jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Kovo G. Akpomie | Soumya Ghosh | Marieka Gryzenhout | Jeanet Conradie
Source: Scientific Reports (article, 2021-04-15)
Field: Materials Science — Biomaterials

One-pot synthesis of zinc oxide nanoparticles via chemical precipitation for bromophenol blue adsorption and the antifungal activity against filamentous fungi In this research, zinc oxide nanoparticles (ZnONPs) were prepared via a facile one-pot chemical precipitation approach and applied in the adsorption of bromophenol blue (BRB) and as antifungal agents against the filamentous fungi and plant pathogens; Alternaria alternata CGJM3078, Alternaria alternata CGJM3006 and Fusarium verticilliodes CGJM3823. The ZnONPs were characterized by the UV-Vis, FTIR, XRD, TGA, BET, SEM, TEM, and EDX techniques, which showed efficient synthesis. The characteristics ZnO UV-Vis absorption band was observed at 375 nm, while the XRD showed an average ZnONPs crystalline size of 47.2 nm. The SEM and TEM images showed an irregular shaped and aggregated porous structure of 65.3 nm average-sized ZnONPs. The TGA showed 22.9% weight loss at 800 °C indicating the high thermal stability of ZnONPs, while BET analysis revealed a surface area, pore volume and pore diameter of 9.259 m<sup>2</sup>/g, 0.03745 cm<sup>3</sup>/g and 9.87 nm respectively. The Freundlich, pseudo-second-order, and intra-particle diffusion models showed R<sup>2</sup> > 0.9494 and SSE < 0.7412, thus, exhibited the best fit to the isotherm and kinetics models. Thermodynamics revealed feasible, endothermic, random, and spontaneous adsorption of BRB onto the synthesized ZnONPs. The antifungal assay conducted depicts strong antifungal activities against all three tested fungi. Noticeably, ZnONPs (0.002-5 mg/mL) showed maximum activities with the largest zone of inhibition against A. alternata CGJM 3006 from 25.09 to 36.28 mm. This was followed by the strain F. verticilliodes CGJM 3823 (range from 23.77 to 34.77 mm) > A. alternata CGJM3078 (range from 22.73 to 30.63 mm) in comparison to Bleach 5% (positive control). Additionally a model was proposed based on the possible underlying mechanisms for the antifungal effect. This research demonstrated the potent use of ZnONPs for the adsorption of BRB and as effective antifungal agents. Alternaria alternata ; Bromophenol blue ; Nuclear chemistry ; Adsorption ; Freundlich equation ; Zinc ; Nanoparticle ; Fourier transform infrared spectroscopy ; Chemistry ; Materials science ; Chromatography ; Chemical engineering ; Nanotechnology ; Organic chemistry ; Botany ; Biology Nanocomposite Films for Food Packaging ; Nanoparticles: synthesis and applications ; Polydiacetylene-based materials and applications
Authors: Helen O. Chukwuemeka‐Okorie | Francis Kalu Ekuma | Kovo G. Akpomie | J. C. Nnaji | Amarachi G. Okereafor
Source: Applied Water Science (article, 2021-01-24)
Field: Chemistry — Analytical Chemistry

Adsorption of tartrazine and sunset yellow anionic dyes onto activated carbon derived from cassava sievate biomass Abstract The toxic effects of anionic dyes such as tartrazine and sunset yellow on humans and the aquatic environment are of serious concern. The need for the removal of these dyes from wastewaters led to the use of adsorption techniques as a cheap and efficient treatment method. Thus, this research was based on the preparation of a low-cost activated carbon derived from cassava sievate designated as CS, which was utilized in the adsorption of sunset yellow and tartrazine from simulated wastewater. The sorption process was carried out under varying process factors in a batch mode. Adsorbent characterization displayed the presence of surface functional groups by the FT-IR and a porous structure as revealed by scanning electron microscopy. Optimum dye uptake was recorded at pH (1.0–2.0), temperature (30–40 °C), CS dosage (0.1 g), and dye concentration (150 mg/L). A maximum CS monolayer uptake of 20.83 and 0.091 mg/g was recorded for tartrazine and sunset yellow dyes, respectively. The pseudo-second-order ( R 2 &gt; 0.99) and Freundlich ( R 2 &gt; 0.92) models were most fitted to the kinetics and isotherm data of the uptake of the dyes on CS. The adsorption equilibrium attainment was reached was within 90 min of dye sequestration. The experimental results revealed that both sunset yellow and tartrazine dyes were considerably adsorbed onto the environmentally compatible and low-cost activated carbon derived from cassava sievate. Tartrazine ; Adsorption ; Activated carbon ; Chemistry ; Sorption ; Nuclear chemistry ; Freundlich equation ; Wastewater ; Langmuir adsorption model ; Chromatography ; Organic chemistry ; Environmental engineering ; Environmental science Dye analysis and toxicity ; Adsorption and biosorption for pollutant removal ; Carbon and Quantum Dots Applications
Authors: Aisha N. Bismillah | Ivan Aprahamian
Source: Chemical Society Reviews (review, 2021-01-01)
Field: Materials Science — Materials Chemistry

Fundamental studies to emerging applications of pyrrole-BF<sub>2</sub> (BOPHY) fluorophores BF<sub>2</sub>-based fluorophores, such as the well-known BODIPY (4,4-difluoro-4-bora-3a,4a-diaza-s-indacene) dye, are prevalently used in diverse research areas (e.g., bioimaging and chemosensing) as they exhibit promising features including high quantum yields, fine-tuned absorption and emission spectra as well as good photostability and biocompatibility. While BODIPY dyes are most commonly used in such applications, other BF<sub>2</sub>-based fluorophores, such as BOPHY (bis(difluoroboron)-1,2-bis((1H-pyrrol-2-yl)methylene)hydrazine - which possess their own characteristic features - are rising in popularity and are being used in a range of applications spanning from molecular sensors to photosensitizers for solar cells. This review examines select examples of BOPHY dyes to highlight the progression of their development while detailing their syntheses and photophysical properties including structure-property relationships. Applications of a number of substituted BOPHYs made by the methods described in this review are also presented. Pyrrole ; Nanotechnology ; Focus (optics) ; Chemistry ; Materials science ; Physics ; Organic chemistry ; Optics Luminescence and Fluorescent Materials ; Organic Light-Emitting Diodes Research ; Porphyrin and Phthalocyanine Chemistry
Authors: Simone Gallarati | Raimón Fabregat | Rubén Laplaza | Sinjini Bhattacharjee | Matthew D. Wodrich | Clémence Corminbœuf
Source: Chemical Science (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

Reaction-based machine learning representations for predicting the enantioselectivity of organocatalysts Hundreds of catalytic methods are developed each year to meet the demand for high-purity chiral compounds. The computational design of enantioselective organocatalysts remains a significant challenge, as catalysts are typically discovered through experimental screening. Recent advances in combining quantum chemical computations and machine learning (ML) hold great potential to propel the next leap forward in asymmetric catalysis. Within the context of quantum chemical machine learning (QML, or atomistic ML), the ML representations used to encode the three-dimensional structure of molecules and evaluate their similarity cannot easily capture the subtle energy differences that govern enantioselectivity. Here, we present a general strategy for improving molecular representations within an atomistic machine learning model to predict the DFT-computed enantiomeric excess of asymmetric propargylation organocatalysts solely from the structure of catalytic cycle intermediates. Mean absolute errors as low as 0.25 kcal mol<sup>-1</sup> were achieved in predictions of the activation energy with respect to DFT computations. By virtue of its design, this strategy is generalisable to other ML models, to experimental data and to any catalytic asymmetric reaction, enabling the rapid screening of structurally diverse organocatalysts from available structural information. Artificial intelligence ; Organocatalysis ; Computer science ; Enantioselective synthesis ; Chemistry ; Combinatorial chemistry ; Machine learning ; Organic chemistry ; Catalysis Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Catalysis and Oxidation Reactions
Authors: Xiaotian Li | Lin Chen | Cheng Shang | Zhi‐Pan Liu
Source: Journal of the American Chemical Society (article, 2021-04-20)
Field: Materials Science — Materials Chemistry

<i>In Situ</i> Surface Structures of PdAg Catalyst and Their Influence on Acetylene Semihydrogenation Revealed by Machine Learning and Experiment PdAg alloy is an industrial catalyst for acetylene-selective hydrogenation in excess ethene. While significant efforts have been devoted to increase the selectivity, there has been little progress in the catalyst performance at low temperatures. Here by combining a machine-learning atomic simulation and catalysis experiment, we clarify the surface status of PdAg alloy catalyst under the reaction conditions and screen out a rutile-TiO<sub>2</sub> supported Pd<sub>1</sub>Ag<sub>3</sub> catalyst with high performance: i.e., 85% selectivity at >96% acetylene conversion over a 100 h period in an experiment. The machine-learning global potential energy surface exploration determines the Pd-Ag-H bulk and surface phase diagrams under the reaction conditions, which reveals two key bulk compositions, Pd<sub>1</sub>Ag<sub>1</sub> (<i>R</i>3̅<i>m</i>) and Pd<sub>1</sub>Ag<sub>3</sub> (<i>Pm</i>3̅<i>m</i>), and quantifies the surface structures with varied Pd:Ag ratios under the reaction conditions. We show that the catalyst activity is controlled by the PdAg patterns on the (111) surface that are variable under reaction conditions, but the selectivity is largely determined by the amount of Pd exposure on the (100) surface. These insights provide the fundamental basis for the rational design of a better catalyst via three measures: (i) controlling the Pd:Ag ratio at 1:3, (ii) reducing the nanoparticle size to limit PdAg local patterns, (iii) searching for active supports to terminate the (100) facets. Catalysis ; Acetylene ; Chemistry ; Selectivity ; Chemical engineering ; Alloy ; Rutile ; In situ ; Phase (matter) ; Nanotechnology ; Organic chemistry ; Materials science Machine Learning in Materials Science ; Catalysis and Hydrodesulfurization Studies ; Catalytic Processes in Materials Science
Authors: Xiaoliang Pan | Junjie Yang | Richard Van | Evgeny Epifanovsky | Junming Ho | Jing Huang | Jingzhi Pu | Ye Mei | Kwangho Nam | Yihan Shao
Source: Journal of Chemical Theory and Computation (article, 2021-09-01)
Field: Materials Science — Materials Chemistry

Machine-Learning-Assisted Free Energy Simulation of Solution-Phase and Enzyme Reactions Despite recent advances in the development of machine learning potentials (MLPs) for biomolecular simulations, there has been limited effort on developing stable and accurate MLPs for enzymatic reactions. Here we report a protocol for performing machine-learning-assisted free energy simulation of solution-phase and enzyme reactions at the ab initio quantum-mechanical/molecular-mechanical (<i>ai</i>-QM/MM) level of accuracy. Within our protocol, the MLP is built to reproduce the <i>ai</i>-QM/MM energy and forces on both QM (reactive) and MM (solvent/enzyme) atoms. As an alternative strategy, a delta machine learning potential (ΔMLP) is trained to reproduce the differences between the <i>ai</i>-QM/MM and semiempirical (<i>se</i>) QM/MM energies and forces. To account for the effect of the condensed-phase environment in both MLP and ΔMLP, the DeePMD representation of a molecular system is extended to incorporate the external electrostatic potential and field on each QM atom. Using the Menshutkin and chorismate mutase reactions as examples, we show that the developed MLP and ΔMLP reproduce the <i>ai</i>-QM/MM energy and forces with errors that on average are less than 1.0 kcal/mol and 1.0 kcal mol<sup>-1</sup> Å<sup>-1</sup>, respectively, for representative configurations along the reaction pathway. For both reactions, MLP/ΔMLP-based simulations yielded free energy profiles that differed by less than 1.0 kcal/mol from the reference <i>ai</i>-QM/MM results at only a fraction of the computational cost. Computer science ; Ab initio ; Quantum ; Energy (signal processing) ; Artificial intelligence ; Chemistry ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Protein Structure and Dynamics ; Enzyme Structure and Function
Authors: Dominik Lemm | Guido Falk von Rudorff | O. Anatole von Lilienfeld
Source: Nature Communications (article, 2021-07-22)
Field: Materials Science — Materials Chemistry

Machine learning based energy-free structure predictions of molecules, transition states, and solids The computational prediction of atomistic structure is a long-standing problem in physics, chemistry, materials, and biology. Conventionally, force-fields or ab initio methods determine structure through energy minimization, which is either approximate or computationally demanding. This accuracy/cost trade-off prohibits the generation of synthetic big data sets accounting for chemical space with atomistic detail. Exploiting implicit correlations among relaxed structures in training data sets, our machine learning model Graph-To-Structure (G2S) generalizes across compound space in order to infer interatomic distances for out-of-sample compounds, effectively enabling the direct reconstruction of coordinates, and thereby bypassing the conventional energy optimization task. The numerical evidence collected includes 3D coordinate predictions for organic molecules, transition states, and crystalline solids. G2S improves systematically with training set size, reaching mean absolute interatomic distance prediction errors of less than 0.2 Å for less than eight thousand training structures - on par or better than conventional structure generators. Applicability tests of G2S include successful predictions for systems which typically require manual intervention, improved initial guesses for subsequent conventional ab initio based relaxation, and input generation for subsequent use of structure based quantum machine learning models. Ab initio ; Energy minimization ; Computer science ; Relaxation (psychology) ; Force field (fiction) ; Statistical physics ; Machine learning ; Algorithm ; Artificial intelligence ; Physics ; Computational chemistry ; Chemistry ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Trevon Badloe | Jihae Lee | Junhwa Seong | Junsuk Rho
Source: Advanced Photonics Research (article, 2021-05-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Tunable Metasurfaces: The Path to Fully Active Nanophotonics In the field of nanophotonics, metasurfaces have come to the forefront in real‐world applications, owing to their accessible exotic optical properties that can be readily designed and fabricated using currently available techniques. The subwavelength dimensions and lightweight characteristics of metasurfaces are attractive qualities for the miniaturization of optical devices and are already exploited in devices that can rival, and sometimes even outperform, conventional bulky optics. Over the past decade, ample research has been undertaken to produce high‐performance metasurfaces with exciting optical properties that cannot be found in nature or achieved with conventional optics. To open the path to widely used devices in our everyday lives, the next obvious step for metasurfaces is the development of tunability. Herein, the techniques and development of applications of tunable metasurfaces are presented through the incorporation of active materials and controllable external stimuli, and the uncovered tunable characteristics are critically analyzed. The review rounds up by proposing the future directions and prospects for actively tunable metasurfaces and their potential practical applications. Nanophotonics ; Miniaturization ; Computer science ; Nanotechnology ; Field (mathematics) ; Path (computing) ; Metamaterial ; Materials science ; Optoelectronics Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Photonic Crystals and Applications
Authors: Lei Feng | Baohua Wu | Susu Zhu | Yong He | Chu Zhang
Source: Frontiers in Nutrition (review, 2021-06-17)
Field: Chemistry — Analytical Chemistry

Application of Visible/Infrared Spectroscopy and Hyperspectral Imaging With Machine Learning Techniques for Identifying Food Varieties and Geographical Origins Food quality and safety are strongly related to human health. Food quality varies with variety and geographical origin, and food fraud is becoming a threat to domestic and global markets. Visible/infrared spectroscopy and hyperspectral imaging techniques, as rapid and non-destructive analytical methods, have been widely utilized to trace food varieties and geographical origins. In this review, we outline recent research progress on identifying food varieties and geographical origins using visible/infrared spectroscopy and hyperspectral imaging with the help of machine learning techniques. The applications of visible, near-infrared, and mid-infrared spectroscopy as well as hyperspectral imaging techniques on crop food, beverage, fruits, nuts, meat, oil, and some other kinds of food are reviewed. Furthermore, existing challenges and prospects are discussed. In general, the existing machine learning techniques contribute to satisfactory classification results. Follow-up researches of food varieties and geographical origins traceability and development of real-time detection equipment are still in demand. Hyperspectral imaging ; Traceability ; Food quality ; Remote sensing ; Quality (philosophy) ; Computer science ; Environmental science ; Geography ; Food science ; Chemistry ; Physics Spectroscopy and Chemometric Analyses ; Identification and Quantification in Food ; Advanced Chemical Sensor Technologies
Authors: Joanna D. Stachowska | A.H. Murphy | Claire L. Mellor | Diogo Fernandes | Ella N. Gibbons | Marta J. Krysmann | Antonios Kelarakis | Engin Burgaz | J. J. Moore | Stephen G. Yeates
Source: Scientific Reports (article, 2021-05-18)
Field: Materials Science — Materials Chemistry

A rich gallery of carbon dots based photoluminescent suspensions and powders derived by citric acid/urea In this study we demonstrate simple guidelines to generate a diverse range of fluorescent materials in both liquid and solid state by focusing on the most popular C-dots precursors, i.e. the binary systems of citric acid and urea. The pyrolytic treatment of those precursors combined with standard size separation techniques (dialysis and filtration), leads to four distinct families of photoluminescent materials in which the emissive signal predominantly arises from C-dots with embedded fluorophores, cyanuric acid-rich C-dots, a blend of molecular fluorophores and a mixture of C-dots with unbound molecular fluorophores, respectively. Within each one of those families the chemical composition and the optical properties of their members can be fine-tuned by adjusting the molar ratio of the reactants. Apart from generating a variety of aqueous dispersions, our approach leads to highly fluorescent powders derived from precursors comprising excessive amounts of urea that is consumed for the build-up of the carbogenic cores, the molecular fluorophores and the solid diluent matrix that suppresses self-quenching effects. Citric acid ; Fluorescence ; Urea ; Cyanuric acid ; Aqueous solution ; Photoluminescence ; Materials science ; Quenching (fluorescence) ; Chemical engineering ; Carbon fibers ; Melamine ; Nanotechnology ; Chemistry ; Organic chemistry Carbon and Quantum Dots Applications ; Nanocluster Synthesis and Applications ; Luminescence and Fluorescent Materials
Authors: Zhantao Chen | Nina Andrejevic | Nathan C. Drucker | Thanh Nguyen | R. Patrick Xian | Tess Smidt | Yao Wang | Ralph Ernstorfer | D. M. Tennant | Maria K. Y. Chan | Mingda Li
Source: Chemical Physics Reviews (article, 2021-07-08)
Field: Materials Science — Materials Chemistry

Machine learning on neutron and x-ray scattering and spectroscopies Neutron and x-ray scattering represent two classes of state-of-the-art materials characterization techniques that measure materials structural and dynamical properties with high precision. These techniques play critical roles in understanding a wide variety of materials systems from catalysts to polymers, nanomaterials to macromolecules, and energy materials to quantum materials. In recent years, neutron and x-ray scattering have received a significant boost due to the development and increased application of machine learning to materials problems. This article reviews the recent progress in applying machine learning techniques to augment various neutron and x-ray techniques, including neutron scattering, x-ray absorption, x-ray scattering, and photoemission. We highlight the integration of machine learning methods into the typical workflow of scattering experiments, focusing on problems that challenge traditional analysis approaches but are addressable through machine learning, including leveraging the knowledge of simple materials to model more complicated systems, learning with limited data or incomplete labels, identifying meaningful spectra and materials representations, mitigating spectral noise, and others. We present an outlook on a few emerging roles machine learning may play in broad types of scattering and spectroscopic problems in the foreseeable future. Neutron scattering ; Scattering ; Characterization (materials science) ; Workflow ; Computer science ; Artificial intelligence ; Neutron ; Variety (cybernetics) ; Nanotechnology ; Materials science ; Machine learning ; Physics ; Optics ; Nuclear physics Machine Learning in Materials Science ; Nuclear Physics and Applications ; X-ray Diffraction in Crystallography
Authors: Yi Jiang | Dong Chen | Xin Chen | Tangyi Li | Guo‐Wei Wei | Feng Pan
Source: npj Computational Materials (article, 2021-02-05)
Field: Materials Science — Materials Chemistry

Topological representations of crystalline compounds for the machine-learning prediction of materials properties Accurate theoretical predictions of desired properties of materials play an important role in materials research and development. Machine learning (ML) can accelerate the materials design by building a model from input data. For complex datasets, such as those of crystalline compounds, a vital issue is how to construct low-dimensional representations for input crystal structures with chemical insights. In this work, we introduce an algebraic topology-based method, called atom-specific persistent homology (ASPH), as a unique representation of crystal structures. The ASPH can capture both pairwise and many-body interactions and reveal the topology-property relationship of a group of atoms at various scales. Combined with composition-based attributes, ASPH-based ML model provides a highly accurate prediction of the formation energy calculated by density functional theory (DFT). After training with more than 30,000 different structure types and compositions, our model achieves a mean absolute error of 61 meV/atom in cross-validation, which outperforms previous work such as Voronoi tessellations and Coulomb matrix method using the same ML algorithm and datasets. Our results indicate that the proposed topology-based method provides a powerful computational tool for predicting materials properties compared to previous works. Crystal structure prediction ; Persistent homology ; Topology (electrical circuits) ; Computer science ; Representation (politics) ; Pairwise comparison ; Computational topology ; Algorithm ; Crystal structure ; Artificial intelligence ; Mathematics ; Chemistry ; Crystallography Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Topological and Geometric Data Analysis