jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Li Hou | Gaoyan Jiang | Ying Sun | Xuanhan Zhang | Juanjuan Huang | Shendong Liu | Tianran Lin | Fanggui Ye | Shulin Zhao
Source: Catalysts (article, 2019-12-12)
Field: Materials Science — Materials Chemistry

Progress and Trend on the Regulation Methods for Nanozyme Activity and Its Application Natural enzymes, such as biocatalysts, are widely used in biosensors, medicine and health, the environmental field, and other fields. However, it is easy for natural enzymes to lose catalytic activity due to their intrinsic shortcomings including a high purification cost, insufficient stability, and difficulties of recycling, which limit their practical applications. The unexpected discovery of the Fe3O4 nanozyme in 2007 has given rise to tremendous efforts for developing natural enzyme substitutes. Nanozymes, which are nanomaterials with enzyme-mimetic catalytic activity, can serve as ideal candidates for artificial mimic enzymes. Nanozymes possess superiorities due to their low cost, high stability, and easy preparation. Although great progress has been made in the development of nanozymes, the catalytic efficiency of existing nanozymes is relatively low compared with natural enzymes. It is still a challenging task to develop nanozymes with a precise regulation of catalytic activity. This review summarizes the classification and various strategies for modulating the activity as well as research progress in the different application fields of nanozymes. Typical examples of the recent research process of nanozymes will be presented and critically discussed. Biochemical engineering ; Nanotechnology ; Computer science ; Process (computing) ; Risk analysis (engineering) ; Engineering ; Materials science ; Business Advanced Nanomaterials in Catalysis ; Nanocluster Synthesis and Applications ; Carbon and Quantum Dots Applications
Authors: Marie S. Norre | Chen Gao | Sourav Dey | Sandeep K. Gupta | Aditya Borah | Ramaswamy Murugavel | Gopalan Rajaraman | Jacob Overgaard
Source: Inorganic Chemistry (article, 2019-12-12)
Field: Materials Science — Electronic, Optical and Magnetic Materials

High-Pressure Crystallographic and Magnetic Studies of Pseudo-<i>D</i><sub>5<i>h</i></sub> Symmetric Dy(III) and Ho(III) Single-Molecule Magnets Single-ion magnets based on lanthanide ions in pseudo-<i>D</i><sub>5<i>h</i></sub> symmetry have gained much attention in recent years as they are reported to possess a large blocking temperature and a large barrier for magnetization reversal. Magneto-structural correlations reveal that the axial O-Ln-O angle is an important parameter to control the barrier, and while it can be fine-tuned by chemical modification, an alternative would be to utilize hydrostatic pressure. Herein, we report the crystal structures and static magnetic properties of two air-stable isostructural lanthanide SIMs under applied pressures. The complexes exhibit pseudo-<i>D</i><sub>5<i>h</i></sub> symmetry around the Ln(III)-ion (Ln = Dy or Ho), which coordinates to five equatorial water molecules and two large neutral phosphonic diamide ligands along the axial direction. High-pressure single-crystal X-ray diffraction experiments revealed two phase-transitions and an increasing deviation from <i>D</i><sub>5<i>h</i></sub>-symmetry between ambient pressure and 3.6 GPa. High-pressure direct-current magnetic measurements of the Dy(III) compound showed large steps in the hysteresis loops near zero field, indicative of quantum tunneling of magnetization (QTM). These steps grow in size with increasing pressure, suggesting that QTM becomes progressively more active, which correlates well with the pressure-induced increased overall deviation from pseudo-<i>D</i><sub>5<i>h</i></sub> symmetry and decreasing axial O-Dy-O angle. A strong temperature dependence of the step size is seen at 0.3 GPa, which shows that the SMM character persists even at this pressure. To understand the origin of significant variation in the tunneling probability upon pressure, we performed a range of <i>ab initio</i> calculations based on the CASSCF/RASSI-SO/SINGLE_ANISO method on both Dy and Ho complexes. From the energies and magnetic anisotropy of the <i>m</i><sub>J</sub> sublevels, we find a complex variation of the energy barrier with pressure, and using a constructed geometrical parameter, <i>R</i>, taking into account changes in both bond angles and distances, we link the magnetic properties to the first coordination sphere of the molecules. Chemistry ; Isostructural ; Hydrostatic pressure ; Magnetization ; Lanthanide ; Quantum tunnelling ; Ion ; Crystallography ; Single-molecule magnet ; Symmetry (geometry) ; Single crystal ; Condensed matter physics ; Molecule ; Magnet ; Phase (matter) ; Magnetic field ; Crystal structure ; Thermodynamics ; Geometry ; Physics Magnetism in coordination complexes ; Lanthanide and Transition Metal Complexes ; Organic and Molecular Conductors Research
Authors: Amanda Andersen | Yaqing Chen | Henrik Birkedal
Source: Biomimetics (review, 2019-04-04)
Field: Materials Science — Surfaces, Coatings and Films

Bioinspired Metal–Polyphenol Materials: Self-Healing and Beyond The blue mussel incorporates the polyphenolic amino acid l-3,4-dihydroxyphenylalanine (DOPA) to achieve self-healing, pH-responsiveness, and impressive underwater adhesion in the byssus threads that ensure the survival of the animal. This is achieved by a pH-dependent and versatile reaction chemistry of polyphenols, including both physical interactions as well as reversible and irreversible chemical bonding. With a short introduction to the biological background, we here review the latest advances in the development of smart materials based on the metal-chelating capabilities of polyphenols. We focus on new ways of utilizing the polyphenolic properties, including studies on the modifications of the nearby chemical environment (on and near the polyphenolic moiety) and on the incorporation of polyphenols into untraditional materials. Polyphenol ; Byssus ; Chemistry ; Metal ; Nanotechnology ; Moiety ; Materials science ; Organic chemistry ; Antioxidant ; Mussel ; Ecology ; Biology Polymer Surface Interaction Studies ; Marine Biology and Environmental Chemistry ; Bone Tissue Engineering Materials
Authors: Jiazhen Chen | Alessandro Motta | Binghao Wang | Yanshan Gao | Tobin J. Marks
Source: Angewandte Chemie International Edition (article, 2019-03-26)
Field: Chemistry — Organic Chemistry

Significant Polar Comonomer Enchainment in Zirconium‐Catalyzed, Masking Reagent‐Free, Ethylene Copolymerizations In principal, the direct copolymerization of ethylene with polar comonomers should be the most efficient means to introduce functional groups into conventional polyolefins but remains a formidable challenge. Despite the tremendous advances in group 4-centered catalysis for olefin polymerization, successful examples of ethylene + polar monomer copolymerization are rare, especially without Lewis acidic masking reagents. Here we report that certain group 4 catalysts are very effective for ethylene + CH<sub>2</sub> =CH(CH<sub>2</sub> )<sub>n</sub> NR<sub>2</sub> copolymerizations with activities up to 3400 Kg copolymer mol<sup>-1</sup> -Zr h<sup>-1</sup> atm<sup>-1</sup> , and with comonomer enchainment up to 5.5 mol % in the absence of masking reagents. Group 4 catalyst-amino-olefin structure-activity-selectivity relationships reflect the preference of olefin activation over free amine coordination, which is supported by mechanistic experiments and DFT analysis. These results illuminate poorly understood facets of d<sup>0</sup> metal-catalyzed polar olefin monomer copolymerization processes. Comonomer ; Copolymer ; Monomer ; Olefin fiber ; Polymer chemistry ; Ethylene ; Reagent ; Catalysis ; Zirconium ; Chemistry ; Post-metallocene catalyst ; Polymerization ; Materials science ; Organic chemistry ; Metallocene ; Polymer Organometallic Complex Synthesis and Catalysis ; Carbon dioxide utilization in catalysis ; Synthetic Organic Chemistry Methods
Authors: Vidur Raj | Teng Lü | Mark Lockrey | Rong Liu | Felipe Kremer | Li Li | Yun Liu | Hark Hoe Tan | C. Jagadish
Source: ACS Applied Materials & Interfaces (article, 2019-06-17)
Field: Materials Science — Materials Chemistry

Introduction of TiO<sub>2</sub> in CuI for Its Improved Performance as a p-Type Transparent Conductor The challenges of making high-performance, low-temperature processed, p-type transparent conductors (TCs) have been the main bottleneck for the development of flexible transparent electronics. Though a few p-type transparent conducting oxides (TCOs) have shown promising results, they need high processing temperature to achieve the required conductivity which makes them unsuitable for organic and flexible electronic applications. Copper iodide is a wide band gap p-type semiconductor that can be heavily doped at low temperature (<100 °C) to achieve conductivity comparable or higher than many of the well-established p-type TCOs. However, as-processed CuI loses its transparency and conductivity with time in an ambient condition which makes them unsuitable for long-term applications. Herein, we propose CuI-TiO<sub>2</sub> composite thin films as a replacement of pure CuI. We show that the introduction of TiO<sub>2</sub> in CuI makes it more stable in ambient conditions while also improving its conductivity and transparency. A detailed comparative analysis between CuI and CuI-TiO<sub>2</sub> composite thin films has been performed to understand the reasons for improved conductivity, transparency, and stability of CuI-TiO<sub>2</sub> samples in comparison to pure CuI samples. The enhanced conductivity in CuI-TiO<sub>2</sub> stems from the highly conductive space-charge layer formation at the CuI-TiO<sub>2</sub> interface, whereas the improved transparency is due to reduced CuI grain growth mobility in the presence of TiO<sub>2</sub>. The improved stability of CuI-TiO<sub>2</sub> in comparison to pure CuI is a result of inhibited recrystallization and grain growth, reduced loss of iodine, and limited oxidation of the CuI phase in the presence of TiO<sub>2</sub>. For optimized fraction of TiO<sub>2</sub>, an average transparency of ∼78% (in 450-800 nm region) and a resistivity of 14 mΩ·cm are achieved, while maintaining a relatively high mobility of ∼3.5 cm<sup>2</sup> V<sup>-1</sup> s<sup>-1</sup> with hole concentration reaching as high as 1.3 × 10<sup>20</sup> cm<sup>-3</sup>. Most importantly, this work opens up the possibility to design a new range of p-type transparent conducting materials using the CuI/insulator composite system such as CuI/SiO<sub>2</sub>, CuI/Al<sub>2</sub>O<sub>3</sub>, CuI/SiN<sub><i>x</i></sub>, and so forth. Materials science ; Conductor ; Type (biology) ; Optoelectronics ; Electrical conductor ; Engineering physics ; Nanotechnology ; Composite material Copper-based nanomaterials and applications ; ZnO doping and properties ; Semiconductor materials and devices
Authors: Marjan Alsadat Kashfipour | Nitin Mehra | Russell S. Dent | Jiahua Zhu
Source: Engineered Science (article, 2019-04-17)
Field: Materials Science — Materials Chemistry

Regulating Intermolecular Chain Interaction of Biopolymer with Natural Polyol for Flexible, Optically Transparent and Thermally Conductive Hybrids Natural polymers like cellulose are rich in hydrophilic groups those are able to form intermolecular and intramolecular hydrogen bonding interactions. The regulation of such interactions can be critical for tuning material functionality. In this work, we designed a sustainable hybrid comprising of sodium carboxymethyl cellulose (SCMC) and xylitol, in which the molecular interaction is tuned for significantly enhanced thermal conductivity (TC). With optimized ratio of SCMC and xylitol, enhanced TC of up to 1.75 times of neat SCMC was achieved in addition to excellent flexibility and optical transparency. These enhancements are attributed to the formation of new H-bonds between SCMC and xylitol molecules resulting in formation of homogenously distributed thermal bridges throughout the polymer matrix. The intermolecular interaction in SCMC-xylitol composites elucidates some of the fundamental factors (e.g. H-bond intensity) responsible for promoted phonon transfer in polymeric materials, and at the same time sheds light on the capability of employing biopolymer-polyol based materials for thermal management applications. Biopolymer ; Intermolecular force ; Hydrogen bond ; Polyol ; Intramolecular force ; Materials science ; Xylitol ; Polymer ; Molecule ; Hydrophobic effect ; Chemical engineering ; Chemical physics ; Carboxymethyl cellulose ; Chemistry ; Composite material ; Organic chemistry ; Fermentation ; Sodium Thermal properties of materials ; Synthesis and properties of polymers ; Aerogels and thermal insulation
Authors: Masanori Sakamoto | Tokuhisa Kawawaki | Masato Kimura | Taizo Yoshinaga | Junie Jhon M. Vequizo | Hironori Matsunaga | Chandana Sampath Kumara Ranasinghe | Akira Yamakata | Hiroyuki Matsuzaki | Akihiro Furube | Toshiharu Teranishi
Source: Nature Communications (article, 2019-01-24)
Field: Materials Science — Materials Chemistry

Clear and transparent nanocrystals for infrared-responsive carrier transfer Infrared-light-induced carrier transfer is a key technology for 'invisible' optical devices for information communication systems and energy devices. However, clear and colourless photo-induced carrier transfer has not yet been demonstrated in the field of photochemistry, to the best of our knowledge. Here, we resolve this problem by employing short-wavelength-infrared (1400-4000 nm) localized surface plasmon resonance-induced electron injection from indium tin oxide nanocrystals to transparent metal oxides. The time-resolved infrared measurements visualize the dynamics of the carrier in this invisible system. Selective excitation of localized surface plasmon resonances causes hot electron injection with high efficiency (33%) and long-lived charge separation (~ 2-200 μs). We anticipate our study not only provides a breakthrough for plasmonic carrier transfer systems but may also stimulate the invention of state-of-the-art invisible optical devices. Plasmon ; Materials science ; Infrared ; Optoelectronics ; Charge carrier ; Indium tin oxide ; Nanocrystal ; Surface plasmon resonance ; Excitation ; Indium ; Nanoparticle ; Nanotechnology ; Thin film ; Optics ; Physics Quantum Dots Synthesis And Properties ; Gold and Silver Nanoparticles Synthesis and Applications ; Copper-based nanomaterials and applications
Authors: Daniel W. Davies | Keith T. Butler | Aron Walsh
Source: Chemistry of Materials (article, 2019-08-26)
Field: Materials Science — Materials Chemistry

Data-Driven Discovery of Photoactive Quaternary Oxides Using First-Principles Machine Learning We present a low-cost, virtual high-throughput materials design workflow and use it to identify earth-abundant materials for solar energy applications from the quaternary oxide chemical space. A statistical model that predicts bandgap from chemical composition is built using supervised machine learning. The trained model forms the first in a hierarchy of screening steps. An ionic substitution algorithm is used to assign crystal structures, and an oxidation state probability model is used to discard unlikely chemistries. We demonstrate the utility of this process for screening over 1 million oxide compositions. We find that, despite the difficulties inherent to identifying stable multicomponent inorganic materials, several compounds produced by our workflow are calculated to be thermodynamically stable or metastable and have desirable optoelectronic properties according to first-principles calculations. The predicted oxides are Li2MnSiO5, MnAg(SeO3)2, and two polymorphs of MnCdGe2O6, all four of which are found to have direct electronic bandgaps in the visible range of the solar spectrum. Band gap ; Metastability ; Ionic bonding ; Oxide ; Workflow ; Characterization (materials science) ; Materials science ; Computer science ; Hierarchy ; Chemical space ; Chemical physics ; Nanotechnology ; Chemistry ; Optoelectronics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Electronic and Structural Properties of Oxides
Authors: Cristiano Fragassa | Matej Babič | Carlos Pérez Bergmann | Giangiacomo Minak
Source: Metals (article, 2019-05-13)
Field: Materials Science — Materials Chemistry

Predicting the Tensile Behaviour of Cast Alloys by a Pattern Recognition Analysis on Experimental Data The ability to accurately predict the mechanical properties of metals is essential for their correct use in the design of structures and components. This is even more important in the presence of materials, such as metal cast alloys, whose properties can vary significantly in relation to their constituent elements, microstructures, process parameters or treatments. This study shows how a machine learning approach, based on pattern recognition analysis on experimental data, is able to offer acceptable precision predictions with respect to the main mechanical properties of metals, as in the case of ductile cast iron and compact graphite cast iron. The metallographic properties, such as graphite, ferrite and perlite content, extrapolated through macro indicators from micrographs by image analysis, are used as inputs for the machine learning algorithms, while the mechanical properties, such as yield strength, ultimate strength, ultimate strain and Young’s modulus, are derived as output. In particular, 3 different machine learning algorithms are trained starting from a dataset of 20–30 data for each material and the results offer high accuracy, often better than other predictive techniques. Concerns regarding the applicability of these predictive techniques in material design and product/process quality control are also discussed. Cast iron ; Ultimate tensile strength ; Materials science ; Ferrite (magnet) ; Graphite ; Experimental data ; Modulus ; Papermaking ; Computer science ; Mechanical engineering ; Metallurgy ; Composite material ; Mathematics ; Engineering Machine Learning in Materials Science ; Microstructure and Mechanical Properties of Steels ; Non-Destructive Testing Techniques
Authors: Rohit Batra | Tran Doan Huan | Chiho Kim | James Chapman | Lihua Chen | Anand Chandrasekaran | Rampi Ramprasad
Source: The Journal of Physical Chemistry C (article, 2019-06-06)
Field: Materials Science — Materials Chemistry

General Atomic Neighborhood Fingerprint for Machine Learning-Based Methods To facilitate chemical space exploration for material screening or to accelerate computationally expensive first-principles simulations, inexpensive surrogate models that capture electronic, atomistic, or macroscopic materials properties have become an increasingly popular tool over the last decade. The most fundamental quantity common across all such machine learning (ML)-based methods is the fingerprint used to numerically represent a material or its structure. To increase the learning capability of the ML methods, the common practice is to construct fingerprints that satisfy the same symmetry relations as displayed by the target material property of interest (for which the ML model is being developed). Thus, in this work, we present a general, simple, and elegant fingerprint that can be used to learn different electronic/atomistic/structural properties, irrespective of their scalar, vector, or tensorial nature. This fingerprint is based on the concept of multipole terms and can be systematically increased in sophistication to achieve a desired level of accuracy. Using the examples of Al, C, and hafnia (HfO2), we demonstrate the applicability of this fingerprint to easily classify different atomistic environments, such as phases, surfaces, point defects, and so forth. Furthermore, we demonstrate the generality and effectiveness of this fingerprint by building an accurate, yet inexpensive, ML-based potential energy model for the case of Al using a reference data set that is obtained from density functional theory computations. Finally, we note that the fingerprint definition presented here has applications in fields beyond materials informatics, such as structure prediction, identification of defects, and detection of new crystal phases. Computer science ; Artificial intelligence ; Fingerprint (computing) ; Machine learning ; Generality ; Algorithm Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Electron and X-Ray Spectroscopy Techniques
Authors: J. P. Coe
Source: Journal of Chemical Theory and Computation (article, 2019-10-17)
Field: Materials Science — Materials Chemistry

Machine Learning Configuration Interaction for ab Initio Potential Energy Curves The concept of machine learning configuration interaction (MLCI) (<i>J. Chem. Theory Comput.</i> 2018, 14, 5739), where an artificial neural network (ANN) learns on the fly to select important configurations, is further developed so that accurate ab initio potential energy curves can be efficiently calculated. This development includes employing the artificial neural network also as a hash function for the efficient deletion of duplicates on the fly so that the single and double space does not need to be stored and this barrier to scalability is removed. In addition, configuration state functions are introduced into the approach so that pure spin states are guaranteed, and the transferability of data between geometries is exploited. This improved approach is demonstrated on potential energy curves for the nitrogen molecule, water, and carbon monoxide. The results are compared with full configuration interaction values, when available, and different transfer protocols are investigated. It is shown that, for all of the considered systems, accurate potential energy curves can now be efficiently computed with MLCI. For the potential curves of N<sub>2</sub> and CO, MLCI can achieve lower errors than stochastically selecting configurations while also using substantially less processor hours. Ab initio ; Computer science ; Potential energy ; Energy (signal processing) ; Learning curve ; Configuration interaction ; Artificial intelligence ; Machine learning ; Physics ; Atomic physics ; Quantum mechanics ; Operating system Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics