jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Martin A. Hubbe | Saeid Azizian | Sigrid Douven
Source: BioResources (review, 2019-08-01)
Field: Chemistry — Physical and Theoretical Chemistry

Implications of apparent pseudo-second-order adsorption kinetics onto cellulosic materials: A review The pseudo-second-order (PSO) kinetic model has become among the most popular ways to fit rate data for adsorption of metal ions, dyes, and other compounds from aqueous solution onto cellulose-based materials. This review first considers published evidence regarding the validity of the mechanistic assumptions underlying application of the PSO model to adsorption kinetics. A literal interpretation of the model requires an assumption that different adsorption sites on a solid substrate randomly collide with each other during a rate-limiting mechanistic step. Because of problems revealed by the literature regarding the usual assumptions associated with the PSO model, this review also considers how else to account for good fits of adsorption data to the PSO model. Studies have shown that adsorption behavior that fits the PSO model well often can be explained by diffusion-based mechanisms. Hypothetical data generated using the assumption of pseudo-first-order rate behavior has been shown to fit the PSO model very well. In light of published evidence, adsorption kinetics of cellulosic materials is expected to mainly depend on diffusion-limited processes, as affected by heterogeneous distributions of pore sizes and continual partitioning of solute species between a dissolved state and a fixed state of adsorption. Adsorption ; Kinetics ; Thermodynamics ; Diffusion ; Cellulose ; Materials science ; Chemistry ; Physical chemistry ; Physics ; Organic chemistry ; Classical mechanics Electrostatics and Colloid Interactions ; Surfactants and Colloidal Systems ; Electrochemical Analysis and Applications
Authors: M.K. Marichelvam | Mohammad Jawaid | Mohammad Asim
Source: Fibers (article, 2019-04-09)
Field: Materials Science — Biomaterials

Corn and Rice Starch-Based Bio-Plastics as Alternative Packaging Materials Due to the negative environmental impacts of synthetic plastics, the development of biodegradable plastics for both industrial and commercial applications is essential today. Researchers have developed various starch-based composites for different applications. The present work investigates the corn and rice starch-based bioplastics for packaging applications. Various samples of bioplastics are produced, with different compositions of corn and rice starch, glycerol, citric acid, and gelatin. The tensile properties were improved after adding rice starch. However, water absorption and water solubility were reduced. On the basis of these results, the best sample was analyzed for thickness testing, biodegradability properties, SEM, hydrophilicity, thermogravimetric analysis, and sealing properties of bioplastic. The results show the suitability of rice and corn-based thermoplastic starch for packaging applications. Bioplastic ; Starch ; Biodegradation ; Thermoplastic ; Thermogravimetric analysis ; Materials science ; Absorption of water ; Citric acid ; Ultimate tensile strength ; Glycerol ; Gelatin ; Corn starch ; Pulp and paper industry ; Composite material ; Food science ; Waste management ; Chemistry ; Organic chemistry Nanocomposite Films for Food Packaging ; biodegradable polymer synthesis and properties ; Natural Fiber Reinforced Composites
Authors: Su Ji Kim | Hyun Soo Kim | Young Rok Seo
Source: Oxidative Medicine and Cellular Longevity (review, 2019-12-20)
Field: Chemistry — Organic Chemistry

Understanding of ROS-Inducing Strategy in Anticancer Therapy Redox homeostasis is essential for the maintenance of diverse cellular processes. Cancer cells have higher levels of reactive oxygen species (ROS) than normal cells as a result of hypermetabolism, but the redox balance is maintained in cancer cells due to their marked antioxidant capacity. Recently, anticancer therapies that induce oxidative stress by increasing ROS and/or inhibiting antioxidant processes have received significant attention. The acceleration of accumulative ROS disrupts redox homeostasis and causes severe damage in cancer cells. In this review, we describe ROS-inducing cancer therapy and the anticancer mechanism employed by prooxidative agents. To understand the comprehensive biological response to certain prooxidative anticancer drugs such as 2-methoxyestradiol, buthionine sulfoximine, cisplatin, doxorubicin, imexon, and motexafin gadolinium, we propose and visualize the drug-gene, drug-cell process, and drug-disease interactions involved in oxidative stress induction and antioxidant process inhibition as well as specific side effects of these drugs using pathway analysis with a big data-based text-mining approach. Our review will be helpful to improve the therapeutic effects of anticancer drugs by providing information about biological changes that occur in response to prooxidants. For future directions, there is still a need for pharmacogenomic studies on prooxidative agents as well as the molecular mechanisms underlying the effects of the prooxidants and/or antioxidant-inhibitor agents for effective anticancer therapy through selective killing of cancer cells. Oxidative stress ; Reactive oxygen species ; Pharmacology ; Cancer cell ; Antioxidant ; Chemistry ; Doxorubicin ; Mitochondrial ROS ; Cancer ; Cisplatin ; Cancer research ; Medicine ; Biochemistry ; Chemotherapy ; Internal medicine Free Radicals and Antioxidants ; Genomics, phytochemicals, and oxidative stress ; Glutathione Transferases and Polymorphisms
Authors: Ting Zhang | Guolong Xing | Weiben Chen | Long Chen
Source: Materials Chemistry Frontiers (article, 2019-10-19)
Field: Materials Science — Materials Chemistry

Porous organic polymers: a promising platform for efficient photocatalysis This review summarizes the latest advances of porous organic polymers (POPs) focusing on their applications in photocatalysis, including photocatalytic chemical transformations, photodetoxification of pollutants from water, and water splitting. Photocatalysis ; Porosity ; Pollutant ; Materials science ; Polymer ; Organic polymer ; Nanotechnology ; Water pollutants ; Chemical engineering ; Environmental chemistry ; Chemistry ; Organic chemistry ; Catalysis ; Composite material ; Engineering Covalent Organic Framework Applications ; Advanced Photocatalysis Techniques ; Metal-Organic Frameworks: Synthesis and Applications
Authors: Hualiang Lv | Zhihong Yang | Samuel Jun Hoong Ong | Chao Wei | Hanbin Liao | Shibo Xi | Yonghua Du | Guangbin Ji | Zhichuan J. Xu
Source: Advanced Functional Materials (article, 2019-02-14)
Field: Materials Science — Electronic, Optical and Magnetic Materials

A Flexible Microwave Shield with Tunable Frequency‐Transmission and Electromagnetic Compatibility Abstract Wireless techniques have improved life quality for many. However, the drawbacks like instable signal and high loss in air of electromagnetic interference hinder its further development. One solution is to develop a smart material or device, which can selectively receive a specific frequency ( f s ) of electromagnetic wave with less loss, and simultaneously show effective shielding against unwanted waves (frequency is denoted as f p ). A bottleneck has been reached, such that using materials alone is unable to achieve the above due to the limitation of the intrinsic physical properties of materials. Here, a strategy combining the material structure design with a voltage control is proposed to overcome the limitation of materials toward the aforementioned task. The efforts are focused on exploring a suitable electrically tunable material with a sensitive response to an external voltage and the flexibility to be engineered to the needed macrostructure. As a result, the f s region can be fine‐tuned to 8–8.4, 8–9.3, and 8–10.3 GHz. Materials science ; Electromagnetic shielding ; Electromagnetic interference ; Electromagnetic compatibility ; Microwave ; Wireless ; EMI ; Voltage ; Acoustics ; Radio frequency ; Bottleneck ; Optoelectronics ; Electromagnetic radiation ; Flexibility (engineering) ; Electronic engineering ; Electrical engineering ; Computer science ; Telecommunications ; Optics ; Composite material ; Physics ; Engineering Electromagnetic wave absorption materials ; Advanced Antenna and Metasurface Technologies ; Metamaterials and Metasurfaces Applications
Authors: Shoshana J. Wodak | Emanuele Paci | Nikolay V. Dokholyan | Igor N. Berezovsky | Amnon Horovitz | Jing Li | Vincent J. Hilser | İvet Bahar | John Karanicolas | Gerhard Stock | Peter Hamm | Roland H. Stote | Jérôme Eberhardt | Yassmine Chebaro | Annick Dejaegere | Marco Cecchini | Jean‐Pierre Changeux | Peter G. Bolhuis | Jocelyne Vreede | Pietro Faccioli | Simone Orioli | Riccardo Ravasio | Le Yan | Carolina Brito | Matthieu Wyart | Paraskevi Gkeka | Ivan Rivalta | Giulia Palermo | J. Andrew McCammon | Joanna Panecka-Hofman | Rebecca C. Wade | Antonella Di Pizio | Masha Y. Niv | Ruth Nussinov | Chung‐Jung Tsai | Hyunbum Jang | Dzmitry Padhorny | Dima Kozakov | Tom McLeish
Source: Structure (review, 2019-02-10)
Field: Chemistry — Spectroscopy

Allostery in Its Many Disguises: From Theory to Applications Allosteric regulation ; Chemistry ; Computer science ; Computational biology ; Biochemistry ; Biology ; Receptor Mass Spectrometry Techniques and Applications ; Protein Structure and Dynamics ; Lipid Membrane Structure and Behavior
Authors: Maryam Azizi‐Lalabadi | Ali Ehsani | Baharak Divband | Mahmood Alizadeh Sani
Source: Scientific Reports (article, 2019-11-25)
Field: Materials Science — Materials Chemistry

Antimicrobial activity of Titanium dioxide and Zinc oxide nanoparticles supported in 4A zeolite and evaluation the morphological characteristic In this study, the antimicrobial activity of titanium dioxide (TiO<sub>2</sub>), zinc oxide (ZnO), and TiO<sub>2</sub>/ZnO nanoparticles supported into 4A zeolite (4A z) was assessed. Based on antimicrobial experiments, minimum inhibitory concentration (MIC<sub>90</sub>), minimum bactericidal concentration (MBC), fractional inhibitory concentration (FIC) and disc diffusion test were determined after 24 h of contact with the prepared nanocomposites. These results are in agreements with the results of disc diffusion test. During the experiments, the numbers of viable bacterial cells of Staphylococcus aureus, Pseudomonas fluorescens, Listeria monocytogenes and Escherichia coli O<sub>157</sub>:H<sub>7</sub> decreased significantly. The crystallinity and morphology of nanoparticles were investigated by X-ray diffraction patterns (XRD), elemental mapping at the microstructural level by scanning electron microscopy (SEM) with energy dispersive X-ray spectrometry (EDS), and transmission electron microscopy (TEM). As a result, it was demonstrated that TiO<sub>2</sub>/ZnO nanoparticles supported in 4A zeolite could lead to an optimum activity as antimicrobial agents. Crystallinity ; Zinc ; Nanoparticle ; Titanium dioxide ; Listeria monocytogenes ; Scanning electron microscope ; Pseudomonas fluorescens ; Minimum inhibitory concentration ; Antimicrobial ; Nuclear chemistry ; Materials science ; Chemical engineering ; Chemistry ; Nanotechnology ; Bacteria ; Organic chemistry ; Metallurgy ; Composite material ; Biology Nanoparticles: synthesis and applications ; TiO2 Photocatalysis and Solar Cells ; Advanced Nanomaterials in Catalysis
Authors: Eunha Kim | Heebeom Koo
Source: Chemical Science (review, 2019-01-01)
Field: Chemistry — Organic Chemistry

Biomedical applications of copper-free click chemistry: <i>in vitro</i>, <i>in vivo</i>, and <i>ex vivo</i> Recently, click chemistry has provided important advances in biomedical research fields. Particularly, copper-free click chemistry including strain-promoted azide-alkyne cycloaddition (SPAAC) and inverse-electron-demand Diels-Alder (iEDDA) reactions enable fast and specific chemical conjugation under aqueous conditions without the need for toxic catalysts. Click chemistry has resulted in a change of paradigm, showing that artificial chemical reactions can occur on cell surfaces, in cell cytosol, or within the body, which is not easy with most other chemical reactions. Click chemistry <i>in vitro</i> allows specific labelling of cellular target proteins and studying of drug target engagement with drug surrogates in live cells. Furthermore, cellular membrane lipids and proteins could be selectively labelled with click chemistry <i>in vitro</i> and cells could be adhered together using click chemistry. Click chemistry <i>in vivo</i> enables efficient and effective molecular imaging and drug delivery for diagnosis and therapy. Click chemistry <i>ex vivo</i> can be used to develop molecular tools to understand tissue development, diagnosis of diseases, and therapeutic monitoring. Overall, the results from research to date suggest that click chemistry has emerged as a valuable tool in biomedical fields as well as in organic chemistry. In vivo ; Click chemistry ; Ex vivo ; Copper ; Cytosol ; Chemistry ; In vitro ; Biophysics ; Cell ; Nanotechnology ; Biochemistry ; Combinatorial chemistry ; Materials science ; Biology ; Organic chemistry ; Biotechnology Click Chemistry and Applications ; Monoclonal and Polyclonal Antibodies Research ; Advanced biosensing and bioanalysis techniques
Authors: Andrea Cárdenas‐Arenas | Adrián Quindimil | Arantxa Davó‐Quiñonero | Esther Bailón‐García | Dolores Lozano‐Castelló | Unai De‐La‐Torre | Beñat Pereda‐Ayo | José A. González‐Marcos | Juan R. González‐Velasco | Agustín Bueno‐López
Source: Applied Catalysis B: Environmental (article, 2019-12-23)
Field: Materials Science — Materials Chemistry

Isotopic and in situ DRIFTS study of the CO2 methanation mechanism using Ni/CeO2 and Ni/Al2O3 catalysts Methanation ; Chemisorption ; Dissociation (chemistry) ; Catalysis ; Chemistry ; Inorganic chemistry ; Desorption ; Oxygen ; Non-blocking I/O ; Water-gas shift reaction ; Chemical engineering ; Photochemistry ; Adsorption ; Physical chemistry ; Organic chemistry Catalytic Processes in Materials Science ; Catalysts for Methane Reforming ; Catalysis and Oxidation Reactions
Authors: Evgeny V. Podryabinkin | Evgenii Tikhonov | Alexander V. Shapeev | Artem R. Oganov
Source: Physical review. B./Physical review. B (article, 2019-02-27)
Field: Materials Science — Materials Chemistry

Accelerating crystal structure prediction by machine-learning interatomic potentials with active learning We propose a methodology for crystal structure prediction that is based on the evolutionary algorithm USPEX and the machine-learning interatomic potentials actively learning on-the-fly. Our methodology allows for an automated construction of an interatomic interaction model from scratch, replacing the expensive density functional theory (DFT) and giving a speedup of several orders of magnitude. Predicted low-energy structures are then tested on DFT, ensuring that our machine-learning model does not introduce any prediction error. We tested our methodology on prediction of crystal structures of carbon, high-pressure phases of sodium, and boron allotropes, including those that have more than 100 atoms in the primitive cell. All the the main allotropes have been reproduced, and a hitherto unknown 54-atom structure of boron has been predicted with very modest computational effort. Interatomic potential ; Crystal structure prediction ; Computer science ; Artificial intelligence ; Crystal structure ; Active learning (machine learning) ; Machine learning ; Materials science ; Statistical physics ; Physics ; Crystallography ; Chemistry ; Computational chemistry ; Molecular dynamics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Crystallography and molecular interactions
Authors: Zhenpeng Zhou | Steven Kearnes | Li Li | Richard N. Zare | Patrick Riley
Source: Scientific Reports (article, 2019-07-24)
Field: Materials Science — Materials Chemistry

Optimization of Molecules via Deep Reinforcement Learning We present a framework, which we call Molecule Deep Q-Networks (MolDQN), for molecule optimization by combining domain knowledge of chemistry and state-of-the-art reinforcement learning techniques (double Q-learning and randomized value functions). We directly define modifications on molecules, thereby ensuring 100% chemical validity. Further, we operate without pre-training on any dataset to avoid possible bias from the choice of that set. MolDQN achieves comparable or better performance against several other recently published algorithms for benchmark molecular optimization tasks. However, we also argue that many of these tasks are not representative of real optimization problems in drug discovery. Inspired by problems faced during medicinal chemistry lead optimization, we extend our model with multi-objective reinforcement learning, which maximizes drug-likeness while maintaining similarity to the original molecule. We further show the path through chemical space to achieve optimization for a molecule to understand how the model works. Reinforcement learning ; Chemical space ; Benchmark (surveying) ; Similarity (geometry) ; Path (computing) ; Domain (mathematical analysis) ; Optimization problem ; Space (punctuation) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Xiang Liu | Jean‐René Hamon
Source: Coordination Chemistry Reviews (article, 2019-03-28)
Field: Chemistry — Inorganic Chemistry

Recent developments in penta-, hexa- and heptadentate Schiff base ligands and their metal complexes Denticity ; Chemistry ; Schiff base ; Supramolecular chemistry ; Lanthanide ; Ligand (biochemistry) ; Main group element ; Coordination complex ; Reactivity (psychology) ; Catalysis ; Combinatorial chemistry ; Metal ; HEXA ; Transition metal ; Nanotechnology ; Stereochemistry ; Crystallography ; Molecule ; Organic chemistry ; Materials science ; Ion Metal-Organic Frameworks: Synthesis and Applications ; Metal complexes synthesis and properties ; Magnetism in coordination complexes
Authors: H. Yamada | Chang Liu | Stephen Wu | Yukinori Koyama | Shenghong Ju | Junichiro Shiomi | Junko Morikawa | Ryo Yoshida
Source: ACS Central Science (article, 2019-09-30)
Field: Materials Science — Materials Chemistry

Predicting Materials Properties with Little Data Using Shotgun Transfer Learning There is a growing demand for the use of machine learning (ML) to derive fast-to-evaluate surrogate models of materials properties. In recent years, a broad array of materials property databases have emerged as part of a digital transformation of materials science. However, recent technological advances in ML are not fully exploited because of the insufficient volume and diversity of materials data. An ML framework called "transfer learning" has considerable potential to overcome the problem of limited amounts of materials data. Transfer learning relies on the concept that various property types, such as physical, chemical, electronic, thermodynamic, and mechanical properties, are physically interrelated. For a given target property to be predicted from a limited supply of training data, models of related proxy properties are pretrained using sufficient data; these models capture common features relevant to the target task. Repurposing of such machine-acquired features on the target task yields outstanding prediction performance even with exceedingly small data sets, as if highly experienced human experts can make rational inferences even for considerably less experienced tasks. In this study, to facilitate widespread use of transfer learning, we develop a pretrained model library called XenonPy.MDL. In this first release, the library comprises more than 140 000 pretrained models for various properties of small molecules, polymers, and inorganic crystalline materials. Along with these pretrained models, we describe some outstanding successes of transfer learning in different scenarios such as building models with only dozens of materials data, increasing the ability of extrapolative prediction through a strategic model transfer, and so on. Remarkably, transfer learning has autonomously identified rather nontrivial transferability across different properties transcending the different disciplines of materials science; for example, our analysis has revealed underlying bridges between small molecules and polymers and between organic and inorganic chemistry. Transfer of learning ; Computer science ; Repurposing ; Property (philosophy) ; Machine learning ; Artificial intelligence ; Task (project management) ; Multi-task learning ; Model building Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Jian‐Cheng Lai | Xiaoyong Jia | Da‐Peng Wang | Yibing Deng | Peng Zheng | Cheng‐Hui Li | Jing‐Lin Zuo | Zhenan Bao
Source: Nature Communications (article, 2019-03-11)
Field: Materials Science — Polymers and Plastics

Thermodynamically stable whilst kinetically labile coordination bonds lead to strong and tough self-healing polymers There is often a trade-off between mechanical properties (modulus and toughness) and dynamic self-healing. Here we report the design and synthesis of a polymer containing thermodynamically stable whilst kinetically labile coordination complex to address this conundrum. The Zn-Hbimcp (Hbimcp = 2,6-bis((imino)methyl)-4-chlorophenol) coordination bond used in this work has a relatively large association constant (2.2 × 10<sup>11</sup>) but also undergoes fast and reversible intra- and inter-molecular ligand exchange processes. The as-prepared Zn(Hbimcp)<sub>2</sub>-PDMS polymer is highly stretchable (up to 2400% strain) with a high toughness of 29.3 MJ m<sup>-3</sup>, and can autonomously self-heal at room temperature. Control experiments showed that the optimal combination of its bond strength and bond dynamics is responsible for the material's mechanical toughness and self-healing property. This molecular design concept points out a promising direction for the preparation of self-healing polymers with excellent mechanical properties. We further show this type of polymer can be potentially used as energy absorbing material. Toughness ; Polymer ; Materials science ; Ligand (biochemistry) ; Self-healing material ; Modulus ; Self-healing ; Elastic modulus ; Composite material ; Chemical engineering ; Chemistry Polymer composites and self-healing ; Advanced Sensor and Energy Harvesting Materials ; Electrospun Nanofibers in Biomedical Applications
Authors: Adam J. Rieth | Ashley M. Wright | Mircea Dincă
Source: Nature Reviews Materials (review, 2019-09-13)
Field: Chemistry — Inorganic Chemistry

Kinetic stability of metal–organic frameworks for corrosive and coordinating gas capture Metal-organic framework ; Porosity ; Materials science ; Inert ; Phase (matter) ; Nanotechnology ; Chemical engineering ; Chemistry ; Adsorption ; Organic chemistry ; Composite material Metal-Organic Frameworks: Synthesis and Applications ; Catalytic Processes in Materials Science ; Gas Sensing Nanomaterials and Sensors
Authors: Jack Payne | Paul McKeown | Matthew D. Jones
Source: Polymer Degradation and Stability (article, 2019-05-16)
Field: Materials Science — Biomaterials

A circular economy approach to plastic waste Bioplastic ; Plastic waste ; Circular economy ; Context (archaeology) ; Waste management ; Business ; Environmental science ; Environmental economics ; Engineering ; Economics ; Ecology ; Biology biodegradable polymer synthesis and properties ; Microplastics and Plastic Pollution ; Carbon dioxide utilization in catalysis
Authors: Florian Häse | Loı̈c M. Roch | Alán Aspuru‐Guzik
Source: Trends in Chemistry (article, 2019-03-06)
Field: Materials Science — Materials Chemistry

Next-Generation Experimentation with Self-Driving Laboratories Process (computing) ; Computer science ; Artificial intelligence ; Key (lock) ; Data science ; Computer security Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Innovative Microfluidic and Catalytic Techniques Innovation