jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Hossein Omidian | Erma J. Gill
Source: Journal of Composites Science (article, 2024-07-12)
Field: Materials Science — Biomaterials

Nanofibrous Scaffolds in Biomedicine This review explores the design, fabrication, and biomedical applications of nanofibrous scaffolds, emphasizing their impact on tissue engineering and regenerative medicine. Advanced techniques like electrospinning and 3D printing have enabled precise control over scaffold architecture, crucial for mimicking native tissue structures. Integrating bioactive materials has significantly enhanced cellular interactions, mechanical properties, and the controlled release of therapeutic agents. Applications span bone, cardiovascular, soft tissue, neural regeneration, wound healing, and advanced drug delivery. Despite these advancements, challenges such as scalability, biocompatibility, and long-term stability remain barriers to clinical translation. Future research should focus on developing smart scaffolds and utilizing AI-enhanced manufacturing for more personalized and effective regenerative therapies. Biomedicine ; Materials science ; Nanotechnology ; Biology ; Bioinformatics Electrospun Nanofibers in Biomedical Applications ; Graphene and Nanomaterials Applications ; Bone Tissue Engineering Materials
Authors: Min-Jee Kim | Woo-Hyeong Yu | Doo-Jin Song | Seungwoo Chun | Moon S. Kim | Ahyeong Lee | Gi‐Young Kim | Beom-Soo Shin | Changyeun Mo
Source: Sensors (article, 2024-02-26)
Field: Chemistry — Analytical Chemistry

Prediction of Soluble-Solid Content in Citrus Fruit Using Visible–Near-Infrared Hyperspectral Imaging Based on Effective-Wavelength Selection Algorithm Citrus fruits were sorted based on external qualities, such as size, weight, and color, and internal qualities, such as soluble solid content (SSC), acidity, and firmness. Visible and near-infrared (VNIR) hyperspectral imaging techniques were used as rapid and nondestructive techniques for determining the internal quality of fruits. The applicability of the VNIR hyperspectral imaging technique for predicting the SSC in citrus fruits was evaluated in this study. A VNIR hyperspectral imaging system with a wavelength range of 400-1000 nm and 100 W light source was used to acquire hyperspectral images from citrus fruits in two orientations (i.e., stem and calyx ends). The SSC prediction model was developed using partial least-squares regression (PLSR). Spectrum preprocessing, effective wavelength selection through competitive adaptive reweighted sampling (CARS), and outlier detection were used to improve the model performance. The performance of each model was evaluated using the coefficient of determination (R<sup>2</sup>) and root mean square error (RMSE). In the present study, the PLSR model was developed using only a citrus cultivar. The SSC prediction CARS-PLSR model with outliers removed exhibited R<sup>2</sup> and RMSE values of approximatively 0.75 and 0.56 °Brix, respectively. The results of this study are expected to be useful in similar fields such as agricultural and food post-harvest management, as well as in the development of an online system for determining the SSC of citrus fruits. Hyperspectral imaging ; Infrared ; Selection (genetic algorithm) ; Content (measure theory) ; Wavelength ; Near-infrared spectroscopy ; Remote sensing ; Computer science ; Algorithm ; Pattern recognition (psychology) ; Artificial intelligence ; Materials science ; Optics ; Optoelectronics ; Mathematics ; Physics ; Geology Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Water Quality Monitoring and Analysis
Authors: Jaymin Vrajlal Sanchaniya | Inga Ļašenko | Valters Gobins | Alaa Kobeissi | Dmitri Goljandin
Source: Polymers (article, 2024-03-20)
Field: Materials Science — Biomaterials

A Finite Element Method for Determining the Mechanical Properties of Electrospun Nanofibrous Mats This study focuses on the mechanical properties of electrospun nanofibrous mats, highlighting the importance of the characteristics of single nanofibers in determining the overall mechanical behavior of the mats. Recognizing the significant impacts of the diameter and structural properties of the nanofibers, this research introduces a novel methodology for deriving the effects of the mechanical properties of single nanofibers on the aggregate mechanical performance of electrospun oriented nanofiber mats. For this purpose, a finite element method (FEM) model is developed to simulate the elastoplastic response of the mats, incorporating the influence of structural parameters on mechanical properties. The validation of the FEM model against experimental data from electrospun polyacrylonitrile (PAN) nanofibers with different orientations demonstrates its effectiveness in capturing the elastic-plastic tensile behaviors of the material and confirms its accuracy in terms of reflecting the complex mechanical interactions within the nanofibrous mats. Through a detailed analysis of how nanofiber diameter, orientation of fibers, length-to-width ratio, and porosity affect the mechanical properties of the mats, this research provides valuable insights for the engineering of nanofibrous materials to meet specific mechanical requirements. These findings improve our understanding of nanofibrous mat structures, allowing for better performance in diverse applications as well as highlighting the critical importance of identifying the properties of single nanofibers and their associated impacts on material design. Finite element method ; Electrospinning ; Materials science ; Composite material ; Mechanical engineering ; Polymer ; Structural engineering ; Engineering Electrospun Nanofibers in Biomedical Applications ; Polymer Nanocomposite Synthesis and Irradiation ; Advanced Sensor and Energy Harvesting Materials
Authors: Eric Lindgren | J. Magnus Rahm | Erik Fransson | Fredrik Eriksson | Nicklas Österbacka | Zheyong Fan | Paul Erhart
Source: The Journal of Open Source Software (article, 2024-03-06)
Field: Materials Science — Materials Chemistry

calorine: A Python package for constructing andsampling neuroevolution potential models Molecular dynamics (MD) simulations are a key tool in computational chemistry, physics, and materials science, aiding the understanding of microscopic processes but also guiding the development of novel materials. A MD simulation requires a model for the interatomic interactions. To this end, one traditionally often uses empirical interatomic potentials or force fields, which are fast but inaccurate, or ab-initio methods based on electronic structure theory such as density functional theory, which are accurate but computationally very expensive (Müser et al., 2023). Machine-learned interatomic potentials (MLIPs) have in recent years emerged as an alternative to these approaches, combining the speed of heuristic force fields with the accuracy of ab-initio techniques (Unke et al., 2021). Neuroevolution potentials (NEPs), implemented in the GPUMD package, in particular, are a highly accurate and efficient class of MLIPs (Fan et al., 2021, 2022; Fan, 2022). NEP models have already been used to study a variety of properties in a range of materials, with recent examples including radiation damage in tungsten (Liu et al., 2023), phase transitions (Fransson, Wiktor, et al., 2023) and dynamics of halide perovskites (Fransson, Rosander, et al., 2023) as well as thermal transport in two-dimensional materials (Sha et al., 2023). Here, we present calorine, a Python package that simplifies the construction, analysis and use of NEP models via GPUMD. Python (programming language) ; Neuroevolution ; Computer science ; Programming language ; Artificial intelligence ; Artificial neural network Machine Learning in Materials Science ; Neural dynamics and brain function ; Functional Brain Connectivity Studies
Authors: Mohamed Moussaoui | Soukayna Baammi | Hatim Soufi | Mouna Baassi | Mohammed Salah | Achraf El Allali | Banan Mohammed | Rachid Daoud | Saïd Belaaouad
Source: ACS Omega (article, 2024-11-08)
Field: Chemistry — Organic Chemistry

Design and Optimization of Quinazoline Derivatives as Potent EGFR Inhibitors for Lung Cancer Treatment: A Comprehensive QSAR, ADMET, and Molecular Modeling Investigation The epidermal growth factor receptor (EGFR) is part of a protein family that controls cell growth and development. Due to its importance, EGFR has been identified as a suitable target for creating novel drugs. For this research, we conducted a 2D-QSAR analysis on a set of 31 molecules derived from quinazoline, which exhibited inhibitory activity against human lung cancer. This investigation incorporated principal component analysis (PCA) and multiple linear regression (MLR), leading to the development of QSAR models with strong predictive capabilities (<i>R</i> <sup>2</sup> = 0.745, <i>R</i> <sup>2</sup>_adj = 0.723, MSE = 0.061, <i>R</i> <sup>2</sup>_test = 0.941, and <i>Q</i> <sup>2</sup>_cv = 0.669). The reliability of these models was confirmed through internal, external, <i>Y</i>-randomization, and applicability domain validations. Leveraging the predictions from the QSAR model, we designed 18 new molecules based on the modifications at the N-3 and C-6 positions of the quinazoline ring, with electronegative substituents at these positions fostering optimal polar interactions and hydrophobic contacts within the ATP-binding site of EGFR, significantly enhancing the inhibitory activity against the lung cancer cell line. Subsequently, ADMET predictions were conducted for these 18 compounds, revealing outstanding ADMET profiles. Molecular docking analyses were performed to investigate the interactions between the newly designed molecules-Pred15, Pred17, Pred20, Pred21-and the EGFR protein, indicating high affinity of these proposed compounds to EGFR. Furthermore, molecular dynamics (MD) simulations were utilized to assess the stability and binding modes of compounds Pred17, Pred20, and Pred21, reinforcing their potential as novel inhibitors against human lung cancer. Overall, our findings suggest that these investigated compounds can serve as effective inhibitors, showcasing the utility of our analytical and design approach in the identification of promising therapeutic agents. Quantitative structure–activity relationship ; EGFR inhibitors ; Quinazoline ; Epidermal growth factor receptor ; Chemistry ; Docking (animal) ; Lung cancer ; Computational biology ; Molecular descriptor ; Stereochemistry ; Combinatorial chemistry ; Biology ; Biochemistry ; Receptor ; Medicine Synthesis and biological activity ; Quinazolinone synthesis and applications ; Computational Drug Discovery Methods
Authors: Juxiang Zhang | Fei Teng | Beiyuan Hu | Wanshan Liu | Yida Huang | Jiao Wu | Yuning Wang | Haiyang Su | Shouzhi Yang | Lumin Zhang | Lingchuan Guo | Zhe Lei | Yan Meng | Xiaoyu Xu | Ruimin Wang | Qingui Bao | Qiongzhu Dong | Jiang Long | Kun Qian
Source: Advanced Materials (article, 2024-01-19)
Field: Chemistry — Spectroscopy

Early Diagnosis and Prognosis Prediction of Pancreatic Cancer Using Engineered Hybrid Core‐Shells in Laser Desorption/Ionization Mass Spectrometry Effective detection of bio-molecules relies on the precise design and preparation of materials, particularly in laser desorption/ionization mass spectrometry (LDI-MS). Despite significant advancements in substrate materials, the performance of single-structured substrates remains suboptimal for LDI-MS analysis of complex systems. Herein, designer Au@SiO<sub>2</sub>@ZrO<sub>2</sub> core-shell substrates are developed for LDI-MS-based early diagnosis and prognosis of pancreatic cancer (PC). Through controlling Au core size and ZrO<sub>2</sub> shell crystallization, signal amplification of metabolites up to 3 orders is not only achieved, but also the synergistic mechanism of the LDI process is revealed. The optimized Au@SiO<sub>2</sub>@ZrO<sub>2</sub> enables a direct record of serum metabolic fingerprints (SMFs) by LDI-MS. Subsequently, SMFs are employed to distinguish early PC (stage I/II) from controls, with an accuracy of 92%. Moreover, a prognostic prediction scoring system is established with enhanced efficacy in predicting PC survival compared to CA19-9 (p < 0.05). This work contributes to material-based cancer diagnosis and prognosis. Materials science ; Mass spectrometry ; Pancreatic cancer ; Desorption ; Ionization ; Core (optical fiber) ; Crystallization ; Substrate (aquarium) ; Analytical Chemistry (journal) ; Nanotechnology ; Cancer ; Chromatography ; Chemical engineering ; Internal medicine ; Chemistry ; Composite material ; Adsorption ; Physical chemistry ; Organic chemistry Mass Spectrometry Techniques and Applications ; Advanced Proteomics Techniques and Applications ; Analytical chemistry methods development
Authors: Francesca Lombardi | Francesca Rosaria Augello | Alessia Ciafarone | Valeria Ciummo | Serena Altamura | Benedetta Cinque | Paola Palumbo
Source: Biomolecules (review, 2024-08-26)
Field: Materials Science — Polymers and Plastics

3D Models Currently Proposed to Investigate Human Skin Aging and Explore Preventive and Reparative Approaches: A Descriptive Review Skin aging is influenced by intrinsic and extrinsic factors that progressively impair skin functionality over time. Investigating the skin aging process requires thorough research using innovative technologies. This review explores the use of in vitro human 3D culture models, serving as valuable alternatives to animal ones, in skin aging research. The aim is to highlight the benefits and necessity of improving the methodology in analyzing the molecular mechanisms underlying human skin aging. Traditional 2D models, including monolayers of keratinocytes, fibroblasts, or melanocytes, even if providing cost-effective and straightforward methods to study critical processes such as extracellular matrix degradation, pigmentation, and the effects of secretome on skin cells, fail to replicate the complex tissue architecture with its intricated interactions. Advanced 3D models (organoid cultures, "skin-on-chip" technologies, reconstructed human skin, and 3D bioprinting) considerably enhance the physiological relevance, enabling a more accurate representation of skin aging and its peculiar features. By reporting the advantages and limitations of 3D models, this review highlights the importance of using advanced in vitro systems to develop practical anti-aging preventive and reparative approaches and improve human translational research in this field. Further exploration of these technologies will provide new opportunities for previously unexplored knowledge on skin aging. Descriptive research ; Medicine ; Data science ; Computer science ; Sociology ; Social science Textile materials and evaluations ; Skin Protection and Aging ; Additive Manufacturing and 3D Printing Technologies
Authors: Kecheng Qu
Source: MATEC Web of Conferences (article, 2024-01-01)
Field: Chemistry — Analytical Chemistry

Research on linear regression algorithm Linear regression is one of the most widely used predictive models in statistics and machine learning. This paper aims to comprehensively discuss the theoretical basis, mathematical principle and application of linear regression algorithm in various fields. Firstly, this paper introduces the research background and significance of linear regression, and summarizes its important role in modern data analysis. Then, the paper elaborates the basic theory of linear regression, including its definition, assumptions, parameter estimation methods and model diagnosis and selection. In addition, different types of linear regression are classified and discussed, such as simple linear regression, multiple linear regression and logistic regression, and the specific application scenarios of each type are analyzed. Proper linear model ; Linear regression ; Algorithm ; Computer science ; Regression ; Bayesian multivariate linear regression ; Mathematics ; Statistics Spectroscopy and Chemometric Analyses ; Traditional Chinese Medicine Analysis
Authors: Yujun Zhong | Zhiyang Li | Anlin Zhang | Yuqing Peng | Hao Zhou | Yaping Guo | Danqing Lu | Lianwu Xie | Shuyun Shi
Source: Microchemical Journal (article, 2024-05-11)
Field: Chemistry — Analytical Chemistry

Gold nanoparticle-mediated molecularly imprinted electrochemical sensor MIP/AuNPs/GCE for highly sensitive and selective detection of neutral phosmet residues in fruits and vegetables Colloidal gold ; Detection limit ; Molecularly imprinted polymer ; Cyclic voltammetry ; Electrochemical gas sensor ; Scanning electron microscope ; Dielectric spectroscopy ; Adsorption ; Materials science ; Nuclear chemistry ; Repeatability ; Chemistry ; Analytical Chemistry (journal) ; Electrochemistry ; Nanoparticle ; Selectivity ; Chromatography ; Electrode ; Nanotechnology ; Organic chemistry ; Catalysis Analytical chemistry methods development ; Electrochemical Analysis and Applications ; Electrochemical sensors and biosensors
Authors: Alok Negi | Mohamed Elkhodbia | Imad Barsoum | Akram Alfantazi
Source: International Journal of Hydrogen Energy (review, 2024-08-01)
Field: Materials Science — Metals and Alloys

Coupled analysis of hydrogen diffusion, deformation, and fracture: a review Hydrogen (H), emerging as a sustainable and promising clean energy source, holds significant potential for transitioning towards a H-based economy, offering a cleaner alternative to traditional fossil fuels. However, hydrogen embrittlement (HE) poses a substantial obstacle to this transition, impacting critical sectors such as transportation, defense, energy production, and construction. Computational modeling, driven by the continuous development of new algorithms and high-performance computing platforms, emerges as an attractive avenue to unravel and address the complexities associated with HE. In particular, a multidisciplinary modeling approach shows potential in investigating the intricate interactions between H and materials across different temporal and spatial scales. Over the last few decades, there have already been many developments in computational modeling investigations based on a coupled study of H diffusion, deformation, and fracture processes to address multifaceted aspects of the HE problem. This comprehensive review sheds light on these advancements, providing insights into the modeling methodologies adopted in these investigations and their results. The review begins with a concise overview of commonly adopted mechanisms to explain HE. Thereafter, the discussion shifts to various advancements in H diffusion modeling, from early works to most recent developments, encompassing diverse aspects, such as H uptake and diffusion through the lattice structure and the role of microstructural traps and material microstructure. The last section of the review focuses on several theoretical and numerical studies that simulate how H affects the fracture characteristics and mechanical properties of various metals and alloys. This discussion includes applications of various state-of-the-art fracture models to predict H-assisted crack growth, as well as a range of theoretical models, continuum-based finite element simulations, and micro-meso scale modeling studies. Multiscale modeling ; Hydrogen embrittlement ; Multidisciplinary approach ; Computer science ; Biochemical engineering ; Fracture (geology) ; Nanotechnology ; Materials science ; Hydrogen ; Chemistry ; Engineering ; Political science Hydrogen embrittlement and corrosion behaviors in metals ; Fatigue and fracture mechanics ; Nuclear Materials and Properties
Authors: Ghulam Murtaza | Syed Shoaib Ahmad Shah | Asad Mumtaz | Ghayoor Abbas Chotana | Ayman Nafady | Md A. Wahab | Manzar Sohail
Source: ACS Omega (article, 2024-03-26)
Field: Chemistry — Inorganic Chemistry

Efficient Adsorption of Methylene Blue Using a Hierarchically Structured Metal–Organic Framework Derived from Layered Double Hydroxide The growing concerns about environmental pollution, particularly water pollution, are causing an increasing alarm in modern society. One promising approach to address this issue involves engineering existing materials to enhance their effectiveness. A one-step solvothermal reconstruction approach was used to build an eco-friendly two-dimensional (2D) AlNiZn-LDH/BDC MOF composite. The characterizations confirm the formation of a metal-organic framework (MOF) at the layered double hydroxide (LDH) surface. The resulting synthesized material, 2D AlNiZn-LDH/BDC MOF, demonstrated remarkable efficacy in decontaminating methylene blue (MB), a model cationic dye found in water systems. The removal performance of 2D AlNiZn-LDH/BDC MOF was significantly higher than that of pristine 2D AlNiZn-LDH. This improvement shows the potential to increase the adsorption capabilities of nanoporous LDH materials by incorporating organic ligands and integrating meso-/microporosity through MOF formation on their surfaces. Furthermore, their kinetic, isothermal, and thermodynamic studies elucidated the adsorption behavior of this composite material. The results of synthesized MOF showed excellent removal efficiency (92.27%) of 10 ppm of MB aqueous solution as compared to pristine LDH. Additionally, the as-synthesized adsorbent could be regenerated for six successive cycles. This method holds promise for the synthesis of novel and highly effective materials to combat water pollution, laying the groundwork for potential advancements in diverse applications. Hydroxide ; Adsorption ; Methylene blue ; Metal-organic framework ; Materials science ; Chemical engineering ; Layered double hydroxides ; Aqueous solution ; Environmental pollution ; Nanoporous ; Cationic polymerization ; Nanotechnology ; Inorganic chemistry ; Chemistry ; Organic chemistry ; Polymer chemistry ; Catalysis ; Environmental science Metal-Organic Frameworks: Synthesis and Applications ; Covalent Organic Framework Applications ; Layered Double Hydroxides Synthesis and Applications
Authors: Ximena Jaramillo-Fierro | Guisella Cuenca
Source: Nanomaterials (article, 2024-04-19)
Field: Materials Science — Materials Chemistry

Theoretical and Experimental Analysis of Hydroxyl and Epoxy Group Effects on Graphene Oxide Properties In this study, we analyzed the impact of hydroxyl and epoxy groups on the properties of graphene oxide (GO) for the adsorption of methylene blue (MB) dye from water, addressing the urgent need for effective water purification methods due to industrial pollution. Employing a dual approach, we integrated experimental techniques with theoretical modeling via density functional theory (DFT) to examine the atomic structure of GO and its adsorption capabilities. The methodology encompasses a series of experiments to evaluate the performance of GO in MB dye adsorption under different conditions, including differences in pH, dye concentration, reaction temperature, and contact time, providing a comprehensive view of its effectiveness. Theoretical DFT calculations provide insights into how hydroxyl and epoxy modifications alter the electronic properties of GO, improving adsorption efficiency. The results demonstrate a significant improvement in the dye adsorption capacity of GO, attributed to the interaction between the functional groups and MB molecules. This study not only confirms the potential of GO as a superior adsorbent for water treatment, but also contributes to the optimization of GO-based materials for environmental remediation, highlighting the synergy between experimental observations and theoretical predictions in advances in materials science to improve sustainability. Adsorption ; Graphene ; Epoxy ; Density functional theory ; Oxide ; Methylene blue ; Materials science ; Molecule ; Functional group ; Chemical engineering ; Nanotechnology ; Computational chemistry ; Chemistry ; Composite material ; Organic chemistry ; Catalysis ; Photocatalysis ; Polymer Graphene research and applications ; Graphene and Nanomaterials Applications ; Adsorption and biosorption for pollutant removal