jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Laura Posch | J. Alves | N. Miret-Roig | Sebastian Ratzenböck | Josefa E. Großschedl | Stefan Meingast | Cameren Swiggum | Ralf Konietzka
Source: Astronomy and Astrophysics (article, 2024-11-13)
Field: Chemistry — Physical and Theoretical Chemistry

The physical properties of cluster chains We explore the kinematics and star formation history of the Scorpius Centaurus (Sco-Cen) OB association following the initial identification of sequential, linearly aligned chains of clusters. Building upon our characterization of the Corona Australis (CrA) chain, we now analyze two additional major cluster chains that exhibit similar characteristics: the Lower Centaurus Crux (LCC) and Upper Scorpius (Upper Sco) chains. All three cluster chains display distinct sequential patterns in (1) the 3D spatial distribution, (2) age, (3) velocity, and (4) mass. The Upper-Sco chain is the most massive and complex cluster chain, possibly consisting of two or more overlapping subchains. We discuss the possible formation of cluster chains and argue for a scenario where feedback from the most massive star formation episode 15 Myr ago initiated the formation of these spatio-temporal cluster sequences. Our results identify cluster chains as a distinct type of stellar structure with well-defined physical properties, formed in environments capable of sustaining stellar feedback over timescales of 5–10 Myr. We find that around 40% of the stellar population in Sco-Cen formed due to triggered star formation, with 35% forming along the three cluster chains. We conclude that cluster chains could be common structures in OB associations, particularly in regions that have similar natal environments as Sco-Cen. Beyond their significance for star formation and stellar feedback, they appear to be promising laboratories for chemical enrichment and the transport of elements from one generation to the next in the same star-forming region. Physics ; Astrophysics ; Cluster (spacecraft) ; Astronomy History and advancements in chemistry
Authors: Necla Toğun | Süleyman Murat Bağdatlı
Source: Journal of Vibration Engineering & Technologies (article, 2024-03-31)
Field: Materials Science — Materials Chemistry

Application of Modified Couple-Stress Theory to Nonlinear Vibration Analysis of Nanobeam with Different Boundary Conditions Abstract Purpose In the present study, the nonlinear vibration analysis of a nanoscale beam with different boundary conditions named as simply supported, clamped-clamped, clamped-simple and clamped-free are investigated numerically. Methods Nanoscale beam is considered as Euler-Bernoulli beam model having size-dependent. This non-classical nanobeam model has a size dependent incorporated with the material length scale parameter. The equation of motion of the system and the related boundary conditions are derived using the modified couple stress theory and employing Hamilton’s principle. Multiple scale method is used to obtain the approximate analytical solution. Result Numerical results by considering the effect of the ratio of beam height to the internal material length scale parameter, h / l and with and without the Poisson effect, υ are graphically presented and tabulated. Conclusion We remark that small size effect and poisson effect have a considerable effect on the linear fundamental frequency and the vibration amplitude. In order to show the accuracy of the results obtained, comparison study is also performed with existing studies in the literature. Vibration ; Nonlinear system ; Stress (linguistics) ; Boundary value problem ; Structural engineering ; Boundary (topology) ; Materials science ; Physics ; Mathematics ; Mathematical analysis ; Engineering ; Acoustics ; Philosophy ; Quantum mechanics Nonlocal and gradient elasticity in micro/nano structures ; Composite Structure Analysis and Optimization ; Numerical methods in engineering
Authors: Alexander Kensert | Pieter Libin | Gert Desmet | Deirdre Cabooter
Source: Journal of Chromatography A (article, 2024-02-28)
Field: Chemistry — Spectroscopy

Deep reinforcement learning for the direct optimization of gradient separations in liquid chromatography Chemistry ; Chromatography ; Hydrophilic interaction chromatography ; High-performance liquid chromatography ; Chromatography column Analytical Chemistry and Chromatography ; Microfluidic and Capillary Electrophoresis Applications ; Chromatography in Natural Products
Authors: Meysam Latifi-Amoghin | Yousef Abbaspour‐Gilandeh | Mohammad Tahmasebi | Mohammad Kaveh | Hany S. El‐Mesery | Mariusz Szymanek | Maciej Sprawka
Source: Applied Sciences (article, 2024-11-23)
Field: Chemistry — Analytical Chemistry

VIS/NIR Spectroscopy as a Non-Destructive Method for Evaluation of Quality Parameters of Three Bell Pepper Varieties Based on Soft Computing Methods Spectroscopic analysis was employed to evaluate the quality of three bell pepper varieties within the 350–1150 nm wavelength range. Quality parameters such as firmness, pH, soluble solids content, titratable acids, vitamin C, total phenols, and anthocyanins were measured. To enhance data reliability, principal component analysis (PCA) was used to identify and remove outliers. Raw spectral data were initially modeled using partial least squares regression (PLSR). To optimize wavelength selection, support vector machines (SVMs) were combined with genetic algorithms (GAs), particle swarm optimization (PSO), ant colony optimization (ACO), and imperial competitive algorithm (ICA). The most effective wavelength selection method was subsequently used for further analysis. Three modeling techniques—PLSR, multiple linear regression (MLR), and artificial neural networks (ANNs)—were applied to the selected wavelengths. PLSR analysis of raw data yielded a maximum R2 value of 0.98 for red pepper pH, while the lowest R2 (0.58) was observed for total phenols in yellow peppers. SVM-PSO was determined to be the optimal wavelength selection algorithm based on ratio of performance to deviation (RPD), root mean square error (RMSE), and correlation values. An average of 15 effective wavelengths were identified using this combined approach. Model performance was evaluated using root mean square error of cross-validation and coefficient of determination (R2). ANN consistently outperformed MLR and PLSR in predicting firmness, pH, soluble solids content, titratable acids, vitamin C, total phenols, and anthocyanins for all three varieties. R2 values for the ANN model ranged from 0.94 to 1.00, demonstrating its superior predictive capability. Based on these results, ANN is recommended as the most suitable method for evaluating the quality parameters of bell peppers using spectroscopic data. Soft computing ; Pepper ; Mathematics ; Quality (philosophy) ; Materials science ; Agricultural engineering ; Horticulture ; Biological system ; Computer science ; Engineering ; Artificial intelligence ; Biology ; Physics Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Postharvest Quality and Shelf Life Management
Authors: Stefan P. Schmid | Leon Schlosser | Frank Glorius | Kjell Jorner
Source: Beilstein Journal of Organic Chemistry (review, 2024-09-10)
Field: Materials Science — Materials Chemistry

Catalysing (organo-)catalysis: Trends in the application of machine learning to enantioselective organocatalysis Organocatalysis has established itself as a third pillar of homogeneous catalysis, besides transition metal catalysis and biocatalysis, as its use for enantioselective reactions has gathered significant interest over the last decades. Concurrent to this development, machine learning (ML) has been increasingly applied in the chemical domain to efficiently uncover hidden patterns in data and accelerate scientific discovery. While the uptake of ML in organocatalysis has been comparably slow, the last two decades have showed an increased interest from the community. This review gives an overview of the work in the field of ML in organocatalysis. The review starts by giving a short primer on ML for experimental chemists, before discussing its application for predicting the selectivity of organocatalytic transformations. Subsequently, we review ML employed for privileged catalysts, before focusing on its application for catalyst and reaction design. Concluding, we give our view on current challenges and future directions for this field, drawing inspiration from the application of ML to other scientific domains. Organocatalysis ; Enantioselective synthesis ; Chemistry ; Catalysis ; Biochemical engineering ; Field (mathematics) ; Combinatorial chemistry ; Nanotechnology ; Organic chemistry Machine Learning in Materials Science ; Asymmetric Hydrogenation and Catalysis ; Computational Drug Discovery Methods
Authors: Sirui Li | Hanguang Zhang | Edward F. Holby | Piotr Zelenay | Wilton J. M. Kort-Kamp
Source: Current Opinion in Electrochemistry (article, 2024-08-23)
Field: Materials Science — Materials Chemistry

Machine learning-guided design, synthesis, and characterization of atomically dispersed electrocatalysts Artificial intelligence ; Characterization (materials science) ; Nanotechnology ; Computer science ; Machine learning ; Convolutional neural network ; Heuristic ; Deep learning ; Materials science Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Fuel Cells and Related Materials
Authors: Han Zhang | Suping Li | Xingming Ma
Source: Drug Design Development and Therapy (review, 2024-08-01)
Field: Materials Science — Materials Chemistry

Transforming Healthcare with Nanomedicine: A SWOT Analysis of Drug Delivery Innovation Nanomedicine holds substantial promise in driving medical innovation. However, successful clinical translation requires addressing safety, cost, and regulatory challenges. Interdisciplinary collaboration and comprehensive strategic planning are crucial for the safe and effective application of nanomedicine. Nanomedicine ; Healthcare delivery ; SWOT analysis ; Drug delivery ; Health care ; Drug ; Business ; Pharmacology ; Medicine ; Nanotechnology ; Marketing ; Nanoparticle ; Materials science ; Political science Nanoparticles: synthesis and applications ; Nanoparticle-Based Drug Delivery
Authors: Zongrui Pei | Junqi Yin | Jörg Neugebauer | Anubhav Jain
Source: Nature Reviews Materials (review, 2024-09-12)
Field: Materials Science — Materials Chemistry

Towards the holistic design of alloys with large language models Computer science Machine Learning in Materials Science ; Catalysis and Hydrodesulfurization Studies ; Titanium Alloys Microstructure and Properties
Authors: Xurui Zhang | Jan C. M. van Hest | Yongjun Men
Source: ACS Applied Nano Materials (article, 2024-06-28)
Field: Chemistry — Organic Chemistry

Review of Shape Transformation Pathways of Polymersomes: Implications for Nanomotor, Biomedicine, and Artificial Cell Mimics Polymersomes have been studied for many years since their discovery, and have consistently been an appealing and widely explored research domain. Their inherent benefits, encompassing stability, versatility, load-carrying capacity, and deformability, endow them with many opportunities to be used in numerous fields of biomedicine, nanocarriers, diagnostics and therapeutics. Nevertheless, shape transformation pathways have not been comprehensively summarized or received adequate scholarly attention. Herein, we summarize typical mechanisms underlying polymersome self-assembly with a particular focus on the shape transformation pathways associated with commonly employed self-assembling methods. Moreover, we provide a succinct overview of the proposed applications involving shape transformation applications, and enumerate the most recent findings in this domain. Polymersome ; Biomedicine ; Transformation (genetics) ; Computational biology ; Biology ; Chemistry ; Bioinformatics ; Amphiphile ; Biochemistry ; Copolymer ; Gene Advanced Polymer Synthesis and Characterization ; Polymer composites and self-healing ; Nanofabrication and Lithography Techniques
Authors: Pinky Kotwal | Rohit Jasrotia | Himanshi Himanshi | Jyoti Prakash | Abhishek Kandwal | Vaseem Raja | Naresh Kumar Sharma | Rajesh Kumar
Source: E3S Web of Conferences (review, 2024-01-01)
Field: Materials Science — Materials Chemistry

A Review on the Synthesis, Properties and Multifunctional Applications of Transition and Rare-Earth Doped Nickel Spinel Ferrite Nickel ferrite have gathered a lot of research interest nowadays, because of their unique magnetic and structural traits, which makes appealing for a variety of scientific applications. In recent years, considerable attention has been placed towards the impact of rare-earth and transition elements doping on the magnetic as well as the structural traits of nickel spinel ferrite. This study delivers a concise summary of scientific advancements in this area. It discusses the processes used for fabricating doped nickel spinel ferrite, the influence of different dopants on the material's magnetic and structural traits, and the mechanisms that underlie these effects. The review also highlights the potential applications of doped nickel spinel ferrite in fields such as magnetic data storage, biomedical, and photocatalysis. Spinel ; Rare earth ; Doping ; Materials science ; Nickel ; Ferrite (magnet) ; Metallurgy ; Optoelectronics ; Composite material Magnetic Properties and Synthesis of Ferrites ; Iron oxide chemistry and applications ; Electromagnetic wave absorption materials
Authors: Vahid Yaghoubi | Barış Kumru
Source: Advanced Sustainable Systems (article, 2024-04-03)
Field: Materials Science — Biomaterials

Retrosynthetic Life Cycle Assessment: A Short Perspective on the Sustainability of Integrating Thermoplastics and Artificial Intelligence Into Composite Systems Abstract Over the past 30 years, the polymer composite industry has flourished, producing advanced structural materials for the aviation, energy, and transportation sectors. However, the use of crosslinked thermoset matrices has been linked to significant end‐of‐life challenges, presenting a critical issue for the industry. Moreover, the industry is characterized by numerous labor‐intensive processes. In alignment with Industry 4.0 principles, two major routes have been identified to enhance sustainability: the utilization of high‐performance thermoplastic matrices and the integration of artificial intelligence in manufacturing. Nevertheless, there are substantial concerns regarding the life cycle assessment of these technologies, which are not accounted for in the initial calculations, including the environmental footprint of polymer synthesis and energy requirements for training AI. This perspective aims to address potential and significant CO 2 emissions from chemical feedstocks and the high computing requirements of these new technologies. Life-cycle assessment ; Perspective (graphical) ; Retrosynthetic analysis ; Composite number ; Sustainability ; Materials science ; Engineering ; Artificial intelligence ; Computer science ; Composite material ; Chemistry ; Ecology ; Biology ; Organic chemistry biodegradable polymer synthesis and properties ; Recycling and Waste Management Techniques ; Polymer crystallization and properties
Authors: Gerson Márquez | Edgar Serrano Pérez | V. Sagredo
Source: Materials Research (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Structural and Magnetic Characterization of Ni-Co Mixed Ferrite Nanopowders Synthesized via Coprecipitation and Sol-Gel Methods Ni0.5Co0.5Fe2O4 nanoparticulate powders were synthesized using coprecipitation and sol-gel methods. The crystalline structure of the nanocompounds was determined through X-ray diffraction analysis. The chemical composition of the synthesized nanopowders was analyzed using infrared spectroscopy measurements. The size, morphology, and aggregation of the nanoparticles were determined from electron microscopy images. The magnetic properties of the nanocompounds were studied through magnetization measurements as a function of temperature and applied magnetic field. The synthesized powders consist of aggregates of nanoparticles with mean particle sizes of 24 nm and 43 nm, with those synthesized using the coprecipitation method being smaller. The compounds exhibited a single crystalline phase corresponding to the cubic spinel structure, with a unit cell parameter of approximately 8.35 Å and an inversion parameter with values of 0.94 and 0.95. The synthesized Ni-Co mixed ferrites presented an ordered magnetic behavior below 320 K, with the nanoparticles being in the blocked magnetic regime. Coprecipitation ; Materials science ; Ferrite (magnet) ; Sol-gel ; Characterization (materials science) ; Chemical engineering ; Nanotechnology ; Composite material Magnetic Properties and Synthesis of Ferrites ; Electromagnetic wave absorption materials ; Magnetic properties of thin films
Authors: Xiaoping Song | Tao Chu | Wanru Shi | Jingyan He
Source: Bioresources and Bioprocessing (article, 2024-07-16)
Field: Materials Science — Biomaterials

Expression, characterization, and application of human-like recombinant gelatin Gelatin is a product obtained through partial hydrolysis and thermal denaturation of collagen, belonging to natural biopeptides. With irreplaceable biological functions in the field of biomedical science and tissue engineering, it has been widely applied. The amino acid sequence of recombinant human-like gelatin was constructed through a newly designed hexamer composed of six protein monomer sequences in series, with the minimum repeating unit being the characteristic Gly-X-Y sequence found in type III human collagen α1 chain. The nucleotide sequence was subsequently inserted into the genome of Pichia pastoris to enable soluble secretion expression of recombinant gelatin. At the shake flask fermentation level, the yield of recombinant gelatin is up to 0.057 g/L, and its purity can rise up to 95% through affinity purification. It was confirmed in the molecular weight determination and amino acid analysis that the amino acid composition of the obtained recombinant gelatin is identical to that of the theoretically designed. Furthermore, scanning electron microscopy revealed that the freeze-dried recombinant gelatin hydrogel exhibited a porous structure. After culturing cells continuously within these gelatin microspheres for two days followed by fluorescence staining and observation through confocal laser scanning microscopy, it was observed that cells clustered together within the gelatin matrix, exhibiting three-dimensional growth characteristics while maintaining good viability. This research presents promising prospects for developing recombinant gelatin as a biomedical material. Gelatin ; Recombinant DNA ; Pichia pastoris ; Biochemistry ; Amino acid ; Chemistry ; Materials science ; Molecular biology ; Biology ; Gene Collagen: Extraction and Characterization ; 3D Printing in Biomedical Research ; Protein Hydrolysis and Bioactive Peptides
Authors: Layale Moussa | Hussein F. Hassan | Ioannis N. Savvaidis | Layal Karam
Source: PLoS ONE (article, 2024-08-23)
Field: Chemistry — Analytical Chemistry

Impact of source, packaging and presence of food safety management system on heavy metals levels in spices and herbs Spices and herbs are susceptible to various contaminants, including heavy metals. Our study aimed to quantify the levels of lead (Pb), mercury (Hg), cadmium (As), and cadmium (Cd) in 96 composite samples of 13 herbs and spices frequently consumed in Lebanon. Twenty percent (19/96) and 4% (4/96) of the samples exceeded the permissible levels of Pb and Cd, respectively and all the samples met the permissible levels of As and Hg according to Codex Alimentarius standards. For Pb and Cd, unpackaged samples had the highest levels of unacceptable samples of 31% (8/26) and 8% (2/26), respectively. The samples locally packaged in companies with and without Food Safety Management System (FSMS) had the same levels of unacceptable samples of 12% (3/26) and 4% (1/26) for Pb and Cd, respectively. Imported packaged samples had unacceptable levels of Pb (28% (5/18)) and were acceptable for the three other heavy metals. Cadmium ; Mercury (programming language) ; Heavy metals ; Food packaging ; Food contaminant ; Food safety ; Contamination ; Food science ; Consumer safety ; Chemistry ; Toxicology ; Environmental science ; Environmental chemistry ; Biology ; Medicine ; Risk analysis (engineering) Heavy Metals in Plants ; Heavy Metal Exposure and Toxicity ; Heavy metals in environment
Authors: Hengrui Zhang | Tianxing Lai | Jie Chen | Arumugam Manthiram | James M. Rondinelli | Wei Chen
Source: PRX Energy (article, 2024-06-12)
Field: Materials Science — Materials Chemistry

Learning Molecular Mixture Property Using Chemistry-Aware Graph Neural Network Recent advances in machine learning (ML) are expediting materials discovery and design. One significant challenge facing ML for materials is the expansive combinatorial space of potential materials formed by diverse constituents and their flexible configurations. This complexity is particularly evident in molecular mixtures, a frequently explored space for materials, such as battery electrolytes. Owing to the complex structures of molecules and the sequence-independent nature of mixtures, conventional ML methods have difficulties in modeling such systems. Here, we present MolSets, a specialized ML model for molecular mixtures, to overcome the difficulties. Representing individual molecules as graphs and their mixture as a set, MolSets leverages a graph neural network and the deep sets architecture to extract information at the molecular level and aggregate it at the mixture level, thus addressing local complexity while retaining global flexibility. We demonstrate the efficacy of MolSets in predicting the conductivity of lithium battery electrolytes and highlight its benefits in the virtual screening of the combinatorial chemical space. Published by the American Physical Society 2024 Expediting ; Molecular graph ; Computer science ; Chemical space ; Expansive ; Flexibility (engineering) ; Artificial neural network ; Graph ; Virtual screening ; Theoretical computer science ; Artificial intelligence ; Topology (electrical circuits) ; Nanotechnology ; Materials science ; Molecular dynamics ; Chemistry ; Drug discovery ; Mathematics ; Computational chemistry ; Engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Fuel Cells and Related Materials
Authors: Tejus Shastry | Yasemin Basdogan | Zhen‐Gang Wang | Sanat K. Kumar | Matthew R. Carbone
Source: Journal of Membrane Science (article, 2024-02-19)
Field: Materials Science — Materials Chemistry

Machine learning-based discovery of molecular descriptors that control polymer gas permeation Computer science ; Polymer ; Hash function ; Monomer ; Uniqueness ; Algorithm ; Biological system ; Artificial intelligence ; Chemistry ; Organic chemistry ; Mathematics Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Membrane Separation and Gas Transport