jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Eid M. Alosime | Omar A. Adam | Ahmed A. Basfar
Source: Materials (article, 2023-05-24)
Field: Materials Science — Materials Chemistry

Encapsulation of Carbon Nanotubes by Styrene and Butyl Acrylate Particles via Suspension Polymerization for Polymerized Toner Applications Electrophotographic printing and copying processes primarily use toner, which is a mixture of colorant, polymer, and additives. Toner can be made using traditional mechanical milling techniques or more contemporary chemical polymerization techniques. Suspension polymerization provides spherical particles with less stabilizer adsorption, homogeneous monomers, higher purity, and easier control of the reaction temperature. In contrast to these advantages, however, the particle size resulting from suspension polymerization is too large for toner. To overcome this disadvantage, devices such as high-speed stirrers and homogenizers can be used to reduce the size of the droplets. This research investigated the use of carbon nanotubes (CNTs) instead of carbon black as the pigment in toner development. We succeeded in achieving a good dispersion of four different types of CNT, specifically modified with NH<sub>2</sub> and Boron or unmodified with long or short chains in water rather than chloroform, using sodium n-dodecyl sulfate as a stabilizer. We then performed polymerization of the monomers styrene and butyl acrylate in the presence of the different CNT types and found that the best monomer conversion and largest particles (in the micron range) occurred with CNTs modified with boron. The insertion of a charge control agent into the polymerized particles was achieved. Monomer conversion of over 90% was realized with all concentrations of MEP-51, whereas conversion was under 70% with all concentrations of MEC-88. Furthermore, analysis with dynamic light scattering and scanning electron microscopy (SEM) indicated that all polymerized particles were in the micron size range, suggesting that our newly developed toner particles were less harmful and environmentally friendly products than those typically and commercially available. The SEM micrographs clearly showed good dispersion and attachment of the CNTs on the polymerized particles (no CNT aggregation was found), which has never been published before. Materials science ; Polymerization ; Chemical engineering ; Suspension polymerization ; Monomer ; Polymer ; Acrylate ; Carbon nanotube ; Dispersion polymerization ; Polyolefin ; Particle size ; Polymer chemistry ; Nanotechnology ; Composite material Carbon Nanotubes in Composites ; Polydiacetylene-based materials and applications ; Nanotechnology research and applications
Authors: Juan C. Reinoso-Burrows | Marcelo Cortés-Carmona | Fabiola Pineda | Mauro Henríquez | Norman Toro | Felipe M. Galleguillos Madrid
Source: Preprints.org (preprint, 2023-06-25)
Field: Materials Science — Materials Chemistry

Cellular Automata Modeling as Tool in The Corrosion Management Cellular automata (CA) models have emerged as a valuable tool in corrosion management. This manuscript provides an overview of the application of cellular automata models in corrosion research, highlighting their benefits and contributions to understanding the complex nature of corrosion processes. Cellular automata models offer a computational approach to simulate corrosion behavior at the microscale, capturing the intricate interactions between electrochemical reactions, material properties, and environmental factors generating a new vision of predictive maintenance. It discusses the key features of cellular automata, such as the grid-based representation of the material surface, the definition of state variables, and the rules governing cell-state transitions. The ability to model local interactions and emergent global behavior makes cellular automata particularly suitable for simulating corrosion processes. Finally, cellular automata models offer a powerful and versatile approach to studying corrosion processes, expanding models that can continue to enhance our understanding of corrosion and contribute to the development of effective corrosion prevention and control strategies. Cellular automaton ; Corrosion ; Microscale chemistry ; Computer science ; Automaton ; Stochastic cellular automaton ; Key (lock) ; Biochemical engineering ; Artificial intelligence ; Materials science ; Engineering ; Mathematics Corrosion Behavior and Inhibition ; Concrete Corrosion and Durability ; Anodic Oxide Films and Nanostructures
Authors: Anas Abu-Odeh | Blas P. Uberuaga | Mark Asta
Source: Acta Materialia (article, 2023-07-26)
Field: Materials Science — Materials Chemistry

Barrier-free predictions of short-range ordering/clustering kinetics in binary FCC solid solutions We present comparisons of kinetic Monte Carlo (kMC) simulations of isothermal short-range ordering (SRO) and clustering (SRC) kinetics in binary FCC alloys with a mean-field concentration wave (CW) model. We find that the CW model is able to give order-of-magnitude agreement with kMC simulations for ordering/clustering relaxation times over a wide range of temperatures and compositions. The advantage of the CW model is that it does not require parameterization of vacancy hopping energy barriers, which, for a concentrated alloy, becomes prohibitive. We assess limits in the accuracy of the model, and discuss the effect of cooling rates as well as the extension to multi-component systems. Ultimately, the simplicity and performance of the CW model compared to kMC simulations suggests that it is a useful tool to connect with models of properties dependent on SRO/SRC as well as for designing thermal treatments to control formation of SRO/SRC. Materials science ; Statistical physics ; Isothermal process ; Monte Carlo method ; Kinetic Monte Carlo ; Thermodynamics ; Range (aeronautics) ; Binary number ; Kinetics ; Cluster analysis ; Relaxation (psychology) ; Cluster (spacecraft) ; Physics ; Computer science Machine Learning in Materials Science ; Thermal properties of materials ; Thermodynamic and Structural Properties of Metals and Alloys
Authors: Е. Д. Мерсон | V. A. Poluyanov | Pavel Myagkikh | Alexander S. Bunev | Dmitri Merson | Alexei Vinogradov
Source: Metals (article, 2023-09-27)
Field: Materials Science — Biomaterials

Improving Corrosion and Stress Corrosion Cracking Performance of Machined Biodegradable Alloy ZX20 by HF-Treatment The treatment with hydrofluoric acid (HF-treatment) was suggested to be an effective way of improving the corrosion resistance of Mg alloys, including Mg-Zn-Ca (ZX) ones used for biodegradable implants. However, the effect of the HF-treatment on the stress corrosion cracking (SCC) susceptibility of ZX alloys has not been reported yet, although this phenomenon can induce premature brittle failures of the metallic medical devices, and thus, it is critical for their in-service structural integrity. In the present study, the effect of the HF-treatment on the microstructure, cytotoxicity, corrosion rate, mechanical properties, and fracture and side surface characteristics of the as-cast ZX20 alloy were investigated with the use of scanning electron microscopy, immersion, and slow-strain rate tensile testing in Hanks’ solution and indirect cell viability tests. It is found that the HF-treatment exerts no cytotoxic effect and results in a significant reduction in corrosion rate (up to 6 times of magnitude) and SCC susceptibility indexes (up to 1.5 times of magnitude). The observed improvement of corrosion and SCC performance of the alloy by the HF-treatment is found to be attributed to three effects, including (i) formation of the protective surface film of MgF2, (ii) removal of surficial contaminations originating from sample preparation procedures, and (iii) dissolution of surficial secondary phase particles. The mechanism of corrosion and SCC in the specimens before and after the HF-treatment are discussed. Corrosion ; Materials science ; Alloy ; Stress corrosion cracking ; Hydrofluoric acid ; Metallurgy ; Dissolution ; Scanning electron microscope ; Brittleness ; Ultimate tensile strength ; Microstructure ; Environmental stress cracking ; Slow strain rate testing ; Intergranular corrosion ; Composite material ; Chemical engineering Magnesium Alloys: Properties and Applications ; Aluminum Alloys Composites Properties ; Corrosion Behavior and Inhibition
Authors: Mark Coletti | Ada Sedova | Rajni Chahal | Luke Gibson | Santanu Roy | Vyacheslav S. Bryantsev
Source:  (article, 2023-08-07)
Field: Materials Science — Materials Chemistry

Multiobjective Hyperparameter Optimization for Deep Learning Interatomic Potential Training Using NSGA-II Deep neural network (DNN) potentials are an emerging tool for simulation of dynamical atomistic systems, with the promise of quantum mechanical accuracy at speedups of 10000 ×. As with other DNN methods, hyperparameters used during training can make a substantial difference in model accuracy, and optimal settings vary with dataset. To enable rapid tuning of hyperparameters for DNN potential training, we developed a scalable multiobjective optimization evolutionary algorithm for supercomputers and tested it on the Summit system at the Oak Ridge Leadership Computing Facility (OLCF). The multiobjective approach is required due to the coupling of two learned values defining the potential: the energy and force. Using a large-scale implementation of the NSGA-II algorithm adapted for training DNN potentials, we discovered several optimal multiobjective combinations, including best choices of activation functions, learning rate scaling scheme, and pairing of the two radial cutoffs used in the three dimensional descriptor function. Hyperparameter ; Computer science ; Scalability ; Evolutionary algorithm ; Artificial neural network ; Benchmark (surveying) ; Artificial intelligence ; Multi-objective optimization ; Deep learning ; Scaling ; Machine learning ; Mathematical optimization ; Mathematics Machine Learning in Materials Science ; Software Engineering Research ; Advanced Multi-Objective Optimization Algorithms
Authors: Jordi‐Roger Riba | Rita Puig | Rosa Cantero
Source: Machines (article, 2023-05-18)
Field: Chemistry — Analytical Chemistry

Portable Instruments Based on NIR Sensors and Multivariate Statistical Methods for a Semiautomatic Quality Control of Textiles Near-infrared (NIR) spectroscopy is a widely used technique for determining the composition of textile fibers. This paper analyzes the possibility of using low-cost portable NIR sensors based on InGaAs PIN photodiode array detectors to acquire the NIR spectra of textile samples. The NIR spectra are then processed by applying a sequential application of multivariate statistical methods (principal component analysis, canonical variate analysis, and the k-nearest neighbor classifier) to classify the textile samples based on their composition. This paper tries to solve a real problem faced by a knitwear manufacturer, which arose because different pieces of the same garment were made with “identical” acrylic yarns from two suppliers. The sweaters had a composition of 50% acrylic, 45% wool, and 5% viscose. The problem occurred after the garments were dyed, where different shades were observed due to the different origins of the acrylic yarns. This is a challenging real-world problem for two reasons. First, there is the need to differentiate between acrylic yarns of different origins, which experts say cannot be visually distinguished before garments are dyed. Second, measurements are made in the field using portable NIR sensors rather than in a controlled laboratory using sophisticated and expensive benchtop NIR spectrometers. The experimental results obtained with the portable sensors achieved a classification accuracy of 95%, slightly lower than the 100% obtained with the high-performance laboratory benchtop NIR spectrometer. The results presented in this paper show that portable NIR sensors combined with appropriate multivariate statistical classification methods can be effectively used for on-site textile quality control. Textile ; Near-infrared spectroscopy ; Spectrometer ; Principal component analysis ; Wool ; Computer science ; Chemometrics ; Classifier (UML) ; Process engineering ; Materials science ; Artificial intelligence ; Optics ; Engineering ; Composite material ; Physics ; Machine learning Spectroscopy and Chemometric Analyses ; Industrial Vision Systems and Defect Detection ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Kyle Boone | Aya Kirahm Cloyd | Emina Derakovic | Paulette Spencer | Candan Tamerler
Source: Applied Sciences (article, 2023-03-06)
Field: Materials Science — Biomaterials

Designing Collagen-Binding Peptide with Enhanced Properties Using Hydropathic Free Energy Predictions Collagen is fundamental to a vast diversity of health functions and potential therapeutics. Short peptides targeting collagen are attractive for designing modular systems for site-specific delivery of bioactive agents. Characterization of peptide-protein binding involves a larger number of potential interactions that require screening methods to target physiological conditions. We build a hydropathy-based free energy estimation tool which allows quick evaluation of peptides binding to collagen. Previous studies showed that pH plays a significant role in collagen structure and stability. Our design tool enables probing peptides for their collagen-binding property across multiple pH conditions. We explored binding features of currently known collagen-binding peptides, collagen type I alpha chain 2 sense peptide (TKKTLRT) and decorin LRR-10 (LRELHLNNN). Based on these analyzes, we engineered a collagen-binding peptide with enhanced properties across a large pH range in contrast to LRR-10 pH dependence. To validate our predictions, we used a quantum-dots-based binding assay to compare the coverage of the peptides on type I collagen. The predicted peptide resulted in improved collagen binding. Hydropathy of the peptide-protein pair is a promising approach to finding compatible pairings with minimal use of computational resources, and our method allows for quick evaluation of peptides for binding to other proteins. Overall, the free-energy-based tool provides an alternative computational screening approach that impacts protein interaction search methods. Peptide ; Chemistry ; Biophysics ; Biochemistry ; Computational biology ; Type I collagen ; Combinatorial chemistry ; Biology Collagen: Extraction and Characterization ; Chemical Synthesis and Analysis ; Protein Hydrolysis and Bioactive Peptides
Authors: Jialu Wang | Rongfei Yu | Zhenying Li | Yang Fen | Linmin Luo | Dandan Wang | Huan Cheng | Yutao Zhang | Qiuyun Zhang
Source: Green Processing and Synthesis (article, 2023-01-01)
Field: Chemistry — Inorganic Chemistry

Heteropolyacid-loaded MOF-derived mesoporous zirconia catalyst for chemical degradation of rhodamine B Abstract In this article, silicotungstic acid (STA)-loaded metal–organic framework (MOF)-derived composites (C-STA@ZrO 2 ) were successfully synthesized by simple strategies. X-ray diffraction, Fourier transform infrared, scanning electron microscopy, energy-dispersive X-ray, N 2 physisorption, UV-vis diffuse reflection spectroscopy, and X-ray photoelectron spectroscopy techniques were used to characterize the as-obtained composites. Intriguingly, C-STA@ZrO 2 exhibits excellent photocatalytic performance, and rhodamine B (RhB) (40 mg·L −1 ) in water can be degraded to 93.9% after 120 min of irradiation. Moreover, various catalysts, catalyst dosage, and dye concentrations on RhB degradation were evaluated. Besides, the reusability of C-STA@ZrO 2 was also investigated. This work may provide a new and significant guideline for exploring excellent performance of MOF-derived hybrid material for wastewater purification. Rhodamine B ; X-ray photoelectron spectroscopy ; Catalysis ; Materials science ; Silicotungstic acid ; Diffuse reflectance infrared fourier transform ; Mesoporous material ; Physisorption ; Photocatalysis ; Fourier transform infrared spectroscopy ; Chemical engineering ; Nuclear chemistry ; Scanning electron microscope ; Cubic zirconia ; Chemistry ; Composite material ; Organic chemistry ; Ceramic Metal-Organic Frameworks: Synthesis and Applications ; Advanced Photocatalysis Techniques ; Advanced Nanomaterials in Catalysis
Authors: Jierui Zhang | Francesco Paesani | Martina Lessio
Source: ChemRxiv (preprint, 2023-04-17)
Field: Chemistry — Inorganic Chemistry

Computational Insights into the Interaction of Water with the UiO-66 Metal-Organic Framework and Its Functionalized Derivatives The UiO-66 metal-organic framework (MOF) has been identified as a promising hydrophilic material for water harvesting. Recent studies show that its water uptake ability at low relative humidity (RH) can be improved by incorporating hydrophilic functional groups into the framework. In this work, we provide computational insights into the adsorption of water in UiO-66 and its functionalized derivatives to reveal the role played by different adsorption sites and functional groups in the adsorption mechanism. We started by developing molecular models for UiO-66, UiO-66-NH2, UiO-66-OH, and UiO-66-(OH)2 compatible with the MB-pol data-driven many-body potential of water. We then benchmarked these models against ab initio data. We used these models to perform molecular dynamics simulations and calculate radial distribution functions, IR spectra, and two-dimensional density distribution maps for water in the MOFs. These results consistently show that the μ3-OH sites are the preferential interaction sites for water in UiO-66 and all its variants, and the formation of localised water clusters inside the octahedral pores is responsible for the abrupt step in the experimental adsorption isotherms. Furthermore, the presence of functional groups in the framework allows water to cluster in the octahedral pores at lower RH, thus making the MOF a more efficient water harvester. Overall, this study provides molecular-level insights into the pore filling process of UiO-66 and its functionalized derivatives, which are needed for the design of efficient water harvesting materials based on MOFs. Metal-organic framework ; Adsorption ; Octahedron ; Density functional theory ; Ab initio ; Chemistry ; Molecular dynamics ; Cluster (spacecraft) ; Molecule ; Metal ; Computational chemistry ; Materials science ; Chemical physics ; Physical chemistry ; Crystallography ; Crystal structure ; Organic chemistry ; Computer science Metal-Organic Frameworks: Synthesis and Applications ; Advanced NMR Techniques and Applications ; X-ray Diffraction in Crystallography
Authors: Zhiwen Li | Zhilin Zhou | Jianghua Wang | Tingxian Tao | Yingqiang Fu
Source: Nanotechnology (article, 2023-06-21)
Field: Materials Science — Materials Chemistry

Aggregation-induced emission enhancement N, S-CQDs for selective detection of CIP in the environment Carbon quantum dots (CQDs) have been extensively researched as fluorescent probes, but there are few reports on fluorescence-enhanced probes. Herein, nitrogen and sulfur co-doped CQDs (N, S-CQDs) with blue aggregation-induced emission enhancement (AIEE) fluorescence were synthesized by a one-step hydrothermal reaction. N, S-CQDs can rely on the presence of -OH, C=O, -NH<sub>2</sub>, and ether bonds on their surfaces and the formation of hydrogen bonds by ciprofloxacin (CIP) containing Ar-F and -COOH functional groups to achieve effective charge transfer. In addition, CIP forces N, S-CQDs to aggregate to form cross-linked structures, which effectively limits the vibration and rotation of N, S-CQDs, leading to enhanced fluorescence of N, S-CQDs. Based on the above intermolecular charge transfer and AIEE between N, S-CQDs and CIP, an efficient and sensitive nano fluorescent probe for the detection of CIP in real water samples was developed, which can achieve sensitive detection of 3.33 × 10<sup>-8</sup>-1.13 × 10<sup>-6</sup>M CIP. Fluorescence ; Intermolecular force ; Materials science ; Hydrogen bond ; Photochemistry ; Sulfur ; Ether ; Hydrothermal circulation ; Electron transfer ; Aggregation-induced emission ; Carbon fibers ; Molecule ; Chemistry ; Chemical engineering ; Organic chemistry Carbon and Quantum Dots Applications ; Nanocluster Synthesis and Applications ; Luminescence and Fluorescent Materials
Authors: Sweta Sneha | S Surjith | S. M. Alex Raj
Source:  (review, 2023-05-19)
Field: Chemistry — Analytical Chemistry

A Review on Food Adulteration Detection Techniques: Methodologies, Applications, and Challenges Food adulteration is common around the globe. To make sure to get high-quality food, and to identify the numerous adulterants in food items. The use of machine learning and deep learning techniques in the detection of food adulteration has gained increasing attention in recent years. This review paper presents an overview of the current state of the art in detecting food adulteration and summarizes various techniques and applications used to detect food adulteration, including traditional analytical techniques and machine learning. The paper also includes a summary of the recent research papers, which includes the objective, techniques, and samples used for adulteration detection in various food items. Additionally, the paper highlights the challenges faced in detecting food adulteration and the rapid evolution of adulteration methods. Computer science ; Food quality ; Quality (philosophy) ; Food products ; Data science ; Food science ; Chemistry Spectroscopy and Chemometric Analyses ; Identification and Quantification in Food ; Advanced Chemical Sensor Technologies
Authors: Agnieszka Szymaszek | Urbano Díaz | Bogdan Samojeden | Monika Motak
Source: Molecules (article, 2023-06-24)
Field: Materials Science — Materials Chemistry

Synthesis, Characterization, and NH3-SCR Catalytic Performance of Fe-Modified MCM-36 Intercalated with Various Pillars Two series of MCM-36 zeolites intercalated with various pillars and modified with iron were synthesized, analyzed with respect to their physicochemical properties, and tested as catalysts for the NH<sub>3</sub>-SCR process. It was found that the characteristic MWW morphology of MCM-36 can be obtained successfully using silica, alumina, and iron oxide as pillars. Additionally, one-pot synthesis of the material with iron resulted in the incorporation of monomeric Fe<sup>3+</sup> species into the framework positions. The results of catalytic tests revealed that the one-pot synthesized sample intercalated with silica and alumina was the most efficient catalyst of NO reduction, exhibiting ca. 100% activity at 250 °C. The outstanding performance of the material was attributed to the abundance of Lewis acid sites and the beneficial influence of alumina on the distribution of iron species in the zeolite. In contrast, the active centers originating from the Fe<sub>2</sub>O<sub>3</sub> pillars improved the NO conversion in the high-temperature range. Nevertheless, the aggregated particles of the metal oxide limited the access of the reacting molecules to the inner structure of the catalyst, which affected the overall activity and promoted the formation of N<sub>2</sub>O above 300 °C. Catalysis ; Zeolite ; Selective catalytic reduction ; Oxide ; Iron oxide ; Metal ; Chemistry ; Lewis acids and bases ; Inorganic chemistry ; Molecule ; Acid strength ; Monomer ; MCM-41 ; Chemical engineering ; Materials science ; Molecular sieve ; Organic chemistry ; Polymer Catalytic Processes in Materials Science ; Zeolite Catalysis and Synthesis ; Nanomaterials for catalytic reactions