ; Reagent ; Nanotechnology ; Antimicrobial ; Chemistry ; Composite material ; Polymer chemistry ; Organic chemistry Advanced Cellulose Research Studies ; Aerogels and thermal insulation ; Microencapsulation and Drying Processes
Authors: A. Escobedo-Morales | L. Tepech-Carrillo | A. Bautista Hernández | J. H. Camacho-García | Diego Cortés‐Arriagada | Ernesto Chigo Anota
Source: Scientific Reports (article, 2019-11-11)
Field: Materials Science — Materials Chemistry

Effect of Chemical Order in the Structural Stability and Physicochemical Properties of B12N12 Fullerenes The effect of chemical order in the structural and physicochemical properties of B<sub>12</sub>N<sub>12</sub> [4,6]-fullerene (BNF) isomers was evaluated using density functional theory and molecular dynamic calculations. The feasibility to find stable BNF isomers with atomic arrangement other than the well-known octahedral T<sub>h</sub>-symmetry was explored. In this study, the number of homonuclear bonds in the modeled nanostructures was used as categorical parameter to describe and quantify the degree of structural order. The BNF without homonuclear bonds was identified as the most energetically favorable isomer. However, a variety of BNF arrays departing from T<sub>h</sub>-symmetry was determined as stable structures also. The calculated vibrational spectra suggest that isomers with chemical disorder can be identified by infrared spectroscopy. In general, formation of homonuclear bonds is possible meanwhile the entropy of the system increases, but at expense of cohesive energy. It is proposed that formation of phase-segregated regions stablishes an apparent limit to the number of homonuclear bonds in stable B<sub>12</sub>N<sub>12</sub> fullerenes. It was found that formation of homonuclear bonds decreases substantially the chemical hardness of BNF isomers and generates zones with large charge density, which might act as reactive sites. Moreover, chemical disorder endows BNF isomers with a permanent electric dipole moment as large as 3.28 D. The obtained results suggest that by manipulating their chemical order, the interaction of BNF's with other molecular entities can be controlled, making them potential candidates for drug delivery, catalysis and sensing. Homonuclear molecule ; Fullerene ; Chemical shift ; Density functional theory ; Chemical bond ; Computational chemistry ; Chemical physics ; Chemistry ; Chemical stability ; Materials science ; Physical chemistry ; Molecule ; Organic chemistry Boron and Carbon Nanomaterials Research ; Fullerene Chemistry and Applications ; Graphene research and applications
Authors: David McDonagh | Chris‐Kriton Skylaris | Graeme M. Day
Source: Journal of Chemical Theory and Computation (article, 2019-02-28)
Field: Materials Science — Materials Chemistry

Machine-Learned Fragment-Based Energies for Crystal Structure Prediction Crystal structure prediction involves a search of a complex configurational space for local minima corresponding to stable crystal structures, which can be performed efficiently using atom-atom force fields for the assessment of intermolecular interactions. However, for challenging systems, the limitations in the accuracy of force fields prevent a reliable assessment of the relative thermodynamic stability of potential structures, while the cost of fully quantum mechanical approaches can limit applications of the methods. We present a method to rapidly improve force field lattice energies by correcting two-body interactions with a higher level of theory in a fragment-based approach and predicting these corrections with machine learning. Corrected lattice energies with commonly used density functionals and second order perturbation theory (MP2) all significantly improve the ranking of experimentally known polymorphs where the rigid molecule model is applicable. The relative lattice energies of known polymorphs are also found to systematically improve with the fragment corrections. Predicting two-body interactions with atom-centered symmetry functions in a Gaussian process is found to give highly accurate results using as little as 10-20% of the data for training, reducing the cost of the energy correction by up to an order of magnitude. The machine learning approach opens up the possibility of more widespread use of fragment-based methods in crystal structure prediction, whose increased accuracy at a low computational cost will benefit applications in areas such as polymorph screening and computer-guided materials discovery. Crystal structure prediction ; Maxima and minima ; Force field (fiction) ; Intermolecular force ; Crystal structure ; Computer science ; Statistical physics ; Lattice (music) ; Atom (system on chip) ; Gaussian ; Crystal (programming language) ; Physics ; Algorithm ; Machine learning ; Molecule ; Artificial intelligence ; Chemistry ; Quantum mechanics ; Mathematics ; Crystallography Machine Learning in Materials Science ; Crystallography and molecular interactions ; Protein Structure and Dynamics
Authors: Han Liu | Zipeng Fu | Kai Yang | Xinyi Xu | Mathieu Bauchy
Source: Journal of Non-Crystalline Solids X (review, 2019-08-22)
Field: Materials Science — Ceramics and Composites

Machine learning for glass science and engineering: A review The design of new glasses is often plagued by poorly efficient Edisonian “trial-and-error” discovery approaches. As an alternative route, the Materials Genome Initiative has largely popularized new approaches relying on artificial intelligence and machine learning for accelerating the discovery and optimization of novel, advanced materials. Here, we review some recent progress in adopting machine learning to accelerate the design of new glasses with tailored properties. Computer science ; Artificial intelligence ; Machine learning ; Data science Glass properties and applications ; Pigment Synthesis and Properties ; Phase-change materials and chalcogenides
Authors: Jiayan Xu | Xiaoming Cao | P. Hu
Source: The Journal of Physical Chemistry C (article, 2019-11-04)
Field: Materials Science — Materials Chemistry

Improved Prediction for the Methane Activation Mechanism on Rutile Metal Oxides by a Machine Learning Model with Geometrical Descriptors Methane activation could occur via either the radical-like or the surface-stabilized mechanism on metal oxides. The linear Brønsted–Evans–Polanyi (BEP) relationship between activation energies and the adsorption energies of products has made it possible to swiftly predict some reaction mechanisms. However, it is not accurate enough to predict the preferential methane activation mechanism on metal oxides. Herein, to improve the prediction for the methane activation mechanism, the machine learning method percentile-LASSO was developed to extract energetic and geometrical descriptors on the basis of a series of surface-stabilized and radical-like transition states of methane activation on rutile-type metal oxides from density functional theory calculations. Revised relations are capable of classifying those two mechanisms on the same surface with a higher accuracy, which will facilitate high-throughput catalyst screening for methane activation on metal oxides. Methane ; Rutile ; Mechanism (biology) ; Metal ; Activation energy ; Catalysis ; Reaction mechanism ; Chemistry ; Adsorption ; Oxide ; Materials science ; Physical chemistry ; Physics ; Organic chemistry Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Catalytic Processes in Materials Science
Authors: Liam Wilbraham | Reiner Sebastian Sprick | Kim E. Jelfs | Martijn A. Zwijnenburg
Source: Chemical Science (article, 2019-01-01)
Field: Materials Science — Materials Chemistry

Mapping binary copolymer property space with neural networks The extremely large number of unique polymer compositions that can be achieved through copolymerisation makes it an attractive strategy for tuning their optoelectronic properties. However, this same attribute also makes it challenging to explore the resulting property space and understand the range of properties that can be realised. In an effort to enable the rapid exploration of this space in the case of binary copolymers, we train a neural network using a tiered data generation strategy to accurately predict the optical and electronic properties of 350 000 binary copolymers that are, in principle, synthesizable from their dihalogen monomers <i>via</i> Yamamoto, or Suzuki-Miyaura and Stille coupling after one-step functionalisation. By extracting general features of this property space that would otherwise be obscured in smaller datasets, we identify simple models that effectively relate the properties of these copolymers to the homopolymers of their constituent monomers, and challenge common ideas behind copolymer design. We find that binary copolymerisation does not appear to allow access to regions of the optoelectronic property space that are not already sampled by the homopolymers, although it conceptually allows for more fine-grained property control. Using the large volume of data available, we test the hypothesis that copolymerisation of 'donor' and 'acceptor' monomers can result in copolymers with a lower optical gap than their related homopolymers. Overall, despite the prevalence of this concept in the literature, we observe that this phenomenon is relatively rare, and propose conditions that greatly enhance the likelihood of its experimental realisation. Finally, through a 'topographical' analysis of the co-polymer property space, we show how this large volume of data can be used to identify dominant monomers in specific regions of property space that may be amenable to a variety of applications, such as organic photovoltaics, light emitting diodes, and thermoelectrics. Copolymer ; Property (philosophy) ; Binary number ; Space (punctuation) ; Materials science ; Polymer ; Artificial neural network ; Computer science ; Polymer chemistry ; Polymer science ; Artificial intelligence ; Mathematics ; Composite material Machine Learning in Materials Science ; Organic Electronics and Photovoltaics ; Advanced Memory and Neural Computing
Authors: Jianfeng Wen | Bingang Xu | Jinyun Zhou
Source: Nano-Micro Letters (article, 2019-10-17)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Toward Flexible and Wearable Embroidered Supercapacitors from Cobalt Phosphides-Decorated Conductive Fibers Wearable supercapacitors (SCs) are gaining prominence as portable energy storage devices. To develop high-performance wearable SCs, the significant relationship among material, structure, and performance inspired us with a delicate design of the highly wearable embroidered supercapacitors made from the conductive fibers composited. By rendering the conductive interdigitally patterned embroidery as both the current collector and skeleton for the SCs, the novel pseudocapacitive material cobalt phosphides were then successfully electrodeposited, forming the first flexible and wearable in-plane embroidery SCs. The electrochemical measurements manifested that the highest specific capacitance was nearly 156.6 mF cm<sup>-2</sup> (65.72 F g<sup>-1</sup>) at the current density of 0.6 mA cm<sup>-2</sup> (0.25 A g<sup>-1</sup>), with a high energy density of 0.013 mWh cm<sup>-2</sup> (5.55 Wh kg<sup>-1</sup>) at a power density of 0.24 mW cm<sup>-2</sup> (100 W kg<sup>-1</sup>). As a demonstration, a monogrammed pattern was ingeniously designed and embroidered on the laboratory gown as the wearable in-plane SCs, which showed both decent electrochemical performance and excellent flexibility. Supercapacitor ; Wearable computer ; Materials science ; Capacitance ; Current density ; Power density ; Electrical conductor ; Wearable technology ; Flexibility (engineering) ; Electrochemistry ; Energy storage ; Energy density ; Nanotechnology ; Composite material ; Computer science ; Engineering physics ; Power (physics) ; Electrode ; Engineering ; Embedded system ; Chemistry ; Physics Supercapacitor Materials and Fabrication ; Advanced battery technologies research ; Electrocatalysts for Energy Conversion
Authors: Er‐Xiong Ding | Aqeel Hussain | Saeed Ahmad | Qiang Zhang | Yongping Liao | Hua Jiang | Esko I. Kauppinen
Source: Nano Research (article, 2019-12-10)
Field: Materials Science — Materials Chemistry

High-performance transparent conducting films of long single-walled carbon nanotubes synthesized from toluene alone Abstract Single-walled carbon nanotube (SWCNT) transparent conducting films (TCFs) are attracting increasing attention due to their exceptional optoelectronic properties. Toluene is a proposed carbon source for SWCNT synthesis, but the growth parameters of SWCNTs and their TCF optoelectronic performance (i.e., sheet resistance versus transmittance) have been insufficiently evaluated. Here, we have for the first time reported a systematic study of the fabrication of high-performance SWCNT TCFs using toluene alone as the carbon source. The mechanisms behind each observed phenomenon were elucidated using optical and microscopy techniques. By optimizing the growth parameters, high yields of SWCNT TCFs exhibiting a considerably low sheet resistance of 57 Ω/sq at 90% transmittance were obtained. This competitive optoelectronic performance is mainly attributable to long SWCNT bundles (mean length is 41.4 μm) in the film. Additionally, a chirality map determined by electron diffraction displays a bimodal distribution of chiral angles divided at 15°, which is close to both armchair and zigzag edges. Our study paved the way towards scaled-up production of SWCNTs for the fabrication of high-performance TCFs for industrial applications. Materials science ; Zigzag ; Transmittance ; Fabrication ; Carbon nanotube ; Sheet resistance ; Toluene ; Optoelectronics ; Diffraction ; Nanotechnology ; Carbon fibers ; Band gap ; Scanning electron microscope ; Optics ; Composite material ; Layer (electronics) ; Chemistry ; Organic chemistry ; Composite number Carbon Nanotubes in Composites ; Nanowire Synthesis and Applications ; Organic Electronics and Photovoltaics
Authors: A. Lázaro | Martí Boada | Ramón Villarino | David Girbau
Source: Sensors (article, 2019-04-11)
Field: Chemistry — Analytical Chemistry

Color Measurement and Analysis of Fruit with a Battery-Less NFC Sensor This paper presents a color-based classification system for grading the ripeness of fruit using a battery-less Near Field Communication (NFC) tag. The tag consists of a color sensor connected to a low-power microcontroller that is connected to an NFC chip. The tag is powered by the energy harvested from the magnetic field generated by a commercial smartphone used as a reader. The raw RGB color data measured by the colorimeter is converted to HSV (hue, saturation, value) color space. The hue angle and saturation are used as features for classification. Different classification algorithms are compared for classifying the ripeness of different fruits in order to show the robustness of the system. The low cost of NFC chips means that tags with sensing capability can be manufactured economically. In addition, nowadays, most commercial smartphones have NFC capability and thus a specific reader is not necessary. The measurement of different samples obtained on different days is used to train the classification algorithms. The results of training the classifiers have been saved to the cloud. A mobile application has been developed for the prediction based on a table-based method, where the boundary decision is downloaded from a cloud service for each product. High accuracy, between 80 and 93%, is obtained depending on the kind of fruit and the algorithm used. Ripeness ; Hue ; RGB color model ; Computer science ; Color space ; HSL and HSV ; Colorimeter ; Microcontroller ; Artificial intelligence ; Computer vision ; Real-time computing ; Computer hardware Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Advanced Chemical Sensor Technologies
Authors: Hongyang Li | Shengyao Jia | Zichun Le
Source: Sensors (article, 2019-10-09)
Field: Chemistry — Analytical Chemistry

Quantitative Analysis of Soil Total Nitrogen Using Hyperspectral Imaging Technology with Extreme Learning Machine Soil nutrient detection is important for precise fertilization. A total of 150 soil samples were picked from Lishui City. In this work, the total nitrogen (TN) content in soil samples was detected in the spectral range of 900-1700 nm using a hyperspectral imaging (HSI) system. Characteristic wavelengths were extracted using uninformative variable elimination (UVE) and the successive projections algorithm (SPA), separately. Partial least squares (PLS) and extreme learning machine (ELM) were used to establish the calibration models with full spectra and characteristic wavelengths, respectively. The results indicated that the prediction effect of the nonlinear ELM model was superior to the linear PLS model. In addition, the models using the characteristic wavelengths could also achieve good results, and the UVE-ELM model performed better, having a correlation coefficient of prediction (r<sub>p</sub>), root-mean-square error of prediction (RMSEP), and residual prediction deviation (RPD) of 0.9408, 0.0075, and 2.97, respectively. The UVE-ELM model was then used to estimate the TN content in the soil sample and obtain a distribution map. The research results indicate that HSI can be used for the detection and visualization of the distribution of TN content in soil, providing a basis for future large-scale monitoring of soil nutrient distribution and rational fertilization. Hyperspectral imaging ; Variable elimination ; Extreme learning machine ; Partial least squares regression ; Residual ; Mean squared error ; Calibration ; Soil test ; Correlation coefficient ; Mathematics ; Soil science ; Artificial intelligence ; Biological system ; Remote sensing ; Environmental science ; Soil water ; Computer science ; Statistics ; Algorithm ; Artificial neural network Spectroscopy and Chemometric Analyses ; Machine Learning and ELM ; Smart Agriculture and AI