jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Shahid Atiq | Muhammad Moeen | Abubakar Siddique | Waseem Aslam | Kholod D. Alsufiani
Source: Energy Science & Engineering (article, 2025-08-17)
Field: Materials Science — Materials Chemistry

Machine Learning‐Based Health Index Prediction for XLPE Power Cables: Enhancing Reliability and Cost‐Effective Maintenance ABSTRACT The reliability of power cables is essential for ensuring efficient and uninterrupted power distribution. Insulation degradation caused by thermal aging, moisture ingress, partial discharge (PD), and natural corrosion significantly impacts cable performance and lifespan. Accurate Health Index (HI) estimation is crucial for maintenance planning, failure prevention, and asset management. However, traditional HI assessment methods, which rely on scoring and weighting techniques, often suffer from data availability and subjective evaluations, leading to reduced accuracy. In contrast, this study introduces advanced machine learning (ML)‐based models for predicting the HI of Cross‐Linked Polyethylene cables using both classification and regression models via Python. Real‐time key diagnostic parameters, including Insulation Resistance, Tan Delta, Loading Conditions, and PD activity, were incorporated to enhance model accuracy. To address missing data challenges, this study employs data imputation strategies, ensuring reliable HI predictions even with incomplete data sets. On the basis of the predictive accuracy, a cost‐saving analysis is conducted to quantify the financial benefits of ML‐driven predictive maintenance, highlighting its potential to optimize maintenance costs. Among the eight regression and classification models, Random Forest Regression achieved the highest accuracy of 99.01%. At the same time, Random Forest Classification reached up to 97.2%, demonstrating the effectiveness of ML in HI assessment. The results emphasize that data‐driven HI assessment enhances predictive decision‐making for power cable systems, offering a superior alternative to conventional approaches. Reliability engineering ; Reliability (semiconductor) ; Index (typography) ; Computer science ; Power (physics) ; Engineering ; Forensic engineering High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Electrical Fault Detection and Protection
Authors: Gabriela Camacho Meneses | Juliane Weber | Raphaël P. Hermann | Anna K. Wanhala | Joanne E. Stubbs | Peter J. Eng | Ke Yuan | Albina Y. Borisevich | Matthew G. Boebinger | Tingting Liu | Andrew G. Stack | Jacquelyn N. Bracco
Source: The Journal of Physical Chemistry C (article, 2025-02-12)
Field: Materials Science — Materials Chemistry

Inhibition of Reaction Layer Formation on MgO(100) by Doping with Trace Amounts of Iron Despite extensive research on MgO's reactivity in the presence of CO<sub>2</sub> under various conditions, little is known about whether impurities incorporated into the solid, such as iron, enhance or impede hydroxylation and carbonation reactions. The purity of the MgO required for the successful implementation of MgO looping as a direct air capture technology affects the deployment costs. With this motivation, we tested how incorporated iron impacts MgO (100) reactivity and passivation layer formation under ambient conditions by using atomic force microscopy, electron microscopy, and synchrotron-based X-ray scattering. Based on electron microprobe analysis, our MgO samples were 0.5 wt % iron, and Mössbauer spectroscopy results indicated that 70% of the iron is present as Fe<sup>(II)</sup>. We find that even these low levels of iron dopants impeded both the hydroxylation at various relative humidities (10%, 33%, 75%, and >95%) and carbonation in CO<sub>2</sub> (33%, 75%, and >95%) on the (100) surface. Crystalline reaction products were formed. Reaction layers on the sample were easily removed by exposing the sample to deionized water for 2 min. Overall, our findings demonstrate that the presence of iron dopants slows the reaction rate of MgO, indicating that MgO without incorporated iron is preferable for mineral looping applications. Doping ; Layer (electronics) ; TRACE (psycholinguistics) ; Trace Amounts ; Chemistry ; Radiochemistry ; Materials science ; Metallurgy ; Organic chemistry Magnesium Oxide Properties and Applications ; Layered Double Hydroxides Synthesis and Applications ; Nuclear materials and radiation effects
Authors: Ifeanyi John Onuorah | Miki Bonacci | Muhammad Maikudi Isah | Marcello Mazzani | R. De Renzi | Giovanni Pizzi | Pietro Bonfà
Source: Digital Discovery (article, 2025-01-01)
Field: Chemistry — Spectroscopy

Automated computational workflows for muon spin spectroscopy Positive muon spin rotation and relaxation spectroscopy is a well established experimental technique. We present here computational workflows to automate the corresponding simulations, boosting the predictive power of this technique. Muon spin spectroscopy ; Spectroscopy ; Muon ; Workflow ; Relaxation (psychology) ; Spin (aerodynamics) ; Physics ; Scattering ; Rotation (mathematics) ; Condensed matter physics ; Nuclear magnetic resonance ; Nuclear physics ; Computer science ; Optics ; Quantum mechanics ; Artificial intelligence ; Medicine Advanced NMR Techniques and Applications ; Muon and positron interactions and applications ; Superconducting Materials and Applications
Authors: Joicy John
Source: RSC Pharmaceutics (article, 2025-10-16)
Field: Materials Science — Biomaterials

Advancements in nano-based drug delivery systems for therapeutics: a comprehensive review In contemporary medicine, nano-based drug delivery systems (NDDS) have become a ground-breaking strategy, offering notable improvements in the regulated and targeted release of medicinal drugs. Drug delivery ; Drug ; Targeted drug delivery ; Delivery system ; MEDLINE Nanoparticle-Based Drug Delivery ; Advanced Drug Delivery Systems ; Graphene and Nanomaterials Applications
Authors: Shinpei Ogawa | Misaki Hanaoka | Manabu Iwakawa | Shoichiro Fukushima | Masaaki Shimatani
Source: Sensors (review, 2025-06-17)
Field: Materials Science — Electronic, Optical and Magnetic Materials

All-Dielectric Metalenses for Long-Wavelength Infrared Imaging Applications: A Review Infrared imaging has gained considerable attention across diverse fields, including security, surveillance, and environmental monitoring. The need to minimize size, weight, power, and cost (SWaP-C) poses challenges for conventional optical systems like refractive lenses. Metalenses with subwavelength surface patterns have emerged as promising solutions to address these limitations. This review provides a comprehensive analysis of all-dielectric metalenses for long-wavelength infrared (LWIR) imaging applications, a critical spectral region for human detection and analytical applications (such as gas analysis). We examine the limitations of conventional infrared (IR) lens materials and highlight the performance advantages of LWIR metalenses. Key design principles, including chromatic and achromatic lens configurations, are discussed alongside their imaging performance. Additionally, we review advanced functionalities such as polarization control, multifocal capabilities, zoom, and reconfigurability. Theoretical performance limits and trade-offs are analyzed to provide insights into design optimization. We identify future challenges related to advanced design methods and fabrication techniques. LWIR metalenses can be expected to overcome the shortcomings of conventional LWIR lenses owing to meta-optics technologies, to achieve SWaP-C and advanced functionalities that cannot be achieved by conventional LWIR lenses. This review will guide researchers in academia and industry to develop LWIR metalenses to advance IR imaging technologies. Reconfigurability ; Achromatic lens ; Infrared ; Materials science ; Night vision ; Lens (geology) ; Computer science ; Optics ; Nanotechnology ; Optoelectronics ; Telecommunications ; Physics Metamaterials and Metasurfaces Applications ; Polydiacetylene-based materials and applications ; Optical Coatings and Gratings
Authors: Swee Qi Pan | Yan Chai Hum | Khin Wee Lai | Wun‐She Yap | Yi Zhang | Hye‐Young Heo | Yee Kai Tee
Source: Artificial Intelligence Review (article, 2025-04-17)
Field: Materials Science — Materials Chemistry

Artificial intelligence in chemical exchange saturation transfer magnetic resonance imaging This review delves into the transformative role of Artificial Intelligence (AI) in advancing Chemical Exchange Saturation Transfer (CEST) Magnetic Resonance Imaging (MRI), a cutting-edge imaging method for non-invasive biochemical mapping. CEST MRI faces many technical challenges that hinder its clinical adoption. AI-driven approaches have emerged as one of the promising solutions to address some of these limitations. The evolution of AI in CEST MRI is traced from its inception, with pioneering studies in AI-driven image analysis, to current trends reflecting a marked increase in AI-related CEST publications. This review highlights AI’s impact on various stages of the CEST MRI pipeline, including accelerated imaging acquisition and reconstruction, improved pre-processing and denoising methods, and advanced quantification techniques. Furthermore, AI has demonstrated potential in clinical applications, such as disease diagnosis, molecular subtyping, and treatment monitoring, underscoring its growing relevance in the field. This review also examines the challenges in AI applications and future directions in CEST MRI, including the use of synthetic data, the explainability and interpretability of AI models, and their implications for clinical adoption. Overall, this review provides a comprehensive understanding of the current state of AI applications in CEST MRI and will inspire further research to unlock the full potential of this powerful molecular imaging technique. Magnetic resonance imaging ; Nuclear magnetic resonance ; Saturation (graph theory) ; Materials science ; Computer science ; Physics ; Medicine ; Radiology Lanthanide and Transition Metal Complexes ; Advanced MRI Techniques and Applications ; Atomic and Subatomic Physics Research
Authors: Yicong Qi | Yin Zhang | Shuqi Tang | Zhen Zeng
Source: Forests (article, 2025-01-19)
Field: Chemistry — Organic Chemistry

Synergizing Wood Science and Interpretable Artificial Intelligence: Detection and Classification of Wood Species Through Hyperspectral Imaging With the increasing demand for wood in the wood market and the frequent trade of high-value wood, the accurate identification of wood varieties has become essential. This study employs two hyperspectral imaging systems—visible and near-infrared spectroscopy (VNIR) and short-wave infrared spectroscopy (SWIR)—in combination with a deep learning model to propose a method for wood species identification. Spectral data from wood samples were obtained through hyperspectral imaging technology, and classification was performed using a combination of convolutional neural networks (CNNs) and Transformer models. Multiple spectral preprocessing and feature extraction techniques were applied to enhance data quality and model performance. The experimental results show that the full-band modeling is significantly better than the feature-band modeling in terms of classification accuracy and robustness. Among them, the classification accuracy of SWIR reaches 100%, the number of model parameters is 1,286,228, the total size of the model is 4.93 MB, and the Floating Point Operations (FLOPs) is 1.29 M. Additionally, the Shapley Additive Explanation (SHAP) technique was utilized for model interpretability, revealing key spectral bands and feature regions that the model emphasizes during classification. Compared with other models, CNN-Transformer is more effective in capturing the key features. This method provides an efficient and reliable tool for the wood industry, particularly in wood processing and trade, offering broad application potential and significant economic benefits. Hyperspectral imaging ; Artificial intelligence ; Pattern recognition (psychology) ; Computer science ; Environmental science ; Remote sensing ; Geography Wood and Agarwood Research
Authors: Md Shafkat Bin Hoque | Eric R. Hoglund | Boyang Zhao | De‐Liang Bao | Hao Zhou | Sandip Thakur | Eric Osei‐Agyemang | Khalid Hattar | Ethan A. Scott | Mythili Surendran | John A. Tomko | John T. Gaskins | Kiumars Aryana | Sara Makarem | Adie Alwen | Andrèa M. Hodge | Ganesh Balasubramanian | Ashutosh Giri | Tianli Feng | Jordan A. Hachtel | Jayakanth Ravichandran | Sokrates T. Pantelides | Patrick E. Hopkins
Source: Nature Communications (article, 2025-07-02)
Field: Materials Science — Materials Chemistry

Ruddlesden-Popper chalcogenides push the limit of mechanical stiffness and glass-like thermal conductivity in single crystals Insulating materials featuring ultralow thermal conductivity for diverse applications also require robust mechanical properties. Conventional thinking, however, which correlates strong bonding with high atomic-vibration-mediated heat conduction, led to diverse weakly bonded materials that feature ultralow thermal conductivity and low elastic moduli. One must, therefore, search for strongly-bonded single crystals in which heat transport is impeded by other means. Here, we report intrinsic, glass-like, ultralow thermal conductivity and ultrahigh elastic-modulus/thermal-conductivity ratio in single-crystalline Ruddlesden-Popper Ba<sub>n+1</sub>Zr<sub>n</sub>S<sub>3n+1</sub>, n = 2, 3, which are derivatives of BaZrS<sub>3</sub>. Their key features are strong anharmonicity and intra-unit-cell rock-salt blocks. The latter produce strongly bonded intrinsic superlattices, impeding heat conduction by broadband reduction of phonon velocities and mean free paths and concomitant strong phonon localization. The present study initiates a paradigm of "mechanically stiff phonon glasses". Thermal conductivity ; Limit (mathematics) ; Materials science ; Stiffness ; Thermal ; Condensed matter physics ; Conductivity ; Composite material ; Physics ; Thermodynamics ; Quantum mechanics ; Mathematics ; Mathematical analysis Advanced Thermoelectric Materials and Devices ; Phase-change materials and chalcogenides ; Chalcogenide Semiconductor Thin Films
Authors: Ali Hussein Khalaf | Bing Lin | Bo Zhang | Ying Xiao | Ahmed N. Abdalla | Junlei Tang
Source: Surface Science and Technology (article, 2025-08-25)
Field: Materials Science — Materials Chemistry

Corrosion rate prediction and inhibitor optimization for L80-1 steel and alloyed materials using response surface methodology Abstract Accurate prediction of corrosion rates and optimization of inhibitor strategies are vital for enhancing material durability and operational efficiency in oilfield environments. This study pioneers the use of Response Surface Methodology (RSM) to predict corrosion behavior and optimize inhibitor efficiency for L80-1 steel compared to L80-3Cr, L80-13Cr, and 2205 duplex stainless steel in oil reservoirs. Through immersion weight loss tests and electrochemical measurements, L80-1 was found to be the most corrosion-prone, with high salinity and temperature accelerating degradation. RSM innovatively modeled the impacts of inhibitor concentration, temperature, pH, and exposure time, achieving a remarkable R 2 value of 0.972 for corrosion rate predictions and 0.8927 for inhibitor efficiency predictions, demonstrating exceptional model accuracy. These robust predictive models provide new insights into the complex interplay of environmental factors and inhibitor performance. The findings enable optimized material selection and corrosion control strategies, significantly improving the safety and efficiency of oil well operations, particularly in Iraqi oilfields. This novel application of RSM offers a systematic approach to corrosion management, advancing the field of materials engineering in challenging oilfield conditions. Corrosion ; Response surface methodology ; Materials science ; Corrosion inhibitor ; Metallurgy ; Mathematics ; Statistics Corrosion Behavior and Inhibition ; Hydrogen embrittlement and corrosion behaviors in metals ; Concrete Corrosion and Durability
Authors: Saba Yaqoob | Zulfiqar Ali | Sajjad Ali | Alberto D’Amore
Source: Physchem (article, 2025-04-03)
Field: Materials Science — Materials Chemistry

Polystyrene–Carbon Nanotube Composites: Interaction Mechanisms, Preparation Methods, Structure, and Rheological Properties—A Review This review focuses on the rheological behavior of polystyrene (PS) composites reinforced with carbon nanotubes (CNTs), providing an in-depth analysis of how CNT incorporation affects the viscosity, elasticity, and flow properties of these materials. The review covers fundamental aspects of PS and CNT structures, emphasizing their influence on the composite’s rheological properties. Key interaction mechanisms, including van der Waals forces and covalent bonding, are discussed for their role in determining material behavior. Various preparation methods, such as melt mixing, solution mixing, and in situ polymerization, are evaluated based on their impact on CNT dispersion and rheological performance. The study examines critical rheological parameters such as relative and complex viscosity, shear thinning, and elasticity, supported by theoretical models and experimental findings. The review also identifies major challenges, such as achieving uniform CNT dispersion and addressing processing limitations, while offering insights into future research directions aimed at improving the rheological performance and scalability of PS/CNT composites for advanced applications. Polystyrene ; Rheology ; Materials science ; Carbon nanotube ; Composite material ; Carbon nanotube metal matrix composites ; Nanotube ; Polymer Carbon Nanotubes in Composites ; Polymer Nanocomposites and Properties ; Polymer crystallization and properties
Authors: José D. Gouveia | José R. B. Gomes
Source: Physical Chemistry Chemical Physics (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

The role of oxygen in the synthesis of the Mo<sub>2</sub>CT<sub><i>x</i></sub> MXene from the Mo<sub>2</sub>Ga<sub>2</sub>C MAX phase MAX phases are layered materials that have been known for over 60 years and MXenes are their metaphorical children, a family of two-dimensional (2D) materials that appeared in 2011. MXenes are often synthesised by etching out the layers of the A element of their parent MAX phases. The remaining 2D MXene layers are very reactive and spontaneously become covered by a layer called the surface termination. The Mo<sub>2</sub>C MXene was first synthesised in 2015, by using hydrofluoric acid to remove the Ga layers from the Mo<sub>2</sub>Ga<sub>2</sub>C MAX phase, and its surface termination was determined to be mostly composed by O groups. While the layers of Mo and C in Mo<sub>2</sub>Ga<sub>2</sub>C are arranged in a face-centred cubic manner, those of Mo<sub>2</sub>C prefer a hexagonal close-packed stacking. This implies that, at some point during the etching process, a phase transition should occur because of the replacement of Ga by O. Herein, we employ density-functional theory calculations to analyse the thermodynamics of this process by assessing the stability of the two stacking phases of the Mo<sub>2</sub>Ga<sub>2</sub>C MAX phase, as the Ga atoms are gradually replaced by O ones. Among other results, we identify the preferred locations of O and F atoms in the Mo<sub>2</sub>Ga<sub>2</sub>C lattice, propose a possible explanation for the greater presence of O atoms over F ones on the surface of as-synthesised Mo<sub>2</sub>C MXenes, evaluate the interaction between O atoms in Mo<sub>2</sub>Ga<sub>2</sub>C crystals, estimate the energy of Ga-Ga bonds, and lastly, show that the relative stability of the two stacking phases of Mo<sub>2</sub>Ga<sub>2</sub>C is reversed when approximately half of the Ga content is replaced by O. Phase (matter) ; Oxygen ; Materials science ; Chemistry ; Nanotechnology ; Organic chemistry MXene and MAX Phase Materials ; 2D Materials and Applications ; Ferroelectric and Negative Capacitance Devices
Authors: Shubhayu Das | Markus Vorholt | Andreas Houben | Richard Dronskowski
Source: Crystals (article, 2025-04-23)
Field: Materials Science — Materials Chemistry

Learning Self-Supervised Representations of Powder-Diffraction Patterns The potential of machine learning (ML) models for predicting crystallographic symmetry information from single-phase powder X-ray diffraction (XRD) patterns is investigated. Given the scarcity of large, labeled experimental datasets, we train our models using simulated XRD patterns generated from crystallographic databases. A key challenge in developing reliable diffraction-based structure-solution tools lies in the limited availability of training data and the presence of natural adversarial examples, which hinder model generalization. To address these issues, we explore multiple training pipelines and testing strategies, including evaluations on experimental XRD data. We introduce a contrastive representation learning approach that significantly outperforms previous supervised learning models in terms of robustness and generalizability, demonstrating improved invariance to experimental effects. These results highlight the potential of self-supervised learning in advancing ML-driven crystallographic analysis. Powder diffraction ; Materials science ; Computer science ; Crystallography ; Chemistry X-ray Diffraction in Crystallography ; Machine Learning in Materials Science ; Crystallography and molecular interactions
Authors: Vrindaa Somjit | Joel Davidsson | Yu Jin | Giulia Galli
Source: npj Computational Materials (article, 2025-03-17)
Field: Materials Science — Materials Chemistry

An NV− center in magnesium oxide as a spin qubit for hybrid quantum technologies Recent predictions suggest that oxides, such as MgO and CaO, could serve as hosts of spin defects with long coherence times and thus be promising materials for quantum applications. However, in most cases, specific defects have not yet been identified. Here, by using a high-throughput first-principles framework and advanced electronic structure methods, we identify a negatively charged complex between a nitrogen interstitial and a magnesium vacancy in MgO with favorable electronic and optical properties for hybrid quantum technologies. We show that this NV− center has stable triplet ground and excited states, with singlet shelving states enabling optical initialization and spin-dependent readout. We predict several properties, including absorption, emission, and zero-phonon line energies, as well as zero-field splitting tensor, and hyperfine interaction parameters, which can aid in the experimental identification of this defect. Our calculations show that due to a strong pseudo-Jahn Teller effect and low-frequency phonon modes, the NV− center in MgO is subject to a substantial vibronic coupling. We discuss design strategies to reduce such coupling and increase the Debye-Waller factor, including the effect of strain and the localization of the defect states. We propose that the favorable properties of the NV− defect, along with the technological maturity of MgO, could enable hybrid classical-quantum applications, such as spintronic quantum sensors and single qubit gates. Qubit ; Center (category theory) ; Magnesium ; Quantum ; Spin (aerodynamics) ; Physics ; Quantum computer ; Nanotechnology ; Condensed matter physics ; Quantum mechanics ; Materials science ; Chemistry ; Crystallography ; Metallurgy Electronic and Structural Properties of Oxides ; Semiconductor materials and devices ; Quantum and electron transport phenomena