jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: N. Ahmed | Usman Ahmed | Azadeh Bashari | Einas M. A. Widaa | Elharam Ali Eltahir Mohammed | Mashair Ahmed Mohammed Yousif | Emadeldeen Noureldaim | Yousif Mohamed Modawy | Montasir Salman
Source: European Journal of Pure and Applied Mathematics (article, 2025-11-05)
Field: Chemistry — Organic Chemistry

Nano Quantum Explanation of the Changes in the Spectra of Some Oils Used for the Determination of the4 Degree of its Purity Ensuring the purity of edible oils is essential for human health, especially given the widespread use of oils in modern diets. Oil impurities, often in the form of nanoscale particles, necessitate advanced analytical techniques, confirmed by a theoretical model, for detection. In this study, a nanoquantum model based on Klein-Gordon and energy-momentum quantum equations beside perturbation theory is developed to theoretically explain how such impurities affect the spectral properties of pure oils. The model predicts that the impurities affect the wave number and length through the medium-field potential and the collision energy resulting from the free vibration. The intensity is also affected by these parameters in addition to the effects of the internal magnetic field along with the friction through the exponential term. This means that the effects of impurities on the intensity should be relatively larger than the wave number and length because of the existence of additional parameters and theenhancing effect of the exponential term. Experimental studies were carried out using UV-VIS and FTIR techniques on pure olive oil mixed with corn, sunflower, and soybean oils. The findings indicated a considerable decrease in the intensity of olive oil when increasing the amount of mixture associated with very slight changes in the number and length of waves. The results of the absorption and transmittance of three bean oil samples consisting of different impurities indicated that the effects of impurities on the intensity are more significant compared to the number and length of the waves, which is consistent with the theoretical findings. The theoretical model also shows that the inner shell electron energies remain largely unaffected by impurities, resulting in spectral convergence at high wave numbers (short wavelengths). In contrast, outer shell energies vary significantly due to environmental perturbations, causing spectral divergence at low wave numbers (long wavelengths). These theoretical predictions are validated using experimental data obtained by Fourier transform infrared (FTIR) and ultraviolet-visible (UV-vis) spectroscopy on various oil samples, including olive and sesame oils. The strong agreement between theoretical expectations and experimental observations confirm the effectiveness of the model in detecting oil purity. Impurity ; Exponential function ; Spectral line ; Intensity (physics) ; Wavenumber ; Transmittance ; Absorption spectroscopy ; Exponential decay ; Absorption (acoustics) Edible Oils Quality and Analysis ; Food Chemistry and Fat Analysis ; Biodiesel Production and Applications
Authors: Ashis Kundu | Florian Knoop | Igor A. Abrikosov
Source: arXiv (Cornell University) (preprint, 2025-08-21)
Field: Materials Science — Materials Chemistry

Revisiting thermal transport in CuCl: First-principles calculations and machine learning force fields Accurate prediction of lattice thermal conductivity ($κ_l$) in strongly anharmonic materials requires renormalized interatomic force constants (IFCs) and appropriate incorporation of diagonal and off-diagonal contributions and higher-order scattering. We investigate CuCl, a highly anharmonic system with a simple zincblende structure and ultralow $κ_l$. Our calculations, including IFC renormalization and four-phonon scattering, show excellent agreement with the experiment, underscoring the critical role of both effects in the accurate estimation of $κ_l$. Furthermore, the unusual pressure dependence of $κ_l$ is explored using a rigorously validated machine-learned force field, with the predicted values showing good agreement with the experimentally observed trend of monotonic decrease. This behavior is primarily driven by a significant increase in four-phonon scattering and a reduction in the group velocity of transverse acoustic modes. Overall, this study establishes a robust framework for modeling thermal transport in strongly anharmonic materials. Anharmonicity ; Thermal conductivity ; Scattering ; Renormalization group ; Thermal ; Renormalization ; Transverse plane Thermal Expansion and Ionic Conductivity ; Machine Learning in Materials Science ; Thermal properties of materials
Authors: Jeela, R. K. | Tosato, G. | Ahmad, M. | Wieler, M. | Koeppe, A. | Nestler, B. | Schneider, D.
Source: Repository KITopen (Karlsruhe Institute of Technology) (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Enhancing Solid Oxide Fuel Cells Development through Bayesian Active Learning Ensuring the sustainable operation of solid-oxide fuel cells (SOFCs) requires an understanding of the components' lifespan. Multiphase-field simulation studies play a major role in understanding the underlying microstructural changes and the resulting property alterations in SOFCs over time. The primary challenge in such simulations lies in identifying a suitable model and defining its parametrization. This study presents an Active Learning framework combined with Bayesian Optimization to identify optimal model parameters to simulate the aging of nickel-gadolinium doped ceria (Ni-GDC) anodes. The study overcomes incompleteness and inconsistency of literature data, and navigates the complex, high-dimensional parameter space, by leveraging experimental microstructure data and the power of the AL framework. The successful parameter search enables simulation studies of Ni-GDC anode aging and performance during long-term SOFC operation. This approach improves the accuracy of phase-field simulations and offers a versatile tool for broader applications in SOFC development, predicting material behavior under various operational conditions. Bayesian optimization ; Fuel cells ; Anode ; Solid oxide fuel cell ; Property (philosophy) ; Bayesian probability ; Active learning (machine learning) Advancements in Solid Oxide Fuel Cells ; Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion
Authors: Tadahiko Hirai | Doki Yamaguchi | Ken Inoue | Ryo Suzuki | Makoto Tanimura | Yuko Kaneda | Masaru Tachibana
Source: ACS Omega (article, 2025-01-08)
Field: Materials Science — Materials Chemistry

Blue Electroluminescent Carbon Dots Derived from Victorian Lignite Carbon dots (CDs) derived from natural products have attracted considerable interest as eco-friendly materials with a wide range of applications, such as bioimaging, sensors, catalysis, and solar energy harvesting. Among these applications, electroluminescence (EL) is particularly desirable for light-emitting devices in display and lighting technologies. Typically, EL devices incorporating CDs feature a layered structure, where CDs function as the central emissive layer, flanked by charge transport layers and electrodes. Under an applied external bias, electrons and holes are introduced into the active CD layer, resulting in EL through radiative recombination. However, achieving EL with natural product-derived CDs has remained elusive due to challenges such as production difficulties and quenching in the solid state. In this study, we present, for the first time, the successful realization of EL from natural product-derived CDs, synthesized using Victorian lignite through a straightforward single-step pyrolysis method. The CDs demonstrated excellent dispersibility in solvents, allowing them to serve as an emissive layer in light-emitting diodes (LEDs). This was achieved by spin-coating a concentrated CD solution between the organic hole and electron transport layers on a glass substrate coated with indium tin oxide. Remarkably, the CDs retained their dispersibility and emissive efficiency both in solution (toluene) and after film formation. Moreover, the resulting LED demonstrated blue EL, characterized by a peak emission at 460 nm and a maximum luminance of 100.4 cd/m<sup>2</sup>. This luminance is comparable to that achieved with CDs synthesized from conventional chemical carbon sources. These results highlight the promise of natural product-derived CDs as sustainable and eco-friendly materials for use in LED applications. Electroluminescence ; Materials science ; Optoelectronics ; Carbon fibers ; Substrate (aquarium) ; Layer (electronics) ; OLED ; Light-emitting diode ; Quenching (fluorescence) ; Nanotechnology ; Fluorescence ; Optics ; Composite material Carbon and Quantum Dots Applications ; Nanocluster Synthesis and Applications ; Quantum Dots Synthesis And Properties
Authors: D. Daskalova | Mustafa S. Özyurt | Holger Lerch | Ulrich Plachetka | Max C. Lemme
Source: Advanced Physics Research (article, 2025-11-28)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Periodic and Statistically Distributed Surface Texturing of Surface‐Enhanced Raman Spectroscopy (SERS) Sensors with Spatially Selective Signal Mapping ABSTRACT Surface‐enhanced Raman spectroscopy (SERS) is a highly sensitive and noninvasive molecular fingerprinting technique already making an impact in fields such as medical diagnosis and environmental monitoring. However, challenges persist in the fabrication of SERS substrates, which delays the adoption of SERS as a standard analytical tool. Here, we experimentally demonstrate and compare two methods for achieving reproducible SERS substrates on a 150 mm wafer scale. In the first approach, we use nanoimprint lithography (NIL) to create highly periodic structures with subwavelength dimensions (&lt; 300 nm). These periodic structures offer high signal uniformity, with a spatial variation of just 7.7% over a 20 × 20 µm area. In the second approach, we use a maskless reactive ion etching (RIE) process to create statistically distributed spikes in silicon wafers. Compared to the periodic structures, the statistical structures offer five times greater signal enhancement and twice the signal‐to‐background ratio at the cost of increased spatial variation. Both substrate types perform similarly or better than commercially available SERS substrates. In addition, we demonstrate spatially selective Raman mapping via microarray templating by photolithography, with potential for multimodal detection of analytes or use in quality control. Surface-enhanced Raman spectroscopy ; Raman spectroscopy ; Wafer ; Nanoimprint lithography ; Fabrication ; SIGNAL (programming language) ; Substrate (aquarium) ; Footprint ; Etching (microfabrication) Gold and Silver Nanoparticles Synthesis and Applications ; Nanofabrication and Lithography Techniques ; Nanowire Synthesis and Applications
Authors: Zainab Yahya Al-Rubaie | Ayad A.H. Faisal
Source: Journal of Ecological Engineering (article, 2025-02-15)
Field: Materials Science — Materials Chemistry

Sorption of tetracycline from contaminated water using magnesium-iron layered double hydroxide alginate beads prepared from&lt;i&gt; Schanginia aegyptica&lt;/i&gt; and scrap iron This research was conducted to produce a nonconventional sorbent with the aid of Schanginia aegyptica and scrap iron solid wastes.The solutions extracted from Schanginia plant parts and scrap iron can be used effectively to prepare magnesium and iron ions, respectively.The prepared sorbent named "magnesium / iron-layered double hydroxide-sodium alginate beads (Mg/Fe-LDH-Na alginate beads)" was studied to capture tetracycline (TC) from the wastewater based on batch study.The best conditions optimized to prepare the afore-mentioned beads were Mg/Fe molar ratio = 3, initial pH 10, and Mg/Fe-LDH dosage = 5 g per 100 mL.Langmuir and pseudo-second-order model adequately describe isotherm and kinetic sorption suggesting a maximal capacity of 2.564 mg/g and there are chemical bonds between TC and alginate beads.The bead characterization tests revealed the formation of Mg/Fe-LDH nanoparticles that are responsible for TC with the presence of functional groups like hydroxyl (OH) groups. Sorption ; Magnesium ; Scrap ; Hydroxide ; Chemistry ; Contamination ; Nuclear chemistry ; Environmental chemistry ; Inorganic chemistry ; Adsorption ; Biology ; Organic chemistry Magnesium Oxide Properties and Applications ; Layered Double Hydroxides Synthesis and Applications
Authors: А. Е. Карманов | Н. Ә. Уахит | А. М. Парамонов
Source: Bulletin of Toraighyrov University Energetics series (article, 2025-09-30)
Field: Chemistry — Analytical Chemistry

ПРОГНОЗИРОВАНИЕ УДЕЛЬНОГО ЭЛЕКТРИЧЕСКОГО СОПРОТИВЛЕНИЯ ПРОКАЛЕННОГО НЕФТЯНОГО КОКСА С ИСПОЛЬЗОВАНИЕМ ИСКУССТВЕННОГО ИНТЕЛЛЕКТА Прокалка нефтяного кокса является ключевым процессом для формирования его товарных свойств, необходимых в алюминиевой промышленности, где удельное электрическое сопротивление (УЭС) существенно влияет на эффективность производства. Цель исследования заключалась в разработке метода прогнозирования УЭС прокаленного нефтяного кокса с использованием искусственного интеллекта для оптимизации режима работы печи и повышения качества продукции. В работе применялись лабораторные испытания в муфельной печи при температурах 900–1300 °C с измерением УЭС, а также методы машинного обучения, реализованные в языке программирования Python. Для обучения модели глубоких нейронных сетей использовались производственные данные. Установлена обратная зависимость УЭС от температуры прокалки: минимальное значение УЭС (459 мкОм·м) достигнуто при 1380 °C, что связано с графитизацией структуры кокса. Разработанная модель продемонстрировала потенциал для прогнозирования УЭС с метриками качества R² = 0,152 и MAE = 10,729 мкОм·м. Исследование подтвердило возможность применения искусственного интеллекта для контроля качества кокса, что способствует снижению энергозатрат и брака. Научная новизна работы заключается в адаптации глубоких нейронных сетей к задаче прогнозирования УЭС нефтяного кокса, а практическая значимость – в возможности интеграции модели в системы управления печами для оперативной корректировки технологического процесса. Ключевые слова: нефтяной кокс, удельное электрическое сопротивление, искусственный интеллект, глубокие нейронные сети, машинное обучение. Identification (biology) ; Product (mathematics) ; Work (physics) Petroleum Processing and Analysis ; Coal and Coke Industries Research ; Industrial Engineering and Technologies
Authors: Tieu-Long Phan | Marcos E. Gonzalez Laffitte | Klaus Weinbauer | Daniel Merkle | Jakob L. Andersen | Rolf Fagerberg | Thomas Gatter | Peter F. Stadler
Source: ChemRxiv (article, 2025-09-01)
Field: Materials Science — Materials Chemistry

SynKit: A Graph-Based Python Framework for Rule-Based Reaction Modeling and Analysis The computational modeling of chemical reactions is fundamental to modern synthetic chemistry, yet progress is often hindered by a fragmented software ecosystem and the challenge of representing complex reaction mechanisms with high fidelity. To address these challenges, we introduce SynKit, a streamlined, open-source Python library that provides a unified and chemically intuitive framework for reaction informatics. SynKit provides a comprehensive suite of tools for core tasks via algorithms implemented directly in the package, including reaction canonicalization and classification of transformation types, while other tasks are accomplished through integrated external libraries, e.g., the construction of synthetic routes via a flexible rule composition engine. Moving beyond the static, net-change representations of the Imaginary Transition State, SynKit introduces the Mechanistic Transition Graph, a graph that explicitly models the sequence of bond-forming and -breaking events, thereby capturing transient intermediates and providing deeper mechanistic insight. The package can be installed with minimal dependencies on the ubiquitous RDKit and NetworkX libraries, and thus SynKit ensures broad accessibility and seamless integration into existing computational workflows for exploring complex Chemical Reaction Networks; when deeper network exploration is required, SynKit can wrap and orchestrate specialist mechanistic packages (e.g., MØD) to perform exhaustive mechanistic enumeration and kinetics-aware analyses. By combining advanced mechanistic modeling with a robust and accessible design, SynKit empowers researchers to advance the field of automated synthesis planning with greater clarity and reproducibility. Python (programming language) ; Computer science ; Programming language ; Graph ; Theoretical computer science Machine Learning in Materials Science
Authors: Seyed Meysam Hashemnejad | A. Doshi
Source: Polymers (article, 2025-09-20)
Field: Materials Science — Polymers and Plastics

Polymer Blend Spiking Method for Quantifying Polypropylene Variants in 100% Polypropylene Blends Understanding the type of polypropylene (PP) in post-consumer recycled (PCR) PP is valuable for optimizing mechanical recycling approaches, especially when blending with virgin polymers for specific applications. However, accurately identifying and quantifying the various types of polypropylene, including isotactic Homo-PP (Homo-PP), Random-PP, and non-crystalline PP components (such as xylene-soluble atactic PP and amorphous ethylene-propylene copolymers), presents significant challenges when dealing with materials composed entirely of polypropylene. To address this, we propose a solution-based crystallization elution fractionation (CEF) technique to determine the composition of different PP variants in PP blend systems. Our approach involves introducing a controlled amount of linear low-density polyethylene (LLDPE) into the 100% PP sample in solution, enabling the separation of Homo-PP from Random-PP. By applying established calibration curves, we quantitatively resolve the content of Homo-PP, Random-PP, and non-crystalline PP. The calibration is effective across the full composition window, enabling accurate quantification of Random-PP and Homo-PP from pure (100%) components to mixed systems spanning ~5 wt.% to 95 wt.% Random-PP. This comprehensive analysis offers valuable insights into the distribution of PP variants within the material, facilitating informed decision-making in recycling and material selection processes, ultimately enhancing the efficiency and sustainability of PP recycling operations. Polypropylene ; Tacticity ; Polymer blend ; Polymer ; Calibration ; Fractionation ; Polyethylene Polymer crystallization and properties ; Microplastics and Plastic Pollution ; Recycling and Waste Management Techniques
Authors: Stella Girousi | Zoi Banti | Sophia Κarastogianni | Rigini Papi | Dilsat Ozkan‐Ariksoysal | Evangelia E. Golia
Source: Preprints.org (preprint, 2025-07-10)
Field: Chemistry — Spectroscopy

Detection of Leucine Based upon the Development of a CPE/ssDNA Biosensor Leucine (Leu) is an amino acid which is considered to be an important compound in health matters and lately is recognized as an indicator of carbon storage. For the first time, an interaction between the amino acid leucine and thermally denatured single stranded (ss) DNA has been demonstrated by applying voltammetry. As a result of interaction the characteristic peak of ssDNA, due to the oxidation of guanine residues, decreased. The interaction behaviour between leucine and ssDNA was studied with UV-vis spectrophotometry, the obtained results are in good agreement. The results of the interaction study were exploited in order to develop a SWV method for the determination of leucine at the ssDNA modified carbon paste electrode (CPE). Different parameters were tested to optimize the conditions of the determination. The peak of guanine was at around +0.86 V. Linearity was observed in the range of 1,4.10-9 - 3,5.10-8 mol/L( r= 0,9990 ) while LOD equals 4,9.10-10 mol/L. The method was applied in spiked soil sample and gave satisfactory results. Biosensor ; Leucine ; Chemistry ; Biochemistry ; Computational biology ; Chromatography ; Biophysics ; Nanotechnology ; Biology ; Materials science ; Amino acid Analytical Chemistry and Chromatography ; Metabolomics and Mass Spectrometry Studies ; Electrochemical sensors and biosensors
Authors: Qi Liu | Xiaoxuan Li | Lixin Fang | Yanhui Wang | Fang Sun | Peng Liu
Source: Biology (article, 2025-10-14)
Field: Materials Science — Materials Chemistry

Toxicological Mechanism of the Size–Form Synergy of Nano-Copper Oxide in Danio rerio CuO-NPs demonstrate significant potential across biomedical, environmental protection, and electronic technology domains. This widespread utilization inevitably leads to their discharge into aquatic ecosystems. Research on the biotoxicity of CuO-NPs constitutes a current scientific priority; however, toxicological impacts related to particle size and morphology remain inadequately documented. The zebrafish (<i>Danio rerio</i> Roloff, 1956) is employed as a model animal organism to assess acute and subchronic toxicity of differentially sized/shaped CuO-NPs. Organ-specific damage manifested in the gills, liver, and muscles. It was found that sheet-shaped CuO-NPs (SC) could induce the most severe histomorphological alterations. Among spherical CuO-NPs (SP), smaller particles exhibited higher toxicity (SC > 40 nm SP-S > 150-250 nm SP-L). Tissue antioxidant capacity followed the same decreasing trend. The three CuO-NPs in the present study reduced microbial alpha-diversity. Altered relative abundance of dominant taxa is observed at the phylum and genus levels. These results expand toxicological datasets for nanomaterial-vertebrate interactions and support environmental risk assessment for nano-pollutants in natural conditions. Danio ; Zebrafish ; Organism ; Toxicity ; Mechanism (biology) ; Antioxidant ; Acute toxicity ; Taxon ; Model organism Nanoparticles: synthesis and applications ; Nanotechnology research and applications
Authors: Bohao Li | Wen-Xuan Qiu | Fengcheng Wu | A. H. MacDonald
Source: arXiv (Cornell University) (preprint, 2025-09-09)
Field: Materials Science — Materials Chemistry

Quantum Phases in Twisted Homobilayer Transition Metal Dichalcogenides Twisted homobilayer transition metal dichalcogenides - specifically twisted bilayer MoTe$_2$ and twisted bilayer WSe$_2$ - have recently emerged as a versatile platform for strongly correlated and topological phases of matter. These two-dimensional systems host tunable flat Chern bands in which Coulomb interactions can dominate over kinetic energy, giving rise to a variety of interaction-driven phenomena. A series of groundbreaking experiments have revealed a rich landscape of quantum phases, including integer and fractional quantum anomalous Hall states, quantum spin Hall states, anomalous Hall metals, zero-field composite Fermi liquids, and unconventional superconductors, along with more conventional topologically trivial correlated states including antiferromagnets. This review surveys recent experimental discoveries and theoretical progress in understanding these phases, with a focus on the key underlying mechanisms - band topology, electron interactions, symmetry breaking, and charge fractionalization. We emphasize the unique physics of twisted TMD homobilayers in comparison to other related systems, discuss open questions, and outline promising directions for future research. Quantum Hall effect ; Symmetry (geometry) ; Spin (aerodynamics) ; Fractional quantum Hall effect ; Coulomb ; Quantum ; Quantum phases ; Charge (physics) ; Quantum phase transition 2D Materials and Applications ; Organic and Molecular Conductors Research ; Inorganic Fluorides and Related Compounds