jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ricardo Braga Nogueira Branco | Kohei Oyama | Olivia J. Cook | Grace M. Mu | Andrea P. Argüelles | Namiko Yamamoto | Charles E. Bakis
Source: Journal of Nanoparticle Research (article, 2025-05-01)
Field: Materials Science — Materials Chemistry

Non-destructive assessment of aligned carbon nanotube structures through correlation of polymer nanocomposite properties via ultrasonic testing Abstract Evaluation of nanoparticle distribution and orientation within polymer nanocomposites is critical to ascertaining structure–property relationships but has been a challenge. Nanoparticles form multi-scale structures consisting of nanometer- and micrometer-scale agglomerations, requiring inspection of high resolution and large field of view (FOV) at the same time. Electron microscopy provides high-resolution 2D images of small FOV. Micro-computed tomography provides 3D images of moderate resolution and FOV, but is limited in its ability to resolve regions of similar elements such as polymers and carbon-based nanoparticles. In this work, an ultrasonic testing (UT) technique of a moderate resolution (sub-millimeter) was used to indirectly assess microstructures of carbon nanotubes (CNTs) within an epoxy matrix over a sizable volume (~ mm in all directions). CNTs were magnetically aligned and agglomerated using two different field strengths, and such CNT micro-structure change affected the fracture toughness data of CNT-epoxy nanocomposites. The propagation speed and energy loss (attenuation) of the reflected wave were correlated to changes of CNT orientation and distribution by magnetic field application. Materials science ; Carbon nanotube ; Nanocomposite ; Composite material ; Ultrasonic sensor ; Polymer ; Polymer nanocomposite ; Acoustics Carbon Nanotubes in Composites ; Thermal properties of materials ; Smart Materials for Construction
Authors: I Uribe | Liulin Deng | Leonard C. Rorrer | Lauren Royer | Miriam Fico | Daniel DeBord
Source:  (preprint, 2025-05-22)
Field: Chemistry — Spectroscopy

High-resolution ion mobility as an alternative to quadrupole-based precursor isolation for reducing chimeric fragmentation spectra in bottom-up proteomics To assess the potential for high resolution ion mobility (HRIM) as an alternative means of precursor isolation for mass spectrometry fragmentation analysis we performed a meta-analysis of predicted tryptic peptide features from the human proteome to measure the rate of chimeric spectrum generation relative to traditional quadrupole-based isolation. Results indicate that for proteomic mixtures, HRIM separation with a peak capacity of 100 produces chimeric spectra at a rate commensurate with a ~5 Th quadrupole isolation window, while providing the additional benefit of generating non-chimeric spectra for many isobaric and isomeric peptides unresolvable with a quadrupole filter. This behavior was verified experimentally using a HRIM-QTOF mass spectrometry system. The ability to combine HRIM and MS isolation resulted in &gt;10x increase in precursor isolation specificity as compared to either of the techniques independently. Fragmentation (computing) ; Quadrupole ; Ion ; High resolution ; Spectral line ; Isolation (microbiology) ; Ion-mobility spectrometry ; Chemistry ; Analytical Chemistry (journal) ; Computer science ; Physics ; Chromatography ; Atomic physics ; Geography ; Bioinformatics ; Biology ; Remote sensing ; Organic chemistry Mass Spectrometry Techniques and Applications ; Advanced Proteomics Techniques and Applications ; Ion-surface interactions and analysis
Authors: Min Wan | Achou Su | Guangjie Han | Zixuan Zhang | Tao Zhang
Source: Journal of Engineering and Applied Science (article, 2025-07-28)
Field: Materials Science — General Materials Science

Micromechanical modeling of polyamide-based hybrid nanocomposite reinforced with PZT piezoelectric particles and carbon nanotubes Abstract This exploration presents a micromechanical analysis of a hybrid nanocomposite comprising spherical lead zirconate titanate (PZT) piezoelectric particles and haphazardly oriented carbon nanotubes (CNTs) embedded within a polyamide matrix. Employing the Mori–Tanaka framework, a two-step modeling approach was created to examine the coupled elastic and piezoelectric properties of the composite. The model explicitly accounts for both uniform distribution and aggregation phenomena of CNTs, reflecting realistic microstructural conditions. Outcomes demonstrate that PZT particles predominantly enhance the piezoelectric response and contribute to increased stiffness, while uniformly dispersed CNTs significantly improve the mechanical attributes, including Young’s and shear moduli. Conversely, CNT aggregation adversely affects both mechanical and piezoelectric traits owing to reduced interfacial effectiveness. Validation against experimental data and literature confirms the accuracy of the model. These outcomes emphasize the importance of distribution control in nanocomposites and provide insights into the design of advanced multifunctional materials. The study paves the way for future research into optimizing nanoparticle distribution techniques and expanding the application of hybrid piezoelectric composites in industrial and environmental monitoring systems. Carbon nanotube ; Materials science ; Polyamide ; Nanocomposite ; Composite material ; Piezoelectricity Material Properties and Applications ; Smart Materials for Construction ; Dielectric materials and actuators
Authors: Reinaldo Ramirez Camba | Cristian Leonardo García García | Milton Garcia Tobar | Jorge Fajardo Merchan
Source: Lubricants (article, 2025-04-08)
Field: Chemistry — Analytical Chemistry

An Integrated Methodological Approach for Interpreting Used Oil Analysis in Diesel Engines This study develops an integrated methodological approach for interpreting used oil analysis results in diesel engines, focusing on optimizing maintenance strategies. The methodology combines a literature review with a quantitative assessment of 156 lubricant analysis reports from a fleet of diesel waste collection trucks operating in Cuenca, Ecuador, a high-altitude city. The framework includes critical limits for key lubricant parameters, correlation analysis, and Principal Component Analysis (PCA) to identify dominant degradation mechanisms. The Binary Segmentation (BS) algorithm is also used for Change-Point Detection. The findings indicate four primary degradation pathways: thermal–chemical degradation influenced by sulfur, oxidation, and soot; metallic wear and base depletion, involving iron, chromium, and copper; external contamination linked to silica and copper; and viscosity alteration due to lubricant aging. Significant degradation shifts were identified at approximately 346 and 444 service hours, suggesting critical points for condition-based maintenance interventions. This study highlights the effectiveness of multivariate statistical tools in enhancing the interpretation of used oil analysis and optimizing predictive maintenance strategies. The integration of Change-Point Detection and multivariate analysis provides a robust framework for defining oil change intervals based on lubricant condition rather than fixed time- or mileage-based criteria. This approach offers practical benefits for fleet operations, enabling the reduction in operational costs, enhancing engine reliability, and minimizing the environmental impact of unnecessary lubricant changes. Diesel fuel ; Petroleum engineering ; Automotive engineering ; Process engineering ; Environmental science ; Biochemical engineering ; Computer science ; Engineering Petroleum Processing and Analysis ; Lubricants and Their Additives ; Scientific Measurement and Uncertainty Evaluation
Authors: YoungJae Son | Tiejun Chen | Sung‐June Baek
Source: Mathematics (article, 2025-02-09)
Field: Chemistry — Analytical Chemistry

Fast Search Using k-d Trees with Fine Search for Spectral Data Identification Spectral identification is an essential technology in various spectroscopic applications, often requiring large spectral databases. However, the reliance on large databases significantly increases computational complexity. To address this issue, we propose a novel fast search algorithm that substantially reduces computational demands compared to existing methods. The proposed method employs principal component transformation (PCT) as its foundational framework, similar to existing techniques. A running average filter is applied to reduce noise in the input data, which reduces the number of principal components (PCs) necessary to represent the data. Subsequently, a k-d tree is employed to identify a relatively similar spectrum, which efficiently constrains the search space. Additionally, fine search strategies leveraging precomputed distances enhance the existing pilot search method by dynamically updating candidate spectra, thereby improving search efficiency. Experimental results demonstrate that the proposed method achieves accuracy comparable to exhaustive search methods while significantly reducing computational complexity relative to existing approaches. Identification (biology) ; Computer science ; Information retrieval ; Biology ; Botany Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Remote-Sensing Image Classification
Authors: Ömer Faruk Can Tiska | Niklas Sülzner | Julia Haberhauer | Patrick Kibies | Stefan M. Kast | Christof Hättig
Source: Physical Chemistry Chemical Physics (article, 2025-01-01)
Field: Chemistry — Organic Chemistry

Comparison of the solvation models COSMO and EC-RISM for the prediction of photoacidity in aqueous solution The embedded cluster reference interaction site model (EC-RISM) and the conductor-like solvation model (COSMO) are compared in terms of their ability to predict excitation energies and photoacidity in aqueous solution. The test set includes photoacids derived from phenol, naphthol, and coumarin as well as photobases derived from quinoline. Vertical excitation energies were calculated using the approximate coupled cluster singles-and-doubles model (CC2). For the neutral and protonated forms of the photobases as well as for the neutral forms of the photoacids, EC-RISM-CC2 and COSMO-CC2 yield excitation energies that agree within approximately ±0.08 eV and are in good agreement with available experimental data. For the deprotonated forms of the photoacids, which in this study are phenolate anions throughout, COSMO significantly underestimates the effects of hydrogen bond donation in aqueous solution. In contrast, EC-RISM provides a more faithful description of these solvation effects due to its ability to model solvent distributions on an atomic level, unlike continuum approaches such as COSMO. As a result, EC-RISM performs better in predicting the photoacidity of the photoacids while yielding similar results for the photobasicity of the photobases. To approximately correct for the remaining electronic structure error of CC2, higher-order corrections were estimated from vacuum calculations with CCSDR(3) and CC3 and added to the EC-RISM-CC2 results. For the excitation energies of the photoacids, the CCSDR(3) and CC3 results exhibit significant differences, with the CC3 corrections leading to better agreement with the available experimental data in solution. However, regarding photoacidity, the estimated EC-RISM-CCSDR(3) and EC-RISM-CC3 results are very similar. Solvation ; COSMO-RS ; Aqueous solution ; Chemistry ; Thermodynamics ; Computational chemistry ; Physical chemistry ; Organic chemistry ; Physics ; Molecule Free Radicals and Antioxidants ; Spectroscopy and Quantum Chemical Studies ; Computational Drug Discovery Methods
Authors: Marzban, F. | Marzban, M. | Mohammadzadeh, K. | Abadeh, A.
Source: Journal of Applied Fluid Mechanics (article, 2025-01-04)
Field: Materials Science — Polymers and Plastics

Thermal Behavior of Carboxymethyl Cellulose Containing Alumina Nanoparticles at the Entrance Region of an Annulus This paper investigates the thermal behavior of non-Newtonian nanofluids, specifically carboxymethyl cellulose (CMC) 0.5% and Al₂O₃ nanoparticles, in the fully developed region of a horizontal annulus. A three-dimensional axisymmetric, steady-state numerical solution is performed using the mixture multiphase model to compare with the results obtained from the single-phase model. The present study examines the effects of nanoparticle volume fraction ranging from 0.5% to 1.5% and particle diameters of 25 nm and 50 nm for various Reynolds numbers (Re) within the laminar flow regime. The results indicate that while the temperature profile distribution is slightly affected by changes in alumina concentration, significant variations are observed in the entrance region. Specifically, as Re is enhanced, the Nusselt number (Nu) is increased. For an outer wall heat flux of 1000 W/m² and a 1% concentration, Nu at the x/L = 0.25 section augments from 6.92 to approximately 13.14 as Re is enhanced from 5 to 500. Additionally, for the same conditions, Nu is about 0.78% higher for Al₂O₃ nanoparticles with a diameter of 25 nm than the ones with a diameter of 50 nm. In all cases, there is an acceptable agreement between the results obtained from the mixture and the single-phase models, with discrepancies of less than 1.13%. Carboxymethyl cellulose ; Annulus (botany) ; Materials science ; Cellulose ; Chemical engineering ; Thermal ; Nanoparticle ; Composite material ; Thermodynamics ; Nanotechnology ; Metallurgy ; Sodium ; Physics Synthesis and properties of polymers ; Material Properties and Applications ; Epoxy Resin Curing Processes
Authors: Jibrilla Abdulrahman | Williams S. Ebhota | Pavel Y. Tabakov
Source: Polymer Composites (article, 2025-06-23)
Field: Materials Science — Polymers and Plastics

Experimental and Numerical Calibration of Mechanical Properties for Banana <scp>Fiber</scp> ‐Reinforced Polymer Composites ABSTRACT Accurate calibration of the elastic–plastic behavior of materials is crucial for ensuring that numerical models accurately represent real‐world material responses, thereby enhancing the reliability and predictive capabilities of simulations and engineering designs. The analytical method of obtaining mechanical properties is time‐consuming. The Abaqus software offers a fast and exact means to determine the mechanical and elastic–plastic properties of materials. The study aims to determine the mechanical properties of biopolymer composites comprising banana fiber particles and high‐density polyethylene with varying particle sizes. Tensile test experiments were conducted on the specimen, and the results were calibrated using Abaqus software. To study the behavior of particles in the matrix numerically, a computer program was written in Python to generate a representative volume element (RVE) for finite element analysis (FEA) of a homogeneous particle‐reinforced polyethylene matrix. The experimental results show that the composites exhibited good mechanical properties, specifically with 65 μm particles demonstrating superior mechanical properties. However, the 150 μm particles showed a 31% higher Young's modulus than the 65 μm sample, highlighting stiffness advantages at larger particle sizes. There is also an agreement between the experimental and numerical calibrated results. In addition, the RVE simulations showed that the incorporation of banana fiber significantly enhances the mechanical performance of the polymer matrix by impeding dislocation movement. The RVE results also identified critical regions where failure is most likely to occur, particularly at the fiber‐matrix interface, where the highest strain was observed. Materials science ; Composite material ; Fiber ; Calibration ; Mathematics Natural Fiber Reinforced Composites ; Mechanical Engineering and Vibrations Research ; Innovations in Concrete and Construction Materials
Authors: Phillip Gräfensteiner | Markus Osenberg | André Hilger | Nicole Bohn | Joachim R. Binder | Ingo Manke | Volker Schmidt | Matthias Neumann
Source: Journal of Mathematics in Industry (article, 2025-05-21)
Field: Materials Science — Materials Chemistry

Data-driven stochastic 3D modeling of the nanoporous binder-conductive additive phase in battery cathodes Abstract A stochastic 3D modeling approach for the nanoporous binder-conductive additive phase in hierarchically structured cathodes of lithium-ion batteries is presented. The binder-conductive additive phase of these electrodes consists of carbon black, polyvinylidene difluoride binder and graphite particles. For its stochastic 3D modeling, a three-step procedure based on methods from stochastic geometry is used. First, the graphite particles are described by a Boolean model with ellipsoidal grains. Second, the mixture of carbon black and binder is modeled by an excursion set of a Gaussian random field in the complement of the graphite particles. Third, large pore regions within the mixture of carbon black and binder are described by a Boolean model with spherical grains. The model parameters are calibrated to 3D image data of cathodes in lithium-ion batteries acquired by focused ion beam scanning electron microscopy. Subsequently, model validation is performed by comparing model realizations with measured image data in terms of various morphological descriptors that are not used for model fitting. Finally, we use the stochastic 3D model for predictive simulations, where we generate virtual, yet realistic, image data of nanoporous binder-conductive additives with varying amounts of graphite particles. Based on these virtual nanostructures, we can investigate structure-property relationships. In particular, we quantitatively study the influence of graphite particles on effective transport properties in the nanoporous binder-conductive additive phase, which have a crucial impact on electrochemical processes in the cathode and thus on the performance of battery cells. Nanoporous ; Electrical conductor ; Battery (electricity) ; Materials science ; Cathode ; Phase (matter) ; Computer science ; Composite material ; Nanotechnology ; Thermodynamics ; Electrical engineering ; Physics ; Power (physics) Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Advanced Electron Microscopy Techniques and Applications
Authors: Zhaoyuan Hou | Feng Tan | Manshu Li | Jiadong Gao | Chunjie Su | Feng Jiao | Yaxuan Wang | Xin Zheng
Source: Agronomy (article, 2025-08-04)
Field: Chemistry — Analytical Chemistry

Quantitative Determination of Nitrogen Content in Cucumber Leaves Using Raman Spectroscopy and Multidimensional Feature Selection Cucumber, a high-yielding crop commonly grown in facility environments, is particularly susceptible to nitrogen (N) deficiency due to its rapid growth and high nutrient demand. This study used cucumber as its experimental subject and established a spectral dataset of leaves under four nutritional conditions, normal supply, nitrogen deficiency, phosphorus deficiency, and potassium deficiency, aiming to develop an efficient and robust method for quantifying N in cucumber leaves using Raman spectroscopy (RS). Spectral data were preprocessed using three baseline correction methods—BaselineWavelet (BW), Iteratively Improve the Moving Average (IIMA), and Iterative Polynomial Fitting (IPF)—and key spectral variables were selected using 4-Dimensional Feature Extraction (4DFE) and Competitive Adaptive Reweighted Sampling (CARS). These selected features were then used to develop a N content prediction model based on Partial Least Squares Regression (PLSR). The results indicated that baseline correction significantly enhanced model performance, with three methods outperforming unprocessed spectra. A further analysis showed that the combination of IPF, 4DFE, and CARS achieved optimal PLSR model performance, achieving determination coefficients (R2) of 0.947 and 0.847 for the calibration and prediction sets, respectively. The corresponding root mean square errors (RMSEC and RMSEP) were 0.250 and 0.368, while the residual predictive deviation (RPDC and RPDP) values reached 4.335 and 2.555. These findings confirm the feasibility of integrating RS with advanced data processing for rapid, non-destructive nitrogen assessment in cucumber leaves, offering a valuable tool for nutrient monitoring in precision agriculture. Raman spectroscopy ; Nitrogen ; Selection (genetic algorithm) ; Content (measure theory) ; Chemistry ; Spectroscopy ; Feature selection ; Analytical Chemistry (journal) ; Computer science ; Artificial intelligence ; Environmental chemistry ; Mathematics ; Physics ; Optics Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Smart Agriculture and AI
Authors: Budi Santoso | Christy Atika Sari | Eko Hari Rachmawanto
Source: Journal of Applied Informatics and Computing (article, 2025-01-11)
Field: Chemistry — Analytical Chemistry

Coffee Beans Classification Using Convolutional Neural Networks Based On Extraction Value Analysis In Grayscale Color Space Coffee is a vital agricultural commodity, and precise classification of coffee beans is crucial for quality assessment and agricultural practices. In this study, we propose a methodology utilizing Convolutional Neural Networks (CNN) based on ResNet-101 architecture for coffee bean classification. The novelty of our approach lies in the integration of comprehensive feature extraction from grayscale coffee bean images, including mean, standard deviation, skewness, energy, entropy, and smoothness, with the transfer learning capabilities of CNN. Through this integration, we achieved exceptional classification performance, with the CNN model attaining accuracy, recall, precision, and F1-score metrics of 99.44% and 100% on the training data, and 100% on the testing data. These results underscore the robustness and generalization capability of our methodology in accurately classifying coffee bean types. While the dataset used in this study is experimental, the comprehensive feature extraction and the effectiveness of the CNN architecture suggest the potential for accurate classification of coffee bean types beyond the experimental data, provided the new data shares similar characteristics to the collected samples. For future research, we recommend exploring the integration of two transfer learning techniques within CNN architectures to further enhance coffee bean classification systems. Specifically, leveraging pre-trained CNN models as a foundation for feature extraction, while simultaneously fine-tuning specific layers to adapt to the nuances of coffee bean classification tasks, could offer improved model performance and scalability. Grayscale ; Convolutional neural network ; Artificial intelligence ; Pattern recognition (psychology) ; Computer science ; Color space ; Extraction (chemistry) ; Mathematics ; Image (mathematics) ; Chromatography ; Chemistry Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI