jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Velid Ünsal | Reşit Yıldız | Aziz Korkmaz | Başak Doğru Mert | Cemile Günbegi Çalışkan | Erkan Öner
Source: BMC Chemistry (article, 2025-01-03)
Field: Chemistry — Organic Chemistry

Evaluation of extra virgin olive oil compounds using computational methods: in vitro, ADMET, DFT, molecular docking and human gene network analysis study This study investigates the phenolic compounds (PC), volatile compounds (VC), and fatty acids (FA) of extra virgin olive oil (EVOO) derived from the Turkish olive variety "Sarı Ulak", along with ADMET, DFT, molecular docking, and gene network analyses of significant molecules identified within the EVOO. Chromatographic methods (GC-FID, HPLC) were employed to characterize FA, PC, and VC profiles, while quality parameters, antioxidant activities (TAC, ABTS, DPPH) were assessed via spectrophotometry. The analysis revealed a complex composition of 40 volatile compounds, with estragole, 7-hydroxyheptene-1, and 3-methoxycinnamaldehyde as the primary components. Hydroxytyrosol, tyrosol, oleuropein, apigenin, ferulic acid, and vanillic acid emerged as main phenolic constituents, with hydroxytyrosol and apigenin exhibiting high bioavailability. Molecular docking highlighted oleuropein and pinoresinol as compounds with strong binding affinities, though only hydroxytyrosol, apigenin, and pinoresinol fully met Lipinski and other drug-likeness criteria. DFT analysis showed that oleuropein and pinoresinol have notable dipole moments, reflecting polar and asymmetrical structures. KEGG enrichment analysis further linked key molecules like oleuropein and apigenin with pathways related to lipid metabolism and atherosclerosis, underscoring their potential bioactivity and relevance in health-related applications. Hydroxytyrosol ; Oleuropein ; Pinoresinol ; Chemistry ; Tyrosol ; Ferulic acid ; ADME ; Vanillic acid ; Estragole ; Apigenin ; Lignan ; Biochemistry ; Chromatography ; Organic chemistry ; Antioxidant ; Polyphenol ; Essential oil ; Flavonoid ; In vitro Edible Oils Quality and Analysis ; Free Radicals and Antioxidants ; Cholesterol and Lipid Metabolism
Authors: Reyhaneh Fatahian | Rasool Erfani
Source: Scientific Reports (article, 2025-04-15)
Field: Materials Science — Biomaterials

Surrogate modeling of electrospun PVA/PLA nanofibers using artificial neural network for biomedical applications Blending poly (lactic acid) (PLA) with poly (vinyl alcohol) (PVA) improves the strength and hydrophilicity of nanofibers, making them suitable for biomedical applications like wound dressings. This study explores how electrospinning parameters-applied voltage, flow rate, and needle-to-collector distance-affect PVA/PLA nanofiber properties, optimizing them using a Taguchi design of experiment (DoE) approach to enhance their mechanical and surface properties for clinical use. Given the high costs and time associated with conducting extensive experimental tests, an artificial neural network based surrogate model is developed to predict experimental outcomes more efficiently, facilitating faster identification of optimal design configurations. Analysis of Variance reveals flow rate as the most significant determinant of fiber diameter. The optimal electrospinning configuration yields nanofibers with an average diameter of 127.6 ± 19.8 nm. These fibers exhibit exceptional tensile strength, flexibility, and a water contact angle of 37°, demonstrating superior hydrophilicity conducive to cell adhesion and proliferation-key factors in promoting wound healing. Comparative analyses confirm that the optimized scaffold (18 cm needle-to-collector distance, 0.6 ml/h flow rate, and 18 kV applied voltage) significantly outperforms alternative configurations, such as 10 cm needle-to-collector distance, 1.2 ml/h flow rate, and 22 kV applied voltage, which display larger diameters, reduced hydrophilicity (contact angle of 72°), and diminished suitability for medical use. Validation experiments affirm the accuracy and reproducibility of the Taguchi optimization, substantiating the methodological rigor and reliability of the findings. This work contributes novel insights into the tunable design of electrospun nanofibers, providing a pathway to developing advanced wound dressings that facilitate tissue integration and accelerate healing. The optimized PVA/PLA nanofibers have the potential to revolutionize wound care by offering a cost-effective and clinically viable solution for enhancing patient recovery, reducing treatment durations, and improving global healthcare outcomes. Nanofiber ; Taguchi methods ; Materials science ; Electrospinning ; Vinyl alcohol ; Contact angle ; Volumetric flow rate ; Ultimate tensile strength ; Biomedical engineering ; Composite material ; Design of experiments ; Fiber ; Nanotechnology ; Polymer ; Mathematics Electrospun Nanofibers in Biomedical Applications ; Tissue Engineering and Regenerative Medicine ; Advanced Sensor and Energy Harvesting Materials
Authors: Than V. Tran | S.S. Nanthakumar | Xiaoying Zhuang
Source: npj Artificial Intelligence (article, 2025-05-14)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Deep learning-based framework for the on-demand inverse design of metamaterials with arbitrary target band gap This study presents a deep learning framework for the forward prediction of band structures and the inverse design of metamaterials. For forward prediction, a convolutional neural network is trained to estimate the bandgap width and mid-frequency of the bandgap of metamaterials. For inverse design, a conditional variational autoencoder (cVAE) network is employed, where band structure features (bandgap width and mid-frequency of the bandgap) serve as inputs, and the network predicts unit cell topologies. Once trained, the cVAE decoder enables the rapid design of metamaterials with user-defined bandgap properties, requiring no prior knowledge. Results show that this approach accurately predicts bandgap characteristics for topologies and efficiently generates designs tailored to target properties. The method accelerates metamaterial design and optimization, offering scalability to asymmetric 2D and 3D structures, and holds promise for driving innovation in metamaterial research. Metamaterial ; Inverse ; Computer science ; Physics ; Optics ; Mathematics ; Geometry Metamaterials and Metasurfaces Applications ; Acoustic Wave Phenomena Research ; Antenna Design and Optimization
Authors: Jakub Kubiak | Piotr Szyk | Beata Czarczyńska-Goślińska | Tomasz Gośliński
Source: Molecules (review, 2025-05-30)
Field: Chemistry — Organic Chemistry

Flavonoids, Chalcones, and Their Fluorinated Derivatives—Recent Advances in Synthesis and Potential Medical Applications Flavonoids and chalcones, widely recognised for their diverse biological activities, have garnered attention due to their potential therapeutic applications. This review discusses fluorinated flavonoids and chalcones, focusing on their prospective anti-inflammatory, antidiabetic, anticancer, antiosteoporotic, cardioprotective, neuroprotective, hepatoprotective, antimicrobial, and antiparasitic applications. The enhanced biological activities of fluorinated derivatives, particularly the antibacterial, antiviral, and anticancer properties, are attributed to the introduction of fluorine groups, which increase lipophilicity and metabolic stability. Key findings indicate that fluorinated flavonoids and chalcones exhibit synergistic effects with antibiotics, inhibit bacterial efflux pumps, and reveal potent antiviral and anticancer properties. However, challenges such as cytotoxicity and structural optimisation have to be addressed. The synthesis of fluorinated flavonoids and chalcones is discussed, with emphasis on various synthetic methods such as condensation and cyclisation reactions starting from fluorinated precursors, as well as fluorination strategies, including the use of molecular fluorine or fluorinating agents. Fluorinated flavonoids and chalcones represent candidates for therapeutic development and have the potential to overcome drug resistance. However, further studies are necessary to adjust their pharmacological profiles. Chemistry ; Lipophilicity ; Chalcone ; Antimicrobial ; Combinatorial chemistry ; Antiparasitic ; Cytotoxicity ; Organic chemistry ; Biochemistry ; In vitro ; Medicine Synthesis and biological activity ; Synthesis of Organic Compounds ; Genomics, phytochemicals, and oxidative stress
Authors: Muhammad Amjad | Meriem Nguir | Xiaolong Ma | Dongsheng Wen
Source: Progress in Additive Manufacturing (article, 2025-05-14)
Field: Materials Science — Surfaces, Coatings and Films

Superhydrophobic 3D-printed microstructures: applications, challenges, and prospects Abstract Superhydrophobic surfaces exhibit remarkably low wettability, enabling various liquids to form droplets and roll off the surface. Beyond their extreme aquaphobic behavior, self-cleaning, anti-corrosive, drag-reductive, oil-repellent, anti-icing, anti-reflection, and anti-biofouling attributes make these structures and surfaces highly valuable for numerous liquid-related applications. Despite these attractive features, developing these intricate three-dimensional microstructures using traditional manufacturing methods presents significant challenges. Recent advancements in 3D microprinting technology, with its unmatched design freedom and precise control over microfeatures, have revolutionized the fabrication of these superhydrophobic microstructures. This work presents a detailed analysis of the benefits and limitations of different 3D-printing technologies in fabricating superhydrophobic microstructures. Applications of these 3D-printed superhydrophobic microstructures in various engineering and scientific domains are analyzed. This work also discusses the need for future improvements in 3D-printing techniques to cope with the scalability issue, optimizing material properties for stability and durability of superhydrophobic microstructures, and developing innovative design strategies for higher levels of superhydrophobicity with desired functionalities. 3d printed ; Microstructure ; Nanotechnology ; Materials science ; Engineering ; Manufacturing engineering ; Composite material Surface Modification and Superhydrophobicity ; Additive Manufacturing and 3D Printing Technologies ; Nanomaterials and Printing Technologies
Authors: James L. Suter | Werner A. Müller | Maxime Vassaux | Antonios D. Anastasiou | Mark Simmons | David A. Tilbrook | Peter V. Coveney
Source: Journal of Chemical Theory and Computation (article, 2025-01-29)
Field: Materials Science — Materials Chemistry

Rapid, Accurate and Reproducible Prediction of the Glass Transition Temperature Using Ensemble-Based Molecular Dynamics Simulation For the computational design of new polymeric materials, accurate methods for determining the glass transition temperature (<i>T</i><sub>g</sub>) are required. We apply an ensemble approach in molecular dynamics (MD) and examine its predictions of <i>T</i><sub>g</sub> and their associated uncertainty. We separate the uncertainty into the aleatoric contributions arising from dynamical chaos and that due to the computational scenarios chosen to compute <i>T</i><sub>g</sub>. We propose a new scenario for computing <i>T</i><sub>g</sub>, where the density-temperature behavior is computed by running all temperatures concurrently, rather than invoking a sequential approach, thereby significantly reducing wall-clock time from days to several hours without increasing the aleatoric uncertainty. On comparing concurrent and sequential scenarios on six highly cross-linked epoxy resins cured with aromatic amines, we find excellent agreement with our experimentally determined <i>T</i><sub>g</sub> using dynamical mechanical analysis for both scenarios. The confidence intervals are found to scale as <i>N</i><sup>-0.5</sup>, where <i>N</i> is the number of members in the ensemble, implying that ensembles comprised of at least ten replicas are required to predict <i>T</i><sub>g</sub> using MD with 95% confidence intervals of less than 20 K. The optimal MD simulation protocol is 4 ns of burn-in time followed by 2 ns of production run time. Molecular dynamics ; Glass transition ; Statistical physics ; Confidence interval ; Uncertainty quantification ; Multiscale modeling ; Ensemble forecasting ; Scale (ratio) ; Ensemble average ; Materials science ; Thermodynamics ; Computer science ; Biological system ; Chemistry ; Physics ; Mathematics ; Computational chemistry ; Statistics ; Machine learning Silicone and Siloxane Chemistry ; Railway Systems and Materials Science ; Thermal and Kinetic Analysis
Authors: Fotios Tsopelas | Chrysanthos Stergiopoulos | Panagiotis Danias | Anna Tsantili‐Kakoulidou
Source: Microchimica Acta (review, 2025-02-04)
Field: Chemistry — Spectroscopy

Biomimetic separations in chemistry and life sciences Since Otto Schmitt introduced the term "biomimetics" in 1957, the imitation of biological systems to develop separation methods and simulate biological processes has seen continuous growth, particularly over the past five decades. The biomimetic approach relies on the use of specific ligands-biospecific, biomimetic, or synthetic-which target biomolecules, such as proteins, antibodies, nucleic acids, enzymes, drugs, pesticides, and other bioactive analytes. This review highlights advances in biomimetic separations, focusing on biomimetic liquid chromatography (including immobilized artificial membrane chromatography, cell membrane chromatography, biomimetic affinity chromatography, weak affinity chromatography, micellar liquid chromatography, immobilized liposome chromatography, and liposome electrokinetic capillary chromatography) for the complex separation and purification of biomolecules and other important chemical compounds. It also explores their application in studying drug-receptor interactions, screening chemical permeability, absorption, distribution, toxicity, as well as predicting environmental risks. Additionally, this review discusses the application of biomimetic magnetic nanoparticles, which leverage biological membranes and proteins for drug discovery, protein purification, and diagnostics. Chemistry ; Biomolecule ; Micellar electrokinetic chromatography ; Affinity chromatography ; Chromatography ; Liposome ; Capillary electrophoresis ; Organic chemistry ; Biochemistry ; Enzyme Analytical Chemistry and Chromatography ; Microfluidic and Capillary Electrophoresis Applications ; Protein purification and stability
Authors: Zhipeng Zhong | Yu Zhuang | Xing Cheng | Jian Zheng | Qianyi Yang | Xiang Li | Yan Chen | Hong Shen | Tie Lin | Wu Shi | Xiangjian Meng | Junhao Chu | Hai Huang | Jianlu Wang
Source: Nature Communications (article, 2025-08-02)
Field: Materials Science — Polymers and Plastics

Ionic-electronic photodetector for vision assistance with in-sensor image processing The human eye is highly advanced but limited by color blindness and poor adaptation to changing light. Artificial photodetectors attempt to mimic vision but often require complex processing to overcome these limitations. Thus, developing photodetectors that complement human vision is crucial to overcoming these limitations. Here, we report a CuInP<sub>2</sub>S<sub>6</sub>-based photodetector array with tunable photoresponse for in-sensor image processing, directly complementing human vision. Through ionic and electronic tuning, the photodetector shows both positive and negative correlations with light intensity and wavelength. It enhances signal-to-background ratio by 880% and suppresses noise by 1,170 times, allowing effective detection of weak signals under strong illumination. Moreover, taking advantage of the distinct photoresponse to red and green light, the photodetector could improve the contrast between red and green patterns up to 43%, offering potential aid for red-green color blindness. This work presents a vision-enhancing photodetector capable of compensating for human visual deficiencies without external computation. Photodetector ; Computer science ; Image processing ; Ionic bonding ; Computer vision ; Optoelectronics ; Materials science ; Image (mathematics) ; Physics ; Ion Transition Metal Oxide Nanomaterials ; Advanced Memory and Neural Computing ; Gas Sensing Nanomaterials and Sensors
Authors: K Kirk Shanahan | Daniel Coen | Wanis Nafo
Source: Exploration of BioMat-X (article, 2025-07-30)
Field: Materials Science — Biomaterials

Polymer-based nanoparticles for cancer theranostics: advances, challenges, and future perspectives Polymer-based nanoparticles have emerged as powerful multifunctional platforms in cancer theranostics, offering the ability to integrate diagnostic imaging and targeted therapy within a single system. These nanocarriers enable improved tumor localization, enhanced contrast agent delivery, and controlled therapeutic release, addressing limitations associated with conventional contrast agents such as poor specificity, rapid clearance, and systemic toxicity. Advances in polymer chemistry and nanoparticle fabrication methods, including solvent evaporation, nanoprecipitation, emulsion-diffusion, and emulsion polymerization, have allowed precise control over particle size, surface charge, and drug-loading efficiency, optimizing biodistribution and imaging performance. Hybrid polymer-inorganic nanoparticles further expand functionality by incorporating magnetic, optical, or radiopaque components, enabling multimodal imaging and stimuli-responsive drug release while maintaining biocompatibility. Key factors influencing the efficiency of polymer nanoparticle-based contrast agents include physicochemical properties such as particle size, morphology, surface functionalization, and responsiveness to tumor microenvironmental stimuli. These attributes collectively govern circulation time, cellular uptake, and accumulation in tumor tissues via passive and active targeting strategies. While promising, the clinical translation of these systems faces challenges including immunogenicity, pharmacokinetic variability, long-term safety concerns, and manufacturing scalability. Recent innovations in ligand functionalization, biomimetic coatings, and multifunctional nanoparticle design continue to advance therapeutic specificity and imaging precision, positioning polymer nanoparticles as versatile candidates for personalized oncologic care. This review provides a comprehensive synthesis of current methods for contrast agent integration, the role of physicochemical properties in performance, biological interactions, safety considerations, recent design innovations, translational barriers, and future research directions for polymer nanoparticle-based cancer theranostics. Nanotechnology ; Polymer ; Nanoparticle ; Materials science ; Composite material Nanoparticle-Based Drug Delivery ; Nanoplatforms for cancer theranostics ; Graphene and Nanomaterials Applications
Authors: Zhengyong Zhang | Jun-wei Su | Huan‐Ming Xiong
Source: Molecules (review, 2025-01-09)
Field: Chemistry — Analytical Chemistry

Technology for the Quantitative Identification of Dairy Products Based on Raman Spectroscopy, Chemometrics, and Machine Learning The technologies used for the characterization and quantitative analysis of dairy products based on Raman spectroscopy have developed rapidly in recent years. At the level of spectral data, there are not only traditional Raman spectra but also two-dimensional correlation spectra, which can provide rich compositional and characteristic information about the samples. In terms of spectral preprocessing, there are various methods, such as normalization, wavelet denoising, and feature extraction. A combination of these methods with appropriate quantitative techniques is beneficial to reveal the differences between samples or improve predictive performance. Quantitative evaluation can be divided into similarity measurement methods and machine learning algorithms. When evaluating small batch samples, similarity measurements can provide quantitative discrimination results. When the sample data are sufficient and matched with Raman spectroscopy parameters, machine learning algorithms suitable for intelligent discrimination can be trained and optimized. Finally, with the rise of deep learning algorithms and fusion strategies, some challenges in this field are proposed. Chemometrics ; Artificial intelligence ; Normalization (sociology) ; Pattern recognition (psychology) ; Raman spectroscopy ; Preprocessor ; Computer science ; Machine learning ; Support vector machine ; Biological system ; Analytical Chemistry (journal) ; Chemistry ; Chromatography Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Identification and Quantification in Food
Authors: Shihao Zang | Sanjib Paul | Cheuk Wai Leung | Michael S. Chen | Theodore Hueckel | Glen M. Hocky | Stefano Sacanna
Source: Nature Communications (article, 2025-04-16)
Field: Materials Science — Materials Chemistry

Direct observation and control of non-classical crystallization pathways in binary colloidal systems Crystallization stands as a prime example of self-assembly. Elementary building blocks converge, seemingly adhering to an intricate blueprint, orchestrating order from chaos. While classical theories describe crystallization as a monomer-by-monomer addition, non-classical pathways introduce complexity. Using microscopic charged particles as monomers, we uncover the mechanisms governing the formation of ionic colloidal crystals. Our findings reveal a two-step process, wherein metastable amorphous blobs condense from the gas phase, before evolving into small binary crystals. These small crystals then grow into large faceted structures via three simultaneous processes: addition of free monomers from bulk, capture and absorption of surrounding blobs, and oriented attachment of other crystals. These complex crystallization pathways occur both in bulk and on surfaces across a range of particle sizes and interaction strengths, resulting in a diverse array of crystal types and morphologies. Harnessing our ability to tune the interaction potential through small changes in salt concentration, we developed a continuous dialysis approach that allows fine control over the interaction strength in both time and space. This method enables us to discover and characterize various crystal structures in a single experiment, including a previously unreported low-density hollow structure and the heteroepitaxial formation of composite crystal structures. Crystallization ; Binary number ; Colloid ; Computer science ; Computational biology ; Nanotechnology ; Materials science ; Chemistry ; Biology ; Physics ; Mathematics ; Thermodynamics ; Physical chemistry Crystallization and Solubility Studies ; Calcium Carbonate Crystallization and Inhibition ; Chemical and Physical Properties in Aqueous Solutions