jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Dan Zhou | Leilei Zhang | Xiaoyan Liu | Haifeng Qi | Qinggang Liu | Ji Yang | Yang Su | Jingyuan Ma | Jianzhong Yin | Aiqin Wang
Source: Nano Research (article, 2021-06-11)
Field: Chemistry — Organic Chemistry

Tuning the coordination environment of single-atom catalyst M-N-C towards selective hydrogenation of functionalized nitroarenes Fine-tuning of the coordination environment of single-atom catalysts (SACs) is effective to optimize their catalytic performances, yet it remains challenging due to the vulnerability of SACs. Herein, we report a new approach to engineering the coordination environment of M-N-C (M = Fe, Co, and Ni) SACs by using glutamic acid as the N/C source and pyrolysis atmosphere as a regulator. Compared with that in N2, NH3 was able to promote the doping of N at T < 700 °C yet etch the N-species at higher temperatures, by which the M-N coordination number (CN) and the electronic structure were delicately tuned. It was found that the electron density of Ni single atoms increased with the decrease of Ni-N CN. As a consequence, the capability of Ni-N-C to dissociate H2 was greatly enhanced and a higher catalytic activity in chemoselective hydrogenation of functionalized nitroarenes was achieved. Moreover, this modulation method could be applied to other transition metals including Fe and Co. In particular, the as-synthesized Co-N-C SAC afforded a turnover frequency of 152.3 h−1 with 99% selectivity to 3-vinylaniline in the hydrogenation of 3-nitrostyrene, which was the highest ever reported thus far and was at least one order of magnitude more active than state-of-the-art noble-metal-free M-N-C catalysts, demonstrating the great potential of engineering the coordination environment of SACs. Catalysis ; Selectivity ; Transition metal ; Coordination complex ; Chemistry ; Atom (system on chip) ; Coordination number ; Metal ; Coordination sphere ; Inorganic chemistry ; Nanotechnology ; Materials science ; Organic chemistry ; Ion Nanomaterials for catalytic reactions ; Ammonia Synthesis and Nitrogen Reduction ; Advanced Photocatalysis Techniques
Authors: Andrea Rónavári | Péter Bélteky | Eszter Boka | Dalma Zakupszky | Nóra Igaz | Bettina Szerencsés | Ilona Pfeiffer | Zoltán Kónya | Mónika Kiricsi
Source: International Journal of Molecular Sciences (article, 2021-08-12)
Field: Materials Science — Materials Chemistry

Polyvinyl-Pyrrolidone-Coated Silver Nanoparticles—The Colloidal, Chemical, and Biological Consequences of Steric Stabilization under Biorelevant Conditions (1) Background: Several properties of silver nanoparticles (AgNPs), such as cytotoxic, anticancer, and antimicrobial activities, have been subjects of intense research; however, important aspects such as nanoparticle aggregation are generally neglected, although a decline in colloidal stability leads to a loss of the desired biological activities. Colloidal stability is affected by pH, ionic strength, or a plethora of biomolecules that interact with AgNPs under biorelevant conditions. (2) Methods: As only a few studies have focused on the relationship between aggregation behavior and the biological properties of AgNPs, here, we have systematically evaluated this issue by completing a thorough analysis of sterically (via polyvinyl-pyrrolidone (PVP)) stabilized AgNPs that were subjected to different circumstances. We assessed ultraviolet-visible light absorption, dynamic light scattering, zeta potential measurements, in vitro cell viability, and microdilution assays to screen both colloidal stability as well as bioactivity. (3) Results: The results revealed that although PVP provided outstanding biorelevant colloidal stability, the chemical stability of AgNPs could not be maintained completely with this capping material. (4) Conclusion: These unexpected findings led to the realization that stabilizing materials have more profound importance in association with biorelevant applications of nanomaterials than just being simple colloidal stabilizers. Colloid ; Chemistry ; Dynamic light scattering ; Silver nanoparticle ; Biomolecule ; Zeta potential ; Ionic strength ; Nanoparticle ; Nanomaterials ; Nanotechnology ; Chemical engineering ; Steric effects ; Chemical stability ; Materials science ; Organic chemistry ; Biochemistry ; Aqueous solution Nanoparticles: synthesis and applications ; Nanoparticle-Based Drug Delivery ; Gold and Silver Nanoparticles Synthesis and Applications
Authors: D.L. Reger | Philipp Haines | Konstantin Amsharov | J.A.R. Schmidt | Tobias Ullrich | Simon Bönisch | Frank Hampel | Andreas Görling | Jenny Nelson | Kim E. Jelfs | Dirk M. Guldi | Norbert Jux
Source: Angewandte Chemie International Edition (article, 2021-05-20)
Field: Chemistry — Organic Chemistry

A Family of Superhelicenes: Easily Tunable, Chiral Nanographenes by Merging Helicity with Planar π Systems We designed a straightforward synthetic route towards a full-fledged family of π-extended helicenes: superhelicenes. They have two hexa-peri-hexabenzocoronenes (HBCs) in common that are connected via a central five-membered ring. By means of structurally altering this 5-membered ring, we realized a versatile library of molecular building blocks. Not only the superhelicene structure, but also their features are tuned with ease. In-depth physico-chemical characterizations served as a proof of concept thereof. The superhelicene enantiomers were separated, their circular dichroism was measured in preliminary studies and concluded with an enantiomeric assignment. Our work was rounded-off by crystal structure analyses. Mixed stacks of M- and P-isomers led to twisted molecular wires. Using such stacks, charge-carrier mobilities were calculated, giving reason to expect outstanding hole transporting properties. Ring (chemistry) ; Planar ; Enantiomer ; Circular dichroism ; Helicity ; Crystallography ; Materials science ; Chemistry ; Stereochemistry ; Physics ; Computer science ; Organic chemistry Synthesis and Properties of Aromatic Compounds ; Advanced NMR Techniques and Applications ; Molecular spectroscopy and chirality
Authors: Gustavo Navarro | Jorge Torres | Marcos Blanco | Jorge Nájera | Miguel Santos-Herrán | Marcos Lafoz
Source: Energies (article, 2021-05-25)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Present and Future of Supercapacitor Technology Applied to Powertrains, Renewable Generation and Grid Connection Applications Energy storage systems (ESS) are becoming essential as a solution for troublesome industrial systems. This study focuses on the application of a type of ESS, a high-power technology known in the literature as supercapacitors or electric double layer capacitors (EDLC). This technology has had a huge impact during the last decade on research related to the electric traction drives, renewable sources and powergrids. Related to this aspect, this paper summarizes the most relevant scientific publications in the last five years that study the use of supercapacitor technology (SCs) in electric traction applications (drives for rail vehicles and drives for road vehicles), generation systems for renewable energy (wind, solar and wave energy), and connection systems to the electric grid (voltage and frequency regulation and microgrids). The technology based on EDLC and the practical aspects that must be taken into account in the op-eration of these systems in industrial applications are briefly described. For each of the aforementioned applications, it is described how the problems are solved by using the energy storage technology, drawing the solutions proposed by different authors. Special attention is paid to the control strategies when combining SCs with other technologies, such as batteries. As a summary, some conclusions are collected drawn from the publications analyzed, evaluating the aspects in which it is necessary to conduct further research in order to facilitate the integration of EDLC technology. Supercapacitor ; Renewable energy ; Energy storage ; Powertrain ; Grid ; Engineering ; Grid connection ; Computer science ; Electrical engineering ; Automotive engineering ; Telecommunications ; Systems engineering ; Wind power ; Power (physics) ; Capacitance Supercapacitor Materials and Fabrication ; Electric and Hybrid Vehicle Technologies ; Microgrid Control and Optimization
Authors: Amir Hajibabaei | Kwang S. Kim
Source: The Journal of Physical Chemistry Letters (article, 2021-08-19)
Field: Materials Science — Materials Chemistry

Universal Machine Learning Interatomic Potentials: Surveying Solid Electrolytes We apply ab initio molecular dynamics (AIMD) with on-the-fly machine learning (ML) of interatomic potentials using the sparse Gaussian process regression (SGPR) algorithm for a survey of Li diffusivity in hundreds of ternary crystals as potential electrolytes for all-solid-state batteries. We show that models generated for these crystals can be easily combined for creating more general and transferable models which can potentially be used for simulating new materials without further training. As examples, universal potentials are created for Li-P-S and Li-Sb-S systems by combining the expert models of the crystals which contained the same set of elements. We also show that combinatorial models of different ternary crystals can be directly applied for modeling composite quaternary ones (e.g., Li-Ge-P-S). This hierarchical approach paves the way for modeling large-scale complexity by a combinatorial approach. Ternary operation ; Interatomic potential ; Statistical physics ; Computer science ; Kriging ; Gaussian ; Process (computing) ; Electrolyte ; Ab initio ; Thermal diffusivity ; Set (abstract data type) ; Molecular dynamics ; Materials science ; Chemical physics ; Thermodynamics ; Machine learning ; Chemistry ; Physics ; Computational chemistry ; Physical chemistry ; Quantum mechanics Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Thermal and Kinetic Analysis
Authors: April M Miksch | Tobias Morawietz | Johannes Kästner | Alexander Urban | Nongnuch Artrith
Source: Machine Learning Science and Technology (article, 2021-04-30)
Field: Materials Science — Materials Chemistry

Strategies for the construction of machine-learning potentials for accurate and efficient atomic-scale simulations Abstract Recent advances in machine-learning interatomic potentials have enabled the efficient modeling of complex atomistic systems with an accuracy that is comparable to that of conventional quantum-mechanics based methods. At the same time, the construction of new machine-learning potentials can seem a daunting task, as it involves data-science techniques that are not yet common in chemistry and materials science. Here, we provide a tutorial-style overview of strategies and best practices for the construction of artificial neural network (ANN) potentials. We illustrate the most important aspects of (a) data collection, (b) model selection, (c) training and validation, and (d) testing and refinement of ANN potentials on the basis of practical examples. Current research in the areas of active learning and delta learning are also discussed in the context of ANN potentials. This tutorial review aims at equipping computational chemists and materials scientists with the required background knowledge for ANN potential construction and application, with the intention to accelerate the adoption of the method, so that it can facilitate exciting research that would otherwise be challenging with conventional strategies. Atomic units ; Scale (ratio) ; Computer science ; Artificial intelligence ; Machine learning ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Electron and X-Ray Spectroscopy Techniques
Authors: Eric Jonas | Stefan Kühn | Nils E. Schlörer
Source: Magnetic Resonance in Chemistry (review, 2021-11-17)
Field: Chemistry — Spectroscopy

Prediction of chemical shift in NMR: A review Calculation of solution-state NMR parameters, including chemical shift values and scalar coupling constants, is often a crucial step for unambiguous structure assignment. Data-driven (sometimes called empirical) methods leverage databases of known parameter values to estimate parameters for unknown or novel molecules. This is in contrast to popular ab initio techniques that use detailed quantum computational chemistry calculations to arrive at parameter estimates. Data-driven methods have the potential to be considerably faster than ab inito techniques and have been the subject of renewed interest over the past decade with the rise of high-quality databases of NMR parameters and novel machine learning methods. Here, we review these methods, their strengths and pitfalls, and the databases they are built on. Chemistry ; Leverage (statistics) ; Ab initio ; Computational chemistry ; Quantum chemical ; Quantum chemistry ; Chemical shift ; J-coupling ; Coupling constant ; Scalar (mathematics) ; Molecule ; Nuclear magnetic resonance spectroscopy ; Statistical physics ; Computer science ; Artificial intelligence ; Quantum mechanics ; Physical chemistry ; Physics ; Organic chemistry ; Mathematics Molecular spectroscopy and chirality ; Metabolomics and Mass Spectrometry Studies ; Spectroscopy and Quantum Chemical Studies
Authors: Vy Anh Tran | Sang‐Wha Lee
Source: RSC Advances (article, 2021-01-01)
Field: Chemistry — Inorganic Chemistry

pH-triggered degradation and release of doxorubicin from zeolitic imidazolate framework-8 (ZIF8) decorated with polyacrylic acid Zeolite imidazolate framework-8 (ZIF8) represents a class of highly porous materials with a very high surface area, large pore volume, thermal stability, and biocompatibility. In this study, ZIF8-based nanostructures demonstrated a high loading capacity for doxorubicin (62 mg Dox per g ZIF8) through the combination of π-π stacking, hydrogen bonding, and electrostatic interactions. Dox-loaded ZIF8 was subsequently decorated with polyacrylic acid (PAA) (ZIF8-Dox@PAA) that showed good dispersity, fluorescent imaging capability, and pH-responsive drug release. The stable localization and association of Dox in ZIF8@PAA were investigated by C<sup>13</sup> nuclear magnetic resonance (NMR) and Fourier transform infrared spectroscopy. The NMR chemical shifts suggest the formation of hydrogen bonding interactions and π-π stacking interactions between the imidazole ring of ZIF8 and the benzene ring of Dox that can significantly improve the storage of Dox in the ZIF8 nanostructure. Additionally, the release mechanism of ZIF8-Dox@PAA was discussed based on the detachment of the PAA layer, enhanced solubility of Dox, and destruction of ZIF8 at different pH conditions. <i>In vitro</i> release test of ZIF8-Dox@PAA at pH 7.4 showed the low release rate of 24.7% even after 100 h. However, ZIF8-Dox@PAA at pH 4.0 exhibited four stages of release profiles, significantly enhanced release rate of 84.7% at the final release stage after 30 h. The release kinetics of ZIF8-Dox@PAA was analyzed by the sigmoidal Hill, exponential Weibull, and two-stage BiDoseResp models. The ZIF8-Dox@PAA nanocarrier demonstrated a promising theranostic nanoplatform equipped with fluorescent bioimaging, pH-responsive controlled drug release, and high drug loading capacity. Polyacrylic acid ; Zeolitic imidazolate framework ; Degradation (telecommunications) ; Imidazolate ; Chemistry ; Doxorubicin ; Chemical engineering ; Nuclear chemistry ; Inorganic chemistry ; Organic chemistry ; Polymer ; Metal-organic framework ; Adsorption ; Computer science Metal-Organic Frameworks: Synthesis and Applications ; Nanoplatforms for cancer theranostics ; Analytical Chemistry and Sensors
Authors: Matthew Witman | Gustav Ek | Sanliang Ling | Jeffery M. Chames | Sapan Agarwal | Justin Wong | Mark D. Allendorf | Martin Sahlberg | Vitalie Stavila
Source: Chemistry of Materials (article, 2021-04-28)
Field: Materials Science — Materials Chemistry

Data-Driven Discovery and Synthesis of High Entropy Alloy Hydrides with Targeted Thermodynamic Stability Solid-state hydrogen storage materials that are optimized for specific use cases could be a crucial facilitator of the hydrogen economy transition. Yet, the discovery of novel hydriding materials has historically been a manual process driven by chemical intuition or experimental trial and error. Data-driven materials’ discovery paradigms provide an alternative to traditional approaches, whereby machine/statistical learning (ML) models are used to efficiently screen materials for desired properties and significantly narrow the scope of expensive/time-consuming first-principles modeling and experimental validation. Here, we specifically focus on a relatively new class of hydrogen storage materials, high entropy alloy (HEA) hydrides, whose vast combinatorial composition space and local structural disorder necessitate a data-driven approach that does not rely on exact crystal structures to make property predictions. Our ML model quickly screens hydride stability within a large HEA space and permits down selection for laboratory validation based on not only targeted thermodynamic properties but also secondary criteria such as alloy phase stability and density. To experimentally verify our predictions, we performed targeted synthesis and characterization of several novel hydrides that demonstrate significant destabilization (70× increase in equilibrium pressure, 20 kJ/molH2 decrease in desorption enthalpy) relative to the benchmark HEA hydride, TiVZrNbHfHx. Ultimately, by providing a large composition space in which hydride thermodynamics can be continuously tuned over a wide range, this work will enable efficient material selection for various applications, especially in areas such as metal hydride-based hydrogen compressors, actuators, and heat pumps. Alloy ; Chemical stability ; Materials science ; Thermodynamics ; Entropy (arrow of time) ; Metallurgy ; Physics Hydrogen Storage and Materials ; Nuclear Materials and Properties ; High Entropy Alloys Studies
Authors: Nikolaos Kaplaneris | Jongwoo Son | Lorena Mendive‐Tapia | Adelina Kopp | Nicole D. Barth | Isaac Maksso | Marc Vendrell | Lutz Ackermann
Source: Nature Communications (article, 2021-06-07)
Field: Chemistry — Organic Chemistry

Chemodivergent manganese-catalyzed C–H activation: modular synthesis of fluorogenic probes Bioorthogonal late-stage diversification of amino acids and peptides bears enormous potential for drug discovery and molecular imaging. Despite major accomplishments, these strategies largely rely on traditional, lengthy prefunctionalization methods, heavily involving precious transition-metal catalysis. Herein, we report on a resource-economical manganese(I)-catalyzed C-H fluorescent labeling of structurally complex peptides ensured by direct alkynylation and alkenylation manifolds. This modular strategy sets the stage for unraveling structure-activity relationships between structurally discrete fluorophores towards the rational design of BODIPY fluorogenic probes for real-time analysis of immune cell function. Bioorthogonal chemistry ; Combinatorial chemistry ; Catalysis ; Modular design ; Chemistry ; Manganese ; Computational biology ; Nanotechnology ; Biochemistry ; Computer science ; Biology ; Click chemistry ; Materials science ; Organic chemistry Catalytic C–H Functionalization Methods ; Click Chemistry and Applications ; Fluorine in Organic Chemistry
Authors: Yaxin Yu | Jie Yang | Kake Zhu | Zhi‐Jun Sui | De Chen | Yi‐An Zhu | Xinggui Zhou
Source: ACS Catalysis (article, 2021-07-06)
Field: Materials Science — Materials Chemistry

High-Throughput Screening of Alloy Catalysts for Dry Methane Reforming Dry methane reforming (DMR) is a promising technique aiming at converting two major greenhouse gases into useful chemical feedstocks. A major challenge in the commercialization of this process is to develop a suitable catalyst with long-term stability, strong catalytic activity, and low cost. In this work, a microkinetic analysis coupled with a descriptor-based approach is conducted to study the trend in the catalytic activity across eight transition metals, where the formation energies of adsorbed C and O are identified as two reactivity descriptors. The catalytic properties of the close-packed (111) and stepped (211) surfaces are compared to show the structure sensitivity of the DMR reaction. The resultant activity map with adsorbate–adsorbate interactions taken into consideration shows that Rh, Ir, and Ni are among the most active elemental metals for this reaction. Then, 1482 A3B1 and 741 A1B1 alloys that contain 39 elements have been screened for the DMR catalyst based on their anticarbonization and antioxidation ability, catalytic activity, and cost, in which an unsupervised machine learning technique is employed to identify the thermodynamically stable alloys upon adsorption and thus to accelerate the screening process. The identification of 23 binary intermetallic compounds as potential DMR catalysts not only gives theoretical evidence in support of the experimentally reported combinations but also provides new guidelines for rationally designing alloy catalysts for the DMR reaction. Catalysis ; Methane ; Carbon dioxide reforming ; Adsorption ; Alloy ; Chemical engineering ; Intermetallic ; Chemistry ; Reactivity (psychology) ; Transition metal ; Steam reforming ; Materials science ; Syngas ; Physical chemistry ; Organic chemistry ; Hydrogen production Catalytic Processes in Materials Science ; Catalysts for Methane Reforming ; Catalysis and Oxidation Reactions
Authors: Bin Wang | Fuxiang Zhang
Source: Angewandte Chemie International Edition (review, 2021-09-29)
Field: Materials Science — Materials Chemistry

Main Descriptors To Correlate Structures with the Performances of Electrocatalysts Traditional trial and error approaches to search for hydrogen/oxygen redox catalysts with high activity and stability are typically tedious and inefficient. There is an urgent need to identify the most important parameters that determine the catalytic performance and so enable the development of design strategies for catalysts. In the past decades, several descriptors have been developed to unravel structure-performance relationships. This Minireview summarizes reactivity descriptors in electrocatalysis including adsorption energy descriptors involving reaction intermediates, electronic descriptors represented by a d-band center, structural descriptors, and universal descriptors, and discusses their merits/limitations. Understanding the trends in electrocatalytic performance and predicting promising catalytic materials using reactivity descriptors should enable the rational construction of catalysts. Artificial intelligence and machine learning have also been adopted to discover new and advanced descriptors. Finally, linear scaling relationships are analyzed and several strategies proposed to circumvent the established scaling relationships and overcome the constraints imposed on the catalytic performance. Computer science ; Rational design ; Reactivity (psychology) ; Molecular descriptor ; Electrocatalyst ; Scaling ; Catalysis ; Artificial intelligence ; Biochemical engineering ; Machine learning ; Chemistry ; Quantitative structure–activity relationship ; Nanotechnology ; Materials science ; Electrochemistry ; Mathematics ; Engineering Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Electrochemical Analysis and Applications