jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Geun Ho Gu | Juhyung Lim | Chengzhang Wan | Tao Cheng | Heting Pu | Sungwon Kim | Juhwan Noh | Changhyeok Choi | Juhwan Kim | William A. Goddard | Xiangfeng Duan | Yousung Jung
Source: Journal of the American Chemical Society (article, 2021-03-17)
Field: Materials Science — Materials Chemistry

Autobifunctional Mechanism of Jagged Pt Nanowires for Hydrogen Evolution Kinetics via End-to-End Simulation The extraordinary mass activity of jagged Pt nanowires can substantially improve the economics of the hydrogen evolution reaction (HER). However, it is a great challenge to fully unveil the HER kinetics driven by the jagged Pt nanowires with their multiscale morphology. Herein we present an end-to-end framework that combines experiment, machine learning, and multiscale advances of the past decade to elucidate the HER kinetics catalyzed by jagged Pt nanowires under alkaline conditions. The bifunctional catalysis conventionally refers to the synergistic increase in activity by the combination of two different catalysts. We report that monometals, such as jagged Pt nanowires, can exhibit bifunctional characteristics owing to its complex surface morphology, where one site prefers electrochemical proton adsorption and another is responsible for activation, resulting in a 4-fold increase in the activity. We find that the conventional design guideline that the sites with a 0 eV Gibbs free energy of adsorption are optimal for HER does not hold under alkaline conditions, and rather, an energy between -0.2 and 0.0 eV is shown to be optimal. At the reaction temperatures, the high activity arises from low-coordination-number (≤7) Pt atoms exposed by the jagged surface. Our current demonstration raises an emerging prospect to understand highly complex kinetic phenomena on the nanoscale in full by implementing end-to-end multiscale strategies. Chemistry ; Kinetics ; Nanowire ; Mechanism (biology) ; Hydrogen ; Nanotechnology ; Organic chemistry ; Classical mechanics Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Fuel Cells and Related Materials
Authors: Tianyu Heng | Dezhi Yang | Ruonan Wang | Li Zhang | Yang Lü | Guanhua Du
Source: ACS Omega (review, 2021-06-11)
Field: Chemistry — Physical and Theoretical Chemistry

Progress in Research on Artificial Intelligence Applied to Polymorphism and Cocrystal Prediction Artificial intelligence (AI) is a technology that builds an artificial system with certain intelligence and uses computer software and hardware to simulate intelligent human behavior. When combined with drug research and development, AI can considerably shorten this cycle, improve research efficiency, and minimize costs. The use of machine learning to discover novel materials and predict material properties has become a new research direction. On the basis of the current status of worldwide research on the combination of AI and crystal form and cocrystal, this mini-review analyzes and explores the application of AI in polymorphism prediction, crystal structure analysis, crystal property prediction, cocrystal former (CCF) screening, cocrystal composition prediction, and cocrystal formation prediction. This study provides insights into the future applications of AI in related fields. Cocrystal ; Artificial intelligence ; Computer science ; Machine learning ; Polymorphism (computer science) ; Crystal structure prediction ; Crystal structure ; Chemistry ; Crystallography Crystallography and molecular interactions ; Crystallization and Solubility Studies ; X-ray Diffraction in Crystallography
Authors: Hossein Salami | Matthew A. McDonald | Andreas S. Bommarius | Ronald W. Rousseau | Martha A. Grover
Source: Organic Process Research & Development (article, 2021-07-01)
Field: Materials Science — Materials Chemistry

<i>In Situ</i> Imaging Combined with Deep Learning for Crystallization Process Monitoring: Application to Cephalexin Production The online detection of a trace amount of an undesired solid phase within a crystal slurry can enable feedback control to improve product purity, decrease batch rejection, and increase process performance. Systems involving the production of one crystalline solid while suppressing the nucleation of a second solid are common, with applications to polymorphs, hydrates/solvates, chiral resolution, and byproducts. Several process analytical technologies (PATs) have already been established for system-specific detection of an undesired solid phase; this study adds to that PAT suite an image-based technique with greater generality and sensitivity than common tools such as Raman spectroscopy and focused beam reflectance measurement. In situ microscope images are analyzed with a convolutional neural network (CNN) to extract image features and classify cropped regions obtained by a sliding window as containing a single particle type or multiple particle types. As an experimental case study, the performance of the technique is evaluated using a system involving contamination of reactive crystallization of cephalexin with phenylglycine, the sparingly soluble byproduct in the enzymatic synthesis of cephalexin. A CNN, ResNet, was retrained for the classification task at hand and showed >98% accuracy on the test data, highlighting the distinct features of different crystal classes used as the basis of process monitoring. Crystallization ; In situ ; Process analytical technology ; Process engineering ; Computer science ; Slurry ; Materials science ; Biological system ; Artificial intelligence ; Chemistry ; Chemical engineering ; Engineering Crystallization and Solubility Studies ; Innovative Microfluidic and Catalytic Techniques Innovation ; Enzyme Structure and Function
Authors: Huziel E. Sauceda | Valentin Vassilev-Galindo | Stefan Chmiela | Klaus-Robert Müller | Alexandre Tkatchenko
Source: Nature Communications (article, 2021-01-19)
Field: Chemistry — Physical and Theoretical Chemistry

Dynamical strengthening of covalent and non-covalent molecular interactions by nuclear quantum effects at finite temperature Nuclear quantum effects (NQE) tend to generate delocalized molecular dynamics due to the inclusion of the zero point energy and its coupling with the anharmonicities in interatomic interactions. Here, we present evidence that NQE often enhance electronic interactions and, in turn, can result in dynamical molecular stabilization at finite temperature. The underlying physical mechanism promoted by NQE depends on the particular interaction under consideration. First, the effective reduction of interatomic distances between functional groups within a molecule can enhance the n → π<sup>*</sup> interaction by increasing the overlap between molecular orbitals or by strengthening electrostatic interactions between neighboring charge densities. Second, NQE can localize methyl rotors by temporarily changing molecular bond orders and leading to the emergence of localized transient rotor states. Third, for noncovalent van der Waals interactions the strengthening comes from the increase of the polarizability given the expanded average interatomic distances induced by NQE. The implications of these boosted interactions include counterintuitive hydroxyl-hydroxyl bonding, hindered methyl rotor dynamics, and molecular stiffening which generates smoother free-energy surfaces. Our findings yield new insights into the versatile role of nuclear quantum fluctuations in molecules and materials. Delocalized electron ; van der Waals force ; Polarizability ; Molecular dynamics ; Quantum ; Coupling (piping) ; Molecule ; Localized molecular orbitals ; Atomic orbital Crystallography and molecular interactions ; Spectroscopy and Quantum Chemical Studies ; Quantum, superfluid, helium dynamics
Authors: Jiuyuan Nie | Chongze Hu | Qizhang Yan | Jian Luo
Source: Nature Communications (article, 2021-04-22)
Field: Materials Science — Materials Chemistry

Discovery of electrochemically induced grain boundary transitions Electric fields and currents, which are used in innovative materials processing and electrochemical energy conversion, can often alter microstructures in unexpected ways. However, little is known about the underlying mechanisms. Using ZnO-Bi<sub>2</sub>O<sub>3</sub> as a model system, this study uncovers how an applied electric current can change the microstructural evolution through an electrochemically induced grain boundary transition. By combining aberration-corrected electron microscopy, photoluminescence spectroscopy, first-principles calculations, a generalizable thermodynamic model, and ab initio molecular dynamics, this study reveals that electrochemical reduction can cause a grain boundary disorder-to-order transition to markedly increase grain boundary diffusivities and mobilities. Consequently, abruptly enhanced or abnormal grain growth takes place. These findings advance our fundamental knowledge of grain boundary complexion (phase-like) transitions and electric field effects on microstructural stability and evolution, with broad scientific and technological impacts. A new method to tailor the grain boundary structures and properties, as well as the microstructures, electrochemically can also be envisioned. Grain boundary ; Electric field ; Electrochemistry ; Microstructure ; Boundary (topology) ; Grain boundary diffusion coefficient ; Current (fluid) ; Electric current ; Field (mathematics) Microstructure and mechanical properties ; Anodic Oxide Films and Nanostructures ; Electromagnetic Effects on Materials
Authors: Ryan D. Norton | Hoa T. Phan | Stephanie N. Gibbons | Amanda J. Haes
Source: Annual Review of Physical Chemistry (review, 2021-12-23)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Quantitative Surface-Enhanced Spectroscopy Surface-enhanced Raman scattering (SERS), a powerful technique for trace molecular detection, depends on chemical and electromagnetic enhancements. While recent advances in instrumentation and substrate design have expanded the utility, reproducibility, and quantitative capabilities of SERS, some challenges persist. In this review, advances in quantitative SERS detection are discussed as they relate to intermolecular interactions, surface selection rules, and target molecule solubility and accessibility. After a brief introduction to Raman scattering and SERS, impacts of surface selection rules and enhancement mechanisms are discussed as they relate to the observation of activation and deactivation of normal Raman modes in SERS. Next, experimental conditions that can be used to tune molecular affinity to and density near SERS substrates are summarized and considered while tuning these parameters is conveyed. Finally, successful examples of quantitative SERS detection are discussed, and future opportunities are outlined. Raman scattering ; Nanotechnology ; Raman spectroscopy ; Surface-enhanced Raman spectroscopy ; Materials science ; Substrate (aquarium) ; Intermolecular force ; Instrumentation (computer programming) ; Molecule ; Chemistry ; Computer science ; Optics ; Physics Gold and Silver Nanoparticles Synthesis and Applications ; Quantum Dots Synthesis And Properties ; Nanocluster Synthesis and Applications
Authors: Qiuyun Zhang | Qizhi Luo | Yaping Wu | Rongfei Yu | Jingsong Cheng | Yutao Zhang
Source: RSC Advances (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

Construction of a Keggin heteropolyacid/Ni-MOF catalyst for esterification of fatty acids This work reports the one-pot solvothermal synthesis of a Keggin heteropolyacid (phosphomolybdic acid, tungstophosphoric acid, or silicotungstic acid) immobilized on Ni-MOF composite catalysts for esterification of fatty acids, and the composites were further analyzed by XRD, FTIR, NH<sub>3</sub>-TPD, SEM, TEM, N<sub>2</sub> adsorption/desorption, and XPS. Among the contrastive syntheses (<i>i.e.</i>, HPW/Ni-MOF, HSiW/Ni-MOF, and HPMo/Ni-MOF), HPMo/Ni-MOF exhibits the most active catalyst toward fatty acids esterification, and the characterization results also revealed that HPMo/Ni-MOF has a strong acidity, large specific surface area, and appropriate average pore size. More significantly, this catalyst exhibits a good catalytic performance (86.1% conversion) during esterification under the optimized reaction conditions, and the HPMo/Ni-MOF catalyst can remain stable after the tenth cycle with a conversion of 73.5%. Intriguingly, the esterification reaction kinetics was studied, and the activation energy was found to be 64.6 kJ mol<sup>-1</sup>. The results indicated that the esterification of fatty acids using the HPMo/Ni-MOF catalyst is a chemically controlled reaction. Catalysis ; Chemistry ; Keggin structure ; Organic chemistry Polyoxometalates: Synthesis and Applications ; Chemical Synthesis and Reactions ; Carbon dioxide utilization in catalysis
Authors: Xingpeng Li | Hongzhe Jiang | Xuesong Jiang | Minghong Shi
Source: Agriculture (article, 2021-12-15)
Field: Chemistry — Analytical Chemistry

Identification of Geographical Origin of Chinese Chestnuts Using Hyperspectral Imaging with 1D-CNN Algorithm The adulteration in Chinese chestnuts affects the quality, taste, and brand value. The objective of this study was to explore the feasibility of the hyperspectral imaging (HSI) technique to determine the geographical origin of Chinese chestnuts. An HSI system in spectral range of 400–1000 nm was applied to identify a total of 417 Chinese chestnuts from three different geographical origins. Principal component analysis (PCA) was preliminarily used to investigate the differences of average spectra of the samples from different geographical origins. A deep-learning-based model (1D-CNN, one-dimensional convolutional neural network) was developed first, and then the model based on full spectra and optimal wavelengths were established for various machine learning methods, including partial least squares-discriminant analysis (PLS-DA) and particle swarm optimization-support vector machine (PSO-SVM). The optimal results based on full spectra for 1D-CNN, PLS-DA, and PSO-SVM models were 97.12%, 97.12%, and 95.68%, respectively. Competitive adaptive reweighted sampling (CARS) and a successive projections algorithm (SPA) were individually utilized for wavelengths selection, and the results of simplified models generally improved. The contrasting results demonstrated that the prediction accuracies of SPA-PLS-DA and 1D-CNN both reached 97.12%, but 1D-CNN presented a higher Kappa coefficient value than SPA-PLS-DA. Meanwhile, the sensitivities and specificities of SPA-PLS-DA and 1D-CNN models were both above 90% for the samples from each geographical origin. These results indicated that both SPA-PLS-DA and 1D-CNN models combined with HSI have great potential for the geographical origin identification of Chinese chestnuts. Hyperspectral imaging ; Pattern recognition (psychology) ; Artificial intelligence ; Principal component analysis ; Partial least squares regression ; Support vector machine ; Convolutional neural network ; Particle swarm optimization ; Computer science ; Mathematics ; Spectral signature ; Linear discriminant analysis ; Algorithm ; Statistics ; Remote sensing ; Geography Spectroscopy and Chemometric Analyses ; Nuts composition and effects ; Identification and Quantification in Food
Authors: John Thomas | Anirudh Raju Natarajan | Anton Van der Ven
Source: npj Computational Materials (article, 2021-10-07)
Field: Materials Science — Materials Chemistry

Comparing crystal structures with symmetry and geometry Abstract Measuring the similarity between two arbitrary crystal structures is a common challenge in crystallography and materials science. Although there are an infinite number of ways to mathematically relate two crystal structures, only a few are physically meaningful. Here we introduce both a geometry-based and a symmetry-adapted similarity metric to compare crystal structures. Using crystal symmetry and combinatorial optimization we describe an algorithm to arrive at the structural relationship that minimizes these similarity metrics across all possible maps between any pair of crystal structures. The approach makes it possible to (i) identify pairs of crystal structures that are identical, (ii) quantitatively measure the similarity between crystal structures, and (iii) find and rank structural transformation pathways between any pair of crystal structures. We discuss the advantages of using the symmetry-adapted cost metric over the geometric cost. Finally, we show that all known structural transformation pathways between common crystal structures are recovered with the mapping algorithm. The methodology presented in this study will be of value to efforts that seek to catalogue crystal structures, identify structural transformation pathways or prune large first-principles datasets used to parameterize on-lattice Hamiltonians. Crystal structure ; Symmetry (geometry) ; Similarity (geometry) ; Crystal (programming language) ; Metric (unit) ; Rank (graph theory) ; Lattice (music) ; Crystal structure prediction ; Mathematics ; Geometry ; Computer science ; Algorithm ; Crystallography ; Physics ; Combinatorics ; Artificial intelligence ; Chemistry ; Image (mathematics) ; Engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Crystallography and molecular interactions
Authors: Candelaria Tejada-Tovar | Ángel Villabona-Ortíz | Ángel Darío González-Delgado
Source: Water (article, 2021-05-15)
Field: Chemistry — Analytical Chemistry

Adsorption of Azo-Anionic Dyes in a Solution Using Modified Coconut (Cocos nucifera) Mesocarp: Kinetic and Equilibrium Study The effect of adsorbent dose and initial concentration on removing the azo-anionic dyes Congo Red andtartrazine present in a synthetic aqueous solution was studied using natural cellulose (CC) and modified cationic cellulose (MCC) from coconut mesocarp. Three levels of adsorbent dosage (5, 8 and 12 mg/L) and initial concentration (40, 70 and 100 mg/L) were used. Cetyl trimethyl ammonium chloride (CTAC) was used as a modifying agent. TGA and DSC showed that the extracted cellulose was of good quality, composed mostly of cellulose with lignin and hemicellulose traces, and 8% moisture. The FTIR spectrum showed the effectiveness of the modification in the structure of the material with symmetric deformation of the C6H6-Cl group in 1472 cm−1 present in the CTAC. It was found that decreasing the adsorbent dosage and increasing the initial concentration favored the dyes’ adsorption capacity on the two bioadsorbents. Tartrazine removals of 5.67 mg/g on CC and 19.61 mg/g on MCC were achieved, and for CR of 15.52 mg/g on CC and 19.99 with MCC with removal percentages over 97% with the quaternized biomass in all cases. The kinetic and equilibrium study was carried out to identify the mechanisms involved in the adsorption process. The Freundlich model can describe the equilibrium isotherm data of tartrazine on CC and MCC. In contrast, those of CR is defined by the Langmuir and Dubinin–Radushkevic models for CC and MCC, respectively Adsorption kinetics showed that equilibrium was reached at 30 min, with rapid adsorption in the initial minutes with the removal of about 97% of the contaminant in the first 5 min; fitting to kinetic models showed that the kinetics of tartrazine on CC was fitted by Elovich (R2 = 0.756), and on MCC the Elovich (R2 = 0.887) and pseudo-second-order (R2 = 0.999) models. Removing CR on CC was fitted by pseudo-first-order, pseudo-second-order and Elovich models (R2 &gt; 0.98), and when using MCC, all models show a good fitting with R2 = 0.99 in all cases. Adsorption ; Tartrazine ; Chemistry ; Freundlich equation ; Cellulose ; Aqueous solution ; Nuclear chemistry ; Cationic polymerization ; Langmuir adsorption model ; Langmuir ; Chromatography ; Organic chemistry Dye analysis and toxicity ; Adsorption and biosorption for pollutant removal ; Dyeing and Modifying Textile Fibers
Authors: Véronique Gomes | Ana Mendes‐Ferreira | Pedro Melo‐Pinto
Source: Sensors (article, 2021-05-15)
Field: Chemistry — Analytical Chemistry

Application of Hyperspectral Imaging and Deep Learning for Robust Prediction of Sugar and pH Levels in Wine Grape Berries Remote sensing technology, such as hyperspectral imaging, in combination with machine learning algorithms, has emerged as a viable tool for rapid and nondestructive assessment of wine grape ripeness. However, the differences in terroir, together with the climatic variations and the variability exhibited by different grape varieties, have a considerable impact on the grape ripening stages within a vintage and between vintages and, consequently, on the robustness of the predictive models. To address this challenge, we present a novel one-dimensional convolutional neural network architecture-based model for the prediction of sugar content and pH, using reflectance hyperspectral data from different vintages. We aimed to evaluate the model's generalization capacity for different varieties and for a different vintage not employed in the training process, using independent test sets. A transfer learning mechanism, based on the proposed convolutional neural network, was also used to evaluate improvements in the model's generalization. Overall, the results for generalization ability showed a very good performance with RMSEP values of 1.118 °Brix and 1.085 °Brix for sugar content and 0.199 and 0.183 for pH, for test sets using different varieties and a different vintage, respectively, improving and updating the current state of the art. Vintage ; Hyperspectral imaging ; Wine ; Convolutional neural network ; Artificial intelligence ; Ripeness ; Sugar ; Computer science ; Robustness (evolution) ; Machine learning ; Artificial neural network ; Deep learning ; Pattern recognition (psychology) ; Mathematics ; Food science ; Ripening ; Chemistry Spectroscopy and Chemometric Analyses ; Horticultural and Viticultural Research ; Fermentation and Sensory Analysis
Authors: Laura N. Neumann | Emad Oveisi | Albrecht Petzold | Robert W. Style | Thomas Thurn‐Albrecht | Christoph Weder | Stephen Schrettl
Source: Science Advances (article, 2021-04-28)
Field: Materials Science — Polymers and Plastics

Dynamics and healing behavior of metallosupramolecular polymers Self-healing or healable polymers can recuperate their function after physical damage. This process involves diffusion of macromolecules across severed interfaces until the structure of the interphase matches that of the pristine material. However, monitoring this nanoscale process and relating it to the mechanical recovery remain elusive. We report that studying diffusion across healed interfaces and a correlation of contact time, diffusion depth, and mechanical properties is possible when two metallosupramolecular polymers assembled with different lanthanoid salts are mended. The materials used display similar properties, while the metal ions can be tracked with high spatial resolution by energy-dispersive x-ray spectrum imaging. We find that healing actual defects requires an interphase thickness in excess of 100 nm, 10 times more than previously established for self-adhesion of smooth films of glassy polymers. Polymer ; Nanotechnology ; Nanoscopic scale ; Dynamics (music) ; Supramolecular polymers ; Supramolecular chemistry ; Materials science ; Computer science ; Chemistry ; Physics ; Composite material Polymer composites and self-healing ; Polydiacetylene-based materials and applications ; Advanced Polymer Synthesis and Characterization