jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yanzhao Pang | Mingliang Jin
Source: Nanomaterials (article, 2023-04-13)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Self-Assembly of Silver Nanowire Films for Surface-Enhanced Raman Scattering Applications The development of SERS detection technology is challenged by the difficulty in obtaining SERS active substrates that are easily prepared, highly sensitive, and reliable. Many high-quality hotspot structures exist in aligned Ag nanowires (NWs) arrays. This study used a simple self-assembly method with a liquid surface to prepare a highly aligned AgNW array film to form a sensitive and reliable SERS substrate. To estimate the signal reproducibility of the AgNW substrate, the RSD of SERS intensity of 1.0 × 10<sup>-10</sup> M Rhodamine 6G (R6G) in an aqueous solution at 1364 cm<sup>-1</sup> was calculated to be as low as 4.7%. The detection ability of the AgNW substrate was close to the single molecule level, and even the R6G signal of 1.0 × 10<sup>-16</sup> M R6G could be detected with a resonance enhancement factor (EF) as high as 6.12 × 10<sup>11</sup> under 532 nm laser excitation. The EF without the resonance effect was 2.35 × 10<sup>6</sup> using 633 nm laser excitation. FDTD simulations have confirmed that the uniform distribution of hot spots inside the aligned AgNW substrate amplifies the SERS signal. Rhodamine 6G ; Materials science ; Raman scattering ; Substrate (aquarium) ; Optoelectronics ; Excitation ; Nanowire ; Laser ; Raman spectroscopy ; Nanotechnology ; Optics ; Fluorescence Gold and Silver Nanoparticles Synthesis and Applications ; Plasmonic and Surface Plasmon Research ; Nanocluster Synthesis and Applications
Authors: Xuezhun Gu | Hao Jiang | Xiaotong Chen | Yu Li | Guoqing Wang
Source: e-Polymers (article, 2023-01-01)
Field: Materials Science — Surfaces, Coatings and Films

A novel fabrication method of slippery lubricant-infused porous surface by thiol-ene click chemistry reaction for anti-fouling and anti-corrosion applications Abstract A novel and simple way for efficiently preparing stable and non-toxic slippery lubricant-infused porous surface (SLIPS) will expand its anti-fouling and anti-corrosion applications in marine environments. Herein, vinyl-terminated polydimethylsiloxane was covalently grafted on the surface of nano-SiO 2 by a thiol-ene click chemistry reaction. After that, SLIPS was efficiently prepared at room temperature via the spraying method on various substrate surfaces. Surface wettability results showed that a water droplet (10 μL) can slip on the surface with an inclination of 10° and a stained water droplet can slip without stain during the slide process, which proved that SLIPS displayed excellent slippery performance. The existence of molecular-level slippery silicone oil film on the rough surface. Anti-fouling and anti-corrosion tests showed that the prepared SLIPS exhibited stable and excellent anti-fouling and anti-corrosion performance after immersion in Pseudoalteromonas sp. culture solution for 14 days. The SLIPS exhibited a value of more than 98% of bacterial attachment inhibition efficiency and a value of 99.9% of corrosion inhibition efficiency. This facile method provides guidance to fabricate SLIPS for its anti-fouling and anti-corrosion applications in marine environments. Fouling ; Corrosion ; Lubricant ; Fabrication ; Wetting ; Materials science ; Chemical engineering ; Contact angle ; Polydimethylsiloxane ; Slip (aerodynamics) ; Chemistry ; Nanotechnology ; Composite material Surface Modification and Superhydrophobicity ; Polymer Surface Interaction Studies ; Advanced Sensor and Energy Harvesting Materials
Authors: Xinyi Xu | Rajan Saini | E. K. Akdoğan | John S. McCloy | Ashutosh Goel
Source: Journal of the American Ceramic Society (article, 2023-01-27)
Field: Materials Science — Ceramics and Composites

Corrosion behavior of Monofrax K‐3 refractory in borosilicate‐based model low activity waste glass melts Abstract Owing to its good chemical and thermal durabilities at high temperatures, Monofrax K‐3 refractory is widely used in nuclear waste vitrification as a lining material in melting vessels. However, the corrosion of K‐3 refractory during the vitrification of nuclear waste is a serious problem because it affects the melter's safety, performance, and lifetime. Therefore, in the present study, we have focused on unearthing the impact of glass network formers, such as SiO 2 , B 2 O 3 , and Al 2 O 3 , in a model nuclear waste glass composition on the corrosion of Monofrax K‐3 refractory. The corrosion tests have been performed per ASTM C621 at 1150°C for 5 days. The dimensional measurements on corroded K‐3 refractory suggest that Al 2 O 3 and SiO 2 tend to reduce the refractory corrosion (neck loss), with the effect of Al 2 O 3 being significant. A corroded region on the K‐3 refractory at the melt–refractory interface is observed. The corrosion occurs via a coupling of the melt infiltration induced by a capillary effect and the dissolution of Al, Mg, and Fe components from K‐3 into the melt through chemical reactions. A Cr‐rich layer is retained on the glass contact surface of the corroded K‐3 refractory. Vitrification ; Corrosion ; Materials science ; Borosilicate glass ; Refractory (planetary science) ; Metallurgy ; Radioactive waste ; Dissolution ; Refractory metals ; Nuclear chemistry ; Chemical engineering ; Chemistry Glass properties and applications ; Recycling and utilization of industrial and municipal waste in materials production ; Nuclear materials and radiation effects
Authors: Balduin Katzer | Daniel Betsche | Klemens Böhm | D. Weygand | Katrin Schulz
Source: Scripta Materialia (article, 2023-10-25)
Field: Materials Science — Materials Chemistry

A graph database for feature characterization of dislocation networks Dislocation ; Materials science ; Graph ; Computer science ; Theoretical computer science Machine Learning in Materials Science ; Corrosion Behavior and Inhibition ; RNA Research and Splicing
Authors: Si Jiang | Sirui Lu | Dong-Ling Deng
Source: Quantum Frontiers (article, 2023-11-22)
Field: Materials Science — Materials Chemistry

Adversarial machine learning phases of matter Abstract We study the robustness of machine learning approaches to adversarial perturbations, with a focus on supervised learning scenarios. We find that typical phase classifiers based on deep neural networks are extremely vulnerable to adversarial perturbations: adding a tiny amount of carefully crafted noises into the original legitimate examples will cause the classifiers to make incorrect predictions at a notably high confidence level. Through the lens of activation maps, we find that some important underlying physical principles and symmetries remain to be adequately captured for classifiers with even near-perfect performance. This explains why adversarial perturbations exist for fooling these classifiers. In addition, we find that, after adversarial training the classifiers will become more consistent with physical laws and consequently more robust to certain kinds of adversarial perturbations. Our results provide valuable guidance for both theoretical and experimental future studies on applying machine learning techniques to condensed matter physics. Adversarial system ; Artificial intelligence ; Computer science ; Machine learning Machine Learning in Materials Science ; Adversarial Robustness in Machine Learning ; Advanced Electron Microscopy Techniques and Applications
Authors: Ewa Ropelewska | Kadir Sabancı | Vanya Slavova | Stefka Genova
Source: European Food Research and Technology (article, 2023-09-12)
Field: Chemistry — Analytical Chemistry

The classification of leek seeds based on fluorescence spectroscopic data using machine learning Abstract The objective of this study was to distinguish leek seeds belonging to the Starozagorski kamush variety and two breeding lines based on the selected fluorescence spectroscopic data. The classification models were developed for three classes of Starozagorski kamush vs. breeding line 4 vs. breeding line 39 and pairs of classes of Starozagorski kamush vs. breeding line 4, Starozagorski kamush vs. breeding line 39, and breeding line 4 vs. breeding line 39. The traditional machine learning algorithms, such as PART, Logistic, Naive Bayes, Random Forest, IBk, and Filtered Classifier were applied. All three classes were distinguished with an average accuracy of up to 93.33% for models built using IBk and Filtered Classifier. In the case of each model, Starozagorski kamush variety was completely different (accuracy of 100%, precision, and F-measure, MCC (Matthews correlation coefficient), and ROC (receiver operating characteristic) area of 1.000) from breeding lines, and the mixing of cases was observed between breeding line 4 and breeding line 39. The models built for pairs of leek seed classes distinguished Starozagorski kamush and breeding line 4 with an average accuracy reaching 100% (Logistic, Naive Bayes, Random Forest, IBk). The classification accuracy of Starozagorski kamush and breeding line 39 also reached 100% (Logistic, Naive Bayes, Random Forest, IBk), whereas breeding line 4 and breeding line 39 were classified with an average accuracy of up to 80% (Logistic, Naive Bayes, Random Forest, Filtered Classifier). The proposed approach combining fluorescence spectroscopy and machine learning may be used in practice to distinguish leek seed varieties and breeding lines. Naive Bayes classifier ; Random forest ; Artificial intelligence ; Classifier (UML) ; Machine learning ; Logistic regression ; Bayes' theorem ; Statistics ; Mathematics ; Computer science ; Support vector machine ; Bayesian probability Spectroscopy and Chemometric Analyses ; Horticultural and Viticultural Research ; Plant Physiology and Cultivation Studies
Authors: Luiz Pereira | Sophie Schuller | Fabian B. Wadsworth | Jérémie Vasseur | Ricardo F. Lancelotti | Kai‐Uwe Hess | Stéphane Gossé | Donald B. Dingwell
Source: International Journal of Applied Glass Science (article, 2023-12-17)
Field: Materials Science — Materials Chemistry

Rheology of a sodium‐molybdenum borosilicate melt undergoing phase separation Abstract During glass production, phase separation can result in the formation of suspended liquid droplets, which can cause changes in the system rheology. In nuclear waste vitrification context, some new glassy matrices may present this phase separation matter, but the mechanisms controlling the viscosity changes have not yet been determined. Here, we measure the viscosity of a sodium‐borosilicate melt containing dissolved MoO 3 at different temperatures and subject to different applied shear strain rates. We observe that (i) the viscosity increases sharply as the temperature decreases and (ii) at any constant temperature below 1000°C, the system presents non‐Newtonian response. Using transmission electron microscope observations coupled with viscosity calculations, we interpret the cause of the observed changes as the result of phase separation. We show that the viscosity increase on cooling is in excess of the predicted temperature dependence for a homogeneous melt of the starting composition. The increase is due to the formation of a second phase and is controlled by chemical and structural modifications of the matrix during the loss of the elements that form the droplets. This work provides insights into the rheology of a system composed of two composition sets due to a miscibility gap. Borosilicate glass ; Rheology ; Materials science ; Viscosity ; Vitrification ; Phase (matter) ; Thermodynamics ; Chemical engineering ; Composite material ; Chemistry ; Organic chemistry Material Dynamics and Properties ; Glass properties and applications ; Geological and Geochemical Analysis
Authors: Chinmay V. Mhatre | Jacob J. Wardzala | Priyanka B. Shukla | Mayank Agrawal | J. Karl Johnson
Source: Nanomaterials (article, 2023-06-02)
Field: Chemistry — Inorganic Chemistry

Calculation of Self, Corrected, and Transport Diffusivities of Isopropyl Alcohol in UiO-66 The UiO-6x family of metal-organic frameworks has been extensively studied for applications in chemical warfare agent (CWA) capture and destruction. An understanding of intrinsic transport phenomena, such as diffusion, is key to understanding experimental results and designing effective materials for CWA capture. However, the relatively large size of CWAs and their simulants makes diffusion in the small-pored pristine UiO-66 very slow and hence impractical to study directly with direct molecular simulations because of the time scales required. We used isopropanol (IPA) as a surrogate for CWAs to investigate the fundamental diffusion mechanisms of a polar molecule within pristine UiO-66. IPA can form hydrogen bonds with the μ3-OH groups bound to the metal oxide clusters in UiO-66, similar to some CWAs, and can be studied by direct molecular dynamics simulations. We report self, corrected, and transport diffusivities of IPA in pristine UiO-66 as a function of loading. Our calculations highlight the importance of the accurate modeling of the hydrogen bonding interactions on diffusivities, with about an order of magnitude decrease in diffusion coefficients when the hydrogen bonding between IPA and the μ3-OH groups is included. We found that a fraction of the IPA molecules have very low mobility during the course of a simulation, while a small fraction are highly mobile, exhibiting mean square displacements far greater than the ensemble average. Diffusion ; Chemical physics ; Hydrogen bond ; Molecule ; Isopropyl alcohol ; Chemistry ; Hydrogen ; Oxide ; Metal-organic framework ; Molecular dynamics ; Chemical polarity ; Mole fraction ; Materials science ; Computational chemistry ; Physical chemistry ; Thermodynamics ; Organic chemistry ; Physics Metal-Organic Frameworks: Synthesis and Applications ; Crystallography and molecular interactions ; Metal complexes synthesis and properties
Authors: Zhongzheng Ma | Yi Duan | Yongqi Deng | Hongdong Quan | Xiuguo Yang | Hongyan Li | Luqian Ye | Bingxia Xu | Lifeng Yan
Source: RSC Sustainability (article, 2023-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Deep eutectic solvent assisted preparation of cellulose nanofibers and graphene composite films for supercapacitors Cellulose nanofibers have been prepared directly from rice biomass with green solvent DES for graphene/CNF composite electrodes of high-performance supercapacitors. Supercapacitor ; Materials science ; Cellulose ; Nanofiber ; Graphene ; Composite number ; Deep eutectic solvent ; Solvent ; Eutectic system ; Chemical engineering ; Composite material ; Electrode ; Nanotechnology ; Capacitance ; Microstructure ; Organic chemistry ; Chemistry Supercapacitor Materials and Fabrication ; Nanomaterials for catalytic reactions ; MXene and MAX Phase Materials
Authors: L. Natrayan | Raviteja Surakasi | Pravin P. Patil | S. Kaliappan | Vijayashree Selvam | P. Murugan
Source: Journal of Nanomaterials (article, 2023-04-27)
Field: Materials Science — Polymers and Plastics

Optimizing Numerous Influencing Parameters of Nano-SiO2/Banana Fiber-Reinforced Hybrid Composites using Taguchi and ANN Approach High specific strength, strength-to-weight ratio, cheap cost, and other advantages, nanofillers are now the subject of most research on natural fibers. The current research’s main goal is to combine the Taguchi and artificial neural networks (ANN) approaches to maximize the mechanical characteristics of nanocomposites. The parameters: (i) nano-SiO2 wt%, (ii) banana fiber wt%, (iii) compression pressure in MPa, and (iv) compression molding temperature in °C were selected to achieve the objectives above. An L16 orthogonal array was used to optimize the process parameters based on the Taguchi technique. According to the intended experiment, mechanical characteristics, such as tension, bending, and impact strength, were assessed. The ANN was used to forecast outcomes that were optimized. The fiber mat thickness of banana fiber and the weight ratio of nano-SiO2 showed a considerable improvement in the mechanical characteristics of hybrid composites. According to the Taguchi technique, the most significant mechanical characteristics were 47.36 MPa tensile, 64.48 MPa flexural, and 35.33 kJ of impact under circumstances of 5% SiO2, 19 MPa pressure, and 110 °C. With 95% accuracy, ANN-predicted mechanical strength. The ANN forecast was more accurate than the regression model and experimental data. The above nanobased hybrid composites are mainly employed to satisfy the needs of the contemporary vehicle sector. Taguchi methods ; Materials science ; Orthogonal array ; Flexural strength ; Composite material ; Ultimate tensile strength ; Fiber ; Bending ; Izod impact strength test ; Nano- ; Artificial neural network ; Tension (geology) ; Natural fiber ; Computer science ; Machine learning Natural Fiber Reinforced Composites ; Tribology and Wear Analysis ; Mechanical Engineering and Vibrations Research
Authors: Stephanie A. Wankowicz | James S. Fraser
Source: ChemRxiv (preprint, 2023-11-21)
Field: Materials Science — Materials Chemistry

Comprehensive Encoding of Conformational and Compositional Protein Structural Ensembles through mmCIF Data Structure In their folded state, biomolecules exchange between multiple conformational states, crucial for their function. However, most structural models derived from experiments and computational predictions only encode a single state. To represent biomolecules more accurately, we must move towards modeling and predicting structural ensembles. Information about structural ensembles exists within experimental data from X-ray crystallography and cryo electron microscopy (cryoEM). While new tools are available to detect conformational and compositional heterogeneity that exist within these ensembles, the legacy PDB data structure does not robustly encapsulate this complexity. We propose modifications to the Macromolecular Crystallographic Information File (mmCIF) to improve the representation and interrelation of conformational and compositional heterogeneity. These modifications will enable improved tools to capture macromolecular ensembles in a way that is human and machine interpretable, potentially catalyzing breakthroughs for ensemble-function predictions, analogous to AlphaFold's achievements with single structure prediction. Conformational ensembles ; Biomolecule ; Protein Data Bank (RCSB PDB) ; Computer science ; Representation (politics) ; Biomolecular structure ; Function (biology) ; Structural bioinformatics ; Structural biology ; Protein structure ; Cryo-electron microscopy ; Biological system ; Chemistry ; Nanotechnology ; Materials science ; Biology Enzyme Structure and Function ; Microbial Metabolic Engineering and Bioproduction ; Protein Structure and Dynamics
Authors: Cuiling Li | Xiu Wang | Liping Chen | Xueguan Zhao | Yang Li | Mingzhou Chen | Haowei Liu | Changyuan Zhai
Source: Agriculture (article, 2023-08-24)
Field: Chemistry — Analytical Chemistry

Grading and Detection Method of Asparagus Stem Blight Based on Hyperspectral Imaging of Asparagus Crowns This study adopted hyperspectral imaging technology combined with machine learning to detect the disease severity of stem blight through the canopy of asparagus mother stem. Several regions of interest were selected from each hyperspectral image, and the reflection spectra of the regions of interest were extracted. There were 503 sets of hyperspectral data in the training set and 167 sets of hyperspectral data in the test set. The data were preprocessed using various methods and the dimension was reduced using PCA. K−nearest neighbours (KNN), decision tree (DT), BP neural network (BPNN), and extreme learning machine (ELM) were used to establish a classification model of asparagus stem blight. The optimal model depended on the preprocessing methods used. When modeling was based on the ELM method, the disease grade discrimination effect of the FD−MSC−ELM model was the best with an accuracy (ACC) of 1.000, a precision (PREC) of 1.000, a recall (REC) of 1.000, an F1-score (F1S) of 1.000, and a norm of the absolute error (NAE) of 0.000, respectively; when the modeling was based on the BPNN method, the discrimination effect of the FD−SNV−BPNN model was the best with an ACC of 0.976, a PREC of 0.975, a REC of 0.978, a F1S of 0.976, and a mean square error (MSE) of 0.072, respectively. The results showed that hyperspectral imaging of the asparagus mother stem canopy combined with machine learning methods could be used to grade and detect stem blight in asparagus mother stems. Hyperspectral imaging ; Asparagus ; Blight ; Artificial intelligence ; Canopy ; Mathematics ; Pattern recognition (psychology) ; Preprocessor ; Test set ; Computer science ; Horticulture ; Botany ; Biology Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Remote Sensing in Agriculture
Authors: Issa Sheibani Amin | Ali Naser Neysari | Raed H. Althomali | Ebraheem Abdu Musad Saleh | Sayfiddin Baymakov | Ahmed Hussien Radie Alawady | Ali Hashiem Alsaalamy | Montather F. Ramadan | Ashima Juyal
Source: Frontiers in Environmental Science (article, 2023-08-21)
Field: Chemistry — Analytical Chemistry

Development of microextraction methods for the determination of sulfamethoxazole in water and biological samples: modelling, optimization and verification by central composite design This study aimed to preconcentration of sulfamethoxazole (SMX) in water and biological samples. Ultrasound-assisted dispersive liquid-liquid microextraction (UA-DLLME) and ultrasound-assisted dispersive solid-phase microextraction (UA-DSPME) methods paired with spectrophotometry were applied to extraction and preconcentration of SMX. ZnFe 2 O 4 nanoparticles were prepared as adsorbent in UA-DSPME method by hydrothermal method. The scanning electron microscopy (SEM) technique showed that the adsorbent had symmetrical, bullet-shaped particles with uniform size. The results of the X-ray diffraction (XRD) showed the successful synthesis of the ZnFe 2 O 4 nanoparticles. Effective parameters in extraction, including ultrasonication time, disperser solvent volume, adsorbent amount, extraction solvent volume, eluent volume, and pH were investigated and optimized. The practical and optimal conditions of the process were determined by the central composite design (CCD). The optimal conditions were 0.024 g of adsorbent, 535 µL of disperser solvent volume, 7.5 min of ultrasonication time, 235 µL of eluent volume, pH of 5, and 185 µL of extraction solvent volume. Linear ranges and detection limits were 20–1,200 μg L −1 and 6 μg L −1 for UA-DSPME and 10–800 μg L −1 and 3 μg L −1 for UA-DLLME. Relative standard deviation (RSD) of less than 4% were obtained for UA-DSPME and UA-DLLME methods. The reusability showed that the ZnFe 2 O 4 adsorbent could extract SMX up to five cycles of adsorption/desorption without significant reduction in its efficiency. Also, interference studies showed that the presence of different cations and anions did not significantly interfere in the extraction of SMX. The outcomes of real-time samples analysis showed that the extraction of SMX for both methods was in the range of 92.44%–99.12%. The results showed the developed methods are simple, sensitive, and suitable for SMX preconcentration in environmental water and biological samples. Adsorption ; Central composite design ; Disperser ; Extraction (chemistry) ; Solvent ; Chromatography ; Response surface methodology ; Sonication ; Detection limit ; Chemistry ; Volume (thermodynamics) ; Desorption ; Solid phase extraction ; Composite number ; Materials science ; Nuclear chemistry ; Organic chemistry ; Composite material Analytical chemistry methods development ; Pharmaceutical and Antibiotic Environmental Impacts ; Biosensors and Analytical Detection