jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Manikandan Arumugam | K. Devarajan | P. Vasanthkumar | Ratchagaraja Dhairiyasamy
Source: Matéria (Rio de Janeiro) (article, 2024-01-01)
Field: Materials Science — Polymers and Plastics

Mechanical characterization of bamboo-reinforced polymer composites: a comparative study of epoxy and polyurethane matrices ABSTRACT The increasing demand for sustainable materials has sparked growing interest in natural fiber composites, such as bamboo-reinforced polymers, due to their renewability, cost-effectiveness, and favorable mechanical properties. This study evaluates the mechanical performance of bamboo-reinforced composites fabricated using epoxy and polyurethane matrices, highlighting their potential as eco-friendly alternatives to synthetic composites. Bamboo&amp;apos;s high strength-to-weight ratio and compatibility with polymer matrices make it a promising reinforcement material for structural applications. Experimental results showed significant improvements in tensile and flexural properties. Unidirectional bamboo-epoxy composites achieved a tensile strength of 125 MPa, a 25-fold increase compared to the pure epoxy matrix (5 MPa). The modulus of elasticity also increased from 20 MPa for pure polyurethane to 4,020 MPa in unidirectional bamboo-polyurethane composites. Flexural strength improved markedly, with unidirectional bamboo-epoxy composites reaching 80 MPa compared to 6 MPa for the pure matrix. Microstructural analysis indicated enhanced fiber-matrix adhesion and optimized fiber orientation as key factors contributing to the improved performance, while voids and fiber pull-out limited further enhancements. These findings demonstrate bamboo composites&amp;apos; viability as sustainable materials with competitive mechanical properties. This study lays the groundwork for future optimization and broader industrial adoption of bamboo-reinforced composites. Composite material ; Epoxy ; Bamboo ; Polyurethane ; Materials science ; Characterization (materials science) ; Composite number ; Polymer Natural Fiber Reinforced Composites ; Bamboo properties and applications ; Innovations in Concrete and Construction Materials
Authors: Aarati Maurya | Shivam Tyagi
Source: Journal of Drug Discovery and Health Sciences (article, 2024-12-30)
Field: Materials Science — Biomaterials

Recent Advances in Nanoparticle-Based Drug Delivery Systems Nanoparticle-based drug delivery systems (NDDS) have emerged as a revolutionary approach in pharmaceutical development, offering significant improvements in drug bioavailability, therapeutic efficacy, and patient compliance. This review provides an overview of recent advances in nanoparticle-based drug delivery systems, focusing on novel nanoparticle formulations, surface modifications for targeted drug delivery, and the mechanisms of controlled release. The article explores various types of nanoparticles, including polymeric nanoparticles, liposomes, lipid nanoparticles, dendrimers, nanogels, and hybrid materials, and their applications in diverse therapeutic areas such as cancer therapy, gene delivery, and vaccine development. A particular emphasis is placed on the advancements in surface modification techniques, such as PEGylation and antibody conjugation, which enhance targeting and minimize off-target effects. Additionally, the review discusses smart nanoparticles that respond to stimuli (e.g., pH, temperature, and light) for controlled and triggered release of drugs, as well as challenges related to toxicity, scalability, and regulatory approval. Furthermore, the article highlights the potential of nanoparticle-based systems in personalized medicine and their future prospects in treating complex diseases like cancer, neurological disorders, and genetic conditions. Despite challenges, such as the need for improved safety profiles and large-scale production techniques, the continued development of nanotechnology holds great promise for transforming drug delivery paradigms and advancing the field of medicine. Drug delivery ; Nanoparticle ; Drug ; Nanotechnology ; Computer science ; Materials science ; Medicine ; Pharmacology Nanoparticle-Based Drug Delivery ; Advanced Drug Delivery Systems
Authors: Gesine Kuhnen | Lisa‐Carina Class | Svenja Badekow | Kim Lara Hanisch | Sascha Rohn | Jürgen Kuballa
Source: Analytical and Bioanalytical Chemistry (article, 2024-04-12)
Field: Chemistry — Spectroscopy

Python workflow for the selection and identification of marker peptides—proof-of-principle study with heated milk The analysis of almost holistic food profiles has developed considerably over the last years. This has also led to larger amounts of data and the ability to obtain more information about health-beneficial and adverse constituents in food than ever before. Especially in the field of proteomics, software is used for evaluation, and these do not provide specific approaches for unique monitoring questions. An additional and more comprehensive way of evaluation can be done with the programming language Python. It offers broad possibilities by a large ecosystem for mass spectrometric data analysis, but needs to be tailored for specific sets of features, the research questions behind. It also offers the applicability of various machine-learning approaches. The aim of the present study was to develop an algorithm for selecting and identifying potential marker peptides from mass spectrometric data. The workflow is divided into three steps: (I) feature engineering, (II) chemometric data analysis, and (III) feature identification. The first step is the transformation of the mass spectrometric data into a structure, which enables the application of existing data analysis packages in Python. The second step is the data analysis for selecting single features. These features are further processed in the third step, which is the feature identification. The data used exemplarily in this proof-of-principle approach was from a study on the influence of a heat treatment on the milk proteome/peptidome. Python (programming language) ; Workflow ; Computer science ; Proof of concept ; Software ; Identification (biology) ; Feature selection ; Data mining ; Artificial intelligence ; Machine learning ; Programming language ; Database Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Identification and Quantification in Food
Authors: Muhammad Hanif Soomro | Salahuddin Salahuddin | Ghulam Irtaza | Ghazanfar Ali | Saima Batool
Source: Kashf Journal of Multidisciplinary Research (article, 2024-11-28)
Field: Chemistry — Analytical Chemistry

USE IMAGE PROCESSING MODEL TO FRUIT QUALITY DETECTION In recent years, artificial intelligence and image processing plays an important role in the agriculture fields such as plant disease detection and plant health issue prediction. The detection of plant quality in the early stages is a difficult task due to the variations in the symptoms, crop species, and climate factors. Several diseases such as late blight and early blight influence the quantity and quality of fruits. Manual detection of fruit quality leaves disease is a quite complex and time-taking process. It requires an expert with high skills to diagnose the fruit quality in the early stage. Therefore, an automated and efficient method is required that can detect the fruit quality. In this research, a novel EfficientB2 convolution neural network model is proposed to extract the deep features from the dataset. The model is evaluated on the Processed Images_Fruits dataset. The result shows that the proposed model achieves efficient and improved results as compared to the previous work. Computer science ; Image processing ; Quality (philosophy) ; Computer vision ; Artificial intelligence ; Image quality ; Image (mathematics) ; Pattern recognition (psychology) ; Physics Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI
Authors: Dyah Nindita Carolina | Mieke Hemiawati Satari | Bambang Pontjo Priosoeryanto | Agus Dwi Susanto | Cortino Sukotjo | Rahmana Emran Kartasasmita
Source: Clinical Cosmetic and Investigational Dentistry (article, 2024-05-01)
Field: Materials Science — Biomaterials

Synthesis and Structural Characterization of Four Different Concentrations of Ant Nest (Myrmecodia pendens) Collagen Membranes with Potential for Medical Applications Increasing the concentration of ant nest affects the values of the membrane's mechanical properties parameters, including the elastic modulus (0.87 Mpa), tensile strength (16.32 N/mm2), maximum elongation (4.96%), elongation at break (5.23%), and maximum force (22.50 N). The average membrane absorption of water (p value <0.01) was also affected. SEM images showed homogeneous mixing, and membrane EDS results consisted of C, O, and Ca composition. However, there was no effect on FTIR functional groups. The anthill membrane with a 1% concentration has the potential to serve as an alternative membrane in guided tissue regeneration. Elongation ; Ultimate tensile strength ; Membrane ; Materials science ; Absorption of water ; Polyvinyl alcohol ; Composite material ; Universal testing machine ; Tensile testing ; Fourier transform infrared spectroscopy ; Chemical engineering ; Chemistry ; Engineering Collagen: Extraction and Characterization ; Silk-based biomaterials and applications ; Periodontal Regeneration and Treatments
Authors: Ashley L. Dey
Source: Molecules (article, 2024-10-22)
Field: Chemistry — Organic Chemistry

11B NMR Together with Infrared Spectroscopy Provides Insight into Structural Elucidation of Quadrivalent Diazaborines &amp; Cyclic Boronate Esters: Intriguing &amp; Little-Explored Imidazo-fused diazaborines, which serve as intermediary structures somewhat alongside benzene and borazine, had been of particular interest to Dewar and Snyder more than 60 years ago. To this end, Dewar utilised his 'π-<i>complex theory</i>'so as to represent '<i>borazaros</i>'as a '<i>quadrivalent</i>' species; however, sadly, modern representations have deviated and leapt into '<i>trivalent</i>' counterparts. Bonding in boron species has never been straightforward, to such an extent that the orthodox '<i>ethane</i>' like diborane, <i>i.e.</i>, H<sub>3</sub>B-BH<sub>3</sub>, which conformed to the paradigmatic rules of molecular structure, in particular, hybridisation and electronegativity, was later evolved to a more realistic '<i>3-centre 2-electron</i>' bonding so as to give the lie to the purported diborane structures of X-ray diffractors. Herein <sup>11</sup>B NMR together with IR spectroscopy sheds light on the nature of bonding in borazaros, and '<i>caged</i>' cyclic oxazaborons so as to reinforce, and reinvigorate the old literature, which could be of interest to both the synthetic, and medicinal chemist alike. Chemistry ; Infrared spectroscopy ; Nuclear magnetic resonance spectroscopy ; Infrared ; Stereochemistry ; Organic chemistry ; Physics Organoboron and organosilicon chemistry ; Boron Compounds in Chemistry ; Chemical Synthesis and Analysis
Authors: Magdalena Piekutowska | Patryk Hara | Katarzyna Pentoś | Tomasz Lenartowicz | Tomasz Wojciechowski | Sebastian Kujawa | Gniewko Niedbała
Source: Agronomy (article, 2024-12-18)
Field: Chemistry — Analytical Chemistry

Predicting Starch Content in Early Potato Varieties Using Neural Networks and Regression Models: A Comparative Study Starch content serves as a crucial indicator of the quality and palatability of potato tubers. It has become a common practice to evaluate the polysaccharide content directly in tubers freshly harvested from the field. This study aims to develop models that can predict starch content prior to the harvesting of potato tubers. Very early potato varieties were cultivated in the northern and northwestern regions of Poland. The research involved constructing multiple linear regression (MLR) and artificial neural network (ANN-MLP) models, drawing on data from eight years of field trials. The independent variables included factors such as sunshine duration, average daily air temperatures, precipitation, soil nutrient levels, and phytophenological data. The NSM demonstrated a higher accuracy in predicting the dependent variable compared to the RSM, with MAPE errors of 7.258% and 9.825%, respectively. This study confirms that artificial neural networks are an effective tool for predicting starch content in very early potato varieties, making them valuable for monitoring potato quality. Artificial neural network ; Regression ; Starch ; Regression analysis ; Content (measure theory) ; Biological system ; Agronomy ; Botany ; Biology ; Mathematics ; Artificial intelligence ; Food science ; Statistics ; Computer science Spectroscopy and Chemometric Analyses ; Multidisciplinary Science and Engineering Research ; Advanced Chemical Sensor Technologies
Authors: Cuiling Li | Dandan Xiang | Shuo Yang | Xiu Wang | Chunyu Li
Source: Agronomy (article, 2024-12-16)
Field: Chemistry — Analytical Chemistry

Fusarium Wilt of Banana Latency and Onset Detection Based on Visible/Near Infrared Spectral Technology Fusarium wilt of banana is a soil-borne vascular disease caused by Fusarium oxysporum f. sp. cubense. The rapid and accurate detection of this disease is of great significance to controlling its spread. The research objective was to explore rapid banana Fusarium wilt latency and onset detection methods and establish a disease severity grading model. Visible/near-infrared spectroscopy analysis combined with machine learning methods were used for the rapid in vivo detection of banana Fusarium wilt. A portable visible/near-infrared spectrum acquisition system was constructed to collect the spectra data of banana Fusarium wilt leaves representing five different disease grades, totaling 106 leaf samples which were randomly divided into a training set with 80 samples and a test set with 26 samples. Different data preprocessing methods were utilized, and Fisher discriminant analysis (FDA), an extreme learning machine (ELM), and a one-dimensional convolutional neural network (1D-CNN) were used to establish the classification models of the disease grades. The classification accuracies of the FDA, ELM, and 1D-CNN models reached 0.891, 0.989, and 0.904, respectively. The results showed that the proposed visible/near infrared spectroscopy detection method could realize the detection of the incubation period of banana Fusarium wilt and the classification of the disease severity and could be a favorable tool for the field diagnosis of banana Fusarium wilt. Fusarium wilt ; Fusarium oxysporum ; Wilt disease ; Fusarium ; Horticulture ; Biology ; Artificial intelligence ; Computer science Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Yalei Tang | Fei Xu | Shoukun Sun | Daniele Salvato | Fidelma Giulia Di Lemma | Min Xian | Daniel J. Murray | C. D. Judge | Luca Capriotti | Tiankai Yao
Source: Materials Characterization (article, 2024-06-17)
Field: Materials Science — Materials Chemistry

Segmentation and Classification of Fission as Pores in Reactor Iirradiated Annular U 10Zr Metallic Fuel Using Machine Learning Models Materials science ; Fission ; Segmentation ; Metal ; Nuclear engineering ; Metallurgy ; Artificial intelligence ; Nuclear physics ; Computer science ; Engineering ; Neutron Nuclear Materials and Properties ; Nuclear reactor physics and engineering ; Nuclear Physics and Applications
Authors: Milinović, Andrijana | Šimunović, Sanda | Keddam, Mourad | Margetić Butković, Mirjana
Source: Tehnicki vjesnik - Technical Gazette (article, 2024-12-31)
Field: Materials Science — Materials Chemistry

Boronizing Kinetics of 30CrNiMo8 Steel Boronizing is a thermochemical treatment performed to obtain hard surface layers that improve wear resistance. In this study, the boronizing kinetics of pack boronized 30CrNiMo8 steel is investigated. Boronizing was performed at 830, 880 and 930 °C for 4, 6 and 8 hours in DURBORID® G powder mixture. The boride layers obtained characteristic but less pronounced sawtooth morphology, which is typical for the boronizing of low-alloy steels. The average layer thicknesses were between 32,9 and 122 µm, depending on the boronizing temperature and duration, with the temperature having a greater influence. The kinetics of boride layer growth was investigated using the Arrhenius equation, and the results confirm that the boronizing process follows the parabolic law, i.e. that the kinetics of boride layer growth is governed by the diffusion of boron atoms. The activation energy is estimated to be 173,45 kJ/mol and the frequency factor to be 1,54 × 10–5 m²/s. Based on these results, a mathematical model was established to describe the relationship between the boride layer thickness, temperature and duration of boronizing of 30CrNiMo8 steel. This model is suitable for practical application as it allows a quick and easy estimation of the boride layer thickness based on the boronizing parameters. Kinetics ; Metallurgy ; Materials science ; Physics Metal Alloys Wear and Properties ; Metal and Thin Film Mechanics ; Powder Metallurgy Techniques and Materials
Authors: Ryan Rutkoski | Lucas Kniess Debarba | Lukas Stilgenbauer | Tay Rosenthal | Marianna Sadagurski | Pavel Nagorny
Source: ChemRxiv (preprint, 2024-01-02)
Field: Materials Science — Biomaterials

Selective (a)-L-Rhamnosylation and Neuroprotective Activity Exploration of Cardiotonic Steroids This work describes the studies on the direct C3-glycosylation of the C19-hydroxylated cardiotonic steroids strophanthidol, anhydro-ouabagenin and ouabagenin using a strategy based on in situ protection of the C5 and C19 hydroxyl groups with boronic acids. While this strategy resulted in a successful one-pot C3-selective glycosylation of strophanthidol and anhydro-ouabegenin, it failed to provide ouabain from ouabagenin. The neuroprotective activity of the synthetic and natural glyco-sides against LPS-induced neuroinflammation was explored in neonatal mice primary glia cells. Co-administration of natural and synthetic C3-glycosides at 200 nM concentrations resulted in the significant reduction of the LPS-induced neuroinflam-matory markers IL-6, IL-1, TNFα, and IKBKE, with the anhydro-ouabagenin-3-(a)-L-rhamnoside (anhydro-ouabain) show-ing the most significant effect. At the same time, unglycosylated anhydro-ouabagenin enhanced rather than suppressed LPS-induced neuroinflammation. Neuroprotection ; Neuroinflammation ; Chemistry ; Glycosylation ; Pharmacology ; Ouabain ; Inflammation ; Biochemistry ; Medicine ; Internal medicine Phytochemistry and Bioactive Compounds ; Phytochemical Studies and Bioactivities ; Natural product bioactivities and synthesis
Authors: Yii Yat Chan | Zi Cheng Tey | Hui‐Qiong Wang
Source: RSC Advances (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Interfacial electronic state between hexagonal ZnO and cubic NiO The interface of two dissimilar materials gives rise to a myriad of interesting structural, magnetic, and electronic properties that may be utilized to produce novel materials with unique characteristics and functions. In particular, growing a cubic oxide film on top of a hexagonal oxide substrate results in such unique properties due to the conflict of their respective stabilization mechanisms within the interface layer. This study aims to elucidate the electronic properties of the interface between hexagonal ZnO and cubic NiO by analyzing the interface electronic states within epitaxial NiO films grown on ZnO substrates, expressed in the form of ultraviolet photoemission spectroscopy (UPS) for valence band structure and X-ray absorption spectroscopy (XAS) spectra for conduction band structure. This is accomplished through a modeling approach in which the film, substrate, and interface signals are assumed to be related to each other by a set of mathematical equations, and then rearranging and modulating the equations to obtain unique UPS and XAS spectra that depict the interface electronic states. Non-blocking I/O ; Hexagonal crystal system ; Materials science ; Cubic crystal system ; Absorption spectroscopy ; Spectral line ; Absorption (acoustics) ; Crystallography ; Condensed matter physics ; Chemistry ; Optics ; Composite material ; Physics ZnO doping and properties ; Transition Metal Oxide Nanomaterials ; Gas Sensing Nanomaterials and Sensors
Authors: Youngmin Lee | Lang S. I. D. Andrew | Cai Duoduo | W Stephen
Source: arXiv (Cornell University) (preprint, 2024-05-02)
Field: Materials Science — Materials Chemistry

The Role of Model Architecture and Scale in Predicting Molecular Properties: Insights from Fine-Tuning RoBERTa, BART, and LLaMA This study introduces a systematic framework to compare the efficacy of Large Language Models (LLMs) for fine-tuning across various cheminformatics tasks. Employing a uniform training methodology, we assessed three well-known models-RoBERTa, BART, and LLaMA-on their ability to predict molecular properties using the Simplified Molecular Input Line Entry System (SMILES) as a universal molecular representation format. Our comparative analysis involved pre-training 18 configurations of these models, with varying parameter sizes and dataset scales, followed by fine-tuning them on six benchmarking tasks from DeepChem. We maintained consistent training environments across models to ensure reliable comparisons. This approach allowed us to assess the influence of model type, size, and training dataset size on model performance. Specifically, we found that LLaMA-based models generally offered the lowest validation loss, suggesting their superior adaptability across tasks and scales. However, we observed that absolute validation loss is not a definitive indicator of model performance - contradicts previous research - at least for fine-tuning tasks: instead, model size plays a crucial role. Through rigorous replication and validation, involving multiple training and fine-tuning cycles, our study not only delineates the strengths and limitations of each model type but also provides a robust methodology for selecting the most suitable LLM for specific cheminformatics applications. This research underscores the importance of considering model architecture and dataset characteristics in deploying AI for molecular property prediction, paving the way for more informed and effective utilization of AI in drug discovery and related fields. Scale (ratio) ; Architecture ; Computer science ; Artificial intelligence ; Geography ; Cartography ; Archaeology Machine Learning in Materials Science