jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ting Liu | Kun Zheng | Shibin Jiang | Zhirui Zeng | Guanxing Zang | Wei Huang | Weiming Zhu
Source: Sensors (article, 2026-02-28)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Dual-Wavelength Metalens Design for Compact LWIR and MWIR Imaging Systems Multispectral infrared imaging systems that simultaneously operate in the long-wave infrared (LWIR) and mid-wave infrared (MWIR) bands offer significant advantages for target detection and recognition. However, conventional infrared optical systems rely on bulky multi-element lens assemblies to accommodate incident wavelengths of LWIR and MWIR bands, making it challenging for compact thermal optics design. Here, we propose and experimentally demonstrate an inverse designed infrared metalens capable of simultaneously focusing LWIR and MWIR radiation at wavelengths of 9.5 μm and 4.75 μm with a focal length variation of 1%. In the experiment, the proposed metalens with detector enables a dual-wavelength thermal imaging with a compact size (26 × 26 × 18 mm<sup>3</sup>) and a light weight (19 g). This work establishes a robust and scalable inverse design strategy for dual-wavelength infrared metalenses and provides a promising route toward compact, integrated, and multifunctional infrared imaging lens. Infrared ; Detector ; Infrared detector ; Lens (geology) ; Wavelength ; Radiation ; Inverse ; Cardinal point Metamaterials and Metasurfaces Applications ; Thermal Radiation and Cooling Technologies ; Transition Metal Oxide Nanomaterials
Authors: Daniela Prinz | Silvester J. Bartsch | Joachim Friske | Martin Krššák | Daniela Laimer-Gruber | T. Helbich | Katja Pinker
Source: European Radiology Experimental (article, 2026-01-19)
Field: Materials Science — Materials Chemistry

Comparison of postprocessing metrics in multimetabolic APT-weighted CEST and 2-deoxy-D-glucose-CEST-MRI for differentiating breast cancer subtypes in a murine model Advanced multimetabolic APTw-CEST and 2-deoxy-D-glucose-CEST postprocessing metrics allowed adequate preclinical murine BC subtyping. AREX showed potential for 2-deoxy-D-glucose-CEST in tumor characterization; however, APTw-CEST remains superior. MTR<sub>asym</sub> failed to distinguish between tumor subtypes in CEST-MRI. Breast cancer ; Breast tumor ; Cancer ; Ultrasound Lanthanide and Transition Metal Complexes ; MRI in cancer diagnosis ; Magnetism in coordination complexes
Authors: Linzhi Yu | Haobijam Johnson Singh | Jesse Pietila | Hümeyra Çağlayan
Source: Light Science & Applications (article, 2026-02-23)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Double-phase metasurface operators for all-optical image processing All-optical image processing offers a high-speed, energy-efficient alternative to conventional electronic systems by leveraging the wave nature of light for parallel computation. However, traditional optical processors rely on bulky components, limiting scalability and integration. Here, we demonstrate a compact metasurface-based platform for analog optical computing. By employing double-phase encoding and polarization multiplexing, our approach enables arbitrary image transformations within a single passive nanophotonic device, eliminating the need for complex optical setups or digital post-processing. We experimentally showcase key computational operations, including first-order differentiation, cross-correlation, vertex detection, and Laplacian differentiation. Additionally, we extend this framework to high-resolution complex holography, achieving subwavelength-scale volumetric wavefront control for depth-resolved reconstructions with high fidelity. Our results establish a scalable and versatile approach to computational optics, with applications including real-time image processing, energy-efficient computing, biomedical imaging, high-fidelity holographic displays, and optical data storage, driving the advancement of intelligent optical processors. Wavefront ; Scalability ; Image processing ; Holography ; Nanophotonics ; Key (lock) ; Digital image processing ; Photonics ; Reconfigurability Metamaterials and Metasurfaces Applications ; Neural Networks and Reservoir Computing ; Random lasers and scattering media
Authors: Jiwon Park
Source: Metallurgical and Materials Transactions B (article, 2026-03-03)
Field: Materials Science — Materials Chemistry

Preface to the Topical Collection “Artificial Intelligence and Machine Learning in Metal Processing and Metallurgical Engineering” Metal working ; Expert system ; Metallurgical industry ; Data collection Machine Learning in Materials Science ; Iron and Steelmaking Processes ; Metallurgical Processes and Thermodynamics
Authors: Shen Song | Xiaodong Song | Haohan Ding | Xiaohui Cui | Zhenqi Xie | He Huang | Guanjun Dong
Source: Sensors (article, 2026-01-30)
Field: Chemistry — Analytical Chemistry

Data-Driven Soft Sensing for Raw Milk Ethanol Stability Prediction Ethanol stability is an important indicator for evaluating the quality and heat-processing suitability of raw milk. Traditional ethanol stability testing relies on destructive laboratory procedures, which are not suitable for large-scale industrial use. In contrast, parameters such as protein, fat, lactose and other basic compositional indicators are already measured routinely in dairy plants through sensor-based or spectroscopic systems. This provides the basis for developing a non-destructive soft sensing approach for ethanol stability. In this study, a soft sensing model was developed to predict ethanol stability from commonly monitored raw-milk intake indicators. An autoencoder was used to examine feature correlations and select variables with stronger relevance to ethanol stability. TabNet was then applied to build the classification model, and a TabDDPM-based data generation method was introduced to address class imbalance in the dataset. The proposed model was trained and tested using three years of industrial raw-milk intake data from a dairy company. It achieved an accuracy of 92.57% and a recall of 90.26% for identifying ethanol-unstable samples. These results demonstrate the model's strong potential for practical engineering applications in real-world dairy quality monitoring. Stability (learning theory) ; Raw milk ; Raw material ; Autoencoder ; Lactose ; Raw data ; Dairy industry Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Fault Detection and Control Systems
Authors: Zouaoui R. Harrat | Aida Achour | Mohammed Chatbi | Marijana Hadzima-Nyarko | Ercan Işık
Source: Modelling—International Open Access Journal of Modelling in Engineering Science (article, 2026-03-25)
Field: Materials Science — Polymers and Plastics

Modelling the Dynamic Response of Clay Nanoparticle-Modified Concrete Beams Resting on Two-Parameter Elastic Foundations This study presents an analytical investigation of the dynamic behavior of concrete beams reinforced with different types of nano-clay (NC) particles and resting on a Winkler–Pasternak elastic foundation. The equivalent elastic properties of the nanocomposite were determined using an Eshelby-based homogenization model. An improved quasi-three-dimensional beam theory was applied to formulate the governing equations of motion, which were subsequently then analytically solved using Navier’s method. The analysis shows that NC reinforcement systematically elevates the natural frequencies of the beam, with the magnitude of improvement varying by particle type and concentration. Increasing the NC volume fraction to 30% leads to a significant rise in the fundamental frequency, reaching about 30% for hectorite (SHca-1) compared with the unreinforced beam, whereas montmorillonite (SWy-1) produces a more moderate increase of approximately 13%. This reinforcing effect remains consistent across different span-to-depth ratios, indicating that the influence of nano-clay content on the dynamic response is largely independent of beam slenderness. Furthermore, increasing the Winkler foundation stiffness results in an almost linear rise in frequency of approximately 18–22%, whereas the Pasternak shear parameter produces a stronger effect, reaching around 25% enhancement depending on the reinforcement type. These results indicate that incorporating nano-clay platelets can be an effective strategy for enhancing the vibrational stiffness of concrete beams and improving their dynamic performance when interacting with supporting soil media. Stiffness ; Volume fraction ; Reinforcement ; Beam (structure) ; Homogenization (climate) ; Timoshenko beam theory ; Shear (geology) Polymer Nanocomposites and Properties ; Composite Material Mechanics ; Innovative concrete reinforcement materials
Authors: Kham Lek Chaton | Eric D. Boittier | M. Devereux | Markus Meuwly
Source: ArXiv.org (article, 2026-03-15)
Field: Materials Science — Materials Chemistry

Explicit, Machine-Learned Two-Body Potentials for Molecular Simulations A new pairwise hybrid machine-learning/molecular mechanics (ML/MM) potential is introduced that is conceived for application to large, heterogeneous condensed-phase systems. The PhysNet ML method describes monomers and short-range dimer interactions, while a classical MM force field describes pairwise interactions beyond a defined switching distance. Models are fitted to MP2 dimer and pairwise cluster energies, and the quality of each model is assessed at different switching distances and using MM approaches with and without detailed distributed charge electrostatics. The applicability of the approach to molecular dynamics simulations is demonstrated for a basic implementation applied to a small model system. Dichloromethane and acetone are used as test systems to demonstrate the accuracy of the approach in describing pairwise reference data, and also to highlight the limitations of the pairwise approach for systems that exhibit significant many-body effects in condensed phase, paving the way for the addition of a general many-body correction in future work. Pairwise comparison ; Molecular dynamics ; Field (mathematics) ; Set (abstract data type) ; Force field (fiction) ; Dimer ; Cluster (spacecraft) Machine Learning in Materials Science ; Advanced Physical and Chemical Molecular Interactions ; Block Copolymer Self-Assembly
Authors: O. M. Ojo | O. V. Olupona | S. A. Abraham | O. J. Oladotun
Source: Journal of applied science and environmental management (article, 2026-01-03)
Field: Materials Science — Polymers and Plastics

Mechanical Evaluation of Hybrid Particle Boards Reinforced with Corn Cobs and Fabric Waste with Low-density Polyethene Waste as binder This study investigated the mechanical performance of hybrid particle boards produced from corn cobs, fabric waste and low-density polyethylene (LDPE) in varying mix ratios, compared with a conventional wood plastic composite (control) using appropriate standard methods. Results revealed that Sample A achieved the highest MOR (6.91 N/mm²), outperforming the control (5.78 N/mm²), Sample B (6.48 N/mm²), and Sample C (5.66 N/mm²). Conversely, Sample C recorded the highest MOE (5116.87 N/mm²), followed by Sample B (4242.12 N/mm²), the control (4146.24 N/mm²), and Sample A (3944.28 N/mm²). Statistical analysis confirmed significant differences in MOR and MOE across the samples (p &lt; 0.05). The findings demonstrate that while Sample A offers superior bending strength, Sample C provides maximum stiffness. These results suggest that hybrid particle boards incorporating agricultural and textile wastes can be tailored for specific applications, balancing toughness and rigidity as needed. Sample (material) ; Composite number ; Control sample ; Polyethylene ; Particle (ecology) ; Particle size ; Toughness Natural Fiber Reinforced Composites ; Wood Treatment and Properties ; Bamboo properties and applications
Authors: Seungwoo Yoon | Dohyun Kang | Eunsue Choi | Sohyun Lee | Seoyeon Kim | Minho Choi | Hyeonsu Heo | Dong-ha Shin | Suha Kwak | Arka Majumdar | Junsuk Rho | Seung-Hwan Baek
Source: ArXiv.org (article, 2026-01-27)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Learned split-spectrum metalens for obstruction-free broadband imaging in the visible Obstructions such as raindrops, fences, or dust degrade captured images, especially when mechanical cleaning is infeasible. Conventional solutions to obstructions rely on a bulky compound optics array or computational inpainting, which compromise compactness or fidelity. Metalenses composed of subwavelength meta-atoms promise compact imaging, but simultaneous achievement of broadband and obstruction-free imaging remains a challenge, since a metalens that images distant scenes across a broadband spectrum cannot properly defocus near-depth occlusions. Here, we introduce a learned split-spectrum metalens that enables broadband obstruction-free imaging. Our approach divides the spectrum of each RGB channel into pass and stop bands with multi-band spectral filtering and learns the metalens to focus light from far objects through pass bands, while filtering focused near-depth light through stop bands. This optical signal is further enhanced using a neural network. Our learned split-spectrum metalens achieves broadband and obstruction-free imaging with relative PSNR gains of 32.29% and improves object detection and semantic segmentation accuracies with absolute gains of +13.54% mAP, +48.45% IoU, and +20.35% mIoU over a conventional hyperbolic design. This promises robust obstruction-free sensing and vision for space-constrained systems, such as mobile robots, drones, and endoscopes. Broadband ; RGB color model ; Focus (optics) ; Visible spectrum ; Channel (broadcasting) ; Segmentation ; Lens (geology) ; Object (grammar) Metamaterials and Metasurfaces Applications ; Random lasers and scattering media ; Advanced Optical Sensing Technologies
Authors: Pauline Ong | Sia Chee Kiong | Cheng Kit Cheong
Source: Food and Bioprocess Technology (article, 2026-03-04)
Field: Chemistry — Analytical Chemistry

Attention Guided Convolutional Neural Networks for Accurate Egg Defect Detection in Food Quality Assessment Egg defect detection is essential for ensuring food safety and quality in the egg production industry. This study introduces a novel approach for egg defect detection by integrating a multi-head self-attention (MHSA) mechanism into the AlexNet convolutional neural network (CNN) to overcome the limitations of traditional CNNs in capturing long-range dependencies and global context. A machine vision system equipped with a constant-speed rotating mechanism was developed to capture multi-view images of each egg’s surface. Each egg was imaged from six angles, generating a dataset of 2400 images categorized into four classes: bloodstained, cracked, dirty, and intact. The collected images were pre-processed through segmentation and cropping before being fed into the MHSA-AlexNet architecture. The extracted feature maps were flattened and passed through an MHSA layer with eight attention heads, followed by a fully connected layer and Softmax classifier. The proposed MHSA-AlexNet model achieved an average accuracy of 0.9847, outperforming the baseline AlexNet and surpassing other state-of-the-art CNNs. The model also surpassed conventional machine learning models, including Naïve Bayes, Random Forest, and Support Vector Machine, by 36.35–66.81%. Comparison with prior studies demonstrated that MHSA-AlexNet achieved superior accuracy, emphasizing the effectiveness of integrating self-attention mechanisms for egg defect detection. Softmax function ; Convolutional neural network ; Pattern recognition (psychology) ; Support vector machine ; Segmentation ; Feature (linguistics) ; Artificial neural network ; Feature extraction Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Food Supply Chain Traceability
Authors: Bilge Berkhan Kastaci
Source: Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi (article, 2026-03-03)
Field: Materials Science — Polymers and Plastics

FARKLI YAPISAL ÖZELLİKLERDEKİ ÖRME KUMAŞLARIN PATLAMA MUKAVEMETİNİN MAKİNE ÖĞRENMESİ YÖNTEMLERİYLE MODELLENMESİ VE TAHMİNİ Örme kumaşların patlama mukavemeti, çok eksenli ve ani basınçlara karşı dayanımı belirleyen kritik bir mekanik özelliktir ve spor–performans, tıbbi tekstil ve koruyucu giyim uygulamalarında önemli bir rol oynar. Ancak geleneksel test yöntemleri zaman ve maliyet açısından sınırlayıcıdır. Bu nedenle, kumaşın yapısal ve fiziksel özelliklerine dayalı tahmin modellerinin geliştirilmesi üretim verimliliği ve kalite kontrol açısından önemli avantajlar sunmaktadır. Bu çalışmada, 56 farklı konstrüksiyona sahip örme kumaştan elde edilen deneysel veriler kullanılarak çeşitli makine öğrenmesi algoritmalarıyla patlama mukavemeti tahmin modelleri geliştirilmiştir. Girdi parametreleri literatür bulguları, korelasyon analizi ve Varyans Şişirme Faktörü (VIF) sonuçlarına göre belirlenmiştir. Doğrusal regresyon (LR), çok değişkenli ayarlanabilir regresyon eğrileri (MLR) ve destek vektör makinelerinde regresyon (SMOreg) modelleri, k-katlı çapraz doğrulamada en yüksek tahmin performansını göstermiştir. Duyarlılık analizi, LR ve SMOreg modellerinde çubuk sıklığının, MLR modelinde ise iplik mukavemetinin tahmin performansını en az etkileyen parametre olduğunu ortaya koymuştur. Bu bulgular, makine öğrenmesi tabanlı yaklaşımların örme kumaşların mekanik özelliklerinin öngörülmesinde etkili ve uygulanabilir bir alternatif sunduğunu göstermektedir. Neutral network Textile materials and evaluations ; 3D Shape Modeling and Analysis ; Adhesion, Friction, and Surface Interactions
Authors: Justus Arweiler | Indra Jungjohann | Aparna Muraleedharan | Heike Leitte | Jakob Burger | Kerstin Münnemann | Fabian Jirasek | Hans Hasse
Source: Scientific Data (preprint, 2026-03-31)
Field: Materials Science — Materials Chemistry

Batch Distillation Data for Developing Machine Learning Anomaly Detection Methods Machine learning (ML) holds great potential to advance anomaly detection (AD) in chemical processes. However, the development of ML-based methods is hindered by the lack of openly available experimental data. To address this gap, we have set up a laboratory-scale batch distillation plant and operated it to generate an extensive experimental dataset, covering fault-free experiments and experiments in which anomalies were intentionally induced, for training advanced ML-based AD methods. In total, 119 experiments were conducted across a wide range of operating conditions and mixtures. Most experiments containing anomalies were paired with a corresponding fault-free one. The dataset that we provide here includes time-series data from numerous sensors and actuators, along with estimates of measurement uncertainty. In addition, unconventional data sources - such as concentration profiles obtained via online benchtop NMR spectroscopy and video and audio recordings - are provided. Extensive metadata and expert annotations of all experiments are included. The anomaly annotations are based on an ontology developed in this work. The data are organized in a structured dataset and made freely available via https://doi.org/10.5281/zenodo.17395543 . This new dataset paves the way for the development of advanced ML-based AD methods. As it includes information on the causes of anomalies, it further enables the development of interpretable and explainable ML approaches, as well as methods for anomaly mitigation. Anomaly detection ; Metadata ; Set (abstract data type) ; Range (aeronautics) ; Anomaly (physics) ; Data set ; Distillation Machine Learning in Materials Science ; Fault Detection and Control Systems ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: L. Della Giovanna | F. Guastavino | E. Torello
Source: Metrology (article, 2026-04-01)
Field: Materials Science — Materials Chemistry

Experimental Study of High-Frequency Current Transformer for Partial Discharge Detection Using Frequency and Impulse Metrics This study presents a characterization method for High-Frequency Current Transformers (HFCTs) intended for partial discharge (PD) measurement in on-line acquisition systems designed for AI-based processing and clustering. The primary objective is to analyze how key design parameters, ferrite core material, and number of turns, influence HFCT frequency response, attenuation, and sensitivity, thereby providing a basis for optimized sensor design when data analysis is to be performed by means of AI-based algorithms. The investigation focuses on the influence of different ferrite core materials and varying secondary turn numbers on the frequency spectrum and the response to IEC 60270-compliant calibrator impulses Both concentrated and well-distributed HFCT secondary winding configurations are analyzed to evaluate their impact on signal behavior and sensitivity. The experimental results are compared with a simplified theoretical model to validate performance trends and identify key design factors. The HFCT response to IEC 60270-compliant calibrator impulses is examined to assess its suitability for PD measurement systems and monitoring. The results highlight the critical role of core selection and the number of turns in shaping HFCT bandwidth, attenuation, and impulse response, which are essential for accurate and reliable PD detection in continuous monitoring systems to perform the diagnostic of the electrical insulation condition. This diagnostic approach is based on the detection of partial discharge (PD) activity over time, with the objective of identifying evolving phenomena by monitoring the amplitude and characteristics of the signals associated with different defects. Therefore, accurate separation of signals originating from different defects and from noise is essential. These results provide a foundation for designing HFCT sensors suitable for integration into advanced diagnostic frameworks, AI-aided for Condition-Based Maintenance (CBM). Partial discharge ; Current transformer ; Transformer ; Impulse (physics) ; Ferrite core ; Signal processing ; Data acquisition ; Rogowski coil ; System of measurement High voltage insulation and dielectric phenomena ; Electrical Fault Detection and Protection ; Power Transformer Diagnostics and Insulation
Authors: Olivia Clifton | A. E. Chávez | Antonio Madrigal | Annie Warren | Paige Yeung | Arnd Scheel
Source: Journal of Nonlinear Waves (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Vacuum bubble and fissure formation in collective motion with competing attractive and repulsive forces We study the continuum limit of the motion of agents in the plane driven by competing short-range repulsion and long-range attractive forces.At a critical parameter value, we find destabilization of a trivial branch of uniformly distributed solutions and analyze bifurcating solutions.Curiously, the bifurcating branch is vertical, leading to a reversible, non-hysteretic phase transition.Near the bifurcation point, we demonstrate scaling laws for the size of vacuum regions, which can form fissures or bubbles.We also study the effect of small noise and the eventual topological transition from vacuum bubbles to isolated particle clusters. Fissure ; Bubble ; Motion (physics) ; Collective motion ; Field (mathematics) Pickering emulsions and particle stabilization ; Fluid Dynamics and Mixing ; Micro and Nano Robotics