jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Denis Gentili | Guido Ori
Source: Nanoscale (review, 2022-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Reversible assembly of nanoparticles: theory, strategies and computational simulations The significant advances in synthesis and functionalization have enabled the preparation of high-quality nanoparticles that have found a plethora of successful applications. The unique physicochemical properties of nanoparticles can be manipulated through the control of size, shape, composition, and surface chemistry, but their technological application possibilities can be further expanded by exploiting the properties that emerge from their assembly. The ability to control the assembly of nanoparticles not only is required for many real technological applications, but allows the combination of the intrinsic properties of nanoparticles and opens the way to the exploitation of their complex interplay, giving access to collective properties. Significant advances and knowledge gained over the past few decades on nanoparticle assembly have made it possible to implement a growing number of strategies for reversible assembly of nanoparticles. In addition to being of interest for basic studies, such advances further broaden the range of applications and the possibility of developing innovative devices using nanoparticles. This review focuses on the reversible assembly of nanoparticles and includes the theoretical aspects related to the concept of reversibility, an up-to-date assessment of the experimental approaches applied to this field and the advanced computational schemes that offer key insights into the assembly mechanisms. We aim to provide readers with a comprehensive guide to address the challenges in assembling reversible nanoparticles and promote their applications. Nanoparticle ; Nanotechnology ; Materials science Gold and Silver Nanoparticles Synthesis and Applications ; Nanocluster Synthesis and Applications ; Micro and Nano Robotics
Authors: Suixuan Li | Zihao Qin | Huan Wu | Man Li | Martin Kunz | Ahmet Alatas | A. Kavner | Yongjie Hu
Source: Nature (article, 2022-11-23)
Field: Materials Science — Materials Chemistry

Anomalous thermal transport under high pressure in boron arsenide Phonon ; Thermal conductivity ; Condensed matter physics ; Anharmonicity ; Hydrostatic pressure ; Thermal conduction ; Raman spectroscopy ; Materials science ; Diamond anvil cell ; Scattering ; Gallium arsenide ; Spectroscopy ; Ambient pressure ; Physics ; Thermodynamics ; High pressure ; Optics Thermal properties of materials ; High-pressure geophysics and materials ; Mechanical and Optical Resonators
Authors: Zipeng Zhong | Jie Song | Zunlei Feng | Tiantao Liu | Lingxiang Jia | Shaolun Yao | Min Wu | Tingjun Hou | Mingli Song
Source: Chemical Science (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Root-aligned SMILES: a tight representation for chemical reaction prediction Chemical reaction prediction, involving forward synthesis and retrosynthesis prediction, is a fundamental problem in organic synthesis. A popular computational paradigm formulates synthesis prediction as a sequence-to-sequence translation problem, where the typical SMILES is adopted for molecule representations. However, the general-purpose SMILES neglects the characteristics of chemical reactions, where the molecular graph topology is largely unaltered from reactants to products, resulting in the suboptimal performance of SMILES if straightforwardly applied. In this article, we propose the root-aligned SMILES (R-SMILES), which specifies a tightly aligned one-to-one mapping between the product and the reactant SMILES for more efficient synthesis prediction. Due to the strict one-to-one mapping and reduced edit distance, the computational model is largely relieved from learning the complex syntax and dedicated to learning the chemical knowledge for reactions. We compare the proposed R-SMILES with various state-of-the-art baselines and show that it significantly outperforms them all, demonstrating the superiority of the proposed method. Computer science ; Representation (politics) ; Smiles rearrangement ; Retrosynthetic analysis ; Sequence (biology) ; Artificial intelligence ; Root (linguistics) ; Graph ; Theoretical computer science ; Natural language processing ; Machine learning ; Chemistry ; Stereochemistry ; Linguistics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemical Synthesis and Analysis
Authors: Jean Armengaud
Source: Environmental Microbiology (article, 2022-10-09)
Field: Chemistry — Spectroscopy

Metaproteomics to understand how microbiota function: The crystal ball predicts a promising future In the medical, environmental, and biotechnological fields, microbial communities have attracted much attention due to their roles and numerous possible applications. The study of these communities is challenging due to their diversity and complexity. Innovative methods are needed to identify the taxonomic components of individual microbiota, their changes over time, and to determine how microoorganisms interact and function. Metaproteomics is based on the identification and quantification of proteins, and can potentially provide this full picture. Due to the wide molecular panorama and functional insights it provides, metaproteomics is gaining momentum in microbiome and holobiont research. Its full potential should be unleashed in the coming years with progress in speed and cost of analyses. In this exploratory crystal ball exercise, I discuss the technical and conceptual advances in metaproteomics that I expect to drive innovative research over the next few years in microbiology. I also debate the concepts of 'microbial dark matter' and 'Metaproteomics-Assembled Proteomes (MAPs)' and present some long-term prospects for metaproteomics in clinical diagnostics and personalized medicine, environmental monitoring, agriculture, and biotechnology. Biology ; Metaproteomics ; Computational biology ; Function (biology) ; Metagenomics ; Evolutionary biology ; Genetics ; Gene Advanced Proteomics Techniques and Applications ; Gut microbiota and health ; Metabolomics and Mass Spectrometry Studies
Authors: Won San Choi | Ha-Jin Lee
Source: Polymers (review, 2022-05-27)
Field: Chemistry — Organic Chemistry

Nanostructured Materials for Water Purification: Adsorption of Heavy Metal Ions and Organic Dyes Chemical water pollution poses a threat to human beings and ecological systems. The purification of water to remove toxic organic and inorganic pollutants is essential for a safe society and a clean environment. Adsorption-based water treatment is considered one of the most effective and economic technologies designed to remove toxic substances. In this article, we review the recent progress in the field of nanostructured materials used for water purification, particularly those used for the adsorption of heavy metal ions and organic dyes. This review includes a range of nanostructured materials such as metal-based nanoparticles, polymer-based nanomaterials, carbon nanomaterials, bio-mass materials, and other types of nanostructured materials. Finally, the current challenges in the fields of adsorption of toxic materials using nanostructured materials are briefly discussed. Adsorption ; Nanomaterials ; Metal ions in aqueous solution ; Portable water purification ; Materials science ; Pollutant ; Activated carbon ; Environmental chemistry ; Nanotechnology ; Water treatment ; Metal ; Environmental science ; Chemistry ; Environmental engineering ; Organic chemistry ; Metallurgy Nanomaterials for catalytic reactions ; Adsorption and biosorption for pollutant removal ; Carbon and Quantum Dots Applications
Authors: Yufei Yang | Fucheng Gao | Yandong Wang | Hui Li | Jie Zhang | Zhiwei Sun | Yanyan Jiang
Source: Molecules (review, 2022-12-01)
Field: Chemistry — Spectroscopy

Fluorescent Organic Small Molecule Probes for Bioimaging and Detection Applications The activity levels of key substances (metal ions, reactive oxygen species, reactive nitrogen, biological small molecules, etc.) in organisms are closely related to intracellular redox reactions, disease occurrence and treatment, as well as drug absorption and distribution. Fluorescence imaging technology provides a visual tool for medicine, showing great potential in the fields of molecular biology, cellular immunology and oncology. In recent years, organic fluorescent probes have attracted much attention in the bioanalytical field. Among various organic fluorescent probes, fluorescent organic small molecule probes (FOSMPs) have become a research hotspot due to their excellent physicochemical properties, such as good photostability, high spatial and temporal resolution, as well as excellent biocompatibility. FOSMPs have proved to be suitable for in vivo bioimaging and detection. On the basis of the introduction of several primary fluorescence mechanisms, the latest progress of FOSMPs in the applications of bioimaging and detection is comprehensively reviewed. Following this, the preparation and application of fluorescent organic nanoparticles (FONPs) that are designed with FOSMPs as fluorophores are overviewed. Additionally, the prospects of FOSMPs in bioimaging and detection are discussed. Fluorescence ; Nanotechnology ; Bioanalysis ; Biocompatibility ; Chemistry ; Materials science ; Organic chemistry Molecular Sensors and Ion Detection ; Luminescence and Fluorescent Materials ; Nanoplatforms for cancer theranostics
Authors: Gunasekar Ramachandran | Ross L. Goodyear | Ilaria Proietti Silvestri | Jianliang Xiao
Source: Organic & Biomolecular Chemistry (review, 2022-01-01)
Field: Chemistry — Inorganic Chemistry

Recent developments in enantio- and diastereoselective hydrogenation of N-heteroaromatic compounds The enantioselective and diastereoselective hydrogenation of N-heteroaromatic compounds is an efficient strategy to access chirally enriched cyclic heterocycles, which often possess highly bio-active properties. This strategy, however, has only been established in recent times. This is in part due to the challenges of the high stability of the aromatic compounds and the presence of heteroatoms that have the potential to poison the chiral catalysts. Additionally, N-heteroaromatic compounds are a structurally diverse family of substrates, each group showing distinct reactivity in hydrogenation. Advances in recent years have allowed various N-heteroaromatic compounds, including pyridines, indoles, quinolines, isoquinolines, quinoxalines and imidazoles, to be hydrogenated with good to excellent enantioselectivity under appropriate reaction conditions. Transition-metal catalysis, utilising iridium, ruthenium, rhodium, and palladium complexes, has been found to play an important role in this field. More recently, organocatalysis has been shown to be efficient for the hydrogenation of certain N-heteroaromatic compounds. This review provides an analysis of the recent developments in the enantioselective and diastereoselective hydrogenation of N-heteroaromatic compounds. The importance of these molecules and their applications to drug discovery has been highlighted throughout the review. Chemistry ; Organic chemistry ; Stereochemistry Asymmetric Hydrogenation and Catalysis ; Surface Chemistry and Catalysis ; Organoboron and organosilicon chemistry
Authors: Jieying Liang | Muhammad Yazid Bin Zulkifli | Joel Yong | Zeping Du | Zhimin Ao | Aditya Rawal | Jason Scott | Jeffrey R. Harmer | Joseph Wang | Kang Liang
Source: Journal of the American Chemical Society (article, 2022-09-08)
Field: Chemistry — Inorganic Chemistry

Locking the Ultrasound-Induced Active Conformation of Metalloenzymes in Metal–Organic Frameworks Enhancing the enzymatic activity inside metal-organic frameworks (MOFs) is a critical challenge in chemical technology and bio-technology, which, if addressed, will broaden their scope in energy, food, environmental, and pharmaceutical industries. Here, we report a simple yet versatile and effective strategy to optimize biocatalytic activity by using MOFs to rapidly "lock" the ultrasound (US)-activated but more fragile conformation of metalloenzymes. The results demonstrate that up to 5.3-fold and 9.3-fold biocatalytic activity enhancement of the free and MOF-immobilized enzymes could be achieved compared to those without US pretreatment, respectively. Using horseradish peroxidase as a model, molecular dynamics simulation demonstrates that the improved activity of the enzyme is driven by an opened gate conformation of the heme active site, which allows more efficient substrate binding to the enzyme. The intact heme active site is confirmed by solid-state UV-vis and electron paramagnetic resonance, while the US-induced enzyme conformation change is confirmed by circular dichroism spectroscopy and Fourier-transform infrared spectroscopy. In addition, the improved activity of the biocomposites does not compromise their stability upon heating or exposure to organic solvent and a digestion cocktail. This rapid locking and immobilization strategy of the US-induced active enzyme conformation in MOFs gives rise to new possibilities for the exploitation of highly efficient biocatalysts for diverse applications. Chemistry ; Metal-organic framework ; Ultrasound ; Metal ; Combinatorial chemistry ; Stereochemistry ; Organic chemistry ; Radiology Metal-Organic Frameworks: Synthesis and Applications ; Molecular Sensors and Ion Detection ; Chemical and Physical Properties in Aqueous Solutions
Authors: Kai Xu | Tero‐Petri Ruoko | Morteza Shokrani | Dorothea Scheunemann | Hassan Abdalla | Hengda Sun | Chi‐Yuan Yang | Yuttapoom Puttisong | Nagesh B. Kolhe | J. Figueroa | Jonas Oshaug Pedersen | Thomas Ederth | Weimin Chen | Magnus Berggren | Samson A. Jenekhe | Daniele Fazzi | Martijn Kemerink | Simone Fabiano
Source: Advanced Functional Materials (article, 2022-02-05)
Field: Materials Science — Polymers and Plastics

On the Origin of Seebeck Coefficient Inversion in Highly Doped Conducting Polymers Abstract A common way of determining the majority charge carriers of pristine and doped semiconducting polymers is to measure the sign of the Seebeck coefficient. However, a polarity change of the Seebeck coefficient has recently been observed to occur in highly doped polymers. Here, it is shown that the Seebeck coefficient inversion is the result of the density of states filling and opening of a hard Coulomb gap around the Fermi energy at high doping levels. Electrochemical n‐doping is used to induce high carrier density (&gt;1 charge/monomer) in the model system poly(benzimidazobenzophenanthroline) (BBL). By combining conductivity and Seebeck coefficient measurements with in situ electron paramagnetic resonance, UV–vis–NIR, Raman spectroelectrochemistry, density functional theory calculations, and kinetic Monte Carlo simulations, the formation of multiply charged species and the opening of a hard Coulomb gap in the density of states, which is responsible for the Seebeck coefficient inversion and drop in electrical conductivity, are uncovered. The findings provide a simple picture that clarifies the roles of energetic disorder and Coulomb interactions in highly doped polymers and have implications for the molecular design of next‐generation conjugated polymers. Seebeck coefficient ; Materials science ; Condensed matter physics ; Doping ; Thermoelectric effect ; Thermoelectric materials ; Density functional theory ; Electrical resistivity and conductivity ; Raman spectroscopy ; Polymer ; Fermi level ; Electron ; Thermal conductivity ; Optoelectronics ; Computational chemistry ; Physics ; Thermodynamics ; Chemistry ; Optics ; Composite material Conducting polymers and applications ; Organic Electronics and Photovoltaics ; Advanced Memory and Neural Computing
Authors: Xuan-Ran Ong | Adrielle Xianwen Chen | Ning Li | Yi Yan Yang | He‐Kuan Luo
Source: Small Science (article, 2022-12-22)
Field: Materials Science — Biomaterials

Nanocellulose: Recent Advances Toward Biomedical Applications Sustainable materials are key to the continual improvement of living standards on this planet with minimal environmental impacts. Nanocellulose combines the fascinating features of nanomaterials with favorable properties of the abundantly available cellulose biopolymer, which in recent years has gained much attention toward biomedical applications by virtue of its unique surface chemistry, remarkable physical features, and inherent biological attributes. Herein, the recent advances in nanocellulose-based biomedical materials, with foci on biomolecule immobilization, drug delivery, cell culture and tissue engineering (TE), antimicrobial strategy, wound healing, and biomedical implants are summarized. Each topic is elaborated with representative examples to present the significance of nanocelluloses in their respective material design principles utilizing different sub-types, including cellulose nanofibers (CNFs), cellulose nanocrystals (CNCs), and bacterial nanocellulose (BNC). The current state of large-scale production of nanocellulose and accelerated development by artificial intelligence and machine learning are also briefly discussed, before ending with its future prospects and potential challenges. Nanocellulose ; Nanotechnology ; Nanofiber ; Bacterial cellulose ; Biopolymer ; Cellulose ; Biochemical engineering ; Materials science ; Chemistry ; Engineering Advanced Cellulose Research Studies ; Electrospun Nanofibers in Biomedical Applications ; Nanocomposite Films for Food Packaging
Authors: Steven Dajnowicz | Garvit Agarwal | James Stevenson | Leif D. Jacobson | Farhad Ramezanghorbani | Karl Leswing | Richard A. Friesner | Mathew D. Halls | Robert Abel
Source: The Journal of Physical Chemistry B (article, 2022-08-16)
Field: Materials Science — Materials Chemistry

High-Dimensional Neural Network Potential for Liquid Electrolyte Simulations Liquid electrolytes are one of the most important components of Li-ion batteries, which are a critical technology of the modern world. However, we still lack the computational tools required to accurately calculate key properties of these materials (viscosity and ionic diffusivity) from first principles necessary to support improved designs. In this work, we report a machine learning-based force field for liquid electrolyte simulations, which bridges the gap between the accuracy of range-separated hybrid density functional theory and the efficiency of classical force fields. Predictions of material properties made with this force field are quantitatively accurate compared to experimental data. Our model uses the QRNN deep neural network architecture, which includes both long-range interactions and global charge equilibration. The training data set is composed solely of non-periodic density functional theory (DFT), allowing the practical use of an accurate theory (here, ωB97X-D3BJ/def2-TZVPD), which would be prohibitively expensive for generating large data sets with periodic DFT. In this report, we focus on seven common carbonates and LiPF<sub>6</sub>, but this methodology has very few assumptions and can be readily applied to any liquid electrolyte system. This provides a promising path forward for large-scale atomistic modeling of many important battery chemistries. Electrolyte ; Computer science ; Density functional theory ; Field (mathematics) ; Battery (electricity) ; Artificial neural network ; Work (physics) ; Range (aeronautics) ; Statistical physics ; Force field (fiction) ; Ionic liquid ; Materials science ; Chemistry ; Artificial intelligence ; Thermodynamics ; Physics ; Computational chemistry ; Mathematics ; Physical chemistry Machine Learning in Materials Science ; Advanced Battery Materials and Technologies ; Fuel Cells and Related Materials
Authors: Qi‐Jun Hong | Sergey V. Ushakov | Axel van de Walle | Alexandra Navrotsky
Source: Proceedings of the National Academy of Sciences (article, 2022-08-31)
Field: Materials Science — Materials Chemistry

Melting temperature prediction using a graph neural network model: From ancient minerals to new materials The melting point is a fundamental property that is time-consuming to measure or compute, thus hindering high-throughput analyses of melting relations and phase diagrams over large sets of candidate compounds. To address this, we build a machine learning model, trained on a database of ∼10,000 compounds, that can predict the melting temperature in a fraction of a second. The model, made publicly available online, features graph neural network and residual neural network architectures. We demonstrate the model's usefulness in diverse applications. For the purpose of materials design and discovery, we show that it can quickly discover novel multicomponent materials with high melting points. These predictions are confirmed by density functional theory calculations and experimentally validated. In an application to planetary science and geology, we employ the model to analyze the melting temperatures of ∼4,800 minerals to uncover correlations relevant to the study of mineral evolution. Artificial neural network ; Computer science ; Graph ; Melting point ; Residual ; Melting temperature ; Property (philosophy) ; Deep learning ; Artificial intelligence ; Machine learning ; Theoretical computer science ; Materials science ; Algorithm Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Geochemistry and Geologic Mapping
Authors: Yanzhou Wang | Zheyong Fan | Ping Qian | Tapio Ala-Nissilä | A. Miguel
Source: Chemistry of Materials (article, 2022-01-04)
Field: Materials Science — Materials Chemistry

Structure and Pore Size Distribution in Nanoporous Carbon &lt;p&gt;We study the structural and mechanical properties of nanoporous (NP) carbon materials by extensive atomistic machine-learning (ML) driven molecular dynamics (MD) simulations. To this end, we retrain a ML Gaussian approximation potential (GAP) for carbon by recalculating the a-C structural database of Deringer and Csányi adding van der Waals interactions. Our GAP enables a notable speedup and improves the accuracy of energy and force predictions. We use the GAP to thoroughly study the atomistic structure and pore-size distribution in computational NP carbon samples. These samples are generated by a melt-graphitization-quench MD procedure over a wide range of densities (from 0.5 to 1.7 g/cm&lt;sup&gt;3&lt;/sup&gt;) with structures containing 131072 atoms. Our results are in good agreement with experimental data for the available observables and provide a comprehensive account of structural (radial and angular distribution functions, motif and ring counts, X-ray diffraction patterns, pore characterization) and mechanical (elastic moduli and their evolution with density) properties. Our results show relatively narrow pore-size distributions, where the peak position and width of the distributions are dictated by the mass density of the materials. Our data allow further work on computational characterization of NP carbon materials, in particular for energy-storage applications, as well as suggest future experimental characterization of NP carbon-based materials.&lt;/p&gt; Nanoporous ; Molecular dynamics ; Materials science ; van der Waals force ; Diffraction ; Carbon fibers ; Characterization (materials science) ; Gaussian ; Work (physics) ; Chemical physics ; Nanotechnology ; Composite material ; Computational chemistry ; Thermodynamics ; Physics ; Chemistry ; Molecule ; Optics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Surface Chemistry and Catalysis