jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Shirly Miluska Gomez Pacheco | Jazmin Nicole Espinoza Herrera | Karina Caracuzma Condor | Marko Antonio Lengua Fernandez
Source: International Journal of Civil Engineering (article, 2025-11-30)
Field: Materials Science — Polymers and Plastics

Evaluation of the Potential of Natural Additives based on Oje, Croton Lechleri, and Copaiba for Waterproof Concrete Compared to Commercial Products Construction on saturated and sloping terrain presents challenges for the stability and durability of concrete due to water infiltration, loss of strength, and accelerated deterioration. This research evaluated the performance of natural Amazonian additives such as bloodwood resin, ojé, and copaiba in the formulation of waterproof concrete, comparing them with Sika-1 and Sika Bitumen in mixtures of 210 kg/cm². An experimental methodology was applied, varying the proportions of additives (1% to 6% relative to the weight of the cement), and compression, indirect tensile, absorption, porosity, and penetration tests were performed according to ASTM standards. The results showed that an optimal dose of 3% bloodwood, ojé and copaiba resin improved mechanical strength (up to 229.96 kg/cm² in compression and 28.03 kg/cm² in traction), reducing absorption (1.55% to 1.66%), porosity (4.2%) and penetration (13.8 mm), with performance similar to or superior to commercial additives. Higher doses did not generate improvements and increased porosity. The cost of concrete with 3% resins was S/ 490.88 /m³, cheaper than Sika-1 (S/ 532.71) and competitive with Sika Bitumen (S/ 475.45). Therefore, the use of Amazonian resins in appropriate proportions is an efficient and sustainable alternative for improving the impermeability and durability of concrete in humid environments, providing technical, economic, and environmental value. Durability ; Absorption of water ; Asphalt ; Penetration (warfare) ; Porosity ; Natural materials Natural Fiber Reinforced Composites ; Concrete and Cement Materials Research ; Microbial Applications in Construction Materials
Authors: Hyeok-Jin Bak | Eunji Kim | Ji‐Hyeon Lee | Sungyul Chang | Dongwon Kwon | Woo-Jin Im | Woon-Ha Hwang | Jae-Ki Chang | Nam-Jin Chung | Wan-Gyu Sang
Source: Frontiers in Plant Science (article, 2025-08-14)
Field: Chemistry — Analytical Chemistry

Deep learning-based semantic segmentation for rice yield estimation by analyzing the dynamic change of panicle coverage This framework proves to be a powerful tool for quantifying rice developmental dynamics and accurately predicting yield using readily available RGB imagery. It holds significant potential for advancing both precision agriculture and crop breeding efforts. Panicle ; Segmentation ; Yield (engineering) ; Estimation ; Artificial intelligence ; Computer science ; Machine learning ; Pattern recognition (psychology) ; Agronomy ; Biology ; Engineering Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI
Authors: Laura Nabukalu | Elisha Kayongo | Vianney Andrew Yiga | Michael Lubwama
Source: Discover Mechanical Engineering (article, 2025-10-09)
Field: Materials Science — Polymers and Plastics

Characteristics of agro-residue reinforced plastic ceiling board composites from rice husks and polyethylene terephthalate Ceiling boards are highly needed in our daily lives due to the need for temporary ceilings in the construction industry. However, the use of ceiling boards in Uganda is affected by climate fluctuation, with a dominant wet season throughout the year which leads to damages to existing ceiling board materials such as cardboards and plywood. These ceiling boards also have low durability, low fire resistance, hence their limited usage in the construction industry. This study has developed a material for ceiling boards that offers suitable mechanical, physical and thermal properties using rice husks as a filler material and polyethylene terephthalate (PET) plastic residue as an adhesive. The study aimed at investigating the effect of raw and treated rice husks composition in PET plastic residues for the development of composite ceiling boards. The ceiling boards were processed using varying amounts (10–30wt.%) of raw rice husks (RHR) and NaOH treated rice husks (RHT) by melt-blending with PET flakes. Fourier Transform Infrared Spectroscopy (FTIR), density tests, water absorption tests, thermoconductivity and mechanical tests were carried out. The functional groups present in raw and treated rice husks composites are predominantly the same, including O–H, C–H, C=O, and C–O groups characteristic of cellulose, hemicellulose, polyesters and lignin with slight change in intensity and sharpness on FTIR spectra graphs. Treated rice husks-PET ceiling limited board composites (RHT30) are better than raw rice husks-PET ceiling board composites (RHR30) for the ceiling board application due to the less water absorption rate 4.23%, lower density 140 kg/m3. The treatment process of the rice husks affected their compatibility and adhesion with PET, resulting in lower mechanical performance. Ceiling board composites were developed with 10–30% composition of rice husks. Mechanical and thermal properties of the composites were determined. Treatment process altered the rice husks` surface chemistry. Husk ; Ceiling (cloud) ; Raw material ; Polyethylene terephthalate ; Wood-plastic composite ; High-density polyethylene ; Absorption of water Natural Fiber Reinforced Composites ; Advanced Cellulose Research Studies
Authors: Johnnie Phuong | Raphael Kircher | Sarah Mross | Billy Salgado | Hans Hasse | Kerstin Münnemann
Source: ChemPhysChem (article, 2025-07-18)
Field: Chemistry — Spectroscopy

Quantitative Nuclear Magnetic Resonance Spectroscopy with Overhauser Dynamic Nuclear Polarization Nuclear magnetic resonance (NMR) spectroscopy is a powerful tool for process monitoring. However, the quantitative analysis of highly diluted components or of components in small sample volumes is difficult with this analytical method due to its inherent lack of sensitivity. The hyperpolarization technique Overhauser dynamic nuclear polarization (ODNP) holds promise to solve this problem because it can provide strong signal enhancements. Furthermore, ODNP operates on short time scales and can therefore be used on flowing samples. Despite these advantages, to our knowledge, ODNP has never been applied for quantitative analysis of mixtures-probably because NMR signal enhancements by ODNP can vary greatly for different molecules, making quantitative analysis of mixtures difficult. We demonstrated that this problem can be solved by a robust calibration: three binary mixtures were studied as test cases in a wide range of concentrations by ODNP-enhanced <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics> <mml:mrow><mml:msup><mml:mrow></mml:mrow> <mml:mn>1</mml:mn></mml:msup> </mml:mrow> <mml:annotation>$^{1}$</mml:annotation></mml:semantics> </mml:math> H and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics> <mml:mrow><mml:msup><mml:mrow></mml:mrow> <mml:mrow><mml:mn>13</mml:mn></mml:mrow> </mml:msup> </mml:mrow> <mml:annotation>$^{13}$</mml:annotation></mml:semantics> </mml:math> C NMR spectroscopy in continuous-flow experiments with a benchtop NMR spectrometer using a new tailored calibration procedure. We show that quantitative analysis with ODNP-enhanced NMR spectroscopy is possible even under these challenging conditions. Nuclear Overhauser effect ; Nuclear magnetic resonance spectroscopy ; Hyperpolarization (physics) ; Spectroscopy ; Two-dimensional nuclear magnetic resonance spectroscopy ; Chemistry ; Analytical Chemistry (journal) ; Nuclear magnetic resonance ; Spectrometer ; Calibration ; Polarization (electrochemistry) ; Quantitative analysis (chemistry) ; Transverse relaxation-optimized spectroscopy ; Fluorine-19 NMR ; Chromatography ; Physical chemistry ; Physics ; Optics Advanced NMR Techniques and Applications ; NMR spectroscopy and applications ; Solid-state spectroscopy and crystallography
Authors: Sobhan Haghparast | Yi Zhang | Qian Tao | Sjoerd Stallinga | Bernd Rieger
Source: Research Square (preprint, 2025-06-13)
Field: Materials Science — Materials Chemistry

Detecting Continuous Structural Heterogeneity in Single Molecule Localization Microscopy Data with a Point Cloud Variational Auto-Encoder Encoder ; Point cloud ; Microscopy ; Computer science ; Single point ; Algorithm ; Cloud computing ; Autoencoder ; Computer vision ; Artificial intelligence ; Physics ; Optics Machine Learning in Materials Science ; Manufacturing Process and Optimization ; Electron and X-Ray Spectroscopy Techniques
Authors: Onur Can Boy | Ulderico Di Caprio | Idelfonso B. R. Nogueira | M. Enis Leblebici
Source: Systems and Control Transactions (article, 2025-06-30)
Field: Materials Science — Materials Chemistry

Structural Optimization of Translucent Monolith Reactors through Multi-objective Bayesian Optimization Photochemical monolith reactors offer advantages over microreactors by providing high mixing efficiency and surface area to volume ratio while being scalable. However, optimizing monolith design parameters like channel number, shape, and stacking is critical to maximizing light usage and reactor efficiency. This work proposes using Bayesian optimization and COMSOL Multiphysics simulations to automatically design translucent monoliths for photochemical reactions. The goal is to maximize both photochemical space-time yield and space-time yield. Ray tracing simulations were performed while evaluating five different channel geometries (circular, elliptical, triangular, square, and pentagonal) and optimizing parameters, including channel diameter, vertical stacking, shape rotation, and ellipse axis ratio. Results showed a clear trade-off between Space-Time Yield (STY) and Photochemical Space-Time Yield (PSTY), with optimized elliptical channels achieving up to 15.3% improvement in STY with minimal PSTY increase and 9.0% enhancement in PSTY with negligible STY decrease. Monolith ; Bayesian optimization ; Computer science ; Bayesian probability ; Mathematical optimization ; Materials science ; Artificial intelligence ; Mathematics ; Chemistry Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Akeme Cyril Njume | Yoshiaki Shinzato | Muneshi Mitsuoka | Yuya Aoyagi | Eizo Taira
Source: Engineering in Agriculture Environment and Food (article, 2025-01-01)
Field: Chemistry — Analytical Chemistry

Evaluation of unified calibration model in predicting the pol content in sugarcane juice extracted using two extraction methods Developing a unified calibration model to assess pol content in sugarcane juice extracted using different methods can substantially reduce the costs of developing individual models and improve operational efficiency.A benchtop spectrophotometer (400-2,500 nm) was used to acquire transflectance spectra of sugarcane juice extracted using the direct extraction method (DEM) and the shredded extraction method (SEM).DEM yielded a higher pol content of 0.67 % compared with that of SEM.The unified partial least squares (PLS) calibration model accurately predicted the pol content in the DEM and SEM samples, as well as that of mixed samples, with root mean square error of prediction (RMSEP) values of 0.23 %, 0.21 %, and 0.23 %, respectively. Calibration ; Extraction (chemistry) ; Content (measure theory) ; Feature extraction Spectroscopy and Chemometric Analyses ; Sugarcane Cultivation and Processing ; Natural Products and Biological Research
Authors: Enes Eken | İsmail Bayraklı
Source: Communications Faculty of Sciences University of Ankara Series A2-A3 Physical Sciences and Engineering (article, 2025-12-24)
Field: Chemistry — Spectroscopy

Improving resolution of grating-coupled external cavity quantum cascade laser without sacrificing time by leveraging transformer encoder The wavelength of a grating-coupled EC-QCL (external cavity quantum cascade laser) can be scanned using a motorized stage on which the diffraction grating is mounted. The speed of the motor affects the resolution of the spectrum. The resolution of the laser signal and the speed of the motor are inversely proportional; as the speed of the motor increases, the resolution decreases. Nevertheless, it is desirable to both increase the motor speed (hence reducing the analysis time) and achieve high resolution. Within the scope of this research, we conducted a new theoretical study that will enable this situation. For this purpose, we developed a Transformer Encoder based model inspired by Bidirectional Encoder Representations from Transformers (BERT) and demonstrated that the model injects correct information into the low resolution signal in a fixed interval. Encoder ; Cascade ; Transformer ; Rotary encoder ; Quantum cascade laser ; Laser ; High resolution Spectroscopy and Laser Applications ; Semiconductor Lasers and Optical Devices ; Chaos control and synchronization
Authors: Vaijayanthi Bhashyam
Source: International Journal of Computational and Theoretical Chemistry (article, 2025-05-29)
Field: Chemistry — Spectroscopy

Before You Click: Understanding the Potential Energy Surface of Water Students and researchers pursuing molecular modeling or computational chemistry use readily available software such as Schrodinger, Gaussian, Arguslab, Hyperchem etc. to facilitate the visualization of molecules and calculation of their energy. A variety of computational tools form the basis of working of such software. The first step in using most of these software programs is to optimize the geometry of the input molecule. During such optimization procedure, the software searches for parameters of geometry such as bond length, bond angle and dihedral angle which results in the molecule’s minimum energy and hence the most stable geometry. While it is pertinent for the student to be able to choose the right computational tool to obtain reliable results, the visualization of potential energy diagram of the molecule is equally important. It may be appropriately said that the core of all the computational tools is rooted in a deep understanding of potential energy diagrams or potential energy surfaces (PESs). Potential energy surfaces are multidimensional graphs of potential energy against the various independent variables of geometrical parameters. They can span from three-dimensional representations (with two dimensions for the independent variables and one for energy) to more complex, higher-dimensional forms. A PES is often compared to a landscape, with hills, valleys, and ridges corresponding to high and low energy configurations. It is a challenge for undergraduate students to understand the PESs of polyatomic molecules as they have always dealt with potential energy diagrams that are of two dimensions only. This article discusses a simplified approach to grasp the concept of PES for polyatomic molecules, using water—the simplest polyatomic molecule—as an example. A three-dimensional PES graph is created in MS Excel using values calculated from the free molecular modeling software, ArgusLab. Additionally, the process of reducing the 3D plot to a 2D plot through slicing is also explained. Computer science ; Surface (topology) ; Energy (signal processing) ; Environmental science ; Mathematics ; Statistics ; Geometry Spectroscopy and Laser Applications ; Quantum, superfluid, helium dynamics ; Spectroscopy and Quantum Chemical Studies
Authors: Zhixiang Yin | Linpei Shou | Yumin Ye
Source: Highlights in Science Engineering and Technology (article, 2025-02-25)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Data-Driven Core Loss Prediction Model Research and Magnetic Component Optimization Design Accurate core loss prediction is essential for the design of high-performance magnetic components for power electronics. In this study, a modified Steinmetz's equation incorporating a temperature factor is introduced to improve the accuracy of core loss prediction. By analyzing the independent and synergistic effects of various factors on core loss using multiple linear regression, partial correlation analysis, and Bayesian factor analysis, this study applies a genetic algorithm to find the conditions for minimizing core loss and constructed a model based on the Random Forest algorithm for predicting the core loss of different materials. In addition, considering that core loss is not the only thing that affects the performance of magnetic elements, this study considers both core loss and transmitted magnetic energy and uses particle swarm optimization (PSO) to determine the optimal conditions for magnetic elements. It is shown that the modified model and optimization method can more accurately predict and optimize the performance of magnetic elements, providing a comprehensive solution for power conversion systems. Component (thermodynamics) ; Core (optical fiber) ; Computer science ; Physics ; Thermodynamics ; Telecommunications Magnetic Properties and Applications ; Non-Destructive Testing Techniques ; Electric Motor Design and Analysis
Authors: Zheng Tan | Qi Yang | Sanzhong Luo
Source: Organic Chemistry Frontiers (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Correction: AI molecular catalysis: where are we now? Correction for ‘AI molecular catalysis: where are we now?’ by Zhenzhi Tan et al. , Org. Chem. Front. , 2025, https://doi.org/10.1039/d4qo02363c. Chemistry ; Combinatorial chemistry ; Nanotechnology Machine Learning in Materials Science ; Innovative Microfluidic and Catalytic Techniques Innovation ; Computational Drug Discovery Methods
Authors: А. Ф. Чешкова
Source: Vavilov Journal of Genetics and Breeding (article, 2025-04-11)
Field: Chemistry — Analytical Chemistry

Identification of fungal diseases in strawberry by analysis of hyperspectral images using machine learning methods Leaf spot, leaf scorch and phomopsis leaf blight are the most common fungal diseases of strawberry in Western Siberia, which significantly reduce its yield and quality. Accurate, fast and non-invasive diagnosis of these diseases is important for strawberry production. This article explores the ability of hyperspectral imaging to detect and differentiate symptoms caused to strawberry leaves by pathogenic fungi Ramularia tulasnei Sacc., Marssonina potentillae Desm. and Dendrophoma obscurans Anders. The reflection spectrum of leaves was acquired with a Photonfocus MV1-D2048x1088-HS05-96-G2-10 hyperspectral camera under laboratory conditions using the line scanning method. Five machine learning methods were considered to differentiate between healthy and diseased leaf areas: Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Linear Discriminant Analysis (LDA), Partial Least Squares Discriminant Analysis (PLS-DA), and Random Forest (RF). In order to reduce the high dimensionality of the extracted spectral data and to increase the speed of their processing, several subsets of optimal wavelengths were selected. The following dimensionality reduction methods were explored: ROC curve analysis method, derivative analysis method, PLS-DA method, and ReliefF method. In addition, 16 vegetation indices were used as features. The support vector machine method demonstrated the highest classification accuracy of 89.9 % on the full range spectral data. When using vegetation indices and optimal wavelengths, the overall classification accuracy of all methods decreased slightly compared to the classification on the full range spectral data. The results of the study confirm the potential of using hyperspectral imaging methods in combination with machine learning for differentiating fungal diseases of strawberries. Hyperspectral imaging ; Linear discriminant analysis ; Artificial intelligence ; Support vector machine ; Dimensionality reduction ; Pattern recognition (psychology) ; Principal component analysis ; Mathematics ; Computer science ; Spectral signature ; Partial least squares regression ; Machine learning ; Remote sensing Spectroscopy and Chemometric Analyses ; Remote Sensing in Agriculture ; Plant Pathogens and Fungal Diseases
Authors: Yusuke Kohigashi | Hiroshi Tanei | Keita Ikeda | Takuya Mitsunobu | Kazuma Ito
Source: Journal of the Japan Institute of Metals and Materials (article, 2025-12-25)
Field: Materials Science — Materials Chemistry

Relationship between Penetration Rate and Grain Boundary Diffusion during the Early Stage of Cu Penetration in Hot Shortness Penetration (warfare) ; Stage (stratigraphy) ; Grain boundary ; Penetration rate ; Penetration depth ; Grain boundary diffusion coefficient Microstructure and mechanical properties ; High-Velocity Impact and Material Behavior ; Metal Forming Simulation Techniques
Authors: Hadis Yari | Mostafa Mostafaei | Nahid Aghilinategh
Source: Journal of Spectroscopy (article, 2025-01-01)
Field: Chemistry — Analytical Chemistry

Detection of Common Adulteration in Lemon Juice With the Help of E‐Nose and Vision Machine This study aims to identify and classify common adulterations (i.e., straw water, citric acid, and industrial lemon juice) in the lemon juice market using an electronic nose and colorimeter. These samples were mixed with natural lemon juice in various percentages and tested with an electronic nose and colorimeter. A pH measurement was also performed on the samples. Principal component analysis (PCA), quadratic discriminant analysis (QDA), linear discriminant analysis (LDA), support vector machine (SVM), support vector regression (SVR), partial least square (PLS), multiple linear regression (MLR), and principal component regression (PCR) were used to classify and predict odor data, color data, and data fusion of color and odor through the standard and zero‐one methods. According to the results, PCA classification using fused data was more accurate than classification using odor data and color data. According to the loading graph, the TGS2620, TGS2610, and TGS822 sensors were most effective in identifying straw water, industrial lemon juice, and citric acid. Also, the LDA method could distinguish different levels of adulteration of industrial lemon juice and straw water, but the levels of adulteration of citric acid cannot be detected. The results showed that the accuracy of the QDA method based on odor data was higher than that of color data. In addition, SVM accuracy classification based on fused data and color data by the standard method was close. However, the MLR model outperformed the other prediction models in predicting color changes, browning indices, and pH values in three types of adulteration added to natural lemon juice. Electronic nose ; Principal component analysis ; Linear discriminant analysis ; Pattern recognition (psychology) ; Quadratic classifier ; Support vector machine ; Odor Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Identification and Quantification in Food