jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: P. Pandiarajan | P. Baskaran | Sivasubramanian Palanisamy | Manickaraj Karuppusamy | K. Marimuthu | Anbarasan Rajan | Mansour I. Almansour | Quanjin Ma | Saleh Alfarraj
Source: BioResources (article, 2025-08-29)
Field: Materials Science — Polymers and Plastics

Enhancing polyester composites with nano Aristida hystrix fibers: Mechanical and microstructural insights The utilization of natural fibers in polymer composites is increasingly popular due to their sustainability, cost-effectiveness, and favorable mechanical properties. This study introduces the novel use of Aristida hystrix fibers, processed for the first time into nano-sized particles via ball milling, to enhance dispersion and bonding within a polyester matrix. These nanoparticles were incorporated into polyester resin at various weight percentages (0 wt%, 1 wt%, 3 wt%, 5 wt%, 7 wt%, and 9 wt%), and composite laminates were fabricated using solvent casting and compression molding techniques. Mechanical properties were evaluated through tensile, flexural, and impact strength tests following ASTM standards. The composite containing 5 wt% nano fiber exhibited the optimum mechanical performance, with tensile strength of 30.13 MPa, flexural strength of 43.685 MPa, and impact strength of 1.87 kJ/m². At higher fiber loadings, particle agglomeration led to performance reduction. Water absorption studies indicated that increased nano fiber content resulted in higher moisture uptake, influencing long-term durability. Scanning Electron Microscopy (SEM) provided insights into fiber–matrix interfacial behavior, dispersion quality, and fracture mechanisms. Overall, this work establishes the first-time development of polyester composites reinforced with nano Aristida hystrix fibers, demonstrating their potential as a sustainable and high-performance material for lightweight structural applications in automotive, aerospace, and marine sectors. Materials science ; Composite material ; Hystrix ; Nano- ; Polyester ; Ecology Natural Fiber Reinforced Composites
Authors: Joseph C. Bear | Jeremy K. Cockcroft | Alexander Rosu-Finsen | Jeffrey H. Williams
Source: CrystEngComm (article, 2025-11-26)
Field: Chemistry — Physical and Theoretical Chemistry

Exploring non-covalent interactions in binary aromatic complexes Crystal structure prediction for systems governed by weak non-covalent interactions remain a significant challenge due to the complex energy landscapes involved. Herein, we have experimentally investigated the impact of systematic halogen substitution in fluorinated aromatic co-formers on the formation, structure, and phase behaviour of donor-acceptor adducts and co-crystals with <i>p</i>-xylene (<i>p</i>-C<sub>6</sub>H<sub>4</sub>Me<sub>2</sub>). Using a combined approach of differential scanning calorimetry (DSC), variable-temperature powder X-ray diffraction (VT-PXRD), and single-crystal X-ray diffraction (SXD), we have characterized a series of co-crystals formed by <i>p</i>-C<sub>6</sub>H<sub>4</sub>Me<sub>2</sub> with C<sub>6</sub>F<sub>5</sub>X (X = Cl, Br, I) and <i>p</i>-C<sub>6</sub>F<sub>4</sub>X<sub>2</sub> derivatives. Our results revealed a clear evolution from columnar π-stacked adducts in the Cl-substituted systems to halogen-bonded structures with the heavier halogens (Br, I). The columnar 1 : 1 adducts exhibit complex solid-state phase behaviour linked to molecular dipole and steric effects, whereas co-crystals involving Br and I show simpler behaviour, with discrete η<sup>2</sup> and η<sup>6</sup> halogen-π interactions both being observed. In one instance, a 1 : 2 co-crystal was formed with antiferroelectric ordering requiring halogen bonding to <i>p</i>-C<sub>6</sub>H<sub>4</sub>Me<sub>2</sub> from two C<sub>6</sub>F<sub>5</sub>I molecules. The results underscore the tunability of solid-state architectures through targeted halogen substitution to probe subtle non-covalent interactions. In summary, this work advances our understanding of weak intermolecular forces in crystalline materials and provides data for the predictive design of functional co-crystals. Intermolecular force ; Steric effects ; Differential scanning calorimetry ; Halogen ; Halogen bond ; Adduct ; Diffraction ; Phase (matter) ; Antiferroelectricity Crystallography and molecular interactions ; Luminescence and Fluorescent Materials ; Synthesis and Properties of Aromatic Compounds
Authors: Oluwasegun Henry Jaiyeoba | Mohammad Karimzadeh Kolamroudi | Cemal Kavalcıoğlu | Cigdem Özkan | Said EL Khatib
Source: Archives of Advanced Engineering Science (review, 2025-06-24)
Field: Materials Science — Materials Chemistry

Implementation Approaches of Thermoelectric Generator in Photovoltaic System: A Review Solar energy is one of the viable solutions for global energy demand. To compete with traditional resources, solar cells have to be reliable and cost-effective. This paper reviews the basics of thermoelectric generators (TEGs), including their working principles and main physical properties. TEGs transform heat directly to electricity based on the Seebeck effect, which generates an electric voltage based on the difference in temperature of a material. It presents methods for energy harvesting by using TEGs integrated with photovoltaic (PV) systems and discusses the application and findings of this hybrid model. By converting solar system waste heat or primary heat flow into additional heating, cooling, and electricity, TEGs enhance PV system efficiency. This paper reviews the methods and effects of integrating thermoelectric devices with PV systems, summarizing key findings from previous studies. The paper concludes that the integration of TEGs significantly improves the functionality and efficiency of PV systems, as evidenced by previous studies. Received: 14 November 2024 | Revised: 27 April 2025 | Accepted: 29 May 2025 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data available on request from the corresponding author upon reasonable request. Author Contribution Statement Oluwasegun Henry Jaiyeob: Conceptualization, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing - original draft, Writing - review &amp; editing, Visualization. Mohammad Karimzadeh Kolamroudi: Conceptualization, Methodology, Validation, Formal analysis, Investigation, Resources, Data curation, Writing - original draft, Writing - review &amp; editing, Visualization, Supervision, Project administration. Cemal Kavalcioglu: Validation, Investigation, Writing - original draft, Writing - review &amp; editing. Çağrı Özkan: Software, Writing - original draft, Writing - review &amp; editing. Said EL Khatib: Conceptualization, Resources, Writing - original draft, Writing - review &amp; editing. Photovoltaic system ; Thermoelectric generator ; Generator (circuit theory) ; Thermoelectric effect ; Engineering physics ; Materials science ; Electrical engineering ; Systems engineering ; Computer science ; Engineering ; Physics ; Power (physics) ; Thermodynamics Advanced Thermoelectric Materials and Devices
Authors: P. K. Hashim | Ashwin T. Shaji | Ammathnadu S. Amrutha | Sk Akhtar Ahmad
Source: RSC Medicinal Chemistry (review, 2025-01-01)
Field: Materials Science — Materials Chemistry

Conceptual expansion of photomedicine for spatiotemporal treatment methods Photomedicine has evolved from basic phototherapy to a broad range of light-based technologies to achieve precise and minimally invasive therapeutic outcomes. Recent advances in light sources, photochemical reactions, and photoswitches have facilitated the development of light-activated methodologies for modulating biological processes. This review discusses the history of light therapy that leads to the emergence of a new field known as photopharmacology, mode of actions in photopharmacology such as photodynamic, photo-uncaging and photoswitchable methods, a few representative examples in photopharmacology, and a brief overview of its associated challenges. The current developments in photopharmacology hold great promise for the treatment of diseases such as cancer, with enhanced therapeutic precision, and minimal side effects. We foresee further expansion of photomedicine for novel approaches in precision medicine and healthcare, and unprecedented treatment methods. Computer science ; Psychology ; Management science ; Medical physics ; Medicine ; Engineering Photochromic and Fluorescence Chemistry ; Photodynamic Therapy Research Studies ; Photoreceptor and optogenetics research
Authors: Aylin Kabaş | Uğur Ercan | Önder Kabaş | Georgiana Moiceanu
Source: Horticulturae (article, 2025-08-16)
Field: Chemistry — Analytical Chemistry

Prediction of Selected Minerals in Beef-Type Tomatoes Using Machine Learning for Digital Agriculture Tomato is one of the most important vegetables due to its high production and nutritional value. With the development of digital agriculture, the tomato breeding and processing industries have seen a rapid increase in the need for simple, low-labor, and inexpensive methods for analyzing tomato composition. This study proposes a digital method to predict four minerals (calcium, potassium, phosphorus, and magnesium) in beef-type tomato using machine learning models, including k-nearest neighbors (kNN), artificial neural networks (ANNs), and Support Vector Regression (SVR). The models were discriminated using the coefficient of determination (R2), root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). The kNN model showed the best performance for estimation of quantity of calcium, potassium, phosphorus, and magnesium. The results demonstrate that kNN consistently outperforms ANNs and SVR across all target nutrients, achieving the highest R2 and the lowest error metrics (RMSE, MAE, and MAPE). Notably, kNN achieved an exceptional R2 of 0.8723 and a remarkably low MAPE of 3.95% in predicting phosphorus. This study highlights how machine learning can provide a versatile, accurate, and efficient solution for tomato mineral analysis in digital agriculture. Agriculture ; Agronomy ; Agricultural engineering ; Mathematics ; Biology ; Engineering Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Advanced Chemical Sensor Technologies
Authors: Shiwank Rana | Appaneni Lakshmi Deepika | Ajay Vikram Singh
Source: Medical and Pharmaceutical Journal (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Safety Prediction for Pre 1988 Permitted Fixed Dose Combinations Using Artificial Intelligence - In Silico Approach Background: Since the Drug Controller General of India (DCGI) approved more than 130 FDCs, the usage of fixed dose combinations (FDCs) has become more popular. FDCs are medications that contain more than one active component, aiming to enhance adherence, reduce adverse effects, and improve effectiveness. In a recent development, the Central Drug Standard Control Organisation (CDSCO) issued a notice on January 11, 2024, calling for the evaluation of pre-1988 permitted FDCs. Objective: This study aimed to determine whether AI algorithms could predict the safety of FDCs by examining their pharmacokinetic properties. Methods: In order to assess pharmacokinetic properties and verify the safety of pre-1988 permitted fixed dose combinations that were approved by The Central Drug Standard Control Organisation, India (CDSCO), this study used a range of AI tools, including ADMET.AI, SwissADME, vNN-ADMET, and AERSMine. Results: The results made by these tools were giving dissimilar properties in the same drug combinations. So, these tools are presently needs to be developed to have more accurate and precise results. Conclusion: This research contributes to the ongoing discourse surrounding FDCs, pharmaceutical innovation, and regulatory practices, providing valuable insights that can shape future approaches in drug development and patient care with the help of artificial intelligence and data-based analysis tools. Additionally, this work helps to strengthen the credibility of the current A.I. development for in-silico pharmacokinetic predictions. In silico ; Artificial intelligence ; Computer science ; Machine learning ; Biology Graphite, nuclear technology, radiation studies ; Radiation Effects in Electronics
Authors: Ramiro Sánchez | Fernando Díaz | Lina Viviana Melo Niño
Source: Chemosensors (article, 2025-10-21)
Field: Chemistry — Organic Chemistry

Portable MOS Electronic Nose Screening of Virgin Olive Oils with HS-SPME-GC–MS Corroboration: Classification and Estimation of Sunflower-Oil Adulteration Extra virgin olive oil (EVOO) can degrade during production or storage to virgin olive oil (VOO) or lampante olive oil (LOO). Fraud can also occur during commercialisation through the adulteration of EVOO (Ad-EVOO) with cheaper sunflower oil (SFO). Therefore, rapid screening techniques for quality control are needed. We evaluated an electronic nose (EN) with chemometrics—linear discriminant analysis (LDA), artificial neural-network discriminant analysis (ANN-DA), and partial least-squares regression (PLS)—in two scenarios: (i) classification into four classes (EVOO, VOO, LOO, and Ad-EVOO adulterated with 25% w/w SFO); and (ii) Ad-EVOO series containing 5–40% w/w SFO. Classes were corroborated by HS-SPME-GC-MS, with elevated (E)-2-hexenal and 3-hexen-1-ol in EVOO and increases in nonanal, ethyl acetate, and 2-propanol in deteriorated oils. Using the EN, LDA separated the classes, and ANN-DA achieved 90% accuracy under cross-validation, with the greatest confusion between VOO and LOO. In adulteration, discrimination emerged from 20% SFO, and PLS estimated %Ad-EVOO with R2pred = 0.972 (RMSEC/RMSEP = 8.059/5.627). In conclusion, the EN provides objective, rapid, and non-destructive screening that supports sensory panels and chromatographic analyses during reception and storage in industrial settings. Electronic nose ; Olive oil ; Linear discriminant analysis ; Sunflower oil ; Edible oil ; Confusion ; Pattern recognition (psychology) Edible Oils Quality and Analysis ; Advanced Chemical Sensor Technologies ; Spectroscopy and Chemometric Analyses
Authors: Zhi Hong Zhu | Yikun Fang | Wei Wu | Bo Zhao
Source: Alloys (article, 2025-10-21)
Field: Materials Science — General Materials Science

Effect of the Cooling Rate on the Solidification Structure and Phase of a 2:17 Samarium–Cobalt Alloy Understanding the way samarium–cobalt alloys solidify at varying cooling rates and the regularities in alloying element distribution is crucial for optimizing subsequent homogenization and annealing processes, leading to an enhancement in the overall quality of the product. The study investigates the effects of rapid water-cooled copper mold (600 °C/min), medium-speed copper mold (100 °C/min), and slow furnace cooling (10 °C/min) on the microstructural evolution, element distribution, and phase transformation of samarium–cobalt (Sm-Co) alloys. The results of the research show that the phase transition temperatures obtained via differential scanning calorimetry (DSC) closely matched those observed in situ by high-temperature laser scanning confocal microscopy (HT-LSCM). Higher cooling rates resulted in notable dendritic refinement and reduced precipitate size. Elemental analysis revealed that Co and Fe exhibited negative segregation, whereas Sm, Cu, and Zr showed positive segregation, with segregation intensity increasing alongside the cooling rate. The principal phases identified included Cu-rich and Zr-rich constituents, the matrix phase, and a gray phase morphologically distinct from the matrix. These correspond to the (Sm, Co, Fe, Cu, Zr)5 phase, (Sm, Zr)(Co, Fe, Cu)3 phase, Sm2(Co, Fe, Cu, Zr)17 phase, and Sm(Co, Fe, Cu, Zr)7 phase. The phase constitution remained consistent across different cooling rates. Differential scanning calorimetry ; Homogenization (climate) ; Alloy ; Copper ; Mold ; Confocal laser scanning microscopy ; Annealing (glass) ; Phase (matter) ; Phase transition Material Properties and Applications ; Metallurgical and Alloy Processes ; Thermodynamic and Structural Properties of Metals and Alloys
Authors: Bernard Piette
Source: Symmetry (article, 2025-06-12)
Field: Materials Science — Biomaterials

Bi-Symmetric Polyhedral Cages with Nearly Maximally Connected Faces and Small Holes Polyhedral cages (p-cages) provide a good description of the geometry of some families of artificial protein cages. In this paper we identify p-cages made out of two families of equivalent polygonal faces/protein rings, where each face has at least four neighbours and where the holes are contributed by at most four faces. We start the construction from a planar graph made out of two families of equivalent nodes. We construct the dual of the solid corresponding to that graph, and we tile its faces with regular or nearly regular polygons. We define an energy function describing the amount of irregularity of the p-cages, which we then minimise using a simulated annealing algorithm. We analyse over 600,000 possible geometries but restrict ourselves to p-cages made out of faces with deformations not exceeding 10%. We then present graphically some of the most promising geometries for protein nanocages. Combinatorics ; Physics ; Geometry ; Topology (electrical circuits) ; Mathematics ; Computer science Supramolecular Self-Assembly in Materials ; Advanced Materials and Mechanics ; Microtubule and mitosis dynamics
Authors: Xiaoqing Song | Haodong Yao | Fei Zhan | Shuguang Chen | Lirong Zheng | Chenyan Ma | Lei Zheng | Haifeng Zhao
Source: Journal of Synchrotron Radiation (article, 2025-07-24)
Field: Materials Science — Materials Chemistry

XASDB: a new database of experimental interactive X-ray absorption spectra We present a dedicated and comprehensive database for experimental X-ray absorption spectroscopy (XAS) data, an integrated and user-friendly platform that combines spectrum visualization, raw data processing, spectrum matching and downloading, thereby offering a holistic solution for understanding XAS data. Leveraging the unique nature of XAS data, we have designed the MySQL table structure and developed professional plotting tools to present the data. Furthermore, the database - named XASDB - incorporates a variety of data processing tools, including data normalization as well as flexible searching and downloading options. A toolkit called XASMatch is integrated in the platform, enabling users to identify the most similar spectra within the database to their input spectra and rank with scores. An API interface is also provided to facilitate sharing data with other XAS databases and users. A total of 152 spectra measured at Beijing Synchrotron Radiation Facility for metals, oxides and minerals standards are included in the database. Applications integrated with artificial intelligence are advancing, since more high solution spectra with new standards will be available after operation of a high energy photon source to be commissioned at the end of the year. Upload ; Database ; Computer science ; X-ray absorption spectroscopy ; Raw data ; Spectral line ; Synchrotron radiation ; Data processing ; Absorption spectroscopy ; World Wide Web ; Physics ; Optics Machine Learning in Materials Science ; Advanced X-ray and CT Imaging ; X-ray Spectroscopy and Fluorescence Analysis
Authors: Quancheng Liu | Jun Zhou | Zhilin Wu | Dandan Ma | Yuxuan Ma | Shuxiang Fan | Yan Lei
Source: Foods (article, 2025-07-18)
Field: Chemistry — Analytical Chemistry

Classification Prediction of Jujube Variety Based on Hyperspectral Imaging: A Comparative Study of Intelligent Optimization Algorithms Accurate classification of jujube varieties is essential for ensuring their quality and medicinal value. Traditional methods, relying on manual detection, are inefficient and fail to meet the demands of modern production and quality control. This study integrates hyperspectral imaging with intelligent optimization algorithms-Zebra Optimization Algorithm (ZOA), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Grey Wolf Optimization (GWO)-and a Support Vector Machine (SVM) model to classify jujube varieties. First, the Isolation Forest (IF) algorithm was employed to remove outliers from the spectral data. The data were then processed using Baseline correction, Multiplicative Scatter Correction (MSC), and Savitzky-Golay first derivative (SG1st) spectral preprocessing techniques, followed by feature enhancement with the Competitive Adaptive Reweighted Sampling (CARS) algorithm. A comparative analysis of the optimization algorithms in the SVM model revealed that SG1st preprocessing significantly boosted classification accuracy. Among the algorithms, GWO demonstrated the best global search ability and generalization performance, effectively enhancing classification accuracy. The GWO-SVM-SG1st model achieved the highest classification accuracy, with 94.641% on the prediction sets. This study showcases the potential of combining hyperspectral imaging with intelligent optimization algorithms, offering an effective solution for jujube variety classification. Hyperspectral imaging ; Support vector machine ; Artificial intelligence ; Preprocessor ; Particle swarm optimization ; Computer science ; Algorithm ; Data pre-processing ; Genetic algorithm ; Pattern recognition (psychology) ; Statistical classification ; Machine learning Spectroscopy and Chemometric Analyses ; Remote Sensing and Land Use ; Ziziphus Jujuba Studies and Applications
Authors: Bin Zeng | Xuefei Wei | Jun Tan | Ke‐Shi Zhang
Source: Materials (article, 2025-08-21)
Field: Materials Science — Metals and Alloys

Low Cycle Fatigue Life Prediction for Hydrogen-Charged HRB400 Steel Based on CPFEM Addressing the limitations of traditional fatigue life prediction methods, which rely on extensive experimental data and incur high costs, and given the current absence of studies that employ deformation inhomogeneity parameters to construct fatigue-indicator parameter (FIP) for predicting low-cycle fatigue (LCF) life of metals in hydrogen environments, this study firstly explores how hydrogen pre-charging influences the LCF behavior of hot-rolled ribbed bar grade 400 (HRB400) steel via experimental and crystal plasticity simulation, and focus on the relationship between the fatigue life and the evolution of microscale deformation inhomogeneity. The experimental results indicate that hydrogen charging causes alterations in cyclic hysteresis, an expansion of the elastic range of the stabilized hysteresis loop, and a significant reduction in LCF life. Secondly, a novel FIP was developed within the crystal plasticity finite element method (CPFEM) framework to predict the LCF life of HRB400 steel under hydrogen influence. This FIP incorporates three internal variables: hydrogen embrittlement index, axial strain variation coefficient, and macroscopic stress ratio. These variables collectively account for the hydrogen charging effects and stress peak impacts on the microscale deformation inhomogeneity. The LCF life of hydrogen-charged HRB400 steel can be predicted using this new FIP. We performed fatigue testing under only one loading condition to measure the corresponding fatigue life and determine the FIP critical value. This helped predict fatigue life under different cyclic loading conditions for the same hydrogen-charged material. We compared the experimental data to validate the novel FIP to accurately predict the LCF life of hydrogen-charged HRB400 steel. The error between the predicted results and the measured results is limited to a factor of two. Materials science ; Hydrogen embrittlement ; Microscale chemistry ; Hydrogen ; Service life ; Hysteresis ; Plasticity ; Structural engineering ; Deformation (meteorology) ; Composite material ; Psychology ; Corrosion ; Engineering ; Condensed matter physics Hydrogen embrittlement and corrosion behaviors in metals ; Fatigue and fracture mechanics ; Non-Destructive Testing Techniques