jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Patrik Tholen | Craig A. Peeples | Raoul Schaper | Ceyda Bayraktar | Turan S. Erkal | Mehmet Menaf Ayhan | Bünyemin Çoşut | Jens Beckmann | A. Özgür Yazaydın | Michael Wark | Gabriel Hanna | Yunus Zorlu | Gündoğ Yücesan
Source: Nature Communications (article, 2020-06-23)
Field: Chemistry — Inorganic Chemistry

Semiconductive microporous hydrogen-bonded organophosphonic acid frameworks Herein, we report a semiconductive, proton-conductive, microporous hydrogen-bonded organic framework (HOF) derived from phenylphosphonic acid and 5,10,15,20-tetrakis[p-phenylphosphonic acid] porphyrin (GTUB5). The structure of GTUB5 was characterized using single crystal X-ray diffraction. A narrow band gap of 1.56 eV was extracted from a UV-Vis spectrum of pure GTUB5 crystals, in excellent agreement with the 1.65 eV band gap obtained from DFT calculations. The same band gap was also measured for GTUB5 in DMSO. The proton conductivity of GTUB5 was measured to be 3.00 × 10<sup>-6</sup> S cm<sup>-1</sup> at 75 °C and 75% relative humidity. The surface area was estimated to be 422 m<sup>2</sup> g<sup>-1</sup> from grand canonical Monte Carlo simulations. XRD showed that GTUB5 is thermally stable under relative humidities of up to 90% at 90 °C. These findings pave the way for a new family of organic, microporous, and semiconducting materials with high surface areas and high thermal stabilities. Microporous material ; Hydrogen ; Materials science ; Metal-organic framework ; Nanotechnology ; Chemistry ; Chemical engineering ; Adsorption ; Composite material ; Physical chemistry ; Organic chemistry ; Engineering Metal-Organic Frameworks: Synthesis and Applications ; Covalent Organic Framework Applications ; Chemical Synthesis and Characterization
Authors: Joel F. Destino | Katie Cunningham
Source: Journal of Chemical Education (article, 2020-08-04)
Field: Chemistry — Physical and Theoretical Chemistry

At-Home Colorimetric and Absorbance-Based Analyses: An Opportunity for Inquiry-Based, Laboratory-Style Learning In light of COVID-19 in spring 2020, we developed a simple and versatile inquiry-based, laboratory-style active learning colorimetry experiment amenable to at-home quantitative analysis. In this experiment, students acquire an external calibration method using aqueous solutions of a self-selected chromophoric analyte from household products using a smartphone camera and RGB image analysis. We report typical student-obtained results for a 5-point external calibration method using RGB image analysis and solutions prepared with green food colorant and blue food colorant. Solutions were prepared using common kitchen measuring tools, glass drinking cups served as sample cells or cuvettes, images were acquired with a smartphone camera, and image analyses were conducted using RGB analysis software. Results show analytical data can be readily obtained for both colorimetric and absorbance-based analyses. Experientially, results show that throughout method development students are challenged with various quantitative analysis learning objectives, including basic concepts about light–matter interactions, analytical solution preparation, chemical concentrations, unit analysis, significant figures, statistical analysis, and analytical figures of merit (i.e., method robustness, linearity, and sensitivity, etc.). Furthermore, by bringing chemistry into the home, students are challenged to be creative and access a wide range of problem-solving and critical thinking skills, some of which may not be exercised in traditional laboratory experiments and projects. RGB color model ; Colorimetry ; Analyte ; Absorbance ; Colorimetric analysis ; Computer science ; Cuvette ; Artificial intelligence ; Chemistry ; Computer vision ; Optics ; Chromatography Various Chemistry Research Topics ; Biosensors and Analytical Detection ; Advanced Chemical Sensor Technologies
Authors: Michalis Konsolakis | Maria Lykaki
Source: Catalysts (article, 2020-02-01)
Field: Materials Science — Materials Chemistry

Recent Advances on the Rational Design of Non-Precious Metal Oxide Catalysts Exemplified by CuOx/CeO2 Binary System: Implications of Size, Shape and Electronic Effects on Intrinsic Reactivity and Metal-Support Interactions Catalysis is an indispensable part of our society, massively involved in numerous energy and environmental applications. Although, noble metals (NMs)-based catalysts are routinely employed in catalysis, their limited resources and high cost hinder the widespread practical application. In this regard, the development of NMs-free metal oxides (MOs) with improved catalytic activity, selectivity and durability is currently one of the main research pillars in the area of heterogeneous catalysis. The present review, involving our recent efforts in the field, aims to provide the latest advances—mainly in the last 10 years—on the rational design of MOs, i.e., the general optimization framework followed to fine-tune non-precious metal oxide sites and their surrounding environment by means of appropriate synthetic and promotional/modification routes, exemplified by CuOx/CeO2 binary system. The fine-tuning of size, shape and electronic/chemical state (e.g., through advanced synthetic routes, special pretreatment protocols, alkali promotion, chemical/structural modification by reduced graphene oxide (rGO)) can exert a profound influence not only to the reactivity of metal sites in its own right, but also to metal-support interfacial activity, offering highly active and stable materials for real-life energy and environmental applications. The main implications of size-, shape- and electronic/chemical-adjustment on the catalytic performance of CuOx/CeO2 binary system during some of the most relevant applications in heterogeneous catalysis, such as CO oxidation, N2O decomposition, preferential oxidation of CO (CO-PROX), water gas shift reaction (WGSR), and CO2 hydrogenation to value-added products, are thoroughly discussed. It is clearly revealed that the rational design and tailoring of NMs-free metal oxides can lead to extremely active composites, with comparable or even superior reactivity than that of NMs-based catalysts. The obtained conclusions could provide rationales and design principles towards the development of cost-effective, highly active NMs-free MOs, paving also the way for the decrease of noble metals content in NMs-based catalysts. Catalysis ; Oxide ; Rational design ; Nanotechnology ; Reactivity (psychology) ; Materials science ; Noble metal ; Graphene ; Precious metal ; Heterogeneous catalysis ; Chemistry ; Organic chemistry ; Metallurgy Catalytic Processes in Materials Science ; Nanomaterials for catalytic reactions ; Catalysis and Oxidation Reactions
Authors: Jacob I. Deneff | Kimberly S. Butler | Lauren E. S. Rohwer | Charles J. Pearce | Nichole Valdez | Mark A. Rodriguez | Ting S. Luk | Dorina F. Sava Gallis
Source: Angewandte Chemie International Edition (article, 2020-11-02)
Field: Chemistry — Inorganic Chemistry

Encoding Multilayer Complexity in Anti‐Counterfeiting Heterometallic MOF‐Based Optical Tags Optical tags provide a way to quickly and unambiguously identify valuable assets. Current tag fluorophore options lack the tunability to allow combined methods of encoding in a single material. Herein we report a design strategy to encode multilayer complexity in a family of heterometallic rare-earth metal-organic frameworks based on highly connected nonanuclear clusters. To impart both intricacy and security, a synergistic approach was implemented resulting in both overt (visible) and covert (near-infrared, NIR) properties, with concomitant multi-emissive spectra and tunable luminescence lifetimes. Tag authentication is validated with a variety of orthogonal detection methodologies. Importantly, the effect induced by subtle compositional changes on intermetallic energy transfer, and thus on the resulting photophysical properties, is demonstrated. This strategy can be widely implemented to create a large library of highly complex, difficult-to-counterfeit optical tags. Materials science ; Fluorophore ; Encoding (memory) ; Energy transfer ; Computer science ; Metal-organic framework ; Optoelectronics ; Nanotechnology ; Fluorescence ; Chemistry ; Physics ; Artificial intelligence ; Chemical physics Metal-Organic Frameworks: Synthesis and Applications ; Nanocluster Synthesis and Applications ; Nanoplatforms for cancer theranostics
Authors: Qingkun Zhao | Huiya Yang | Jiabin Liu | Haofei Zhou | Hongtao Wang | Wei Yang
Source: Materials & Design (article, 2020-10-20)
Field: Materials Science — Materials Chemistry

Machine learning-assisted discovery of strong and conductive Cu alloys: Data mining from discarded experiments and physical features Copper alloys with high strength and electrical conductivity are ideal candidates for a wide range of civilian and engineering applications. Traditional methods of alloy design, such as “trial and error” experiments, are costly and time-consuming, limiting the discovery of Cu alloys. Machine learning (ML)-based technology facilitates a better understanding on inter-relationships within massive experimental datasets, and shortens the development cycle of new alloy systems. Herein, we propose a method of material design based on ML to discover high-performance Cu alloys. The ML models are trained from “discarded” experimental data that show undesirable hardness and/or electrical conductivity. We constructed effective Gaussian process regression-based models successfully from limited training data by engaging additional features. The predicted Cu alloys were experimentally synthesized and characterized, exhibiting superior hardness or electrical conductivity with respect to original training data. The increase of hardness is due to precipitation of second-phase Co-Ti and Fe-Ti compounds during aging, while the rise of electrical conductivity is caused by a purification effect of the precipitates on Cu matrix. The impact of physical features on ML models was further evaluated by a genetic algorithm. Our findings suggest that ML holds great potential for developing high-performance Cu alloys. Materials science ; Electrical resistivity and conductivity ; Alloy ; Electrical conductor ; Precipitation ; Conductivity ; Limiting ; Kriging ; Range (aeronautics) ; Process (computing) ; Precipitation hardening ; Machine learning ; Gaussian process ; Metallurgy ; Biological system ; Gaussian ; Computer science ; Mechanical engineering ; Composite material ; Electrical engineering Machine Learning in Materials Science ; Hydrogen embrittlement and corrosion behaviors in metals ; Advanced Materials Characterization Techniques
Authors: Masashi Tsubaki | Teruyasu Mizoguchi
Source: Physical Review Letters (article, 2020-11-10)
Field: Materials Science — Materials Chemistry

Quantum Deep Field: Data-Driven Wave Function, Electron Density Generation, and Atomization Energy Prediction and Extrapolation with Machine Learning Deep neural networks (DNNs) have been used to successfully predict molecular properties calculated based on the Kohn-Sham density functional theory (KS-DFT). Although this prediction is fast and accurate, we believe that a DNN model for KS-DFT must not only predict the properties but also provide the electron density of a molecule. This Letter presents the quantum deep field (QDF), which provides the electron density with an unsupervised but end-to-end physics-informed modeling by learning the atomization energy on a large-scale dataset. QDF performed well at atomization energy prediction, generated valid electron density, and demonstrated extrapolation. Extrapolation ; Wave function ; Artificial neural network ; Statistical physics ; Field (mathematics) ; Electron ; Physics ; Density functional theory ; Function (biology) ; Quantum ; Computer science ; Electron density ; Energy (signal processing) ; Artificial intelligence ; Computational physics ; Quantum mechanics ; Statistics ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Yujie Yan | Qizhen Chen | Xiaomin Wu | Xiumei Wang | Enlong Li | Yudan Ke | Yuan Liu | Huipeng Chen | Tailiang Guo
Source: ACS Applied Materials & Interfaces (article, 2020-10-21)
Field: Materials Science — Polymers and Plastics

High-Performance Organic Electrochemical Transistors with Nanoscale Channel Length and Their Application to Artificial Synapse Organic electrochemical transistors (OECTs) have attracted considerable interests for various applications ranging from biosensors to digital logic circuits and artificial synapses. However, the majority of reported OECTs utilize large channel length up to several or several tens of micrometers, which limits the device performance and leads to low transistor densities. Here, we demonstrate a new design of vertical OECT architecture with a nanoscale channel length down to ∼100 nm. The devices exhibit a high on-state current of over 20 mA under a low bias voltage of 0.5 V, a fast transient response of less than 300 μs, and an extraordinary transconductance up to 68.88 mS, representing a record-high value for OECTs. The excellent electrical performance is attributed to the novel structure with a nanoscale channel length defined by the channel material thickness, which is intrinsically different from that of conventional OECTs with the channel length limited by the lithography resolution. Owing to the low thermal budget, we fabricate flexible devices on a flexible substrate, which exhibit unprecedented endurance characteristics and mechanical robustness after 1000 blending cycles. Furthermore, the proposed device is capable of mimicking biological inhibitory synapses for application in intelligent artificial neural networks. Our work not only pushes the performance limit of OECTs but also opens up a new design of OECTs for high-performance biosensors, digital logic, and neuromorphic devices. Materials science ; Transistor ; Nanotechnology ; Transconductance ; Nanoscopic scale ; Optoelectronics ; Biosensor ; Neuromorphic engineering ; Electronic circuit ; Voltage ; Computer science ; Electrical engineering ; Artificial neural network Conducting polymers and applications ; Advanced Memory and Neural Computing ; Organic Electronics and Photovoltaics
Authors: Tanjin He | Wenhao Sun | Haoyan Huo | Olga Kononova | Ziqin Rong | Vahe Tshitoyan | Tiago Botari | Gerbrand Ceder
Source: Chemistry of Materials (article, 2020-08-19)
Field: Materials Science — Materials Chemistry

Similarity of Precursors in Solid-State Synthesis as Text-Mined from Scientific Literature Collecting and analyzing the vast amount of information available in the solid-state chemistry literature may accelerate our understanding of materials synthesis. However, one major problem is the difficulty of identifying which materials from a synthesis paragraph are precursors or are target materials. In this study, we developed a two-step chemical named entity recognition model to identify precursors and targets, based on information from the context around material entities. Using the extracted data, we conducted a meta-analysis to study the similarities and differences between precursors in the context of solid-state synthesis. To quantify precursor similarity, we built a substitution model to calculate the viability of substituting one precursor with another while retaining the target. From a hierarchical clustering of the precursors, we demonstrate that the "chemical similarity"of precursors can be extracted from text data. Quantifying the similarity of precursors helps provide a foundation for suggesting candidate reactants in a predictive synthesis model. Solid-state ; Similarity (geometry) ; Scientific literature ; Information retrieval ; Materials science ; Chemistry ; Computer science ; Artificial intelligence ; Physical chemistry ; Paleontology ; Geology Machine Learning in Materials Science ; Topic Modeling ; Data Quality and Management
Authors: Puneet Mishra | G. Polder | Nastassia Vilfan
Source: Current Robotics Reports (review, 2020-04-02)
Field: Chemistry — Analytical Chemistry

Close Range Spectral Imaging for Disease Detection in Plants Using Autonomous Platforms: a Review on Recent Studies Abstract Purpose of Review A short introduction to the spectral imaging (SI) of plants along with a comprehensive overview of the recent research works related to disease detection in plants using autonomous phenotyping platforms is provided. Key benefits and challenges of SI for plant disease detection on robotic platforms are highlighted. Recent Findings SI is becoming a potential tool for autonomous platforms for non-destructive plant assessment. This is because it can provide information on the plant pigments such as chlorophylls, anthocyanins and carotenoids and supports quantification of biochemical parameters such as sugars, proteins, different nutrients, water and fat content. A plant suffering from diseases will exhibit different physicochemical parameters compared with a healthy plant, allowing the SI to capture those differences as a function of reflected or absorbed light. Summary Potential of SI to non-destructively capture physicochemical parameters in plants makes it a key technique to support disease detection on autonomous platforms. SI can be broadly used for crop disease detection by quantification of physicochemical changes in the plants. Plant disease ; Computer science ; Nutrient ; Biochemical engineering ; Environmental science ; Nanotechnology ; Biotechnology ; Biology ; Materials science ; Engineering ; Ecology Spectroscopy and Chemometric Analyses ; Remote Sensing in Agriculture ; Smart Agriculture and AI
Authors: Jógvan Magnus Haugaard Olsen | Simen Reine | Olav Vahtras | Erik Rosendahl Kjellgren | Peter Reinholdt | Karen Oda Hjorth Minde Dundas | Xin Li | Janusz Cukras | Magnus Ringholm | Erik D. Hedegård | Roberto Di Remigio | Nanna Holmgaard List | Rasmus Faber | Bruno Nunes Cabral Tenorio | Radovan Bast | Thomas Bondo Pedersen | Žilvinas Rinkevičius | Stephan P. A. Sauer | Kurt V. Mikkelsen | Jacob Kongsted | Sonia Coriani | Kenneth Ruud | Trygve Helgaker | Hans Jørgen Aa. Jensen | Patrick Norman
Source: The Journal of Chemical Physics (article, 2020-06-05)
Field: Chemistry — Spectroscopy

Dalton Project: A Python platform for molecular- and electronic-structure simulations of complex systems The Dalton Project provides a uniform platform access to the underlying full-fledged quantum chemistry codes Dalton and LSDalton as well as the PyFraME package for automatized fragmentation and parameterization of complex molecular environments. The platform is written in Python and defines a means for library communication and interaction. Intermediate data such as integrals are exposed to the platform and made accessible to the user in the form of NumPy arrays, and the resulting data are extracted, analyzed, and visualized. Complex computational protocols that may, for instance, arise due to a need for environment fragmentation and configuration-space sampling of biochemical systems are readily assisted by the platform. The platform is designed to host additional software libraries and will serve as a hub for future modular software development efforts in the distributed Dalton community. Python (programming language) ; Computer science ; Modular design ; Software ; Fragmentation (computing) ; Programming language ; Computational science Mass Spectrometry Techniques and Applications ; Spectroscopy and Quantum Chemical Studies ; Protein Structure and Dynamics
Authors: Shushu Huang | Wei Deng | Long Zhang | Dengyao Yang | Qiang Gao | Zhengfang Tian | Limin Guo | Tatsumi Ishihara
Source: Microporous and Mesoporous Materials (article, 2020-04-08)
Field: Chemistry — Inorganic Chemistry

Adsorptive properties in toluene removal over hierarchical zeolites Toluene ; Adsorption ; Mordenite ; Chemistry ; Zeolite ; Desorption ; Freundlich equation ; Diffusion ; Relative humidity ; Chemical engineering ; Inorganic chemistry ; Organic chemistry ; Catalysis ; Thermodynamics Zeolite Catalysis and Synthesis ; Catalytic Processes in Materials Science ; Mesoporous Materials and Catalysis
Authors: Anastasiia S. Kholtobina | Reinhard Pıppan | Lorenz Romaner | Daniel Scheiber | Werner Ecker | Vsevolod I. Razumovskiy
Source: Materials (article, 2020-05-15)
Field: Materials Science — Metals and Alloys

Hydrogen Trapping in bcc Iron Fundamental understanding of H localization in steel is an important step towards theoretical descriptions of hydrogen embrittlement mechanisms at the atomic level. In this paper, we investigate the interaction between atomic H and defects in ferromagnetic body-centered cubic (bcc) iron using density functional theory (DFT) calculations. Hydrogen trapping profiles in the bulk lattice, at vacancies, dislocations and grain boundaries (GBs) are calculated and used to evaluate the concentrations of H at these defects as a function of temperature. The results on H-trapping at GBs enable further investigating H-enhanced decohesion at GBs in Fe. A hierarchy map of trapping energies associated with the most common crystal lattice defects is presented and the most attractive H-trapping sites are identified. Trapping ; Materials science ; Grain boundary ; Hydrogen ; Density functional theory ; Embrittlement ; Ferromagnetism ; Lattice (music) ; Hydrogen embrittlement ; Chemical physics ; Condensed matter physics ; Crystallography ; Molecular physics ; Computational chemistry ; Chemistry ; Metallurgy ; Physics ; Corrosion ; Microstructure Hydrogen embrittlement and corrosion behaviors in metals ; Nuclear Materials and Properties ; Fusion materials and technologies
Authors: Yun Zhang | Xiaojie Xu
Source: Shape Memory and Superelasticity (article, 2020-10-15)
Field: Materials Science — Materials Chemistry

Transformation Temperature Predictions Through Computational Intelligence for NiTi-Based Shape Memory Alloys Nickel titanium ; Shape-memory alloy ; Transformation (genetics) ; Materials science ; Metallurgy ; Chemistry Shape Memory Alloy Transformations ; Transition Metal Oxide Nanomaterials ; Advanced Memory and Neural Computing
Authors: Calogero Giancarlo Piscopo | S. Loebbecke
Source: ChemPlusChem (review, 2020-03-01)
Field: Chemistry — Inorganic Chemistry

Strategies to Enhance Carbon Dioxide Capture in Metal‐Organic Frameworks The dramatic increase of atmospheric CO<sub>2</sub> concentration is responsible for a fast and potentially unpredictable global climate change. Therefore, the implementation of negative carbon technologies such as direct air capture (DAC) is needed. Metal-organic frameworks (MOFs) have the potential to perform CO<sub>2</sub> DAC and achieve unprecedented performances. Herein strategies to improve the CO<sub>2</sub> capture efficiency of MOFs and their potential implementation in real applications are discussed. Three main categories of targeted modifications to the frameworks are usually performed to enhance the CO<sub>2</sub> uptake capacity and adsorption selectivity: 1) modifications to the metal unit; 2) modifications to the linker unit; 3) confinement of solvents within MOFs. The synthesis of MOF composites using other porous materials as support is also a useful method to improve the CO<sub>2</sub> capture performances. Another approach involves the synthesis of amine-functionalized MOFs that can chemically bind carbon dioxide. Metal-organic framework ; Adsorption ; Carbon dioxide ; Nanotechnology ; Carbon fibers ; Materials science ; Amine gas treating ; Carbon capture and storage (timeline) ; Linker ; Process engineering ; Computer science ; Chemistry ; Organic chemistry ; Climate change ; Engineering Metal-Organic Frameworks: Synthesis and Applications ; Carbon Dioxide Capture Technologies ; Covalent Organic Framework Applications