jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Hayk Minassian | Armen Melikyan | Manuel R. Gonçalves | Petros Petrosyan
Source: RSC Advances (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Nonlinear light absorption in Ti <sub>3</sub> C <sub>2</sub> T <sub> <i>x</i> </sub> MXene: a theoretical study Nonlinear absorption of MXenes has been investigated experimentally in several recent publications, and applications of these new 2D materials in broadband ultrafast photonics has already been demonstrated. In particular, MXenes have been used in the generation of highly stable femtosecond pulses in mode-locked fiber lasers. The optical nonlinearities appearing in different MXenes in the vis-NIR wavelength range have been explained theoretically based on the theory of saturation of nonlinear absorption in a two-level atomic system. This approach requires a fitting procedure applied to determine the saturation intensity of the MXene. However, this approximation does not account for the band structure of MXene, and therefore the microscopic character of the saturation of nonlinear absorption is still not understood. In this communication we use published results of calculations of the band structure of titanium carbide MXene and apply the density matrix theory to calculate the nonlinear absorption coefficient for a two-band model, in the resonance approximation. The latest experimental results on nonlinear transmittance at pump wavelengths between 1000 nm and 1500 nm are discussed in this framework and the saturation dynamics of absorption is revealed. Absorption (acoustics) ; Nonlinear system ; Chemistry ; Crystallography ; Analytical Chemistry (journal) ; Physical chemistry ; Materials science ; Atomic physics ; Physics ; Optics ; Quantum mechanics ; Environmental chemistry MXene and MAX Phase Materials ; 2D Materials and Applications ; Boron and Carbon Nanomaterials Research
Authors: Ashutosh Kumar Maurya | Sweta Saswati Rath | Ashish Kumar Maurya | V.B. Sameer Kumar | Jordi Muntané | Rajendra Pilankatta
Source: Preprints.org (preprint, 2025-11-04)
Field: Materials Science — Biomaterials

Translational Nanotechnology in Oncology: Integrating Nanoscale Innovation into Precision Cancer Diagnosis and Therapy Cancer remains one of the most formidable challenges in global health, characterized by immense biological complexity, heterogeneity, and resistance to conventional therapies. Despite significant advances in molecular biology and immuno-oncology, limitations in early detection, targeted delivery, and therapeutic precision continue to hinder effective management. Over the past two decades, nanotechnology has emerged as a transformative platform, offering novel strategies for both cancer diagnostics and treatment. Engineered nanomaterials—ranging from liposomes and polymeric nanoparticles to quantum dots and metallic nanostructures—enable precise tumor targeting, enhanced imaging resolution, and controlled therapeutic release. In diagnostics, nanoscale biosensors and imaging probes have revolutionized the sensitivity and specificity of biomarker detection and visualization, while in therapeutics, nanocarriers have minimized systemic toxicity and improved pharmacokinetics. Recent innovations, including smart and stimuli-responsive nanoplatforms, nanorobotics, and integrated “theranostic” systems, are reshaping personalized cancer care. However, translational hurdles persist, particularly in large-scale manufacturing, biosafety, and regulatory validation. This review critically examines the evolution of nanotechnology in oncology, emphasizing current applications, clinical progress, and future prospects toward achieving precision nanomedicine in cancer diagnosis and therapy. Nanomedicine ; Nanocarriers ; Precision medicine ; Cancer therapy ; Nanosensor ; Cancer ; Personalized medicine ; Transformative learning ; Cancer biomarkers Nanoparticle-Based Drug Delivery ; Advanced biosensing and bioanalysis techniques ; Nanoplatforms for cancer theranostics
Authors: H W Liu | Kangyan Liu | B. S. Zhang | Z Chen | Yi Yang | Qiang Sun | Tao Ye | Bed Poudel | Kai Wang | Congcong Wu
Source: Carbon Energy (article, 2025-12-25)
Field: Materials Science — Materials Chemistry

Data‐Driven Design of Scalable Perovskite Film Fabrication via Machine Learning–Guided Processing ABSTRACT The key challenge in the preparation of perovskite solar cells is to enhance the reproducibility of PSC manufacturing, particularly by better controlling multiple high‐dimensional process parameters. This study proposes a machine learning (ML) approach to efficiently predict and analyze perovskite film fabrication processes. By evaluating five classic ML algorithms on 130 experimental data sets from blade‐coating parameters, the Random Forest (RF) model was identified as the most effective, enabling rapid prediction of over 100,000 parameter sets in just 10 min‐equivalent to 3 years of manual experimentation. The RF model demonstrated strong predictive accuracy, with an R 2 close to 0.8. This approach led to the identification of optimal process parameter combinations, significantly improving the reproducibility of PSCs and reducing performance variance by approximately threefold, thereby advancing the development of scalable manufacturing processes. Fabrication ; Scalability ; Reproducibility ; Perovskite (structure) ; Process (computing) ; Identification (biology) ; Key (lock) Machine Learning in Materials Science ; Perovskite Materials and Applications ; Explainable Artificial Intelligence (XAI)
Authors: Yao Li | Xiangrui Wang | Dingyuan Liang | Xiaoli Zhao | Zhaomin Dong | Yingchen Bai | Wen‐Xiong Wang | Willie J.G.M. Peijnenburg | Ying Wang | Wenhong Fan
Source: Aquatic Toxicology (article, 2025-02-11)
Field: Materials Science — Materials Chemistry

Modelling the size distribution and bioaccumulation of gold nanoparticles under mixture exposure Bioaccumulation ; Environmental chemistry ; Nanoparticle ; Colloidal gold ; Chemistry ; Environmental science ; Nanotechnology ; Materials science Nanoparticles: synthesis and applications ; Heavy metals in environment
Authors: Radost Herboth | Dmytro Dudariev | Alexander P. Lyubartsev
Source: Crystal Growth & Design (article, 2025-08-16)
Field: Materials Science — Materials Chemistry

Estimating the Solubility of Active Pharmaceutical Ingredients Using Molecular Dynamics The solubility of molecular crystals is of interest in many areas of chemistry, of which pharmaceutical applications are of particular importance. Predicting solubility using atomistic first-principle methods, that compare the chemical potential of the solid and the solvated phase, has become more common, but remains challenging due to the difficulty in modeling both the crystal form, including its polymorphs, and the interactions with the solvent. Here we aim to compute the solubilities of three active pharmaceutical ingredients of increasing size and complexity: paracetamol, carbamazepine and indomethacin. The known anhydrous polymorphs of each compound are considered in the calculation of the free energy of the solid form and the solvation is explored both in water and in some organic solvents (ethanol, methanol and acetonitrile). The compounds are categorized as poorly soluble or well-soluble based on the comparison of the solid form free energy and the solvation free energy of a single molecule at infinite dilution. Poor solubility then prompts the use of a solubility estimation based on excess free energies, while for well-soluble molecules their chemical potential has to be calculated as a function of concentration. This is done using the recently developed S0 method. While promising results are obtained for paracetamol, carbamazepine and indomethacin predictions systematically underestimate the solubility. This can be ascribed to incomplete descriptions of intermolecular interactions by the force field, but the order of stability of the solid forms and systematic nature of the deviations point toward additional problems originating in the accuracy of the method used to calculate the solid free energies. Solubility ; Active ingredient ; Molecular dynamics ; Chemistry ; Pharmaceutical sciences ; Biochemical engineering ; Biological system ; Nanotechnology ; Computational chemistry ; Materials science ; Organic chemistry ; Pharmacology ; Medicine ; Engineering Crystallization and Solubility Studies ; Pharmacological Effects of Natural Compounds ; Analytical Chemistry and Chromatography
Authors: Abu Hassan | Zakiah Mohd Yusoff | Nurlaila Ismail | Mohd Nasir Taib
Source: IAES International Journal of Artificial Intelligence (article, 2025-08-01)
Field: Chemistry — Organic Chemistry

Application of self-organizing map for modeling the Aquilaria malaccensis oil using chemical compound &lt;span&gt;Agarwood oil, known as ‘black gold’ or the ‘wood of God,’ is a globally prized essential oil derived naturally from the &lt;/span&gt;&lt;em&gt;&lt;span&gt;Aquilaria&lt;/span&gt;&lt;/em&gt;&lt;span&gt; tree. Despite its significance, the current non-standardized grading system varies worldwide, relying on subjective assessments. This paper addresses the need for a consistent classification model by presenting an overview of &lt;/span&gt;&lt;em&gt;&lt;span&gt;Aquilaria malaccensis&lt;/span&gt;&lt;/em&gt;&lt;span&gt; oil quality using the self-organizing map (SOM) algorithm. Derived from the Thymelaeaceae family, &lt;/span&gt;&lt;em&gt;&lt;span&gt;Aquilaria malaccensis&lt;/span&gt;&lt;/em&gt;&lt;span&gt; is a primary source of agarwood trees in the Malay Archipelago. Agarwood oil extraction involves traditional methods like solvent extraction and hydro-distillation, yielding a complex mixture of chromone derivatives, oxygenated sesquiterpenes, and sesquiterpene hydrocarbons. This study categorizes agarwood oil into high and low grades based on chemical compounds, utilizing the SOM algorithm with inputs of three specific compounds: β-agarofuran, α-agarofuran, and 10-epi-φ-eudesmol. Findings demonstrate the efficacy of SOM-based quality grading in distinguishing agarwood oil grades, offering a significant contribution to the field. The non-standardized grading system's inefficiency and subjectivity underscore the necessity for a standardized model, making this research crucial for the agarwood industry's advancement.&lt;/span&gt; Computer science Wood and Agarwood Research ; Image Retrieval and Classification Techniques
Authors: Jihad Oumerri | Wan Nurhidayah Wan Hanaffi | Hicham Qayouh | Ana‐Andreea Arteni | Mohammed Lahcini | Jean‐Luc Six | Khalid Ferji
Source: ACS Applied Polymer Materials (article, 2025-07-29)
Field: Chemistry — Organic Chemistry

Binary-Stabilized Morphological Control of Cationic Oligochitosan Nano-Objects in Polymerization-Induced Self-Assembly Preparing functional nano-objects by polymerization-induced self-assembly (PISA) from polyelectrolyte-based stabilizers has remained challenging due to strong electrostatic interactions that prevent morphological evolution beyond spherical assemblies. To overcome this limitation, we developed a binary stabilization strategy combining cationic oligochitosan (OCS) and neutral dextran (Dex) stabilizers in a photoinitiated reversible addition–fragmentation chain transfer polymerization-induced self-assembly (photo-RAFT PISA) approach. By fine-tuning the molar fraction of glucosamine units (fGaU) and glucosidic units (fGU), we successfully unlocked morphology transitions, forming complex nanostructures. Our results demonstrate that this dual stabilization mechanism effectively balances electrostatic and steric repulsions, thereby enabling controlled self-assembly in polyelectrolyte-driven PISA. This work provides a scalable and bioinspired strategy for engineering complex polysaccharide-based nanostructures with promising applications in drug delivery and biomaterial design. Cationic polymerization ; Nano- ; Polymerization ; Self-assembly ; Materials science ; Polymer chemistry ; Chemical engineering ; Nanotechnology ; Composite material ; Polymer ; Engineering Advanced Polymer Synthesis and Characterization ; Surfactants and Colloidal Systems ; Polymer Surface Interaction Studies
Authors: Tzu-Chieh Wei | Weixing Wan | C L Chen
Source: ArXiv.org (preprint, 2025-04-16)
Field: Materials Science — Materials Chemistry

Deep Material Network: Overview, applications and current directions The Deep Material Network (DMN) has emerged as a powerful framework for multiscale materials modeling, enabling efficient and accurate prediction of material behavior across different length scales. Unlike conventional data-driven approaches, the trainable parameters in DMN possess clear physical interpretations-they encode the geometric characteristics of representative volume elements (RVEs) rather than serving as purely statistical fitting parameters . By employing a hierarchical tree structure, DMN learns the homogenization behavior associated with microstructural geometry. Consequently, it can be trained exclusively on linear elastic datasets while effectively extrapolating to nonlinear responses during online prediction, making it a highly efficient and scalable approach for multiscale simulations. From a broader perspective, DMN can be viewed as a physics-informed reduced-order model that captures the essential micromechanical features governing macroscopic behavior. Its hierarchical formulation provides a compact yet interpretable representation of the RVE response, significantly reducing computational costs compared to direct numerical simulations. This review elaborates on the theoretical foundation, training methodology, and recent extensions of DMN, emphasizing its role as a unifying framework that connects data-driven learning with physically interpretable multiscale modeling. Microscale chemistry ; Multiscale modeling ; Generalization ; Scalability ; Nonlinear system ; Key (lock) ; Artificial neural network ; Material properties ; Tree (set theory) Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Composite Material Mechanics
Authors: Yi Ouyang | Meng Chen | Siwei Pan | Qing Wang | Yihua Qian | Yuanyuan Li | Yong Liu | Pengfei Fang
Source: Materials (article, 2025-11-21)
Field: Materials Science — Materials Chemistry

Optimization of Anti-Fouling Piezoelectric Composite Coating for High-Voltage Insulators in Converter Stations Under the DC field, live contamination is more likely to deposit on the surface of insulators due to the action of the external electric field. The deposition of dirt on the surface of Ultra High Voltage (UHV) insulators can lead to the occurrence of flashover phenomena, causing significant economic losses. Due to the particularity of UHV insulators, many traditional surface anti-pollution technologies designed for normal voltage insulators are not applicable to them. In order to prevent the harm of contamination accumulation affecting the safe operation of transmission lines, in this study, tetragonal BaTiO<sub>3</sub> was mixed into room-temperature vulcanized silicone rubber for the first time to prepare a composite coating with piezoelectric properties. This coating can use the piezoelectric effect to remove the contamination adhering to the surface of UHV insulators under a DC field. In this study, the piezoelectric properties of the prepared tetragonal BaTiO<sub>3</sub> were verified through material characterization. The results show that the introduction of piezoelectric fillers can significantly accelerate the dissipation of charges on the insulator surface under slight disturbances, which helps to reduce the accumulation of charged pollutants on the insulator surface. The anti-pollution performance under electric field conditions was verified through a simulation experimental device. Finally, through experiments in a real converter station environment, the anti-pollution effect of the insulator under actual working conditions was verified. Insulator (electricity) ; Coating ; Piezoelectricity ; Silicone rubber ; Electric field ; Vulcanization ; Voltage ; Curing (chemistry) High voltage insulation and dielectric phenomena ; Dielectric materials and actuators ; Power Transformer Diagnostics and Insulation
Authors: Yusuf Kuvvetli | Ebru Çalışkan
Source: Nicel Bilimler Dergisi (article, 2025-12-31)
Field: Materials Science — Polymers and Plastics

Garment Time Prediction of Denim Products using Artificial Neural Networks and Ensemble Methods Textile products are subject to frequent changes due to fashion trends, yet maintaining low production costs remains essential. Production time affects these costs significantly and numerous factors can affect production time, including the complexity of the design, the type of fabric, specific parts, and operational characteristics. This study aims to predict the garment time of denim products using information from sample items, rather than relying on traditional time measurement methods. To achieve this, a dataset of 79 attributes was created and later refined to 53 features through preprocessing. These selected features were then used to predict the standard garment time using artificial neural networks, extreme gradient boosting, and random forest algorithms. Based on these results, the study proposes an alternative approach for managers to predict production time using sample-based parameters, eliminating the need for time-consuming observational studies. The findings indicate that ensemble algorithms such as extreme gradient boosting, and random forest demonstrate a high degree of efficacy, exhibiting an absolute average error of approximately 10% in the test data. Production (economics) ; Denim ; Artificial neural network ; Random forest ; Sample (material) ; Lead time Textile materials and evaluations ; 3D Shape Modeling and Analysis ; Fashion and Cultural Textiles
Authors: Mikhail Kurbakov | Valentina Sulimova | Oleg Seredin | Andrey Kopylov
Source: Computer Optics (article, 2025-06-01)
Field: Materials Science — Materials Chemistry

Interpretable graph methods for determining nanoparticles ordering in electron microscopy image An important step in determining the properties of carbon materials is the analysis of images from a scanning electron microscope (SEM). These images show the material surface after the application of metal nanoparticles. The order of these nanoparticles is a key characteristic that affects the material properties. We have previously proposed an approach to formalize the order features based on the identification of lines by nanoparticles in the SEM image. This paper proposes a novel approach to line allocation that is based on the concept of constructing a minimum spanning forest. Additionally, it introduces a set of novel ordering functions that are derived from this approach. The experimental study demonstrates that the combination of these new and previously extracted features improves the recognition quality of SEM images with ordered and disordered nanoparticles arrangements. This approach allows us to gain a better understanding of the nanoparticles arrangement and their effect on the material properties. Nanoparticle ; Scanning electron microscope ; Graph ; Set (abstract data type) ; Image (mathematics) ; Surface (topology) ; Line (geometry) ; Electron microscope Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Computational Drug Discovery Methods
Authors: Simone Braccia | Oluseyi Moses Ajayi | Silvano Palladino | Lorenzo Emiliano Imbò | Rosa Bellavita | Annarita Falanga | Luigi Alfano | Alessia Giordano | Stefania Galdiero
Source: Annals of Research in Oncology (article, 2025-07-01)
Field: Materials Science — Biomaterials

Current advances and future vision of drug delivery systems for cancer Conventional cancer treatments often damage both tumour and healthy cells, leading to significant systemic toxicity. This has created an urgent need for the advancements of targeted drug delivery systems that can selectively reach cancer cells or tissues and release the drug precisely at the intended site. Recent advancements in anticancer therapy have highlighted the importance … Continued Current (fluid) ; Drug delivery ; Medicine ; Cancer ; Cancer drugs ; Medical physics ; Computer science ; Intensive care medicine ; Nanotechnology ; Engineering ; Internal medicine Nanoparticle-Based Drug Delivery ; RNA Interference and Gene Delivery ; Computational Drug Discovery Methods
Authors: Jin Wang | Fengping Luo | Tao Zheng | Bowen Zhang | Yuxin Liu | Denghuang Chen | Xinyue Xie | Mohan Chen | Hong-Bo Zhou | Fei Gao | Jianming Xue | Yugang Wang | Chenxu Wang
Source: ArXiv.org (preprint, 2025-04-28)
Field: Materials Science — Materials Chemistry

Evolution of cavities in BCC-Fe with coexisting H and He under fusion environments In the fusion environment, understanding the synergistic effects of transmutation-produced hydrogen (H), helium (He), and irradiation-induced displacement damage in iron-based alloys is crucial for the development of structural materials for fusion reactors. When H and He atoms are simultaneously introduced into the matrix, the interaction between irradiation-induced cavity defects (voids and bubbles) with H and He, along with their evolutionary behavior remains poorly understood. In this study, the evolutionary behavior of cavities in body-centered cubic (BCC) iron (Fe) with H and He atoms is systematically investigated through a combination of molecular dynamics (MD) calculations and statistical thermodynamics. First, an efficient and suitable set of Fe-H-He ternary potential functions for describing interatomic interactions is established. Based on the newly developed MD model, the evolutionary behavior of H/He atoms and cavities is systematically investigated under various temperature and cavity structure conditions. Specifically, the kinetic process of H/He capture by cavities is elucidated for different scenarios. Additionally, thermodynamic analyses are employed to assess the feasibility of cavity trapping of H under varying conditions. The results exhibit strong consistency with experimental results and provide significant evidence supporting the formation of the core-shell structure (where He is confined at the cavity center while H accumulates at the surface) from both kinetic and thermodynamic perspectives. This work provides mechanistic insights into the nucleation and growth of cavities over extended temporal and spatial scales in the presence of H-He synergies. Fusion ; Nucleation ; Molecular dynamics ; Ternary operation ; Kinetic energy ; Hydrogen ; Work (physics) Fusion materials and technologies ; Hydrogen embrittlement and corrosion behaviors in metals ; Hydrogen Storage and Materials