jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Qingjun Peng | Hui Du | Zezhong Zheng | Haowei Zhu | Yuhang Fang
Source: Sensors (article, 2025-07-03)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Prediction of Magnetic Fields in Single-Phase Transformers Under Excitation Inrush Based on Machine Learning With the digital transformation of power systems, higher demands are being placed on smart grids for the timely and precise acquisition of the status of transmission and transformation equipment during operational and maintenance processes. When a transformer is energized under no-load conditions, an excitation inrush phenomenon occurs in the windings, posing a hazard to the stable operation of the power system. A machine learning approach is proposed in this paper for predicting the internal magnetic field of transformers under excitation inrush condition, enabling the monitoring of transformer operation status. Experimental results indicate that the mean absolute percentage error (MAPE) for predicting the transformer's magnetic field using the deep neural network (DNN) model is 4.02%. The average time to obtain a single magnetic field data prediction is 0.41 s, which is 46.68 times faster than traditional finite element analysis (FEA) method, validating the effectiveness of machine learning for magnetic field prediction. Excitation ; Transformer ; Inrush current ; Magnetic field ; Single phase ; Computer science ; Materials science ; Engineering ; Biomedical engineering ; Artificial intelligence ; Electrical engineering ; Physics ; Voltage Magnetic Properties and Applications ; Non-Destructive Testing Techniques ; Machine Fault Diagnosis Techniques
Authors: Matthew Thelen | Sebastian A. Flores | James A. Kelley | Brianna R. Swank | Jill K. Winkler‐Moser | Matthew Fhaner
Source:  (preprint, 2025-04-09)
Field: Chemistry — Organic Chemistry

Toward Rapid Analysis of Unsaturated Fatty Acid Oxidation in Edible Oils via Square-Wave Voltammetry Square-wave voltammetry (SWV) was investigated as a potential analytical tool for quantitation of unsaturated fatty acid oxidation in edible oils exposed to high heat. A traditional linear regression calibration curve and a novel single-point calibrat Edible oil ; Voltammetry ; Chemistry ; Square wave ; Fatty acid ; Cyclic voltammetry ; Food science ; Organic chemistry ; Chromatography ; Electrochemistry ; Physics ; Electrode Edible Oils Quality and Analysis ; Electrochemical sensors and biosensors
Authors: Zhenyu Zheng | Chuanchuan Yang | Haolan Yang | Cheng Zhang | Hongbin Li
Source: Electronics (article, 2025-09-30)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Metasurface Design on Low-Emissivity Glass via a Physically Constrained Search Method Low-emissivity (Low-E) glass, crucial for thermal insulation, significantly attenuates wireless signals, hindering 5G communication. Metasurface technology offers a solution, but the existing designs often neglect the etching ratio constraint and lack physical interpretability. In this work, we propose a physically constrained search method that incorporates prior knowledge of the capacitive equivalent circuit to guide the design of metasurfaces on Low-E glass. First, the equivalent circuit type of the metasurface is determined as a capacitive structure through transmission line model analysis. Then, a random walk-based search is conducted within the solution space of topological patterns corresponding to capacitive structures, ensuring etching ratio constraints and maintaining structural continuity. Using this method, we design a metasurface pattern optimized for 5G communication, which demonstrates over 30 dB improvement in signal transmission compared with full-coating Low-E glass. A fabricated 300 mm × 300 mm prototype, etched with a ratio of 19.5%, demonstrates a minimum transmission loss of 2.509 dB across the 24–30 GHz band with a 3 dB bandwidth of 4.28 GHz, fully covering the 5G n258 band (24.25–27.5 GHz). Additionally, the prototype maintains a transmission coefficient reduction of no more than 3 dB under oblique incidence angles from 0° to 50°, enabling robust 5G connectivity. Capacitive sensing ; Bandwidth (computing) ; Transmission line ; Wireless ; Transmission (telecommunications) ; Capacitance ; Reduction (mathematics) ; Equivalent circuit Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Millimeter-Wave Propagation and Modeling
Authors: K. Minakawa | Tokio Nakada | Reiji Kaneko | Takashi Ikuno
Source: Scientific Reports (article, 2025-12-18)
Field: Materials Science — Materials Chemistry

Mapping defect distribution in transparent single-walled carbon nanotube film with electrical resistance tomography We extend the application of electrical resistance tomography (ERT) to visualize IG/ID distribution in transparent single-walled carbon nanotube (SWCNT) thin films. By establishing the correlation between disorder and resistance through experimental measurements, we successfully converted ERT-reconstructed conductivity distributions into IG/ID maps. This approach enabled the detection of a defect region with an IG/ID ratio of approximately 15 embedded in a pristine SWCNT film (IG/ID = 18.1), corresponding to a minimum detectable resistance ratio (R/R0) of 1.59. Moreover, in a sample containing two defect regions introduced by plasma irradiation at 20 and 40 W, ERT successfully distinguished them despite the small difference in IG/ID ratio (1.1). Simulation studies revealed that positional deviations in reconstructed images can be attributed to electrode misalignment, contact resistance variation, and plasma-induced defect expansion. These results demonstrate the feasibility of ERT as a non-destructive, high-resolution technique for evaluating disorder variations in conductive thin films. Furthermore, due to its conductivity-based imaging principle, the method is applicable to other materials such as graphene, indium tin oxide (ITO), and metal nanowire networks, offering promise for real-time monitoring and quality assurance in large-area optoelectronic device manufacturing. Carbon nanotube ; Electrical resistivity and conductivity ; Electrical resistance and conductance ; Contact resistance ; Indium tin oxide ; Thin film ; Nanowire ; Electrode ; Tomography Carbon Nanotubes in Composites ; Electrical and Bioimpedance Tomography ; Nanomaterials and Printing Technologies
Authors: D. Lita Pansy | M. Murali
Source: Journal of Computer Science (article, 2025-05-01)
Field: Chemistry — Analytical Chemistry

Enhancing Plant Disease Identification Through Hyperspectral Imaging and AC-FNet Framework The precise identification of plant diseases is essential for optimizing agricultural practices. The impact of these diseases on food production often leads to significant revenue loss. Detecting diseases that primarily manifest symptoms in plant leaves has conventionally depended on visual inspection by plant pathologists. However, modern methodologies use machine learning and computer vision techniques to facilitate disease identification, thus addressing the limitations of traditional approaches. However, in most of the existing works, the quality of the leaf image, smaller image regions, and chlorophyll content-based vegetation index features were not concentrated, which is required to be analyzed for the efficient detection of multiple leaf diseases. Therefore, this study highlights the necessity for a robust diagnostic system that tackles existing challenges in disease identification. Hyperspectral Images (HSI) serve as a valuable resource due to their rich spectral information. This research proposes a framework for multiple disease identification using hyperspectral data. The process begins by identifying target regions within the hyperspectral image through the Jeffries-Matusita-based Simple Linear Iterative Clustering (JM-SLIC) technique. Subsequently, the segmented image undergoes preprocessing, involving dead pixel replacement and noise removal. To approximate the resolution effectively, the Stochastic gradient-based Bi-cubic interpolation (S-BI) technique is employed. The resolution-approximated spectral images are then subjected to unmixing, followed by the estimation of chlorophyll content-based indexes. These indexes, combined with various features, contribute to disease identification within the leaf using the Atrous Convolution-based FractalNet (AC-FNet) model. The experimental outcomes strongly support the effectiveness of the proposed framework in detecting plant diseases efficiently. Hyperspectral imaging ; Computer science ; Identification (biology) ; Artificial intelligence ; Computer vision ; Remote sensing ; Pattern recognition (psychology) ; Geology Spectroscopy and Chemometric Analyses
Authors: Hava Taslak | Burhanettin Yalçınkaya | Zeliha Eskin | Hülya Yılmaz | Oğuz Öztürk
Source: Sakarya Üniversitesi Holistik Sağlık Dergisi (article, 2025-08-31)
Field: Materials Science — Biomaterials

The Role of Liposomal Delivery Systems in the Treatment of Triple Negative Breast Cancer This study aims to highlight the potential of liposomal nanocarrier systems in addressing the challenges associated with the treatment of triple-negative breast cancer (TNBC) and to provide a literature-based foundation for their use in targeted therapeutic approaches. This study evaluating the efficacy of liposomal drug delivery systems in TNBC treatment, along with current developments reported in the literature. Particular emphasis is placed on surface modifications involving PEGylation, antibodies, aptamers, and small molecules, and their impact on therapeutic success. TNBC accounts for approximately 20% of all breast cancer cases and represents a highly aggressive subtype characterized by treatment resistance and high metastatic potential. Conventional treatment methods often fall short, with recurrence observed in about 40% of cases and mortality reaching 80–90% due to therapy-resistant tumors. Liposomes have garnered attention due to their ability to enhance drug bioavailability, reduce systemic toxicity, and provide tumor site-specific targeting. Numerous formulations have been developed, ranging from PEGylated liposomes to antibody- and aptamer-conjugated systems, demonstrating therapeutic efficacy in various TNBC cell lines and animal models. Given the aggressive nature of TNBC and the limited treatment options, liposomal nanocarrier systems offer a promising alternative. The integration of these systems with specific targeting modifications may lay the groundwork for future personalized and more effective TNBC therapies. To facilitate clinical translation, it is essential to establish standardized production protocols, streamline regulatory processes, and strengthen interdisciplinary collaborations. Nanocarriers ; Triple-negative breast cancer ; Targeted drug delivery ; Drug delivery ; Breast cancer ; Liposome ; Cancer Nanoparticle-Based Drug Delivery ; Nanoplatforms for cancer theranostics ; RNA Interference and Gene Delivery
Authors: Lukas Kampik | Scott Gruber | Klemens Weisleitner | Gerald Bauer | H. Steiner | Leo Tous | Seraphin Hubert Unterberger | Johannes Dominikus Pallua
Source: Textiles (article, 2025-09-28)
Field: Chemistry — Analytical Chemistry

Hyperspectral Imaging for Non-Destructive Detection of Chemical Residues on Textiles Detecting chemical residues on surfaces is critical in environmental monitoring, industrial hygiene, public health, and incident management after chemical releases. Compounds such as acrylonitrile (ACN) and tetraethylguanidine (TEG), widely used in chemical processes, can pose risks upon residual exposure. Hyperspectral imaging (HSI), a high-resolution, non-destructive method, offers a secure and effective solution to identify and spatially map chemical contaminants based on spectral signatures. In this study, we present an HSI-based framework to detect and differentiate ACN and TEG residues on textile surfaces. High-resolution spectral data were collected from three representative textiles using a hyperspectral camera operating in the short-wave infrared range. The spectral datasets were processed using standard normal variate transformation, Savitzky–Golay filtering, and principal component analysis to enhance contrast and identify material-specific features. The results demonstrate the effectiveness of this approach in resolving spectral differences corresponding to distinct chemical residues and concentrations but also provide a practical and scalable method for detecting chemical contaminants in consumer and industrial textile materials, supporting reliable residue assessment and holding promise for broader applications in safety-critical fields. Hyperspectral imaging ; Chemical imaging ; Textile ; Residual ; Principal component analysis ; Residue (chemistry) ; Agrochemical Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Cultural Heritage Materials Analysis
Authors: Xiaoqiu Yuan
Source: Digital Commons - RU (Rockefeller University) (article, 2025-09-08)
Field: Chemistry — Organic Chemistry

Chemical Tools for Exploring IFITM3 S-Palmitoylation and Mechanism Protein S-palmitoylation is a reversible post-translational lipid modification that regulates the trafficking, stability, and activity of proteins in eukaryotes. The detection of fatty-acylated proteins has been challenging but recent advances in chemical labeling methods have enabled more sensitive detection and proteomic analyses, which I summarize in Chapter 1. The proteomic analysis of S-palmitoylated proteins in dendritic cells and macrophages by our laboratory revealed that the interferon-induced transmembrane proteins (IFITMs) are S-fatty-acylated at conserved cysteine (Cys) residues. IFITMs are unique interferon-induced proteins that restrict the infection of multiple pathogenic viruses. Initial studies by our laboratory showed that S-fatty-acylation on three conserved Cys residues were crucial for IFITM3 anti-influenza virus activity. However, endogenous levels and site-specific functions of S-palmitoylation on the individual IFITM3 Cys residues were unknown and are addressed in Chapter 2. In collaboration with other members of the Hang laboratory, we discovered that endogenous IFITM3 is fully S-fatty-acylated in IFN-stimulated mammalian cells and that Cys72 in particular plays an important role in IFITM3 antiviral activity. My additional biochemical studies suggest S-palmitoylation may regulate IFITM3 protein turnover in mammalian cells. IFITMs appear to inhibit virus entry into mammalian cells, but the precise mechanisms have been unclear due to limited methods for live-cell imaging and IFITM3 protein-protein interaction studies. To address these limitations, I explored amber codon suppression technology for site-specific IFITM3 labeling with unnatural amino acids for bioorthogonal imaging and covalent protein crosslinking in mammalian cells in Chapter 3. Using the pyrrolysyl-tRNA synthetase (PylRS)/Pyl tRNACUA system, I showed that unnatural amino acid can be site-specifically incorporated throughout IFITM3 in mammalian cells. The site-specific labeling of IFITM3 with unnatural amino acids has provided new opportunities for live-cell imaging and photocrosslinking studies with specific interacting proteins in Chapter 4. My thesis studies have revealed additional insight into IFITM3 regulation by S-palmitoylation and established new tools to explore the antiviral mechanism of IFITMs. Mechanism (biology) ; Palmitoylation ; Chemistry ; Computer science ; Epistemology ; Biochemistry ; Philosophy ; Cysteine Synthesis and Catalytic Reactions ; Chemical Synthesis and Reactions ; Synthetic Organic Chemistry Methods
Authors: Rilwan Kayode Apalowo | Aizat Abas | Dimitrios Chronopoulos
Source: Acta Acustica (article, 2025-01-01)
Field: Materials Science — General Materials Science

Impacts of pressure and pre-stress loads on the vibroacoustic response of laminated composite structures This study investigates the effects of tensile pre-stress, compressive pre-stress, and surface pressure loads on the vibroacoustic behavior of laminated composite panels using a wave-based Statistical Energy Analysis (SEA) framework. A Wave Finite Element (WFE) method is used to compute wave dispersion, which informs the calculation of modal density, acoustic radiation efficiency, and sound transmission loss (STL). The numerical framework is validated against benchmark experimental and numerical results, with maximum discrepancies below 10%. Results reveal that tensile pre-stress decreases dispersion and modal density while shifting the coincidence frequency downward – from 15.2 kHz (0 N) to 5.1 kHz (100 N). In contrast, compressive pre-stress increases modal density and raises the coincidence frequency, reaching 108.5 kHz at 400 N. Surface pressure primarily affects low-frequency dynamics, increasing dispersion and reducing modal density, with STL coincidence frequency shifting from 12 kHz (0.6 GPa) to 20 kHz (0 GPa). Under combined loading, the dynamic response is intermediate, with pre-stress modulating the out-of-plane stiffening effect of pressure. These results offer critical insight for tailoring vibroacoustic performance in lightweight composite structures, particularly in aerospace and transport systems subjected to operational loading. Composite number ; Structural engineering ; Stress (linguistics) ; Materials science ; Composite material ; Acoustics ; Engineering ; Physics Material Properties and Applications ; Structural Analysis of Composite Materials ; Acoustic Wave Phenomena Research
Authors: Ariful Rahaman | Elias Khalil | Md. Hamid Miah | Sabbir Ahmed | Arman Hossen | S Khatun
Source: Advanced Research Journal (article, 2025-05-17)
Field: Materials Science — Polymers and Plastics

Predictive modeling of tearing strength in laser-engraved denim garments using Multiple Linear Regression This study presents the development of a Multiple Linear Regression (MLR) model to predict the tearing strength of laser-engraved denim garments in both the warp and weft directions, based on key input parameters: Dot Per Inch (DPI) and pixel time. The model achieved excellent predictive accuracy, with R² values of 0.9967 for the warp direction and 0.9911 for the weft direction, indicating that over 99% of the variability in tearing strength was explained by the model. The Pearson correlation coefficients (0.9983 for warp, 0.9956 for weft) and Spearman’s rank correlation coefficients (1 for warp, 0.9833 for weft) further confirm the strength of the relationship between the predicted and actual values. The Mean Absolute Percentage Error (MAEP) values of 0.8783% (warp) and 1.6837% (weft) demonstrate the model’s high accuracy, with significantly lower errors compared to previous fuzzy logic model. Residual analysis confirmed the assumptions of normality, homoscedasticity, and independence, validating the model’s reliability. The MLR model provides a robust tool for optimizing laser engraving parameters in denim manufacturing, reducing the need for trial-and-error testing and ensuring consistent product quality. Denim ; Engraving ; Tearing ; Linear regression ; Materials science ; Composite material ; Mathematics ; Statistics Textile materials and evaluations ; Thermoregulation and physiological responses ; Bee Products Chemical Analysis
Authors: Ximeng Zhu | Yongchun Wei | Xiaogang Liu
Source: Molecules (article, 2025-01-12)
Field: Chemistry — Physical and Theoretical Chemistry

Computational Chemistry Study of pH-Responsive Fluorescent Probes and Development of Supporting Software This study employs quantum chemical computational methods to predict the spectroscopic properties of fluorescent probes 2,6-bis(2-benzimidazolyl)pyridine (BBP) and (<i>E</i>)-3-(2-(1<i>H</i>-benzo[<i>d</i>]imidazol-2-yl)vinyl)-9-(2-(2-methoxyethoxy)ethyl)-9<i>H</i>-carbazole (BIMC). Using time-dependent density functional theory (TDDFT), we successfully predicted the fluorescence emission wavelengths of BBP under various protonation states, achieving an average deviation of 6.0% from experimental excitation energies. Molecular dynamics simulations elucidated the microscopic mechanism underlying BBP's fluorescence quenching under acidic conditions. The spectroscopic predictions for BIMC were performed using the STEOM-DLPNO-CCSD method, yielding an average deviation of merely 0.57% from experimental values. Based on Einstein's spontaneous emission formula and empirical internal conversion rate formulas, we calculated fluorescence quantum yields for spectral intensity calibration, enabling the accurate prediction of experimental spectra. To streamline the computational workflow, we developed and open-sourced the EasySpecCalc software v0.0.1 on GitHub, aiming to facilitate the design and development of fluorescent probes. Time-dependent density functional theory ; Fluorescence ; Density functional theory ; Chemistry ; Protonation ; Quantum chemistry ; Quenching (fluorescence) ; Photochemistry ; Analytical Chemistry (journal) ; Computational chemistry ; Molecule ; Physics ; Optics ; Organic chemistry Photochemistry and Electron Transfer Studies ; Synthesis and Biological Evaluation ; DNA and Nucleic Acid Chemistry
Authors: Anna de Juan | Rodrigo Rocha de Oliveira
Source: Analytical and Bioanalytical Chemistry (review, 2025-10-28)
Field: Chemistry — Analytical Chemistry

Hyperspectral image and chemometrics. A step beyond classical spectroscopic PAT tools Hyperspectral imaging (HSI) is a very complete analytical measurement that encloses rich spatial and chemical information. This double side enables HSI to outperform classical spectroscopic measurements and vision systems based only on color information. However, HSI requires powerful data analysis tools for interpretation and to facilitate its implementation in process analytical technology (PAT) contexts. This work is a brief perspective that shows a diversity of PAT challenges that can be solved with the combined use of HSI and dedicated chemometric procedures. Hyperspectral imaging ; Perspective (graphical) ; Process (computing) ; Interpretation (philosophy) ; Chemical imaging ; Image processing Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Hend Ben Hadj Salah | Oussama Trad | Afef Trad | Borhen Louhichi
Source: Research Square (preprint, 2025-08-14)
Field: Materials Science — Polymers and Plastics

Mechanical and Microstructural Evaluation of Palm, Alfa, and Luffa Fibers as Sustainable Reinforcements in Bio-Composites: A Comparative Study under Uniform Processing Ultimate tensile strength ; Flexural strength ; Natural fiber ; Flexural modulus ; Fiber ; Bending ; Modulus ; Young's modulus Natural Fiber Reinforced Composites ; Bamboo properties and applications ; Mechanical Behavior of Composites