jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Muhammad Imran | Muhammad Sufyan Ramzan | Ahmad Kaleem Qureshi | Muhammad Azhar Khan | Muhammad Tariq
Source: Biosensors (review, 2018-10-19)
Field: Materials Science — Materials Chemistry

Emerging Applications of Porphyrins and Metalloporphyrins in Biomedicine and Diagnostic Magnetic Resonance Imaging In recent years, scientific advancements have constantly increased at a significant rate in the field of biomedical science. Keeping this in view, the application of porphyrins and metalloporphyrins in the field of biomedical science is gaining substantial importance. Porphyrins are the most widely studied tetrapyrrole-based compounds because of their important roles in vital biological processes. The cavity of porphyrins containing four pyrrolic nitrogens is well suited for the binding majority of metal ions to form metalloporphyrins. Porphyrins and metalloporphyrins possess peculiar photochemical, photophysical, and photoredox properties which are tunable through structural modifications. Their beneficial photophysical properties, such as the long wavelength of emission and absorption, high singlet oxygen quantum yield, and low in vivo toxicity, have drawn scientists' interest to discover new dimensions in the biomedical field. Applications of porphyrins and metalloporphyrins have been pursued in the perspective of contrast agents for magnetic resonance imaging (MRI), photodynamic therapy (PDT) of cancer, bio-imaging, and other biomedical applications. This review discusses photophysics and the photochemistry of porphyrins and their metal complexes. Secondly, it explains the current developments and mode of action for contrast agents for MRI. Moreover, the application of porphyrin and metalloporphyrin-based molecules as a photosensitizer in PDT of cancer, the mechanism of the generation of reactive oxygen species (ROS), factors that determine the efficiency of PDT, and the developments to improve this technology are delineated. The last part explores the most recent research and developments on metalloporphyrin-based materials in bio-imaging, drug delivery, and the determination of ferrochelatase in bone marrow indicating their prospective clinical applications. Biomedicine ; Magnetic resonance imaging ; Nanotechnology ; Medical imaging ; Nuclear magnetic resonance ; Materials science ; Medicine ; Radiology ; Bioinformatics ; Physics ; Biology Porphyrin and Phthalocyanine Chemistry ; Photodynamic Therapy Research Studies ; Nanoplatforms for cancer theranostics
Authors: Indumathi Sathisaran | Sameer V. Dalvi
Source: Pharmaceutics (review, 2018-07-31)
Field: Chemistry — Physical and Theoretical Chemistry

Engineering Cocrystals of Poorly Water-Soluble Drugs to Enhance Dissolution in Aqueous Medium Biopharmaceutics Classification System (BCS) Class II and IV drugs suffer from poor aqueous solubility and hence low bioavailability. Most of these drugs are hydrophobic and cannot be developed into a pharmaceutical formulation due to their poor aqueous solubility. One of the ways to enhance the aqueous solubility of poorlywater-soluble drugs is to use the principles of crystal engineering to formulate cocrystals of these molecules with water-soluble molecules (which are generally called coformers). Many researchers have shown that the cocrystals significantly enhance the aqueous solubility of poorly water-soluble drugs. In this review, we present a consolidated account of reports available in the literature related to the cocrystallization of poorly water-soluble drugs. The current practice to formulate new drug cocrystals with enhanced solubility involves a lot of empiricism. Therefore, in this work, attempts have been made to understand a general framework involved in successful (and unsuccessful) cocrystallization events which can yield different solid forms such as cocrystals, cocrystal polymorphs, cocrystal hydrates/solvates, salts, coamorphous solids, eutectics and solid solutions. The rationale behind screening suitable coformers for cocrystallization has been explained based on the rules of five i.e., hydrogen bonding, halogen bonding (and in general non-covalent bonding), length of carbon chain, molecular recognition points and coformer aqueous solubility. Different techniques to screen coformers for effective cocrystallization and methods to synthesize cocrystals have been discussed. Recent advances in technologies for continuous and solvent-free production of cocrystals have also been discussed. Furthermore, mechanisms involved in solubilization of these solid forms and the parameters influencing dissolution and stability of specific solid forms have been discussed. Overall, this review provides a consolidated account of the rationale for design of cocrystals, past efforts, recent developments and future perspectives for cocrystallization research which will be extremely useful for researchers working in pharmaceutical formulation development. Cocrystal ; Solubility ; Biopharmaceutics Classification System ; Aqueous solution ; Dissolution ; Chemistry ; Crystal engineering ; Halogen bond ; Organic chemistry ; Hydrogen bond ; Combinatorial chemistry ; Materials science ; Molecule Crystallography and molecular interactions ; Crystallization and Solubility Studies ; Crystal structures of chemical compounds
Authors: Zhimin Lv | Haiyan Wang | Changlun Chen | Shimin Yang | Lei Chen | Ahmed Alsaedi | Tasawar Hayat
Source: Journal of Colloid and Interface Science (review, 2018-11-16)
Field: Chemistry — Inorganic Chemistry

Enhanced removal of uranium(VI) from aqueous solution by a novel Mg-MOF-74-derived porous MgO/carbon adsorbent Adsorption ; Aqueous solution ; Zeta potential ; Carbon fibers ; Porosity ; Chemistry ; Uranium ; X-ray photoelectron spectroscopy ; Chemical engineering ; Diffusion ; Wastewater ; Composite number ; Nuclear chemistry ; Materials science ; Organic chemistry ; Waste management ; Composite material ; Metallurgy ; Nanoparticle Radioactive element chemistry and processing ; Chemical Synthesis and Characterization ; Geochemistry and Elemental Analysis
Authors: Qing‐Yuan Yang | Prem Lama | Susan Sen | Matteo Lusi | Kai‐Jie Chen | Wen‐Yang Gao | Mohana Shivanna | Tony Pham | Nobuhiko Hosono | Shinpei Kusaka | John J. Perry | Shengqian Ma | Brian Space | Leonard J. Barbour | Susumu Kitagawa | Michael J. Zaworotko
Source: Angewandte Chemie International Edition (article, 2018-03-25)
Field: Chemistry — Inorganic Chemistry

Reversible Switching between Highly Porous and Nonporous Phases of an Interpenetrated Diamondoid Coordination Network That Exhibits Gate‐Opening at Methane Storage Pressures Herein, we report that a new flexible coordination network, NiL<sub>2</sub> (L=4-(4-pyridyl)-biphenyl-4-carboxylic acid), with diamondoid topology switches between non-porous (closed) and several porous (open) phases at specific CO<sub>2</sub> and CH<sub>4</sub> pressures. These phases are manifested by multi-step low-pressure isotherms for CO<sub>2</sub> or a single-step high-pressure isotherm for CH<sub>4</sub> . The potential methane working capacity of NiL<sub>2</sub> approaches that of compressed natural gas but at much lower pressures. The guest-induced phase transitions of NiL<sub>2</sub> were studied by single-crystal XRD, in situ variable pressure powder XRD, synchrotron powder XRD, pressure-gradient differential scanning calorimetry (P-DSC), and molecular modeling. The detailed structural information provides insight into the extreme flexibility of NiL<sub>2</sub> . Specifically, the extended linker ligand, L, undergoes ligand contortion and interactions between interpenetrated networks or sorbate-sorbent interactions enable the observed switching. Diamondoid ; Differential scanning calorimetry ; Methane ; Materials science ; Porosity ; Porous medium ; Ligand (biochemistry) ; Chemical engineering ; Crystallography ; Chemistry ; Molecule ; Thermodynamics ; Composite material ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Crystallography and molecular interactions ; Supramolecular Chemistry and Complexes
Authors: Jon Paul Janet | Lydia Chan | Heather J. Kulik
Source: The Journal of Physical Chemistry Letters (article, 2018-02-09)
Field: Materials Science — Materials Chemistry

Accelerating Chemical Discovery with Machine Learning: Simulated Evolution of Spin Crossover Complexes with an Artificial Neural Network Machine learning (ML) has emerged as a powerful complement to simulation for materials discovery by reducing time for evaluation of energies and properties at accuracy competitive with first-principles methods. We use genetic algorithm (GA) optimization to discover unconventional spin-crossover complexes in combination with efficient scoring from an artificial neural network (ANN) that predicts spin-state splitting of inorganic complexes. We explore a compound space of over 5600 candidate materials derived from eight metal/oxidation state combinations and a 32-ligand pool. We introduce a strategy for error-aware ML-driven discovery by limiting how far the GA travels away from the nearest ANN training points while maximizing property (i.e., spin-splitting) fitness, leading to discovery of 80% of the leads from full chemical space enumeration. Over a 51-complex subset, average unsigned errors (4.5 kcal/mol) are close to the ANN's baseline 3 kcal/mol error. By obtaining leads from the trained ANN within seconds rather than days from a DFT-driven GA, this strategy demonstrates the power of ML for accelerating inorganic material discovery. Spin crossover ; Artificial neural network ; Genetic algorithm ; Crossover ; Computer science ; Spin (aerodynamics) ; Artificial intelligence ; Chemical space ; Machine learning ; Drug discovery ; Physics ; Chemistry ; Crystallography ; Thermodynamics Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Spectroscopy and Quantum Chemical Studies
Authors: Rebecca L. Greenaway | Valentina Santolini | Michael J. Bennison | Ben M. Alston | C. J. Pugh | Marc A. Little | Marcin Miklitz | E. G. B. Eden-Rump | Rob Clowes | A. Shakil | H. J. Cuthbertson | H. Armstrong | Michael E. Briggs | Kim E. Jelfs | Andrew I. Cooper
Source: Nature Communications (article, 2018-07-16)
Field: Chemistry — Organic Chemistry

High-throughput discovery of organic cages and catenanes using computational screening fused with robotic synthesis Supramolecular synthesis is a powerful strategy for assembling complex molecules, but to do this by targeted design is challenging. This is because multicomponent assembly reactions have the potential to form a wide variety of products. High-throughput screening can explore a broad synthetic space, but this is inefficient and inelegant when applied blindly. Here we fuse computation with robotic synthesis to create a hybrid discovery workflow for discovering new organic cage molecules, and by extension, other supramolecular systems. A total of 78 precursor combinations were investigated by computation and experiment, leading to 33 cages that were formed cleanly in one-pot syntheses. Comparison of calculations with experimental outcomes across this broad library shows that computation has the power to focus experiments, for example by identifying linkers that are less likely to be reliable for cage formation. Screening also led to the unplanned discovery of a new cage topology-doubly bridged, triply interlocked cage catenanes. Supramolecular chemistry ; Drug discovery ; Workflow ; Catenane ; Computation ; Computer science ; Throughput ; Chemical space ; Topology (electrical circuits) ; Nanotechnology ; Combinatorial chemistry ; Materials science ; Molecule ; Chemistry ; Bioinformatics ; Mathematics ; Algorithm ; Biology Supramolecular Chemistry and Complexes ; Supramolecular Self-Assembly in Materials ; Bacteriophages and microbial interactions
Authors: Volker L. Deringer | Chris J. Pickard | Gábor Cśanyi
Source: Physical Review Letters (article, 2018-04-10)
Field: Materials Science — Materials Chemistry

Data-Driven Learning of Total and Local Energies in Elemental Boron The allotropes of boron continue to challenge structural elucidation and solid-state theory. Here we use machine learning combined with random structure searching (RSS) algorithms to systematically construct an interatomic potential for boron. Starting from ensembles of randomized atomic configurations, we use alternating single-point quantum-mechanical energy and force computations, Gaussian approximation potential (GAP) fitting, and GAP-driven RSS to iteratively generate a representation of the element's potential-energy surface. Beyond the total energies of the very different boron allotropes, our model readily provides atom-resolved, local energies and thus deepened insight into the frustrated β-rhombohedral boron structure. Our results open the door for the efficient and automated generation of GAPs, and other machine-learning-based interatomic potentials, and suggest their usefulness as a tool for materials discovery. Boron ; Atom (system on chip) ; Materials science ; Gaussian ; Interatomic potential ; Band gap ; Chemical physics ; Statistical physics ; Physics ; Computer science ; Quantum mechanics ; Molecular dynamics Machine Learning in Materials Science ; Boron and Carbon Nanomaterials Research ; Boron Compounds in Chemistry
Authors: Greta Becker | Frederik R. Wurm
Source: Chemical Society Reviews (review, 2018-01-01)
Field: Materials Science — Biomaterials

Functional biodegradable polymers <i>via</i> ring-opening polymerization of monomers without protective groups Biodegradable polymers are of current interest and chemical functionality in such materials is often demanded in advanced biomedical applications. Functional groups often are not tolerated in the polymerization process of ring-opening polymerization (ROP) and therefore protective groups need to be applied. Advantageously, several orthogonally reactive functions are available, which do not demand protection during ROP. We give an insight into available, orthogonally reactive cyclic monomers and the corresponding functional synthetic and biodegradable polymers, obtained from ROP. Functionalities in the monomer are reviewed, which are tolerated by ROP without further protection and allow further post-modification of the corresponding chemically functional polymers after polymerization. Synthetic concepts to these monomers are summarized in detail, preferably using precursor molecules. Post-modification strategies for the reported functionalities are presented and selected applications highlighted. Polymerization ; Monomer ; Chain-growth polymerization ; Polymer ; Polymer chemistry ; Ionic polymerization ; Functional polymers ; Reversible addition−fragmentation chain-transfer polymerization ; Ring-opening polymerization ; Chemistry ; Chain transfer ; Cationic polymerization ; Precipitation polymerization ; Materials science ; Radical polymerization ; Organic chemistry biodegradable polymer synthesis and properties ; Carbon dioxide utilization in catalysis ; Advanced Polymer Synthesis and Characterization
Authors: Xiaofeng Yi | Fuliang Sun | Zhenhua Han | Fuhao Han | Jiarui He | Minrui Ou | Junjie Gu | Xiaoping Xu
Source: Ecotoxicology and Environmental Safety (article, 2018-05-02)
Field: Chemistry — Inorganic Chemistry

Graphene oxide encapsulated polyvinyl alcohol/sodium alginate hydrogel microspheres for Cu (II) and U (VI) removal Polyvinyl alcohol ; Adsorption ; Langmuir adsorption model ; Self-healing hydrogels ; Chemical engineering ; Aqueous solution ; Nuclear chemistry ; Sorption ; Oxide ; Sorbent ; Chemistry ; X-ray photoelectron spectroscopy ; Desorption ; Metal ions in aqueous solution ; Materials science ; Polymer chemistry ; Metal ; Organic chemistry Radioactive element chemistry and processing ; Extraction and Separation Processes ; Adsorption and biosorption for pollutant removal
Authors: Jarl Ivar van der Vlugt
Source: Chemistry - A European Journal (article, 2018-08-07)
Field: Chemistry — Organic Chemistry

Radical‐Type Reactivity and Catalysis by Single‐Electron Transfer to or from Redox‐Active Ligands Controlled ligand-based redox-activity and chemical non-innocence are rapidly gaining importance for selective (catalytic) processes. This Concept aims to provide an overview of the progress regarding ligand-to-substrate single-electron transfer as a relatively new mode of operation to exploit ligand-centered reactivity and catalysis based thereon. Redox ; Reactivity (psychology) ; Electron transfer ; Chemistry ; Catalysis ; Photochemistry ; Inorganic chemistry ; Organic chemistry ; Medicine Oxidative Organic Chemistry Reactions ; Radical Photochemical Reactions ; CO2 Reduction Techniques and Catalysts
Authors: Ruijin Cang | Hechao Li | Houpu Yao | Yang Jiao | Yi Ren
Source: Computational Materials Science (article, 2018-04-20)
Field: Materials Science — Materials Chemistry

Improving direct physical properties prediction of heterogeneous materials from imaging data via convolutional neural network and a morphology-aware generative model Computer science ; Generative grammar ; Artificial neural network ; Generative model ; Property (philosophy) ; Artificial intelligence ; Computation ; Nonlinear system ; Convolutional neural network ; Machine learning ; Algorithm Machine Learning in Materials Science ; Neural Networks and Applications ; Injection Molding Process and Properties
Authors: Jake Graser | Steven K. Kauwe | Taylor D. Sparks
Source: Chemistry of Materials (review, 2018-05-19)
Field: Materials Science — Materials Chemistry

Machine Learning and Energy Minimization Approaches for Crystal Structure Predictions: A Review and New Horizons Predicting crystal structure has always been a challenging problem for physical sciences. Recently, computational methods have been built to predict crystal structure with success but have been limited in scope and computational time. In this paper, we review computational methods such as density functional theory and machine learning methods used to predict crystal structure. We also explored the breadth versus accuracy of building a model to predict across any crystal structure using machine learning. We extracted 24 913 unique chemical formulas existing between 290 and 310 K from the Pearson Crystal Database. Of these 24 913 formulas, there exists 10 711 unique crystal structures referred to as entry prototypes. Common entries might have hundreds of chemical compositions, while the vast majority of entry prototypes is represented by fewer than ten unique compositions. To include all data in our predictions, entry prototypes that lacked a minimum number of representatives were relabeled as “Other”. By selecting the minimum numbers to be 150, 100, 70, 40, 20, and 10, we explored how limiting class sizes affected performance. Using each minimum number to reorganize the data, we looked at the classification performance metrics: accuracy, precision, and recall. Accuracy ranged from 97 ± 2 to 85 ± 2%; average precision ranged from 86 ± 2 to 79 ± 2%, while average recall ranged from 73 ± 2 to 54 ± 2% for minimum-class representatives from 150 to 10, respectively. Crystal (programming language) ; Crystal structure prediction ; Artificial intelligence ; Machine learning ; Computer science ; Limiting ; Energy minimization ; Crystal structure ; Algorithm ; Minification ; Class (philosophy) ; Mathematics ; Chemistry ; Crystallography ; Engineering ; Computational chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Priyadarshi Chakraborty | Tom Guterman | Nofar Adadi | Moran Yadid | Tamar Brosh | Lihi Adler‐Abramovich | Tal Dvir | Ehud Gazit
Source: ACS Nano (article, 2018-12-27)
Field: Materials Science — Biomaterials

A Self-Healing, All-Organic, Conducting, Composite Peptide Hydrogel as Pressure Sensor and Electrogenic Cell Soft Substrate Conducting polymer hydrogels (CPHs) emerge as excellent functional materials, as they harness the advantages of conducting polymers with the mechanical properties and continuous 3D nanostructures of hydrogels. This bicomponent organization results in soft, all-organic, conducting micro-/nanostructures with multifarious material applications. However, the application of CPHs as functional materials for biomedical applications is currently limited due to the necessity to combine the features of biocompatibility, self-healing, and fine-tuning of the mechanical properties. To overcome this issue, we choose to combine a protected dipeptide as the supramolecular gelator, owing to its intrinsic biocompatibility and excellent gelation ability, with the conductive polymer polyaniline (PAni), which was polymerized in situ. Thus, a two-component, all-organic, conducting hydrogel was formed. Spectroscopic evidence reveals the formation of the emeraldine salt form of PAni by intrinsic doping. The composite hydrogel is mechanically rigid with a very high storage modulus ( G') value of ∼2 MPa, and the rigidity was tuned by changing the peptide concentration. The hydrogel exhibits ohmic conductivity, pressure sensitivity, and, importantly, self-healing features. By virtue of its self-healing property, the polymeric nonmetallic hydrogel can reinstate its intrinsic conductivity when two of its macroscopically separated blocks are rejoined. High cell viability of cardiomyocytes grown on the composite hydrogel demonstrates its noncytotoxicity. These combined attributes of the hydrogel allowed its utilization for dynamic range pressure sensing and as a conductive interface for electrogenic cardiac cells. The composite hydrogel supports cardiomyocyte organization into a spontaneously contracting system. The composite hydrogel thus has considerable potential for various applications. Self-healing ; Substrate (aquarium) ; Materials science ; Composite number ; Peptide ; Nanotechnology ; Self-healing hydrogels ; Self-assembling peptide ; Biophysics ; Chemical engineering ; Chemistry ; Polymer chemistry ; Composite material ; Medicine ; Biochemistry Supramolecular Self-Assembly in Materials ; Polydiacetylene-based materials and applications ; Conducting polymers and applications
Authors: Weizhong Zhang | Lin Liu | Hongmin Chen | Kai Hu | Ian Delahunty | Shi Gao | Jin Xie
Source: Theranostics (review, 2018-01-01)
Field: Materials Science — Materials Chemistry

Surface impact on nanoparticle-based magnetic resonance imaging contrast agents Magnetic resonance imaging (MRI) is one of the most widely used diagnostic tools in the clinic. To improve imaging quality, MRI contrast agents, which can modulate local T<sub>1</sub> and T<sub>2</sub> relaxation times, are often injected prior to or during MRI scans. However, clinically used contrast agents, including Gd<sup>3+</sup>-based chelates and iron oxide nanoparticles (IONPs), afford mediocre contrast abilities. To address this issue, there has been extensive research on developing alternative MRI contrast agents with superior r<sub>1</sub> and r<sub>2</sub> relaxivities. These efforts are facilitated by the fast progress in nanotechnology, which allows for preparation of magnetic nanoparticles (NPs) with varied size, shape, crystallinity, and composition. Studies suggest that surface coatings can also largely affect T<sub>1</sub> and T<sub>2</sub> relaxations and can be tailored in favor of a high r<sub>1</sub> or r<sub>2</sub>. However, the surface impact of NPs has been less emphasized. Herein, we review recent progress on developing NP-based T<sub>1</sub> and T<sub>2</sub> contrast agents, with a focus on the surface impact. Magnetic resonance imaging ; Contrast (vision) ; Nanoparticle ; Materials science ; Nanotechnology ; Nuclear magnetic resonance ; Relaxation (psychology) ; MRI contrast agent ; Contrast enhancement ; Magnetic nanoparticles ; Biomedical engineering ; Computer science ; Medicine ; Radiology ; Physics ; Artificial intelligence Lanthanide and Transition Metal Complexes ; Nanoparticle-Based Drug Delivery ; Nanocluster Synthesis and Applications