jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Lina Papadimitriou | Maria Graigkioti | Eirini D. Koutsouroubi | Konstantinos Pagonidis | Yannis Papaharilaou | Anthi Ranella | Alexandros Lappas
Source: MedComm – Biomaterials and Applications (article, 2025-11-02)
Field: Materials Science — Biomaterials

VCAM‐1‐Functionalized Iron Oxide Nanoclusters for Targeted MRI‐Based Diagnosis of Atherosclerosis ABSTRACT This study explores the development and characterization of iron oxide nanoclusters (NCs) functionalized with vascular cell adhesion molecule 1 (VCAM‐1) for targeted magnetic resonance imaging (MRI) of early atherosclerotic lesions. The NCs were synthesized via a high‐temperature polyol method and functionalized using 1‐ethyl‐3‐(3‐dimethylaminopropyl) carbodiimide/N‐hydroxysuccinimide (EDC/NHS) chemistry to enable conjugation with VCAM‐1 antibodies. Dynamic light scattering and transmission electron microscopy TEM confirmed controlled growth of NCs with a size ranging from 40 nm, in the parent to 110 nm post‐functionalization, maintaining though colloidal stability in aqueous media. Cytotoxicity assays using mesenchymal stem cells (MSCs) demonstrated high biocompatibility. Confocal and electron microscopy confirmed specific binding of VCAM‐1‐NCs to VCAM‐1‐overexpressing MSCs under inflammatory conditions, with internalization through the endolysosomal pathway. The functionalized NCs remained bound under shear stress in an orbital flow model, mimicking early atherosclerotic conditions. MRI phantom analysis demonstrated preserved contrast capability despite increased T 2 * relaxation times following antibody conjugation. These findings highlight the potential of VCAM‐1‐NCs as noninvasive imaging agents for early‐stage atherosclerosis and vascular inflammation. Although this study is limited by the lack of in vivo validation and therapeutic evaluation, it provides a strong foundation for future translational research. Nanoclusters ; Dynamic light scattering ; In vivo ; Iron oxide nanoparticles ; Magnetic resonance imaging ; Transmission electron microscopy ; Cytotoxicity ; Internalization ; Confocal microscopy ; Nanoparticle Nanoparticle-Based Drug Delivery ; Characterization and Applications of Magnetic Nanoparticles ; Iron oxide chemistry and applications
Authors: Billy Alberto Ávila-Camacho | Martial Pabon | Norma Aurea Rangel-Vázquez | Edgar Márquez | Hilda Elizabeth Reynel Ávila | Didilia Ileana Mendoza‐Castillo | Yectli A. Huerta
Source: Polysaccharides (article, 2025-10-09)
Field: Chemistry — Analytical Chemistry

Adsorption of Pharmaceutical Compounds from Water on Chitosan/Glutaraldehyde Hydrogels: Theoretical and Experimental Analysis Chitosan-based hydrogels are used in the adsorption of pharmaceutical compounds from water. The adsorption process of diclofenac and naproxen on chitosan hydrogels cross-linked with glutaraldehyde has been studied theoretically and experimentally. According to the thermodynamic properties, the adsorption processes were spontaneous and endothermic, due to the negative values of Gibbs free energy, and the enthalpies of formation were positive. Furthermore, the different systems were studied by electrostatic potential maps, where the functional groups (amino and hydroxyl) represented the active sites of the hydrogel. The maximum adsorption capacity obtained for diclofenac and naproxen was 108.85 and 97.22 mg/g, respectively, at a temperature of 308.15 K. On the other hand, the adsorbent was characterized by FTIR (Fourier Transform Infrared Spectroscopy) and XRD (X-ray Diffraction) before and after the adsorption of the drugs to confirm the binding of the adsorbates on the surface of the material. Adsorption ; Naproxen ; Fourier transform infrared spectroscopy ; Diclofenac ; Glutaraldehyde ; Infrared spectroscopy Analytical chemistry methods development ; Adsorption and biosorption for pollutant removal ; Nanomaterials for catalytic reactions
Authors: C M Sulaikha | Anitha Somasundaram
Source: International Journal of Basic and Applied Sciences (article, 2025-09-13)
Field: Chemistry — Analytical Chemistry

A Hybrid Decision Logic Framework Combining Fuzzy Systems and Machine Learning Classifier for Post-Fermentation Tea Quality Grading This study addresses the need for reliable post-fermentation tea quality grading by proposing a hybrid decision logic framework that com-‎bines narrowband spectrophotometric sensing, computer vision-based fermentation indicators, and intelligent classification techniques. The ‎proposed methodology employs low-cost sensing and digital imaging for feature extraction, followed by a two-stage modeling approach ‎that integrates rule-based fuzzy inference system (FIS) with machine learning (ML) classification. After feature extraction, statistical and ‎ML-based feature selection techniques were applied to identify the most informative features. The initial stage employs a FIS to identify the ‎grade and its confidence score based on recurrent fuzzy patterns corresponding to each tea grade. In the second stage, a light-gradient boost‎ing machine LightGBM classifier is trained to enhance predictive accuracy and generate probabilistic confidence scores. Finally, hybrid ‎decision logic is applied to finalize the grade. The proposed hybrid model achieved an accuracy of 99.00%, precision of 99.53%, recall of ‎‎98.27%, and F1-score of 98.74%, outperforming both traditional ML and state-of-the-art models. It demonstrated robust generalization ‎across varying sample sizes and cross-validation folds. Ablation studies confirmed the efficiency of the FIS, showing strong performance ‎with reduced feature sets. Furthermore, the framework maintained shorter execution times than ensemble-based methods, making it suitable ‎for real-time and resource-constrained environments‎. Fuzzy logic ; Feature selection ; Interpretability ; Classifier (UML) ; Support vector machine ; Probabilistic logic ; Inference ; Feature (linguistics) Spectroscopy and Chemometric Analyses
Authors: Yan Li | Nan Lu
Source: Scientific Journal of Technology (article, 2025-06-20)
Field: Chemistry — Inorganic Chemistry

Prospect of Modification Treatment and Application of Natural Zeolite Materials The efficient and extensive utilization of natural zeolites is restricted by factors such as pore blockage, weak ion exchange selectivity, and small adsorption capacity. Modifying natural zeolite materials is an important means to enhance their performance and value. Based on existing research results, this paper summarizes the characteristics of inorganic treatment, thermal modification, organic modification, metal loading and composite modification of zeolite materials, as well as their main applicable conditions and influencing factors. Finally, it looks forward to the challenges of large-scale production and application of modified natural zeolites, and proposes to strengthen research and practice in green and efficient modification processes, new functional targeted modification, intelligent manufacturing and application monitoring, etc., to provide scientific references for the high-value utilization of natural zeolite resources. Zeolite ; Adsorption ; Surface modification ; Natural (archaeology) ; Ion exchange ; Materials science ; Nanotechnology ; Chemical engineering ; Engineering ; Chemistry ; Catalysis ; Organic chemistry ; Ion ; Geology Zeolite Catalysis and Synthesis ; Mesoporous Materials and Catalysis ; Metal-Organic Frameworks: Synthesis and Applications
Authors: S. Sudha | Sivaranjani Nafeeza
Source: Journal of Technology Informatics and Engineering (article, 2025-08-25)
Field: Chemistry — Analytical Chemistry

Predicting and Inspecting Food Contamination Using AI based Hyperspectral Imaging Growing consumer demands and intricate supply networks are making it more difficult for the global food industry to maintain high standards of quality and ensure food safety. Conventional inspection techniques sometimes take a lot of time, cause damage, and are inaccurate enough to miss contaminants or quality problems early. These drawbacks emphasize how sophisticated, effective, and non-invasive technology is required for food quality monitoring. This effort aims to investigate the use of Hyperspectral Imaging (HSI) in conjunction with Artificial Intelligence (AI) for food contamination inspection and prediction. Food's chemical and physical characteristics that are undetectable to the human eye can be revealed by hyperspectral imaging, which takes pictures at a variety of wavelengths. The findings show that AI-based HSI offers notable advantages over traditional techniques in terms of quick, accurate, and non-destructive examination. It makes early contamination detection possible and aids in preserving food quality throughout the supply chain. By reducing waste, guaranteeing product authenticity, and boosting customer trust in food items, our effort helps worldwide food safety and advance the development of smarter food inspection systems. Hyperspectral imaging ; Contamination ; Environmental science ; Artificial intelligence ; Remote sensing ; Computer science ; Geography ; Biology ; Ecology Spectroscopy and Chemometric Analyses
Authors: Tamer A. Elkhatib | S. Elnahwy
Source: Optical and Quantum Electronics (article, 2025-12-24)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Parametric geometric meta-electronics: review and perspectives This paper presents a novel physics approach in the field of electromagnetic and electronics engineering, combining the principle of metamaterials that may exist naturally in semiconductor electronic devices with a parametric design and geometric optimization. By manipulating the geometric parameters of a simple transistor, diode, or resistor at the sub-wavelength scale, unexpected quantum localized oscillations (Plasmons) can be achieved for their charge carriers (i.e., electrons), which can be triggered by inputting electromagnetic or electronic signals at specific frequencies that match their natural resonance frequencies. The proper and precise control of these tunable sub-wavelength physical phenomena will lead to an emerging electronics discipline focusing on creating engineered electronics and integrated circuits with unprecedented functionalities beyond the limits of traditional electronics and integrated circuits. The applications of parametric geometric meta-electronics span a wide range of active tunable detectors and emitters across any frequency band. This meta-electronic physical approach enables an alternative room-temperature solution to the fundamental extreme cooling requirements of superconductors and quantum computers. Electronics ; Metamaterial ; Parametric statistics ; Resistor ; Electromagnetic field ; Electronic circuit ; Electronic component ; Electromagnetic interference ; Field (mathematics) Metamaterials and Metasurfaces Applications ; Topological Materials and Phenomena ; Quantum Mechanics and Non-Hermitian Physics
Authors: Steven T. Cerabona | Gordon G. Brown | Leah B. Casabianca
Source: Journal of Molecular Modeling (article, 2025-02-24)
Field: Chemistry — Spectroscopy

Quantum mechanical and machine learning prediction of rotational energy barriers in halogenated aromatic alcohols DFT calculations were done with Gaussian 16 software at the B3LYP/6-311 + G(d.p) level of theory. Machine learning was done using the classification and regression training (caret) package in R version 4.4.0. Electronegativity ; Substituent ; Density functional theory ; Chemistry ; Computational chemistry ; Gaussian ; Molecule ; Context (archaeology) ; Artificial intelligence ; Machine learning ; Computer science ; Organic chemistry Molecular Spectroscopy and Structure ; Chemical Reactions and Mechanisms ; Photochemistry and Electron Transfer Studies
Authors: Ayhan Orhan | Nurettin Çek
Source: Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi (article, 2025-06-03)
Field: Materials Science — Materials Chemistry

TURBA ÇÖZELTİSİNİN TİTANYUMUN ÇUKURLAŞMA KOROZYONU ÜZERİNDEKİ ROLÜNÜN ORTAYA ÇIKARILMASI Ucuz olması ve lokal korozyona karşı daha az duyarlı olduğu gerekçesiyle karbon çeliğinden üretilen yeraltı boru hatları, korozyon saldırısına karşı yetersiz kalmaktadırlar. Bu sorun nedeniyle yeraltı boru hatlarının ömrü kısalır, ekonomik ve can kayıpları ortaya çıkar. Bu nedenle, yeraltı boru hattı uygulamalarında kullanılan çelik malzemelere yeni bir seçenek olabilecek malzemelere ihtiyaç duyulduğu gerçeği halen güncelliğini korumaktadır. Ayrıca, dünyada önemli bir alan kaplayan ve yeraltı boru hatlarının geçtiği turba ortamlarının korozif etkisi hakkında çalışmalar oldukça fakirdir. Bu nedenle, bu çalışmada, turba çözeltisindeki titanyum malzemenin çukurlaşma korozyonu özellikleri, üç elektrotlu elektrokimyasal hücrede potansiyostat/galvanostat cihazı kullanılarak araştırılmıştır. Araştırmalar kapsamında açık devre potansiyeli ve döngüsel potansiyodinamik kutuplaşma testleri gerçekleştirilmiştir. Bahse konu testler birbirleriyle tutarlı olarak gerçekleşmiş ve bu testler turba çözeltisinde titanyum malzemenin çukurlaşma korozyonuna karşı direnç gösterdiğini ortaya koymuştur. Bu nedenle, çukurcuk korozyonuna karşı direnç göstermesi nedeniyle turba ortamlarından geçen yeraltı boru hatlarında kullanılan çelik esaslı malzemeler yerine titanyum esaslı boruların kullanılması tavsiye edilmektedir. Humanities ; Art Corrosion Behavior and Inhibition ; Hydrogen embrittlement and corrosion behaviors in metals ; Concrete Corrosion and Durability
Authors: Xufei Fang
Source: ArXiv.org (preprint, 2025-06-05)
Field: Materials Science — Materials Chemistry

A brief history of dislocations in ceramics: From Steinsalz to quantum wires Dislocations in ceramics have enjoyed a long yet underappreciated research history. This brief historical overview and reflection on the current challenges provides new insights into using this line defect as a rediscovered tool for engineering functional ceramics. Reflection (computer programming) ; Dislocation ; Line (geometry) ; Quantum ; Field (mathematics) Electronic and Structural Properties of Oxides ; Advanced Materials Characterization Techniques ; Advanced ceramic materials synthesis
Authors: Beatriz Rosales‐Reina | Guillermo Cruz‐Quesada | Pablo Pujol | Santiago Reinoso | César Elosúa | Gurutze Arzamendi | María Victoria López‐Ramón | Julián J. Garrido
Source: Preprints.org (preprint, 2025-10-31)
Field: Chemistry — Spectroscopy

Decomposition of Organochlorinated Silica Xerogels at High Temperature: A Kinetic Study Hybrid silica xerogels functionalised with chlorinated organosilanes combine tunable porosity and surface chemistry, making them attractive for applications in sensing, membrane technology, and photonics. The main objective of this work is to investigate the thermal decomposition kinetics of organochlorinated xerogels and to establish a correlation with the released volatile compounds identified in a prior TGA/FTIR/GC–MS study. The materials were synthesised via the sol-gel process using organochlorin-ated alkoxysilane precursors and yielding highly condensed nanostructures in which the precursor nature strongly influences the morphology and textural properties. The N2 adsorption analyses reveal that increasing the precursor content decreases the spe-cific surface area and pore volume while promoting the formation of periodic domains, which are observed even at low organosilane molar percentages. The incorporation of such organic moieties imparts chemical reactivity and flexibility to the silica matrix, although it may also affect thermal stability, which is critical considering that decom-position can release hazardous volatiles such as benzene. A comprehensive thermal characterisation of a series of xerogels containing chloroalkyl and -aryl groups was conducted using TGA coupled to FT–IR and GC–MS, identifying two main decomposi-tion stages and the following order of thermal stability according to the organosilane moiety: 4-chlorophenyl &amp;gt; chloromethyl &amp;gt; 3-chloropropyl &amp;gt; 2-chloroethyl. Kinetic and mechanistic insights were obtained through the Flynn–Wall–Ozawa isoconversional method and Criado master plots, using TGA/DSC measurements under nitrogen at multiple heating rates (5, 10, 20, 30 and 40 K min⁻¹). These results provide a framework for designing thermally stable hybrid materials for advanced technological applications. Thermal decomposition ; Thermal stability ; Porosity ; Decomposition ; Reactivity (psychology) ; Adsorption ; Kinetics ; Thermal analysis ; Specific surface area Aerogels and thermal insulation ; Mesoporous Materials and Catalysis ; Adsorption, diffusion, and thermodynamic properties of materials
Authors: Yue Kan | Fengxia Hu | Jiantao Wang | Jiazheng Hao | Q. Li | Yi Cao | Feiran Shen | Wen Yin | Lunhua He | Bo Su | Jing Wang | Jirong Sun | Zhiguo Chen | Changqing Jin | Xianran Xing | Yunzhong Chen | Tongyun Zhao | Wei Zhai | B. Wei | Baogen Shen
Source: NPG Asia Materials (article, 2025-10-03)
Field: Materials Science — Materials Chemistry

Colossal barocaloric effect in GdCl3-doped H2O for sustainable cooling Abstract H 2 O exists everywhere and its huge latent heat across ice-water phase transition empowers it to be a potential candidate for barocaloric refrigeration applications. Here we report a colossal and reversible barocaloric effect (BCE) in doped H 2 O, where the large hysteresis caused by supercooling is solved by adding 1.33 wt% GdCl 3 . Thereby the reversible entropy change ΔS r ∼728 J kg -1 K -1 has been demonstrated under a low pressure of 0.1 GPa and a more attractive colossal one (1018 J kg -1 K -1 ) can be achieved at 0.16 GPa, exceeding those of all other BCE materials and most of the harmful Freon in vapor compression refrigeration. Neutron measurements combined with molecular dynamics simulations demonstrated that the colossal BCE originates from the breakage/formation of H-bonds in H 2 O. Phonon density of states and Raman spectra validate the change of H-bonds from perspective of dynamics. The super BCE performance and the ubiquitous, non-toxic characters make H 2 O attractive as barocaloric refrigerant for sustainable cooling, more importantly, it is inferred that H-bond engineering can be an attractive approach for designing novel caloric materials. Supercooling ; Freon ; Refrigerant ; Raman spectroscopy ; Phase transition ; Refrigeration ; Magnetic refrigeration ; Colossal magnetoresistance ; Subcooling Thermal Expansion and Ionic Conductivity ; Magnetic and transport properties of perovskites and related materials ; Phase Change Materials Research
Authors: Youjun Zhao
Source: ArXiv.org (preprint, 2025-12-11)
Field: Materials Science — Materials Chemistry

Template-Free Retrosynthesis with Graph-Prior Augmented Transformers Retrosynthesis reaction prediction aims to infer plausible reactant molecules for a given product and is a important problem in computer-aided organic synthesis. Despite recent progress, many existing models still fall short of the accuracy and robustness required for practical deployment. In this paper, we present a template-free, Transformer-based framework that removes the need for handcrafted reaction templates or additional chemical rule engines. Our model injects molecular graph information into the attention mechanism to jointly exploit SMILES sequences and structural cues, and further applies a paired data augmentation strategy to enhance training diversity and scale. Extensive experiments on the USPTO-50K benchmark demonstrate that our approach achieves state-of-the-art performance among template-free methods and substantially outperforms a vanilla Transformer baseline. Retrosynthetic analysis ; Exploit ; Transformer ; Robustness (evolution) ; Benchmark (surveying) ; Training set Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Asymmetric Hydrogenation and Catalysis
Authors: Parham Ghayour
Source: Research Square (preprint, 2025-12-16)
Field: Materials Science — Materials Chemistry

Q-CVaR-Fold: A Quantum CVaR Contrastive LearningFramework for Protein-Folding Decoy Scoring Decoy ; Discriminative model ; Parameterized complexity ; Ranking (information retrieval) ; Representation (politics) ; Computational complexity theory ; Quantum ; Nonlinear system ; Reduction (mathematics) Machine Learning in Materials Science ; Protein Structure and Dynamics ; Advanced Graph Neural Networks
Authors: Johanna Hartley | L.F. Mortimer | Jeff Peakall | Richard A. Bourne | Jonathan M. Dodds | Martyn Barnes | Michael Fairweather | Timothy N. Hunter
Source: Flow Measurement and Instrumentation (article, 2025-04-28)
Field: Chemistry — Physical and Theoretical Chemistry

Convolutional neural networks to characterise particle suspensions from ultrasonic backscatter Ultrasonic backscatter has been used extensively across many applications to characterise suspended particles. It is of particular interest in nuclear decommissioning, as it allows online characterisation without the need to sample, or even contact the suspension in some cases. Industrial processes often utilise dynamic changes to suspended particle concentrations and particle size distributions (PSDs), and as such, characterisation of both simultaneously would be advantageous. At present, there is limited scope within existing analytical methods to achieve this, where the concentration or PSD of the target system must be known to calculate the other. Machine learning (ML) is a method that when trained on representative data, can use non-linear multi-variable minimisations to estimate both concentration and PSD simultaneously and, as such, this study aims to demonstrate that an artificial neural network (ANN) and convolutional neural network (CNN) can accomplish this. A training library of nine spherical glass bead suspension systems, comprising of variable median particle size and coefficient of variation, across six concentrations was compiled using a commercial backscatter instrument at 2 and 4 MHz. The hyperparameters of an ANN and CNN were optimised on these acoustic profiles, before being used to predict median particle size, coefficient of variation, and concentration from acoustic profiles at 2 and 4 MHz of two “unknown” suspensions. While neither the ANN or CNN predictions proved to be successful for estimating the coefficient of variation, moderate agreement between predicted and true values were found for median particle size and concentration from the ANN, while the CNN achieved good agreement for median particle size and very good agreement when predicting particle concentration. Consequently, this study was able to successfully determine that a CNN could simultaneously estimate a median particle size and concentration using ultrasonic backscatter data gathered on an “unknown” suspension. • MHz ultrasonic (US) backscatter characterisation of bidisperse suspensions. • ANN and CNN used in ML evaluation of training sets from different size mixtures. • Particle size distribution (PSD), coefficient of variation and concentration analysed. • CNN predicted PSDs and concentrations from “unknown” mixtures accurately. • Additional features are required for better prediction of Coefficient of Variation. Convolutional neural network ; Backscatter (email) ; Ultrasonic sensor ; Particle (ecology) ; Computer science ; Materials science ; Environmental science ; Artificial intelligence ; Acoustics ; Geology ; Physics ; Telecommunications Electrostatics and Colloid Interactions ; Electrical and Bioimpedance Tomography ; Flow Measurement and Analysis