jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ibrahim S. Al Nasr | Ismail Daoud | Waleed S. Koko | Tariq A. Khan | Rainer Schobert | Ridha Ben Said | Noureddine Amdouni | Seyfeddine Rahali | Ali O. Al-Ghamdi | Bernhard Biersack
Source: Inorganica Chimica Acta (article, 2024-03-05)
Field: Chemistry — Organic Chemistry

Activity of (η6-arene)dichloridoruthenium(II) complexes with antifungal imidazolyl-based ligands against Toxoplasma gondii and Leishmania major A series of six (η6-arene)Ru(II) complexes with antifungal imidazole drug ligands such as clotrimazole, bifonazole and climbazole was prepared and analyzed. They were tested for their activities against Leishmania major and Toxoplasma gondii parasites, and were compared with the activities of the metal-free azole ligands. Activities superior to those of the azole ligands were observed for the Ru complexes, which indicate the beneficial role of the (η6-arene)ruthenium(II) moiety in terms of antiparasitic properties. The modifications of the arene ligand also played a role in the biological activity. Docking of selected ruthenium complexes into T. gondii kinase CDPK1 and ADME-T calculations revealed promising binding affinities and drug-like properties. Chemistry ; Toxoplasma gondii ; Antifungal ; Leishmania ; Stereochemistry ; Protozoa ; Microbiology ; Parasite hosting ; Immunology Synthesis and Biological Evaluation ; Synthesis and Characterization of Heterocyclic Compounds ; Synthesis and biological activity
Authors: Eric Vogelsberg | Jan Griebel | Iryna Engelmann | Jens Bauer | Florian Taube | Björn Corzilius | Stefan Zahn | Axel Kahnt | Kirill Yu. Monakhov
Source: Advanced Science (article, 2024-06-13)
Field: Materials Science — Materials Chemistry

Reversible Optical Switching of Polyoxovanadates and Their Communication via Photoexcited States The 2-bit Lindqvist-type polyoxometalate (POM) [V<sub>6</sub>O<sub>13</sub>((OCH<sub>2</sub>)<sub>3</sub>CCH<sub>2</sub>N<sub>3</sub>)<sub>2</sub>]<sup>2-</sup> with a diamagnetic {V<sub>6</sub>O<sub>19</sub>} core and azide termini shows six fully oxidized V<sup>V</sup> centers in solution as well as the solid state, according to <sup>51</sup>V NMR spectroscopy. Under UV irradiation, it exhibits reversible switching between its ground S<sub>0</sub> state and the energetically higher lying states in acetonitrile and water solutions. TD-DFT calculations demonstrate that this process is mainly initialized by excitation from the S<sub>0</sub> to S<sub>9</sub> state. Pulse radiolysis transient absorption spectroscopy experiments with a solvated electron point out photochemically induced charge disproportionation of V<sup>V</sup> into V<sup>IV</sup> and electron communication between the POM molecules via their excited states. The existence of this unique POM-to-POM electron communication is also indicated by X-ray photoelectron spectroscopy (XPS) studies on gold-metalized silicon wafers (Au//SiO<sub>2</sub>//Si) under ambient conditions. The amount of reduced vanadium centers in the "confined" environment increases substantially after beam irradiation with soft X-rays compared to non-irradiated samples. The excited state of one POM anion seems to give rise to subsequent electron transfer from another POM anion. However, this reaction is prohibited as soon as the relaxed T<sub>1</sub> state of the POM is reached. Materials science ; Optoelectronics ; Optical switch Polyoxometalates: Synthesis and Applications ; Molecular Sensors and Ion Detection ; Sulfur-Based Synthesis Techniques
Authors: Sebastian Hagel | Fokko Schütt
Source: Clean Technologies (article, 2024-03-05)
Field: Materials Science — Biomaterials

Reinforcement Fiber Production from Wheat Straw for Wastepaper-Based Packaging Using Steam Refining with Sodium Carbonate Locally sourced agricultural residues are a promising feedstock for the production of reinforcement fibers for wastepaper-based packaging papers. An eco-friendly high yield process to generate fibers from wheat straw using high pressure steam and sodium carbonate is presented. The wheat straw was impregnated with up to 16% of sodium carbonate and steam treated for 10 min at temperatures from 148 °C to 203 °C. The pulps were characterized concerning their chemical composition and test sheets with 100% straw fibers and with 15% and 30% straw fibers blended with recycled pulp were prepared. Fiber yields ranged from 70% to 45%, wherein more severe treatment conditions contributed to increased paper strength but lower yields. At comparable fiber yields, treatments featuring a higher chemical input, coupled with lower treatment temperatures, resulted in improved paper strength. By blending recycled pulp with up to 30% of straw fibers with a beating degree of roughly 45 °SR, the burst, compression and tensile strength was enhanced by up to 66%, 74% and 59%, respectively. As the enhancement effect decreases with a high steam treatment intensity and a high proportion of wheat straw, a moderate treatment and limited use of wheat straw may be the best choice. Straw ; Sodium carbonate ; Refining (metallurgy) ; Pulp and paper industry ; Production (economics) ; Reinforcement ; Materials science ; Waste management ; Environmental science ; Engineering ; Sodium ; Composite material ; Chemistry ; Metallurgy Advanced Cellulose Research Studies ; Natural Fiber Reinforced Composites ; Lignin and Wood Chemistry
Authors: Yen-Ting Lin | Cheng‐Chien Kuo
Source: Applied Sciences (article, 2024-02-13)
Field: Materials Science — Materials Chemistry

Real-Time Salt Contamination Monitoring System and Method for Transmission Line Insulator Based on Artificial Intelligence Insulators on overhead power lines have long been exposed to the outdoors and are susceptible to pollution and salt contamination. Due to factors such as wind and gravity, pollution in the atmosphere gradually deposits on the surface of the insulator. In humid and windy conditions, conductive pollutants begin to dissolve in the water on the surface of the insulator, increasing the leakage current and affecting insulation performance. This study mainly uses a data acquisition system to measure the leakage current of the insulator and weather parameters (including temperature, relative humidity, pressure, wind speed, and ultraviolet) around the insulator. Artificial intelligence is then applied to establish a prediction model for leakage current based on weather parameters. The established model accurately predicts insulator leakage current through weather parameters. In order to observe the real-time status of the insulator, this study establishes a monitoring platform that integrates the predicted leakage current with weather parameters. It allows users or maintenance personnel to connect to the server through the network to observe the predicted results and weather parameters. The results can establish a real-time salt contamination monitoring system for insulators on transmission lines, enabling operation and maintenance personnel to understand the actual insulation situation of the insulator in real-time. This can not only prevent power outages due to salt contamination or pollution but also reduce the workload for maintenance personnel. Moreover, the maintenance strategy is upgraded from time-base maintenance to condition-base maintenance, significantly improving the efficiency of operation and maintenance for power lines. Insulator (electricity) ; Environmental science ; Contamination ; Electric power transmission ; Transmission line ; Icing ; Leakage (economics) ; Arc flash ; Marine engineering ; Engineering ; Meteorology ; Electrical engineering High voltage insulation and dielectric phenomena ; Traffic Prediction and Management Techniques ; Anomaly Detection Techniques and Applications
Authors: Mariame Taibi | Marouane Aouji | Hamada Imtara | Feras Abujaber | Asmaa Oubihi | Abdelmalek Ouannou | Lhoussain Hajji | Abdelaaty A. Shahat | Omar M. Noman | Mahmoud Tarayrah | Rachid Bengueddour | Oussama Hassani
Source: Frontiers in Sustainable Food Systems (article, 2024-11-29)
Field: Materials Science — Materials Chemistry

Novel biosynthesis of silver nanoparticles using Ulva lactuca and their potential toward environment and agricultural purposes Introduction In the global field of nanotechnology, the production of silver nanoparticles (AgNPs) is an environmentally beneficial technique. To prevent the diseases caused by many commonly used pesticides, it is essential to synthesize safe alternatives from low-cost sources. Material and methods This study focuses on the eco-friendly extracellular production of AgNPs using an aqueous extract of Ulva lactuca as a reducing agent in a silver nitrate solution. The biosynthetic reaction was confirmed through various analytical techniques, including ultraviolet–visible (UV–Vis) spectroscopy, Fourier transform infrared spectroscopy (FT-IR), scanning electron microscope (SEM), and energy dispersive X-ray analysis (EDAX). Moreover, antioxidant activity was confirmed. It has also been evaluated against a variety of bacteria and fungi that have an impact on humans, animals, and plants. Results and discussion The mean size of the nanoparticles (NPs) varied from 45–61 nm. The disk diffusion testing results show that, in comparison to the tested antibiotics, Staphylococcus aureus , Escherichia coli , Staphylococcus epidermidis , and Klebsiella pneumoniae were more responsive to U. lactuca -silver nanoparticles (U-AgNPs) extract. eldor 50% sc fungicide was used at the prescribed dose of 0.5 μL/mL, and the antifungal activity of aqueous and U-AgNPs extracts was examined at three concentrations of 0.62, 1.25, and 2.5 mg/mL. Through the suppression of spore germination, in vitro investigations demonstrated that U-AgNPs at varying doses greatly hindered the process of reproductive growth of Botrytis cinerea . Comparing U-AgNPs to the standard synthetic fungicide Teldor 50% sc, it is noteworthy to observe that a substantial control efficacy against tomato and strawberry gray mold was noticed, especially at concentrations of 2.5mg/mL on the entire tomato and strawberry plants and also on the detached leaves and fruits without causing any appearance damage. In summary, this study advances the field of agronanotechnology by demonstrating the potential application of a fungicide based on NPs to prevent gray mold on tomato and strawberry plants in greenhouse environments and throughout the postharvest phase. Ulva lactuca ; Lactuca ; Agriculture ; Biosynthesis ; Botany ; Chemistry ; Biology ; Biochemistry ; Ecology ; Gene Nanoparticles: synthesis and applications ; Moringa oleifera research and applications ; Seaweed-derived Bioactive Compounds
Authors: J. W. Green | Xiaorui Liu | Rong Yin
Source: Fibers (review, 2024-10-31)
Field: Materials Science — Polymers and Plastics

A Brief Review of Hemp Fiber Length Measurement Techniques Accurate fiber length measurement is essential for the processing and quality management of textile products. This article reviews the current methods used to measure fiber length, including manual, photoelectric, capacitive, and optical techniques. Existing sample preparation processes for natural fiber characterization have been primarily developed for cotton and wool fibers. However, hemp fibers present unique challenges due to their greater length variability, high strength, and low elongation, making some traditional sample preparation methods less effective. Image processing offers a promising approach for scalable and precise measurement of hemp fiber length. Nevertheless, current image processing techniques are limited by the inability to effectively handle overlapping fibers, which increases both the time and cost of testing. Continued research into developing more advanced segmentation algorithms could lead to more widely adopted commercial methods for fiber measurement. Fiber ; Materials science ; Computer science ; Composite material Textile materials and evaluations ; Image Enhancement Techniques ; Fire Detection and Safety Systems
Authors: Olatunde Awotunde | Jiaqi Lu | Jin Cai | Nicholas Roseboom | Sarah Honegger | Ornella Joseph | A C Wicks | Kathleen L. Hayes | Marya Lieberman
Source: Analytical Methods (article, 2024-01-01)
Field: Chemistry — Analytical Chemistry

Mitigating the impact of gelatin capsule variability on detection of substandard and falsified pharmaceuticals with near-IR spectroscopy Portable NIR spectrometers are effective in detecting authentic pharmaceutical products in intact capsule formulations, which can be used to screen for substandard or falsified versions of those authentic products. However, the chemometric models are trained on libraries of authentic products, and are generally unreliable for detection of quality problems in products from outside their training set, even for products that are nominally the same active pharmaceutical ingredient and same dosage as products in the training set. As part of our research directed at developing better non-brand-specific strategies for pharmaceutical screening, we investigated the impact of capsule composition on NIR modeling. We found that capsule features like gelatin type, color, or thickness, give rise to a similar amount of variance in the NIR spectra as the type of API stored within the capsules. Our results highlight the efficacy of orthogonal projection to latent structures in mitigating the impacts of different types of capsules on the accuracy of NIR chemometric models for classification and regression analysis of lab-made samples. The models showed good performance for classification of field-collected doxycycline capsules as good or bad quality when an NIR-based % w/w metric was used, identifying five samples that were adulterated with talc. However, the % w/w was systematically underestimated, so when evaluating the capsules based on their absolute API content according to the monograph standard, the classification accuracy decreased from 100% to 70%. The underestimation was attributed to an unforeseen variability in the quantities and types of excipients present in the capsules. Ingredient ; Chemometrics ; Capsule ; Near-infrared spectroscopy ; Talc ; Active ingredient ; Chromatography ; Computer science ; Pattern recognition (psychology) ; Chemistry ; Artificial intelligence ; Materials science ; Food science ; Medicine ; Optics ; Physics Spectroscopy and Chemometric Analyses ; Pharmaceutical Quality and Counterfeiting ; Identification and Quantification in Food
Authors: Zerin Mahzabin Khan | Junru Zhang | Jessica M. Gannon | Blake N. Johnson | Scott S. Verbridge | Eli Vlaisavljevich
Source: Annals of Biomedical Engineering (article, 2024-08-30)
Field: Materials Science — Biomaterials

Development of an Injectable Hydrogel for Histotripsy Ablation Toward Future Glioblastoma Therapy Applications Glioblastoma (GBM) is the most common and malignant type of primary brain tumor. Even after surgery and chemoradiotherapy, residual GBM cells can infiltrate the healthy brain parenchyma to form secondary tumors. To mitigate GBM recurrence, we recently developed an injectable hydrogel that can be crosslinked in the resection cavity to attract, collect, and ablate residual GBM cells. We previously optimized a thiol-Michael addition hydrogel for physical, chemical, and biological compatibility with the GBM microenvironment and demonstrated CXCL12-mediated chemotaxis can attract and entrap GBM cells into this hydrogel. In this study, we synthesize hydrogels under conditions mimicking GBM resection cavities and assess feasibility of histotripsy to ablate hydrogel-encapsulated cells. The results showed the hydrogel synthesis was bio-orthogonal, not shear-thinning, and can be scaled up for injection into GBM resection mimics in vitro. Experiments also demonstrated ultrasound imaging can distinguish the synthetic hydrogel from healthy porcine brain tissue. Finally, a 500 kHz transducer applied focused ultrasound treatment to the synthetic hydrogels, with results demonstrating precise histotripsy bubble clouds could be sustained in order to uniformly ablate red blood cells encapsulated by the hydrogel for homogeneous, mechanical fractionation of the entrapped cells. Overall, this hydrogel is a promising platform for biomaterials-based GBM treatment. Glioblastoma ; Ablation ; Tumor ablation ; Ablation Therapy ; Biomedical engineering ; Materials science ; Medicine ; Cancer research ; Internal medicine ; Cancer ; Prostate cancer Nanoparticle-Based Drug Delivery ; Glioma Diagnosis and Treatment ; 3D Printing in Biomedical Research
Authors: Sowmya Nag Karnam | Veenadevi Siddanahundi Vaddagallaiah | Pradeep Kooganahalli Rangnaik | Akshaya Kumar | Charan Kumar | Bidadi Mahesh Vishwanath
Source: Engineering Technology & Applied Science Research (article, 2024-10-09)
Field: Chemistry — Analytical Chemistry

Precised Cashew Classification Using Machine Learning This study explores the utilization of deep learning techniques for accurate cashew classification to improve efficiency and accuracy in the cashew industry. YOLOv5, YOLOv9, and a Convolutional Neural Network (CNN) were evaluated in classifying cashews into whole, broken, split-up, split-down, and defect categories. A comprehensive labeled dataset was built to train the models, applying data augmentation to increase robustness. YOLOv5 achieved the highest accuracy of 97.65% and the fastest inference time (0.025 s per image) among the models, making it suitable for real-time applications. Although CNN offered a simpler architecture, YOLOv5's superior performance places it as the most promising candidate for large-scale cashew classification deployment. Machine learning ; Artificial intelligence ; Mathematics ; Engineering ; Computer science ; Biochemical engineering Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Ginkgo biloba and Cashew Applications
Authors: Feilan Li | Jingfei Shen | Qianfeng Yang | Yongning Wei | Yifan Zuo | Yujie Wang | Jingming Ning | Luqing Li
Source: Food Chemistry X (article, 2024-06-13)
Field: Chemistry — Analytical Chemistry

Monitoring quality changes in green tea during storage: A hyperspectral imaging method The quality of green tea deteriorates the longer it is stored. However, there is a lack of accurate and rapid methods for determining the storage period of tea. In this study, hyperspectral imaging (HSI) was used to determine the storage period of green teas stored at 4 °C (set 1) and 25 °C (set 2), and to quantify and visualize the main chemical components (e.g. catechins). In this study, three prediction algorithms were compared, in which partial least squares discriminant analysis outperformed the other models in qualitative discrimination, with 98% and 96% correct discrimination for two sets, respectively. Moreover, quantitative models for ester catechins, simple catechins, and total catechins were developed with Rp > 0.90 and RPD > 1.0, indicating that the models were reliable. Further, a more intuitive visualization of catechin content was achieved. In conclusion, the HSI provides a rapid, non-destructive method to determine the freshness of stored green tea. Hyperspectral imaging ; Green tea ; Partial least squares regression ; Catechin ; Chemistry ; Mathematics ; Artificial intelligence ; Pattern recognition (psychology) ; Biological system ; Chromatography ; Food science ; Polyphenol ; Computer science ; Statistics ; Biology Spectroscopy and Chemometric Analyses ; Tea Polyphenols and Effects ; Advanced Chemical Sensor Technologies
Authors: Haonan Wang | James Zou | Michael C. Mozer | Anirudh Goyal | Alex Lamb | Linjun Zhang | Su, Weijie J | Zhun Deng | Michael Qizhe Xie | H. Alex Brown | Kenji Kawaguchi
Source: arXiv (Cornell University) (preprint, 2024-01-03)
Field: Materials Science — Materials Chemistry

Can AI Be as Creative as Humans? Creativity serves as a cornerstone for societal progress and innovation. With the rise of advanced generative AI models capable of tasks once reserved for human creativity, the study of AI's creative potential becomes imperative for its responsible development and application. In this paper, we prove in theory that AI can be as creative as humans under the condition that it can properly fit the data generated by human creators. Therefore, the debate on AI's creativity is reduced into the question of its ability to fit a sufficient amount of data. To arrive at this conclusion, this paper first addresses the complexities in defining creativity by introducing a new concept called Relative Creativity. Rather than attempting to define creativity universally, we shift the focus to whether AI can match the creative abilities of a hypothetical human. The methodological shift leads to a statistically quantifiable assessment of AI's creativity, term Statistical Creativity. This concept, statistically comparing the creative abilities of AI with those of specific human groups, facilitates theoretical exploration of AI's creative potential. Our analysis reveals that by fitting extensive conditional data without marginalizing out the generative conditions, AI can emerge as a hypothetical new creator. The creator possesses the same creative abilities on par with the human creators it was trained on. Building on theoretical findings, we discuss the application in prompt-conditioned autoregressive models, providing a practical means for evaluating creative abilities of generative AI models, such as Large Language Models (LLMs). Additionally, this study provides an actionable training guideline, bridging the theoretical quantification of creativity with practical model training. Creativity ; Generative grammar ; Computational creativity ; Creativity technique ; Cornerstone ; Computer science ; Generative model ; Cognitive science ; Artificial intelligence ; Epistemology ; Psychology ; Cognitive psychology ; Social psychology Machine Learning in Materials Science ; Topic Modeling ; Language and cultural evolution
Authors: Lukas Seifert | Lisa Leuchtenberger-Engel | Christian Hopmann
Source: Polymers (article, 2024-08-16)
Field: Materials Science — Polymers and Plastics

Enhancing the Quality of Polypropylene Recyclates: Predictive Modelling of the Melt Flow Rate and Shear Viscosity The extensive use of polypropylene (PP) in various industries has heightened interest in developing efficient methods for recycling and optimising its mixtures. This study focuses on formulating predictive models for the Melt Flow Rate (MFR) and shear viscosity of PP blends. The investigation involved characterising various grades, including virgin homopolymers, copolymers, and post-consumer recyclates, in accordance with ISO 1133 standards. The research examined both binary and ternary blends, utilising traditional mixing rules and symbolic regression to predict rheological properties. High accuracy was achieved with the Arrhenius and Cragoe models, attaining R<sup>2</sup> values over 0.99. Symbolic regression further enhanced these models, offering significant improvements. To mitigate overfitting, empirical noise and variable swapping were introduced, increasing the models' robustness and generalisability. The results demonstrated that the developed models could reliably predict MFR and shear viscosity, providing a valuable tool for improving the quality and consistency of PP mixtures. These advancements support the development of recycling technologies and sustainable practices in the polymer industry by optimising processing and enhancing the use of recycled materials. Polypropylene ; Shear rate ; Materials science ; Shear (geology) ; Viscosity ; Composite material ; Melt flow index ; Shear viscosity ; Flow properties ; Thermodynamics ; Polymer science ; Mechanics ; Polymer ; Physics ; Copolymer Polymer crystallization and properties ; Injection Molding Process and Properties ; Rheology and Fluid Dynamics Studies