jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: M. Kabilan | M. Jayakumar | K. Sundharaiya | G. Sathish | Adnan A. Khan
Source: Journal of Phytonanotechnology and Pharmaceutical Sciences (article, 2025-12-01)
Field: Materials Science — Materials Chemistry

Nanotechnology as a tool to enhance the pharmacological potential of phytochemicals derived from horticultural crops Horticultural crops, encompassing fruits, vegetables, spices, plantation and ornamental plants, are abundant sources of bioactive phytochemicals such as phenolics, flavonoids, alkaloids, terpenoids, carotenoids, saponins, glycosides and sulfur-containing compounds.These phytochemicals exhibit diverse pharmacological properties, including antioxidant, anti-inflammatory, antimicrobial, antiviral, anticancer, antidiabetic, cardioprotective and neuroprotective activities and are strongly associated with reduced risk of chronic and lifestyle-related diseases.Despite their promising therapeutic potential, the practical application of horticultural crop phytochemicals is limited by several challenges, including poor aqueous solubility, low gastrointestinal absorption, rapid metabolism and clearance, chemical instability, dose-related toxicity and limited target specificity.Nanotechnology has emerged as an effective strategy to overcome these limitations and enhance phytochemical pharmacology.Nanoencapsulation and nano-delivery systems improve solubility, stability, bioavailability and pharmacokinetic behavior of phytochemicals, while enabling controlled, sustained and targeted delivery.A wide range of nanocarriers, including lipid-based and polymeric nanoparticles, metallic and metal oxide nanoparticles, nanoemulsions, nanogels, solid lipid nanoparticles and nanostructured lipid carriers, have demonstrated improved therapeutic efficacy and safety profiles for phytochemical-based formulations.The development of green and phytoengineered nanotechnology further strengthens this approach by utilizing horticultural crop extracts for eco-friendly nanoparticle synthesis.Plant-mediated fabrication reduces the use of toxic chemicals and energy-intensive processes while enhancing biocompatibility and therapeutic synergy through phytochemical-mediated reduction and stabilization.At the cellular and molecular levels, nano-phytochemical systems exhibit enhanced cellular uptake, efficient intracellular trafficking, improved interaction with biomolecules and receptors and modulation of key signaling pathways and gene expression, leading to superior pharmacological outcomes at lower doses.Overall, the integration of nanotechnology with horticultural crop phytochemicals offers a transformative pathway for developing sustainable, effective and evidence-based plant-derived therapeutics, with expanding applications in pharmaceutical, nutraceutical, functional food and cosmeceutical industries. Applications of nanotechnology ; Agrochemical ; Agriculture ; Quality (philosophy) Nanoparticles: synthesis and applications ; Phytochemicals and Antioxidant Activities ; Curcumin's Biomedical Applications
Authors: Fatouma Mohamed Abdoul‐Latif | Ibrahim Houmed Aboubaker | Houda Mohamed | Ayoub Ainane | Mouhcine Chakrouni | Ali Merito Ali | Pannaga Pavan Jutur | Tarik Ainane
Source: Molecules (article, 2025-08-11)
Field: Chemistry — Organic Chemistry

Evaluation of the Anticancer Properties of Lamellar Alkaloid Drivatives Extracted from the Tunicate Didemnum abradatum (Moucha Island Sea, Djibouti): Pharmacological and Computational Approach This study aimed to evaluate the anticancer activity of lamellar alkaloid derivatives extracted from the tunicate <i>Didemnum abradatum</i> from Moucha Island (Djibouti), focusing on their antiviability against human cell lines and using biocomputational analyses via the Integrated Biomolecular Profiling and Mechanism Evaluation (IBProME) method to understand their mechanisms of action. Two alkaloids were isolated, lamellarin D and lamellarin T, whose structures were confirmed by state-of-the-art analytical techniques. Cell viability tests were performed on PC3, A549 and JIMT-T1 cell lines, and IBProME analyses were used to predict their interactions with p53 protein and evaluate their toxicological and pharmacokinetic profiles. The results showed that lamellarin D was particularly effective against prostate and lung cancer cells, with respective IC<sub>50</sub> values of 5.25 µg/mL and 8.64 µg/mL, close to those of doxorubicin. In contrast, lamellarin T showed less marked activity but remains promising. Computational analyses via IBProME highlighted differences in chemical reactivity between the two compounds, with lamellarin D being more reactive. Toxicity tests revealed that lamellarin D exhibited lower acute toxicity than lamellarin T. In terms of pharmacokinetic properties, both molecules showed low absorption and moderate bioavailability, although lamellarin T displayed more marked lipophilicity. These results suggest that lamellars, particularly lamellarin D, have therapeutic potential for the treatment of certain types of cancer. Alkaloid ; Tunicate ; Biology ; Stereochemistry ; Chemistry Synthesis and Characterization of Pyrroles ; Computational Drug Discovery Methods ; Marine Sponges and Natural Products
Authors: Blanche Krieguer | Fabien Girard | Samuel Dorey | Nathalie Dupuy | Sylvain R. A. Marque
Source: European Journal of Pharmaceutical Sciences (article, 2025-12-02)
Field: Materials Science — Polymers and Plastics

Comparative analysis of irradiation technologies on high-density polyethylene (HDPE) for biopharmaceutical applications The impacts of e-beam and X-ray irradiation on medical-grade high-density polyethylene (HDPE) are compared with that of gamma irradiation to evaluate their potential effects on pharmaceutical applications. An extensive suite of analytical techniques was employed to evaluate the chemical and physical transformations of irradiated HDPE. Key findings indicated that irradiation did not significantly alter the mechanical properties, as tensile strength and elongation at break remained stable across all irradiation types and doses. Thermal analysis via Differential Scanning Calorimetry (DSC) revealed a slight decrease in melting temperature at higher doses, with an equivalent melting temperature peak observed for all three irradiation technologies. Electron Spin Resonance (ESR) detected alkyl and allyl radicals, which decreased over time, showing no significant differences attributable to irradiation type or dose. Colorimetric analysis indicated yellowing in the samples, linked to specific additives. The quantification of methionine oxidized byproducts by High-Performance Liquid Chromatography (HPLC) demonstrated that the oxidation potential was equivalent for the three irradiation technologies for HDPE. The study concluded that there was a lack of significant impact of irradiation technologies on several physical, chemical and mechanical properties of HDPE. Irradiation ; Differential scanning calorimetry ; Polyethylene ; Alkyl ; Ultimate tensile strength ; Electron paramagnetic resonance ; Thermal analysis Polymer crystallization and properties ; Polymer Nanocomposite Synthesis and Irradiation ; biodegradable polymer synthesis and properties
Authors: Lorenzo Manunza | Riccardo Dettori | Antonio Cappai | Claudio Melis
Source: Preprints.org (preprint, 2025-04-17)
Field: Materials Science — Materials Chemistry

Investigating Thermal Conductivity in Graphene Moiré Superlattices Through Approach-to-Equilibrium Molecular Dynamics We investigate the thermal conductivity of graphene Moiré superlattices formed by twisting bilayer graphene (TBG) at small angles, employing approach-to-equilibrium molecular dynamics and lattice dynamics calculations based on the Boltzmann Transport Equation. Our simulations reveal a non-monotonic dependence of the thermal conductivity on the twisting angle, with a local minimum near the first magic angle (θ∼1.1∘). This behavior is attributed to the evolution of local stacking configurations—AA, AB, and saddle-point (SP)—across the Moiré superlattice, which strongly affect phonon transport. A detailed analysis of phonon mean free paths and mode-resolved thermal conductivity shows that AA stacking suppresses thermal transport while, AB and SP stackings exhibit enhanced conductivity owing to more efficient low-frequency phonon transport. Furthermore, we establish a direct correlation between the thermal conductivity of twisted structures and the relative abundance of stacking domains within the Moiré supercell. Our results demonstrate that even very small changes in twisting angle (&amp;amp;lt;2∘) can lead to thermal conductivity variations of over 30%, emphasizing the high tunability of thermal transport in TBG. Graphene ; Superlattice ; Thermal conductivity ; Molecular dynamics ; Moiré pattern ; Condensed matter physics ; Materials science ; Dynamics (music) ; Statistical physics ; Chemical physics ; Nanotechnology ; Physics ; Quantum mechanics ; Optics ; Composite material Thermal properties of materials ; Graphene research and applications ; Advanced Thermodynamics and Statistical Mechanics
Authors: Sandhya R. Jalgar | Anand M. Hunashyal | UmaM Prabhu | B M Gurumurthy | Pavan Hiremath | Nithesh Naik
Source: Journal of Composites Science (article, 2025-04-24)
Field: Materials Science — Polymers and Plastics

Dynamic Mechanical Analysis and Optimization of Vibration Damping in Epoxy-Based Nano Cement Composite Dampers for Sustainable Structures Traditional cement-based materials often fall short in delivering both high mechanical strength and effective vibration damping. Although nano-modified composites have shown promise, a gap remains in understanding the interaction between nanofillers and polymeric phases in epoxy-based cement systems. This study investigates the development of epoxy-based cement composite dampers with enhanced mechanical strength and vibration damping for structural applications. The composite integrates nano-SiO2 and graphene to improve the energy dissipation, structural integrity, and long-term performance. A comprehensive experimental and mathematical modeling approach was employed to evaluate the storage modulus, loss modulus, and damping factor (tan δ) using Dynamic Mechanical Analysis (DMA). The results indicated that incorporating 2.0 wt.% nano-SiO2 and 0.05 wt.% graphene leads to an optimum increase in both mechanical and damping properties, achieving a 92% enhancement in compressive strength and a 38% improvement in damping factor compared to conventional cement composites. Beyond this optimal composition, agglomeration effects reduce the reinforcement efficiency. Microstructural investigations using TEM and EDX confirmed the homogeneous dispersion of the nanofillers, leading to enhanced matrix densification and improved interfacial bonding. A validated mathematical model was proposed to predict viscoelastic behavior, correlating well with experimental findings. These results highlight the potential of epoxy-based cement composites for high-performance damping applications in sustainable infrastructures. Epoxy ; Damper ; Vibration ; Materials science ; Nano- ; Cement ; Structural engineering ; Composite material ; Composite number ; Engineering ; Acoustics ; Physics Natural Fiber Reinforced Composites ; Innovative concrete reinforcement materials ; Polymer Nanocomposites and Properties
Authors: Krisna Dwipa Muhdi | Ahmad Atif Fikri
Source: Journal of Mechanical Engineering Science and Innovation (article, 2025-05-01)
Field: Chemistry — Inorganic Chemistry

Quantum Mechanics Approach for Metal-Organic Frameworks Deformation Effect on Carbon Capture Performance: A Density Functional Theory Study Increasing carbon dioxide (CO₂) emissions from fossil fuel combustion demand the development of effective and efficient carbon capture technologies. Metal-Organic Frameworks (MOFs) are excellent candidates as adsorbent materials because they have uniform pores, specific surface area, and can modified according to purpose. However, performance of MOFs may decrease due to structural deformation during adsorption-desorption process, especially under extreme conditions. This study uses a quantum mechanical approach, namely Density Functional Theory (DFT), to analyze effect of deformation, specifically hMOF-13, on its performance in CO₂ adsorption. Through modeling the atomic structure of hMOF-13, an understanding of the quantum interactions between atoms, changes in position of atoms and cells due to deformation is obtained. Simulation results show that mechanical deformation of hMOF-13 decreases CO₂ adsorption performance through pore narrowing and electrostatic charge redistribution. In addition, excessive deformation can trigger structural failures that reduce regeneration cycles and lower carbon capture efficiency. Insights from this study can guide the subsequent development of MOFs with enhanced mechanical resistance, contributing to the optimization of industrial-scale carbon capture processes. By improving the structural stability of MOFs, industries can achieve higher adsorption efficiency, longer material life, and reduced operational costs, making carbon capture technology more feasible and sustainable. Density functional theory ; Deformation (meteorology) ; Adsorption ; Carbon fibers ; Fossil fuel ; Structural stability ; Quantum ; Structural material Metal-Organic Frameworks: Synthesis and Applications ; Carbon Dioxide Capture Technologies ; Covalent Organic Framework Applications
Authors: Tedi Sumardi | Iqbal Robiyana | Rozeff Permana | M. Agung Suhendra
Source: Jurnal Penelitian Pendidikan IPA (article, 2025-11-25)
Field: Chemistry — Analytical Chemistry

Thermal Image-Based Classification of Okra Maturity: A Comparative Study of CNN, SVM, and LSTM Post-harvest quality assessment remains a major challenge in agriculture, particularly for okra (Abelmoschus esculentus), which deteriorates rapidly due to high moisture content. Traditional grading based on manual inspection often results in inconsistency and product damage. This study explores thermal imaging as a non-destructive alternative for okra maturity classification. A dataset of 501 thermal images was acquired under controlled conditions and analyzed using three machine learning models: Convolutional Neural Network (CNN), Support Vector Machine (SVM) with Histogram of Oriented Gradients (HOG) features, and Long Short-Term Memory (LSTM) network. Experimental results show that CNN achieved the highest accuracy (99.01%), outperforming SVM (95.05%) and LSTM (91.09%). Confusion matrix and ROC analyses confirmed CNN’s superiority in capturing spatial thermal patterns related to maturity stages. Compared with RGB or hyperspectral imaging reported in prior studies, thermal imaging integrated with AI provides a more robust, illumination-independent, and non-destructive solution. The findings demonstrate the potential of CNN-based thermal imaging systems for automated sorting of okra in agricultural supply chains. Future work will focus on larger datasets, multi-class maturity levels, and real-time implementation to enhance practical deployment in post-harvest management. Support vector machine ; Convolutional neural network ; Hyperspectral imaging ; Pattern recognition (psychology) ; RGB color model ; Confusion matrix ; Histogram ; Artificial neural network Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Food Drying and Modeling
Authors: Edwin K Sijabat | Samsul Arifin
Source: International Journal on Computational Engineering (article, 2025-09-27)
Field: Materials Science — Biomaterials

A Simple Bibliometric Insight into Nanocellulose Publications and Research Focus Nanocellulose has garnered increasing attention due to its renewable nature, biocompatibility, and broad range of potential applications, particularly in materials science. This study presents a lightweight bibliometric and text mining analysis to explore recent publication trends, research focuses, and keyword patterns in nanocellulose-related studies. Using metadata retrieved from the Scopus database for the years 2021 to 2025, we compiled a dataset of 2,971 documents that mention "Cellulose," "Nanocellulose," or "Nano-cellulose" and are indexed under the subject category of Materials Science. Our methodology involved combining bibliometric visualization using VOSviewer with text analysis tools such as Python-based Latent Dirichlet Allocation (LDA), WordCloud, and matplotlib. The analysis revealed major contributing countries, institutions, journals, and authors. Materials Science and Chemistry were identified as the dominant subject areas, with China emerging as the leading contributor in terms of both publications and funding. Keyword mapping uncovered frequent terms such as "composites," "films," "biodegradability," and "hydrogels," indicating major research themes. In addition, co-citation analysis revealed distinct clusters that connect application-based studies with core nanocellulose material research. The integration of Gen-AI, including concensus.app, further supported the synthesis of emerging patterns and hotspots. This study offers a compact, accessible overview for researchers and stakeholders interested in the evolving landscape of nanocellulose research, helping to inform strategic planning, academic collaboration, and future investigations in the field of sustainable nanomaterials. Nanocellulose ; Latent Dirichlet allocation ; Scopus ; Metadata ; Bibliometrics ; Subject (documents) ; Topic model ; Visualization ; Citation analysis Advanced Cellulose Research Studies ; Electrospun Nanofibers in Biomedical Applications ; Biosensors and Analytical Detection
Authors: M. G. Isaenkova | O. S. Krymskaya | V. P. Tyutin | Olga Romanovna Boldyreva | Vladimir Aleksandrovich Fesenko
Source: Materials Technologies Design (article, 2025-12-19)
Field: Materials Science — Materials Chemistry

Modeling of hot rolling texture in CM-2A molybdenum alloy using a cellular automaton A combined model of plastic deformation and recrystallization was developed and verified to predict the evolution of crystallographic texture during annealing and hot rolling of CM-2A molybdenum alloy. The model takes into account the activation of the {110}&lt;111&gt;, {112}&lt;111&gt;, and {123}&lt;111&gt; slip systems, strain accumulation, and the nucleation and growth of nuclei misoriented relative to the deformed matrix. The nucleation and growth parameters were calibrated using literature data on static recrystallization and critical degrees of deformation of pure molybdenum and then verified using experimental kinetic recrystallization curves. Various hot rolling schemes were simulated, which made it possible to reproduce the main patterns of γ-fiber formation, {100}&lt;110&gt; and {111}&lt;110&gt; texture components, and the effect of temperature and degree of deformation on the proportion of the recrystallized component. The obtained results demonstrate the applicability of the developed model for analyzing texture formation during deformation and dynamic recrystallization, which allows its use in optimizing process routes for the manufacture of isotropic molybdenum sheets. Nucleation ; Recrystallization (geology) ; Molybdenum ; Annealing (glass) ; Alloy ; Slip (aerodynamics) ; Isotropy ; Dynamic recrystallization Microstructure and mechanical properties ; Metallurgy and Material Forming ; Metal Forming Simulation Techniques
Authors: Rong Yang | Zhe Zhang | Bin Tang
Source: RSC Advances (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Density functional study of PuC and PuC <sub>0.75</sub> O <sub>0.25</sub> We study the structural, magnetic, electronic, thermodynamic and elastic properties of PuC and PuC<sub>0.75</sub>O<sub>0.25</sub> using density-functional theory (DFT) and DFT + <i>U</i>. The nonmagnetic (NM), ferromagnetic (FM) and antiferromagnetic (AFM) configurations are considered in this work. Total energy results obtained with DFT + <i>U</i> indicate that PuC<sub>0.75</sub>O<sub>0.25</sub> has an AFM ground state, matching the AFM nature of stoichiometric PuC. Calculated electronic properties reveal a new density of states peak in PuC<sub>0.75</sub>O<sub>0.25</sub>, a consequence of C/O substitution in PuC. Thermodynamically, PuC<sub>0.75</sub>O<sub>0.25</sub> exhibits higher enthalpy difference (<i>H</i> <sub><i>T</i></sub> -<i>H</i> <sub>298</sub>), entropy difference (<i>S</i> <sub><i>T</i></sub> -<i>S</i> <sub>298</sub>) and heat capacity (<i>C</i> <sub>v</sub> and <i>C</i> <sub>p</sub>) than PuC at the same temperature; elastically, it is predicted to be harder, owing to the stronger ionic character of Pu-O <i>versus</i> Pu-C bonds. Crucially, the formation energy of the oxygen-substitution defect is calculated to be highly spontaneous (-5.11 eV), revealing the fundamental driving force for the oxidation and chemical aging of PuC. These results are intended to provide a valuable reference for further theoretical and experimental investigations of PuC and PuC<sub>0.75</sub>O<sub>0.25</sub>. Antiferromagnetism ; Density functional theory ; Stoichiometry ; Ferromagnetism ; Character (mathematics) ; Enthalpy ; Entropy (arrow of time) ; Ionic bonding Nuclear Materials and Properties ; Rare-earth and actinide compounds ; Boron and Carbon Nanomaterials Research
Authors: Eric Verret | Sophie Duquesne | Anthony Collin | Martin Stievenard
Source: ChemRxiv (preprint, 2025-04-08)
Field: Materials Science — Polymers and Plastics

Enhancing Flame Retardancy in Polypropylene Composites: A Bayesian Optimization Approach This study presents a novel application of Multi-Objective Bayesian Optimization (MOBO) to enhance the formulation of flame-retardant polypropylene (PP) composites. Our goal was to optimize the chemical composition of intumescent polypropylene (PP) formulations by maximizing the Limiting Oxygen Index (LOI) and minimizing filler content. Two different initialization procedures were studied: one using a Space Filling Design and the other based on literature-based formulations. The optimization model is based on a Gaussian Process (GP), employing the parallel acquisition function qNEHVI for suggesting experiments. Our research successfully navigates the complexities of material property optimization by proposing an optimal formulation within a constrained evaluation framework. This involved five iterations of three parallel evaluations, totaling a budget of 20 points. This work underscores MOBO’s potential as a transformative tool in advanced materials science, particularly for developing high-performance flame-retardant materials. Polypropylene ; Composite material ; Materials science ; Bayesian optimization ; Bayesian probability ; Computer science ; Mathematical optimization ; Mathematics ; Artificial intelligence Flame retardant materials and properties ; Environmental and Industrial Safety ; Polymer Science and PVC