jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Naoya Osato | Akinori Joko | Hayata Ikeda | Noriyoshi Arai
Source: Journal of Surfactants and Detergents (article, 2025-07-28)
Field: Chemistry — Organic Chemistry

Effect of Propylene–Oxide Segment Length on Cleansing Performance of Detergents With Nonionic Surfactants ABSTRACT Understanding the relationship between surfactant molecular structure and cleansing performance is essential for the rational design of high‐performance detergents. In this study, we investigate the effect of propylene oxide (PO) chain length on the detergency of nonionic surfactants through a combination of experimental measurements and coarse‐grained molecular simulations. Experimentally, detergency tests revealed that increasing the PO content leads to a systematic decrease in cleansing performance. Dissipative particle dynamics simulations were performed to elucidate the molecular basis of this behavior. In bulk aqueous solutions, all surfactants self‐assembled into micelles, with longer PO chains resulting in slightly larger and more diffuse micellar structures. At a model dirt surface, surfactants with longer PO chains exhibited stronger adsorption and slower desorption under flow conditions compared to surfactants without PO units. These findings suggest that while PO segments enhance interfacial adsorption, excessive retention at the surface can hinder soil removal, thereby reducing overall detergency. This combined experimental and simulation approach provides molecular‐level insights into designing optimized nonionic surfactants for practical cleansing applications. Chemistry ; Propylene oxide ; Chromatography ; Nonionic surfactant ; Chemical engineering ; Pulmonary surfactant ; Organic chemistry ; Ethylene oxide ; Polymer ; Copolymer ; Biochemistry Surfactants and Colloidal Systems ; Advanced Polymer Synthesis and Characterization ; Block Copolymer Self-Assembly
Authors: Sylvester Andrew Ekong | Paul O. Adebambo | B.I. Adetunji | Oghenekevwe T. Uto | G.A. Adebayo
Source: Discover Chemistry. (article, 2025-12-24)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Electronic, mechanical, dynamical, thermodynamic and thermoelectric study of ScNiSb Heusler-like alloy: planewave density functional theory approach We apply First-Principles Density Functional Theory and Boltzmann’s transport theory to study the electronic structure and transport properties of ScNiSb Alloy. We also calculated the mechanical and thermodynamic properties, as well as other quantities such as Lame constants, Kleinman parameter, and micro-hardness. The half-Heusler nature of the compound is revealed in the ground-state electronic structure calculations, which yield an energy bandgap of 0.27 eV. The mechanical stability of ScNiSb was verified by subjecting the calculated elastic constants to the conditions for elasticity in a cubic crystal. From the computed transport properties, we found highly significant effects in the region of n-type carrier concentration. However, the p-type half-Heusler-like compound is structurally and mechanically stable. The compound has a maximum power factor [PF (= S2σ/τ)] of 7.8 × 1011 µW/cmK2s at 800 K. Our calculated Figure of merit and lattice thermal conductivity revealed that ScNiSb is a suitable candidate for power generation. Density functional theory ; Thermoelectric effect ; Seebeck coefficient ; Thermal conductivity ; Figure of merit ; Elasticity (physics) ; Work (physics) ; Electronic band structure ; Band gap Heusler alloys: electronic and magnetic properties ; Advanced Thermoelectric Materials and Devices ; Thermal properties of materials
Authors: Niang Saye | Jihad Oumerri | Ana‐Andreea Arteni | Jean‐Luc Six | Khalid Ferji
Source: Macromolecular Rapid Communications (article, 2025-07-04)
Field: Chemistry — Organic Chemistry

Visible‐Light‐Driven PET‐RAFT Emulsion PISA of Biobased Graft Copolymers Toward Spherical Nanoparticle Formulation We report the formulation of dextran-based nano-objects via polymerization-induced self-assembly (PISA) in aqueous emulsion, using photoinduced electron/energy transfer RAFT (PET-RAFT) polymerization under visible light irradiation (λ = 500 nm). A dextran macromolecular chain transfer agent (DexCTA), bearing multiple trithiocarbonate groups, was employed as a hydrophilic scaffold for the grafting of poly(methyl methacrylate) (PMMA) chains. Eosin Y was used as a metal-free photocatalyst to mediate polymerization in water under mild conditions. By systematically varying the PMMA graft degree of polymerization (X = 100-400) and solids content (5-15% w/w), we investigated the influence of formulation parameters on nanoparticle morphology and size. High monomer conversion (>99%) was achieved within 2 h of irradiation. Transmission electron microscopy revealed spherical morphologies across all conditions, with increasing particle size as a function of X and solids content. This study highlights the potential of PET-RAFT emulsion PISA as a green and versatile strategy for the design of polymeric nanomaterials. Raft ; Materials science ; Chain transfer ; Polymerization ; Emulsion polymerization ; Chemical engineering ; Copolymer ; Nanoparticle ; Polymer chemistry ; Reversible addition−fragmentation chain-transfer polymerization ; Monomer ; Radical polymerization ; Nanotechnology ; Polymer ; Composite material Advanced Polymer Synthesis and Characterization ; Pickering emulsions and particle stabilization ; Polymer Surface Interaction Studies
Authors: Ronick Spenly Shadrack | Daniel Tari | Hancy Tabi | Jacinta Botleng | Rolina Kelep | Ladyshia Regenvanu | Mowa Pakoasongi | Edword Butjukabwaelep | Galana Siro | Atanas Pipite | Vincent Lebot
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2025-10-06)
Field: Chemistry — Analytical Chemistry

Geographical origin and cultivar differentiation of Kava ( <i>Piper methysticum</i> ) using Artificial Neural Network with FTIR Spectroscopy: A Novel Method Abstract This study presents a novel method for authenticating the geographical origin and cultivar of kava ( Piper methysticum ) by combining Fourier Transform Infrared (FTIR) spectroscopy with Artificial Neural Networks (ANN). A spectral database of kava varieties from four (4) countries in the Pacific Island region, namely Vanuatu, Fiji, Papua New Guinea, and Hawaii, was used for regional authentication. For samples collected within Vanuatu, spectral data were obtained from the acetone extract of both fresh and dried kava. The ANN predictive model was trained on geographical origin (countries or islands of origin), quality (noble vs tudei), and between different cultivars. ANN achieved near-perfect performance, with generalized R-Square of 0.99 (training), 0.84 (validation), and 0.95 (test) for geographical origin prediction. Class-specific accuracy was 100% for Vanuatu, Papua New Guinea, and Hawaii. Although Fiji exhibited lower validation accuracy (33.3%), the ANN model of a single hidden layer with five TanH neurons and 5-fold cross validation achieved near-perfect classification prediction accuracy (R 2 of 0.99), demonstrating the method’s robustness for geographical authentication. Significantly, the model demonstrated perfect classification (100% accuracy) for Malo and Santo Island kava samples, highlighting its ability to authenticate micro-regional origins within Vanuatu. For variety differentiation, ANN achieved 100% accuracy for noble versus tudei cultivars, ensuring compliance with Vanuatu’s noble-only export policy. ATR-FTIR spectra of fresh and dried kava acetone extracts exhibited visually distinct patterns among kava cultivars at spectral regions of 1750 cm -1 to 1525 cm -1 and 1124 cm -1 to 900 cm -1 , indicating potential for direct differentiation and fraud detection without the need for advanced machine learning algorithms or specialized expertise. These findings position ANN-FTIR as a rapid, non-destructive, and cost-effective solution for food authentication, geographical indication labeling, and export certification, supporting international standards such as Codex Alimentarius and International Standards Organization (ISO) guidelines. Graphical abstract Kava ; Cultivar ; Artificial neural network ; Robustness (evolution) ; Pattern recognition (psychology) ; Spectral analysis Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Identification and Quantification in Food
Authors: S Fricke | Velencia J. Witherspoon | Jérémy Demarteau | Brett A. Helms | Jeffrey A. Reimer
Source: ChemRxiv (preprint, 2025-04-07)
Field: Chemistry — Organic Chemistry

Correlated exchange in recyclable polymers via multidimensional diffusion power spectra Understanding molecular interactions in complex environments is essential for developing sustainable materials with tailored properties. To expedite chemical recycling of polymers, dynamics of these materials must be explored. Correlated movement between structurally distinct components within crosslinked polymers undergoing deconstruction to monomers maps chemical structures to network dynamics, providing unique insights into interactions of polymers in reactive chemical environments and their reactivity therein. Here, we construct an NMR experiment to detect frequency-correlated exchange, which informs on exchange processes between molecules in distinct dynamic regimes. This approach extends a modulated-gradient echo train to 3D, using a unilateral magnet as a low-cost portable sensor. Results link proximally diffusing molecular moieties in operando settings and resolve dynamics of multiple components in complex mixtures. We corroborate frequency-correlated exchange with simulated data and benchmark the technique using linear and crosslinked polymers as standards with known domains arising from topology. Notably, we found that exchange between two frequencies indicates mutually diffusing molecules between regimes of motional averaging, porous confinement, and network localization. These insights enable comprehensive assessments of the influence and extent of crosslinking on polymer deconstruction. We expect this approach to enable measurement of heterogeneous reactions and improve understanding of network dynamics in complex materials. Diffusion ; Polymer ; Spectral line ; Power (physics) ; Statistical physics ; Materials science ; Analytical Chemistry (journal) ; Chemistry ; Physics ; Thermodynamics ; Chromatography ; Organic chemistry ; Quantum mechanics Advanced Polymer Synthesis and Characterization ; NMR spectroscopy and applications
Authors: Sunil P. Gawali and D. V. Mane
Source: International Journal of Advanced Research in Science Communication and Technology (article, 2025-07-30)
Field: Materials Science — Materials Chemistry

Antioxidant and Antimicrobial Activity of Pure and Ag-Doped CuFe2O4 Nanoparticles Prepared by Microwave-Assisted Green Synthesis This investigation demonstrates the successful fabrication of silver-substituted copper ferrite nanoparticles (AgxCu1-xFe2O4, where x = 0.05, 0.1, 0.2) through microwave-assisted biosynthesis utilizing Asteracantha longifolia leaf extract as both reducing and stabilizing agent. X-ray diffraction analysis confirmed single-phase cubic spinel structures with lattice parameters ranging from 8.294 to 8.165 Å. Electron microscopy revealed morphological transitions from spherical to irregular geometries with crystallite sizes between 15.43 and 18.85 nm. Enhanced optical absorption in the UV-visible region (300-800 nm) resulted from surface plasmon resonance effects of silver nanodomains. Magnetic characterization showed decreased saturation magnetization correlating with silver content, attributed to diamagnetic Ag⁺ ions disrupting magnetic exchange interactions. Thermal analysis indicated structural stability up to 600°C with minimal mass loss. Antioxidant evaluation through DPPH radical scavenging demonstrated concentration-dependent activity enhancement, with AgxCu1-xFe2O4 exhibiting 55.35% radical neutralization efficiency compared to 41.87% for undoped copper ferrite. Copper ; Crystallite ; Nanoparticle ; Spinel ; Surface plasmon resonance ; Diamagnetism ; DPPH ; Transmission electron microscopy ; Thermal stability ; Absorption spectroscopy Magnetic Properties and Synthesis of Ferrites ; Nanoparticles: synthesis and applications ; Electromagnetic wave absorption materials
Authors: Natalia Kireeva | А. Yu. Tsivadze
Source: Advanced Theory and Simulations (article, 2025-04-22)
Field: Materials Science — Materials Chemistry

Input Consistency Regularization of Experimental Data for Modeling Functional Characteristics of A2M3O12${\rm A}_{2} {\rm M}_{3} {\rm O}_{12}$ Family of Compounds Abstract The prediction confidence is one of the goals of any machine learning‐based study with no respect if this is distinguished as the aim of the study or is the associated desired concomitant. The possibility do not import the additional error into the pre‐experimental estimation of the studied characteristics should be the goal of machine learning‐based approaches in any case limited in their accuracy by the precision and confidence of the experimental data. The representation theory and information geometry come to the fore to achieve the stated desiderates. In this study, the approaches related to the input consistency regularization are considered which may provide with the required enhancement in the prediction confidence and are able to recoup the part of the experimental error associated with obtaining the data using the methods of different precision or with the discrepancy in the results obtained. The methodology of regularization of the input data consistency is considered in this study in relation to the problem of the predictive modeling of the functional characteristics of family of ceramics with negative/close‐to‐zero thermal expansion (NTE or ZTE) properties. It is showed that using diffusion models as the input consistency regularization procedure allows one to achieve the reduction in predictive error for about thirty percent in its absolute value. The Hessian‐based analysis of the loss function landscape is considered as the criterion of the generalizability and model performance. The continuity of the property change as a function of the data description coupled with the analysis of p ‐values for the experiment‐prediction output are considered as the auxiliary criteria concerned with the input consistency regularization. Consistency (knowledge bases) ; Regularization (linguistics) ; Mathematics ; Combinatorics ; Computer science ; Discrete mathematics ; Artificial intelligence Solid-state spectroscopy and crystallography ; Thermal Expansion and Ionic Conductivity ; X-ray Diffraction in Crystallography
Authors: Vincent Vangrunderbeek | Leonardo Bertolucci Coelho | Mats Meeusen | Herman Terryn | Mesfin Haile Mamme
Source: npj Materials Degradation (article, 2025-12-19)
Field: Materials Science — Materials Chemistry

A field-based computer vision and corrosion sensor approach to study electrolyte dynamics in atmospheric corrosion Abstract Transient electrolyte films govern atmospheric corrosion but remain difficult to monitor outdoors. Here, a novel field approach integrates imaging, environmental logging, AI and real-time corrosion sensing to capture data on surface wetting, weather and electrochemical response. Across multiple wetting events, incorporating image-derived electrolyte coverage improved prediction performance over a weather-only model. The approach enables linking electrolyte geometry with corrosion sensor signals, validating mechanistic models and comparing laboratory and field wetting. Electrolyte ; Corrosion ; Wetting ; Field (mathematics) ; Electrochemical gas sensor Corrosion Behavior and Inhibition ; Concrete Corrosion and Durability ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Jian Cao | Zhiyang Zhang | Heming Wang | Jun Xu | Ling Lan | Simon J. L. Billinge | Gu Ran
Source: ArXiv.org (preprint, 2025-09-01)
Field: Materials Science — Materials Chemistry

CbLDM: A Diffusion Model for recovering nanostructure from atomic pair distribution function The nanostructure inverse problem is an attractive problem that helps researchers to understand the relationship between the properties and the structure of nanomaterials. This study focuses on the problem of recovering the model system of monometallic nanoparticles (MMNPs) from their pair distribution function (PDF) and regards it as a highly ill-posed conditional generation task. This study proposes a Condition-based Latent Diffusion Model (CbLDM) as a feasible solution to this problem. This model demonstrates an acceleration approach within the framework of a latent diffusion model by using conditional priors to estimate the conditional posterior distribution, which is an approximate distribution of p(z|x). In addition, this study uses Laplacian matrix instead of distance matrix to recover the nanostructure, which helps to improve stability. Our study demonstrates that a latent diffusion model with a conditional prior can generate nanostructures that are consistent with PDF observations and physically meaningful, thereby laying the groundwork for subsequent more complex inverse problems. Conditional probability distribution ; Inverse problem ; Nanostructure ; Inverse ; Matrix (chemical analysis) ; Diffusion ; Prior probability Copper-based nanomaterials and applications ; X-ray Diffraction in Crystallography ; Advanced biosensing and bioanalysis techniques
Authors: Punit Kumar
Source: IOSR Journal of Applied Physics (article, 2025-06-01)
Field: Materials Science — Biomaterials

Investigating The Use Of Plasma-Polymerized Films For Controlled Drug Release The integration of plasma-polymerized coatings into drug delivery systems (DDS) represents a cutting-edge advancement in biomedical engineering, offering significant improvements in precision, efficiency, and therapeutic outcomes. Plasma-based coating techniques enable the deposition of ultrathin, conformal, and chemically tailored polymer films onto a variety of substrates, including drug-loaded nanoparticles, implants, and microcapsules. These coatings serve not only as physical barriers but also as functional interfaces that can modulate drug release profiles based on site-specific conditions such as pH, temperature, or enzymatic activity. This paper delves into the underlying physicochemical mechanisms of plasma polymerization, the influence of coating parameters on drug diffusion kinetics, and the role of surface energy and chemistry in biocompatibility and drug stability. Further, it reviews the latest experimental and clinical advancements, highlighting key challenges such as scalability, reproducibility, and regulatory considerations. Through this comprehensive study, we aim to establish a robust scientific framework to guide future innovations in plasma-assisted drug delivery technologies. Plasma ; Drug ; Plasma polymerization ; Materials science ; Polymerization ; Pharmacology ; Medicine ; Composite material ; Physics ; Polymer ; Nuclear physics biodegradable polymer synthesis and properties ; Electrospun Nanofibers in Biomedical Applications ; Dendrimers and Hyperbranched Polymers
Authors: Shunsaku Yasumura | Ken‐ichi Shimizu
Source: Bulletin of the Chemical Society of Japan (article, 2025-09-01)
Field: Chemistry — Inorganic Chemistry

Designing zeolite-based catalysis using automated reaction route mapping Abstract The artificial force-induced reaction technique, known as the automated reaction route mapping method, is an efficient approach for exploring complete reaction pathways, identifying both equilibrium and transition state structures. This method requires only the initial structure, enabling automated exploration of reaction networks and computational screening of catalytic activity based on calculated activation barriers. This review provides an overview of the rational design of zeolite-based catalysis, mainly for exhaust gas purification using the artificial force-induced reaction method. Detailed reaction mechanisms were elucidated for the H₂-assisted selective catalytic reduction over Ag-zeolites, NH₃ decomposition over Cu-zeolites, and CH₄ combustion with O₃ over H- and Co-zeolites. Zeolites, characterized by their atomically defined porous frameworks, facilitate the experimental reproduction of active sites. Consequently, they serve as optimal platforms for the artificial force-induced reaction-guided computational design of heterogeneous catalysts, effectively bridging theoretical predictions with experimental validation. The integration of AFIR with zeolite systems thus presents a powerful strategy for the rational development of solid-state catalysts. Catalysis ; Reaction conditions ; Rational design ; Decomposition ; Combustion ; Heterogeneous catalysis ; Reaction mechanism Zeolite Catalysis and Synthesis ; Catalysis and Hydrodesulfurization Studies ; Catalytic Processes in Materials Science
Authors: Bibha Mishra A. | Vidisha Tomer
Source: Food Frontiers (article, 2025-11-20)
Field: Materials Science — Materials Chemistry

Recent Insights on Synthesis, Stability, and Complexities of Water‐in‐Oil Pickering Emulsion: From Theory to Applications ABSTRACT In an era of surging non‐communicable disease cases due to increased trans‐fat/saturated fat consumption, water‐in‐oil Pickering emulsion (w/o PE) has emerged as a potent fat replacer. The use of solid particles to stabilize these emulsions provides longer stability, environmental sustainability, and a decreased need for surfactants. However, development of w/o PE has been challenging, as it is highly prone to de‐stabilization, resulting in phase separation, Ostwald ripening, and sedimentation. This review provides comprehensive information pertaining to the role of each component, such as oil phase properties (type, polarity, density, fatty acid concentration, viscosity), water fraction, and use of different solid particles (natural/synthetic) for the development of w/o PE. An insight into the influence of various internal (water/oil ratio, particle characteristics, pH) and external factors (temperature and time) over emulsion system stability has been provided. Impact of varying properties of internal and external parameters on the rheology, such as shear viscosity, viscoelastic property, and slip melting point, which helps to determine the quality of developed emulsion, has also been emphasized. By highlighting the role of w/o PE in different emerging sectors like biomedical, encapsulation and delivery, and eco‐friendly packaging, the current review aims to consolidate the scattered information and fill the existing information gap towards the development of stabilized emulsion. The information provided in the article can serve as a cornerstone for future advanced research studies in the development and stability of emulsion. Pickering emulsion ; Rheology ; Emulsion ; Phase (matter) ; Information system Pickering emulsions and particle stabilization ; Proteins in Food Systems ; Food Chemistry and Fat Analysis