jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Wenxia Li | Zihan Wei | Zhengdong Liu | Yujun Du | Jiahui Zheng | Huaping Wang | Shuo Zhang
Source: Textile Research Journal (article, 2021-04-09)
Field: Chemistry — Analytical Chemistry

Qualitative identification of waste textiles based on near-infrared spectroscopy and the back propagation artificial neural network Hand sorting for different types of waste textiles is time-consuming, laborious and inaccurate. The non-destructive and efficient identification of fibers in waste fabrics is of great significance to the reuse of textile materials. In this paper, 593 samples were selected as the research objects, including polyester, cotton, wool, viscose, nylon, silk, acrylic, polyester/nylon, polyester/cotton, polyester/wool and silk/cotton waste textiles. The near-infrared spectrum of each sample was obtained by a portable near-infrared spectrometer, and the influence of environmental humidity and fabric thickness on the near-infrared spectrum of the sample was discussed to obtain the best test conditions. On this basis, the back propagation artificial neural network (BP-ANN) was applied to the qualitative classification of waste textiles to complete the automatic identification of fabric components in the sorting process. Firstly, a standard sample set was established by waveform clipping and normalization, and a BP-ANN deep web suitable for near-infrared spectroscopy was established. Then the BP network was trained according to the input near-infrared spectrum data of known sample categories and the classification results of the preset 11 types of labels, and the weights and thresholds of each layer were adjusted in the repeated training process. Finally, a 1500 × 100 × 11 network structure was established when the network error was the smallest, and the number of corresponding hidden layer nodes was 100. When the number of training steps was 500, the sum of squared errors reached 0.001, and the model recognition effect was the best. Meanwhile, the validity of the model was verified by inspecting additional 299 samples outside the model, and the recognition accuracy rate of the established model also exceeded 99%, which verified the effectiveness of the model. These results show that this near-infrared qualitative analysis model can more accurately classify and identify waste textiles, especially polyester waste textiles. In addition, it provides a new idea for the recycling and reuse of waste textiles for enterprises. Wool ; Polyester ; Artificial neural network ; Textile ; Materials science ; Backpropagation ; Sample (material) ; Artificial intelligence ; Sorting ; Pattern recognition (psychology) ; Computer science ; Biological system ; Process engineering ; Pulp and paper industry ; Composite material ; Engineering ; Algorithm ; Chemistry Spectroscopy and Chemometric Analyses ; Industrial Vision Systems and Defect Detection ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Amr Awad Ibrahim | Shaimaa L. Ali | Mina Shawky Adly | S.A. El-Hakam | S.E. Samra | Awad I. Ahmed
Source: RSC Advances (article, 2021-01-01)
Field: Chemistry — Organic Chemistry

Green construction of eco-friendly phosphotungstic acid Sr-MOF catalysts for crystal violet removal and synthesis of coumarin and xanthene compounds There is an urgent need to improve engineering and synthetic chemistry, either through the use of eco-friendly starting materials or the proper design of novel synthesis routes. This reduces the contamination of toxic chemicals and helps the disposal of organic dyes. In the current work, a metal-organic framework-based Sr(ii) was fabricated to achieve the desired goal for dye removal and catalysis. Sr-MOF-based phosphotungstic acid (PWA/Sr-MOF) was hydrothermally synthesized to study its adsorption and catalytic activities. Remarkably, about 99.9% of crystal violet (CV) dye was removed using PWA/Sr-MOF within 90 min at room temperature. Various factors have been studied to investigate the optimum conditions such as pH of solution, initial dye concentration, contact time, and temperature. The maximum adsorption capacity of CV dye was reached after 90 min and well fitted the pseudo-second kinetic order and Langmuir adsorption isotherm. Coumarin and xanthene reactions were chosen to test the catalytic activity of the prepared PWA/Sr-MOF at 373 K. Furthermore, structural and chemical characterization of the fabricated samples was obtained using FT-IR, XRD, TGA, DTA, TEM, EDX, and XPS. PWA/Sr-MOF can be considered as a promising and green framework in the material design used to study catalytic and adsorption performances. Xanthene ; Phosphotungstic acid ; Coumarin ; Catalysis ; Crystal violet ; Environmentally friendly ; Chemistry ; Crystal structure ; Organic chemistry ; Combinatorial chemistry Chemical Synthesis and Reactions ; Metal-Organic Frameworks: Synthesis and Applications ; Polyoxometalates: Synthesis and Applications
Authors: Eleonora Cara | Irdi Murataj | Gianluca Milano | Natascia De Leo | Luca Boarino | Federico Ferrarese Lupi
Source: Nanomaterials (review, 2021-04-13)
Field: Materials Science — Materials Chemistry

Recent Advances in Sequential Infiltration Synthesis (SIS) of Block Copolymers (BCPs) In the continuous downscaling of device features, the microelectronics industry is facing the intrinsic limits of conventional lithographic techniques. The development of new synthetic approaches for large-scale nanopatterned materials with enhanced performances is therefore required in the pursuit of the fabrication of next-generation devices. Self-assembled materials as block copolymers (BCPs) provide great control on the definition of nanopatterns, promising to be ideal candidates as templates for the selective incorporation of a variety of inorganic materials when combined with sequential infiltration synthesis (SIS). In this review, we report the latest advances in nanostructured inorganic materials synthesized by infiltration of self-assembled BCPs. We report a comprehensive description of the chemical and physical characterization techniques used for <i>in situ</i> studies of the process mechanism and <i>ex situ</i> measurements of the resulting properties of infiltrated polymers. Finally, emerging optical and electrical properties of such materials are discussed. Nanotechnology ; Materials science ; Microelectronics ; Template ; Characterization (materials science) ; Lithography ; Fabrication ; Copolymer ; Polymer ; Nanoscopic scale ; Computer science ; Optoelectronics ; Composite material Block Copolymer Self-Assembly ; Machine Learning in Materials Science ; Advancements in Battery Materials
Authors: Marco Piccini | Jasmine C. Lightfoot | Bernardo Castro‐Dominguez | Antoine Buchard
Source: ACS Applied Polymer Materials (article, 2021-10-27)
Field: Materials Science — Biomaterials

Xylose-Based Polyethers and Polyesters Via ADMET Polymerization toward Polyethylene-Like Materials One of the challenges of developing bioderived polymers is to obtain materials with competitive properties. This study investigates the structure-properties relationships of polyesters and polyethers that can be derived from d-xylose through metathesis polymerization, in order to produce bioderived plastic materials that are sourced from sustainable feedstocks and whose properties can compete with those of polyolefins such as polyethylene. Bicyclic diol 1,2-O-isopropylidene-α-d-xylofuranose was coupled with ω-unsaturated fatty acids and alcohols of different chain lengths (C11, C5, C3), and the resulting α,ω-unsaturated esters and ethers polymerized via an acyclic diene metathesis (ADMET) polymerization using a commercial Grubbs second-generation catalyst, obtaining polymers with Mn up to 63.0 kg mol–1. Glass transition temperatures (Tg) decreased linearly with increasing chain length and were lower for polyethers (−32 to 14 °C) compared to polyesters (−14 to 45 °C). ADMET polymers could be modified postpolymerization by reacting their internal carbon–carbon double bonds. Thiol–ene reaction with methyl thioglycolate lowered the Tg while allowing insertion of additional functional groups. Alkene hydrogenation turned the polyester and polyether with C20 hydrocarbon linkers into semicrystalline polymers with Tm ≈ 50 °C. The latter, when cast into films, displayed remarkable polyethylene-like properties. Hot-pressed films proved ductile materials (Young modulus Ey 60–110 MPa, elongation at break εb 670–1000%, ultimate tensile strength σb 8–10 MPa), while uniaxially oriented films proved very strong yet flexible materials (Ey 190–200 MPa, εb 160–350%, σb 43–66 MPa). Gas barrier properties were comparable to those of commercial polyolefins. Polyethers were resistant to hydrolysis, while polyesters depolymerized under alkaline conditions. Polyester ; Polymerization ; Polyethylene ; Polymer ; Metathesis ; Polymer chemistry ; Materials science ; Acyclic diene metathesis ; Xylose ; Crystallinity ; Organic chemistry ; Chemistry ; Chemical engineering ; Composite material biodegradable polymer synthesis and properties ; Synthetic Organic Chemistry Methods ; Model-Driven Software Engineering Techniques
Authors: Amy M. McKenna | Martha L. Chacón‐Patiño | Germain Salvato Vallverdu | Brice Bouyssière | Pierre Giusti | Carlos Afonso | Quan Shi | Marianny Y. Combariza
Source: Energy & Fuels (article, 2021-08-19)
Field: Chemistry — Analytical Chemistry

Advances and Challenges in the Molecular Characterization of Petroporphyrins Petroporphyrins, geoporphyrins, or metalloporphyrins are the original petroleum biomarkers, identified by Alfred Treibs more than a century ago and the first molecular evidence for the biogenic origins of petroleum. Since discovery, analytical strategies have been developed to identify porphyrins in petroleum and its fractions. This review focuses on the advances enabled by ultrahigh resolving power Fourier transform ion cyclotron resonance mass spectrometry in tribute to Professor Alan G. Marshall. Fourier transform ion cyclotron resonance ; Tribute ; Characterization (materials science) ; Petroleum ; Mass spectrometry ; Nanotechnology ; Chemistry ; Materials science ; Archaeology ; History ; Chromatography ; Organic chemistry Petroleum Processing and Analysis ; Hydrocarbon exploration and reservoir analysis ; Microbial bioremediation and biosurfactants
Authors: J. Li | A. Hallil | A. Metsue | A. Oudriss | J. Bouhattate | X. Feaugas
Source: Scientific Reports (article, 2021-07-30)
Field: Materials Science — Metals and Alloys

Antagonist effects of grain boundaries between the trapping process and the fast diffusion path in nickel bicrystals Hydrogen-grain-boundaries interactions and their role in intergranular fracture are well accepted as one of the key features in understanding hydrogen embrittlement in a large variety of common engineer situations. These interactions implicate some fundamental processes classified as segregation, trapping and diffusion of the solute which can be studied as a function of grain boundary configuration. In the present study, we carried out an extensive analysis of four grain-boundaries based on the complementary of atomistic calculations and experimental data. We demonstrate that elastic deformation has an important contribution on the segregation energy which cannot be simply reduced to a volume change and need to consider the deviatoric part of strain. Additionally, some significant configurations of the segregation energy depend on the long-range elastic distortion and allows to rationalize the elastic contribution in three terms. By investigating the different energy barriers involved to reach all the segregation sites, the antagonist impact of grain boundaries on hydrogen diffusion and trapping process was elucidated. The segregation energy and migration energy are two fundamental parameters in order to classify the grain-boundaries as a trapping location or short circuit for diffusion. Grain boundary ; Materials science ; Diffusion ; Trapping ; Grain boundary diffusion coefficient ; Embrittlement ; Elastic energy ; Effective diffusion coefficient ; Intergranular fracture ; Intergranular corrosion ; Chemical physics ; Condensed matter physics ; Thermodynamics ; Metallurgy ; Chemistry ; Physics ; Corrosion ; Microstructure Hydrogen embrittlement and corrosion behaviors in metals ; Nuclear Materials and Properties ; Corrosion Behavior and Inhibition
Authors: Lívia B. Pártay | Gábor Cśanyi | Noam Bernstein
Source: The European Physical Journal B (article, 2021-08-01)
Field: Materials Science — Materials Chemistry

Nested sampling for materials Abstract We review the materials science applications of the nested sampling (NS) method, which was originally conceived for calculating the evidence in Bayesian inference. We describe how NS can be adapted to sample the potential energy surface (PES) of atomistic systems, providing a straightforward approximation for the partition function and allowing the evaluation of thermodynamic variables at arbitrary temperatures. After an overview of the basic method, we describe a number of extensions, including using variable cells for constant pressure sampling, the semi-grand-canonical approach for multicomponent systems, parallelizing the algorithm, and visualizing the results. We cover the range of materials applications of NS from the past decade, from exploring the PES of Lennard–Jones clusters to that of multicomponent condensed phase systems. We highlight examples how the information gained via NS promotes the understanding of materials properties through a novel way of visualizing the PES, identifying thermodynamically relevant basins, and calculating the entire pressure–temperature(–composition) phase diagram. Graphic abstract Computer science ; Inference ; Sampling (signal processing) ; Gibbs sampling ; Phase diagram ; Cover (algebra) ; Partition (number theory) ; Range (aeronautics) ; Statistical physics ; Bayesian inference ; Theoretical computer science ; Bayesian probability ; Phase (matter) ; Mathematics ; Physics ; Artificial intelligence ; Materials science ; Mechanical engineering Machine Learning in Materials Science ; Phase Equilibria and Thermodynamics ; High-pressure geophysics and materials
Authors: Anand Pai | R. Divakaran | Sanchit Anand | B. Satish Shenoy
Source: Journal of Dynamic Behavior of Materials (article, 2021-07-20)
Field: Materials Science — Materials Chemistry

Advances in the Whipple Shield Design and Development: Abstract Safety of satellites as well as spacecrafts during space missions is a primary objective to preserve the physical and virtual assets onboard. Whipple shields belong to the class of protective equipment provided on the surface of the spacecrafts and satellites, to sustain impacts from the ultra-high speed debris, which can otherwise cause considerable damage to the corresponding structures. Recent works on whipple shields are focussed on determining the response of different geometrical arrangements and material properties under hyper-velocity impact at projectile speeds of 3-18 km/s. Advances in the whipple shield design include integrated and mechanised models employing high performance materials like fiber-metal laminates ensuring better operational capability. The forward bumper of the whipple shield is the first line of defence as it regulates the state of projectile after the primary impact. Use of aluminium alloys for front bumpers is popular, owing to their lightweight and strength characteristics. The advances for the front bumper have seen usage of ceramic, metallic foams, and super composite mixtures, which resulted in enhanced performance, durability and safety of the whipple shields. This work is a comprehensive coverage of the latest materials used for whipple shields, their performance characterization—both experimental and theoretical, and applications. Projectile ; Shields ; Shield ; Spacecraft ; Materials science ; Aerospace engineering ; Propulsion ; Computer science ; Mechanical engineering ; Marine engineering ; Geology ; Engineering High-Velocity Impact and Material Behavior ; Structural Response to Dynamic Loads ; Energetic Materials and Combustion
Authors: Aybike URAL | Zeynep Hilal Kilimci
Source: International Journal of Computational and Experimental Science and Engineering (article, 2021-11-30)
Field: Materials Science — Electronic, Optical and Magnetic Materials

The Prediction of Chiral Metamaterial Resonance using Convolutional Neural Networks and Conventional Machine Learning Algorithms Electromagnetic resonance is the most important distinguishing property of metamaterials to examine many unusual phenomena. The resonant response of metamaterials can depend many parameters such as geometry, incident wave polarization. The estimation and the design of the unit cells can be challenging for the required application. The research on resonant behavior can yield promising applications. We investigate the resonance frequency of the chiral resonator as a unit of chiral metamaterial employing both traditional machine learning algorithms and convolutional deep neural networks. To our knowledge, this is the very first attempt on chiral metamaterials in that comparing the impact of various machine learning algorithms and deep learning model. The effect of geometrical parameters of the chiral resonator on the resonance frequency is studied. For this purpose, convolutional neural networks, support vector machines, naive Bayes, decision trees, random forests are employed for classification of resonance frequency. Extensive experiments are performed by varying training set percentages, epoch sizes, and data sets. Convolutional neural network ; Metamaterial ; Computer science ; Resonance (particle physics) ; Algorithm ; Artificial intelligence ; Artificial neural network ; Resonator ; Hyperparameter ; Machine learning ; Bayes' theorem ; Physics ; Particle physics ; Bayesian probability ; Optics Metamaterials and Metasurfaces Applications ; Speech and Audio Processing ; Terahertz technology and applications
Authors: Jiansheng Lin | Linghong Huang | Haibo Ou | An Chen | Rong Xiang | Zonghua Liu
Source: RSC Advances (article, 2021-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Effects of ZIF-8 MOFs on structure and function of blood components ZIF-8 MOFs, with their large specific surface area and void volume, unique biodegradability and pH sensitivity, and significant loading capacity, have been widely used as carrier materials for bioactive molecules such as drugs, vaccines and genes. In these applications, ZIF-8 MOFs are usually delivered intravenously. Therefore, it is necessary to know the interaction between ZIF-8 MOFs and blood components, which from this sense is a key factor affecting their delivery effectiveness and biosafety. However, until now there has been no report on the evaluation of hemocompatibility of ZIF-8 MOFs. The lack of biosafety information of ZIF-8 MOFs seriously impedes their clinical applications. In this work, we studied the biosafety of two different sizes of ZIF-8 MOFs, mainly focusing on their <i>in vivo</i> and <i>in vitro</i> effects on the key components of blood (red blood cells (RBCs), platelets, <i>etc.</i>) and the coagulation function. It was found that, <i>in vitro</i>, a high concentration of ZIF-8 MOFs could induce RBC aggregation and hemolysis, and prolong the coagulation time. <i>In vivo</i>, intravenous administration of 45 mg kg<sup>-1</sup> ZIF-8 MOFs significantly disturbed the RBC and platelet-related blood routine indexes, as well as coagulation function indexes, but it did not cause significant abnormalities in blood coagulation and tissue structures (heart, liver, spleen, lung, and kidney). Function (biology) ; Chemical engineering ; Chemistry ; Materials science ; Biology ; Engineering ; Evolutionary biology Magnetism in coordination complexes ; Electron Spin Resonance Studies ; Biochemical effects in animals
Authors: Shilong Liu | Yanjing Su | Haiqing Yin | Dawei Zhang | Jie He | Haiyou Huang | Xue Jiang | Xuan Wang | Haiyan Gong | Zhuang Li | Hao Xiu | Jiawang Wan | Xiaotong Zhang
Source: npj Computational Materials (article, 2021-06-08)
Field: Materials Science — Materials Chemistry

An infrastructure with user-centered presentation data model for integrated management of materials data and services Abstract With scientific research in materials science becoming more data intensive and collaborative after the announcement of the Materials Genome Initiative, the need for modern data infrastructures that facilitate the sharing of materials data and analysis tools is compelling in the materials community. In this paper, we describe the challenges of developing such infrastructure and introduce an emerging architecture with high usability. We call this architecture the Materials Genome Engineering Databases (MGED). MGED provides cloud-hosted services with features to simplify the process of collecting datasets from diverse data providers, unify data representation forms with user-centered presentation data model, and accelerate data discovery with advanced search capabilities. MGED also provides a standard service management framework to enable finding and sharing of tools for analyzing and processing data. We describe MGED’s design, current status, and how MGED supports integrated management of shared data and services. Computer science ; Cloud computing ; Data management ; Data science ; Data sharing ; Usability ; Presentation (obstetrics) ; Data integration ; Process (computing) ; Data as a service ; Architecture ; World Wide Web ; Service (business) ; Database Machine Learning in Materials Science ; Innovative Microfluidic and Catalytic Techniques Innovation ; Catalysis and Oxidation Reactions
Authors: Fabio Corti | Michelangelo-Santo Gulino | Maurizio Laschi | Gabriele Maria Lozito | Luca Pugi | Alberto Reatti | Dario Vangi
Source: Energies (article, 2021-10-19)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Time-Domain Circuit Modelling for Hybrid Supercapacitors Classic circuit modeling for supercapacitors is limited in representing the strongly non-linear behavior of the hybrid supercapacitor technology. In this work, two novel modeling techniques suitable to represent the time-domain electrical behavior of a hybrid supercapacitor are presented. The first technique enhances a well-affirmed circuit model by introducing specific non-linearities. The second technique models the device through a black-box approach with a neural network. Both the modeling techniques are validated experimentally using a workbench to acquire data from a real hybrid supercapacitor. The proposed models, suitable for different supercapacitor technologies, achieve higher accuracy and generalization capabilities compared to those already presented in the literature. Both modeling techniques allow for an accurate representation of both short-time domain and steady-state simulations, providing a valuable asset in electrical designs featuring supercapacitors. Supercapacitor ; Computer science ; Representation (politics) ; Workbench ; Generalization ; Electronic engineering ; Domain (mathematical analysis) ; Black box ; Capacitance ; Engineering ; Artificial intelligence ; Mathematics Supercapacitor Materials and Fabrication ; Electric and Hybrid Vehicle Technologies ; Advanced Battery Technologies Research