jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Wenchang Wei | Yuye Zhang | Xuanhao Fu | Shuo Yuan | Chenxi Shi | Zhicheng Su | Shunjing Luo | Haiqiang Chen | Jun‐Wei Zha | Yiyi Zhang
Source: High Voltage (article, 2025-06-01)
Field: Materials Science — Biomaterials

Nano‐kaolin enhances the mechanical, electrical, and thermal properties of cellulose insulating paper Abstract There is an extremely urgent demand in the realm of power equipment, including power transformers, motors, and cables. Specifically, there is a pressing need for cellulose‐based composite insulating paper that can exhibit high thermal conductivity, superior mechanical properties, and robust insulation characteristics. In response to this demand, this study adopted a ‘simulation‐guided experimental research’ methodology. Firstly, based on molecular dynamics (MD) simulations, nano‐kaolin (KL)/cellulose composite models with different contents were constructed. Then, according to the simulation results, the corresponding proportions of nano‐KL/cellulose insulating paper were prepared. The simulation and experimental findings further reveal a significant effect of nano‐KL. To be more precise, nano‐KL can effectively fill the microscopic defects and voids within the cellulose structure. Moreover, nano‐KL forms an orderly and regular thermal conductivity network in conjunction with cellulose. As a result, this network structure elevates the paper's overall thermal conductivity. Owing to its low‐dielectric‐loss characteristics, nano‐KL reduces the microscopic charge polarisation phenomenon within the composite structure. It curbs the migration of electrons, alleviates the concentration of electric field stress, and ultimately improves the electrical insulation performance of the modified insulating paper. Notably, the 4 wt% nano‐KL/cellulose insulating paper exhibits optimal performance, and its tensile strength, thermal conductivity, volume resistivity, dielectric loss, and breakdown strength are 55.81 MPa, 0.201 W·m −1 ·K −1 , 4.58 × 10 15 Ω·m, 0.25%, and 57.81 kV/mm. This study demonstrates MD simulations' feasibility and effectiveness in providing theories and protocols for experiments. Cellulose ; Nano- ; Materials science ; Thermal ; Composite material ; Chemical engineering ; Engineering ; Thermodynamics ; Physics Advanced Cellulose Research Studies ; High voltage insulation and dielectric phenomena ; Lignin and Wood Chemistry
Authors: S.J.C. Masuku | J. Z. Msomi | Tebogo Sfiso Mahule | V. V. Srinivasu
Source: Journal of Superconductivity and Novel Magnetism (article, 2025-02-21)
Field: Materials Science — Materials Chemistry

Synthesis and Electron Spin Resonance Study of Zn0.7Ni0.3RE0.1Fe1.9O4 (RE = Gd, Sm, Dy) Nanostructured Ferrites Abstract The glycol-thermal technique synthesized Zn 0.7 Ni 0.3 RE 0.1 Fe 1.9 O 4 (RE = 0, Gd, Sm, Dy) nanostructured ferrites with fine particles in the 8–13 nm range. X-ray diffraction (XRD) data confirmed a single-phase cubic spinel structure with no impurity peaks for the samples investigated. High-resolution transmission electron microscopy (HrTEM) showed nearly spherical particle images for the pure Zn 0.7 Ni 0.3 Fe 2 O 4 . HrTEM images for Zn 0.7 Ni 0.3 RE 0.1 Fe 1.9 O 4 (RE = Gd, Sm, or Dy) rare-earth-substituted fine particles revealed agglomerated particles with irregular shapes. Reduced particle sizes of rare-earth-substituted Zn-Ni compared to undoped compounds have been explained based on electron–electron repulsion of 4f electrons. Electron spin dynamics were investigated by electron spin resonance (ESR) measurements. A broad ESR signal with the highest intensity has been observed for the Sm-doped compound (Zn 0.7 Ni 0.3 Sm 0.1 Fe 1.9 O 4 ) with a particle size of about 8 nm. Broadening of the signal revealed stronger magnetic dipole interactions and narrower signals for Gd- and Dy-doped compounds indicate stronger superexchange interactions. The slightest ESR signal observed for the Gd-based (Zn 0.7 Ni 0.3 Gd 0.1 Fe 1.9 O 4 ) compound has been attributed to high spin–orbit coupling and the paramagnetic nature of Gd ions disrupting magnetic resonance. The evolution of the magnetic parameters, such as signal line width, intensities, and g -factors, as a function of the type of rare-earth ions, has been presented and correlated with the outer 4f electron number of rare-earth ions and particle size. Rare-earth-doped Zn 0.7 Ni 0.3 RE 0.1 Fe 1.9 O 4 (RE = Gd, Sm, Dy) nanostructured ferrites exhibit enhanced magnetic and structural properties, critical for advanced applications such as magnetic separation and biomedical imaging. This study demonstrates the influence of rare-earth doping on ESR parameters, particle size, and magnetization, providing insights into the role of 4f electron configurations. Materials science ; Electron paramagnetic resonance ; Nuclear magnetic resonance ; Condensed matter physics ; Spin (aerodynamics) ; Physics ; Thermodynamics Magnetic Properties and Synthesis of Ferrites ; Iron oxide chemistry and applications ; Multiferroics and related materials
Authors: Pallavi S. Salunke | Manisha R. Patil | Kanak S. Alone | Akash V. Fulari | Vinod K. Barote | Maheshkumar L. Mane | R.H. Kadam | S. T. Alone | Sagar E. Shirsath | Vinod N. Dhage
Source: RSC Advances (article, 2025-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

High-efficiency X-band electromagnetic wave absorption in Co–Al engineered Ba–Sr hexaferrites: role of cationic substitution and microstructural tuning The growing demand for high-performance electromagnetic (EM) absorbers and interference (EMI) shielding materials has motivated the design of structurally tunable ferrites with balanced dielectric and magnetic losses. In this study, crystalline Ba<sub>0.5</sub>Sr<sub>0.5</sub>Y<sub>1.0</sub>Fe<sub>11-<i>x</i></sub> (Co <sub><i>x</i>/2</sub>Al <sub><i>x</i>/2</sub>)O<sub>19</sub> (<i>x</i> = 0.00-1.00) hexaferrites were synthesized <i>via</i> a sol-gel auto-combustion route to investigate the correlation between multi-cation substitution, microstructure, and microwave absorption. Rietveld-refined X-ray diffraction confirmed single-phase M-type hexagonal symmetry (<i>P</i>6<sub>3</sub>/<i>mmc</i>) with systematic lattice contraction from 693.53 Å<sup>3</sup> (<i>x</i> = 0.0) to 671.27 Å<sup>3</sup> (<i>x</i> = 1.0) due to smaller Co<sup>2+</sup> and Al<sup>3+</sup> ionic radii. Field emission scanning electron microscopy and energy-dispersive X-ray analyses revealed dense, homogeneous microstructures with uniform dopant distribution. Magnetic measurements showed a progressive softening with substitution saturation magnetization (<i>M</i> <sub>s</sub>) decreased from 54.79 emu g<sup>-1</sup> (<i>x</i> = 0.0) to 41.80 emu g<sup>-1</sup> (<i>x</i> = 1.0), while coercivity (<i>H</i> <sub>c</sub>) dropped from 1788.8 Oe to 77.7 Oe attributed to dilution of Fe<sup>3+</sup>-O-Fe<sup>3+</sup> superexchange and reduced anisotropy constant (<i>K</i> <sub>1</sub> ≈ 10<sup>5</sup> to 10<sup>3</sup> erg cm<sup>-3</sup>). The optimized composition, <i>x</i> = 0.50, achieved outstanding microwave absorption with a minimum reflection loss (RL<sub>min</sub>) of -47.58 dB at 9.27 GHz (6 mm thickness) and an effective absorption bandwidth of 0.609 GHz, confirming nearly complete EM attenuation. Dielectric and magnetic spectra revealed that balanced permittivity (<i>ε</i>', <i>ε</i>″) and permeability (<i>µ</i>', <i>µ</i>″) ensured impedance matching (<i>Z</i> <sub>in</sub> ≈ <i>Z</i> <sub>0</sub>), while enhanced attenuation constant (<i>α</i>) and reduced eddy current loss promoted multi-mechanistic energy dissipation. These findings demonstrate that Co-Al co-substitution effectively tailors lattice strain, anisotropy, and interfacial polarization, providing a rational design strategy for high-efficiency microwave absorbers and EMI-shielding hexaferrites in the X-band region. Reflection loss ; Microwave ; Permittivity ; Dielectric ; Electromagnetic shielding ; Magnetization ; Dielectric loss ; Coercivity ; Scattering Electromagnetic wave absorption materials ; Magnetic Properties and Synthesis of Ferrites ; Metallic Glasses and Amorphous Alloys
Authors: Daniel Voßwinkel | Theresa Block | Rainer Pöttgen
Source: Zeitschrift für Kristallographie - New Crystal Structures (article, 2025-04-10)
Field: Chemistry — Inorganic Chemistry

Crystal structure of Ce<sub>9</sub>Ir<sub>37</sub>Ge<sub>25</sub> Abstract Ce 9 Ir 37 Ge 25 , hexagonal, <m:math xmlns:m="http://www.w3.org/1998/Math/MathML"> <m:mrow> <m:mi>P</m:mi> <m:mover accent="true"> <m:mn>6</m:mn> <m:mo>‾</m:mo> </m:mover> <m:mi>m</m:mi> <m:mn>2</m:mn> </m:mrow> </m:math> $P\overline{6}m2$ (no. 187), a = 18.3707(16) Å, c = 3.9695(4) Å, and V = 1160.16(18) Å 3 , Z = 1, R gt ( F ) = 0.0315, wR ref ( F 2 ) = 0.0799, T = 293 K. Crystal structure ; Crystallography ; Crystal (programming language) ; Materials science ; Germanium ; Chemistry ; Silicon ; Computer science ; Optoelectronics Inorganic Chemistry and Materials ; Rare-earth and actinide compounds ; Crystal Structures and Properties
Authors: Stefan Abi-Karam | Cong Hao
Source:  (preprint, 2025-06-26)
Field: Materials Science — Materials Chemistry

HLS-Eval: A Benchmark and Framework for Evaluating LLMs on High-Level Synthesis Design Tasks The rapid scaling of large language model (LLM) training and inference has accelerated their adoption in semiconductor design across academia and industry. Most prior works benchmark LLMs for design tasks involving hardware description languages (HDLs), primarily Verilog. Meanwhile, designers are increasingly using high-level synthesis (HLS) to develop domain-specific accelerators and other hardware systems. However, benchmarks and tooling to comprehensively evaluate LLMs for HLS design tasks remain scarce.To address this, we introduce HLS-Eval, the first comprehensive benchmark and evaluation framework for LLM-driven HLS design tasks. HLS-Eval focuses on evaluating two key high-level tasks: 1) generating HLS code from natural language descriptions, and 2) making HLS-specific code edits to existing HLS code, primarily targeting HW optimization and performance gains. To evaluate these tasks, we construct the HLS-Eval benchmark consisting of 94 unique designs as benchmark cases. These designs are drawn from a diverse mix of established community HLS benchmarks and novel sources. Using a semi-automated flow, we prepare each case to be "LLM-ready", supplemented with a natural language description and a corresponding testbench for C-simulation and HLS synthesis validation.Beyond the benchmark designs, HLS-Eval provides a framework for automated, parallel evaluation of both local and hosted LLMs across various HLS design tasks. It features a parallel evaluation engine, seamless HLS tool interfaces for LLMs, and an abstraction to support different LLM interaction paradigms, all wrapped in a modular Python API. With HLS-Eval, users can rapidly prototype and evaluate new benchmark sources, HLS design tasks, and LLM methodologies, speeding up the research and development of new AI-driven HLS workflows.We demonstrate the utility of HLS-Eval through baseline evaluations of popular open-source LLMs for HLS code generation and code editing tasks targeting Vitis HLS. Our evaluation assesses LLM-generated HLS code across four critical metrics: "parseability", "compilability", "runnability", and "synthesizability" — mirroring the iterative debugging and testing process of an HLS designer. Additionally, we provide pass@k metrics for common design tasks, establishing clear evaluation criteria, baselines, and the necessary infrastructure for others in the LLM hardware design community to build upon.We open-source our benchmark, framework, and evaluation results at https://github.com/sharc-lab/hls-eval. Benchmark (surveying) ; Computer science ; High-level synthesis ; Computer architecture ; Embedded system ; Geography Machine Learning in Materials Science
Authors: Emilio Seijo-Bestilleiro | Ignacio Arias-Fernández | Diego Carro‐López | Manuel Naveiro
Source: Fuels (article, 2025-09-13)
Field: Chemistry — Analytical Chemistry

Opportunities for Emission Reduction in the Transformation of Petroleum Refining Crude oil accounts for approximately 40% of global energy consumption, and the refining sector is a major contributor to greenhouse gas (GHG) emissions, particularly through the production of hard-to-abate fuels such as aviation fuel and fuel oil. This study disaggregates the refinery into its key process units to identify decarbonization opportunities along the entire production chain. Units are categorized into combustion-based processes—including crude and vacuum distillation, hydrogen production, coking, and fluid catalytic cracking—and non-combustion processes, which exhibit lower emission intensities. The analysis reveals that GHG emissions can be reduced by up to 60% with currently available technologies, without requiring major structural changes. Electrification, residual heat recovery, renewable hydrogen for desulfurization, and process optimization through digital twins are identified as priority measures, many of which are also economically viable in the short term. However, achieving full decarbonization and alignment with net-zero targets will require the deployment of carbon capture technologies. These results highlight the significant potential for emission reduction in refineries and reinforce their strategic role in enabling the transition toward low-carbon fuels. Oil refinery ; Greenhouse gas ; Refining (metallurgy) ; Renewable energy ; Refinery ; Production (economics) ; Process (computing) ; Petroleum ; Energy source Petroleum Processing and Analysis ; Process Optimization and Integration ; Global Energy and Sustainability Research
Authors: Hui Zhang | Jiahui Gu | Long Wen | Xiangjun Zou | Xiaojuan Li
Source: Journal of Food Science (article, 2025-07-01)
Field: Chemistry — Analytical Chemistry

Prediction of Mild Moldy‐Core Disease in Apples Based on Fusion Features of Near‐Infrared Transmission Spectroscopy and Acoustic Vibration Signals In response to the low accuracy of single-method detection of mild moldy-core in apples, a heterogeneous information fusion method based on near-infrared transmission spectroscopy and acoustic vibration technology was proposed. For near-infrared spectral signals, the impact of different preprocessing and feature extraction methods on the modeling performance was analyzed, and the optimal spectral feature bands were determined. For acoustic vibration signals, seven time-domain features and six frequency-domain features were selected using one-way ANOVA. Subsequently, the spectral feature bands and acoustic vibration features were concatenated to form a fused feature vector. Support vector machines (SVM) were employed to construct discrimination models based on single-source features and fused features, and different optimization algorithms were applied to optimize the parameters 'c' and 'g.' Model performance analysis results showed that the FF-SSA-SVM model, which integrates 18 spectral feature bands from near-infrared transmission spectroscopy and 13 time-domain and frequency-domain features, achieved the best performance in discriminating mild moldy-core. Specifically, the accuracy and F1_score reached 97.97% and 97.45% respectively. It demonstrates that the proposed method of fused near-infrared transmission spectroscopy and acoustic vibration features can significantly improve the accuracy of detecting mild moldy-core in apples. Additionally, the AUC of the test set reached 0.9757, indicating that the model has good generalization and robustness for imbalanced datasets. Therefore, this study provides an effective nondestructive testing method for mild moldy-core of apples and can give some research ideas for internal quality detection of other fruits and vegetables. PRACTICAL APPLICATION: This study provides an approach for the precise detection of mild moldy-core in apples during industrial production. Support vector machine ; Frequency domain ; Feature (linguistics) ; Pattern recognition (psychology) ; Computer science ; Robustness (evolution) ; Vibration ; Feature extraction ; Artificial intelligence ; Spectroscopy ; Time domain ; Preprocessor ; Biological system ; Acoustics ; Chemistry ; Computer vision ; Physics Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Nicolò Alagna | Brigitta Dúzs | Vincent Dietrich | Ali Tayefeh Younesi | L. A. Lehmann | Ronald Ulbricht | Heinz Köppl | Andreas Walther | Susanne Gerber
Source: Communications Chemistry (article, 2025-05-15)
Field: Materials Science — Materials Chemistry

Deep Learning Reaction Framework (DLRN) for kinetic modeling of time-resolved data Model-based analysis is essential for extracting information about chemical reaction kinetics in full detail from time-resolved data sets. This approach combines experimental hypotheses with mathematical and physical models, enabling a concise description of complex system dynamics and the extraction of kinetic parameters like kinetic pathways, time constants, and species amplitudes. However, building the final kinetic model requires several intermediate steps, including testing various assumptions and models across multiple experiments. In complex cases, some intermediate states may be unknown and are often simplified. This approach requires expertise in modeling and data comprehension, as poor decisions at any stage during data analysis can lead to an incorrect kinetic model, resulting in inaccurate results. Here, we introduce DLRN, a new deep learning-based framework, designed to rapidly provide a kinetic reaction network, time constants, and amplitude for the system, with comparable performance and, in part, even better than a classical fitting analysis. We demonstrate DLRN's utility in analyzing multiple timescales datasets with complex kinetics, different 2D systems such as time-resolved spectra and agarose gel electrophoresis data, experimental datasets as nitrogen vacancy and strand displacement circuit (using photoluminescence and transient absorption techniques), even in scenarios where the initial state is a hidden, non-emitting dark state. Kinetic energy ; Computer science ; Biological system ; Synthetic data ; Statistical physics ; Data mining ; Algorithm ; Artificial intelligence ; Physics Machine Learning in Materials Science ; Mass Spectrometry Techniques and Applications ; Fuel Cells and Related Materials
Authors: Andréia Coelho Domingos Domingos | Leandro Duarte | Alan Petrônio Pinheiro | Fabrício Augusto Matheus Moura | Lorenço Santos Vasconcelos | Daniel Pereira de Carvalho | Fernando Elias de Freitas Fadel | Patrícia N. Sakai
Source: Energies (article, 2025-05-21)
Field: Materials Science — Materials Chemistry

Comparative Analysis of Insulation Aging in Cross-Linked Polyethylene and Ethylene–Propylene Rubber Cables Through the Progression Rate of Partial Discharge In order to ensure the continuous and reliable supply of electrical energy to the power grid, it is necessary to evaluate and monitor the degree of impairment of the insulation of electrical cables, as throughout its service life, insulation around cables suffers degradation due to numerous stress factors, which can arise from both environmental and operational causes. This aspect has aroused deep interest among energy professionals, as well as the industrial sector, with focus mainly placed on the undesirable effect caused by unexpected and sudden process stoppages, as well as their consequent financial and social impacts. That said, this article presents a methodology for evaluating the degree of insulation aging using the partial discharge progression curve. For this purpose, a thermal oven was duly constructed, in accordance with the technical premises presented in the literature, capable of homogeneously heating conductor samples. After thermal cycles, these conductors were aptly handled and tested in a controlled laboratory environment to determine the partial discharge progression curve. Through accurate data processing, a correlation was obtained between the degradation of the insulation and the rate of increase in partial discharge. The results are promising, as they provide support for maintenance agents’ ability to monitor and intervene regarding conductors. Ethylene propylene rubber ; Cross-linked polyethylene ; Polyethylene ; Partial discharge ; Natural rubber ; Composite material ; Materials science ; Ethylene ; Forensic engineering ; Polymer ; Chemistry ; Voltage ; Organic chemistry ; Engineering ; Electrical engineering ; Copolymer ; Catalysis High voltage insulation and dielectric phenomena ; Electrical Fault Detection and Protection ; Power Transformer Diagnostics and Insulation
Authors: Nawal A. Rajab | Halah Talal Sulaiman
Source: Iraqi Journal of Pharmaceutical Sciences ( P-ISSN 1683 - 3597 E-ISSN 2521 - 3512) (article, 2025-06-25)
Field: Materials Science — Biomaterials

Olmesartan Medoxomil Nanomicelle Using Soluplus for Dissolution Enhancement :Preparation, In-vitro and Ex-vivo Evaluation The antihypertensive medication olmesartan medoxomil (OLM) is pharmacologically selective angiotensin-II receptor antagonist. Pharmaceutically speaking, OLM is a class II medication (low solubility, high permeability) that is practically insoluble.Due to its extremely poor solubility, which negatively affects its usefulness, oral medication have a low (26%) bioavailability. The current strategy involves generating OLM as a micellar dispersion in the nano range scale utilizing the film hydration process. In order to prepare transparent aqueous formulations, increase drug solubilization, and administer medication orally, Soluplus (SLP) was employed as a micellar nanocarrier.Different SLP concentrations were used to make eight formulations. Micelle size, polydispersity, morphology, encapsulation efficiency and in vitro release testing were used to gauge the micellar system's concentration-dependent characteristics. The systems' particle sizes ranged from 48.9 ±0.98 nm (F8) to 461.3± 5.07 nm (F1), with approved poly dispersity index values. OLM release behavior in vitro from micelles with particle size less than 100 nm against a pure medication aqueous suspension was evaluated. In comparison to pure drug powder suspension, all tested formulas demonstrated a significant increase in drug release at p˂0.05. The chosen formulation was subjected to lyophilization , fourier transform infrared (FTIR) spectroscopy and differential scanning calorimetry (DSC) analysis, transmission electron microscopy (TEM) observation ,stability and ex vivo permeation investigations utilizing the non everted intestinal sac technique. F7 with SLP concentration 1.6% showed higher percentage of drug release with particle size of 51.09 nm. DSC of lyophilized F7 formula showed absence of crystalline state which mean complete encapsulation of drug within nanocarrier .The FTIR revealed no incompatibility between drug and excipient. The results of particle size analysis were analogous to TEM image.The selected formula was stable upon storage and dilution with water.The ex vivo study showed improvement in the permeability of the formulated nanomicelle (NM) and the permeability coefficient was increased more than two time compared to the pure drug dispersion. Accordingly it deduced that formulating OLM-NM based on SLP is a promising strategy. It can improve OLM permeability by two and release. Olmesartan ; Ex vivo ; Pharmacology ; In vivo ; Chemistry ; Chromatography ; In vitro ; Medicine ; Internal medicine ; Biochemistry ; Biology ; Biotechnology Nanoparticle-Based Drug Delivery ; Advanced Drug Delivery Systems ; Drug Solubulity and Delivery Systems