jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Robin F. Weitkamp | Beate Neumann | Hans‐Georg Stammler | Berthold Hoge
Source: Chemistry - A European Journal (review, 2021-05-25)
Field: Chemistry — Inorganic Chemistry

Phosphorus‐Containing Superbases: Recent Progress in the Chemistry of Electron‐Abundant Phosphines and Phosphazenes The renaissance of Brønsted superbases is primarily based on their pronounced capacity for a large variety of chemical transformations under mild reaction conditions. Four major set screws are available for the selective tuning of the basicity: the nature of the basic center (N, P, …), the degree of electron donation by substituents to the central atom, the possibility of charge delocalization, and the energy gain by hydrogen bonding. Within the past decades, a plethora of neutral electron-rich phosphine and phosphazene bases have appeared in the literature. Their outstanding properties and advantages over inorganic or charged bases have now made them indispensable as auxiliary bases in deprotonation processes. Herein, an update of the chemistry of basic phosphines and phosphazenes is given. In addition, due to widespread interest, their use in catalysis or as ligands in coordination chemistry is highlighted. Chemistry ; Phosphazene ; Deprotonation ; Phosphine ; Delocalized electron ; Catalysis ; Organic chemistry ; Ion ; Polymer Asymmetric Hydrogenation and Catalysis ; Organophosphorus compounds synthesis ; N-Heterocyclic Carbenes in Organic and Inorganic Chemistry
Authors: Idris Temitope Bello | Shuo Zhai | Qijiao He | Qidong Xu | Meng Ni
Source: International Journal of Hydrogen Energy (article, 2021-06-16)
Field: Materials Science — Materials Chemistry

Scientometric review of advancements in the development of high-performance cathode for low and intermediate temperature solid oxide fuel cells: Three decades in retrospect Solid oxide fuel cells (SOFCs) have the potential to replace conventional thermal power plants due to their high efficiency and low emission. As the activation loss of the cathode usually limits the SOFC performance, the development of high-performance and durable cathode materials has received extensive attention in the past few decades. It is therefore essential to keep track of the research progress to identify significant research gaps and future directions. In this study, we retrieved the bibliometric data of 1101 cutting-edge research articles focused on cathode development for SOFCs and conducted a scientometric review. Even though significant research in cathode development for intermediate to low temperature SOFCs started in the 1990s, significant growth in the research output appeared in the year 2000 and remarkably continued till 2010 before exhibiting a sinusoidal pattern. Overall, there is a record of average decadal progress in this research area. We found that only a small percentage of countries in the world (i.e., about 29%) are involved in the research for the development of intermediate to low temperature SOFC cathodes. A highlight of core assessment criteria for cathode developments is presented with a summary of the most recent articles (i.e., including those in 2021). This paper can help early-stage researchers, journal outlets, governments, funding authorities, and investors understand the current progress in this area and how close researchers are to a breakthrough that could lead to the commercialization of this emerging technology. Commercialization ; Cathode ; Research development ; Fuel cells ; Nanotechnology ; Oxide ; Engineering physics ; Materials science ; Business ; Electrical engineering ; Engineering ; Metallurgy ; Chemical engineering ; Geology ; Marketing Advancements in Solid Oxide Fuel Cells ; Electronic and Structural Properties of Oxides ; Catalysis and Oxidation Reactions
Authors: Jon G. Bell | Thomas Graule | Michael Stuer
Source: Applied Physics Reviews (article, 2021-09-01)
Field: Materials Science — Materials Chemistry

Barium titanate-based thermistors: Past achievements, state of the art, and future perspectives Barium titanate materials displaying a positive temperature coefficient of resistivity are ubiquitously employed as thermistors in electrical current and voltage control systems, as well as in gas and thermal sensing applications. The positive temperature coefficient of resistivity effect is widely accepted to be a grain boundary-based phenomenon, although detailed studies on grain boundary structure and chemistry, and their influence on the resulting electrical properties, are seriously lacking. Tailoring of the positive temperature coefficient of resistivity electrical characteristics, for specific high-value applications, will require improved understanding and control over grain boundary phenomenon. A comprehensive overview of the development of barium titanate-based positive temperature coefficient of resistivity ceramics is initially presented. We then advance to a discussion on emerging grain boundary characterization techniques, specifically, a stereographic analysis of electron backscatter diffraction data that could assist in enhancing control over BaTiO3 defect chemistry and microstructure, through characterization and subsequent manipulation of the population of grain boundary types. These techniques have great potential for increasing the understanding of the delicate interplay between processing conditions, chemistry, microstructure, and functional electrical properties, and are relevant to the development of advanced, high-performance ceramics and electroceramics in general. Contemporary advancements in the field, such as lead-free positive temperature coefficient of resistivity effect materials and multilayer miniaturized systems based on hypostoichiometric barium compositions, are reviewed. Finally, perspectives on future lines of thermistor research, with a focus on the energy sector, are presented including applications in gas separation and chemical sensing. Barium titanate ; Grain boundary ; Materials science ; Electroceramics ; Temperature coefficient ; Thermistor ; Electrical resistivity and conductivity ; Microstructure ; Ceramic ; Engineering physics ; Mineralogy ; Nanotechnology ; Composite material ; Electrical engineering ; Chemistry ; Physics ; Fabrication ; Engineering Ferroelectric and Piezoelectric Materials ; Dielectric properties of ceramics ; Electronic and Structural Properties of Oxides
Authors: Jana Ramon | Ranhua Xiong | Stefaan C. De Smedt | Koen Raemdonck | Kevin Braeckmans
Source: Current Opinion in Colloid & Interface Science (article, 2021-03-30)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Vapor nanobubble-mediated photoporation constitutes a versatile intracellular delivery technology Photothermal therapy ; Nanotechnology ; Intracellular ; Endosome ; Materials science ; Effector ; Transfection ; Nanomaterials ; Cell ; Membrane ; Laser ; Biophysics ; Chemistry ; Cell biology ; Biology ; Optics Gold and Silver Nanoparticles Synthesis and Applications ; 3D Printing in Biomedical Research ; Nanoplatforms for cancer theranostics
Authors: Chengxi Zhao | Linjiang Chen | Yu Che | Zhongfu Pang | Xiaofeng Wu | Yunxiang Lu | Honglai Liu | Graeme M. Day | Andrew I. Cooper
Source: Nature Communications (article, 2021-02-05)
Field: Chemistry — Physical and Theoretical Chemistry

Digital navigation of energy–structure–function maps for hydrogen-bonded porous molecular crystals Energy-structure-function (ESF) maps can aid the targeted discovery of porous molecular crystals by predicting the stable crystalline arrangements along with their functions of interest. Here, we compute ESF maps for a series of rigid molecules that comprise either a triptycene or a spiro-biphenyl core, functionalized with six different hydrogen-bonding moieties. We show that the positioning of the hydrogen-bonding sites, as well as their number, has a profound influence on the shape of the resulting ESF maps, revealing promising structure-function spaces for future experiments. We also demonstrate a simple and general approach to representing and inspecting the high-dimensional data of an ESF map, enabling an efficient navigation of the ESF data to identify 'landmark' structures that are energetically favourable or functionally interesting. This is a step toward the automated analysis of ESF maps, an important goal for closed-loop, autonomous searches for molecular crystals with useful functions. Function (biology) ; Materials science ; Porosity ; Hydrogen ; Energy (signal processing) ; Nanotechnology ; Chemical physics ; Chemistry ; Physics ; Biology ; Cell biology ; Composite material Crystallography and molecular interactions ; Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science
Authors: Evan Walter Clark Spotte‐Smith | Samuel M. Blau | Xiaowei Xie | Hetal D. Patel | Mingjian Wen | Brandon M. Wood | Shyam Dwaraknath | Kristin A. Persson
Source: Scientific Data (article, 2021-08-05)
Field: Materials Science — Materials Chemistry

Quantum chemical calculations of lithium-ion battery electrolyte and interphase species Lithium-ion batteries (LIBs) represent the state of the art in high-density energy storage. To further advance LIB technology, a fundamental understanding of the underlying chemical processes is required. In particular, the decomposition of electrolyte species and associated formation of the solid electrolyte interphase (SEI) is critical for LIB performance. However, SEI formation is poorly understood, in part due to insufficient exploration of the vast reactive space. The Lithium-Ion Battery Electrolyte (LIBE) dataset reported here aims to provide accurate first-principles data to improve the understanding of SEI species and associated reactions. The dataset was generated by fragmenting a set of principal molecules, including solvents, salts, and SEI products, and then selectively recombining a subset of the fragments. All candidate molecules were analyzed at the ωB97X-V/def2-TZVPPD/SMD level of theory at various charges and spin multiplicities. In total, LIBE contains structural, thermodynamic, and vibrational information on over 17,000 unique species. In addition to studies of reactivity in LIBs, this dataset may prove useful for machine learning of molecular and reaction properties. Electrolyte ; Lithium (medication) ; Battery (electricity) ; Interphase ; Ion ; Molecule ; Chemistry ; Reactivity (psychology) ; Density functional theory ; Electrochemistry ; Chemical physics ; Computational chemistry ; Materials science ; Physical chemistry ; Electrode ; Physics ; Organic chemistry ; Thermodynamics Machine Learning in Materials Science ; Advancements in Battery Materials ; Advanced Chemical Physics Studies
Authors: Taher Hajilounezhad | Rina Bao | Kannappan Palaniappan | Filiz Bunyak | Prasad Calyam | Matthew R. Maschmann
Source: npj Computational Materials (article, 2021-08-19)
Field: Materials Science — Materials Chemistry

Predicting carbon nanotube forest attributes and mechanical properties using simulated images and deep learning Abstract Understanding and controlling the self-assembly of vertically oriented carbon nanotube (CNT) forests is essential for realizing their potential in myriad applications. The governing process–structure–property mechanisms are poorly understood, and the processing parameter space is far too vast to exhaustively explore experimentally. We overcome these limitations by using a physics-based simulation as a high-throughput virtual laboratory and image-based machine learning to relate CNT forest synthesis attributes to their mechanical performance. Using CNTNet, our image-based deep learning classifier module trained with synthetic imagery, combinations of CNT diameter, density, and population growth rate classes were labeled with an accuracy of &gt;91%. The CNTNet regression module predicted CNT forest stiffness and buckling load properties with a lower root-mean-square error than that of a regression predictor based on CNT physical parameters. These results demonstrate that image-based machine learning trained using only simulated imagery can distinguish subtle CNT forest morphological features to predict physical material properties with high accuracy. CNTNet paves the way to incorporate scanning electron microscope imagery for high-throughput material discovery. Artificial intelligence ; Computer science ; Deep learning ; Stiffness ; Carbon nanotube ; Random forest ; Pattern recognition (psychology) ; Classifier (UML) ; Machine learning ; Throughput ; Materials science ; Nanotechnology ; Composite material Carbon Nanotubes in Composites ; Machine Learning in Materials Science ; Force Microscopy Techniques and Applications
Authors: Hongyang Dong | Keith T. Butler | Dorota Matras | Stephen W. T. Price | Yaroslav Odarchenko | Rahul Khatry | Andrew J. Thompson | Vesna Middelkoop | Simon D. M. Jacques | Andrew M. Beale | Antonis Vamvakeros
Source: npj Computational Materials (article, 2021-05-21)
Field: Materials Science — Materials Chemistry

A deep convolutional neural network for real-time full profile analysis of big powder diffraction data Abstract We present Parameter Quantification Network (PQ-Net), a regression deep convolutional neural network providing quantitative analysis of powder X-ray diffraction patterns from multi-phase systems. The network is tested against simulated and experimental datasets of increasing complexity with the last one being an X-ray diffraction computed tomography dataset of a multi-phase Ni-Pd/CeO 2 -ZrO 2 /Al 2 O 3 catalytic material system consisting of ca. 20,000 diffraction patterns. It is shown that the network predicts accurate scale factor, lattice parameter and crystallite size maps for all phases, which are comparable to those obtained through full profile analysis using the Rietveld method, also providing a reliable uncertainty measure on the results. The main advantage of PQ-Net is its ability to yield these results orders of magnitude faster showing its potential as a tool for real-time diffraction data analysis during in situ/operando experiments. Diffraction ; Convolutional neural network ; Crystallite ; Rietveld refinement ; Powder diffraction ; Artificial neural network ; Materials science ; Phase (matter) ; X-ray crystallography ; Computer science ; Biological system ; Analytical Chemistry (journal) ; Artificial intelligence ; Optics ; Crystallography ; Chemistry ; Physics ; Chromatography X-ray Diffraction in Crystallography ; Machine Learning in Materials Science ; Advanced materials and composites
Authors: Yu‐Jia Zeng | Yexin Feng | Li‐Ming Tang | Ke‐Qiu Chen
Source: Applied Physics Letters (article, 2021-05-03)
Field: Materials Science — Materials Chemistry

Effect of out-of-plane strain on the phonon structures and anharmonicity of twisted multilayer graphene Twisted bilayer and multilayer two-dimensional materials linked by van der Waals interactions exhibit various unique physical properties. The phonon properties of such systems are of great importance, but have not been explored in detail. In this work, we use a hybrid neural-network potential to systematically investigate the evolution of the phonon structure of twisted multilayer graphene under out-of-plane strain. With increasing out-of-plane strain, the evolution of the phonon structure of the moiré superlattice exhibits different behavior from that of AA and AB stacked multilayer graphene. Meanwhile, with twisting of the interlayer, a higher Grüneisen parameter and a lower phonon group velocity can be obtained. A possible method is revealed by which phonon anharmonic scattering in stacked multilayer graphene could be enhanced by varying the twist angle in combination with out-of-plane strain. Our work shows that the application of out-of-plane strain can serve as an effective way to amplify the effect of twist angle on phonon structures of twisted multilayer two-dimensional systems, with potential application to thermoelectric and thermal logical devices. Phonon ; Anharmonicity ; Graphene ; Materials science ; Condensed matter physics ; Superlattice ; Thermoelectric effect ; Scattering ; Twist ; Work (physics) ; van der Waals force ; Nanotechnology ; Optics ; Optoelectronics ; Physics ; Geometry ; Quantum mechanics Graphene research and applications ; Thermal properties of materials ; 2D Materials and Applications
Authors: Xinxing Li | Anuja Kulkarni | Xun Liu | Wei‐Qiang Gao | Lin Huang | Zhiqian Hu | Kun Qian
Source: Small Methods (article, 2021-01-15)
Field: Chemistry — Inorganic Chemistry

Metal‐Organic Framework Hybrids Aid Metabolic Profiling for Colorectal Cancer Colorectal cancer (CRC) is the third most common fatal cancer worldwide, accounting for ≈10% of cancer-related mortality. Metabolic shift occurs from the very early stage during the development of CRC, which is of significant etiological and diagnostic importance toward precision medicine. Here, an advanced molecular tool to characterize the metabolic alterations in CRC, based on metal-organic framework (MOF) hybrids is reported. Consuming only 500 nL of plasma without any sample pretreatment, MOF hybrids yield direct metabolic fingerprints by laser desorption/ionization mass spectrometry in seconds. A diagnostic prediction model by a machine learning algorithm is constructed, to discriminate CRC patients from normal controls with an average area under the curve of 0.947 for the discovery cohort and 0.912 for the independent validation cohort. In addition, CRC-specific metabolic signature consisting of 34 potential biomarkers, based on the aforementioned diagnostic model is identified. The results advance the design of nanomaterial-based platforms for metabolic analysis and establish a new liquid biopsy tool for CRC screening compatible with the current clinical workflow in practice. Profiling (computer programming) ; Colorectal cancer ; Hybrid ; Environmental science ; Internal medicine ; Oncology ; Computational biology ; Medicine ; Biology ; Cancer ; Computer science ; Botany Metal-Organic Frameworks: Synthesis and Applications ; Inflammatory mediators and NSAID effects ; Nanoplatforms for cancer theranostics
Authors: Lukas Turcani | Andrew Tarzia | Filip Szczypiński | Kim E. Jelfs
Source: The Journal of Chemical Physics (article, 2021-06-01)
Field: Materials Science — Materials Chemistry

<i>stk</i> : An extendable Python framework for automated molecular and supramolecular structure assembly and discovery Computational software workflows are emerging as all-in-one solutions to speed up the discovery of new materials. Many computational approaches require the generation of realistic structural models for property prediction and candidate screening. However, molecular and supramolecular materials represent classes of materials with many potential applications for which there is no go-to database of existing structures or general protocol for generating structures. Here, we report a new version of the supramolecular toolkit, stk, an open-source, extendable, and modular Python framework for general structure generation of (supra)molecular structures. Our construction approach works on arbitrary building blocks and topologies and minimizes the input required from the user, making stk user-friendly and applicable to many material classes. This version of stk includes metal-containing structures and rotaxanes as well as general implementation and interface improvements. Additionally, this version includes built-in tools for exploring chemical space with an evolutionary algorithm and tools for database generation and visualization. The latest version of stk is freely available at github.com/lukasturcani/stk. Python (programming language) ; Workflow ; Computer science ; Modular design ; Software ; Supramolecular chemistry ; Visualization ; Graphical user interface ; Computational science ; Data structure ; Interface (matter) ; Programming language ; Data mining ; Database ; Parallel computing ; Chemistry Machine Learning in Materials Science ; Metal-Organic Frameworks: Synthesis and Applications ; Supramolecular Chemistry and Complexes
Authors: Stephanie Spence | Wah-Keat Lee | Feng Lin | Xianghui Xiao
Source: Nanotechnology (review, 2021-07-27)
Field: Materials Science — Materials Chemistry

Transmission x-ray microscopy and its applications in battery material research—a short review Transmission x-ray microscopy (TXM), which can provide morphological and chemical structural information inside of battery component materials at tens of nanometer scale, has become a powerful tool in battery research. This article presents a short review of the TXM, including its instrumentation, battery research applications, and the practical sample preparation and data analysis in the TXM applications. A brief discussion on the challenges and opportunities in the TXM applications is presented at the end. Battery (electricity) ; Materials science ; Nanometre ; Instrumentation (computer programming) ; Transmission (telecommunications) ; Microscopy ; Nanotechnology ; X-ray ; Optics ; Computer science ; Telecommunications ; Physics X-ray Diffraction in Crystallography ; Electron and X-Ray Spectroscopy Techniques ; Advanced Electron Microscopy Techniques and Applications
Authors: Stephanie Rankin‐Turner | Liam M. Heaney
Source: Analytical Science Advances (review, 2021-02-06)
Field: Chemistry — Spectroscopy

Applications of ambient ionization mass spectrometry in 2020: An annual review Recent developments in mass spectrometry (MS) analyses have seen a concerted effort to reduce the complexity of analytical workflows through the simplification (or removal) of sample preparation and the shortening of run-to-run analysis times. Ambient ionization mass spectrometry (AIMS) is an exemplar MS-based technology that has swiftly developed into a popular and powerful tool in analytical science. This increase in interest and demonstrable applications is down to its capacity to enable the rapid analysis of a diverse range of samples, typically in their native state or following a minimalistic sample preparation approach. The field of AIMS is constantly improving and expanding, with developments of powerful and novel techniques, improvements to existing instrumentation, and exciting new applications added with each year that passes. This annual review provides an overview of applications of AIMS techniques over the past year (2020), with a particular focus on the application of AIMS in a number of key fields of research including biomedical sciences, forensics and security, food sciences, the environment, and chemical synthesis. Novel ambient ionization techniques are introduced, including picolitre pressure-probe electrospray ionization and fiber spray ionization, in addition to modifications and improvements to existing techniques such as hand-held devices for ease of use, and USB-powered ion sources for on-site analysis. In all, the information provided in this review supports the view that AIMS has become a leading approach in MS-based analyses and that improvements to existing methods, alongside the development of novel approaches, will continue across the foreseeable future. Ambient ionization ; Computer science ; Workflow ; Instrumentation (computer programming) ; Ionization ; Focus (optics) ; Mass spectrometry ; Field (mathematics) ; Nanotechnology ; Process engineering ; Data science ; Chemistry ; Engineering ; Chemical ionization ; Materials science ; Physics ; Ion ; Database Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Advanced Chemical Sensor Technologies