jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Agil Azimzada | Ibrahim Jreije | Madjid Hadioui | Phil Shaw | Jeffrey M. Farner | Kevin J. Wilkinson
Source: Environmental Science & Technology (article, 2021-06-28)
Field: Materials Science — Materials Chemistry

Quantification and Characterization of Ti-, Ce-, and Ag-Nanoparticles in Global Surface Waters and Precipitation Nanoparticle (NP) emissions to the environment are increasing as a result of anthropogenic activities, prompting concerns for ecosystems and human health. In order to evaluate the risk of NPs, it is necessary to know their concentrations in various environmental compartments on regional and global scales; however, these data have remained largely elusive due to the analytical difficulties of measuring NPs in complex natural matrices. Here, we measure NP concentrations and sizes for Ti-, Ce-, and Ag-containing NPs in numerous global surface waters and precipitation samples, and we provide insights into their compositions and origins (natural or anthropogenic). The results link NP occurrences and distributions to particle type, origin, and sampling location. Based on measurements from 46 sites across 13 countries, total Ti- and Ce-NP concentrations (regardless of origin) were often found to be within 10<sup>4</sup> to 10<sup>7</sup> NP mL<sup>-1</sup>, whereas Ag NPs exhibited sporadic occurrences with low concentrations generally up to 10<sup>5</sup> NP mL<sup>-1</sup>. This generally corresponded to mass concentrations of <1 ng L<sup>-1</sup> for Ag-NPs, <100 ng L<sup>-1</sup> for Ce-NPs, and <10 μg L<sup>-1</sup> for Ti-NPs, given that measured sizes were often below 15 nm for Ce- and Ag-NPs and above 30 nm for Ti-NPs. In view of current toxicological data, the observed NP levels do not yet appear to exceed toxicity thresholds for the environment or human health; however, NPs of likely anthropogenic origins appear to be already substantial in certain areas, such as urban centers. This work lays the foundation for broader experimental NP surveys, which will be critical for reliable NP risk assessments and the regulation of nano-enabled products. Precipitation ; Environmental chemistry ; Nanoparticle ; Human health ; Ecosystem ; Environmental science ; Chemistry ; Nanotechnology ; Materials science ; Ecology ; Geography ; Biology ; Meteorology Nanoparticles: synthesis and applications ; Heavy metals in environment ; Air Quality and Health Impacts
Authors: Shengtai Hou | Xuefeng Ma | Shu Yuan | Jiafeng Bao | Qiuyue Zhang | Mingshu Chen | Pengfei Zhang | Sheng Dai
Source: Nature Communications (article, 2021-10-11)
Field: Materials Science — Materials Chemistry

Self-regeneration of supported transition metals by a high entropy-driven principle The sintering of Supported Transition Metal Catalysts (STMCs) is a core issue during high temperature catalysis. Perovskite oxides as host matrix for STMCs are proven to be sintering-resistance, leading to a family of self-regenerative materials. However, none other design principles for self-regenerative catalysts were put forward since 2002, which cannot satisfy diverse catalytic processes. Herein, inspired by the principle of high entropy-stabilized structure, a concept whether entropy driving force could promote the self-regeneration process is proposed. To verify it, a high entropy cubic Zr<sub>0.5</sub>(NiFeCuMnCo)<sub>0.5</sub>O<sub>x</sub> is constructed as a host model, and interestingly in situ reversible exsolution-dissolution of supported metallic species are observed in multi redox cycles. Notably, in situ exsolved transition metals from high entropy Zr<sub>0.5</sub>(NiFeCuMnCo)<sub>0.5</sub>O<sub>x</sub> support, whose entropic contribution (TΔS<sub>config</sub> = T⋆12.7 J mol<sup>-1</sup> K<sup>-1</sup>) is predominant in ∆G, affording ultrahigh thermal stability in long-term CO<sub>2</sub> hydrogenation (400 °C, >500 h). Current theory may inspire more STWCs with excellent sintering-resistance performance. Regeneration (biology) ; Entropy (arrow of time) ; Statistical physics ; Physics ; Biology ; Thermodynamics ; Cell biology Catalytic Processes in Materials Science ; High Entropy Alloys Studies ; Catalysis and Oxidation Reactions
Authors: Chunming Tan | Fei Han | Shiqi Zhang | Pinglan Li | Nan Shang
Source: International Journal of Molecular Sciences (review, 2021-09-07)
Field: Materials Science — Biomaterials

Novel Bio-Based Materials and Applications in Antimicrobial Food Packaging: Recent Advances and Future Trends Food microbial contamination not only poses the problems of food insecurity and economic loss, but also contributes to food waste, which is another global environmental problem. Therefore, effective packaging is a compelling obstacle for shielding food items from outside contaminants and maintaining its quality. Traditionally, food is packaged with plastic that is rarely recyclable, negatively impacting the environment. Bio-based materials have attracted widespread attention for food packaging applications since they are biodegradable, renewable, and have a low carbon footprint. They provide a great opportunity to reduce the extensive use of fossil fuels and develop food packaging materials with good properties, addressing environmental problems and contributing significantly to sustainable development. Presently, the developments in food chemistry, technology, and biotechnology have allowed us to fine-tune new methodologies useful for addressing major safety and environmental concerns regarding packaging materials. This review presents a comprehensive overview of the development and potential for application of new bio-based materials from different sources in antimicrobial food packaging, including carbohydrate (polysaccharide)-based materials, protein-based materials, lipid-based materials, antibacterial agents, and bio-based composites, which can solve the issues of both environmental impact and prevent foodborne pathogens and spoilage microorganisms. In addition, future trends are discussed, as well as the antimicrobial compounds incorporated in packaging materials such as nanoparticles (NPs), nanofillers (NFs), and bio-nanocomposites. Food packaging ; Carbon footprint ; Food spoilage ; Active packaging ; Biochemical engineering ; Food waste ; Biotechnology ; Food safety ; Environmental science ; Business ; Waste management ; Engineering ; Food science ; Chemistry ; Greenhouse gas ; Biology Nanocomposite Films for Food Packaging ; biodegradable polymer synthesis and properties ; Microplastics and Plastic Pollution
Authors: Jiayi Tang | Chao Su | Zongping Shao
Source: Small Methods (review, 2021-10-27)
Field: Materials Science — Materials Chemistry

Covalent Organic Framework (COF)‐Based Hybrids for Electrocatalysis: Recent Advances and Perspectives Developing highly efficient electrocatalysts for renewable energy conversion and environment purification has long been a research priority in the past 15 years. Covalent organic frameworks (COFs) have emerged as a burgeoning family of organic materials internally connected by covalent bonds and have been explored as promising candidates in electrocatalysis. The reticular geometry of COFs can provide an excellent platform for precise incorporation of the active sites in the framework, and the fine-tuning hierarchical porous architectures can enable efficient accessibility of the active sites and mass transportation. Considerable advances are made in rational design and controllable fabrication of COF-based organic-inorganic hybrids, that containing organic frameworks and inorganic electroactive species to induce novel physicochemical properties, and take advantage of the synergistic effect for targeted electrocatalysis with the hybrid system. Branches of COF-based hybrids containing a diversity form of metals, metal compounds, as well as metal-free carbons have come to the fore as highly promising electrocatalysts. This review aims to provide a systematic and profound understanding of the design principles behind the COF-based hybrids for electrocatalysis applications. Particularly, the structure-activity relationship and the synergistic effects in the COF-based hybrid systems are discussed to shed some light on the future design of next-generation electrocatalysts. Electrocatalyst ; Rational design ; Covalent bond ; Nanotechnology ; Hybrid material ; Design elements and principles ; Materials science ; Chemistry ; Computer science ; Electrochemistry ; Organic chemistry ; Electrode Covalent Organic Framework Applications ; Advanced Photocatalysis Techniques ; Metal-Organic Frameworks: Synthesis and Applications
Authors: Qian Zhao | Maxim Avdeev | Liquan Chen | Siqi Shi
Source: Science Bulletin (article, 2021-04-23)
Field: Materials Science — Materials Chemistry

Machine learning prediction of activation energy in cubic Li-argyrodites with hierarchically encoding crystal structure-based (HECS) descriptors Ionic conductivity ; Crystal structure ; Mean squared error ; Activation energy ; Ion ; Chemistry ; Materials science ; Mathematics ; Electrolyte ; Physical chemistry ; Statistics ; Crystallography Machine Learning in Materials Science ; Advanced Battery Materials and Technologies ; Fuel Cells and Related Materials
Authors: Sushil Kumar | Gergő Ignácz | György Székely
Source: Green Chemistry (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

Synthesis of covalent organic frameworks using sustainable solvents and machine learning Covalent organic frameworks have been prepared in sustainable solvents by a solvothermal method, and their porosity and crystallinity were predicted using QSPR and machine learning approaches. Crystallinity ; Covalent bond ; Porosity ; Quantitative structure–activity relationship ; Chemistry ; Organic chemistry ; Stereochemistry Covalent Organic Framework Applications ; Metal-Organic Frameworks: Synthesis and Applications ; Advanced Photocatalysis Techniques
Authors: Airlie J. McCoy | Massimo D. Sammito | Randy J. Read
Source: Acta Crystallographica Section D Structural Biology (article, 2021-12-22)
Field: Materials Science — Materials Chemistry

Implications of <i>AlphaFold</i> 2 for crystallographic phasing by molecular replacement The AlphaFold2 results in the 14th edition of Critical Assessment of Structure Prediction (CASP14) showed that accurate (low root-mean-square deviation) in silico models of protein structure domains are on the horizon, whether or not the protein is related to known structures through high-coverage sequence similarity. As highly accurate models become available, generated by harnessing the power of correlated mutations and deep learning, one of the aspects of structural biology to be impacted will be methods of phasing in crystallography. Here, the data from CASP14 are used to explore the prospects for changes in phasing methods, and in particular to explore the prospects for molecular-replacement phasing using in silico models. Phaser ; In silico ; Molecular replacement ; Sequence (biology) ; Protein structure ; Mutation ; Structural biology Enzyme Structure and Function ; Protein Structure and Dynamics ; Bacterial Genetics and Biotechnology
Authors: Sidrish Zahra | Liang Ma | Wenjiao Wang | Jian Li | Dexu Chen | Yifeng Liu | Yuedan Zhou | Na Li | Yongjun Huang | Guangjun Wen
Source: Frontiers in Physics (review, 2021-01-14)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Electromagnetic Metasurfaces and Reconfigurable Metasurfaces: A Review Metamaterial is a kind of material/structure that is artificially designed and has exceptional electromagnetic properties and/or other physical properties, not found in nature. A class of electromagnetic metamaterial with only one or a few layers of periodic or aperiodic arranged cell structures in the direction of electromagnetic waves propagation can be referred to as a metasurface. Metasurface can be considered as a two-dimensional representation of metamaterial and can realize the controlling of the amplitude, phase, and polarization state/direction of the incident electromagnetic wave. According to the novel electromagnetic characteristics of metasurface and its big advantages, a series of new planar devices and systems based on metasurface can be developed. The goal of this review article is firstly to provide introductions for basic metasurface, its significance properties, and application principles. Meanwhile, the main research progresses of regular metasurfaces and the newly developed reconfigurable metasurfaces are analyzed, focusing on the types of amplitude modulation, phase modulation, polarization modulation, and multidimensional modulation. Finally, the research significances of metasurface development trend and important engineering practical applications are analyzed in the end. Metamaterial ; Aperiodic graph ; Electromagnetic radiation ; Polarization (electrochemistry) ; Planar ; Physics ; Modulation (music) ; Amplitude modulation ; Phase modulation ; Optics ; Computer science ; Frequency modulation ; Acoustics ; Telecommunications ; Radio frequency ; Mathematics Metamaterials and Metasurfaces Applications ; Antenna Design and Analysis ; Advanced Antenna and Metasurface Technologies
Authors: María Paulina Romero | Fernanda Alves | Mirian Denise Stringasci | Hilde Harb Buzzá | Heloísa Ciol | Natália Mayumi Inada | Vanderlei Salvador Bagnato
Source: Frontiers in Microbiology (article, 2021-06-21)
Field: Materials Science — Materials Chemistry

One-Pot Microwave-Assisted Synthesis of Carbon Dots and in vivo and in vitro Antimicrobial Photodynamic Applications Carbon-based photosensitizers are more attractive than the other ones based on their low cost, high stability, broadband of light absorption, tunable emission spectra, high quantum yield, water solubility, high resistance to metabolic degradation, and selective delivery. These properties allow multiple applications in the field of biology and medicine. The present study evaluated <i>in vitro</i> and <i>in vivo</i> the antimicrobial photodynamic effect of a one-pot microwave produced C-DOTS based on citric acid. The <i>in vitro</i> assays assessed the effectiveness of illuminated C-DOTS (C-DOTS + light) against <i>Staphylococcus aureus</i> suspension and biofilm. The concentrations of 6.9 and 13.8 mg/mL of C-DOTS and light doses of 20 and 40 J/cm<sup>2</sup> were able to reduce significantly the microorganisms. Based on these parameters and results, the <i>in vivo</i> experiments were conducted in mice, evaluating this treatment on wounds contaminated with <i>S. aureus.</i> The viability test showed that C-DOTS-mediated photodynamic inactivation reduced 10<sup>4</sup> log of the bacteria present on the skin lesions. These results, altogether, showed that antibacterial photodynamic therapy using C-DOTS is a promising and viable treatment for Gram-positive bacteria-infected wounds. Photodynamic therapy ; In vivo ; Antimicrobial ; Staphylococcus aureus ; In vitro ; Chemistry ; Quantum dot ; Microbiology ; Bacteria ; Nanotechnology ; Materials science ; Biology ; Biochemistry ; Organic chemistry ; Biotechnology Carbon and Quantum Dots Applications ; Photodynamic Therapy Research Studies ; Graphene and Nanomaterials Applications
Authors: Norfadilah Rosle | Nor Asiah Muhamad | Mohamad Nur Khairul Hafizi Rohani | Mohamad Kamarol Mohd Jamil
Source: IEEE Access (review, 2021-01-01)
Field: Materials Science — Materials Chemistry

Partial Discharges Classification Methods in XLPE Cable: A Review Partial discharge (PD) signal classification analysis on cross-linked polyethylene (XLPE) cables is complex, requiring a comprehensive understanding of the characteristics of PD patterns. In the realm of high-voltage electrical insulation, PD pattern characteristics, such as PD charge and inception voltage, are essential as assessment criteria in diagnostics systems using PD classifiers. This paper provides a review of various PD patterns and classifiers used by previous researchers, specifically for XLPE cables. In addition, the differences of the research on various sensor development based on PD detection in the past 27 years are also discussed. The repeatability, recognition accuracy, recognition speed, and effect of feature sizes on each PD classification method are reviewed and explained. The review indicates that the pattern recognition for PD signal using artificial neural network (ANN) exhibits better performance in terms of accuracy and repeatability than the other methods, and the reduction of feature size does not affect the accuracy of ANN. Partial discharge ; Repeatability ; Pattern recognition (psychology) ; Artificial neural network ; Artificial intelligence ; SIGNAL (programming language) ; Computer science ; Voltage ; Feature (linguistics) ; Feature extraction ; Polyethylene ; Materials science ; Engineering ; Electrical engineering ; Statistics ; Mathematics High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Advanced Sensor and Energy Harvesting Materials
Authors: Stéfano Frizzo Stefenon | Matheus Henrique Dal Molin Ribeiro | Ademir Nied | Viviana Cocco Mariani | Leandro dos Santos Coelho | Valderi Reis Quietinho Leithardt | Luís Augusto Silva | Laio Oriel Seman
Source: IEEE Access (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

Hybrid Wavelet Stacking Ensemble Model for Insulators Contamination Forecasting Contaminated insulators can have higher surface conductivity, which can result in irreversible failures in the electrical power system. In this paper, the ultrasound equipment is used to assist in the prediction of failure identification in porcelain insulators of the 13.8 kV, 60 Hz pin profile. To perform the laboratory analysis, insulators from a problematic branch are removed after an inspection of the electrical system and are evaluated in the laboratory under controlled conditions. To perform the time series predictions, the stacking ensemble learning model is applied with the wavelet transform for signal filtering and noise reduction. For a complete analysis of the model, variations in its configuration were evaluated. The results of root mean square error (RMSE), mean absolute percentage error (MAPE), mean absolute error (MAE), and coefficient of determination (R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) are presented. To validate the result, a benchmarking is presented with well-established models, such as an adaptive neuro-fuzzy inference system (ANFIS) and long-term short-term memory (LSTM). Mean absolute percentage error ; Mean squared error ; Adaptive neuro fuzzy inference system ; Computer science ; Wavelet ; Root mean square ; Discrete wavelet transform ; Artificial intelligence ; Wavelet transform ; Fuzzy logic ; Pattern recognition (psychology) ; Statistics ; Mathematics ; Fuzzy control system ; Engineering ; Electrical engineering High voltage insulation and dielectric phenomena ; Non-Destructive Testing Techniques ; Power Transformer Diagnostics and Insulation
Authors: Maria Frizzarin | Isobel Claire Gormley | D.P. Berry | Thomas Brendan Murphy | Alessandro Casa | Allison Lynch | S. McParland
Source: Journal of Dairy Science (article, 2021-04-16)
Field: Chemistry — Analytical Chemistry

Predicting cow milk quality traits from routinely available milk spectra using statistical machine learning methods Numerous statistical machine learning methods suitable for application to highly correlated features, as those that exist for spectral data, could potentially improve prediction performance over the commonly used partial least squares approach. Milk samples from 622 individual cows with known detailed protein composition and technological trait data accompanied by mid-infrared spectra were available to assess the predictive ability of different regression and classification algorithms. The regression-based approaches were partial least squares regression (PLSR), ridge regression (RR), least absolute shrinkage and selection operator (LASSO), elastic net, principal component regression, projection pursuit regression, spike and slab regression, random forests, boosting decision trees, neural networks (NN), and a post-hoc approach of model averaging (MA). Several classification methods (i.e., partial least squares discriminant analysis (PLSDA), random forests, boosting decision trees, and support vector machines (SVM)) were also used after stratifying the traits of interest into categories. In the regression analyses, MA was the best prediction method for 6 of the 14 traits investigated [curd firmness at 60 min, α<sub>S1</sub>-casein (CN), α<sub>S2</sub>-CN, κ-CN, α-lactalbumin, and β-lactoglobulin B], whereas NN and RR were the best algorithms for 3 traits each (rennet coagulation time, curd-firming time, and heat stability, and curd firmness at 30 min, β-CN, and β-lactoglobulin A, respectively), PLSR was best for pH, and LASSO was best for CN micelle size. When traits were divided into 2 classes, SVM had the greatest accuracy for the majority of the traits investigated. Although the well-established PLSR-based method performed competitively, the application of statistical machine learning methods for regression analyses reduced the root mean square error compared with PLSR from between 0.18% (κ-CN) to 3.67% (heat stability). The use of modern statistical machine learning methods for trait prediction from mid-infrared spectroscopy may improve the prediction accuracy for some traits. Partial least squares regression ; Lasso (programming language) ; Support vector machine ; Artificial intelligence ; Mathematics ; Regression ; Elastic net regularization ; Statistics ; Boosting (machine learning) ; Principal component analysis ; Linear discriminant analysis ; Random forest ; Machine learning ; Computer science Spectroscopy and Chemometric Analyses ; Milk Quality and Mastitis in Dairy Cows ; Genetic and phenotypic traits in livestock
Authors: Yong Ai | Hui‐Peng Lv | Zhong‐Xia Wang | Wei‐Qiang Liao | Ren‐Gen Xiong
Source: Trends in Chemistry (article, 2021-10-13)
Field: Materials Science — Materials Chemistry

H/F substitution for advanced molecular ferroelectrics Substitution (logic) ; Materials science ; Computer science ; Programming language Photochromic and Fluorescence Chemistry ; Organic Chemistry Cycloaddition Reactions ; Radical Photochemical Reactions
Authors: Andrea Dodero | Guy Schlatter | Anne Hébraud | Silvia Vicini | Maila Castellano
Source: Carbohydrate Polymers (review, 2021-04-07)
Field: Materials Science — Biomaterials

Polymer-free cyclodextrin and natural polymer-cyclodextrin electrospun nanofibers: A comprehensive review on current applications and future perspectives Nanofiber ; Cyclodextrin ; Electrospinning ; Nanotechnology ; Polymer ; Natural polymers ; Biopolymer ; Materials science ; Chemistry ; Organic chemistry ; Composite material Electrospun Nanofibers in Biomedical Applications ; Electrohydrodynamics and Fluid Dynamics