jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Atul Dhall | William T. Self
Source: Antioxidants (review, 2018-07-24)
Field: Materials Science — Materials Chemistry

Cerium Oxide Nanoparticles: A Brief Review of Their Synthesis Methods and Biomedical Applications Cerium oxide nanoparticles (CeNPs) exhibit antioxidant properties both in vitro and in vivo. This is due to the self-regeneration of their surface, which is based on redox-cycling between 3+ and 4+ states for cerium, in response to their immediate environment. Additionally, oxygen vacancies in the lattice structure allow for alternating between CeO₂ and CeO<sub>2-<i>x</i></sub> during redox reactions. Research to identify and characterize the biomedical applications of CeNPs has been heavily focused on investigating their use in treating diseases that are characterized by higher levels of reactive oxygen species (ROS). Although the bio-mimetic activities of CeNPs have been extensively studied in vitro, in vivo interactions and associated protein corona formation are not well understood. This review describes: (1) the methods of synthesis for CeNPs, including the recent green synthesis methods that offer enhanced biocompatibility and a need for establishing a reference CeNP material for consistency across studies; (2) their enzyme-mimetic activities, with a focus on their antioxidant activities; and, (3) recent experimental evidence that demonstrates their ROS scavenging abilities and their potential use in personalized medicine. Cerium oxide ; Nanoparticle ; Nanotechnology ; Cerium ; Materials science ; Oxide ; Metallurgy Advanced Nanomaterials in Catalysis ; Electrochemical sensors and biosensors ; Carbon and Quantum Dots Applications
Authors: Olga Barrera | David Bombač | Yi‐Sheng Chen | Thomas D. Daff | E.I. Galindo-Nava | Peng Gong | Daniel Haley | Robert Horton | Ivaylo H. Katzarov | James R. Kermode | C. Liverani | Miles Alexander Stopher | F. Sweeney
Source: Journal of Materials Science (review, 2018-02-06)
Field: Materials Science — Metals and Alloys

Understanding and mitigating hydrogen embrittlement of steels: a review of experimental, modelling and design progress from atomistic to continuum Hydrogen embrittlement is a complex phenomenon, involving several length- and timescales, that affects a large class of metals. It can significantly reduce the ductility and load-bearing capacity and cause cracking and catastrophic brittle failures at stresses below the yield stress of susceptible materials. Despite a large research effort in attempting to understand the mechanisms of failure and in developing potential mitigating solutions, hydrogen embrittlement mechanisms are still not completely understood. There are controversial opinions in the literature regarding the underlying mechanisms and related experimental evidence supporting each of these theories. The aim of this paper is to provide a detailed review up to the current state of the art on the effect of hydrogen on the degradation of metals, with a particular focus on steels. Here, we describe the effect of hydrogen in steels from the atomistic to the continuum scale by reporting theoretical evidence supported by quantum calculation and modern experimental characterisation methods, macroscopic effects that influence the mechanical properties of steels and established damaging mechanisms for the embrittlement of steels. Furthermore, we give an insight into current approaches and new mitigation strategies used to design new steels resistant to hydrogen embrittlement. Hydrogen embrittlement ; Materials science ; Embrittlement ; Brittleness ; Hydrogen ; Cracking ; Ductility (Earth science) ; Metallurgy ; Composite material ; Creep ; Corrosion Hydrogen embrittlement and corrosion behaviors in metals ; Corrosion Behavior and Inhibition ; Nuclear Materials and Properties
Authors: Zifeng Lin | Eider Goikolea | Andrea Balducci | Katsuhiko Naoi | Pierre‐Louis Taberna | Mathieu Salanne | Gleb Yushin | Patrice Simon
Source: Materials Today (article, 2018-02-19)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Materials for supercapacitors: When Li-ion battery power is not enough Supercapacitor ; Pseudocapacitor ; Materials science ; Capacitor ; Nanotechnology ; Battery (electricity) ; Energy storage ; MXenes ; Electrochemistry ; Electrode ; Power (physics) ; Electrical engineering ; Voltage ; Chemistry ; Engineering ; Physics Supercapacitor Materials and Fabrication ; Advancements in Battery Materials ; Electrocatalysts for Energy Conversion
Authors: Vivek K. Bajpai | Madhu Kamle | Shruti Shukla | Dipendra Kumar Mahato | Pranjal Chandra | Seung Kyu Hwang | Pradeep Kumar | Yun Suk Huh | Young‐Kyu Han
Source: Journal of Food and Drug Analysis (review, 2018-07-03)
Field: Materials Science — Materials Chemistry

Prospects of using nanotechnology for food preservation, safety, and security The rapid development of nanotechnology has transformed many domains of food science, especially those that involve the processing, packaging, storage, transportation, functionality, and other safety aspects of food. A wide range of nanostructured materials (NSMs), from inorganic metal, metal oxides, and their nanocomposites to nano-organic materials with bioactive agents, has been applied to the food industry. Despite the huge benefits nanotechnology has to offer, there are emerging concerns regarding the use of nanotechnology, as the accumulation of NSMs in human bodies and in the environment can cause several health and safety hazards. Therefore, safety and health concerns as well as regulatory policies must be considered while manufacturing, processing, intelligently and actively packaging, and consuming nano-processed food products. This review aims to provide a basic understanding regarding the applications of nanotechnology in the food packaging and processing industries and to identify the future prospects and potential risks associated with the use of NSMs. Food safety ; Nanotechnology ; Food security ; Business ; Chemistry ; Food science ; Biology ; Materials science ; Agriculture Nanoparticles: synthesis and applications ; Nanotechnology research and applications ; Chemistry and Chemical Engineering
Authors: Kristof T. Schütt | Pan Kessel | Michael Gastegger | Kim A. Nicoli | Alexandre Tkatchenko | K. Müller
Source: Journal of Chemical Theory and Computation (article, 2018-11-27)
Field: Materials Science — Materials Chemistry

SchNetPack: A Deep Learning Toolbox For Atomistic Systems SchNetPack is a toolbox for the development and application of deep neural networks that predict potential energy surfaces and other quantum-chemical properties of molecules and materials. It contains basic building blocks of atomistic neural networks, manages their training, and provides simple access to common benchmark datasets. This allows for an easy implementation and evaluation of new models. For now, SchNetPack includes implementations of (weighted) atom-centered symmetry functions and the deep tensor neural network SchNet, as well as ready-to-use scripts that allow one to train these models on molecule and material datasets. Based on the PyTorch deep learning framework, SchNetPack allows one to efficiently apply the neural networks to large datasets with millions of reference calculations, as well as parallelize the model across multiple GPUs. Finally, SchNetPack provides an interface to the Atomic Simulation Environment in order to make trained models easily accessible to researchers that are not yet familiar with neural networks. Computer science ; Toolbox ; Benchmark (surveying) ; Deep learning ; Artificial neural network ; Artificial intelligence ; Interface (matter) ; Deep neural networks ; Scripting language ; Machine learning ; Simple (philosophy) ; Parallel computing ; Programming language Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: Ya Zhuo | Aria Mansouri Tehrani | Jakoah Brgoch
Source: The Journal of Physical Chemistry Letters (article, 2018-03-13)
Field: Materials Science — Materials Chemistry

Predicting the Band Gaps of Inorganic Solids by Machine Learning A machine-learning model is developed that can accurately predict the band gap of inorganic solids based only on composition. This method uses support vector classification to first separate metals from nonmetals, followed by quantitatively predicting the band gap of the nonmetals using support vector regression. The superb accuracy of the regression model is obtained by using a training set composed entirely of experimentally measured band gaps and utilizing only compositional descriptors. In fact, because of the unique training set of experimental data, the machine learning predicted band gaps are significantly closer to the experimentally reported values than DFT (PBE-level) calculated band gaps. Not only does this resulting tool provide the ability to accurately predict the band gap for any composition but also the versatility and speed of the prediction based only on composition will make this a great resource to screen inorganic phase space and direct the development of functional inorganic materials. Band gap ; Support vector machine ; Machine learning ; Artificial intelligence ; Regression ; Set (abstract data type) ; Computer science ; Regression analysis ; Training set ; Materials science ; Algorithm ; Mathematics ; Statistics ; Optoelectronics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Electronic and Structural Properties of Oxides
Authors: Hanyu Gao | Thomas J. Struble | Connor W. Coley | Yuran Wang | William H. Green | Klavs F. Jensen
Source: ACS Central Science (article, 2018-11-16)
Field: Materials Science — Materials Chemistry

Using Machine Learning To Predict Suitable Conditions for Organic Reactions Reaction condition recommendation is an essential element for the realization of computer-assisted synthetic planning. Accurate suggestions of reaction conditions are required for experimental validation and can have a significant effect on the success or failure of an attempted transformation. However, de novo condition recommendation remains a challenging and under-explored problem and relies heavily on chemists' knowledge and experience. In this work, we develop a neural-network model to predict the chemical context (catalyst(s), solvent(s), reagent(s)), as well as the temperature most suitable for any particular organic reaction. Trained on ∼10 million examples from Reaxys, the model is able to propose conditions where a close match to the recorded catalyst, solvent, and reagent is found within the top-10 predictions 69.6% of the time, with top-10 accuracies for individual species reaching 80-90%. Temperature is accurately predicted within ±20 °C from the recorded temperature in 60-70% of test cases, with higher accuracy for cases with correct chemical context predictions. The utility of the model is illustrated through several examples spanning a range of common reaction classes. We also demonstrate that the model implicitly learns a continuous numerical embedding of solvent and reagent species that captures their functional similarity. Reagent ; Context (archaeology) ; Embedding ; Computer science ; Realization (probability) ; Artificial neural network ; Similarity (geometry) ; Solvent ; Transformation (genetics) ; Artificial intelligence ; Organic solvent ; Machine learning ; Biochemical engineering ; Biological system ; Chemistry ; Organic chemistry ; Chemical engineering ; Mathematics ; Engineering ; Geology Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemical Synthesis and Analysis
Authors: Seyed Majid Azimi | Dominik Britz | Michael Engstler | Mario Fritz | Frank Mücklich
Source: Scientific Reports (article, 2018-01-26)
Field: Materials Science — Materials Chemistry

Advanced Steel Microstructural Classification by Deep Learning Methods The inner structure of a material is called microstructure. It stores the genesis of a material and determines all its physical and chemical properties. While microstructural characterization is widely spread and well known, the microstructural classification is mostly done manually by human experts, which gives rise to uncertainties due to subjectivity. Since the microstructure could be a combination of different phases or constituents with complex substructures its automatic classification is very challenging and only a few prior studies exist. Prior works focused on designed and engineered features by experts and classified microstructures separately from the feature extraction step. Recently, Deep Learning methods have shown strong performance in vision applications by learning the features from data together with the classification step. In this work, we propose a Deep Learning method for microstructural classification in the examples of certain microstructural constituents of low carbon steel. This novel method employs pixel-wise segmentation via Fully Convolutional Neural Network (FCNN) accompanied by a max-voting scheme. Our system achieves 93.94% classification accuracy, drastically outperforming the state-of-the-art method of 48.89% accuracy. Beyond the strong performance of our method, this line of research offers a more robust and first of all objective way for the difficult task of steel quality appreciation. Deep learning ; Convolutional neural network ; Segmentation ; Artificial neural network ; Pattern recognition (psychology) ; Feature extraction ; Feature (linguistics) ; Task (project management) Machine Learning in Materials Science ; Advanced Neural Network Applications ; Big Data and Digital Economy
Authors: Nicolai Bache | Philipp E. Geyer | Dorte B. Bekker‐Jensen | Ole Hoerning | Lasse Gaarde Falkenby | Peter V. Treit | Sophia Doll | Igor Paron | Johannes Müller | Florian Meier | Jesper V. Olsen | Ole Vorm | Matthias Mann
Source: Molecular & Cellular Proteomics (article, 2018-08-13)
Field: Chemistry — Spectroscopy

A Novel LC System Embeds Analytes in Pre-formed Gradients for Rapid, Ultra-robust Proteomics To further integrate mass spectrometry (MS)-based proteomics into biomedical research and especially into clinical settings, high throughput and robustness are essential requirements. They are largely met in high-flow rate chromatographic systems for small molecules but these are not sufficiently sensitive for proteomics applications. Here we describe a new concept that delivers on these requirements while maintaining the sensitivity of current nano-flow LC systems. Low-pressure pumps elute the sample from a disposable trap column, simultaneously forming a chromatographic gradient that is stored in a long storage loop. An auxiliary gradient creates an offset, ensuring the re-focusing of the peptides before the separation on the analytical column by a single high-pressure pump. This simplified design enables robust operation over thousands of sample injections. Furthermore, the steps between injections are performed in parallel, reducing overhead time to a few minutes and allowing analysis of more than 200 samples per day. From fractionated HeLa cell lysates, deep proteomes covering more than 130,000 sequence unique peptides and close to 10,000 proteins were rapidly acquired. Using this data as a library, we demonstrate quantitation of 5200 proteins in only 21 min. Thus, the new system - termed Evosep One - analyzes samples in an extremely robust and high throughput manner, without sacrificing in depth proteomics coverage. Analyte ; Proteomics ; Chemistry ; Chromatography ; Computational biology ; Biology ; Biochemistry Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Glycosylation and Glycoproteins Research
Authors: Zilan Li | Zechao Zhuang | Fan Lv | Han Zhu | Liang Zhou | Mingchuan Luo | Jiexin Zhu | Zhiquan Lang | Shihao Feng | Wei Chen | Liqiang Mai | Shaojun Guo
Source: Advanced Materials (article, 2018-09-10)
Field: Materials Science — Materials Chemistry

The Marriage of the FeN<sub>4</sub> Moiety and MXene Boosts Oxygen Reduction Catalysis: Fe 3d Electron Delocalization Matters Iron-nitrogen-carbon (Fe-N-C) is hitherto considered as one of the most satisfactory alternatives to platinum for the oxygen reduction reaction (ORR). Major efforts currently are devoted to the identification and maximization of carbon-enclosed FeN<sub>4</sub> moieties, which act as catalytically active centers. However, fine-tuning of their intrinsic ORR activity remains a huge challenge. Herein, a twofold activity improvement of pristine Fe-N-C through introducing Ti<sub>3</sub> C<sub>2</sub> T<sub>x</sub> MXene as a support is realized. A series of spectroscopy and magnetic measurements reveal that the marriage of FeN<sub>4</sub> moiety and MXene can induce remarkable Fe 3d electron delocalization and spin-state transition of Fe(II) ions. The lower local electron density and higher spin state of the Fe(II) centers greatly favor the Fe <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow><mml:msub><mml:mi>d</mml:mi> <mml:mrow><mml:msup><mml:mi>z</mml:mi> <mml:mn>2</mml:mn></mml:msup> </mml:mrow> </mml:msub> </mml:mrow> </mml:math> electron transfer, and lead to an easier oxygen adsorption and reduction on active FeN<sub>4</sub> sites, and thus an enhanced ORR activity. The optimized catalyst shows a two- and fivefold higher specific ORR activity than those of pristine catalyst and Pt/C, respectively, even exceeding most Fe-N-C catalysts ever reported. This work opens up a new pathway in the rational design of Fe-N-C catalysts, and reflects the critical influence of Fe 3d electron states in FeN<sub>4</sub> moiety supported on MXene in ORR catalysis. Moiety ; Delocalized electron ; Materials science ; Catalysis ; Electron ; Oxygen ; Electron delocalization ; Oxygen reduction reaction ; Reduction (mathematics) ; Photochemistry ; Nanotechnology ; Stereochemistry ; Organic chemistry ; Physical chemistry ; Chemistry ; Physics ; Quantum mechanics MXene and MAX Phase Materials ; Electrocatalysts for Energy Conversion ; Advanced Photocatalysis Techniques
Authors: Rossana Boni | Azam Ali | Amin Shavandi | Andrew N. Clarkson
Source: Journal of Biomedical Science (review, 2018-12-01)
Field: Materials Science — Biomaterials

Current and novel polymeric biomaterials for neural tissue engineering The nervous system is a crucial component of the body and damages to this system, either by of injury or disease, can result in serious or potentially lethal consequences. Restoring the damaged nervous system is a great challenge due to the complex physiology system and limited regenerative capacity.Polymers, either synthetic or natural in origin, have been extensively evaluated as a solution for restoring functions in damaged neural tissues. Polymers offer a wide range of versatility, in particular regarding shape and mechanical characteristics, and their biocompatibility is unmatched by other biomaterials, such as metals and ceramics. Several studies have shown that polymers can be shaped into suitable support structures, including nerve conduits, scaffolds, and electrospun matrices, capable of improving the regeneration of damaged neural tissues. In general, natural polymers offer the advantage of better biocompatibility and bioactivity, while synthetic or non-natural polymers have better mechanical properties and structural stability. Often, combinations of the two allow for the development of polymeric conduits able to mimic the native physiological environment of healthy neural tissues and, consequently, regulate cell behaviour and support the regeneration of injured nervous tissues.Currently, most of neural tissue engineering applications are in pre-clinical study, in particular for use in the central nervous system, however collagen polymer conduits aimed at regeneration of peripheral nerves have already been successfully tested in clinical trials.This review highlights different types of natural and synthetic polymers used in neural tissue engineering and their advantages and disadvantages for neural regeneration. Tissue engineering ; Neural tissue engineering ; Biomedical engineering ; Neural engineering ; Nanotechnology ; Materials science ; Computer science ; Engineering ; Artificial intelligence Electrospun Nanofibers in Biomedical Applications ; Graphene and Nanomaterials Applications ; 3D Printing in Biomedical Research
Authors: Bryan R. Goldsmith | Jacques A. Esterhuizen | Jin‐Xun Liu | Christopher J. Bartel | Christopher Sutton
Source: AIChE Journal (article, 2018-05-07)
Field: Materials Science — Materials Chemistry

Machine learning for heterogeneous catalyst design and discovery Peer Reviewed DEPT ; Library science ; Chemistry ; Computer science Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Catalysis and Hydrodesulfurization Studies
Authors: Valentin Stanev | Corey Oses | A. Gilad Kusne | Efrain Rodriguez | Johnpierre Paglione | Stefano Curtarolo | Ichiro Takeuchi
Source: npj Computational Materials (article, 2018-06-22)
Field: Materials Science — Materials Chemistry

Machine learning modeling of superconducting critical temperature Abstract Superconductivity has been the focus of enormous research effort since its discovery more than a century ago. Yet, some features of this unique phenomenon remain poorly understood; prime among these is the connection between superconductivity and chemical/structural properties of materials. To bridge the gap, several machine learning schemes are developed herein to model the critical temperatures ( T c ) of the 12,000+ known superconductors available via the SuperCon database. Materials are first divided into two classes based on their T c values, above and below 10 K, and a classification model predicting this label is trained. The model uses coarse-grained features based only on the chemical compositions. It shows strong predictive power, with out-of-sample accuracy of about 92%. Separate regression models are developed to predict the values of T c for cuprate, iron-based, and low- T c compounds. These models also demonstrate good performance, with learned predictors offering potential insights into the mechanisms behind superconductivity in different families of materials. To improve the accuracy and interpretability of these models, new features are incorporated using materials data from the AFLOW Online Repositories. Finally, the classification and regression models are combined into a single-integrated pipeline and employed to search the entire Inorganic Crystallographic Structure Database (ICSD) for potential new superconductors. We identify &gt;30 non-cuprate and non-iron-based oxides as candidate materials. Interpretability ; Pipeline (software) ; Identification (biology) ; Superconductivity ; Regression ; Predictive modelling ; High-temperature superconductivity ; Regression analysis Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Inorganic Chemistry and Materials