jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Zi‐You Tian | Zhengchu Zhang | Shuo Wang | Hua Lu
Source: Nature Communications (article, 2021-10-04)
Field: Materials Science — Polymers and Plastics

A moisture-tolerant route to unprotected α/β-amino acid N-carboxyanhydrides and facile synthesis of hyperbranched polypeptides A great hurdle in the production of synthetic polypeptides lies in the access of N-carboxyanhydrides (NCA) monomers, which requires dry solvents, Schlenk line/gloveboxe, and protection of side-chain functional groups. Here we report a robust method for preparing unprotected NCA monomers in air and under moisture. The method employs epoxy compounds as ultra-fast scavengers of hydrogen chloride to allow assisted ring-closure and prevent NCA from acid-catalyzed decomposition under moist conditions. The broad scope and functional group tolerance of the method are demonstrated by the facile synthesis of over 30 different α/β-amino acid NCAs, including many otherwise inaccessible compounds with reactive functional groups, at high yield, high purity, and up to decagram scales. The utility of the method and the unprotected NCAs is demonstrated by the facile synthesis of two water-soluble polypeptides that are promising candidates for drug delivery and protein modification. Overall, our strategy holds great potential for facilitating the synthesis of NCA and expanding the industrial application of synthetic polypeptides. Monomer ; Chemistry ; Yield (engineering) ; Combinatorial chemistry ; Amino acid ; Functional group ; Organic chemistry ; Biochemistry ; Materials science ; Polymer Dendrimers and Hyperbranched Polymers ; Chemical Synthesis and Analysis ; RNA Interference and Gene Delivery
Authors: Xin Dai | Wenhao Fu | Huanyu Chi | Vince St. Dollente Mesias | Hongni Zhu | Cheuk Wai Leung | Wei Liu | Jinqing Huang
Source: Nature Communications (article, 2021-02-26)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Optical tweezers-controlled hotspot for sensitive and reproducible surface-enhanced Raman spectroscopy characterization of native protein structures Surface-enhanced Raman spectroscopy (SERS) has emerged as a powerful tool to detect biomolecules in aqueous environments. However, it is challenging to identify protein structures at low concentrations, especially for the proteins existing in an equilibrium mixture of various conformations. Here, we develop an in situ optical tweezers-coupled Raman spectroscopy to visualize and control the hotspot between two Ag nanoparticle-coated silica beads, generating tunable and reproducible SERS enhancements with single-molecule level sensitivity. This dynamic SERS detection window is placed in a microfluidic flow chamber to detect the passing-by proteins, which precisely characterizes the structures of three globular proteins without perturbation to their native states. Moreover, it directly identifies the structural features of the transient species of alpha-synuclein among its predominant monomers at physiological concentration of 1 μM by reducing the ensemble averaging. Hence, this SERS platform holds the promise to resolve the structural details of dynamic, heterogeneous, and complex biological systems. Biomolecule ; Raman spectroscopy ; Optical tweezers ; Surface-enhanced Raman spectroscopy ; Microfluidics ; Nanotechnology ; Materials science ; Nanoparticle ; Monomer ; Molecule ; Chemistry ; Raman scattering ; Optics ; Physics Gold and Silver Nanoparticles Synthesis and Applications ; Spectroscopy Techniques in Biomedical and Chemical Research ; Protein Interaction Studies and Fluorescence Analysis
Authors: Laura Scalfi | Mathieu Salanne | Benjamin Rotenberg
Source: Annual Review of Physical Chemistry (article, 2021-01-05)
Field: Chemistry — Physical and Theoretical Chemistry

Molecular Simulation of Electrode-Solution Interfaces Many key industrial processes, from electricity production, conversion, and storage to electrocatalysis or electrochemistry in general, rely on physical mechanisms occurring at the interface between a metallic electrode and an electrolyte solution, summarized by the concept of an electric double layer, with the accumulation/depletion of electrons on the metal side and of ions on the liquid side. While electrostatic interactions play an essential role in the structure, thermodynamics, dynamics, and reactivity of electrode-electrolyte interfaces, these properties also crucially depend on the nature of the ions and solvent, as well as that of the metal itself. Such interfaces pose many challenges for modeling because they are a place where quantum chemistry meets statistical physics. In the present review, we explore the recent advances in the description and understanding of electrode-electrolyte interfaces with classical molecular simulations, with a focus on planar interfaces and solvent-based liquids, from pure solvent to water-in-salt electrolytes. Electrochemistry ; Electrocatalyst ; Electrolyte ; Interface (matter) ; Ion ; Electrode ; Molecular dynamics ; Focus (optics) Electrostatics and Colloid Interactions ; Spectroscopy and Quantum Chemical Studies ; Electrochemical Analysis and Applications
Authors: Jianwei Cao | Soumya Mukherjee | Tony Pham | Yu Wang | Teng Wang | Tao Zhang | Xue Jiang | Hui-Juan Tang | Katherine A. Forrest | Brian Space | Michael J. Zaworotko | Kai‐Jie Chen
Source: Nature Communications (article, 2021-11-11)
Field: Chemistry — Inorganic Chemistry

One-step ethylene production from a four-component gas mixture by a single physisorbent One-step adsorptive purification of ethylene (C<sub>2</sub>H<sub>4</sub>) from four-component gas mixtures comprising acetylene (C<sub>2</sub>H<sub>2</sub>), ethylene (C<sub>2</sub>H<sub>4</sub>), ethane (C<sub>2</sub>H<sub>6</sub>) and carbon dioxide (CO<sub>2</sub>) is an unmet challenge in the area of commodity purification. Herein, we report that the ultramicroporous sorbent Zn-atz-oba (H<sub>2</sub>oba = 4,4-dicarboxyl diphenyl ether; Hatz = 3-amino-1,2,4-triazole) enables selective adsorption of C<sub>2</sub>H<sub>2</sub>, C<sub>2</sub>H<sub>6</sub> and CO<sub>2</sub> over C<sub>2</sub>H<sub>4</sub> thanks to the binding sites that lie in its undulating pores. Molecular simulations provide insight into the binding sites in Zn-atz-oba that are responsible for coadsorption of C<sub>2</sub>H<sub>2</sub>, C<sub>2</sub>H<sub>6</sub> and CO<sub>2</sub> over C<sub>2</sub>H<sub>4</sub>. Dynamic breakthrough experiments demonstrate that the selective binding exhibited by Zn-atz-oba can produce polymer-grade purity (>99.95%) C<sub>2</sub>H<sub>4</sub> from binary (1:1 for C<sub>2</sub>H<sub>4</sub>/C<sub>2</sub>H<sub>6</sub>), ternary (1:1:1 for C<sub>2</sub>H<sub>2</sub>/C<sub>2</sub>H<sub>4</sub>/C<sub>2</sub>H<sub>6</sub>) and quaternary (1:1:1:1 for C<sub>2</sub>H<sub>2</sub>/C<sub>2</sub>H<sub>4</sub>/C<sub>2</sub>H<sub>6</sub>/CO<sub>2</sub>) gas mixtures in a single step. Ternary operation ; Ethylene ; Acetylene ; Adsorption ; Sorbent ; Component (thermodynamics) ; Carbon dioxide ; Materials science ; Selective adsorption ; Chemical engineering ; Chemistry ; Organic chemistry ; Catalysis ; Thermodynamics Metal-Organic Frameworks: Synthesis and Applications ; Carbon Dioxide Capture Technologies ; Covalent Organic Framework Applications
Authors: Ahmed Al Saqr | El‐Sayed Khafagy | Ahmed Alalaiwe | Mohammed F. Aldawsari | Saad M. Alshahrani | Md. Khalid Anwer | Salman Khan | Amr S. Abu Lila | Hany H. Arab | Wael A. H. Hegazy
Source: Nanomaterials (article, 2021-03-22)
Field: Materials Science — Materials Chemistry

Synthesis of Gold Nanoparticles by Using Green Machinery: Characterization and In Vitro Toxicity Green synthesis of gold nanoparticles (GNPs) with plant extracts has gained considerable interest in the field of biomedicine. Recently, the bioreduction nature of herbal extracts has helped to synthesize spherical GNPs of different potential from gold salt. In this study, a fast ecofriendly method was adopted for the synthesis of GNPs using fresh peel (aqueous) extracts of <i>Benincasa hispida</i>, which acted as reducing and stabilizing agents. The biosynthesized GNPs were characterized by UV-VIS and Fourier transform infrared spectroscopy, transmission electron microscopy (TEM), and dynamic light scattering. In addition, the in vitro antibacterial and anticancer activities of synthesized GNPs were investigated. The formation of gold nanoparticles was confirmed by the existence of a sharp absorption peak at 520 nm, corresponding to the surface plasmon resonance (SPR) band of the GNPs. TEM analysis revealed that the prepared GNPs were spherical in shape and had an average particle size of 22.18 ± 2 nm. Most importantly, the synthesized GNPs exhibited considerable antibacterial activity against different Gram-positive and Gram-negative bacteria. Furthermore, the biosynthesized GNPs exerted remarkable in vitro cytotoxicity against human cervical cancer cell line, while sparing normal human primary osteoblast cells. Such cytotoxic effect was attributed to the increased production of reactive oxygen species (ROS) that contributed to the damage of HeLa cells. Collectively, peel extracts of <i>B. hispida</i> can be efficiently used for the synthesis of GNPs, which can be adopted as a natural source of antimicrobial and anticancer agent. Colloidal gold ; HeLa ; Surface plasmon resonance ; Cytotoxicity ; Antibacterial activity ; Nuclear chemistry ; Nanoparticle ; Chemistry ; Fourier transform infrared spectroscopy ; Dynamic light scattering ; In vitro ; Nanotechnology ; Materials science ; Combinatorial chemistry ; Biophysics ; Chemical engineering ; Bacteria ; Biochemistry ; Biology Nanoparticles: synthesis and applications
Authors: Yani Guan | Donovan Chaffart | Guihua Liu | Zhaoyang Tan | Dongsheng Zhang | Yanji Wang | Jingde Li | Luis Ricardez‐Sandoval
Source: Chemical Engineering Science (article, 2021-10-25)
Field: Materials Science — Materials Chemistry

Machine learning in solid heterogeneous catalysis: Recent developments, challenges and perspectives Catalysis ; Heterogeneous catalysis ; Biochemical engineering ; Identification (biology) ; Computer science ; Nanotechnology ; Process engineering ; Machine learning ; Materials science ; Chemistry ; Engineering Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; Electrocatalysts for Energy Conversion
Authors: Ali Riza Durmaz | Martin Müller | Bo Lei | Akhil Thomas | Dominik Britz | Elizabeth A. Holm | Christoph Eberl | Frank Mücklich | Peter Gumbsch
Source: Nature Communications (article, 2021-11-01)
Field: Materials Science — Materials Chemistry

A deep learning approach for complex microstructure inference Automated, reliable, and objective microstructure inference from micrographs is essential for a comprehensive understanding of process-microstructure-property relations and tailored materials development. However, such inference, with the increasing complexity of microstructures, requires advanced segmentation methodologies. While deep learning offers new opportunities, an intuition about the required data quality/quantity and a methodological guideline for microstructure quantification is still missing. This, along with deep learning's seemingly intransparent decision-making process, hampers its breakthrough in this field. We apply a multidisciplinary deep learning approach, devoting equal attention to specimen preparation and imaging, and train distinct U-Net architectures with 30-50 micrographs of different imaging modalities and electron backscatter diffraction-informed annotations. On the challenging task of lath-bainite segmentation in complex-phase steel, we achieve accuracies of 90% rivaling expert segmentations. Further, we discuss the impact of image context, pre-training with domain-extrinsic data, and data augmentation. Network visualization techniques demonstrate plausible model decisions based on grain boundary morphology. Deep learning ; Inference ; Artificial intelligence ; Computer science ; Segmentation ; Visualization ; Machine learning ; Context (archaeology) ; Data science ; Geology Machine Learning in Materials Science ; Hydrogen embrittlement and corrosion behaviors in metals ; Mineral Processing and Grinding
Authors: Giulia Rossella Delpiano | Davide Tocco | Luca Medda | Edmond Magner | Andrea Salis
Source: International Journal of Molecular Sciences (article, 2021-01-14)
Field: Chemistry — Inorganic Chemistry

Adsorption of Malachite Green and Alizarin Red S Dyes Using Fe-BTC Metal Organic Framework as Adsorbent Synthetic organic dyes are widely used in various industrial sectors but are also among the most harmful water pollutants. In the last decade, significant efforts have been made to develop improved materials for the removal of dyes from water, in particular, on nanostructured adsorbent materials. Metal organic frameworks (MOFs) are an attractive class of hybrid nanostructured materials with an extremely wide range of applications including adsorption. In the present work, an iron-based Fe-BTC MOF, prepared according to a rapid, aqueous-based procedure, was used as an adsorbent for the removal of alizarin red S (ARS) and malachite green (MG) dyes from water. The synthesized material was characterized in detail, while the adsorption of the dyes was monitored by UV-Vis spectroscopy. An optimal adsorption pH of 4, likely due to the establishment of favorable interactions between dyes and Fe-BTC, was found. At this pH and at a temperature of 298 K, adsorption equilibrium was reached in less than 30 min following a pseudo-second order kinetics, with k″ of 4.29 × 10<sup>-3</sup> and 3.98 × 10<sup>-2</sup> g∙mg<sup>-1</sup> min<sup>-1</sup> for ARS and MG, respectively. The adsorption isotherm followed the Langmuir model with maximal adsorption capacities of 80 mg∙g<sup>-1</sup> (ARS) and 177 mg∙g<sup>-1</sup> (MG), and K<sub>L</sub> of 9.30·10<sup>3</sup> L∙mg<sup>-1</sup> (ARS) and 51.56·10<sup>3</sup> L∙mg<sup>-1</sup> (MG). Malachite green ; Adsorption ; ALIZARIN RED ; Aqueous solution ; Chemistry ; Alizarin ; Langmuir adsorption model ; Metal-organic framework ; Nuclear chemistry ; Langmuir ; Inorganic chemistry ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Adsorption and biosorption for pollutant removal ; Mercury impact and mitigation studies
Authors: Christian Künkel | Johannes T. Margraf | Ke Chen | Harald Oberhofer | Karsten Reuter
Source: Nature Communications (article, 2021-04-23)
Field: Materials Science — Materials Chemistry

Active discovery of organic semiconductors The versatility of organic molecules generates a rich design space for organic semiconductors (OSCs) considered for electronics applications. Offering unparalleled promise for materials discovery, the vastness of this design space also dictates efficient search strategies. Here, we present an active machine learning (AML) approach that explores an unlimited search space through consecutive application of molecular morphing operations. Evaluating the suitability of OSC candidates on the basis of charge injection and mobility descriptors, the approach successively queries predictive-quality first-principles calculations to build a refining surrogate model. The AML approach is optimized in a truncated test space, providing deep methodological insight by visualizing it as a chemical space network. Significantly outperforming a conventional computational funnel, the optimized AML approach rapidly identifies well-known and hitherto unknown molecular OSC candidates with superior charge conduction properties. Most importantly, it constantly finds further candidates with highest efficiency while continuing its exploration of the endless design space. Chemical space ; Computer science ; Space (punctuation) ; Organic semiconductor ; Quality (philosophy) ; Electronics ; Funnel ; Morphing ; Nanotechnology ; Biochemical engineering ; Artificial intelligence ; Materials science ; Physics ; Drug discovery ; Bioinformatics ; Engineering ; Electrical engineering ; Biology ; Mechanical engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Organic Electronics and Photovoltaics
Authors: Esther Heid | William H. Green
Source: Journal of Chemical Information and Modeling (review, 2021-11-04)
Field: Materials Science — Materials Chemistry

Machine Learning of Reaction Properties via Learned Representations of the Condensed Graph of Reaction The estimation of chemical reaction properties such as activation energies, rates, or yields is a central topic of computational chemistry. In contrast to molecular properties, where machine learning approaches such as graph convolutional neural networks (GCNNs) have excelled for a wide variety of tasks, no general and transferable adaptations of GCNNs for reactions have been developed yet. We therefore combined a popular cheminformatics reaction representation, the so-called condensed graph of reaction (CGR), with a recent GCNN architecture to arrive at a versatile, robust, and compact deep learning model. The CGR is a superposition of the reactant and product graphs of a chemical reaction and thus an ideal input for graph-based machine learning approaches. The model learns to create a data-driven, task-dependent reaction embedding that does not rely on expert knowledge, similar to current molecular GCNNs. Our approach outperforms current state-of-the-art models in accuracy, is applicable even to imbalanced reactions, and possesses excellent predictive capabilities for diverse target properties, such as activation energies, reaction enthalpies, rate constants, yields, or reaction classes. We furthermore curated a large set of atom-mapped reactions along with their target properties, which can serve as benchmark data sets for future work. All data sets and the developed reaction GCNN model are available online, free of charge, and open source. Cheminformatics ; Computer science ; Benchmark (surveying) ; Convolutional neural network ; Graph ; Molecular graph ; Embedding ; Artificial intelligence ; Machine learning ; Reaction rate ; Theoretical computer science ; Biological system ; Chemistry ; Computational chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Lei Tao | Guang Chen | Ying Li
Source: Patterns (article, 2021-03-26)
Field: Materials Science — Materials Chemistry

Machine learning discovery of high-temperature polymers To formulate a machine learning (ML) model to establish the polymer's structure-property correlation for glass transition temperature <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow><mml:msub><mml:mi>T</mml:mi> <mml:mi>g</mml:mi></mml:msub> </mml:mrow> </mml:math> , we collect a diverse set of nearly 13,000 real homopolymers from the largest polymer database, PoLyInfo. We train the deep neural network (DNN) model with 6,923 experimental <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow><mml:msub><mml:mi>T</mml:mi> <mml:mi>g</mml:mi></mml:msub> </mml:mrow> </mml:math> values using Morgan fingerprint representations of chemical structures for these polymers. Interestingly, the trained DNN model can reasonably predict the unknown <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow><mml:msub><mml:mi>T</mml:mi> <mml:mi>g</mml:mi></mml:msub> </mml:mrow> </mml:math> values of polymers with distinct molecular structures, in comparison with molecular dynamics simulations and experimental results. With the validated transferability and generalization ability, the ML model is utilized for high-throughput screening of nearly one million hypothetical polymers. We identify more than 65,000 promising candidates with <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow><mml:msub><mml:mi>T</mml:mi> <mml:mi>g</mml:mi></mml:msub> </mml:mrow> </mml:math> > 200°C, which is 30 times more than existing known high-temperature polymers (∼2,000 from PoLyInfo). The discovery of this large number of promising candidates will be of significant interest in the development and design of high-temperature polymers. Polymer ; Glass transition ; Generalization ; Computer science ; Artificial neural network ; Transferability ; Deep learning ; Fingerprint (computing) ; Materials science ; Artificial intelligence ; Biological system ; Machine learning ; Nanotechnology ; Mathematics ; Composite material Machine Learning in Materials Science ; Polymer crystallization and properties ; Computational Drug Discovery Methods
Authors: Rohit Anand | Arun S. Nissimagoudar | Muhammad Umer | Miran Ha | Mohammad Zafari | Sohaib Umer | Geunsik Lee | Kwang S. Kim
Source: Advanced Energy Materials (article, 2021-11-07)
Field: Materials Science — Materials Chemistry

Late Transition Metal Doped MXenes Showing Superb Bifunctional Electrocatalytic Activities for Water Splitting via Distinctive Mechanistic Pathways Abstract MXenes have been widely used as substrates of hybrid electrocatalysts for water splitting due to their stability and metallic properties. However, tuning MXenes towards superb hydrogen/oxygen evolution reaction (HER/OER) activity has remained elusive. Using first‐principles calculations along with machine learning (ML) based descriptors, it is shown that late transition metal doping is able to significantly promote HER/OER activities. Both single‐atom adsorption onto a stable hollow site above the outer oxygen layer single‐atom catalyst 1 (SAC1), and single‐atom replacement at a sub‐surface metal layer (SAC2) are considered. An adsorbate evolving mechanism (AEM) is preferred for SAC1, while the increased M‐O bond covalency for SAC2 makes lattice oxygen mechanism (LOM) favored. It is found that a single Ni or Co atom embedded into MXenes provides a suitable number of electrons for optimal AEM and raises the O 2p band towards activated LOM. The stability and superb bifunctional catalytic capability of MXene combinations (Ni‐doped Sc 3 N 2 O 2 and Ni‐doped Nb 3 C 2 O 2 ) towards both HER and OER are demonstrated. The electronic and geometric descriptors used in the ML analysis work universally for classification of high‐performing HER/OER catalysts. This work provides a rational strategy for promoting bifunctional electrocatalytic activities based on low‐cost MXenes metals. MXenes ; Bifunctional ; Oxygen evolution ; Water splitting ; Materials science ; Doping ; Catalysis ; Transition metal ; Metal ; Chemical engineering ; Nanotechnology ; Chemical physics ; Physical chemistry ; Chemistry ; Optoelectronics ; Electrochemistry ; Metallurgy ; Organic chemistry MXene and MAX Phase Materials ; Electrocatalysts for Energy Conversion ; Advanced Photocatalysis Techniques
Authors: Wei Yang | Tyler J. Nigon | Ziyuan Hao | Gabriel Dias Paiao | Fabián G. Fernández | D. J. Mulla | Ce Yang
Source: Computers and Electronics in Agriculture (article, 2021-03-26)
Field: Chemistry — Analytical Chemistry

Estimation of corn yield based on hyperspectral imagery and convolutional neural network Hyperspectral imaging ; Convolutional neural network ; Artificial intelligence ; Remote sensing ; Pattern recognition (psychology) ; Computer science ; Computer vision ; Mathematics ; Geography Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Remote Sensing in Agriculture