jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Eduardo Pérez‐Botella | Susana Valencia | Fernando Rey
Source: Chemical Reviews (review, 2022-10-19)
Field: Chemistry — Inorganic Chemistry

Zeolites in Adsorption Processes: State of the Art and Future Prospects Zeolites have been widely used as catalysts, ion exchangers, and adsorbents since their industrial breakthrough in the 1950s and continue to be state-of the-art adsorbents in many separation processes. Furthermore, their properties make them materials of choice for developing and emerging separation applications. The aim of this review is to put into context the relevance of zeolites and their use and prospects in adsorption technology. It has been divided into three different sections, i.e., zeolites, adsorption on nanoporous materials, and chemical separations by zeolites. In the first section, zeolites are explained in terms of their structure, composition, preparation, and properties, and a brief review of their applications is given. In the second section, the fundamentals of adsorption science are presented, with special attention to its industrial application and our case of interest, which is adsorption on zeolites. Finally, the state-of-the-art relevant separations related to chemical and energy production, in which zeolites have a practical or potential applicability, are presented. The replacement of some of the current separation methods by optimized adsorption processes using zeolites could mean an improvement in terms of sustainability and energy savings. Different separation mechanisms and the underlying adsorption properties that make zeolites interesting for these applications are discussed. Adsorption ; Nanoporous ; Chemistry ; Context (archaeology) ; Zeolite ; Ion exchange ; Catalysis ; Nanotechnology ; Process engineering ; Chemical engineering ; Ion ; Organic chemistry ; Materials science ; Engineering Zeolite Catalysis and Synthesis ; Membrane Separation and Gas Transport ; Mesoporous Materials and Catalysis
Authors: Chi Chen | Shyue Ping Ong
Source: Nature Computational Science (article, 2022-11-28)
Field: Materials Science — Materials Chemistry

A universal graph deep learning interatomic potential for the periodic table Computer science ; Graph ; Periodic table ; Interatomic potential ; Molecular dynamics ; Computational science ; Relaxation (psychology) ; Statistical physics ; Theoretical computer science ; Computational chemistry ; Physics ; Chemistry ; Quantum mechanics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: Hualiang Lv | Zhihong Yang | Hongge Pan | Renbing Wu
Source: Progress in Materials Science (article, 2022-03-17)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Electromagnetic absorption materials: Current progress and new frontiers Absorption (acoustics) ; Materials science ; Nanotechnology ; Current (fluid) ; Biochemical engineering ; Engineering physics ; Computer science ; Process engineering ; Engineering ; Electrical engineering ; Composite material Electromagnetic wave absorption materials ; Advanced Antenna and Metasurface Technologies ; Metamaterials and Metasurfaces Applications
Authors: Grzegorz Greczyński | Lars Hultman
Source: Journal of Applied Physics (article, 2022-07-01)
Field: Materials Science — Surfaces, Coatings and Films

A step-by-step guide to perform x-ray photoelectron spectroscopy There is a growing concern within the surface science community that the massive increase in the number of XPS articles over the last few decades is accompanied by a decrease in work quality including in many cases meaningless chemical bond assignment. Should this trend continue, it would have disastrous consequences for scientific research. While there are many factors responsible for this situation, the lack of insight of physical principles combined with seeming ease of XPS operation and insufficient training are certainly the major ones. To counter that, we offer a comprehensive tutorial written in the form of a step-by-step guide starting from experimental planning, through sample selection and handling, instrument setup, data acquisition, spectra analysis, and results presentation. Six application examples highlight the broad range of research questions that can be answered by XPS. The topic selection and the discussion level are intended to be accessible for novices yet challenging possible preconceptions of experienced practitioners. The analyses of thin film samples are chosen for model cases as this is from where the bulk of XPS reports presently emanate and also where the author's key expertise lies. At the same time, the majority of discussed topics is applicable to surface science in general and is, thus, of relevance for the analyses of any type of sample and material class. The tutorial contains ca. 160 original spectra and over 290 references for further reading. Particular attention is paid to the correct workflow, development of good research practices, and solid knowledge of factors that impact the quality and reliability of the obtained information. What matters in the end is that the conclusions from the analysis can be trusted. Our aspiration is that after reading this tutorial each practitioner will be able to perform error-free data analysis and draw meaningful insights from the rich well of XPS. X-ray photoelectron spectroscopy ; Workflow ; Relevance (law) ; Computer science ; Sample (material) ; Presentation (obstetrics) ; Reading (process) ; Quality (philosophy) ; Data science ; Chemistry ; Medicine ; Engineering ; Physics ; Political science Electron and X-Ray Spectroscopy Techniques ; Electronic and Structural Properties of Oxides ; Semiconductor materials and devices
Authors: Patrick Reiser | Marlen Neubert | André Eberhard | Luca Torresi | Chen Zhou | Chen Shao | Houssam Metni | Clint van Hoesel | Henrik Schopmans | Timo Sommer | Pascal Friederich
Source: Communications Materials (review, 2022-11-26)
Field: Materials Science — Materials Chemistry

Graph neural networks for materials science and chemistry Machine learning plays an increasingly important role in many areas of chemistry and materials science, being used to predict materials properties, accelerate simulations, design new structures, and predict synthesis routes of new materials. Graph neural networks (GNNs) are one of the fastest growing classes of machine learning models. They are of particular relevance for chemistry and materials science, as they directly work on a graph or structural representation of molecules and materials and therefore have full access to all relevant information required to characterize materials. In this Review, we provide an overview of the basic principles of GNNs, widely used datasets, and state-of-the-art architectures, followed by a discussion of a wide range of recent applications of GNNs in chemistry and materials science, and concluding with a road-map for the further development and application of GNNs. Computer science ; Graph ; Relevance (law) ; Representation (politics) ; Data science ; Cheminformatics ; Artificial intelligence ; Theoretical computer science ; Chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Asif Ali | Yi Wai Chiang | Rafael M. Santos
Source: Minerals (review, 2022-02-06)
Field: Materials Science — Materials Chemistry

X-ray Diffraction Techniques for Mineral Characterization: A Review for Engineers of the Fundamentals, Applications, and Research Directions X-ray diffraction (XRD) is an important and widely used material characterization technique. With the recent development in material science technology and understanding, various new materials are being developed, which requires upgrading the existing analytical techniques such that emerging intricate problems can be solved. Although XRD is a well-established non-destructive technique, it still requires further improvements in its characterization capabilities, especially when dealing with complex mineral structures. The present review conducts comprehensive discussions on atomic crystal structure, XRD principle, its applications, uncertainty during XRD analysis, and required safety precautions. The future research directions, especially the use of artificial intelligence and machine learning tools, for improving the effectiveness and accuracy of the XRD technique, are discussed for mineral characterization. The topics covered include how XRD patterns can be utilized for a thorough understanding of the crystalline structure, size, and orientation, dislocation density, phase identification, quantification, and transformation, information about lattice parameters, residual stress, and strain, and thermal expansion coefficient of materials. All these important discussions on XRD analysis for mineral characterization are compiled in this comprehensive review, so that it can benefit specialists and engineers in the chemical, mining, iron, metallurgy, and steel industries. Characterization (materials science) ; Materials science ; Residual stress ; Diffraction ; Computer science ; Nanotechnology ; Metallurgy ; Physics X-ray Diffraction in Crystallography ; Mineral Processing and Grinding ; Metal Extraction and Bioleaching
Authors: Mustafa Ergin Şahın | Frede Blaabjerg | Ariya Sangwongwanich
Source: Energies (review, 2022-01-18)
Field: Materials Science — Electronic, Optical and Magnetic Materials

A Comprehensive Review on Supercapacitor Applications and Developments The storage of enormous energies is a significant challenge for electrical generation. Researchers have studied energy storage methods and increased efficiency for many years. In recent years, researchers have been exploring new materials and techniques to store more significant amounts of energy more efficiently. In particular, renewable energy sources and electric vehicle technologies are triggering these scientific studies. Scientists and manufacturers recently proposed the supercapacitor (SC) as an alternating or hybrid storage device. This paper aims to provide a comprehensive review of SC applications and their developments. Accordingly, a detailed literature review was first carried out. The historical results of SCs are revealed in this paper. The structure, working principle, and materials of SC are given in detail to be analysed more effectively. The advantages and disadvantages, market profile, and new technologies with manufacturer corporations are investigated to produce a techno-economic analysis of SCs. The electric vehicle, power systems, hybrid energy storage systems with integration of renewable energy sources, and other applications of SCs are investigated in this paper. Additionally, SC modelling design principles with charge and discharge tests are explored. Other components and their price to produce a compact module for high power density are also investigated. Supercapacitor ; Chemistry ; Capacitance Supercapacitor Materials and Fabrication ; Advancements in Battery Materials ; Advanced Battery Technologies Research
Authors: Jireh Yi-Le Chan | Steven Mun Hong Leow | Khean Thye Bea | Wai Khuen Cheng | Seuk Wai Phoong | Zeng‐Wei Hong | Yen‐Lin Chen
Source: Mathematics (review, 2022-04-12)
Field: Chemistry — Analytical Chemistry

Mitigating the Multicollinearity Problem and Its Machine Learning Approach: A Review Technologies have driven big data collection across many fields, such as genomics and business intelligence. This results in a significant increase in variables and data points (observations) collected and stored. Although this presents opportunities to better model the relationship between predictors and the response variables, this also causes serious problems during data analysis, one of which is the multicollinearity problem. The two main approaches used to mitigate multicollinearity are variable selection methods and modified estimator methods. However, variable selection methods may negate efforts to collect more data as new data may eventually be dropped from modeling, while recent studies suggest that optimization approaches via machine learning handle data with multicollinearity better than statistical estimators. Therefore, this study details the chronological developments to mitigate the effects of multicollinearity and up-to-date recommendations to better mitigate multicollinearity. Multicollinearity ; Variance inflation factor ; Estimator ; Computer science ; Feature selection ; Variable (mathematics) ; Econometrics ; Machine learning ; Big data ; Variables ; Statistics ; Data mining ; Data science ; Regression analysis ; Mathematics Spectroscopy and Chemometric Analyses ; Advanced Statistical Methods and Models ; Computational Drug Discovery Methods
Authors: Dhriti Nepal | Saewon Kang | Katarina Adstedt | Krishan Kanhaiya | Michael R. Bockstaller | L. Catherine Brinson | Markus J. Buehler | Peter V. Coveney | Kaushik Dayal | Jaafar A. El‐Awady | Luke C. Henderson | David L. Kaplan | Sinan Keten | Nicholas A. Kotov | George C. Schatz | Silvia Vignolini | Fritz Vollrath | Yusu Wang | Boris I. Yakobson | Vladimir V. Tsukruk | Hendrik Heinz
Source: Nature Materials (review, 2022-11-28)
Field: Materials Science — Biomaterials

Hierarchically structured bioinspired nanocomposites Robustness (evolution) ; Nanotechnology ; Biomimetics ; Computer science ; Cornerstone ; Materials science ; Characterization (materials science) ; Nanocomposite ; Multidisciplinary approach ; Biochemical engineering ; Engineering Calcium Carbonate Crystallization and Inhibition ; Bone Tissue Engineering Materials ; Advanced Materials and Mechanics
Authors: Max C. Lemme | Deji Akinwande | Cedric Huyghebaert | Christoph Stampfer
Source: Nature Communications (article, 2022-03-16)
Field: Materials Science — Materials Chemistry

2D materials for future heterogeneous electronics Graphene and two-dimensional materials (2DM) remain an active field of research in science and engineering over 15 years after the first reports of 2DM. The vast amount of available data and the high performance of device demonstrators leave little doubt about the potential of 2DM for applications in electronics, photonics and sensing. So where are the integrated chips and enabled products? We try to answer this by summarizing the main challenges and opportunities that have thus far prevented 2DM applications. Electronics ; Computer science ; Nanotechnology ; Graphene ; Photonics ; Data science ; Engineering ; Physics ; Materials science ; Electrical engineering ; Optoelectronics 2D Materials and Applications ; Graphene research and applications ; Advanced Memory and Neural Computing
Authors: Andreas‐David Brunner | Marvin Thielert | Catherine G. Vasilopoulou | Constantin Ammar | Fabian Coscia | Andreas Mund | Ole Hoerning | Nicolai Bache | Amalia Apalategui | Markus Lubeck | Sabrina Richter | David S. Fischer | Oliver Raether | Melvin A. Park | Florian Meier | Fabian J. Theis | Matthias Mann
Source: Molecular Systems Biology (article, 2022-02-28)
Field: Chemistry — Spectroscopy

Ultra‐high sensitivity mass spectrometry quantifies single‐cell proteome changes upon perturbation Single-cell technologies are revolutionizing biology but are today mainly limited to imaging and deep sequencing. However, proteins are the main drivers of cellular function and in-depth characterization of individual cells by mass spectrometry (MS)-based proteomics would thus be highly valuable and complementary. Here, we develop a robust workflow combining miniaturized sample preparation, very low flow-rate chromatography, and a novel trapped ion mobility mass spectrometer, resulting in a more than 10-fold improved sensitivity. We precisely and robustly quantify proteomes and their changes in single, FACS-isolated cells. Arresting cells at defined stages of the cell cycle by drug treatment retrieves expected key regulators. Furthermore, it highlights potential novel ones and allows cell phase prediction. Comparing the variability in more than 430 single-cell proteomes to transcriptome data revealed a stable-core proteome despite perturbation, while the transcriptome appears stochastic. Our technology can readily be applied to ultra-high sensitivity analyses of tissue material, posttranslational modifications, and small molecule studies from small cell counts to gain unprecedented insights into cellular heterogeneity in health and disease. Proteome ; Biology ; Proteomics ; Transcriptome ; Mass spectrometry ; Computational biology ; Single-cell analysis ; Systems biology ; Cell ; Cell biology ; Bioinformatics ; Biophysics ; Biochemistry ; Chemistry ; Chromatography ; Gene ; Gene expression Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Single-cell and spatial transcriptomics
Authors: Zhijie Chen | Kent O. Kirlikovali | Karam B. Idrees | Megan C. Wasson | Omar K. Farha
Source: Chem (article, 2022-02-10)
Field: Chemistry — Inorganic Chemistry

Porous materials for hydrogen storage Hydrogen storage ; Commercialization ; Materials science ; Nanotechnology ; Porosity ; Hydrogen ; Porous medium ; Metal-organic framework ; Gravimetric analysis ; Process engineering ; Chemistry ; Engineering ; Composite material ; Organic chemistry ; Business Metal-Organic Frameworks: Synthesis and Applications ; Covalent Organic Framework Applications ; Membrane Separation and Gas Transport
Authors: Zhonglei Ma | Xiaolian Xiang | Liang Shao | Yali Zhang | Junwei Gu
Source: Angewandte Chemie International Edition (article, 2022-02-05)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Multifunctional Wearable Silver Nanowire Decorated Leather Nanocomposites for Joule Heating, Electromagnetic Interference Shielding and Piezoresistive Sensing Multifunctional wearable electronic devices based on natural materials are highly desirable for versatile applications of energy conversion, electronic skin and artificial intelligence. Herein, multifunctional wearable silver nanowire decorated leather (AgNW/leather) nanocomposites with hierarchical structures for integrated visual Joule heating, electromagnetic interference (EMI) shielding and piezoresistive sensing are fabricated via the facile vacuum-assisted filtration process. The AgNWs penetrate the micro-nanoporous structures in the corium side of leather constructing highly-efficient conductive networks. The resultant flexible and mechanically strong AgNW/leather nanocomposites exhibit extremely low sheet resistance of 0.8 Ω/sq, superior visual Joule heating temperatures up to 108 °C at low supplied voltage of 2.0 V due to efficient energy conversion, excellent EMI shielding effectiveness (EMI SE) of ≈55 dB and outstanding piezoresistive sensing ability in human motion detection. This work demonstrates the fabrication of multifunctional AgNW/leather nanocomposites for next-generation wearable electronic devices in energy conversion, electronic skin and artificial intelligence, etc. Materials science ; Piezoresistive effect ; Electromagnetic interference ; Electromagnetic shielding ; Nanocomposite ; Joule heating ; EMI ; Nanotechnology ; Electrical conductor ; Optoelectronics ; Electronics ; Wearable computer ; Fabrication ; Triboelectric effect ; Composite material ; Electrical engineering ; Computer science Electromagnetic wave absorption materials ; Advanced Sensor and Energy Harvesting Materials ; Nanomaterials and Printing Technologies
Authors: Haoxin Mai | Tu C. Le | Dehong Chen | David A. Winkler | Rachel A. Caruso
Source: Chemical Reviews (review, 2022-07-21)
Field: Materials Science — Materials Chemistry

Machine Learning for Electrocatalyst and Photocatalyst Design and Discovery Electrocatalysts and photocatalysts are key to a sustainable future, generating clean fuels, reducing the impact of global warming, and providing solutions to environmental pollution. Improved processes for catalyst design and a better understanding of electro/photocatalytic processes are essential for improving catalyst effectiveness. Recent advances in data science and artificial intelligence have great potential to accelerate electrocatalysis and photocatalysis research, particularly the rapid exploration of large materials chemistry spaces through machine learning. Here a comprehensive introduction to, and critical review of, machine learning techniques used in electrocatalysis and photocatalysis research are provided. Sources of electro/photocatalyst data and current approaches to representing these materials by mathematical features are described, the most commonly used machine learning methods summarized, and the quality and utility of electro/photocatalyst models evaluated. Illustrations of how machine learning models are applied to novel electro/photocatalyst discovery and used to elucidate electrocatalytic or photocatalytic reaction mechanisms are provided. The review offers a guide for materials scientists on the selection of machine learning methods for electrocatalysis and photocatalysis research. The application of machine learning to catalysis science represents a paradigm shift in the way advanced, next-generation catalysts will be designed and synthesized. Photocatalysis ; Electrocatalyst ; Nanotechnology ; Chemistry ; Biochemical engineering ; Computer science ; Catalysis ; Materials science ; Engineering ; Electrochemistry Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Advanced Photocatalysis Techniques
Authors: Zhenpeng Yao | Yanwei Lum | Andrew Johnston | L.M. Mejía-Mendoza | Xin Zhou | Yonggang Wen | Alán Aspuru‐Guzik | Edward H. Sargent | Zhi Wei Seh
Source: Nature Reviews Materials (review, 2022-10-18)
Field: Materials Science — Materials Chemistry

Machine learning for a sustainable energy future Renewable energy ; Computer science ; Photovoltaics ; Energy engineering ; Energy management ; Workflow ; Fossil fuel ; Energy storage ; Efficient energy use ; Systems engineering ; Energy (signal processing) ; Risk analysis (engineering) ; Environmental economics ; Photovoltaic system ; Engineering ; Business ; Database ; Electrical engineering Machine Learning in Materials Science ; Fuel Cells and Related Materials ; CO2 Reduction Techniques and Catalysts
Authors: María Vallet‐Regí | Ferdi Schüth | Daniel Lozano | Montserrat Colilla | Miguel Manzano
Source: Chemical Society Reviews (review, 2022-01-01)
Field: Materials Science — Materials Chemistry

Engineering mesoporous silica nanoparticles for drug delivery: where are we after two decades? The present review details a chronological description of the events that took place during the development of mesoporous materials, their different synthetic routes and their use as drug delivery systems. The outstanding textural properties of these materials quickly inspired their translation to the nanoscale dimension leading to mesoporous silica nanoparticles (MSNs). The different aspects of introducing pharmaceutical agents into the pores of these nanocarriers, together with their possible biodistribution and clearance routes, would be described here. The development of smart nanocarriers that are able to release a high local concentration of the therapeutic cargo on-demand after the application of certain stimuli would be reviewed here, together with their ability to deliver the therapeutic cargo to precise locations in the body. The huge progress in the design and development of MSNs for biomedical applications, including the potential treatment of different diseases, during the last 20 years will be collated here, together with the required work that still needs to be done to achieve the clinical translation of these materials. This review was conceived to stand out from past reports since it aims to tell the story of the development of mesoporous materials and their use as drug delivery systems by some of the story makers, who could be considered to be among the pioneers in this area. Mesoporous silica ; Drug delivery ; Nanoparticle ; Nanotechnology ; Drug ; Mesoporous material ; Chemistry ; Materials science ; Pharmacology ; Medicine ; Organic chemistry ; Catalysis Mesoporous Materials and Catalysis ; Nanoparticle-Based Drug Delivery ; Aerogels and thermal insulation