jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Abel Arce-Ortíz | Luis Jorge Corzo‐Ríos | Cristian Jiménez‐Martínez | Zendy Evelyn Olivo‐Vidal | Gabriel Alfonso Gutiérrez‐Rebolledo | Xariss M. Sánchez‐Chino
Source: European Journal of Lipid Science and Technology (article, 2025-11-03)
Field: Chemistry — Inorganic Chemistry

Characterization of Seed Oil From Caco ( <i>Chrysobalanus icaco</i> L.) and Potential of Its Fatty Acids as a Dermal Anti‐Inflammatory Agent in Murine Models ABSTRACT Caco ( Chrysobalanus icaco L.) seeds are rich in oil. This work aimed to characterize and study the anti‐inflammatory properties of Caco seed oil (CSO) using the model of acute auricular edema induced by 12‐ O ‐tetradecanoylphorbol‐13‐acetate (TPA) in female CD1 mice. A final yield greater than 40% was achieved for CSO extraction. The extract presented a specific gravity of 0.939 g/mL, refractive index of 1.52, acidity index of 1.18 mg KOH/g of oil, saponification index of 181.48 mg KOH/g of oil, iodine index of 78.60 g I 2 /100 g of oil, peroxide index of 5.99 mEq O 2 /kg of oil, and ester index of 179.64 mg KOH/g of oil. Stearic (50.80%), oleic (17.87%), and linoleic (15.33%) fatty acids were identified, along with two new compounds for this species: pucinic and parinaric acids. In terms of acute topical anti‐inflammatory effect, CSO demonstrated greater inhibition (60%) of ear edema at 0.5 mg/ear, approaching the potency of indomethacin (66%), but at 2 mg/ear (control). Physicochemical analysis demonstrated that CSO could be stored for an extended period. Furthermore, it could be used as a therapeutic agent in acute inflammatory processes due to the presence of oleic, linoleic, pucinic, and parinaric fatty acids. Saponification ; Potency ; Oleic acid ; Yield (engineering) ; Edema ; Therapeutic index ; Linoleic acid ; Corn oil Coconut Research and Applications ; Essential Oils and Antimicrobial Activity ; African Botany and Ecology Studies
Authors: Nobuhiro Hayashi | Sing Ying Wong | Yudai Hiratsuka | Youko Oono | Shingo Nakamura
Source: Crop Science (article, 2025-11-01)
Field: Chemistry — Spectroscopy

Toward the application of artificial intelligence (AI) proteomics in the agriculture Abstract Proteomics enables the comprehensive analysis of proteins that drive cells and is thus the ultimate method for profiling biological systems. However, proteomics analyses are time‐consuming and expensive, which has limited their applications to basic science and advanced medical research. The recent development of technologies enabling the generalization of proteomics has led to its application in new fields. Artificial intelligence (AI) is particularly useful for mining of proteomics data to yield new knowledge, as it allows for the integration of a wide variety of metadata—information considered necessary to explain experimental data. Recent improvements in the capabilities of AI have facilitated the practical and social implementation of proteomics. In this review, we describe how AI proteomics has expanded the scope of biological testing and discuss its potential and prospects for applications in agriculture. The potential of AI proteomics to provide detailed information on the state of seed germination and dormancy is discussed. In addition, we will discuss results of an investigation into barley leaves using high‐throughput proteomics technology, which is the fundamental technology of AI proteomics. In the future, increasing the amount of data and analyzing it with AI is likely to yield insights that were not previously available. Furthermore, the introduction of this technology into the field is expected to enable more accurate and effective crop management. Proteomics ; Scope (computer science) ; Profiling (computer programming) ; Emerging technologies ; Agriculture Advanced Proteomics Techniques and Applications ; Machine Learning in Bioinformatics ; Transgenic Plants and Applications
Authors: Jing-Ning Zhang | Kan Jin | Shuaiqi Zhao | Shun Wang | Lingyun Ding | Di Xu
Source: Processes (article, 2025-12-10)
Field: Materials Science — Polymers and Plastics

Research Situation and Prospect Analysis of Corrosion of Rubber Materials: A Study Based on Bibliometric Mapping Rubber materials are widely used in industrial sectors such as petrochemicals and energy due to their excellent elasticity, flexibility, and sealing properties. However, in complex and demanding service environments, rubber is susceptible to swelling or dissolution caused by medium corrosion, leading to seal failure or structural damage and ultimately resulting in safety incidents. To systematically review research progress and trends in this field, this paper employs 729 literature samples from the Web of Science core database (2008–2025) and utilizes VOSviewer and Citespace knowledge mapping tools to conduct a bibliometric analysis of corrosion research on rubber materials. This reveals existing knowledge gaps, technological challenges, hot trends, and future demands. Findings indicate that China, the United States, and India lead in publication volume. Research spans multiple disciplines including multidisciplinary materials science, applied physics, and polymer science, reflecting a cross-disciplinary nature. Current rubber corrosion studies focus on three key areas: fundamental properties and performance of rubber materials, corrosion mechanisms, and protective/corrosion-resistant technologies. Key international challenges include unclear mechanisms of complex media interactions, insufficient multi-scale characterization and life prediction, and limited adaptability to service environments. Future development trends will concentrate on three aspects: systematic research on novel multi-media coupling effects, precision in micro-mechanism and life prediction, and highly reliable advancement in green modification and high-performance protection. Multidisciplinary approach ; Adaptability ; Corrosion ; Natural rubber ; Service (business) ; Flexibility (engineering) Polymer Nanocomposites and Properties ; Polymer composites and self-healing ; Machine Learning in Materials Science
Authors: D. R. Fedotova | M. S. Murga | Artem R. Oganov
Source:  (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Study of The Reconstructed Surface of Silicon Carbide This work is devoted to the theoretical investigation of the reconstructed surface of silicon carbide particles, which can take place in process of formation of aromatic molecules in the envelopes of the asymptotic giant branch stars. Modern computational chemistry methods are applied as a tool. Using these methods, the most stable and probable reconstruction of the silicon carbide surface has been identified, which can be used for further calculations of surface reactions and identification of the catalytic properties of silicon carbide. Silicon carbide ; Materials science ; Surface (topology) ; Silicon ; Surface reconstruction ; Optoelectronics ; Composite material ; Geometry ; Mathematics Machine Learning in Materials Science ; Ion-surface interactions and analysis ; Semiconductor materials and devices
Authors: Andrea Zavala-Guzmán | Everardo Vargas-Rodríguez | Ana Dinora Guzman–Chavez | Raja Kamarulzaman Raja Ibrahim | Mario Alberto García-Ramírez
Source: Laser Physics Letters (article, 2025-11-01)
Field: Chemistry — Spectroscopy

Artificial neural networks to control the wavelength and peak intensity of a DFB laser diode emission Abstract In this work a methodology for estimating the operating current and temperature of a distributed feedback (DFB) laser required to get an emission with particular central wavelength and peak intensity is presented. It is based on an ensemble of six artificial neural networks (ANNs), in which their inputs are the target wavelength and peak intensity, and their outputs are the operating current and temperature of the DFB laser. Here, ANNs can establish non-linear relationships between the temperature and current of the laser with the wavelength and intensity of its emissions. Moreover, it is shown by experimental results that the operating DFB laser diode current and temperature are correctly estimated by our algorithm, since the emission obtained achieved, with high precision, the target central wavelength and peak intensity. Finally, with the proposed methodology it is possible to implement, in practically any research lab, a precise control system for establishing the central wavelength and peak intensity of an emission by setting the current and the temperature of the thermoelectric cooler of a DFB laser diode. Laser ; Wavelength ; Intensity (physics) ; Diode ; Current (fluid) ; Laser diode ; Thermoelectric cooling ; Artificial neural network Spectroscopy and Laser Applications ; Semiconductor Lasers and Optical Devices ; Semiconductor Quantum Structures and Devices
Authors: Alya Athirah Mohd Idris | Saliza Asman | Kavirajaa Pandian Sambasevam | Norazlin Abdullah
Source: Journal of Polymer Research (article, 2025-09-01)
Field: Chemistry — Analytical Chemistry

Selective recognition of paracetamol using optimized deep eutectic solvent-molecularly imprinted polymer An optimized deep eutectic solvent-molecularly imprinted polymer (DES-MIP) was synthesized via a straightforward bulk polymerization using choline chloride (ChCl) and methacrylic acid (MAA) (1:2) as the functional monomer, paracetamol as the template, and ethylene glycol dimethacrylate (EGDMA) as the crosslinker. Response surface methodology (RSM) with central composite design (CCD) optimized the main constituent ratio (template, functional monomer, and crosslinker), yielding a synthesis ratio of 1:4:20 mmol and a highest binding capacity of 8.899 mg/g. The model’s robustness was supported by a high F-value (54.05) and low p-value (< 0.001). At the same time, analysis of variance (ANOVA) indicated that the template and functional monomer significantly influenced binding capacity compared to the crosslinker. Characterization of DES-MIP included analyses of functional groups, molecular structure morphology, thermogravimetric properties and porosity analysis. Selectivity tests demonstrated a superior binding capacity (8.89 mg/g) and imprinting factor (3.85) for paracetamol relative to salicylic acid ( 4.42 mg/g, 3.05) and aspirin (4.08 mg/g, 3.85). The absorption kinetics and isotherm were best described by pseudo-second-order kinetics and the Langmuir model, respectively, at an optimum pH of 7 using 5 mg of DES-MIP as adsorbent. DES-MIP showed good stability, retaining 19.5% efficiency after 20 reusability cycles. Application to real samples of herbal drinks, medicine, and health Supplements achieved recoveries of 96.46%, 88.80%, and 86.20%, with relative standard deviations (RSD) of 1.25%, 2.42%, and 2.56%. These findings highlight DES-MIP as a promising alternative for paracetamol removal or extraction with high selectivity, good reusability, and reliable analytical performance. Ethylene glycol dimethacrylate ; Methacrylic acid ; Molecularly imprinted polymer ; Central composite design ; Polymer ; Precipitation polymerization ; In situ polymerization ; Bulk polymerization ; Deep eutectic solvent ; Selectivity Analytical chemistry methods development ; Electrochemical sensors and biosensors ; Analytical Chemistry and Chromatography
Authors: Niamh Lehane | Patrick J. Guiry
Source: Organic & Biomolecular Chemistry (article, 2025-12-24)
Field: Chemistry — Organic Chemistry

Recent advances in Pd-catalysed decarboxylative asymmetric allylic alkylation This comprehensive review on the Pd-catalysed decarboxylative asymmetric allylic alkylation details the development of this important transformation from its origins to more recent advances. Selected examples of this synthetically mild methodology being applied in natural product synthesis are given. The mechanism is explored in detail, to provide a thorough insight on the current understanding, which is built upon the combination of experimental and computational studies. Tsuji–Trost reaction ; Alkylation ; Natural product ; Allylic rearrangement ; Product (mathematics) Asymmetric Synthesis and Catalysis ; Catalytic C–H Functionalization Methods ; Asymmetric Hydrogenation and Catalysis
Authors: Yunisa Tri Suci | I Wayan Budiastra | Yohanes Aris Purwanto | Slamet Widodo
Source: INMATEH Agricultural Engineering (article, 2025-08-25)
Field: Chemistry — Analytical Chemistry

DETERMINATION OF OIL PALM FRUIT MATURITY USING A PORTABLE INSTRUMENT BASED ON UV-VIS-NIR SENSOR An accurate method for determining palm fruit maturity is needed by the palm oil industry to increase productivity and quality of crude palm oil. This study developed a portable instrument based on the AS7265x sensor (410–940 nm) to determine oil palm maturity accurately. Reflectance from 270 oil palm fruits at various maturity were measured, pre-processed, and classified into three maturity levels using PCA. PCA with SNV pre-treatment explained 97% of the variance. Classification was validated using SVM, RF, and KNN, with KNN achieving 100% accuracy. The instrument with SNV-PCA-KNN method has the potential to be used for oil palm fruit maturity classification. Palm oil ; Maturity (psychological) ; Environmental science ; Agricultural engineering ; Pulp and paper industry ; Process engineering ; Food science ; Engineering ; Chemistry ; Agricultural science Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Wenshuo Zhu | Yuan Xue | Yourong Chen | Peng Cui
Source: Textiles (article, 2025-11-10)
Field: Materials Science — Polymers and Plastics

Research on the Construction of a Full-Color-Gamut Color Spinning Method and Neural Network Color Prediction System This paper aims to develop a comprehensive technology for full-color-gamut spinning to enable the precise design and production of blended yarns. A grid-based color-mixing model with six regions is built as a cylindrical color model by mixing eight primary color fibers. Using a three-element synergistic regulation mechanism comprising a numerically controlled rotor-spun system, a color-spinning method is created that integrates the full-color-gamut grid-based color-mixing model. Consequently, 273 blended yarn fabrics are produced. Leveraging the regional variations in blending primary color fibers, a neural network color-prediction system is designed. It is trained on reflectance and blending-ratio data from 255 samples and validated with 18 samples across different color-mixing regions. The results show a mean color difference prediction of 0.909 and an average blending ratio error of 1.76% for the 18 samples. This indicates that the color-prediction system can accurately predict the colors of blended yarns within the full-color-gamut range, providing theoretical support for yarn production. Spinning ; Artificial neural network ; Color difference ; Color model ; Reflectivity ; Yarn ; Color analysis Textile materials and evaluations ; Dyeing and Modifying Textile Fibers ; Color Science and Applications
Authors: Yuki Murai | Itsuo Hanasaki
Source: Physics of Fluids (article, 2025-09-01)
Field: Materials Science — Biomaterials

Emergent rheological nonuniformity of hydrogel from drying fiber suspension In contrast to the densely packed solid structures of crystals or glasses, gels can take a wide range of concentrations for the suspended medium. The consequent structures are random networks that support the fluid. Therefore, structural characteristics are fundamentally diverse. Drying of a colloidal suspension in liquid can cause gelation by an increase in concentration. The nonequilibrium process of drying can be affected by the initial condition. In this article, we report the structural variation of the hydrogel triggered by the difference in initial concentration. We employ cellulose nanofibers to form hydrogels in the drying process of aqueous dispersion. The structural characteristics of hydrogel are rheologically evaluated by the dynamics of dispersed probe particles through optical microscopy movie data analysis based on the single particle tracking. In particular, we focus on the nonuniformity in the structure through multimodality of probe diffusion. The nonuniformity of the hydrogel structure emerges in the specific combination range of initial and transient concentrations, characteristic to the nonequilibrium nature in terms of path dependence of the process. Physics ; Rheology ; Suspension (topology) ; Fiber ; Mechanics ; Chemical engineering ; Composite material ; Polymer science ; Thermodynamics Advanced Cellulose Research Studies ; Pickering emulsions and particle stabilization ; Textile materials and evaluations
Authors: X. Zhong | Huicheng Li | Yixin Cai | Ying Deng | Haobin Xu | Jun Tian | Shuang Liu | Maomao Hou | Haiyong Weng | Lijin Wang | Miaohong Ruan | Fenglin Zhong | Chunhui Zhu | Xu Lu
Source: Horticulturae (article, 2025-09-05)
Field: Chemistry — Analytical Chemistry

Early Detection of Tomato Gray Mold Based on Multispectral Imaging and Machine Learning Gray mold is one of the major diseases affecting tomato production. Its early symptoms are often inconspicuous, yet the disease spreads rapidly, leading to severe economic losses. Therefore, the development of efficient and non-destructive early detection technologies is of critical importance. At present, multispectral imaging-based detection methods are constrained by two major bottlenecks: limited sample size and single modality, which hinder precise recognition at the early stage of infection. To address these challenges, this study explores a detection approach integrating multispectral fluorescence and reflectance imaging, combined with machine learning algorithms, to enhance early recognition of tomato gray mold. Particular emphasis is placed on evaluating the effectiveness of multimodal information fusion in extracting early disease features, and on elucidating the quantitative relationships between disease progression and key physiological indicators such as chlorophyll content, water content, malondialdehyde levels, and antioxidant enzyme activities. Furthermore, an improved WGAN-GP (Wasserstein Generative Adversarial Network with Gradient Penalty) is employed to alleviate data scarcity under small-sample conditions. The results demonstrate that multimodal data fusion significantly improves model sensitivity to early-stage disease detection, while WGAN-GP-based data augmentation effectively enhances learning performance with limited samples. The Random Forest model achieved an early recognition precision of 97.21% on augmented datasets, and transfer learning models attained an overall precision of 97.56% in classifying different disease stages. This study provides an effective approach for the early prediction of tomato gray mold, with potential application value in optimizing disease management strategies and reducing environmental impact. Multispectral image ; Gray (unit) ; Mold ; Horticulture ; Artificial intelligence ; Botany ; Biology ; Computer science ; Medicine Spectroscopy and Chemometric Analyses ; Remote Sensing in Agriculture ; Smart Agriculture and AI
Authors: Josef Kehrein | Robert Luxenhofer | Alex Bunker
Source: ChemRxiv (preprint, 2025-01-06)
Field: Chemistry — Organic Chemistry

Moving Beyond PEG: Poly(2-oxazolines) and Poly(2-oxazines) Alter the Surface Properties of Lipid Nanoparticles Nucleic acid carrying lipid nanoparticles have received great attention since the COVID-19 pandemic. To extend their usage to other therapeutic fields, understanding the impact of tuning their components is crucial. Here, we performed all-atom molecular dynamics simulations on lipid nanoparticles coated with hydrophilic poly(2-oxazoline)s and poly(2-oxazine)s, resembling biocompatible substitutes for the gold standard poly(ethylene glycol). Our results show distinctly different interaction profiles, with the alternatives less likely to address ionizable lipids on the outer membrane. The observed changes in nanoparticle surface coverage and membrane properties could influence particle size, protein corona formation and, ultimately, the biological fate in vivo. Ethylene glycol ; Nanoparticle ; Nucleic acid ; PEG ratio ; Oxazoline ; Nanotechnology ; Lipid bilayer ; Chemistry ; Biophysics ; Colloidal gold ; Chemical engineering ; PEGylation ; Biocompatible material ; Molecular dynamics ; Membrane ; Materials science ; Polyethylene glycol ; Organic chemistry ; Biochemistry ; Computational chemistry Advanced Polymer Synthesis and Characterization
Authors: Sukrit Kumar Yadhav | Arun Devkota | Ganesh Paudel | Om Shree Rijal | Hari Krishna Neupane | Rajendra Parajuli
Source: Amrit Research Journal (article, 2025-12-19)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Study the Structural, Dynamical, Electronic, and Magnetic Properties of CoSb3 Skutterudite Material: First-Principles Approach Skutterudite compounds have potential applications in optoelectronic, thermoelectric, and spintronic devices. In this work, we have explored the physical properties of the CoSb3 skutterudite compound using the density functional theory (DFT) method with the GGA: PBE+U functional implemented in the VASP computational package. The structural properties of CoSb3 have been investigated by optimizing its lattice parameters and bond lengths, as well as estimating its formation and cohesive energies. The results indicate that the compound is structurally stable. The dynamical properties of CoSb3 were analyzed based on the obtained phonon dispersion curves. The absence of negative frequency modes at all high-symmetry points confirms that the material is dynamically stable. The electronic and magnetic properties were determined through the analysis of the band structure, density of states (DOS), and partial density of states (PDOS) plots. From the band structure and DOS results, CoSb3 exhibits metallic behavior, as band states are observed around the Fermi energy level. For the magnetic properties, the DOS and PDOS analyses reveal that the compound possesses a finite magnetic moment, indicating that CoSb3 is magnetic in nature. The observed magnetic moment arises from the dominant distribution of down-spin electronic states in the atomic orbitals of the constituent elements. Skutterudite ; Density functional theory ; Magnetic moment ; Density of states ; Spintronics ; Electronic band structure ; Fermi level Heusler alloys: electronic and magnetic properties ; Advanced Thermoelectric Materials and Devices ; Boron and Carbon Nanomaterials Research