jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Somayyeh Asgari | Tapio Fabritius
Source: Scientific Reports (article, 2021-12-13)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Graphene-based dual-functional chiral metamirror composed of complementary 90° rotated U-shaped resonator arrays and its equivalent circuit model An equivalent circuit model (ECM) using a MATLAB code to analyze a tunable two-layered graphene-based chiral dual-function metamirror, is proposed in this work. The investigated metastructure is composed of complementary U-shaped graphene resonator arrays in the terahertz (THz) region. The ECM analysis could be used for any two-layered chiral metastructure for any frequencies, containing resonators with a thickness less than λ/50. The characteristics of the proposed tunable metamirror were analyzed numerically using the finite element method (FEM) in CST Software to verify the ECM analysis. The proposed metamirror can be used in polarization-sensitive devices in the THz region with simpler biasing without a need for ion gels or similar. It works as a broadband TE and multiband (four bands) TM mirror in the 0.3-4.5 THz bandwidth with a strong linear dichroism (LD) response (up to 96%). The designed mirror is a dynamically tunable, dual-functional structure, requiring only 90° rotation of the incident electromagnetic fields to switch between broadband and multiband spectral behavior making it a promising candidate for future THz intelligent systems. The proposed ECM is in agreement with the FEM results. The ECM analysis provides a simple, fast, and effective way to understand the metamirror's behavior and guides for the design and analysis of graphene-based chiral metastructures in the THz region. Terahertz radiation ; Graphene ; Resonator ; Equivalent circuit ; Materials science ; Finite element method ; Broadband ; Optoelectronics ; Optics ; Physics ; Nanotechnology ; Voltage Metamaterials and Metasurfaces Applications ; Terahertz technology and applications ; Plasmonic and Surface Plasmon Research
Authors: Ting Zhang | Yanbo Huang | Krishna N. Reddy | Pingting Yang | Xiaohu Zhao | Jingcheng Zhang
Source: Agronomy (article, 2021-03-19)
Field: Chemistry — Analytical Chemistry

Using Machine Learning and Hyperspectral Images to Assess Damages to Corn Plant Caused by Glyphosate and to Evaluate Recoverability Glyphosate is the most widely used herbicide in crop production due to the widespread adoption of glyphosate-resistant (GR) crops. However, the spray of glyphosate onto non-target crops from ground or aerial applications can cause severe injury to non-GR corn plants. To evaluate the crop damage of the non-GR corn plants from glyphosate and the recoverability of the damaged plants, we used the hyperspectral imaging (HSI) technique in field experiments with different glyphosate application rates. This study investigated the spectral characteristic of corn plants and assessed the corn plant damage from glyphosate. Based on HSI image analysis, a spectral variation pattern was observed at 1 week after treatment (WAT), 2 WAT, and 3 WAT from the glyphosate-treated non-GR corn plants. It was further found that the corn plants treated with glyphosate rates equal to or higher than 0.5X (X = 0.866 kilograms acid equivalents/hectare (kg ae/ha) represents the recommended spray rate for GR corn) would suffer unrecoverable damage. Using the Jeffries–Matusita distance as the spectral sensitivity criterion, three sensitive bands from the measured spectra were selected to create two spectral indices for crop recoverability differentiation in band ratio and normalization forms, respectively. With the two spectral indices, the corn plants recoverable and unrecoverable from damage were classified with an overall accuracy greater than 95%. Then, three machine learning algorithms (k-nearest neighbors, random forest, and support vector machine) were respectively combined with the successive projections algorithm to create models to relate selected feature spectral bands to glyphosate spray rates. The results indicated that the models achieved reasonable accuracy, especially in the group of recoverable plants. This study illustrated the potential of the hyperspectral imaging technique for evaluating crop damage from herbicides and recoverability of the injured plants using different data analysis and machine learning modeling approaches for practical weed management in crop fields. Glyphosate ; Hyperspectral imaging ; Agronomy ; Crop ; Mathematics ; Environmental science ; Artificial intelligence ; Biology ; Computer science Spectroscopy and Chemometric Analyses ; Weed Control and Herbicide Applications ; Remote Sensing in Agriculture
Authors: Jesse Lee Kar Ming | Mohd Shamsul Anuar | Syahmeer How | Samsul Bahari Mohd Noor | Zalizawati Abdullah | Farah Saleena Taip
Source: Foods (article, 2021-11-05)
Field: Chemistry — Inorganic Chemistry

Development of an Artificial Neural Network Utilizing Particle Swarm Optimization for Modeling the Spray Drying of Coconut Milk Spray drying techniques are one of the methods to preserve and extend the shelf-life of coconut milk. The objective of this research was to create a particle swarm optimization-enhanced artificial neural network (PSO-ANN) that could predict the coconut milk spray drying process. The parameters for PSO tuning were selected as the number of particles and acceleration constant, respectively, for both global and personal best using a 2<sup>k</sup> factorial design. The optimal PSO settings were recorded as global best, C<sub>1</sub> = 4.0; personal best, C<sub>2</sub> = 0; and number of particles = 100. When comparing different types of spray drying models, PSO-ANN had an MSE value of 0.077, GA-ANN had an MSE of 0.033, while ANN had an MSE of 0.082. Sensitivity analysis was conducted on all three models to evaluate the significance level of each parameter on the model, and it was discovered that inlet temperature had the most significant influence on the model performance. In conclusion, the PSO-ANN was found to be more effective than ANN but less effective than GA-ANN in predicting the quality of coconut milk powder. Particle swarm optimization ; Spray drying ; Artificial neural network ; Factorial experiment ; Mathematics ; Biological system ; Computer science ; Artificial intelligence ; Statistics ; Algorithm ; Chromatography ; Chemistry ; Biology Coconut Research and Applications ; Microencapsulation and Drying Processes ; Food Chemistry and Fat Analysis
Authors: Douglas N. Rutledge | Jean‐Michel Roger | Matthieu Lesnoff
Source: Frontiers in Analytical Science (article, 2021-10-18)
Field: Chemistry — Analytical Chemistry

Different Methods for Determining the Dimensionality of Multivariate Models A tricky aspect in the use of all multivariate analysis methods is the choice of the number of Latent Variables to use in the model, whether in the case of exploratory methods such as Principal Components Analysis (PCA) or predictive methods such as Principal Components Regression (PCR), Partial Least Squares regression (PLS). For exploratory methods, we want to know which Latent Variables deserve to be selected for interpretation and which contain only noise. For predictive methods, we want to ensure that we include all the variability of interest for the prediction, without introducing variability that would lead to a reduction in the quality of the predictions for samples other than those used to create the multivariate model. Multivariate statistics ; Partial least squares regression ; Principal component analysis ; Latent variable ; Multivariate analysis ; Statistics ; Dimensionality reduction ; Regression analysis ; Computer science ; Principal component regression ; Regression ; Mathematics ; Econometrics ; Artificial intelligence Spectroscopy and Chemometric Analyses ; Advanced Statistical Methods and Models ; Sensory Analysis and Statistical Methods
Authors: Muhammad Yasir | Tomáš Šopí­k | Lenka Lovecká | Dušan Kimmer | Vladimír Sedlařík
Source: Nanotechnology (article, 2021-11-02)
Field: Materials Science — Biomaterials

The adsorption, kinetics, and interaction mechanisms of various types of estrogen on electrospun polymeric nanofiber membranes This study focuses on the adsorption kinetics of four highly potent sex hormones (estrone (E1), 17<i>β</i>-estradiol (E2), 17<i>α</i>-ethinylestradiol (EE2), and estriol (E3)), present in water reservoirs, which are considered a major cause of fish feminization, low sperm count in males, breast and ovarian cancer in females induced by hormonal imbalance. Herein, electrospun polymeric nanostructures were produced from cellulose acetate, polyamide, polyethersulfone, polyurethanes (918 and elastollan), and polyacrylonitrile (PAN) to simultaneously adsorbing these estrogenic hormones in a single step process and to compare their performance. These nanofibers possessed an average fiber diameter in the range 174-330 nm and their specific surface area ranged between 10.2 and 20.9 m<sup>2</sup>g<sup>-1</sup>. The adsorption-desorption process was investigated in four cycles to determine the effective reusability of the adsorption systems. A one-step high-performance liquid chromatography technique was developed to detect and quantify concurrently each hormone present in the solution. Experimental data were obtained to determine the adsorption kinetics by applying pseudo-first-order, pseudo-second-order and intraparticle diffusion models. Findings showed that E1, E2 and EE2 best fitted pseudo-second-order kinetics, while E3 followed pseudo-first-order kinetics. It was found that polyurethane Elastollan nanofibers had maximum adsorption capacities of 0.801, 0.590, 0.736 and 0.382 mg g<sup>-1</sup>for E1, E2, EE2 and E3, respectively. In addition, the results revealed that polyurethane Elastollan nanofibers had the highest percentage efficiency of estrogens removal at ∼58.9% due to its strong hydrogen bonding with estrogenic hormones, while the least removal efficiency for PAN at ∼35.1%. Consecutive adsorption-desorption cycles demonstrated that polyurethane maintained the best efficiency, even after being repeatedly used four times compared to the other polymers. Overall, the findings indicate that all the studied nanostructures have the potential to be effective adsorbents for concurrently eradicating such estrogens from the environment. Polyacrylonitrile ; Adsorption ; Nanofiber ; Materials science ; Kinetics ; Chemical engineering ; Desorption ; Estriol ; Polymer chemistry ; Chromatography ; Chemistry ; Organic chemistry ; Hormone ; Polymer ; Nanotechnology ; Composite material ; Biochemistry Electrospun Nanofibers in Biomedical Applications ; Membrane Separation Technologies ; Advanced biosensing and bioanalysis techniques
Authors: Paulina Skupin-Mrugalska | Tomasz Zalewski | Philipp A. Elvang | Grzegorz Nowaczyk | Mikołaj Czajkowski | Hanna Piotrowska‐Kempisty
Source: Colloids and Surfaces B Biointerfaces (article, 2021-05-21)
Field: Materials Science — Biomaterials

Insight into theranostic nanovesicles prepared by thin lipid hydration and microfluidic method Liposomes are phospholipid-based self-assembled nanoparticles. Various components can be solubilized in the lipid bilayer, encapsulated in the aqueous core or attached to the surface, making liposomes attractive platforms for multimodality functionalization. Here we describe theranostic liposomes delivering a magnetic resonance contrast agent (lipid derivative of gadopentetic acid) and a hydrophobic photosensitizer (zinc phthalocyanine, ZnPc) for photodynamic therapy of cancer. For the first time, this theranostic system was prepared by the microfluidic method. Analogous formulations were produced by thin lipid film hydration (TLH) with down-sizing performed by extrusion for comparison purposes. We demonstrated double the loading capacity of ZnPc into liposomes made by microfluidics compared to TLH/extrusion. Microfluidics resulted in the theranostic nanoliposomes characterized by sizes =2.5x smaller than vesicles prepared by TLH/extrusion. Increased relaxivity was observed for liposomes manufactured by microfluidics compared to TLH, despite a slightly lower Gd chelate recovery. We attributed the improved relaxation to the increased surface area/volume ratio of vesicles and decreased phosphatidylcholine/ZnPc molar ratio, which affected water molecules' diffusion through the liposomal membrane. Finally, we showed photodynamic efficacy of ZnPc loaded into theranostic liposomes in head and neck cancer model, resulting in IC<sub>50</sub> of 0.22 - 0.61 μM, depending on the formulation and cell line used. We demonstrate microfluidics' feasibility to be used for theranostic liposome manufacturing and co-entrapment of therapeutic and imaging components in a single-step process with a high yield. Liposome ; Microfluidics ; Lipid bilayer ; Extrusion ; Vesicle ; Chemistry ; Phosphatidylcholine ; Nanotechnology ; Materials science ; Drug delivery ; Phospholipid ; Membrane ; Biochemistry Nanoparticle-Based Drug Delivery ; Nanoplatforms for cancer theranostics ; Lipid Membrane Structure and Behavior
Authors: Kaiming Cheng | Huixia Xu | Lijun Zhang | Jixue Zhou | Xitao Wang | Yong Du | Ming Chen
Source: npj Computational Materials (article, 2021-07-23)
Field: Materials Science — Materials Chemistry

Computational engineering of the oxygen electrode-electrolyte interface in solid oxide fuel cells Abstract The Ce 0.8 Gd 0.2 O 2− δ (CGO) interlayer is commonly applied in solid oxide fuel cells (SOFCs) to prevent chemical reactions between the (La 1− x Sr x )(Co 1− y Fe y )O 3− δ (LSCF) oxygen electrode and the Y 2 O 3 -stabilized ZrO 2 (YSZ) electrolyte. However, formation of the YSZ–CGO solid solution with low ionic conductivity and the SrZrO 3 (SZO) insulating phase still happens during cell production and long-term operation, causing poor performance and degradation. Unlike many experimental investigations exploring these phenomena, consistent and quantitative computational modeling of the microstructure evolution at the oxygen electrode–electrolyte interface is scarce. We combine thermodynamic, 1D kinetic, and 3D phase-field modeling to computationally reproduce the element redistribution, microstructure evolution, and corresponding ohmic loss of this interface. The influences of different ceramic processing techniques for the CGO interlayer, i.e., screen printing and physical laser deposition (PLD), and of different processing and long-term operating parameters are explored, representing a successful case of quantitative computational engineering of the oxygen electrode–electrolyte interface in SOFCs. Electrolyte ; Materials science ; Oxide ; Solid oxide fuel cell ; Chemical engineering ; Ionic conductivity ; Electrode ; Clark electrode ; Microstructure ; Yttria-stabilized zirconia ; Ceramic ; Fast ion conductor ; Oxygen ; Composite material ; Metallurgy ; Physical chemistry ; Chemistry ; Cubic zirconia Advancements in Solid Oxide Fuel Cells ; Electronic and Structural Properties of Oxides ; Catalytic Processes in Materials Science
Authors: Abourehab Hammam | Yi. Cao | A. A. El-Geassy | Mohamed Elsadek | Ying Li | Han Wei | Mamdouh Omran | Yaowei Yu
Source: Metals (article, 2021-07-19)
Field: Materials Science — Materials Chemistry

Non-Isothermal Reduction Kinetics of Iron Ore Fines with Carbon-Bearing Materials This study investigates the non-isothermal reduction of iron ore fines with two different carbon-bearing materials using the thermogravimetric technique. The iron ore fines/carbon composites were heated from room temperature up to 1100 °C with different heating rates (5, 10, 15, and 20 °C/min) under an argon atmosphere. The effect of heating rates and carbon sources on the reduction rate was intensively investigated. Reflected light and scanning electron microscopes were used to examine the morphological structure of the reduced composite. The results showed that the heating rates affected the reduction extent and the reduction rate. Under the same heating rate, the rates of reduction were relatively higher by using charcoal than coal. The reduction behavior of iron ore-coal was proceeded step wisely as follows: Fe2O3 → Fe3O4 → FeO → Fe. The reduction of iron ore/charcoal was proceeded from Fe2O3 to FeO and finally from FeO to metallic iron. The reduction kinetics was deduced by applying two different methods (model-free and model-fitting). The calculated activation energies of Fe2O3/charcoal and of Fe2O3/coal are 40.50–190.12 kJ/mol and 55.02–220.12 kJ/mol, respectively. These indicated that the reduction is controlled by gas diffusion at the initial stages and by nucleation reaction at the final stages. Iron ore ; Carbon fibers ; Isothermal process ; Charcoal ; Nucleation ; Materials science ; Thermogravimetric analysis ; Coal ; Kinetics ; Scanning electron microscope ; Metallurgy ; Direct reduced iron ; Adsorption ; Chemistry ; Composite material ; Composite number ; Thermodynamics ; Organic chemistry Thermal and Kinetic Analysis ; Iron and Steelmaking Processes ; Advanced materials and composites
Authors: Troy D. Loeffler | Suvo Banik | Tarak K. Patra | Michael Sternberg | Subramanian K. R. S. Sankaranarayanan
Source: Journal of Physics Communications (article, 2021-02-11)
Field: Materials Science — Materials Chemistry

Reinforcement learning in discrete action space applied to inverse defect design Abstract Reinforcement learning (RL) algorithms that include Monte Carlo Tree Search (MCTS) have found tremendous success in computer games such as Go, Shiga and Chess. Such learning algorithms have demonstrated super-human capabilities in navigating through an exhaustive discrete action search space. Motivated by their success in computer games, we demonstrate that RL can be applied to inverse materials design problems. We deploy RL for a representative case of the optimal atomic scale inverse design of extended defects via rearrangement of chalcogen (e.g. S) vacancies in 2D transition metal dichalcogenides (e.g. MoS 2 ). These defect rearrangements and their dynamics are important from the perspective of tunable phase transition in 2D materials i.e. 2H (semi-conducting) to 1T (metallic) in MoS 2 . We demonstrate the ability of MCTS interfaced with a reactive molecular dynamics simulator to efficiently sample the defect phase space and perform inverse design—starting from randomly distributed S vacancies, the optimal defect rearrangement of defects corresponds a line defect of S vacancies. We compare MCTS performance with evolutionary optimization i.e. genetic algorithms and show that MCTS converges to a better optimal solution (lower objective) and in fewer evaluations compared to GA. We also comprehensively evaluate and discuss the effect of MCTS hyperparameters on the convergence to solution. Overall, our study demonstrates the effectives of using RL approaches that operate in discrete action space for inverse defect design problems. Inverse ; Reinforcement learning ; Computer science ; Convergence (economics) ; Action (physics) ; Hyperparameter ; Space (punctuation) ; Genetic algorithm ; Tree (set theory) ; Mathematical optimization ; Algorithm ; Artificial intelligence ; Mathematics ; Machine learning ; Physics Machine Learning in Materials Science ; Reinforcement Learning in Robotics ; Optimization and Search Problems
Authors: Harald Rupp | Wolfgang H. Binder
Source: Frontiers in Chemistry (review, 2021-11-29)
Field: Materials Science — Polymers and Plastics

3D Printing of Solvent-Free Supramolecular Polymers Additive manufacturing has significantly changed polymer science and technology by engineering complex material shapes and compositions. With the advent of dynamic properties in polymeric materials as a fundamental principle to achieve, e.g., self-healing properties, the use of supramolecular chemistry as a tool for molecular ordering has become important. By adjusting molecular nanoscopic (supramolecular) bonds in polymers, rheological properties, immanent for 3D printing, can be adjusted, resulting in shape persistence and improved printing. We here review recent progress in the 3D printing of supramolecular polymers, with a focus on fused deposition modelling (FDM) to overcome some of its limitations still being present up to date and open perspectives for their application. Supramolecular chemistry ; Nanotechnology ; 3D printing ; Polymer ; Supramolecular polymers ; Materials science ; Nanoscopic scale ; Rheology ; Supramolecular assembly ; Polymer science ; Chemistry ; Molecule ; Organic chemistry ; Composite material Polymer composites and self-healing ; Additive Manufacturing and 3D Printing Technologies ; Advanced Polymer Synthesis and Characterization
Authors: Jingya Hao | Wenhui Miao | Sheng‐Mei Lu | Yu Cheng | Guoqing Jia | Can Li
Source: Chemical Science (article, 2021-01-01)
Field: Chemistry — Organic Chemistry

Controllable stereoinversion in DNA-catalyzed olefin cyclopropanation <i>via</i> cofactor modification The assembly of DNA with metal-complex cofactors can form promising biocatalysts for asymmetric reactions, although catalytic performance is typically limited by low enantioselectivities and stereo-control remains a challenge. Here, we engineer G-quadruplex-based DNA biocatalysts for an asymmetric cyclopropanation reaction, achieving enantiomeric excess (ee<sub>trans</sub>) values of up to +91% with controllable stereoinversion, where the enantioselectivity switches to -72% ee<sub>trans</sub> through modification of the Fe-porphyrin cofactor. Complementary circular dichroism, nuclear magnetic resonance, and fluorescence titration experiments show that the porphyrin ligand of the cofactor participates in the regulation of the catalytic enantioselectivity <i>via</i> a synergetic effect with DNA residues at the active site. These findings underline the important role of cofactor modification in DNA catalysis and thus pave the way for the rational engineering of DNA-based biocatalysts. Cyclopropanation ; Cofactor ; Chemistry ; Olefin fiber ; Catalysis ; Stereochemistry ; DNA ; Combinatorial chemistry ; Enzyme ; Biochemistry Cyclopropane Reaction Mechanisms
Authors: Nipaphorn Ponpao | Warapong Senapak | Rungnapha Saeeng | Jaray Jaratjaroonphong | Uthaiwan Sirion
Source: RSC Advances (article, 2021-01-01)
Field: Chemistry — Organic Chemistry

Metal- and solvent-free synthesis of aniline- and phenol-based triarylmethanes<i>via</i>Brönsted acidic ionic liquid catalyzed Friedel-Crafts reaction A beneficial, scalable and efficient methodology for the synthesis of aniline-based triarylmethanes has been established through the double Friedel-Crafts reaction of commercial aldehydes and primary, secondary or tertiary anilines using Brönsted acidic ionic liquid as a powerful catalyst, namely [bsmim][NTf<sub>2</sub>]. This protocol was successfully performed under metal- and solvent-free conditions with a broad range of substrates, giving the corresponding aniline-based triarylmethane products in good to excellent yields (up to 99%). In addition, alternative aromatic nucleophiles such as phenols and electron-rich arenes were also studied using this useful approach to achieve a diversity of triarylmethane derivatives in high to excellent yields. Friedel–Crafts reaction ; Ionic liquid ; Aniline ; Catalysis ; Chemistry ; Phenol ; Solvent ; Metal ; Organic chemistry ; Brønsted–Lowry acid–base theory ; Inorganic chemistry Synthesis of Indole Derivatives ; Chemical Synthesis and Reactions ; Ionic liquids properties and applications