jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Shaochun He | Junting Li | Xiaoqian Cao | Fei Xie | Hui Ying Yang | Cheng Wang | Carla Bittencourt | Wenjiang Li
Source: International Journal of Biological Macromolecules (article, 2023-05-26)
Field: Chemistry — Spectroscopy

Regenerated cellulose/chitosan composite aerogel with highly efficient adsorption for anionic dyes A novel reusable, high-compressible cotton regenerated cellulose/chitosan composite aerogel (RC/CSCA) was prepared using N-methylmorpholine-N-oxide (NMMO) as the green cellulose solvent, and glutaraldehyde (GA) as the crosslinking agent. The regenerated cellulose obtained from cotton pulp could chemically crosslink with chitosan and GA, to form a stable 3D porous structure. The GA played an essential role in preventing shrinkage and preserving the deformation recovery ability of RC/CSCA. Due to the ultralow density (13.92 mg/cm<sup>3</sup>), thermal stability (above 300 °C), and high porosity (97.36 %), the positively charged RC/CSCA can be used as a novel biocomposite adsorbent for effective and selective removal of toxic anionic dyes from wastewater, showing an excellent adsorption capacity, environmental adaptability, and recyclability. The maximal adsorption capacity and removal efficiency of RC/CSCA for methyl orange (MO) was 742.68 mg/g and 95.83 %. Chitosan ; Adsorption ; Cellulose ; Aerogel ; Chemical engineering ; Glutaraldehyde ; Regenerated cellulose ; Thermal stability ; Biocomposite ; Pulp (tooth) ; Composite number ; Materials science ; Porosity ; Chemistry ; Organic chemistry ; Composite material Aerogels and thermal insulation ; Advanced Cellulose Research Studies ; Adsorption and biosorption for pollutant removal
Authors: Shangjie Lian | Dimitrios A. Lamprou | Min Zhao
Source: International Journal of Pharmaceutics (review, 2023-11-27)
Field: Materials Science — Biomaterials

Electrospinning technologies for the delivery of Biopharmaceuticals: Current status and future trends Electrospinning ; Biopharmaceutical ; Nanotechnology ; Biochemical engineering ; Computer science ; Materials science ; Engineering ; Biotechnology ; Polymer ; Composite material Electrospun Nanofibers in Biomedical Applications ; Tissue Engineering and Regenerative Medicine ; Electrohydrodynamics and Fluid Dynamics
Authors: Vafa Fakhri | Chia‐Hung Su | Masoud Tavakoli Dare | M. Bazmi | Aliakbar Jafari | Vahid Pirouzfar
Source: Journal of Materials Chemistry B (review, 2023-01-01)
Field: Materials Science — Biomaterials

Harnessing the power of polyol-based polyesters for biomedical innovations: synthesis, properties, and biodegradation Polyesters based on polyols have emerged as promising biomaterials for various biomedical applications, such as tissue engineering, drug delivery systems, and regenerative medicine, due to their biocompatibility, biodegradability, and versatile physicochemical properties. This review article provides an overview of the synthesis methods, performance, and biodegradation mechanisms of polyol-based polyesters, highlighting their potential for use in a wide range of biomedical applications. The synthesis techniques, such as simple polycondensation and enzymatic polymerization, allow for the fine-tuning of polyester structure and molecular weight, thereby enabling the tailoring of material properties to specific application requirements. The physicochemical properties of polyol-based polyesters, such as hydrophilicity, crystallinity, and mechanical properties, can be altered by incorporating different polyols. The article highlights the influence of various factors, such as molecular weight, crosslinking density, and degradation medium, on the biodegradation behavior of these materials, and the importance of understanding these factors for controlling degradation rates. Future research directions include the development of novel polyesters with improved properties, optimization of degradation rates, and exploration of advanced processing techniques for fabricating scaffolds and drug delivery systems. Overall, polyol-based polyesters hold significant potential in the field of biomedical applications, paving the way for groundbreaking advancements and innovative solutions that could revolutionize patient care and treatment outcomes. Biocompatibility ; Polyester ; Biodegradation ; Polyol ; Materials science ; Drug delivery ; Tissue engineering ; Nanotechnology ; Organic chemistry ; Biomedical engineering ; Chemistry ; Polyurethane ; Composite material ; Engineering biodegradable polymer synthesis and properties ; Electrospun Nanofibers in Biomedical Applications ; Polymer composites and self-healing
Authors: Yana Shymborska | Andrzej Budkowski | Joanna Raczkowska | Volodymyr Donchak | Yuriy Melnyk | Viktor Vasiichuk | Yurij Stetsyshyn
Source: The Chemical Record (review, 2023-09-05)
Field: Chemistry — Organic Chemistry

Switching it Up: The Promise of Stimuli‐Responsive Polymer Systems in Biomedical Science Responsive polymer systems have the ability to change properties or behavior in response to external stimuli. The properties of responsive polymer systems can be fine-tuned by adjusting the stimuli, enabling tailored responses for specific applications. These systems have applications in drug delivery, biosensors, tissue engineering, and more, as their ability to adapt and respond to dynamic environments leads to improved performance. However, challenges such as synthesis complexity, sensitivity limitations, and manufacturing issues need to be addressed for successful implementation. In our review, we provide a comprehensive summary on stimuli-responsive polymer systems, delving into the intricacies of their mechanisms and actions. Future developments should focus on precision medicine, multifunctionality, reversibility, bioinspired designs, and integration with advanced technologies, driving the dynamic growth of sensitive polymer systems in biomedical applications. Nanotechnology ; Computer science ; Drug delivery ; Biochemical engineering ; Systems engineering ; Materials science ; Engineering Advanced Polymer Synthesis and Characterization ; Hydrogels: synthesis, properties, applications ; Polymer Surface Interaction Studies
Authors: Francesca Arcudi | Luka Đorđević∞
Source: Small (review, 2023-04-20)
Field: Materials Science — Materials Chemistry

Supramolecular Chemistry of Carbon‐Based Dots Offers Widespread Opportunities Carbon dots are an emerging class of nanomaterials that has recently attracted considerable attention for applications that span from biomedicine to energy. These photoluminescent carbon nanoparticles are defined by characteristic sizes of <10 nm, a carbon-based core and various functional groups at their surface. Although the surface groups are widely used to establish non-covalent bonds (through electrostatic interactions, coordinative bonds, and hydrogen bonds) with various other (bio)molecules and polymers, the carbonaceous core could also establish non-covalent bonds (ππ stacking or hydrophobic interactions) with π-extended or apolar compounds. The surface functional groups, in addition, can be modified by various post-synthetic chemical procedures to fine-tune the supramolecular interactions. Our contribution categorizes and analyzes the interactions that are commonly used to engineer carbon dots-based materials and discusses how they have allowed preparation of functional assemblies and architectures used for sensing, (bio)imaging, therapeutic applications, catalysis, and devices. Using non-covalent interactions as a bottom-up approach to prepare carbon dots-based assemblies and composites can exploit the unique features of supramolecular chemistry, which include adaptability, tunability, and stimuli-responsiveness due to the dynamic nature of the non-covalent interactions. It is expected that focusing on the various supramolecular possibilities will influence the future development of this class of nanomaterials. Supramolecular chemistry ; Covalent bond ; Nanotechnology ; Nanomaterials ; Materials science ; Non-covalent interactions ; Stacking ; Nanoparticle ; Carbon fibers ; Hydrogen bond ; Surface modification ; Quantum dot ; Molecule ; Chemistry ; Organic chemistry ; Composite number Carbon and Quantum Dots Applications ; Nanocluster Synthesis and Applications ; Luminescence and Fluorescent Materials
Authors: Alexandre Adam | Damien Mertz
Source: Nanomaterials (review, 2023-04-12)
Field: Materials Science — Biomaterials

Iron Oxide@Mesoporous Silica Core-Shell Nanoparticles as Multimodal Platforms for Magnetic Resonance Imaging, Magnetic Hyperthermia, Near-Infrared Light Photothermia, and Drug Delivery The design of core-shell nanocomposites composed of an iron oxide core and a silica shell offers promising applications in the nanomedicine field, especially for developing efficient theranostic systems which may be useful for cancer treatments. This review article addresses the different ways to build iron oxide@silica core-shell nanoparticles and it reviews their properties and developments for hyperthermia therapies (magnetically or light-induced), combined with drug delivery and MRI imaging. It also highlights the various challenges encountered, such as the issues associated with in vivo injection in terms of NP-cell interactions or the control of the heat dissipation from the core of the NP to the external environment at the macro or nanoscale. Nanomedicine ; Mesoporous silica ; Materials science ; Nanotechnology ; Drug delivery ; Nanoparticle ; Iron oxide nanoparticles ; Iron oxide ; Nanocomposite ; Magnetic hyperthermia ; Magnetic resonance imaging ; Thermal management of electronic devices and systems ; Magnetic nanoparticles ; Magnetic core ; Heat generation ; Mesoporous material ; Chemistry ; Medicine ; Engineering ; Mechanical engineering ; Metallurgy ; Electromagnetic coil ; Radiology Nanoparticle-Based Drug Delivery ; Characterization and Applications of Magnetic Nanoparticles ; Microfluidic and Bio-sensing Technologies
Authors: Tsukasa Abe | Naoki Sanada | Keisuke Takeuchi | Atsushi Okazawa | Shûichi Hiraoka
Source: Journal of the American Chemical Society (article, 2023-12-14)
Field: Chemistry — Organic Chemistry

Assembly of Six Types of Heteroleptic Pd <sub>2</sub> L <sub>4</sub> Cages under Kinetic Control Heteroleptic assemblies composed of several kinds of building blocks have been seen in nature. It is still unclear how natural systems design and create such complicated assemblies selectively. Past efforts on multicomponent self-assembly of artificial metal-organic cages have mainly focused on finding a suitable combination of building blocks to lead to a single multicomponent self-assembly as the thermodynamically most stable product. Here, we present another approach to selectively produce multicomponent Pd(II)-based self-assemblies under kinetic control based on the selective ligand exchanges of weak Pd-L coordination bonds retaining the original orientation of the metal centers in a kinetically stabilized cyclic structure and on local reversibility given in certain areas of the energy landscape in the presence of the assist molecule that facilitates error correction of coordination bonds. The kinetic approach enabled us to build all six types of Pd<sub>2</sub>L<sub>4</sub> cages and heteroleptic tetranuclear cages composed of three kinds of ditopic ligands. Although the cage complexes thus obtained are metastable, they are stable for 1 month or more at room temperature. Chemistry ; Kinetic energy ; Kinetic control ; Ligand (biochemistry) ; Molecule ; Self-assembly ; Metal ; Palladium ; Chemical physics ; Nanotechnology ; Combinatorial chemistry ; Crystallography ; Catalysis ; Organic chemistry Supramolecular Chemistry and Complexes ; Supramolecular Self-Assembly in Materials ; Metal-Organic Frameworks: Synthesis and Applications
Authors: Gerda Rogl | P. Rogl
Source: Crystals (article, 2023-07-24)
Field: Materials Science — Materials Chemistry

Development of Thermoelectric Half-Heusler Alloys over the Past 25 Years Half-Heusler alloys are among the most promising thermoelectric materials. In the present review, thermoelectric properties (at 300 K and 800 K) of more than 1100 compositions from more than 220 publications between 1998 and 2023 were collected and evaluated. The dependence of the peak figure of merit, ZTmax, of p- and n-type half-Heusler alloys on the publishing year and the peak temperature is displayed in several figures. Furthermore, plots of ZT vs. the electrical resistivity, the Seebeck coefficient and the thermal conductivity at 300 K and 800 K are shown and discussed. Especially thermal conductivity vs. power factor leads to a good overview of ZT. For both p- and n-type individually separated into systems, ZTs and peak ZTs in dependence on the composition are displayed and discussed. This overview can help to find the ideal half-Heusler alloy for practical use. Thermoelectric effect ; Seebeck coefficient ; Materials science ; Thermal conductivity ; Thermoelectric materials ; Electrical resistivity and conductivity ; Condensed matter physics ; Figure of merit ; Alloy ; Engineering physics ; Metallurgy ; Thermodynamics ; Optoelectronics ; Physics ; Composite material Advanced Thermoelectric Materials and Devices ; Heusler alloys: electronic and magnetic properties ; Topological Materials and Phenomena
Authors: Yang Zhong | Hongyu Yu | Mao Su | Xin-Gao Gong | Hongjun Xiang
Source: npj Computational Materials (article, 2023-10-06)
Field: Materials Science — Materials Chemistry

Transferable equivariant graph neural networks for the Hamiltonians of molecules and solids Abstract This work presents an E(3) equivariant graph neural network called HamGNN, which can fit the electronic Hamiltonian matrix of molecules and solids by a complete data-driven method. Unlike invariant models that achieve equivariance approximately through data augmentation, HamGNN employs E(3) equivariant convolutions to construct the Hamiltonian matrix, ensuring strict adherence to all equivariant constraints inherent in the physical system. In contrast to previous models with limited transferability, HamGNN demonstrates exceptional accuracy on various datasets, including QM9 molecular datasets, carbon allotropes, silicon allotropes, SiO 2 isomers, and Bi x Se y compounds. The trained HamGNN models exhibit accurate predictions of electronic structures for large crystals beyond the training set, including the Moiré twisted bilayer MoS 2 and silicon supercells with dislocation defects, showcasing remarkable transferability and generalization capabilities. The HamGNN model, trained on small systems, can serve as an efficient alternative to density functional theory (DFT) for accurately computing the electronic structures of large systems. Equivariant map ; Transferability ; Hamiltonian (control theory) ; Bilayer ; Computer science ; Hamiltonian matrix ; Invariant (physics) ; Artificial neural network ; Materials science ; Chemical physics ; Pure mathematics ; Mathematics ; Physics ; Quantum mechanics ; Chemistry ; Artificial intelligence ; Symmetric matrix ; Machine learning ; Eigenvalues and eigenvectors Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Inorganic Chemistry and Materials
Authors: Lawson T. Glasby | Kristian Gubsch | Rosalee Bence | Rama Oktavian | Kesler Isoko | Seyed Mohamad Moosavi | Joan Cordiner | Jason C. Cole | Peyman Z. Moghadam
Source: Chemistry of Materials (article, 2023-05-18)
Field: Chemistry — Inorganic Chemistry

DigiMOF: A Database of Metal–Organic Framework Synthesis Information Generated via Text Mining The vastness of materials space, particularly that which is concerned with metal-organic frameworks (MOFs), creates the critical problem of performing efficient identification of promising materials for specific applications. Although high-throughput computational approaches, including the use of machine learning, have been useful in rapid screening and rational design of MOFs, they tend to neglect descriptors related to their synthesis. One way to improve the efficiency of MOF discovery is to data-mine published MOF papers to extract the materials informatics knowledge contained within journal articles. Here, by adapting the chemistry-aware natural language processing tool, ChemDataExtractor (CDE), we generated an open-source database of MOFs focused on their synthetic properties: the DigiMOF database. Using the CDE web scraping package alongside the Cambridge Structural Database (CSD) MOF subset, we automatically downloaded 43,281 unique MOF journal articles, extracted 15,501 unique MOF materials, and text-mined over 52,680 associated properties including the synthesis method, solvent, organic linker, metal precursor, and topology. Additionally, we developed an alternative data extraction technique to obtain and transform the chemical names assigned to each CSD entry in order to determine linker types for each structure in the CSD MOF subset. This data enabled us to match MOFs to a list of known linkers provided by Tokyo Chemical Industry UK Ltd. (TCI) and analyze the cost of these important chemicals. This centralized, structured database reveals the MOF synthetic data embedded within thousands of MOF publications and contains further topology, metal type, accessible surface area, largest cavity diameter, pore limiting diameter, open metal sites, and density calculations for all 3D MOFs in the CSD MOF subset. The DigiMOF database and associated software are publicly available for other researchers to rapidly search for MOFs with specific properties, conduct further analysis of alternative MOF production pathways, and create additional parsers to search for additional desirable properties. Chemical space ; Computer science ; Limiting ; Very large database ; Database ; Metal-organic framework ; Chemical database ; Data mining ; Chemistry ; Drug discovery ; Engineering Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; X-ray Diffraction in Crystallography
Authors: Stephen Xie | Matthias Rupp | Richard G. Hennig
Source: npj Computational Materials (article, 2023-09-02)
Field: Materials Science — Materials Chemistry

Ultra-fast interpretable machine-learning potentials Abstract All-atom dynamics simulations are an indispensable quantitative tool in physics, chemistry, and materials science, but large systems and long simulation times remain challenging due to the trade-off between computational efficiency and predictive accuracy. To address this challenge, we combine effective two- and three-body potentials in a cubic B-spline basis with regularized linear regression to obtain machine-learning potentials that are physically interpretable, sufficiently accurate for applications, as fast as the fastest traditional empirical potentials, and two to four orders of magnitude faster than state-of-the-art machine-learning potentials. For data from empirical potentials, we demonstrate the exact retrieval of the potential. For data from density functional theory, the predicted energies, forces, and derived properties, including phonon spectra, elastic constants, and melting points, closely match those of the reference method. The introduced potentials might contribute towards accurate all-atom dynamics simulations of large atomistic systems over long-time scales. Computer science ; Eigenvalues and eigenvectors ; Statistical physics ; Phonon ; Atom (system on chip) ; Basis (linear algebra) ; Molecular dynamics ; Artificial intelligence ; Algorithm ; Machine learning ; Physics ; Mathematics ; Quantum mechanics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Fuel Cells and Related Materials
Authors: Filip Formalik | Kaihang Shi | Faramarz Joodaki | Xijun Wang | Randall Q. Snurr
Source: Advanced Functional Materials (article, 2023-10-17)
Field: Chemistry — Inorganic Chemistry

Exploring the Structural, Dynamic, and Functional Properties of Metal‐Organic Frameworks through Molecular Modeling Abstract This review spotlights the role of atomic‐level modeling in research on metal‐organic frameworks (MOFs), especially the key methodologies of density functional theory (DFT), Monte Carlo (MC) simulations, and molecular dynamics (MD) simulations. The discussion focuses on how periodic and cluster‐based DFT calculations can provide novel insights into MOF properties, with a focus on predicting structural transformations, understanding thermodynamic properties and catalysis, and providing information or properties that are fed into classical simulations such as force field parameters or partial charges. Classical simulation methods, highlighting force field selection, databases of MOFs for high‐throughput screening, and the synergistic nature of MC and MD simulations, are described. By predicting equilibrium thermodynamic and dynamic properties, these methods offer a wide perspective on MOF behavior and mechanisms. Additionally, the incorporation of machine learning (ML) techniques into quantum and classical simulations is discussed. These methods can enhance accuracy, expedite simulation setup, reduce computational costs, as well as predict key parameters, optimize geometries, and estimate MOF stability. By charting the growth and promise of computational research in the MOF field, the aim is to provide insights and recommendations to facilitate the incorporation of computational modeling more broadly into MOF research. Molecular dynamics ; Density functional theory ; Force field (fiction) ; Field (mathematics) ; Monte Carlo method ; Computer science ; Key (lock) ; Statistical physics ; Materials science ; Stability (learning theory) ; Metal-organic framework ; Multiscale modeling ; Nanotechnology ; Biochemical engineering ; Computational chemistry ; Machine learning ; Artificial intelligence ; Physics ; Chemistry ; Physical chemistry Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; X-ray Diffraction in Crystallography
Authors: Luigi Bonati | Enrico Trizio | Andrea Rizzi | Michele Parrinello
Source: The Journal of Chemical Physics (article, 2023-07-06)
Field: Materials Science — Materials Chemistry

A unified framework for machine learning collective variables for enhanced sampling simulations: <tt>mlcolvar</tt> Identifying a reduced set of collective variables is critical for understanding atomistic simulations and accelerating them through enhanced sampling techniques. Recently, several methods have been proposed to learn these variables directly from atomistic data. Depending on the type of data available, the learning process can be framed as dimensionality reduction, classification of metastable states, or identification of slow modes. Here, we present mlcolvar, a Python library that simplifies the construction of these variables and their use in the context of enhanced sampling through a contributed interface to the PLUMED software. The library is organized modularly to facilitate the extension and cross-contamination of these methodologies. In this spirit, we developed a general multi-task learning framework in which multiple objective functions and data from different simulations can be combined to improve the collective variables. The library's versatility is demonstrated through simple examples that are prototypical of realistic scenarios. Computer science ; Python (programming language) ; Dimensionality reduction ; Machine learning ; Curse of dimensionality ; Software ; Process (computing) ; Artificial intelligence ; Programming language Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Protein Structure and Dynamics