jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jenifer R. Hajzus | Lisa C. Shriver‐Lake | Scott N. Dean | Jeffrey S. Erickson | Daniel Zabetakis | Joel P. Golden | Daniel J. Pennachio | Rachael L. Myers‐Ward | Scott A. Trammell
Source: Sensors (article, 2022-07-19)
Field: Materials Science — Materials Chemistry

Modifications of Epitaxial Graphene on SiC for the Electrochemical Detection and Identification of Heavy Metal Salts in Seawater The electrochemical detection of heavy metal ions is reported using an inexpensive portable in-house built potentiostat and epitaxial graphene. Monolayer, hydrogen-intercalated quasi-freestanding bilayer, and multilayer epitaxial graphene were each tested as working electrodes before and after modification with an oxygen plasma etch to introduce oxygen chemical groups to the surface. The graphene samples were characterized using X-ray photoelectron spectroscopy, atomic force microscopy, Raman spectroscopy, and van der Pauw Hall measurements. Dose-response curves in seawater were evaluated with added trace levels of four heavy metal salts (CdCl<sub>2</sub>, CuSO<sub>4</sub>, HgCl<sub>2</sub>, and PbCl<sub>2</sub>), along with detection algorithms based on machine learning and library development for each form of graphene and its oxygen plasma modification. Oxygen plasma-modified, hydrogen-intercalated quasi-freestanding bilayer epitaxial graphene was found to perform best for correctly identifying heavy metals in seawater. Graphene ; Materials science ; Potentiostat ; Raman spectroscopy ; Van der Pauw method ; X-ray photoelectron spectroscopy ; Analytical Chemistry (journal) ; Bilayer graphene ; Monolayer ; Bilayer ; Chemical engineering ; Electrochemistry ; Inorganic chemistry ; Nanotechnology ; Electrode ; Chemistry ; Membrane ; Physical chemistry ; Hall effect ; Environmental chemistry ; Optics Graphene research and applications ; Electrochemical Analysis and Applications ; Advancements in Battery Materials
Authors: Chenlei Ru | Wu Wen | Yi Zhong
Source: Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy (article, 2022-06-09)
Field: Chemistry — Analytical Chemistry

Raman spectroscopy for on-line monitoring of botanical extraction process using convolutional neural network with background subtraction Convolutional neural network ; Artificial intelligence ; Computer science ; Preprocessor ; Pattern recognition (psychology) ; Partial least squares regression ; Background subtraction ; Extraction (chemistry) ; Subtraction ; Biological system ; Mathematics ; Chemistry ; Machine learning ; Chromatography ; Pixel Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Traditional Chinese Medicine Analysis
Authors: Zi Yuan | Lincong Chen | Jian‐Qiao Sun | Wenwei Ye
Source: Probabilistic Engineering Mechanics (article, 2022-12-26)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Transient response of Bouc–Wen hysteretic system under random excitation via RBFNN method Probability density function ; Nonlinear system ; Discretization ; Transient (computer programming) ; Control theory (sociology) ; Transient response ; Computer science ; Mathematical optimization ; Applied mathematics ; Mathematics ; Engineering ; Physics ; Mathematical analysis ; Statistics ; Artificial intelligence Magnetic Properties and Applications ; Structural Health Monitoring Techniques ; Probabilistic and Robust Engineering Design
Authors: Junyu Ren | Shihui Wang | Kexin Bi | Min Cheng | Chong Liu | Li Zhou | Xiaoyu Xue | Xu Ji
Source: ACS Applied Materials & Interfaces (article, 2022-12-28)
Field: Chemistry — Inorganic Chemistry

Machine Learning-Enabled Framework for High-Throughput Screening of MOFs: Application in Radon/Indoor Air Separation Radon and its progeny may cause severe health hazards, especially for people working in underground spaces. Therefore, in this study, a hybrid artificial intelligence machine learning-enabled framework is proposed for high-throughput screening of metal-organic frameworks (MOFs) as adsorbents for radon separation from indoor air. MOFs from a specific database were initially screened using a pore-limiting diameter filter. Subsequently, random forest classification and grand canonical Monte Carlo simulations were implemented to identify MOFs with a high adsorbent performance score (APS) and high regenerability (<i>R</i> %). Interpretability and trustworthiness were determined by variable importance analysis , and adsorption mechanisms were elucidated by calculating the adsorption sites using Materials Studio. Notably, two MOF candidates were discovered with higher APS values in both the radon/N<sub>2</sub> and radon/O<sub>2</sub> systems compared with that of ZrSQU which is the best-performing MOF thus far, with <i>R</i> % values exceeding 85%. Furthermore, the proposed framework can be flexibly applied to multiple data sets due to good performance in model transfer. Therefore, the proposed framework has the potential to provide guidelines for the strategic design of MOFs for radon separation. Interpretability ; Radon ; Metal-organic framework ; Limiting ; Separation (statistics) ; Adsorption ; Materials science ; Monte Carlo method ; Modular design ; Throughput ; Sorption ; Computer science ; Process engineering ; Artificial intelligence ; Machine learning ; Mechanical engineering ; Engineering ; Physics Metal-Organic Frameworks: Synthesis and Applications ; Radioactivity and Radon Measurements ; Radiation Shielding Materials Analysis
Authors: Sichao Li | Amanda S. Barnard
Source: Chemosphere (article, 2022-05-23)
Field: Materials Science — Materials Chemistry

Safety-by-design using forward and inverse multi-target machine learning Computer science ; Nanoparticle ; Material Design ; Transparency (behavior) ; Product design ; Process (computing) ; New product development ; Biochemical engineering ; Product (mathematics) ; Nanotechnology ; Process engineering ; Materials science ; Engineering ; Mathematics Nanoparticles: synthesis and applications ; Chemistry and Chemical Engineering ; TiO2 Photocatalysis and Solar Cells
Authors: Nicholas Stiles Wilkins | Kasturi Nagesh Pai | Arvind Rajendran
Source: The Canadian Journal of Chemical Engineering (article, 2022-05-16)
Field: Chemistry — Inorganic Chemistry

Optimization of pressure‐vacuum swing adsorption processes for nitrogen rejection from natural gas streams using a nitrogen selective metal organic framework Abstract A vanadium(II/III) metal–organic framework (MOF), V 2 Cl 2.8 (btdd), that is selective to N 2 over CH 4 was recently discovered. Process optimizations were performed to determine the performance of this MOF to reach the pipeline transport purity of 96 mol% CH 4 . Two cycles were considered: the basic three‐step cycle and the Skarstrom cycle. First, the three‐step cycle was considered with a wide range of operating conditions. Three inlet compositions (55/45, 80/20, and 92/8 mol% CH 4 /N 2 ), three process temperatures (30, 40, and 50°C) and a range of adsorption pressures (100–500 kPa) were considered. A detailed process model in tandem with machine learning‐aided optimization was employed to determine the optimal set of operating conditions. The three‐step cycle was unable to meet the 96 mol% CH 4 purity requirement in most cases studied. However, the Skarstrom cycle was able to meet the 96 mol% CH 4 purity requirement in all cases studied. The maximum recovery, at a purity of 96 mol%, was at 84.2% for the Skarstrom cycle with a methane feed composition of 80 mol% at 50°C and an adsorption pressure of 100 kPa. For the Skarstrom cycle, at a feed temperature of 50°C, an adsorption pressure of 100 kPa and a feed methane composition of 92 mol%, the productivity could be as high as 21.18 tonnes per day CH 4 m −3 at a recovery of 73%. The achievable recovery‐productivity values were comparable to a carbon molecular sieve process reported in the literature at similar operating conditions. Adsorption ; Methane ; Nitrogen ; Chemistry ; Pressure swing adsorption ; Natural gas ; Analytical Chemistry (journal) ; Chromatography ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Carbon Dioxide Capture Technologies ; Industrial Gas Emission Control
Authors: J. Lund | H. Wang | Richard D. Braatz | R. Edwin Garcı́a
Source: Materials Advances (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Machine learning of phase diagrams A ML strategy is presented to infer the free energy state functions by using phase diagram images as input, resulting in optimized properties 3–5 orders of magnitude faster and dramatically increased accuracy as compared to current approaches. Phase diagram ; Phase (matter) ; Computer science ; Diagram ; Energy (signal processing) ; Current (fluid) ; Magnitude (astronomy) ; Algorithm ; Statistical physics ; Artificial intelligence ; Mathematics ; Statistics ; Physics ; Thermodynamics Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; X-ray Diffraction in Crystallography
Authors: Jonathan Tersur Orasugh | Suprakas Sinha Ray
Source: Polymers (review, 2022-02-11)
Field: Materials Science — Materials Chemistry

Prospect of DFT Utilization in Polymer-Graphene Composites for Electromagnetic Interference Shielding Application: A Review The improvement in current materials science has prompted a developing need to capture the peculiarities that determine the properties of materials and how they are processed on an atomistic level. Quantum mechanics laws control the interface among atoms and electrons; thus, exact and proficient techniques for fixing the major quantum-mechanical conditions for complex many-particle, many-electron frameworks should be created. Density functional theory (DFT) marks an unequivocal advance in these endeavours. DFT has had a rapid influence on quintessential and industrial research during the last decade. The DFT system describes periodic structural systems of 2D or 3D electronics with the utilization of Bloch's theorem in the direction of Kohn-Sham wavefunctions for the significant facilitation of these schemes. This article introduces and discusses the infinite systems modelling approach required for graphene-based polymer composites or their hybrids. Aiming to understand electronic structure computations as per physics, the impressions of band structures and atomic structure envisioned along with orbital predicted density states are beneficial. Convergence facets coupled with the basic functions number and the <i>k</i>-points number are necessary to explain for every physicochemical characteristic in these materials. Proper utilization of DFT in graphene-based polymer composites for materials in EMI SE presents the potential of taking this niche to unprecedented heights within the next decades. The application of this system in graphene-based composites by researchers, along with their performance, is reviewed. Graphene ; Density functional theory ; Electromagnetic shielding ; Materials science ; Electronic structure ; Wave function ; Nanotechnology ; Electromagnetic interference ; Quantum ; Electron ; Statistical physics ; Mechanical engineering ; Computer science ; Composite material ; Physics ; Quantum mechanics ; Engineering ; Telecommunications Graphene research and applications ; Boron and Carbon Nanomaterials Research ; Advanced Energy Technologies and Civil Engineering Innovations
Authors: Cristiana Cesari | Cristina Femoni | Maria Carmela Iapalucci | Stefano Zacchini
Source: Inorganica Chimica Acta (article, 2022-10-05)
Field: Materials Science — Materials Chemistry

Molecular Fe, CO and Ni carbide carbonyl clusters and Nanoclusters† Nanoclusters ; Chemistry ; Carbide ; Context (archaeology) ; Section (typography) ; Nanotechnology ; Crystallography ; Organic chemistry ; Materials science Nanocluster Synthesis and Applications ; Graph theory and applications ; Metal-Organic Frameworks: Synthesis and Applications
Authors: Ondřej Veselý | Russell E. Morris | Jiřı́ Čejka
Source: Microporous and Mesoporous Materials (article, 2022-12-01)
Field: Chemistry — Inorganic Chemistry

Beyond traditional synthesis of zeolites: The impact of germanosilicate chemistry in the search for new materials Zeolites are one of the key materials in the chemical industry with their applications ranging from water purification to gas separation or oil refining. In the recent years, the research of zeolites focuses more and more on specialised applications including fine chemical synthesis or separation of small organic compounds, which increase the demand for new types of zeolites with unique structural and chemical properties. Introduction of tailored organic structure-directing agents and especially the introduction of germanium stimulated the discovery of new zeolite frameworks and particularly led to a discovery of numerous new extra-large pore zeolites with attractive properties for selective catalysis. However, the germanosilicate chemistry also opened up an unparalleled opportunity for tailoring even more new zeolites and zeolite-derived materials by controlled post-synthetic transformation of the germanosilicates into layered zeolites or to new zeolite frameworks by solid-, liquid- or vapour-phase transformations such as the Assembly-Disassembly-Organisation-Reassembly process. The following research provided new strategies for preparation of porous materials with characteristics and properties tailored on-demand. Zeolite ; Fine chemical ; Refining (metallurgy) ; Nanotechnology ; Porosity ; Chemistry ; Chemical engineering ; Commodity chemicals ; Molecular sieve ; Materials science ; Catalysis ; Organic chemistry ; Engineering Zeolite Catalysis and Synthesis ; Chemical Synthesis and Characterization ; Mesoporous Materials and Catalysis
Authors: Farhad Gharagheizi | Zhenzi Yu | David S. Sholl
Source: ACS Applied Materials & Interfaces (article, 2022-09-08)
Field: Chemistry — Inorganic Chemistry

Curated Collection of More than 20,000 Experimentally Reported One-Dimensional Metal–Organic Frameworks A collection of more than 20,000 experimentally derived crystal structures for metal-organic frameworks (MOFs) that do not have two- or three-dimensional covalently bonded networks has been developed from the materials available at the Cambridge Crystallographic Data Centre. Of these 20,000 1D MOFs, more than 12,000 structures have been verified to be solvent-free and in exact agreement with the stoichiometry of the synthesized materials. More than 10% of the complete data set comprise materials including two or more distinct metals. The band gaps of more than 12,000 1D MOFs have been computed at the density functional theory-generalized gradient approximation level, finding more than 2000 materials that have a zero band gap. Molecular simulations of CH<sub>4</sub> adsorption in a small number of 1D MOFs indicated that adsorbate-induced deformation plays a significant role in determining adsorption isotherms in these materials. As a result, methods that have been used previously for high-throughput predictions of molecular adsorption in 3D MOFs are not suitable for 1D MOFs. Materials science ; Metal-organic framework ; Nanotechnology ; Metal ; Metallurgy ; Organic chemistry ; Adsorption Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Enhanced Oil Recovery Techniques
Authors: Jack D. Sundberg | Daniel L. Druffel | Lauren M. McRae | Matthew G. Lanetti | Jacob T. Pawlik | Scott C. Warren
Source: npj Computational Materials (article, 2022-05-06)
Field: Chemistry — Inorganic Chemistry

High-throughput discovery of fluoride-ion conductors via a decoupled, dynamic, and iterative (DDI) framework Abstract Fluoride–ion batteries are a promising alternative to lithium–ion batteries with higher theoretical capacities and working voltages, but they have experienced limited success due to the poor ionic conductivities of known electrolytes and electrodes. Here, we report a high-throughput computational screening of 9747 fluoride-containing materials in search of fluoride-ion conductors. Via a combination of empirical, lightweight DFT, and nudged elastic band (NEB) calculations, we identified &gt;10 crystal systems with high fluoride mobility. We applied a search strategy where calculations are performed in any order (decoupled), computational resources are reassigned based on need (dynamic), and predictive models are repeatedly updated (iterative). Unlike hierarchical searches, our decoupled, dynamic, and iterative framework (DDI) began by calculating high-quality barrier heights for fluoride-ion mobility in a large and diverse group of materials. This high-quality dataset provided a benchmark against which a rapid calculation method could be refined. This accurate method was then used to measure the barrier heights for 6797 fluoride–ion pathways. The final dataset has allowed us to discover many fascinating, high-performance conductors and to derive the design rules that govern their performance. These materials will accelerate experimental research into fluoride–ion batteries, while the design rules will provide an improved foundation for understanding ionic conduction. Fluoride ; Benchmark (surveying) ; Throughput ; Ionic bonding ; Computer science ; Ion ; Electrical conductor ; Lithium (medication) ; Materials science ; Quality (philosophy) ; Chemistry ; Physics ; Inorganic chemistry ; Telecommunications ; Composite material Inorganic Fluorides and Related Compounds ; Machine Learning in Materials Science ; Inorganic Chemistry and Materials
Authors: Shigeru Yamaguchi
Source: Organic & Biomolecular Chemistry (review, 2022-01-01)
Field: Materials Science — Materials Chemistry

Molecular field analysis for data-driven molecular design in asymmetric catalysis This review highlights the recent advances (2019-present) in the use of MFA (molecular field analysis) for data-driven catalyst design, enabling to improve selectivities/reaction outcomes in asymmetric catalysis. Successful examples of MFA-based molecular design and how to design molecules by MFA are described, including how to generate and evaluate MFA-based regression models, and future challenges in MFA-based molecular design in molecular catalysis. Chemistry ; Catalysis ; Molecular model ; Enantioselective synthesis ; Nanotechnology ; Field (mathematics) ; Biochemical engineering ; Organic chemistry ; Engineering ; Materials science Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Asymmetric Hydrogenation and Catalysis
Authors: Noha Al-Awaji | Moufida Boukriba | Hajo Idriss | Mohamed Ali Ben Aissa | M. Bououdina | A. Modwi
Source: Advances in Materials Science and Engineering (article, 2022-12-31)
Field: Materials Science — Materials Chemistry

Green Synthesis Ca-MgO Nanosorbent for the Uptake of Cobalt Ions from Aqueous Media Ca-doped magnesium oxide nanosorbent has been prepared by sol-gel in the presence of gum arabic extract and assessed for the removal of cobalt ions (Co (II)) from aqueous media. X-ray diffraction analysis indicates the formation of MgO and CaO composites with a mean crystallite size of 6 nm having a large surface area of 50 m2/g. Scanning and transmission electron microscopy observations reveal spherical-shaped particles at the nanoscale around 20 nm. Besides, X-ray photoelectron spectroscopy and Fourier transform infrared analyses manifest the existence of functional groups, which will facilitate the adsorption of metal ions. The adsorption capability of the CaO-MgO nanosorbent achieved 469.5 mg/g for Co (II) ions under optimum operating conditions. Furthermore, the experimental data elucidate that the adsorption process is primarily diffusion-based and that both intraparticle and boundary layer diffusion appear to play a key role in the rate-controlling step. The findings of this study highlight the remediation of contaminated water by cobalt ions by CaO-MgO nanostructure and can be extended to other metal ions and organic pollutants. Materials science ; Adsorption ; Cobalt ; Metal ions in aqueous solution ; Cobalt oxide ; Aqueous solution ; Crystallite ; X-ray photoelectron spectroscopy ; Inorganic chemistry ; Fourier transform infrared spectroscopy ; Ion ; Magnesium ; Chemical engineering ; Analytical Chemistry (journal) ; Metal ; Physical chemistry ; Metallurgy ; Chemistry ; Organic chemistry Magnesium Oxide Properties and Applications ; Adsorption and biosorption for pollutant removal ; Geochemistry and Elemental Analysis
Authors: Óscar López | José M. Padrón
Source: Catalysts (article, 2022-01-28)
Field: Chemistry — Inorganic Chemistry

Iridium- and Palladium-Based Catalysts in the Pharmaceutical Industry Transition metal catalysts play a vital role in a wide range of industrial organic processes. The large-scale production of chemicals relying on catalyzed organic reactions represents a sustainable approach to supply society with end products for many daily life applications. Homogeneous (mainly for academic uses) and heterogeneous (crucial in industrial processes) metal-based catalysts have been developed for a plethora of organic reactions. The search for more sustainable strategies has led to the development of a countless number of metal-supported catalysts, nanosystems, and electrochemical and photochemical catalysts. In this work, although a vast number of transition metals can be used in this context, special attention is devoted to Ir- and Pd-based catalysts in the industrial manufacture of pharmaceutical drugs. Pd is by far the most widely used and versatile catalyst not only in academia but also in industry. Moreover, Ir-based complexes have emerged as attractive catalysts, particularly in asymmetric hydrogenation reactions. Ir- and Pd-based asymmetric reductions, aminations, cross-coupling reactions, and C–H activation are covered herein in the production of biologically active compounds or precursors; adaptation to bulk conditions is particularly highlighted. Catalysis ; Context (archaeology) ; Iridium ; Homogeneous ; Palladium ; Nanotechnology ; Chemistry ; Combinatorial chemistry ; Biochemical engineering ; Materials science ; Organic chemistry ; Engineering Asymmetric Hydrogenation and Catalysis ; Nanomaterials for catalytic reactions ; Catalytic Cross-Coupling Reactions
Authors: Federico Frateloreto | Francesco Tavani | Marika Di Berto Mancini | Daniele Del Giudice | Giorgio Capocasa | Isabelle Kieffer | Osvaldo Lanzalunga | Stefano Di Stefano | P. D’Angelo
Source: The Journal of Physical Chemistry Letters (article, 2022-06-13)
Field: Chemistry — Spectroscopy

Following a Silent Metal Ion: A Combined X-ray Absorption and Nuclear Magnetic Resonance Spectroscopic Study of the Zn<sup>2+</sup> Cation Dissipative Translocation between Two Different Ligands The dissipative translocation of the Zn<sup>2+</sup> ion between two prototypical coordination complexes has been investigated by combining X-ray absorption and <sup>1</sup>H NMR spectroscopy. An integrated experimental and theoretical approach, based on state-of-the-art Multivariate Curve Resolution and DFT based theoretical analyses, is presented as a means to understand the concentration time evolution of all relevant Zn and organic species in the investigated processes, and accurately characterize the solution structures of the key metal coordination complexes. Specifically, we investigate the dissipative translocation of the Zn<sup>2+</sup> cation from hexaaza-18-crown-6 to two terpyridine moieties and back again to hexaaza-18-crown-6 using 2-cyano-2-phenylpropanoic acid and its <i>para</i>-chloro derivative as fuels. Our interdisciplinary approach has been proven to be a valuable tool to shed light on reactive systems containing metal ions that are silent to other spectroscopic methods. These combined experimental approaches will enable future applications to chemical and biological systems in a predictive manner. Metal ; Absorption (acoustics) ; Resonance (particle physics) ; Ion ; Nuclear magnetic resonance ; Materials science ; Chemistry ; Analytical Chemistry (journal) ; Atomic physics ; Physics Molecular Sensors and Ion Detection ; Metal complexes synthesis and properties ; DNA and Nucleic Acid Chemistry