jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Suiying Ye | Tian Tian | Andrew J. Christofferson | Sofia Erikson | Jakub Jagielski | Zhi Luo | Sudhir Kumar | Chih‐Jen Shih | Jean‐Christophe Leroux | Yinyin Bao
Source: Science Advances (article, 2021-04-07)
Field: Materials Science — Materials Chemistry

Continuous color tuning of single-fluorophore emission via polymerization-mediated through-space charge transfer Tuning emission color of molecular fluorophores is of fundamental interest as it directly reflects the manipulation of excited states at the quantum mechanical level. Despite recent progress in molecular design and engineering on single fluorophores, a systematic methodology to obtain multicolor emission in aggregated or solid states, which gives rise to practical implications, remains scarce. In this study, we present a general strategy to continuously tune the emission color of a single-fluorophore aggregate by polymerization-mediated through-space charge transfer (TSCT). Using a library of well-defined styrenic donor (D) polymers grown from an acceptor (A) fluorophore by controlled radical polymerization, we found that the solid-state emission color can be fine-tuned by varying three molecular parameters: (i) the monomer substituent, (ii) the end groups of the polymer, and (iii) the polymer chain length. Experimental and theoretical investigations reveal that the color tunability originates from the structurally dependent TSCT process that regulates charge transfer energy. Fluorophore ; Charge (physics) ; Polymerization ; Materials science ; Polymer ; Space (punctuation) ; Fluorescence ; Transfer (computing) ; Photochemistry ; Simple (philosophy) ; Chemical physics ; Optoelectronics ; Nanotechnology ; Chemistry ; Optics ; Physics ; Computer science ; Composite material ; Quantum mechanics Luminescence and Fluorescent Materials ; Organic Light-Emitting Diodes Research ; Molecular Sensors and Ion Detection
Authors: I. Ben Djemaa | Stéphane Auguste | Wiebke Drenckhan | Sébastien Andrieux
Source: Advances in Colloid and Interface Science (review, 2021-06-29)
Field: Materials Science — Materials Chemistry

Hydrogel foams from liquid foam templates: Properties and optimisation Template ; Materials science ; Nanotechnology ; Chemical engineering ; Composite material ; Engineering Pickering emulsions and particle stabilization ; Hydrogels: synthesis, properties, applications ; biodegradable polymer synthesis and properties
Authors: Shangzhi Chen | Stefano Rossi | Ravi Shanker | Giancarlo Cincotti | Sampath Gamage | Philipp Kühne | V. Stanishev | Isak Engquist | Magnus Berggren | Jesper Edberg | Vanya Darakchieva | Magnus P. Jonsson
Source: Advanced Materials (article, 2021-07-05)
Field: Materials Science — Polymers and Plastics

Tunable Structural Color Images by UV‐Patterned Conducting Polymer Nanofilms on Metal Surfaces Precise manipulation of light-matter interactions has enabled a wide variety of approaches to create bright and vivid structural colors. Techniques utilizing photonic crystals, Fabry-Pérot cavities, plasmonics, or high-refractive-index dielectric metasurfaces have been studied for applications ranging from optical coatings to reflective displays. However, complicated fabrication procedures for sub-wavelength nanostructures, limited active areas, and inherent absence of tunability of these approaches impede their further development toward flexible, large-scale, and switchable devices compatible with facile and cost-effective production. Here, a novel method is presented to generate structural color images based on monochromic conducting polymer films prepared on metallic surfaces via vapor phase polymerization and ultraviolet (UV) light patterning. Varying the UV dose enables synergistic control of both nanoscale film thickness and polymer permittivity, which generates controllable structural colors from violet to red. Together with grayscale photomasks this enables facile fabrication of high-resolution structural color images. Dynamic tuning of colored surfaces and images via electrochemical modulation of the polymer redox state is further demonstrated. The simple structure, facile fabrication, wide color gamut, and dynamic color tuning make this concept competitive for applications like multifunctional displays. Structural coloration ; Materials science ; Gamut ; Photomask ; Fabrication ; Optoelectronics ; Polymer ; Nanotechnology ; Ultraviolet ; Electroactive polymers ; Flexible display ; Nanoimprint lithography ; Optics ; Photonic crystal ; Resist ; Layer (electronics) Conducting polymers and applications ; Photonic Crystals and Applications ; Polydiacetylene-based materials and applications
Authors: Li‐Cheng Xu | Shuo‐Qing Zhang | Xin Li | Miao‐Jiong Tang | Pei‐Pei Xie | Xin Hong
Source: Angewandte Chemie International Edition (article, 2021-08-09)
Field: Materials Science — Materials Chemistry

Towards Data‐Driven Design of Asymmetric Hydrogenation of Olefins: Database and Hierarchical Learning Asymmetric hydrogenation of olefins is one of the most powerful asymmetric transformations in molecular synthesis. Although several privileged catalyst scaffolds are available, the catalyst development for asymmetric hydrogenation is still a time- and resource-consuming process due to the lack of predictive catalyst design strategy. Targeting the data-driven design of asymmetric catalysis, we herein report the development of a standardized database that contains the detailed information of over 12000 literature asymmetric hydrogenations of olefins. This database provides a valuable platform for the machine learning applications in asymmetric catalysis. Based on this database, we developed a hierarchical learning approach to achieve predictive machine leaning model using only dozens of enantioselectivity data with the target olefin, which offers a useful solution for the few-shot learning problem and will facilitate the reaction optimization with new olefin substrate in catalysis screening. Olefin fiber ; Computer science ; Catalysis ; Asymmetric hydrogenation ; Enantioselective synthesis ; Process (computing) ; Substrate (aquarium) ; Database ; Artificial intelligence ; Chemistry ; Organic chemistry Machine Learning in Materials Science ; Asymmetric Hydrogenation and Catalysis ; Topic Modeling
Authors: Seoin Back | Jonggeol Na | Zachary W. Ulissi
Source: ACS Catalysis (article, 2021-02-10)
Field: Materials Science — Materials Chemistry

Efficient Discovery of Active, Selective, and Stable Catalysts for Electrochemical H <sub>2</sub> O <sub>2</sub> Synthesis through Active Motif Screening Electrochemical reduction of O2 provides a clean and decentralized pathway to produce H2O2 compared to the current energy-intensive anthraquinone process. As the electrochemical reduction of O2 proceeds via either a two-electron or a four-electron pathway, it is thus essential to control the selectivity as well as to maximize the catalytic activity. Siahrostami et al. [ Nat. Mater. 2013, 12, 1137] demonstrated a novel approach to control the reaction pathway by optimizing an adsorption ensemble to tune adsorption sites of reaction intermediates, identified Pt–Hg catalysts from density functional theory (DFT) calculations, and experimentally validated this catalyst. Inspired by this concept, in this work, we apply a state-of-the-art high-throughput screening to develop an O2 reduction catalyst for selective H2O2 production. Starting from the Materials Project database, we evaluate activity, selectivity, and electrochemical stability. To efficiently perform the screening, we introduce an active-motif-based approach, which pre-screens unpromising materials and performs DFT calculations only for promising materials, which significantly reduces the number of the required calculations. Finally, we discuss a strategy for efficient future high-throughput screening using a machine learning pipeline consisting of a nonlinear dimension reduction and a density-based clustering. Catalysis ; Electrochemistry ; Density functional theory ; Combinatorial chemistry ; Selectivity ; Chemistry ; High-throughput screening ; Active site ; Nanotechnology ; Materials science ; Computational chemistry ; Organic chemistry ; Physical chemistry Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; CO2 Reduction Techniques and Catalysts
Authors: Bo Yang | Cheng Chen | Fangfang Chen | Chen Chen | Jun Tang | Rui Gao | Xiaoyi Lv
Source: Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy (article, 2021-05-12)
Field: Chemistry — Analytical Chemistry

Identification of cumin and fennel from different regions based on generative adversarial networks and near infrared spectroscopy Principal component analysis ; Artificial intelligence ; Chemometrics ; Pattern recognition (psychology) ; Chemistry ; Linear discriminant analysis ; Cuminum ; Artificial neural network ; Curse of dimensionality ; Machine learning ; Computer science ; Food science ; Essential oil ; Chromatography Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Essential Oils and Antimicrobial Activity
Authors: Zhiliang Wang | Yuang Gu | Lingxia Zheng | Jingwei Hou | Huajun Zheng | Shijing Sun | Lianzhou Wang
Source: Advanced Materials (article, 2021-12-29)
Field: Materials Science — Materials Chemistry

Machine Learning Guided Dopant Selection for Metal Oxide‐Based Photoelectrochemical Water Splitting: The Case Study of Fe<sub>2</sub>O<sub>3</sub> and CuO Doping is an effective strategy for tuning metal oxide-based semiconductors for solar-driven photoelectrochemical (PEC) water splitting. Despite decades of extensive research effort, the dopant selection is still largely dependent on a trial-and-error approach. Machine learning (ML) is promising in providing predictable insights on the dopant selection for high-performing PEC systems because it can uncover correlations from the seemingly ambiguous linkages between vast features of dopants and the PEC performance of doped photoelectrodes. Herein, the authors successfully build ML model to predict the doping effect of 17 metal dopants into hematite (Fe<sub>2</sub> O<sub>3</sub> ), a prototype photoelectrode material. Their findings disclose the critical parameters from the 10 intrinsic features of each dopant. The model is further experimentally validated by the coherent prediction on Y and La dopants' behaviors. Further interpretation of the ML model suggests that the chemical state is the most significant selection criteria, meanwhile, dopants with higher metal-oxygen bond formation enthalpy and larger ionic radius are favored in improving the charge separation and transfer (CST) in the Fe<sub>2</sub> O<sub>3</sub> photoanodes. The generic feature of this ML guided selection criteria has been further extended to CuO-based photoelectrodes showing improved CST by alkaline metal ions doping. Dopant ; Ionic radius ; Materials science ; Doping ; Water splitting ; Oxide ; Ionic bonding ; Metal ; Inorganic chemistry ; Nanotechnology ; Chemical physics ; Ion ; Optoelectronics ; Metallurgy ; Chemistry ; Photocatalysis ; Catalysis Copper-based nanomaterials and applications ; Advanced Photocatalysis Techniques ; Iron oxide chemistry and applications
Authors: Wei-Chih Chen | Joanna N. Schmidt | Da Yan | Yogesh K. Vohra | Cheng-Chien Chen
Source: npj Computational Materials (article, 2021-07-21)
Field: Materials Science — Materials Chemistry

Machine learning and evolutionary prediction of superhard B-C-N compounds Abstract We build random forests models to predict elastic properties and mechanical hardness of a compound, using only its chemical formula as input. The model training uses over 10,000 target compounds and 60 features based on stoichiometric attributes, elemental properties, orbital occupations, and ionic bonding levels. Using the models, we construct triangular graphs for B-C-N compounds to map out their bulk and shear moduli, as well as hardness values. The graphs indicate that a 1:1 B-N ratio can lead to various superhard compositions. We also validate the machine learning results by evolutionary structure prediction and density functional theory. Our study shows that BC 10 N, B 4 C 5 N 3 , and B 2 C 3 N exhibit dynamically stable phases with hardness values &gt;40 GPa, which are superhard materials that potentially could be synthesized by low-temperature plasma methods. Artificial intelligence ; Computer science ; Machine learning ; Materials science Boron and Carbon Nanomaterials Research ; MXene and MAX Phase Materials ; Metal and Thin Film Mechanics
Authors: Wenshuo Liang | Guimin Lu | Jianguo Yu
Source: ACS Applied Materials & Interfaces (article, 2021-01-12)
Field: Materials Science — Materials Chemistry

Machine-Learning-Driven Simulations on Microstructure and Thermophysical Properties of MgCl<sub>2</sub>–KCl Eutectic Theoretical studies on the MgCl2–KCl eutectic heavily rely on ab initio calculations based on density functional theory (DFT). However, neither large-scale nor long-time calculations are feasible in the framework of the ab initio method, which makes it challenging to accurately predict some properties. To address this issue, a scheme based on ab initio calculation, deep neural networks, and machine learning is introduced. By training on high-quality data sets generated by ab initio calculations, a deep potential (DP) is constructed to describe the interaction between atoms. This work shows that the DP enables higher efficiency and similar accuracy relative to DFT. By performing molecular dynamics simulations with DP, the microstructure and thermophysical properties of the MgCl2–KCl eutectic (32:68 mol %) are investigated. The structural evolution with temperature is analyzed through partial radial distribution functions, coordination numbers, angular distribution functions, and structural factors. Meanwhile, the estimated thermophysical properties are discussed, including density, thermal expansion coefficient, shear viscosity, self-diffusion coefficient, and specific heat capacity. It reveals that the Mg2+ ions in this system have a distorted tetrahedral geometry rather than an octahedral one (with vacancies). The microstructure of the MgCl2–KCl eutectic shows the feature of medium-range order, and this feature will be enhanced at a higher temperature. All predicted thermophysical properties are in good agreement with the experimental results. The hydrodynamic radius determined from the shear viscosity and self-diffusion coefficient shows that the Mg2+ ions have a strong local structure and diffuse as if with an intact coordination shell. Overall, this work provides a thorough understanding of the microstructure and enriches the data of the thermophysical properties of the MgCl2–KCl eutectic. Materials science ; Eutectic system ; Ab initio ; Thermodynamics ; Microstructure ; Diffusion ; Molecular dynamics ; Ab initio quantum chemistry methods ; Density functional theory ; Computational chemistry ; Physics ; Chemistry ; Molecule ; Composite material Thermal Expansion and Ionic Conductivity ; Chemical Looping and Thermochemical Processes ; Molten salt chemistry and electrochemical processes
Authors: Mingliang Ge | Yueying Li | Caiping Zhu | Guodong Liang | Jahangir Alam S.M. | Guoqing Hu | Yuee Gui | Junaebur Rashid M.
Source: Journal of Materials Science (article, 2021-06-03)
Field: Materials Science — Biomaterials

Preparation of organic-modified magadiite–magnetic nanocomposite particles as an effective nanohybrid drug carrier material for cancer treatment and its properties of sustained release mechanism by Korsmeyer–Peppas kinetic model Ammonium bromide ; Materials science ; Nanocomposite ; Nanoparticle ; Drug delivery ; Drug carrier ; Diffusion ; Chemical engineering ; Magnetic nanoparticles ; Coprecipitation ; Nuclear chemistry ; Nanotechnology ; Chemistry ; Pulmonary surfactant Clay minerals and soil interactions ; Nanoparticle-Based Drug Delivery ; Iron oxide chemistry and applications
Authors: Ernesto Tinajero‐Díaz | Daniela Salado‐Leza | Carmen González | Moisés Martínez‐Velázquez | Zaira López | Jorge Bravo‐Madrigal | Peter Knauth | F. Hernández | Sara Elisa Herrera-Rodríguez | Rosa E. Navarro | Alejandro Cabrera‐Wrooman | Edgar Krötzsch | Zaira Y. García‐Carvajal | Rodolfo Hernández‐Gutiérrez
Source: Pharmaceutics (review, 2021-10-18)
Field: Materials Science — Materials Chemistry

Green Metallic Nanoparticles for Cancer Therapy: Evaluation Models and Cancer Applications Metal-based nanoparticles are widely used to deliver bioactive molecules and drugs to improve cancer therapy. Several research works have highlighted the synthesis of gold and silver nanoparticles by green chemistry, using biological entities to minimize the use of solvents and control their physicochemical and biological properties. Recent advances in evaluating the anticancer effect of green biogenic Au and Ag nanoparticles are mainly focused on the use of conventional 2D cell culture and in vivo murine models that allow determination of the half-maximal inhibitory concentration, a critical parameter to move forward clinical trials. However, the interaction between nanoparticles and the tumor microenvironment is not yet fully understood. Therefore, it is necessary to develop more human-like evaluation models or to improve the existing ones for a better understanding of the molecular bases of cancer. This review provides recent advances in biosynthesized Au and Ag nanoparticles for seven of the most common and relevant cancers and their biological assessment. In addition, it provides a general idea of the in silico, in vitro, ex vivo, and in vivo models used for the anticancer evaluation of green biogenic metal-based nanoparticles. Nanotechnology ; Nanoparticle ; In silico ; In vivo ; Cancer ; Ex vivo ; Cancer therapy ; Silver nanoparticle ; Chemistry ; Biochemical engineering ; Computational biology ; Computer science ; Materials science ; Biotechnology ; Biology ; Biochemistry ; Engineering Nanoparticles: synthesis and applications ; Nanoparticle-Based Drug Delivery ; Nanoplatforms for cancer theranostics
Authors: Huiyu Li | Liheng Bian | Kai Gu | Hao Fu | Gaoling Yang | Haizheng Zhong | Jun Zhang
Source: Advanced Optical Materials (article, 2021-05-07)
Field: Materials Science — Materials Chemistry

A Near‐Infrared Miniature Quantum Dot Spectrometer Abstract The acquisition of near‐infrared (NIR) spectral information is most notably accessed in the scientific analysis and industrial technology. With the development of artificial intelligence technology, miniature NIR spectrometers are usually required to realize compact equipment. Quantum dot spectrometer is demonstrated to be a promising reconstructive spectrometer; however, the spectral range is limited in the visible range due to the lack of high‐resolution NIR detectors and quantum dot filters. In this work, a NIR quantum dot spectrometer is constructed by integrating 195 finely spectra‐tunable PbS and PbSe based NIR quantum dots as filters and a NIR complementary metal‐oxide semiconductor as detector. The spectra‐tunable PbS and PbSe quantum dots are achieved by controlling the key parameters of colloidal synthesis, ligand exchange, and cation exchange. Combined with the compressive‐sensing based total‐variation algorithm, the NIR quantum dot spectrometer reaches an average spectral resolution of 6 nm in the wide spectral range of 900–1700 nm, showing the bright prospect in developing compact NIR spectrometer for artificial intelligence technology. Spectrometer ; Quantum dot ; Materials science ; Spectral resolution ; Near-infrared spectroscopy ; Detector ; Optoelectronics ; Spectroscopy ; Quantum dot laser ; Optics ; Spectral line ; Semiconductor ; Physics ; Semiconductor laser theory Quantum Dots Synthesis And Properties ; Advanced biosensing and bioanalysis techniques ; Biosensors and Analytical Detection
Authors: Hongtao Guo | Yang Li | Yuan Ji | Yiming Chen | Kunming Liu | Bin Shen | Shuijian He | Gaigai Duan | Jingquan Han | Shaohua Jiang
Source: Composites Communications (article, 2021-07-27)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Highly flexible carbon nanotubes/aramid nanofibers composite papers with ordered and layered structures for efficient electromagnetic interference shielding Electromagnetic shielding ; Materials science ; EMI ; Electromagnetic interference ; Composite number ; Carbon nanotube ; Aramid ; Electrical conductor ; Composite material ; Nanocomposite ; Electronics ; Flexible electronics ; Nanofiber ; Optoelectronics ; Nanotechnology ; Electrical engineering ; Fiber ; Computer science ; Telecommunications Electromagnetic wave absorption materials ; Advanced Antenna and Metasurface Technologies
Authors: Ran Zhang | Zhanping You | Jie Ji | Qingwen Shi | Zhi Suo
Source: Molecules (review, 2021-08-20)
Field: Materials Science — Polymers and Plastics

A Review of Characteristics of Bio-Oils and Their Utilization as Additives of Asphalts Transforming waste biomass materials into bio-oils in order to partially substitute petroleum asphalt can reduce environmental pollution and fossil energy consumption and has economic benefits. The characteristics of bio-oils and their utilization as additives of asphalts are the focus of this review. First, physicochemical properties of various bio-oils are characterized. Then, conventional, rheological, and chemical properties of bio-oil modified asphalt binders are synthetically reviewed, as well as road performance of bio-oil modified asphalt mixtures. Finally, performance optimization is discussed for bio-asphalt binders and mixtures. This review indicates that bio-oils are highly complex materials that contain various compounds. Moreover, bio-oils are source-depending materials for which its properties vary with different sources. Most bio-oils have a favorable stimulus upon the low temperature performance of asphalt binders and mixtures but exhibit a negative impact on their high-temperature performance. Moreover, a large amount of oxygen element, oxygen-comprising functional groups, and light components in plant-based bio-oils result in higher sensitivity to ageing of bio-oil modified asphalts. In order to increase the performance of bio-asphalts, most research has been limited to adding additive agents to bio-asphalts; therefore, more reasonable optimization methods need to be proposed. Furthermore, upcoming exploration is also needed to identify reasonable evaluation indicators of bio-oils, modification mechanisms of bio-asphalts, and long-term performance tracking in field applications of bio-asphalts during pavement service life. Asphalt ; Environmental science ; Petroleum ; Materials science ; Rheology ; Pulp and paper industry ; Biochemical engineering ; Waste management ; Composite material ; Chemistry ; Engineering ; Organic chemistry Natural Fiber Reinforced Composites ; Asphalt Pavement Performance Evaluation ; Lignin and Wood Chemistry
Authors: Kalyan Ghosh | Siowwoon Ng | Christian Iffelsberger | Martin Pumera
Source: Applied Materials Today (article, 2021-12-10)
Field: Materials Science — Electronic, Optical and Magnetic Materials

2D MoS2/carbon/polylactic acid filament for 3D printing: Photo and electrochemical energy conversion and storage Materials science ; Fused filament fabrication ; Polylactic acid ; Fabrication ; Nanotechnology ; 3D printing ; Electrode ; Polymer ; Fused deposition modeling ; Conductive polymer ; Carbon nanotube ; Supercapacitor ; Composite material ; Electrochemistry ; Chemistry Supercapacitor Materials and Fabrication ; Conducting polymers and applications ; Advanced Sensor and Energy Harvesting Materials