jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Hao Guo | Jan‐Lukas Kirchhoff | Carsten Strohmann | Bastian Grabe | Charles C. J. Loh
Source: Angewandte Chemie International Edition (article, 2023-12-20)
Field: Chemistry — Organic Chemistry

Exploiting π and Chalcogen Interactions for the β‐Selective Glycosylation of Indoles through Glycal Conformational Distortion Harnessing unconventional noncovalent interactions (NCIs) is emerging as a formidable synthetic approach in difficult-to-access glycosidic chemical space. C-Glycosylation, in particular, has gained a flurry of recent attention. However, most reported methods are restricted to the relatively facile access to α-C-glycosides. Herein, we disclose a β-stereoselective glycosylation of indoles by employing a phosphonoselenide catalyst. The robustness of this protocol is exemplified by its amenability for reaction at both the indolyl C- and N- reactivity sites. In contrast to previous reports, in which the chalcogens were solely involved in Lewis acidic activation, our mechanistic investigation unraveled that the often neglected flanking aromatic substituents of phosphonoselenides can substantially contribute to catalysis by engaging in π-interactions. Computations and NMR spectroscopy indicated that the chalcogenic and aromatic components of the catalyst can be collectively exploited to foster conformational distortion of the glycal away from the usual half-chair to the boat conformation, which liberates the convex β-face for nucleophilic attack. Chemistry ; Glycosylation ; Glycal ; Carbohydrate chemistry ; Nucleophile ; Glycosidic bond ; Catalysis ; Combinatorial chemistry ; Lewis acids and bases ; Stereochemistry ; Organic chemistry ; Stereoselectivity ; Biochemistry Carbohydrate Chemistry and Synthesis ; Chemical Synthesis and Analysis ; Sulfur-Based Synthesis Techniques
Authors: Jiaxiang Guo | Zeyi Li | Xinyu Tian | Tianyu Zhang | Yue Wang | Chuandong Dou
Source: Angewandte Chemie International Edition (article, 2023-01-05)
Field: Chemistry — Organic Chemistry

Diradical B/N‐Doped Polycyclic Hydrocarbons Heterocyclic diradicaloids with atom-precise control over open-shell nature are promising materials for organic electronics and spintronics. Herein, we disclose quinoidal π-extension of a B/N-heterocycle for generating B/N-type organic diradicaloids. Two quinoidal π-extended B/N-doped polycyclic hydrocarbons that feature fusion of the B/N-heterocycle motif with the antiaromatic s-indacene or dicyclopenta[b,g]naphthalene core were synthesized. This quinoidal π-extension and B/N-heterocycle leads to their open-shell electronic nature, which stands in contrast to the multiple-resonance effect of conventional B/N-type emitters. These B/N-type diradicaloids have modulated (anti)aromaticity and enhanced diradical characters comparing with the all-carbon analogues, as well as intriguing properties, such as magnetic activities, narrow energy gaps and highly red-shifted absorptions. This study thus opens the new space for both of B/N-doped polycyclic π-systems and heterocyclic diradicaloids. Diradical ; Antiaromaticity ; Aromaticity ; Chemistry ; Spintronics ; Atom (system on chip) ; Doping ; Crystallography ; Stereochemistry ; Open shell ; Materials science ; Molecule ; Organic chemistry ; Atomic physics ; Physics ; Optoelectronics ; Condensed matter physics ; Ferromagnetism Synthesis and Properties of Aromatic Compounds ; Organoboron and organosilicon chemistry ; Luminescence and Fluorescent Materials
Authors: Matineh Ghomi | Ehsan Nazarzadeh Zare‬ | Hadis Alidadi | Nahid Pourreza | Azarmidokht Sheini | Navid Rabiee | Virgilio Mattoli | Xianfeng Chen | Pooyan Makvandi
Source: Advanced Composites and Hybrid Materials (article, 2023-01-31)
Field: Materials Science — Materials Chemistry

A multifunctional bioresponsive and fluorescent active nanogel composite for breast cancer therapy and bioimaging Abstract Bio-responsive nanocomposites with facile fabrication and rational design are of great importance in the diagnosis and treatment of cancer. Herein, the combination of bioimaging due to the presence of QD and controlled drug delivery via nanogel was employed for cancer treatment. To this aim, we fabricated a traceable and bioresponsive fluorescent active nanogel composite by integrating creatinine-functionalized carbon dots (QD) into a lecithin-inulin nanogel. Subsequently, PEGylation and integration of herceptin on the nanogel were carried out to improve the biofate and its ability to target HER2-positive breast cancer. The assessment of cellular uptake demonstrated that this nanogel was effectively internalized by SK-BR-3 cells and the production of reactive oxygen species was significantly boosted. Moreover, the nanogel composite led to high expression of P53 and Bax genes along with a low expression of Bcl-2 gene (as promotors of the apoptosis signaling pathway). The cellular uptake of herceptin was enhanced. It led to inhibiting the proliferation of the breast cancer cells as well as the reduction of viability of SK-BR-3 cancer cells (HER-2 positive) compared with MDA-MB-231 cells (triple-negative). The intraperitoneal injection of the developed formulation to MCF-7 breast cancer cell-bearing BALB/c mice supported the observation of tumor growth inhibition. Overall, herceptin-adorned PEGylated lecithin-inulin nanogel composite can be a promising theranostic candidate for targeting HER-2-positive breast cancer. Graphical Abstract Nanogel ; Cytotoxicity ; Cancer research ; Chemistry ; Breast cancer ; Cancer cell ; Cancer ; Drug delivery ; Pharmacology ; Nanotechnology ; Materials science ; Biochemistry ; Medicine ; Internal medicine ; In vitro Carbon and Quantum Dots Applications ; Graphene and Nanomaterials Applications ; Nanocluster Synthesis and Applications
Authors: Cataldo Valentini | Verónica Montes‐García | Pietro Antonio Livio | Tomasz Chudziak | Jésus Raya | Artur Ciesielski | Paolo Samorı́
Source: Nanoscale (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Tuning the electrical properties of graphene oxide through low-temperature thermal annealing During the last fifteen years, the reduction of electrically insulating graphene oxide (GO) through the elimination of oxygen containing functional groups and the restoration of sp<sup>2</sup> conjugation yielding its conducting form, known as reduced graphene oxide (rGO), has been widely investigated as a scalable and low-cost method to produce materials featuring graphene-like characteristics. Among various protocols, thermal annealing represents an attractive green approach compatible with industrial processes. However, the high temperatures typically required to accomplish this process are energetically demanding and are incompatible with the use of plastic substrates often desired for flexible electronics applications. Here, we report a systematic study on the low-temperature annealing of GO by optimizing different annealing conditions, <i>i.e.</i>, temperature, time, and reduction atmosphere. We show that the reduction is accompanied by structural changes of GO, which affect its electrochemical performance when used as an electrode material in supercapacitors. We demonstrate that thermally-reduced GO (TrGO) obtained under air or inert atmosphere at relatively low temperatures (<300 °C) exhibits low film resistivities (10<sup>-2</sup>-10<sup>-4</sup> Ω m) combined with unaltered resistance after 2000 bending cycles when supported on plastic substrates. Moreover, it exhibits enhanced electrochemical characteristics with a specific capacitance of 208 F g<sup>-1</sup> and a capacitance retention of >99% after 2000 cycles. The reported strategy is an important step forward toward the development of environmentally friendly TrGO for future electrical or electrochemical applications. Graphene ; Materials science ; Annealing (glass) ; Oxide ; Supercapacitor ; Capacitance ; Nanotechnology ; Electrochemistry ; Electrode ; Chemical engineering ; Thermal treatment ; Optoelectronics ; Composite material ; Metallurgy ; Chemistry Graphene research and applications ; Supercapacitor Materials and Fabrication ; Advancements in Battery Materials
Authors: Tian Luo | Zihao Wei
Source: Food Frontiers (article, 2023-06-10)
Field: Materials Science — Materials Chemistry

Recent progress in food‐grade double emulsions: Fabrication, stability, applications, and future trends Abstract Compared with ordinary emulsions, the unique structures endow double emulsions with special functions, which makes them have broad prospects in delivering biologically active substances and replacing fats. In recent years, due to the instability of double emulsions, numerous experiments have been carried out on their preparation technology as well as stabilization methods. This review emphatically summarizes the research progress of double emulsions, including preparation techniques, factors affecting stability, stabilization mechanisms, and related applications. At present, in addition to the traditional mechanical agitation and ultrasonic emulsification, membrane emulsification, phase inversion, and microfluidic emulsification are the main research directions, all of which can improve the stability. Moreover, the stabilizer is also a crucial factor affecting the stability of the double emulsion. Hence, in this article emphasis is placed on surfactants, biopolymers, and solid particles. The applications of double emulsions in food industry can be divided into the following categories: encapsulating substances, controlling the release, and being served as fat substitutes. Furthermore, in the future, the main challenges are to commercialize the double emulsions and enhance their safety in food applications. Additionally, developing high internal phase Pickering double emulsions and O/W/O double emulsions may become the major research trends. Pickering emulsion ; Emulsion ; Nanotechnology ; Stabilizer (aeronautics) ; Materials science ; Phase inversion ; Process engineering ; Chemical engineering ; Chemistry ; Mechanical engineering ; Membrane ; Engineering ; Nanoparticle Pickering emulsions and particle stabilization ; Proteins in Food Systems ; Surfactants and Colloidal Systems
Authors: Xiaoliang Wang | Lilin He | Jacob Sumner | Shuo Qian | Qiu Zhang | Hugh O’Neill | Yimin Mao | Cheng‐Xia Chen | Abdullah M. Al‐Enizi | Ayman Nafady | Shengqian Ma
Source: Nature Communications (article, 2023-02-21)
Field: Chemistry — Inorganic Chemistry

Spatially confined protein assembly in hierarchical mesoporous metal-organic framework Immobilization of biomolecules into porous materials could lead to significantly enhanced performance in terms of stability towards harsh reaction conditions and easier separation for their reuse. Metal-Organic Frameworks (MOFs), offering unique structural features, have emerged as a promising platform for immobilizing large biomolecules. Although many indirect methods have been used to investigate the immobilized biomolecules for diverse applications, understanding their spatial arrangement in the pores of MOFs is still preliminary due to the difficulties in directly monitoring their conformations. To gain insights into the spatial arrangement of biomolecules within the nanopores. We used in situ small-angle neutron scattering (SANS) to probe deuterated green fluorescent protein (d-GFP) entrapped in a mesoporous MOF. Our work revealed that GFP molecules are spatially arranged in adjacent nanosized cavities of MOF-919 to form "assembly" through adsorbate-adsorbate interactions across pore apertures. Our findings, therefore, lay a crucial foundation for the identification of proteins structural basics under confinement environment of MOFs. Biomolecule ; Nanotechnology ; Mesoporous material ; Nanopore ; Materials science ; Metal-organic framework ; Molecule ; Chemistry ; Adsorption ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Nanoparticle-Based Drug Delivery ; X-ray Diffraction in Crystallography
Authors: Tanjin He | Haoyan Huo | Christopher J. Bartel | Zheren Wang | Kevin Cruse | Gerbrand Ceder
Source: Science Advances (article, 2023-06-09)
Field: Materials Science — Materials Chemistry

Precursor recommendation for inorganic synthesis by machine learning materials similarity from scientific literature Synthesis prediction is a key accelerator for the rapid design of advanced materials. However, determining synthesis variables such as the choice of precursor materials is challenging for inorganic materials because the sequence of reactions during heating is not well understood. In this work, we use a knowledge base of 29,900 solid-state synthesis recipes, text-mined from the scientific literature, to automatically learn which precursors to recommend for the synthesis of a novel target material. The data-driven approach learns chemical similarity of materials and refers the synthesis of a new target to precedent synthesis procedures of similar materials, mimicking human synthesis design. When proposing five precursor sets for each of 2654 unseen test target materials, the recommendation strategy achieves a success rate of at least 82%. Our approach captures decades of heuristic synthesis data in a mathematical form, making it accessible for use in recommendation engines and autonomous laboratories. Computer science ; Similarity (geometry) ; Key (lock) ; Heuristic ; Scientific literature ; Base (topology) ; Sequence (biology) ; Artificial intelligence ; Chemistry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Topic Modeling
Authors: Hang Xiao | Rong Li | Xiaoyang Shi | Yan Chen | Liangliang Zhu | Xi Chen | Lei Wang
Source: Nature Communications (article, 2023-11-02)
Field: Materials Science — Materials Chemistry

An invertible, invariant crystal representation for inverse design of solid-state materials using generative deep learning The past decade has witnessed rapid progress in deep learning for molecular design, owing to the availability of invertible and invariant representations for molecules such as simplified molecular-input line-entry system (SMILES), which has powered cheminformatics since the late 1980s. However, the design of elemental components and their structural arrangement in solid-state materials to achieve certain desired properties is still a long-standing challenge in physics, chemistry and biology. This is primarily due to, unlike molecular inverse design, the lack of an invertible crystal representation that satisfies translational, rotational, and permutational invariances. To address this issue, we have developed a simplified line-input crystal-encoding system (SLICES), which is a string-based crystal representation that satisfies both invertibility and invariances. The reconstruction routine of SLICES successfully reconstructed 94.95% of over 40,000 structurally and chemically diverse crystal structures, showcasing an unprecedented invertibility. Furthermore, by only encoding compositional and topological data, SLICES guarantees invariances. We demonstrate the application of SLICES in the inverse design of direct narrow-gap semiconductors for optoelectronic applications. As a string-based, invertible, and invariant crystal representation, SLICES shows promise as a useful tool for in silico materials discovery. Invertible matrix ; Invariant (physics) ; Crystal (programming language) ; Representation (politics) ; Inverse ; String (physics) ; Computer science ; Mathematics ; Pure mathematics ; Physics ; Theoretical physics ; Geometry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Crystallography and molecular interactions
Authors: Yihang Li | Ruijie Zhu | Yuanqing Wang | Lingyan Feng | Yi Liu
Source: npj Computational Materials (article, 2023-06-22)
Field: Materials Science — Materials Chemistry

Center-environment deep transfer machine learning across crystal structures: from spinel oxides to perovskite oxides Abstract In data-driven materials design where the target materials have limited data, the transfer machine learning from large known source materials, becomes a demanding strategy especially across different crystal structures. In this work, we proposed a deep transfer learning approach to predict thermodynamically stable perovskite oxides based on a large computational dataset of spinel oxides. The deep neural network (DNN) source domain model with “Center-Environment” (CE) features was first developed using the formation energy of 5329 spinel oxide structures and then was fine-tuned by learning a small dataset of 855 perovskite oxide structures, leading to a transfer learning model with good transferability in the target domain of perovskite oxides. Based on the transferred model, we further predicted the formation energy of potential 5329 perovskite structures with combination of 73 elements. Combining the criteria of formation energy and structure factors including tolerance factor (0.7 &lt; t ≤ 1.1) and octahedron factor (0.45 &lt; μ &lt; 0.7), we predicted 1314 thermodynamically stable perovskite oxides, among which 144 oxides were reported to be synthesized experimentally, 10 oxides were predicted computationally by other literatures, 301 oxides were recorded in the Materials Project database, and 859 oxides have been first reported. Combing with the structure-informed features the transfer machine learning approach in this work takes the advantage of existing data to predict new structures at a lower cost, providing an effective acceleration strategy for the expensive high-throughput computational screening in materials design. The predicted stable novel perovskite oxides serve as a rich platform for exploring potential renewable energy and electronic materials applications. Perovskite (structure) ; Spinel ; Transfer of learning ; Machine learning ; Materials science ; Oxide ; Artificial intelligence ; Computer science ; Crystal structure ; Nanotechnology ; Chemistry ; Crystallography ; Metallurgy Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Advanced Photocatalysis Techniques
Authors: Jiahui Liu | Jesper Byggmästar | Zheyong Fan | Ping Qian | Yanjing Su
Source: Physical review. B./Physical review. B (article, 2023-08-24)
Field: Materials Science — Materials Chemistry

Large-scale machine-learning molecular dynamics simulation of primary radiation damage in tungsten Simulating collision cascades and radiation damage poses a long-standing challenge for existing interatomic potentials, both in terms of accuracy and efficiency. Machine-learning-based interatomic potentials have shown sufficiently high accuracy for radiation damage simulations, but most existing ones are still not efficient enough to model high-energy collision cascades with sufficiently large space and timescales. To this end, we here extend the highly efficient neuroevolution potential (NEP) framework by combining it with the Ziegler-Biersack-Littmark (ZBL) screened nuclear repulsion potential, obtaining a NEP-ZBL framework. We train a NEP-ZBL model for tungsten and demonstrate its accuracy in terms of the elastic properties, melting point, and various energetics of defects that are relevant for radiation damage. We then perform large-scale molecular dynamics simulations with the NEP-ZBL model with up to 8.1 million atoms and 240 ps (using a single 40-GB A100 GPU) to study the difference of primary radiation damage in both bulk and thin-foil tungsten. While our findings for bulk tungsten are consistent with existing results simulated by embedded atom method models, the radiation damage differs significantly in foils and shows that larger and more vacancy clusters as well as smaller and fewer interstitial clusters are produced due to the presence of a free surface. Molecular dynamics ; Tungsten ; Embedded atom model ; Interatomic potential ; Collision ; Radiation damage ; Radiation ; Atom (system on chip) ; Physics ; Vacancy defect ; Atomic physics ; Materials science ; Nuclear physics ; Computer science ; Condensed matter physics ; Parallel computing ; Quantum mechanics Nuclear Materials and Properties ; Fusion materials and technologies ; Nuclear Physics and Applications
Authors: Kaixuan Gu | Sen Lin
Source: Angewandte Chemie International Edition (article, 2023-10-13)
Field: Materials Science — Materials Chemistry

Sustained Hydrogen Spillover on Pt/Cu(111) Single‐Atom Alloy: Dynamic Insights into Gas‐Induced Chemical Processes Hydrogen spillover, involving the surface migration of dissociated hydrogen atoms from active metal sites to the relatively inert catalyst support, plays a crucial role in hydrogen-involved catalytic processes. However, a comprehensive understanding of how H atoms are driven to spill over from active sites onto the catalyst support is still lacking. Here, we examine the atomic-scale perspective of the H spillover process on a Pt/Cu(111) single atom alloy surface using machine-learning accelerated molecular dynamics calculations based on density functional theory. Our results show that when an impinging H<sub>2</sub> dissociates at an active Pt site, the Pt atom undergoes deactivation due to the dissociated hydrogen atoms that attach to it. Interestingly, collisions between H<sub>2</sub> and sticking H atoms facilitate H spillover onto the host Cu, leading to the reactivation of the Pt atom and the realization of a continuous H spillover process. This work underscores the importance of the interaction between gas molecules and adsorbates as a driving force in elucidating chemical processes under a gaseous atmosphere, which has so far been underappreciated in thermodynamic studies. Hydrogen spillover ; Hydrogen ; Catalysis ; Chemical physics ; Density functional theory ; Spillover effect ; Hydrogen atom ; Atom (system on chip) ; Molecule ; Molecular dynamics ; Metal ; Materials science ; Inert gas ; Chemistry ; Nanotechnology ; Computational chemistry ; Metallurgy ; Organic chemistry Catalytic Processes in Materials Science ; Advanced Chemical Physics Studies ; Machine Learning in Materials Science
Authors: Pengfei Hou | Yumiao Tian | Yu Xie | Fei Du | Gang Chen | Aleksandra Vojvodić | Jianzhong Wu | Xing Meng
Source: Angewandte Chemie International Edition (article, 2023-06-14)
Field: Materials Science — Materials Chemistry

Unraveling the Oxidation Behaviors of MXenes in Aqueous Systems by Active‐Learning‐Potential Molecular‐Dynamics Simulation MXenes are 2D materials with great potential in various applications. However, the degradation of MXenes in humid environments has become a main obstacle in their practical use. Here we combine deep neural networks and an active learning scheme to develop a neural network potential (NNP) for aqueous MXene systems with ab initio precision but low cost. The oxidation behaviors of super large aqueous MXene systems are investigated systematically at nanosecond timescales for the first time. The oxidation process of MXenes is clearly displayed at the atomic level. Free protons and oxides greatly inhibit subsequent oxidation reactions, leading to the degree of oxidation of MXenes to exponentially decay with time, which is consistent with the oxidation rate of MXenes measured experimentally. Importantly, this computational study represents the first exploration of the kinetic process of oxidation of super-sized aqueous MXene systems. It opens a promising avenue for the future development of effective protection strategies aimed at controlling the stability of MXenes. MXenes ; Aqueous solution ; Process (computing) ; Molecular dynamics ; Computer science ; Materials science ; Chemistry ; Nanotechnology ; Biological system ; Computational chemistry ; Physical chemistry MXene and MAX Phase Materials ; Advanced Memory and Neural Computing ; 2D Materials and Applications