jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: D Swamydoss
Source: Communications on Applied Nonlinear Analysis (article, 2024-08-23)
Field: Chemistry — Analytical Chemistry

Integrating Nonlinear Dynamics and Statistical Methods in Deep Learning Models for Biochemical Component Analysis Nonlinear correlations in data that analysis of biological components often requires might be challenging for conventional analytical methods. The intricate nonlinear patterns in biological data could be difficult for conventional biochemical analysis methods to detect, thereby generating less accurate predictions and insights. This disparity highlights the need of more robust analytical techniques able to effectively model and grasp these complexity. We suggest to include nonlinear dynamics with statistical approaches inside deep learning models to raise the accuracy and interpretability of biochemical component analysis. We especially incorporate nonlinear dynamic systems theory into a deep neural network (DNN) architecture to raise the model's potential to identify complex temporal and spatial patterns in biochemical datasets. Embedded into the network design, dynamic system equations are applied in statistical techniques such variance decomposition and regression analysis to enhance model predictions. The proposed method was evaluated on diverse sized biochemical datasets (150,300, 450, and 600 samples). With a 600 sample dataset, the combined DNN model obtained a prediction accuracy of 92.5% compared to 85.7% with conventional techniques. Moreover, the improved model interpretability by 18% found by variance explained in the biochemical component analysis. Including nonlinear dynamics and statistical methods clearly improves the performance and interpretability of biochemical component analysis models, the results demonstrate. Component (thermodynamics) ; Nonlinear system ; Computer science ; Dynamics (music) ; Component analysis ; Artificial intelligence ; Statistical analysis ; Biological system ; Machine learning ; Mathematics ; Statistics ; Psychology ; Physics ; Biology Spectroscopy and Chemometric Analyses
Authors: Kamila Ścibisz | J. Krawczyk
Source: Archives of Metallurgy and Materials (article, 2024-04-11)
Field: Materials Science — Materials Chemistry

Quantitative and Qualitative Evaluation of Non-Metallic Inclusions in High-Silicon Steel after Hot Rolling This paper presents an analysis of non-metallic inclusions occurring in high-silicon steels containing about 3% Si of terms of their type, volume fraction and morphology. The inclusions were divided into 3 main groups: oxides, sulfides, nitrides which together can also form complex. The work was based on numerous metallographic observations in two sections (longitudinal and transverse to the rolling direction). The study was performed on three casts differing in chemical composition. The analyzed casts were characterized by a different content of non-metallic inclusions, which can be associated with slight differences in chemical composition. The analyzed results showed that the most common inclusions were oxides and nitrides. Sulfides occurred sporadically. Non-metallic inclusions ; Materials science ; Silicon ; Metallurgy ; Chemical composition ; Volume fraction ; Silicon nitride ; Metal ; Nitride ; Inclusion (mineral) ; Composite material ; Mineralogy ; Chemistry Metal Alloys Wear and Properties ; Microstructure and Mechanical Properties of Steels ; Metallurgical Processes and Thermodynamics
Authors: Sameed Hameed | Yaohua Liu | K. S. Rabinovich | Gyeonghun Kim | P. Wochner | G. Christiani | Г. Логвенов | Kohei Higuchi | N. B. Brookes | E. Weschke | F. Yakhou-Harris | A. V. Boris | B. Keimer | M. Minola
Source: arXiv (Cornell University) (preprint, 2024-08-13)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Interplay between electronic and lattice superstructures in La$_{2-x}$Ca$_{x}$CuO$_{4}$ Complex oxides are well known to develop oxygen ordering patterns with well defined periodicities, but their interplay with electronic correlations remains largely unexplored. Here, we report resonant and non-resonant x-ray diffraction data indicating a four-unit-cell periodic superstructure related to oxygen vacancy ordering in La$_{2-x}$Ca$_{x}$CuO$_{4}$ films with doping levels from the underdoped ($x = 0.15$) to the extremely overdoped ($x = 0.50$) regime. Whereas the lattice superstructure is temperature independent up to 300 K, a strongly temperature dependent electronic charge density wave (CDW) is observed in the underdoped and slightly overdoped regimes ($x\leq0.20$). The periodicity, in-plane and out-of-plane correlation lengths of the CDW are locked in by the lattice superstructure. Our results highlight the necessity to consider lattice and electronic energetics on equal footing in the high-temperature oxygen-disordered phase to explain oxygen ordering phenomena in complex oxides. Condensed matter physics ; Lattice (music) ; Materials science ; Crystallography ; Physics ; Chemistry Magnetic and transport properties of perovskites and related materials ; Physics of Superconductivity and Magnetism ; Advanced Condensed Matter Physics
Authors: Hiromi Miura | Yoshitake Matsuo | Yojiro Oba | Y. Aoyagi | Chihiro Watanabe
Source: MATERIALS TRANSACTIONS (article, 2024-12-26)
Field: Materials Science — Materials Chemistry

Strengthening Mechanisms of Heterogeneous Nano-Structured Cu-Zn-Si Alloy Bars Fabricated by Caliber Rolling Hot-extruded Cu-Zn-Si alloy bars with various chemical compositions were cold-caliber rolled down to 91.7% reduction at maximum. Heterogeneous nano-structure, in which coarse initial grains were subdivided mainly by mechanical twins and shear bands, was gradually developed with increasing reduction. The as-rolled bars exhibited an extraordinarily high tensile strength of 988 MPa at best with a reasonable ductility of 5.9%. The tensile strength was further slightly raised to 995 MPa by low-temperature annealing. 3D atom-probe analyses revealed dense Si segregation at twin boundaries and increase in the amount of segregation after annealing. Multi-scale simulation indicated that strain field formed by the Si segregation at twin boundary obstructed dislocation glide to develop geometrically necessary dislocations, which resulted in higher yield stress and work-hardening rate to cause higher tensile strength. It was found that strengthening mechanisms of heterogeneous nano-structured Cu-Zn-Si alloy bars are, therefore, complicatedly combined ones of grain refinement, work hardening, solid-solution hardening and grain-boundary segregation. Materials science ; Caliber ; Alloy ; Nano- ; Metallurgy ; Nanotechnology ; Composite material Microstructure and mechanical properties ; Surface Treatment and Residual Stress ; Advanced Welding Techniques Analysis
Authors: Rusl Mahdi Obaid | Khdeeja Jabbar Ali
Source: Journal of Kufa for Chemical Sciences (article, 2024-11-01)
Field: Chemistry — Analytical Chemistry

New Sensitive Spectrophotometric Method for Methyldopa Determination in Different Pharmaceutical Samples Methyldopa (M. dopa) drug concentration in pharmaceutical and pure forms is determined using a simple spectrophotometric method. In this work, the medication is first oxidized using an excess of N-bromosuccinimide (NBS), and then it is reduced with 3,3-diaminobenzidine (DAB). At 513 nm, the resultant chemical is detected. Beer's law was found to be applicable within concentration ranges of 0.5-10 mg/L, with a molar absorptivity of 25.87×103 L mol-1 cm-1. The common excipients and additives did not interfere in their determination of both methods. The proposed method was effectively utilized to analyze the mentioned drugs in different forms of dosages. Methyldopa ; Molar absorptivity ; Chromatography ; Chemistry ; Spectrophotometry ; Drug ; Pharmacology ; Medicine Analytical Methods in Pharmaceuticals ; Electrochemical sensors and biosensors ; Pharmacological Effects and Assays
Authors: В. В. Алексеенко | A. B. Gonzhitov | S. P. Bugdaev
Source: Izvestiya vuzov Investitsii Stroitelstvo Nedvizhimost (article, 2024-04-05)
Field: Materials Science — Polymers and Plastics

Influence of composition on physical and mechanical properties of modified PVC The present article aims to create polyvinyl chloride composites used in polymer hoses, to study their composition, as well as physical and mechanical characteristics. Polyvinyl chloride is a waterinsoluble material, resistant to weak acids, alkalis, alcohols, and mineral oils. The main characteristics limiting the use of flexible polymer hoses include elasticity at low temperatures and bending resistance at positive temperatures. Hose flexibility is determined by the properties of plasticized polyvinyl chloride together with its ultimate elongation at break and thickness. To study the properties of modified polyvinyl chloride, a MiniTPA-100 extruder was used to create test samples. The samples were cooled in a special refrigeration chamber. Elasticity measurements were performed employing a standard tensile testing scheme on an Instron 5980 testing machine. The measurements were taken at 20 °C, -40 °C, and -50 °C. The investigation of the compositions of polymer composites was conducted using extraction methods with various solvents. These studies enable one to evaluate the properties of polymer composites. Before testing, the samples were held at specified temperatures. The strain rate equaled 3 mm/sec for all samples. The proposed laboratory techniques give an opportunity to evaluate most of the performance characteristics of polyvinyl chloride composites. The obtained results indicate that the performance characteristics of flexible polymer hoses can be fully predicted based on laboratory testing of the materials composing the hose. The results also confirm the potential of work on polyvinyl chloride modification using thermoplastic elastomers. Composition (language) ; Environmental science ; Materials science ; Art ; Literature Polymer Science and PVC ; Polymer Nanocomposites and Properties ; Polymer crystallization and properties
Authors: L. Perez Barboza | E. López Cuéllar | H. Guajardo | Armando Salinas‐Rodríguez
Source: Metals (article, 2024-04-26)
Field: Materials Science — Materials Chemistry

Effect of Initial Microstructure on the Temperature Dependence of the Flow Stress and Deformation Microstructure under Uniaxial Compression of Ti-407 In this study, the influence of initial microstructure and deformation temperature on the flow stress behavior and microstructural evolution of TIMETAL®407 (Ti-407) alloy are investigated. For this purpose, compression cylinders were β-annealed at 940 °C and then cooled to room temperature using furnace cooling, static air, and water quenching to promote three initial microstructures with different α lath thicknesses. The annealed cylinders were compressed isothermally in the range of 750 °C to 910 °C at a constant crosshead speed of 0.05 mm/s up to an engineering strain of −0.8. The resulting stress–strain curves are discussed in terms of the morphology and distribution of the α and β phases. It was found that flow stress is inversely proportional to deformation temperature for all initial microstructures. At the lowest temperatures, compressive yield strength was higher in water-quenched and air-cooled samples than in furnace-cooled specimens, suggesting that the acicular α-phase morphology obtained by rapid cooling could enhance mechanical strength by hindering dislocation motion. Two high-temperature flow regimes were determined based on the shape of the flow stress curves, indicating microstructural changes occurring during deformation. At higher temperatures, the effect of the initial microstructure is negligible as the primary α phase is transformed to the β phase at around 850 °C irrespective of the initial α-lath thickness. Microstructure ; Materials science ; Flow stress ; Deformation (meteorology) ; Compression (physics) ; Composite material ; Stress (linguistics) Titanium Alloys Microstructure and Properties ; Advanced materials and composites ; Metallurgy and Material Forming
Authors: Sakhinana Sagar Srinivas | Rajat Kumar Sarkar | Venkataramana Runkana
Source: arXiv (Cornell University) (preprint, 2024-08-21)
Field: Materials Science — Surfaces, Coatings and Films

EMCNet : Graph-Nets for Electron Micrographs Classification Characterization of materials via electron micrographs is an important and challenging task in several materials processing industries. Classification of electron micrographs is complex due to the high intra-class dissimilarity, high inter-class similarity, and multi-spatial scales of patterns. However, existing methods are ineffective in learning complex image patterns. We propose an effective end-to-end electron micrograph representation learning-based framework for nanomaterial identification to overcome the challenges. We demonstrate that our framework outperforms the popular baselines on the open-source datasets in nanomaterials-based identification tasks. The ablation studies are reported in great detail to support the efficacy of our approach. Electron micrographs ; Micrograph ; Graph ; Artificial intelligence ; Computer science ; Electron microscope ; Physics ; Theoretical computer science ; Optics ; Scanning electron microscope Electron and X-Ray Spectroscopy Techniques ; Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications
Authors: Suraj Arya | Anju Anju | Nor Azuana Ramli
Source: International Journal of Informatics and Communication Technology (IJ-ICT) (article, 2024-12-22)
Field: Chemistry — Analytical Chemistry

Prediction of international rice production using long short-term memory and machine learning models Rice, a staple food source globally, is in high demand and production across the world. Its consumption varies in different countries, with each nation having its unique way of incorporating rice into its diet. Recognizing the global nature of rice, its production is a crucial aspect of ensuring its availability, agriculture forecasting, economic stability, and food security. By predicting its production, we can develop a global plan for its production and stock, thereby preventing issues like famine. This paper proposes machine learning (ML) and deep learning (DL) models like linear regression, ridge regression, random forest (RF), adaptive boosting (AdaBoost), categorical boosting (CatBoost), extreme gradient boosting (XGBoost), gradient boosting, decision tree, and long short-term memory (LSTM) to predict international rice production. A total of nine ML and one DL models are trained and tested on the international dataset, which contains the rice production details of 192 countries over the last 62 years. Notably, linear regression and the LSTM algorithm predict rice production with the highest percentage of R-squared (R2 ), 98.40% and 98.19%, respectively. These predictions and the developed models can play a vital role in resolving crop-related international problems, uniting the global agricultural community in a common cause. Term (time) ; Production (economics) ; Long short term memory ; Computer science ; Artificial intelligence ; Machine learning ; Economics ; Artificial neural network ; Recurrent neural network ; Macroeconomics Spectroscopy and Chemometric Analyses
Authors: Isabela Arantes Ferreira | Gessica Wernke | Alexandre Diório | Rosângela Bergamasco | Marcelo Fernandes Vieira
Source: Revista Brasileira de Ciências Ambientais (article, 2024-08-10)
Field: Chemistry — Analytical Chemistry

Sunflower seed husks as a cost-effective adsorbent for chloroquine removal from water The increased production and consumption of pharmaceuticals represent a potential environmental threat. Thus, advanced treatments are necessary to remove pharmaceutical products from water. A promising removal alternative is low-cost adsorbents, due to their availability, low processing, and favorable results. This study used sunflower seed husks (SSH) and chemically treated sunflower seed husks (TSSH) as bioadsorbents to remove chloroquine from water. Results showed that the pseudo-second-order is the kinetic model with the best fit for both adsorbents. As for isothermic models, the best fit for SSH was Langmuir, and for TSSH, Freundlich. Maximum adsorption capacities of qe=168.09±22.98 mg/g and qe=185.91±27.23 mg/g were found through the isothermic models for SSH and TSSH, respectively. In addition, SSH reached 80% of its initial adsorption capacity after three adsorption-desorption cycles, indicating physisorption and good applicability due to its reusability. Thus, SSH is an efficient adsorbent for chloroquine removal with excellent regeneration capacity, low production cost, and low waste production. This study serves as a model for the use of SSH in the removal of contaminants of emerging concern. Husk ; Sunflower ; Adsorption ; Chloroquine ; Sunflower seed ; Chemistry ; Biotechnology ; Pulp and paper industry ; Agronomy ; Botany ; Biology ; Engineering ; Organic chemistry ; Malaria Dye analysis and toxicity ; Analytical chemistry methods development ; Electrochemical sensors and biosensors
Authors: Shoshi Tokuno | Kouki Kimizuka | Yuichiro Tanaka | Yuki Usami | Hirofumi Tanaka | Hakaru Tamukoh
Source: Nonlinear Theory and Its Applications IEICE (article, 2024-01-01)
Field: Chemistry — Organic Chemistry

Object recognition and grasping point detection using carbon nanotube - polydimethylsiloxane nanocomposite sensor This paper presents a system for processing tactile information using a carbon nanotube (CNT)-polydimethylsiloxane (PDMS) nanocomposite sensor designed for robotic applications. This study introduces an approach for recognizing objects and detecting optimal grasping points using tactile data from a sensor-equipped robotic hand. The sensor is expected to be more efficient than a computerized implementation using sensor dynamics directly in the computation. The experiments demonstrated the system's ability to classify nine types of objects with an accuracy of 83.56% and to discern two grasping points on four object types, achieving a 71.7% success rate. Polydimethylsiloxane ; Carbon nanotube ; Nanocomposite ; Materials science ; Point (geometry) ; Multispectral image ; Nanotechnology ; Artificial intelligence ; Computer vision ; Computer science ; Composite material ; Biomedical engineering ; Medicine ; Mathematics ; Geometry Polydiacetylene-based materials and applications
Authors: Longze Li | John W. Merickel | Yalei Tang | Rongjie Song | Joshua Rittenhouse | Aleksandar Vakanski | Fei Xu
Source: Open MIND (preprint, 2024-09-11)
Field: Materials Science — Materials Chemistry

Dataset of Tensile Properties for Sub-sized Specimens of Nuclear Structural Materials Mechanical testing with sub-sized specimens plays an important role in the nuclear industry, facilitating tests in confined experimental spaces with lower irradiation levels and accelerating the qualification of new materials. The reduced size of specimens results in different material behavior at the microscale, mesoscale, and macroscale, in comparison to standard-sized specimens, which is referred to as the specimen size effect. Although analytical models have been proposed to correlate the properties of sub-sized specimens to standard-sized specimens, these models lack broad applicability across different materials and testing conditions. The objective of this study is to create the first large public dataset of tensile properties for sub-sized specimens used in nuclear structural materials. We performed an extensive literature review of relevant publications and extracted over 1,000 tensile testing records comprising 54 parameters including material type and composition, manufacturing information, irradiation conditions, specimen dimensions, and tensile properties. The dataset can serve as a valuable resource to investigate the specimen size effect and develop computational methods to correlate the tensile properties of sub-sized specimens. Ultimate tensile strength ; Materials science ; Composite material ; Nuclear engineering ; Engineering Graphite, nuclear technology, radiation studies
Authors: Stefania Porcu | Riccardo Corpino | Carlo Maria Carbonaro | Pier Carlo Ricci | Attilio V. Vargiu | Anna Laura Sanna | Giuseppe Sforazzini | Daniele Chiriu
Source: Molecules (article, 2024-06-13)
Field: Materials Science — Polymers and Plastics

Promising Molecular Architectures for Two-Photon Probes in the Diagnosis of α-Synuclein Aggregates The abnormal deposition of protein in the brain is the central factor in neurodegenerative disorders (NDs). These detrimental aggregates, stemming from the misfolding and subsequent irregular aggregation of α-synuclein protein, are primarily accountable for conditions such as Parkinson's disease, Alzheimer's disease, and dementia. Two-photon-excited (TPE) probes are a promising tool for the early-stage diagnosis of these pathologies as they provide accurate spatial resolution, minimal intrusion, and the ability for prolonged observation. To identify compounds with the potential to function as diagnostic probes using two-photon techniques, we explore three distinct categories of compounds: Hydroxyl azobenzene (AZO-OH); Dicyano-vinyl bithiophene (DCVBT); and Tetra-amino phthalocyanine (PcZnNH<sub>2</sub>). The molecules were structurally and optically characterized using a multi-technique approach via UV-vis absorption, Raman spectroscopy, three-dimensional fluorescence mapping (PLE), time-resolved photoluminescence (TRPL), and pump and probe measurements. Furthermore, quantum chemical and molecular docking calculations were performed to provide insights into the photophysical properties of the compounds as well as to assess their affinity with the α-synuclein protein. This innovative approach seeks to enhance the accuracy of in vivo probing, contributing to early Parkinson's disease (PD) detection and ultimately allowing for targeted intervention strategies. Photoluminescence ; Fluorescence ; Two-photon excitation microscopy ; Chemistry ; Dementia with Lewy bodies ; In vivo ; Intramolecular force ; Photochemistry ; Biophysics ; Materials science ; Dementia ; Disease ; Medicine ; Biology ; Stereochemistry ; Optoelectronics ; Pathology ; Optics Conducting polymers and applications ; Electrochemical sensors and biosensors ; Advanced Fluorescence Microscopy Techniques
Authors: Cristiana Luminița Gîjiu | Manuela Rossemary Apetroaei | Miruna-Maria Apetroaei | Raluca Isopescu | Mirela Anton | 12:00:00 AM | Ileana Rău | V. Schröder
Source: Revue Roumaine de Chimie (article, 2024-09-23)
Field: Materials Science — Biomaterials

Optimization of chitosan extraction from crustacean exoskeleton wastes The degree of deacetylation, DDA is the most important characteristic for a specific chitosan sample as it mainly influences its chemical and biological properties. Several natural wastes may be used as sources for chitin as for example crabs’ and shrimps’ exoskeletons, and the yield of chitosan extraction is generally small and depends on the chitin extraction and the deacetylation step. The simultaneous maximization of yield and deacetylation degree DDA pointed out some optimal operating conditions of the deacetylation procedure. The maximum yield and maximum DDA can be obtained working with higher NaOH concentrations and longer duration of this step. Chitosan ; Exoskeleton ; Extraction (chemistry) ; Crustacean ; Chemistry ; Pulp and paper industry ; Chromatography ; Biochemical engineering ; Fishery ; Biology ; Engineering ; Ecology ; Biochemistry Nanocomposite Films for Food Packaging