jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Travis C. Green
Source: OhioLink ETD Center (Ohio Library and Information Network) (article, 2020-01-01)
Field: Chemistry — Physical and Theoretical Chemistry

Functional Materials and Chemistry Education: Biomimetic Metallopolymers, Photoresponsive Gels and Infrared Cameras Functional and stimuli responsive materials are becoming more and more important as technology continues to develop.Many of these materials are based on polymers that contain specific functional moieties that give the bulk material the desired properties.An emerging class of materials known as metallopolymers have been shown to be useful in this area.Metallopolymers are comprised of a polymer backbone that has been modified with a metal binding moiety and coordinated to a metal ion.In some cases these metal ions act as a cross linking agent to form a supramolecular material.Our group has focused on strategically choosing the metal complex to achieve specific functionalities.Traditionally we have focused on creating photoresponsive materials and used this as a way to change the mechanical properties of the material.While we have success in this area this is just the surface of the capabilities of these metallopolymer materials.The projects detailed in this dissertation focus on trying to create biomimetic materials and new photoresponsive materials from these coordination materials.The biomimetic catalysis project studied mimics of the catechol oxidase enzyme and focused on comparing a metallopolymer analog and a small model complex.The two catalysts exhibited differences in reactivity and the factors leading to this were studied through a variety of methods.The photoresponsive materials were focused on leucoindigo containing poly(vinyl alcohol) hydrogels that exhibited photoresponsiveness.The project revealed that the photochemistry of leucoindigo was affected by the polymer and by the inclusion of various metal ions.The third and final project described in this work focusses on using newly affordable technology for chemical education purposes. Infrared ; Chemistry ; Biomimetic materials ; Nanotechnology ; Polymer science ; Materials science ; Optics ; Physics Various Chemistry Research Topics
Authors: Jose M. Peña
Source: arXiv (Cornell University) (preprint, 2020-02-12)
Field: Chemistry — Physical and Theoretical Chemistry

Factorization of the Partial Covariance in Singly-Connected Path\n Diagrams We extend path analysis by showing that, for a singly-connected path diagram,\nthe partial covariance of two random variables factorizes over the nodes and\nedges in the path between the variables. This result allows us to determine the\ncontribution of each node and edge to the partial covariance. It also allows us\nto show that Simpson's paradox cannot occur in singly-connected path diagrams.\n Covariance ; Path (computing) ; Diagram ; Enhanced Data Rates for GSM Evolution ; Mathematics ; Factorization ; Computer science ; Statistics ; Algorithm ; Artificial intelligence History and advancements in chemistry
Authors: Yasset Pérez‐Riverol | Jingwen Bai | Chakradhar Bandla | David García‐Seisdedos | Suresh Hewapathirana | Selvakumar Kamatchinathan | Deepti J Kundu | Ananth Prakash | Anika Frericks-Zipper | Martin Eisenacher | Mathias Walzer | Shengbo Wang | Alvis Brāzma | Juan Antonio Vizcaíno
Source: Nucleic Acids Research (article, 2021-10-16)
Field: Chemistry — Spectroscopy

The PRIDE database resources in 2022: a hub for mass spectrometry-based proteomics evidences The PRoteomics IDEntifications (PRIDE) database (https://www.ebi.ac.uk/pride/) is the world's largest data repository of mass spectrometry-based proteomics data. PRIDE is one of the founding members of the global ProteomeXchange (PX) consortium and an ELIXIR core data resource. In this manuscript, we summarize the developments in PRIDE resources and related tools since the previous update manuscript was published in Nucleic Acids Research in 2019. The number of submitted datasets to PRIDE Archive (the archival component of PRIDE) has reached on average around 500 datasets per month during 2021. In addition to continuous improvements in PRIDE Archive data pipelines and infrastructure, the PRIDE Spectra Archive has been developed to provide direct access to the submitted mass spectra using Universal Spectrum Identifiers. As a key point, the file format MAGE-TAB for proteomics has been developed to enable the improvement of sample metadata annotation. Additionally, the resource PRIDE Peptidome provides access to aggregated peptide/protein evidences across PRIDE Archive. Furthermore, we will describe how PRIDE has increased its efforts to reuse and disseminate high-quality proteomics data into other added-value resources such as UniProt, Ensembl and Expression Atlas. Pride ; Proteomics ; Metadata ; Identifier ; UniProt ; Computer science ; Ensembl ; Resource (disambiguation) ; World Wide Web ; Biology ; Database ; Genomics ; Genetics ; Political science Advanced Proteomics Techniques and Applications ; Genomics and Phylogenetic Studies ; Mass Spectrometry Techniques and Applications
Authors: Dmitry Olegovich Bokov | Abduladheem Turki Jalil | Supat Chupradit | Wanich Suksatan | Mohammad Javed Ansari | Iman H. Shewael | G. H. Valiev | Ehsan Kianfar
Source: Advances in Materials Science and Engineering (article, 2021-01-01)
Field: Chemistry — Spectroscopy

Nanomaterial by Sol‐Gel Method: Synthesis and Application The sol‐gel process is a more chemical method (wet chemical method) for the synthesis of various nanostructures, especially metal oxide nanoparticles. In this method, the molecular precursor (usually metal alkoxide) is dissolved in water or alcohol and converted to gel by heating and stirring by hydrolysis/alcoholysis. Since the gel obtained from the hydrolysis/alcoholysis process is wet or damp, it should be dried using appropriate methods depending on the desired properties and application of the gel. For example, if it is an alcoholic solution, the drying process is done by burning alcohol. After the drying stage, the produced gels are powdered and then calcined. The sol‐gel method is a cost‐effective method and due to the low reaction temperature there is good control over the chemical composition of the products. The sol‐gel method can be used in the process of making ceramics as a molding material and can be used as an intermediate between thin films of metal oxides in various applications. The materials obtained from the sol‐gel method are used in various optical, electronic, energy, surface engineering, biosensors, and pharmaceutical and separation technologies (such as chromatography). The sol‐gel method is a conventional and industrial method for the synthesis of nanoparticles with different chemical composition. The basis of the sol‐gel method is the production of a homogeneous sol from the precursors and its conversion into a gel. The solvent in the gel is then removed from the gel structure and the remaining gel is dried. The properties of the dried gel depend significantly on the drying method. In other words, the “removing solvent method” is selected according to the application in which the gel will be used. Dried gels in various ways are used in industries such as surface coating, building insulation, and the production of special clothing. It is worth mentioning that, by grinding the gel by special mills, it is possible to achieve nanoparticles. Materials science ; Nanomaterials ; Sol-gel ; Chemical engineering ; Nanotechnology Aerogels and thermal insulation ; Pigment Synthesis and Properties ; TiO2 Photocatalysis and Solar Cells
Authors: Michael Naguib | Michel W. Barsoum | Yury Gogotsi
Source: Advanced Materials (article, 2021-08-16)
Field: Materials Science — Materials Chemistry

Ten Years of Progress in the Synthesis and Development of MXenes Since their discovery in 2011, the number of 2D transition metal carbides and nitrides (MXenes) has steadily increased. Currently more than 40 MXene compositions exist. The ultimate number is far greater and in time they may develop into the largest family of 2D materials known. MXenes' unique properties, such as their metal-like electrical conductivity reaching ≈20 000 S cm<sup>-1</sup> , render them quite useful in a large number of applications, including energy storage, optoelectronic, biomedical, communications, and environmental. The number of MXene papers and patents published has been growing quickly. The first MXene generation is synthesized using selective etching of metal layers from the MAX phases, layered transition metal carbides and carbonitrides using hydrofluoric acid. Since then, multiple synthesis approaches have been developed, including selective etching in a mixture of fluoride salts and various acids, non-aqueous etchants, halogens, and molten salts, allowing for the synthesis of new MXenes with better control over their surface chemistries. Herein, a brief historical overview of the first 10 years of MXene research and a perspective on their synthesis and future development are provided. The fact that their production is readily scalable in aqueous environments, with high yields bodes well for their commercialization. MXenes ; Materials science ; Nanotechnology ; Etching (microfabrication) ; MAX phases ; Carbide ; Nitride ; Transition metal ; Hydrofluoric acid ; Metallurgy ; Chemistry ; Layer (electronics) ; Organic chemistry ; Catalysis MXene and MAX Phase Materials ; 2D Materials and Applications ; Graphene and Nanomaterials Applications
Authors: Volker L. Deringer | Albert P. Bartók | Noam Bernstein | David M. Wilkins | Michele Ceriotti | Gábor Cśanyi
Source: Chemical Reviews (review, 2021-08-16)
Field: Materials Science — Materials Chemistry

Gaussian Process Regression for Materials and Molecules We provide an introduction to Gaussian process regression (GPR) machine-learning methods in computational materials science and chemistry. The focus of the present review is on the regression of atomistic properties: in particular, on the construction of interatomic potentials, or force fields, in the Gaussian Approximation Potential (GAP) framework; beyond this, we also discuss the fitting of arbitrary scalar, vectorial, and tensorial quantities. Methodological aspects of reference data generation, representation, and regression, as well as the question of how a data-driven model may be validated, are reviewed and critically discussed. A survey of applications to a variety of research questions in chemistry and materials science illustrates the rapid growth in the field. A vision is outlined for the development of the methodology in the years to come. Kriging ; Chemistry ; Gaussian process ; Statistical physics ; Gaussian ; Representation (politics) ; Scalar (mathematics) ; Regression ; Field (mathematics) ; Machine learning ; Computational chemistry ; Computer science ; Physics ; Mathematics ; Statistics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Ralph Freund | Orysia Zaremba | Giel Arnauts | Rob Ameloot | Grigorii Skorupskii | Mircea Dincă | Anastasiya Bavykina | Jorge Gascón | Aleksander Ejsmont | Joanna Gościańska | Markus J. Kalmutzki | Ulrich Lächelt | Evelyn Ploetz | Christian S. Diercks | Stefan Wuttke
Source: Angewandte Chemie International Edition (review, 2021-05-14)
Field: Chemistry — Inorganic Chemistry

The Current Status of MOF and COF Applications The amalgamation of different disciplines is at the heart of reticular chemistry and has broadened the boundaries of chemistry by opening up an infinite space of chemical composition, structure, and material properties. Reticular design has enabled the precise prediction of crystalline framework structures, tunability of chemical composition, incorporation of various functionalities onto the framework backbone, and as a consequence, fine-tuning of metal-organic framework (MOF) and covalent organic framework (COF) properties beyond that of any other material class. Leveraging the unique properties of reticular materials has resulted in significant advances from both a fundamental and an applied perspective. Here, we wish to review the milestones in MOF and COF research and give a critical view on progress in their real-world applications. Finally, we briefly discuss the major challenges in the field that need to be addressed to pave the way for industrial applications. Reticular connective tissue ; Nanotechnology ; Metal-organic framework ; Chemical space ; Materials science ; Covalent bond ; Computer science ; Chemistry ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Covalent Organic Framework Applications ; Advanced Photocatalysis Techniques
Authors: Zhe Cheng | Maoyu Li | Raja Dey | Yongheng Chen
Source: Journal of Hematology & Oncology (review, 2021-05-31)
Field: Materials Science — Biomaterials

Nanomaterials for cancer therapy: current progress and perspectives Cancer is a disease with complex pathological process. Current chemotherapy faces problems such as lack of specificity, cytotoxicity, induction of multi-drug resistance and stem-like cells growth. Nanomaterials are materials in the nanorange 1-100 nm which possess unique optical, magnetic, and electrical properties. Nanomaterials used in cancer therapy can be classified into several main categories. Targeting cancer cells, tumor microenvironment, and immune system, these nanomaterials have been modified for a wide range of cancer therapies to overcome toxicity and lack of specificity, enhance drug capacity as well as bioavailability. Although the number of studies has been increasing, the number of approved nano-drugs has not increased much over the years. To better improve clinical translation, further research is needed for targeted drug delivery by nano-carriers to reduce toxicity, enhance permeability and retention effects, and minimize the shielding effect of protein corona. This review summarizes novel nanomaterials fabricated in research and clinical use, discusses current limitations and obstacles that hinder the translation from research to clinical use, and provides suggestions for more efficient adoption of nanomaterials in cancer therapy. Medicine ; Cancer ; Nanotechnology ; Drug delivery ; Pharmacology ; Cancer research ; Internal medicine ; Materials science Nanoparticle-Based Drug Delivery ; Nanoplatforms for cancer theranostics ; Graphene and Nanomaterials Applications
Authors: Xiaojia Zhao | Pradip Pachfule | Arne Thomas
Source: Chemical Society Reviews (review, 2021-01-01)
Field: Materials Science — Materials Chemistry

Covalent organic frameworks (COFs) for electrochemical applications Covalent organic frameworks are a class of extended crystalline organic materials that possess unique architectures with high surface areas and tuneable pore sizes. Since the first discovery of the topological frameworks in 2005, COFs have been applied as promising materials in diverse areas such as separation and purification, sensing or catalysis. Considering the need for renewable and clean energy production, many research efforts have recently focused on the application of porous materials for electrochemical energy storage and conversion. In this respect, considerable efforts have been devoted to the design and synthesis of COF-based materials for electrochemical applications, including electrodes and membranes for fuel cells, supercapacitors and batteries. This review article highlights the design principles and strategies for the synthesis of COFs with a special focus on their potential for electrochemical applications. Recently suggested hybrid COF materials or COFs with hierarchical porosity will be discussed, which can alleviate the most challenging drawback of COFs for these applications. Finally, the major challenges and future trends of COF materials in electrochemical applications are outlined. Electrochemistry ; Covalent bond ; Nanotechnology ; Computer science ; Chemistry ; Combinatorial chemistry ; Biochemical engineering ; Materials science ; Organic chemistry ; Engineering ; Electrode Covalent Organic Framework Applications ; Metal-Organic Frameworks: Synthesis and Applications ; Advanced battery technologies research
Authors: Benjamin J. Shields | Jason M. Stevens | Jun Li | Marvin Parasram | Farhan Damani | Jesus I. Martinez Alvarado | Jacob M. Janey | Ryan P. Adams | Abigail G. Doyle
Source: Nature (article, 2021-02-03)
Field: Materials Science — Materials Chemistry

Bayesian reaction optimization as a tool for chemical synthesis Bayesian optimization ; Computer science ; Machine learning ; Engineering optimization ; Bayesian probability ; Benchmark (surveying) ; Artificial intelligence ; Optimization problem ; Algorithm Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Jörg Behler
Source: Chemical Reviews (review, 2021-03-29)
Field: Materials Science — Materials Chemistry

Four Generations of High-Dimensional Neural Network Potentials Since their introduction about 25 years ago, machine learning (ML) potentials have become an important tool in the field of atomistic simulations. After the initial decade, in which neural networks were successfully used to construct potentials for rather small molecular systems, the development of high-dimensional neural network potentials (HDNNPs) in 2007 opened the way for the application of ML potentials in simulations of large systems containing thousands of atoms. To date, many other types of ML potentials have been proposed continuously increasing the range of problems that can be studied. In this review, the methodology of the family of HDNNPs including new recent developments will be discussed using a classification scheme into four generations of potentials, which is also applicable to many other types of ML potentials. The first generation is formed by early neural network potentials designed for low-dimensional systems. High-dimensional neural network potentials established the second generation and are based on three key steps: first, the expression of the total energy as a sum of environment-dependent atomic energy contributions; second, the description of the atomic environments by atom-centered symmetry functions as descriptors fulfilling the requirements of rotational, translational, and permutation invariance; and third, the iterative construction of the reference electronic structure data sets by active learning. In third-generation HDNNPs, in addition, long-range interactions are included employing environment-dependent partial charges expressed by atomic neural networks. In fourth-generation HDNNPs, which are just emerging, in addition, nonlocal phenomena such as long-range charge transfer can be included. The applicability and remaining limitations of HDNNPs are discussed along with an outlook at possible future developments. Artificial neural network ; Chemistry ; Atom (system on chip) ; Construct (python library) ; Range (aeronautics) ; Atomic charge ; Artificial intelligence ; Permutation (music) ; Statistical physics ; Biological system ; Computer science ; Physics ; Molecule Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Advanced Chemical Physics Studies
Authors: John A. Keith | Valentín Vassilev-Galindo | Bingqing Cheng | Stefan Chmiela | Michael Gastegger | Klaus‐Robert Müller | Alexandre Tkatchenko
Source: Chemical Reviews (review, 2021-07-07)
Field: Materials Science — Materials Chemistry

Combining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical Systems Machine learning models are poised to make a transformative impact on chemical sciences by dramatically accelerating computational algorithms and amplifying insights available from computational chemistry methods. However, achieving this requires a confluence and coaction of expertise in computer science and physical sciences. This Review is written for new and experienced researchers working at the intersection of both fields. We first provide concise tutorials of computational chemistry and machine learning methods, showing how insights involving both can be achieved. We follow with a critical review of noteworthy applications that demonstrate how computational chemistry and machine learning can be used together to provide insightful (and useful) predictions in molecular and materials modeling, retrosyntheses, catalysis, and drug design. Chemistry ; Transformative learning ; Computational model ; Artificial intelligence ; Intersection (aeronautics) ; Machine learning ; Computer science ; Management science ; Biochemical engineering ; Nanotechnology Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Catalysis and Oxidation Reactions
Authors: Md. Saifur Rahman | Md. Saif Hasan | Ashis Sutradhar Nitai | Sunghyun Nam | Aneek Krishna Karmakar | Md. Shameem Ahsan | Muhammad J. A. Shiddiky | Mohammad Boshir Ahmed
Source: Polymers (review, 2021-04-20)
Field: Materials Science — Biomaterials

Recent Developments of Carboxymethyl Cellulose Carboxymethyl cellulose (CMC) is one of the most promising cellulose derivatives. Due to its characteristic surface properties, mechanical strength, tunable hydrophilicity, viscous properties, availability and abundance of raw materials, low-cost synthesis process, and likewise many contrasting aspects, it is now widely used in various advanced application fields, for example, food, paper, textile, and pharmaceutical industries, biomedical engineering, wastewater treatment, energy production, and storage energy production, and storage and so on. Many research articles have been reported on CMC, depending on their sources and application fields. Thus, a comprehensive and well-organized review is in great demand that can provide an up-to-date and in-depth review on CMC. Herein, this review aims to provide compact information of the synthesis to the advanced applications of this material in various fields. Finally, this article covers the insights of future CMC research that could guide researchers working in this prominent field. Carboxymethyl cellulose ; Raw material ; Cellulose ; Textile ; Process (computing) ; Process engineering ; Materials science ; Nanotechnology ; Production (economics) ; Biochemical engineering ; Computer science ; Engineering ; Chemical engineering ; Composite material ; Chemistry ; Organic chemistry Advanced Cellulose Research Studies ; Electrospun Nanofibers in Biomedical Applications ; Electrohydrodynamics and Fluid Dynamics
Authors: Dewei Ni | Yuan Cheng | Ping Zhang | Ji‐Xuan Liu | Ji Zou | Bowen Chen | Haoyang Wu | Hejun Li | Shaoming Dong | Jiecai Han | Xinghong Zhang | Qiangang Fu | Guo‐Jun Zhang
Source: Journal of Advanced Ceramics (article, 2021-12-24)
Field: Materials Science — Ceramics and Composites

Advances in ultra-high temperature ceramics, composites, and coatings Abstract Ultra-high temperature ceramics (UHTCs) are generally referred to the carbides, nitrides, and borides of the transition metals, with the Group IVB compounds (Zr &amp; Hf) and TaC as the main focus. The UHTCs are endowed with ultra-high melting points, excellent mechanical properties, and ablation resistance at elevated temperatures. These unique combinations of properties make them promising materials for extremely environmental structural applications in rocket and hypersonic vehicles, particularly nozzles, leading edges, and engine components, etc. In addition to bulk UHTCs, UHTC coatings and fiber reinforced UHTC composites are extensively developed and applied to avoid the intrinsic brittleness and poor thermal shock resistance of bulk ceramics. Recently, highentropy UHTCs are developed rapidly and attract a lot of attention as an emerging direction for ultra-high temperature materials. This review presents the state of the art of processing approaches, microstructure design and properties of UHTCs from bulk materials to composites and coatings, as well as the future directions. Materials science ; Structural material ; Ceramic ; Carbide ; Composite material ; Microstructure ; Thermal shock ; Brittleness ; Metallurgy Advanced ceramic materials synthesis ; Advanced materials and composites ; Energetic Materials and Combustion