jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Marko Nagode | Domen Šeruga | Simon Oman | Jernej Klemenc
Source: International Journal of Mechanical Sciences (article, 2026-02-17)
Field: Materials Science — Materials Chemistry

A unified elasto-viscoplastic model for rate-dependent materials This paper presents a generalized elasto-viscoplasticity framework for modelling material behaviour under static, impact, and potentially cyclic loading. A theoretical equivalence between the classical Cowper–Symonds formulation and Chaboche’s viscoplasticity law is established, demonstrating that both can be expressed within a unified constitutive structure. Based on this equivalence, a new procedure is proposed for identifying elastoplastic and viscoplastic parameters directly from tensile, compressive, or strain-controlled tests performed at different strain rates. The method eliminates the need for additional creep or relaxation experiments and enables decomposition of the stress–strain response into elastoplastic and viscoplastic contributions. Several variants of the viscoplasticity law are introduced, each satisfying relevant physical constraints, and a regression-based strategy is employed for robust parameter identification. The framework is validated using four materially diverse datasets: X2CrNi18-9 stainless steel, Ti6Al4V titanium alloy, short glass fibre-reinforced polybutylene terephthalate (PBT), and hydroxyl-terminated polybutadiene (HTPB) propellant. In all cases, excellent agreement with experimental data is obtained. The proposed formulation further enables prediction of stress–strain behaviour at the limiting cases of zero and infinitely high strain rates, which cannot be accessed experimentally. The results demonstrate that a single constitutive law can capture rate-dependent responses across metals, polymers, and elastomers, making it suitable for structural analyses under monotonic loading, with potential extension to cyclic, multiaxial, and thermo-mechanical conditions. • Unified elasto-viscoplastic law for static, impact, and potentially cyclic loading. • Demonstrates equivalence between Cowper–Symonds and Chaboche formulations. • Material parameters identified directly from standard strain-rate-controlled tests. • Predicts stress–strain response at zero and infinite strain rates beyond experiments. • Validated on metals, polymers, and elastomers with superior accuracy to Perzyna law. Viscoplasticity ; Constitutive equation ; Monotonic function ; Creep ; Plasticity ; Equivalence (formal languages) ; Relaxation (psychology) High-Velocity Impact and Material Behavior ; Energetic Materials and Combustion ; Cellular and Composite Structures
Authors: Leonardo M. Corrêa | Simon M. Fairclough | Kaleigh M. R. Scher | Supriya Atta | D. Dumont dos Santos | Caterina Ducati | Laura Fabris | Daniel Ugarte
Source: Small (article, 2026-03-29)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Essential Role of Icosahedral Symmetry in the 3D Shape of AuAg Plasmonic Nanostars With High Aspect‐Ratio Legs Plasmonic nanoparticles play an essential role in improving the sensitivity of different optical methods, and multibranched nanostars are predicted to show the highest enhancement factor for several applications in the near infrared region. The structural complexity of these particles represents a serious challenge with respect to gathering precise knowledge of atomic arrangement and 3D star morphology, making it difficult to accurately predict their optical response and to devise new synthetic methods addressing various applications. Here, we have used 4D-STEM diffraction mapping and a thorough crystallographic analysis to determine leg configuration in space, so that a better understanding of plasmonic properties and growth mechanism can be obtained. Our results show that although electron microscopy images may show a certain shape diversity with stars displaying 4- 5- or 6 branches, the orientation of these legs in space follows the positions associated with the apexes of an icosahedral core. The direct application of the measured star structures has been used as quantitative structural input in optical simulations, which show excellent agreement with experimental measurements. This evidence confirms the crucial role of icosahedral symmetry for explaining the growth mechanism and plasmonic response of gold nanostars with high-aspect-ratio legs. Icosahedral symmetry ; Plasmon ; Symmetry (geometry) ; Orientation (vector space) ; Diffraction ; Nanoparticle ; Infrared ; Star (game theory) Gold and Silver Nanoparticles Synthesis and Applications ; Plasmonic and Surface Plasmon Research ; Metamaterials and Metasurfaces Applications
Authors: Amal M. Aladwani | Esraa M. Bakhsh | Ekram Y. Danish | Zainab M. Hritani | Kalsoom Akhtar | Sher Bahadar Khan
Source: Sensors (article, 2026-02-28)
Field: Chemistry — Electrochemistry

Design of Electrochemical Sensor Based on Pumpkin Peel Biomass-Derived Carbon Black-Modified Electrode for the Detection of Lead Ions This study reports a sustainable and experimentally simple electrochemical platform for monitoring trace Pb<sup>2+</sup> using pumpkin peel-derived carbon black (CB) as a modifier on a Nafion-coated glassy carbon electrode (CB/Nafion-GCE). Agricultural waste pumpkin peels were converted into CB, offering a low-cost and environmentally friendly sensing material. CB produced at 650 °C was systematically characterized by SEM, TEM, EDX, XRD, FT-IR, and BET, revealing a mesoporous structure, high surface area, and partial graphitization that enlarged the electroactive area and reduced charge transfer resistance relative to the bare GCE. Under optimized square wave anodic stripping voltammetry (SWASV) conditions, the glassy carbon electrode modified with CB produced at 650 °C (CB650-GCE) exhibited a well-defined linear response towards Pb<sup>2+</sup> with a limit of detection of approximately 0.19 µM and a limit of quantification of about 0.58 µM, together with good selectivity against common coexisting metal ions. The sensor also achieved satisfactory recoveries in tap and seawater samples, demonstrating its potential as a practical, green analytical tool for routine lead surveillance in environmental waters. Detection limit ; Electrochemical gas sensor ; Electrode ; Carbon fibers ; Anodic stripping voltammetry ; Electrochemistry ; Square wave ; Anode ; Carbon black ; Stripping (fiber) Electrochemical Analysis and Applications ; Electrochemical sensors and biosensors ; Analytical Chemistry and Sensors
Authors: Yevgeniya Karibjanova | Dimitri Radajewski | Sébastien Teychené | Isaac Rodríguez-Ruiz
Source: Journal of Molecular Liquids (article, 2026-02-15)
Field: Materials Science — Materials Chemistry

Microfluidics resolves non-classical lysozyme nucleation driven by attractive protein interactions Protein crystalline nucleation pathways still remain a hot research topic after decades, due to the experimental challenges involved in its study and its vast implications in several applied and research fields. Here, we report a comprehensive investigation of lysozyme crystal nucleation, employing a high-throughput droplet-based microfluidic chip, developed ad-hoc for performing crystal nucleation kinetic measurements, and Dynamic Light Scattering (DLS) for local solution structure analysis. A temperature-controlled system with 500 independent nL-scale droplet microreactors enabled statistical analysis of nucleation events, and two distinct nucleation rates were identified, one attributed to prevalent heterogeneous nucleation (likely on impurities and/or denatured protein molecules), and the other to nucleation in the bulk of the solution. CNT-derived kinetic parameters for bulk nucleation aligned with previously reported values, yet deviated significantly from classical homogeneous predictions: the estimated density of active nucleation sites was found to be several orders of magnitude lower than the protein monomer concentration, strongly contradicting a homogeneous mechanism. Furthermore, when approaching the binodal, nucleation kinetics exhibited a marked deviation from the CNT trend, indicating a rupture in the nucleation behavior. DLS analysis further indicated that in supersaturated conditions, proteins do not remain monomeric but form dynamic locally densified regions. Results show that lysozyme nucleation proceeds via transient densifications driven by attractive intermolecular interactions, challenging the classical view of a homogeneous, monomer-dominated solution and underscoring the importance of mesoscopic solution structure. • High-throughput microfluidics resolves two distinct nucleation regimes • Bulk nucleation rates defy CNT predictions with extremely low density of nucleation sites • Bulk nucleation deviates from CNT trend near the LLPS boundary • DLS measurements reveal transient densifications in the solution • Nucleation proceeds via dynamic fluctuations driven by attractive protein interactions Nucleation ; Protein crystallization ; Supersaturation ; Classical nucleation theory ; Crystal (programming language) ; Lysozyme ; Monomer Enzyme Structure and Function ; Mass Spectrometry Techniques and Applications ; Protein Structure and Dynamics
Authors: Ryosuke Jinnouchi | Masao Shibata
Source: Chemical Communications (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Local environment neighbor sensitivity analysis: visualization of cation effect at liquid-solid interface We present a method to extract the effects of environmental neighbors on local energies in heterogeneous systems from machine-learning force fields, which predict interatomic interactions with near first-principles accuracy. Applied... Sensitivity (control systems) ; Visualization ; Interface (matter) ; Interatomic potential ; Molecular dynamics ; k-nearest neighbors algorithm Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Block Copolymer Self-Assembly
Authors: Melissa T. Manetsch | David W. Kastner | Yuriy Román‐Leshkov | Heather J. Kulik
Source: ChemRxiv (article, 2026-01-16)
Field: Materials Science — Materials Chemistry

pyEF: A Python Framework for QM and QM/MM Atom-Wise Electric Field Analysis We introduce pyEF, a software package for computing molecular electric fields, electrostatic interaction energies, and electrostatic potentials from quantum mechanical (QM) atom-centered multipole expansions with atom-wise decomposable contributions. We demonstrate the computational efficiency and accuracy of this QM-derived electric field evaluation tool through several tests. To assess the influence of the underlying QM method and charge partitioning scheme on these electrostatic quantities, we analyze over 250 configurations of an acetone solute molecule in five solvents of variable polarity. We find that electric field calculations are highly sensitive to the choice of charge partitioning method. Even among real-space charge schemes, acetone Stark tuning rates differ by up to a factor of two. Benchmarking computed solvent dipole moments against experimental bulk values, we conclude that the CM5, ADCH, and Hirshfeld-I charge schemes most reliably capture solvent electrostatics and therefore provide a more faithful foundation for computing electric fields. When constructed from these real-space charges, electric fields are nearly insensitive to basis set size and monotonically increase in magnitude with higher Fock exchange. We also demonstrate efficient convergence of QM electrostatics when more distant molecules are represented solely by MM point charges, reducing computational overhead. Leveraging these findings, we demonstrate the use of pyEF to deduce environmental effects on a transition metal complex from a Ga4L612- nanocage and quantify the dominant role of organic linkers in orchestrating electrostatic preorganization. Electrostatics ; Electric field ; Dipole ; Multipole expansion ; Charge (physics) ; Static electricity ; Electric charge ; Python (programming language) ; Point particle Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Spectroscopy and Quantum Chemical Studies
Authors: Mehrab Mahdian | Ferenc Ender | Tamás Párdy
Source: Scientific Reports (article, 2026-02-25)
Field: Materials Science — Biomaterials

A surrogate-based inverse design framework for targeted diameter control of electrospun nanofibers Electrospinning is a high-throughput technique for producing nanofibers. The diameter of such nanofibers governs key properties such as surface area, porosity, and mechanical strength. Precise diameter control is therefore crucial for applications from filtration to tissue engineering, yet optimizing processing conditions for targeted diameter fabrication typically relies on slow, costly trial-and-error experiments. This study presents a data-driven inverse-design framework that replaces traditional trial-and-error optimization with predictive modeling to achieve precise diameter control. Eleven regression models were evaluated on a dataset of 96 poly(vinyl alcohol) (PVA) experiments, with Extreme Gradient Boosting (XGBoost) emerging as the best surrogate (test [Formula: see text]). SHAP analysis confirmed applied voltage and solution concentration as the most influential parameters, consistent with physical principles. In the optimization stage, Particle Swarm Optimization (PSO) achieved the highest inverse design accuracy ([Formula: see text], MAE [Formula: see text]). This framework enables rapid, efficient design of nanofibers with specified properties and is readily adaptable to other materials and fabrication processes. Electrospinning ; Fabrication ; Particle swarm optimization ; Nanofiber ; Inverse ; Response surface methodology ; Boosting (machine learning) ; Inverse problem Electrospun Nanofibers in Biomedical Applications ; Electrohydrodynamics and Fluid Dynamics ; Advanced Multi-Objective Optimization Algorithms
Authors: Andrey N. Kuskov | Lydia-Nefeli Thrapsanioti | Ekaterina V. Kukovyakina | Anne V. Yagolovich | Elizaveta Vlaskina | Petros Tzanakakis | Aikaterini Berdiaki | Dragana Nikitovic
Source: Molecules (article, 2026-01-10)
Field: Materials Science — Biomaterials

Peptide-Functionalized Iron Oxide Nanoparticles for Cancer Therapy: Targeting Strategies, Mechanisms, and Translational Opportunities Therapeutic peptides have emerged as promising tools in oncology due to their high specificity, favorable safety profile, and capacity to target molecular hallmarks of cancer. Their clinical translation, however, remains limited by poor stability, rapid proteolytic degradation, and inefficient biodistribution. Iron oxide nanoparticles (IONPs) offer a compelling solution to these challenges. Owing to their biocompatibility, magnetic properties, and ability to serve as both drug carriers and imaging agents, IONPs have become a versatile platform for precision nanomedicine. The integration of peptides with IONPs has generated a new class of hybrid systems that combine the biological accuracy of peptide ligands with the multifunctionality of magnetic nanomaterials. Peptide functionalization enables selective tumor targeting and deeper tissue penetration, while the IONP core supports controlled delivery, MRI-based tracking, and activation of therapeutic mechanisms such as magnetic hyperthermia. These hybrids also influence the tumor microenvironment (TME), facilitating stromal remodeling and improved drug accessibility. Importantly, the iron-driven redox chemistry inherent to IONPs can trigger regulated cell death pathways, including ferroptosis and autophagy, inhibiting opportunities to overcome resistance in aggressive or refractory tumors. As advances in peptide engineering, nanotechnology, and artificial intelligence accelerate design and optimization, peptide-IONP conjugates are poised for translational progress. Their combined targeting precision, imaging capability, and therapeutic versatility position them as promising candidates for next-generation cancer theranostics. Iron oxide nanoparticles ; Nanomedicine ; Magnetic nanoparticles ; Peptide ; Cancer ; Drug delivery ; Targeted drug delivery ; Cancer therapy ; Cancer cell Nanoparticle-Based Drug Delivery ; Nanoplatforms for cancer theranostics ; Ferroptosis and cancer prognosis
Authors: Li Li | Hang Yang | Wei Han
Source: Chemical Science (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Beyond water: a mini review on hybrid electrolyte strategies for advanced aqueous aluminum batteries Aqueous aluminum metal batteries (AAMBs) are considered a highly promising technology for future sustainable energy storage due to their high theoretical capacity, low cost, and environmental compatibility. However, achieving highly reversible and fast reaction kinetics remains a critical challenge that limits their advancement. In this perspective article, we argue that water-organic hybrid electrolytes (WOHEs) possess distinct advantages in concurrently optimizing the performance of both the anode and cathode in AAMBs through modifying the solvation structure and/or suppressing water activity. Finally, we highlight the necessity of in-depth investigations into the regulatory mechanisms of WOHEs on the electrochemical behavior at the electrode/electrolyte interface, particularly the formation of the solid electrolyte interphase (SEI) on the anode/cathode surface. Addressing these key aspects will facilitate the development and practical deployment of AAMBs. Electrolyte ; Anode ; Cathode ; Electrochemistry ; Energy storage ; Battery (electricity) ; Interphase ; Aqueous solution Thermal Expansion and Ionic Conductivity ; Advancements in Battery Materials ; Advanced battery technologies research
Authors: Yiran Ma | Jerome Le Ny | Zhichao Chen | Zhihuan Song
Source: ArXiv.org (article, 2026-04-02)
Field: Materials Science — Materials Chemistry

Towards Intrinsically Calibrated Uncertainty Quantification in Industrial Data-Driven Models via Diffusion Sampler In modern process industries, data-driven models are important tools for real-time monitoring when key performance indicators are difficult to measure directly. While accurate predictions are essential, reliable uncertainty quantification (UQ) is equally critical for safety, reliability, and decision-making, but remains a major challenge in current data-driven approaches. In this work, we introduce a diffusion-based posterior sampling framework that inherently produces well-calibrated predictive uncertainty via faithful posterior sampling, eliminating the need for post-hoc calibration. In extensive evaluations on synthetic distributions, the Raman-based phenylacetic acid soft sensor benchmark, and a real ammonia synthesis case study, our method achieves practical improvements over existing UQ techniques in both uncertainty calibration and predictive accuracy. These results highlight diffusion samplers as a principled and scalable paradigm for advancing uncertainty-aware modeling in industrial applications. Uncertainty quantification ; Calibration ; Measure (data warehouse) ; Uncertainty analysis ; Measurement uncertainty ; Sampling (signal processing) ; Process (computing) ; Scalability Machine Learning in Materials Science ; Fault Detection and Control Systems ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Jacob Votava | Emily Davidson | Michael A. Webb
Source: ChemRxiv (article, 2026-03-08)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Impact of Methylation on the Nematic Phase of Phenyl Benzoate Mesogens and Dimers Liquid crystals (LCs) possess anisotropic mechanical and optical properties with applications ranging from soft robotics to display technology. Despite advances in the precise synthesis of liquid crystalline materials, the microscopic origins of substituent effects, which impact functional performance, are not always well understood. Here, we use molecular dynamics (MD) simulations to investigate how methyl substitution affects the nematic-phase behavior of liquid crystal monomers and dimers composed of phenyl benzoate cores flanked by aliphatic tails. Methylation induces a decrease in the nematic-isotropic transition temperature. Using data-driven analysis, we find that for monomers this decrease is associated with an increase in flexibility of core-adjacent aliphatic torsions that influence overall mesogen conformation. For dimers, this manifests as a shift from a continuum of accessible conformations in the isotropic phase to occupying more distinct hairpin and extended states in the nematic phase. The latter exhibits a bend angle consistent with experimental signatures of a modulated nematic phase. Together, these results show how minor changes in chemical structure can impact the conformational ensemble of liquid crystals, trading local conformational entropy for global nematic order, in turn influencing their macroscopic transition temperatures. Liquid crystal ; Mesogen ; Monomer ; Substituent ; Phase transition ; Phase (matter) ; Anisotropy Liquid Crystal Research Advancements ; Advanced Materials and Mechanics ; Micro and Nano Robotics
Authors: Hamza Abu Owida | Suleiman Ibrahim Mohammad | Qusay Abdulsattar Mohammed | Asokan Vasudevan | Normurоt Fayzullaev | Subbulakshmi Ganesan | Rahul Sharma | Anupam Agarwal | Yesudass Sasikumar | Milad Safamanesh
Source: RSC Advances (article, 2026-01-01)
Field: Chemistry — Analytical Chemistry

Multiphysics-guided design of ZIF-67/MWCNT-modified electrodes for highly selective electrochemical detection of sunset yellow in complex food matrices The development of reliable sensing platforms for synthetic food additives remains a critical challenge due to severe matrix interferences that limit selectivity and analytical accuracy. In this work, a multiphysics-guided framework is employed to design a ZIF-67/MWCNT-modified glassy carbon electrode (GCE) for the highly selective electrochemical detection of sunset yellow (SY) in complex food matrices. By integrating experimental electrochemical analysis with COMSOL-based modeling of mass transport, adsorption dynamics, charge transfer, and thermal effects, this study provides a mechanistic basis for material-analyte interactions that govern sensor performance. The ZIF-67/MWCNT hybrid exhibits synergistic surface chemistry, where π-π stacking between the azo-aromatic structure of SY and the graphitic domains of MWCNTs, together with electrostatic interactions with Co<sup>2+</sup> centers in ZIF-67, yields a high adsorption constant (<i>K</i> <sub>ads</sub> = 5.41 × 10<sup>4</sup> m<sup>3</sup> mol<sup>-1</sup>) and a dominant surface flux (3.47 × 10<sup>-7</sup> mol m<sup>-2</sup> s<sup>-1</sup>), surpassing those of common interferents. The optimized electrode delivers a steady-state current density of 5.22 µA m<sup>-2</sup> at pH 7 and a 5 µm composite layer, while maintaining negligible faradaic contributions from ascorbic acid, citric acid, aspartame, and acesulfame potassium. Parametric simulations reveal robust performance under thermal variations (298-328 K), minimal sensitivity to electrolyte disturbances, and a direct correlation between surface heterogeneity and current attenuation. Model validation against experimental electrochemical impedance spectroscopy yields a low RMSE (0.0621), confirming predictive accuracy. These findings demonstrate how multiphysics analysis can rationally guide electrode engineering, offering a powerful design strategy for next-generation electrochemical sensors. The proposed platform provides a selective, sensitive, and scalable solution for trace-level SY detection, underscoring its relevance for food safety monitoring and real-sample analysis. Electrochemical gas sensor ; Electrode ; Electrochemistry ; Dielectric spectroscopy ; Detection limit ; Electrolyte ; Stacking ; Analytical Chemistry (journal) ; Ascorbic acid Dye analysis and toxicity ; Electrochemical sensors and biosensors ; Advanced Nanomaterials in Catalysis