jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Shogo Kawaguchi
Source: Vacuum and Surface Science (article, 2025-02-09)
Field: Materials Science — Materials Chemistry

Automation and Current Status of Synchrotron Powder Diffraction Experiments Automation of synchrotron radiation powder diffraction experiments at SPring-8 is described, focusing on the BL02B2 powder diffractometer and the BL13XU high-resolution instrument. The development of automation, including data acquisition, sample exchange, and instrument switching, has greatly improved experimental efficiency ; the integration of the MYTHEN detector and automated systems for temperature control, sample preparation, and gas handling has improved measurement speed and accuracy. These developments enable high-throughput, in-situ, and operando measurements that support further materials innovation and facilitate the application of data-driven analysis to large experimental data sets. Synchrotron ; Current (fluid) ; Automation ; Powder diffraction ; Materials science ; Diffraction ; Engineering physics ; Computer science ; Systems engineering ; Crystallography ; Engineering ; Optics ; Physics ; Mechanical engineering ; Electrical engineering ; Chemistry X-ray Diffraction in Crystallography ; Crystallization and Solubility Studies ; Iron and Steelmaking Processes
Authors: Liew, Kai Bin | Phang, Hiu Ching | Tan, Vinie Ying Xuan | Kee, Phei Er | Ming, Long Chiau | Vijayaraj Kumar, Palanirajan | Lee, Siew-Keah | Helal Uddin, A.B.M. | Gilhotra, Ritu M. | Ramanunny, Arya Kadukkattil
Source: The International Islamic University Malaysia Repository (The International Islamic University Malaysia) (article, 2025-04-16)
Field: Materials Science — Biomaterials

Nanoparticles as novel drug delivery systems for cancer treatment: current status and future perspectives Cancer continues to pose a significant global health challenge, demanding innovative therapeutic approaches to overcome the limitations of conventional treatments like chemotherapy and radiotherapy. Nanoparticles (NPs) have emerged as promising tools for cancer therapy due to their unique physicochemical properties that enable targeted drug delivery, reduced systemic toxicity, and enhanced therapeutic efficacy. This comprehensive review delves into the mechanisms of NP-based drug delivery, highlighting both passive and active targeting strategies. It categorizes and discusses diverse NP types, including polymeric, lipid-based, and metallic nanoparticles, emphasizing their applications in enhancing the bioavailability and specificity of anticancer agents. This review also explores the integration of advanced technologies, such as theranostics and artificial intelligence, to optimize NP design and functionality for personalized medicine. However, challenges remain, including issues related to toxicity, drug resistance, and manufacturing scalability. Addressing these barriers re- quires interdisciplinary research focused on developing stimuli-responsive NPs, improving biocompatibility, and incorporating multimodal therapeutic platforms. Although substantial progress has been made, this review is limited by the paucity of clinical trials validating NP efficacy and safety in diverse patient populations. Fu- ture endeavors should prioritize translational research to bridge the gap between preclinical innovations and clinical applications, ensuring that these transformative technologies benefit a broader spectrum of cancer pa- tients. The review underscores the immense potential of NPs in redefining cancer treatment while advocating for sustained research to address existing limitations and unlock their full therapeutic promise. Cancer ; Clinical trial ; Drug delivery ; Transformative learning ; Cancer therapy ; Translational research ; Cancer treatment ; Targeted drug delivery Nanoparticle-Based Drug Delivery ; Nanoplatforms for cancer theranostics ; Nanoparticles: synthesis and applications
Authors: A. N. Phuge1, V. U. Barge2*
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2025-11-07)
Field: Chemistry — Spectroscopy

Analytical Perspectives on Chromatographic Method Development for Impurity Profiling in Pharmaceuticals The analysis of unwanted elements plays a crucial role in pharmaceutical quality assurance, demanding advanced analytical techniques to ensure the safety of medical products and compliance with legal standards. The transformation from traditional method development towards systematic, quality by: Pharmaceutical impurity analytical landscape has been changed by design (QbD) approaches. analysis. Scope: The current review meticulously examines contemporary chromatographic methodologies and techniques employed in the field. Our work examines current efforts in pharmaceutical development and validation, with particular attention to new technologies like impurity profiling. We also present an detailed analysis of recent progress in the Analytical QbD. In addition, we investigate orthogonal separation techniques, sophisticated hyphenated mass spectrometry methodologies, and the foundational principles of green analytical chemistry. Study Results: Ultra-high performance liquid chromatography with mass spectrometry Ultra-High Pressure Liquid Chromatography-mass spectrometry (UHPLC-MS/MS) has become the best method for the identification of impurities. While the use of HPLC-UV is crucial for routine quantitation. Design of Experiments (DoE) methodologies, combined with Monte Carlo simulations and Method Operable Design Region (MODR) concepts, enable robust method development with probabilistic performance assurance. Two-dimensional liquid chromatography and ion-pair-free stationary phases address complex separation challenges, particularly for oligonucleotide therapeutics. Future Direction: The incorporation of artificial intelligence in method development, standardization of green analytical metrics, and automation of the lifecycle are major areas for development. The coming together of the principles of AQbD and sustainability perspectives and regulatory harmonization will drive the next generation of analytical methodologies. Quality by Design ; Standardization ; Analytical technique ; Profiling (computer programming) ; Mass spectrometry ; Automation ; Harmonization ; Good laboratory practice Analytical Chemistry and Chromatography ; Analytical Methods in Pharmaceuticals ; Pesticide Residue Analysis and Safety
Authors: Jing Chen | Ying‐Chang Liang | Xiangxi Li | Xinyu Liu | Hao Jiang | Xiren Miao | Liping Zhang
Source: IEEE Transactions on Power Electronics (article, 2025-10-31)
Field: Materials Science — Electronic, Optical and Magnetic Materials

A Magnetic Core Loss Modeling Method Based on Domain Adaptation Using Multiple Kernel Maximum Mean Discrepancy Core loss is a crucial performance indicator for magnetic materials, significantly influencing the operational stability of electrical equipment regarding efficiency, dependability, and compactness. Traditional core loss calculation models are not very accurate since core loss depends on many different characteristics and operating conditions. Additionally, there are numerous kinds of core materials used in engineering applications, which makes gathering a lot of information about them all challenging. The paper proposes a magnetic core loss modeling approach based on multi-kernel maximum mean discrepancy (MKMMD) domain adaptation to overcome these drawbacks. The method achieves cross-domain feature alignment by quantifying the differences in feature distributions across diverse magnetic core material datasets, enabling rapid adaptation to new magnetic core materials. In this paper, experiments are conducted for three core materials, including N27, 3C94, and 78, in a small-sample scenario with 5% of the conventional training data size. The results demonstrate a 6.28% performance improvement compared to traditional data-driven methods without domain adaptation, indicating that the proposed approach achieves high computational accuracy while significantly reducing the required sample size for magnetic core materials. Core (optical fiber) ; Domain adaptation ; Feature (linguistics) ; Stability (learning theory) ; Kernel (algebra) ; Domain (mathematical analysis) ; Adaptation (eye) ; Magnetic domain Magnetic Properties and Applications ; Power Transformer Diagnostics and Insulation ; Model Reduction and Neural Networks
Authors: Negareh Mahboubi | Junyao Xie | Biao Huang
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Physics-Guided Transfer Learning for Bayesian Optimization of Chemical Port-Hamiltonian Systems Hamiltonian (control theory) ; Port (circuit theory) ; Bayesian probability ; Bayesian optimization ; Physics ; Computer science ; Mathematical optimization ; Engineering ; Mathematics ; Artificial intelligence ; Mechanical engineering Machine Learning in Materials Science ; Advanced Control Systems Optimization ; Fuel Cells and Related Materials
Authors: Monika Kundu | Ananta Vashisth | Prameela Krishnan
Source: Journal of Food Science and Technology (article, 2025-11-24)
Field: Chemistry — Analytical Chemistry

Instrumental detection of fish freshness in research and food industry: a review Fish <Actinopterygii> ; Limiting ; Quality (philosophy) ; Key (lock) ; Food supply ; Fish products Spectroscopy and Chemometric Analyses ; Water Quality Monitoring Technologies ; Water Quality Monitoring and Analysis
Authors: Aoi Watanabe | Ryuhei Sato | Ikuya Kinefuchi | Yasushi Shibuta
Source: The Journal of Chemical Physics (article, 2025-09-17)
Field: Materials Science — Materials Chemistry

douka: A universal platform of data assimilation for materials modeling A large-scale, general-purpose data assimilation (DA) platform for materials modeling, douka, was developed and applied to nonlinear materials models. The platform demonstrated its effectiveness in estimating physical properties that cannot be directly obtained from observed data. Furthermore, it enables state estimation with quantified uncertainty, thereby providing researchers with a new aspect for analyzing the underlying physical process and guiding future model refinements. DA was successfully performed using experimental images of oxygen evolution reaction at a water electrolysis electrode, enabling the estimation of oxygen gas injection velocity and bubble contact angle. Furthermore, large-scale ensemble DA was conducted on the supercomputer Fugaku, achieving state estimation with up to 8192 ensemble members. The results confirmed that runtime scaling for the prediction step follows the weak scaling law, ensuring computational efficiency even with increased ensemble sizes. These findings highlight the potential of douka as a new approach for data-driven materials science, integrating experimental data with numerical simulation. Data assimilation ; Scaling ; Nonlinear system ; Supercomputer ; Process (computing) ; Data modeling ; Bubble Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Advancements in Solid Oxide Fuel Cells
Authors: Bowen Su | JF Ying | Huan Chen | Bo Liu | Kai Sun
Source:  (article, 2025-10-19)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Prediction Accuracy Enhancement of Transformer Core Loss Under Complex Excitation Based on Pre-Trained Foundation Model Magnetic components are crucial in modern power electronic systems, and accurately predicting magnetic core losses can significantly enhance both the power density and efficiency of these systems. However, current model-based methods for estimating magnetic core losses often suffer from low accuracy. On the other hand, data-driven approaches typically focus on specific materials, limiting their ability to generalize and transfer to other materials. This article introduces a bidirectional time series regression model built upon the Mamba foundation model. By pre-training the model on a large dataset, we can significantly improve its generalization ability, enabling accurate material transfer predictions with minimal fine-tuning. Through comparisons with several existing baseline models, we demonstrate the superior performance of the proposed approach. The model proves highly effective for transfer learning under complex excitation conditions, highlighting the full potential of pre-trained foundation models in magnetic core loss prediction. Transformer ; Excitation ; Generalization ; Core (optical fiber) ; Limiting ; Magnetic core ; Maximum power transfer theorem ; Foundation (evidence) Magnetic Properties and Applications ; Advanced DC-DC Converters ; Silicon Carbide Semiconductor Technologies
Authors: Yitong Xie (18096069) | Xin Guo (123593) | Daihui Zhang (14438962) | Xiaoping Shen (637465) | Qingfeng Sun (1924555) | Fuxiang Chu (2193157)
Source: Figshare (article, 2025-06-07)
Field: Materials Science — Biomaterials

Life CycleAssessment and Environmental Cost Evaluationof Biomimetic Cellulose-Enhanced Hydrogels Cellulose-based hydrogels, recognized for their exceptional strength and toughness, have become promising alternatives to petroleum-based functional materials in soft robotics and wearable devices applications. However, the current lack of a detailed understanding of its environmental characteristics and robust quantitative research poses significant challenges to the long-term, sustainable design of biomass materials. To address this research gap and avoid shifting environmental problems in biomass material design, this study developed a sustainable assessment model using life cycle assessment to quantify and identify the drivers of environmental implications in the biomimetic cellulose-enhanced hydrogel fabrication process. The results indicated that preparation of cellulose skeleton and acrylamide solution are the main contributors to environmental burdens, accounting for 68% and 29% of the overall environmental impact. The main driving indicators come from primary energy demand (PED), CO<sub>2</sub>, SO<sub>2</sub>, NO<sub><i>x</i></sub>, and NH<sub>3</sub>–N during raw material extraction and electricity consumption. The scenario analysis revealed that optimizing materials and energy inputs collaboratively can reduce environmental implications and costs by 72.9% and 88.7%, respectively. This life cycle thinking-based quantitative model provides both data-driven insights for the design of cellulose-based materials, avoiding the risk of environmental problem shifting and offering practical guidance for advancing the sustainable development of biomass-derived functional materials. Life-cycle assessment ; Environmental impact assessment ; Sustainability ; Sustainable design ; Raw material ; Biomass (ecology) Advanced Cellulose Research Studies ; Hydrogels: synthesis, properties, applications ; Electrospun Nanofibers in Biomedical Applications
Authors: Shamil Mirishov Zenfira Huseynli Zenfira Huseynli
Source: ETM Equipment Technologies Materials (article, 2025-03-17)
Field: Materials Science — Materials Chemistry

INCREASING WEAR RESISTANCE OF PUMPJACK GEARBOX This article explores constructive improvements aimed at reducing wear processes and extending the service life of gear reducers used in pumpjacks. Proper operation of the reducer is crucial for the safe, efficient, and environmentally friendly performance of the pumpjack. In oil production, gear reducers of pumpjacks are subjected to high loads and friction during long-term operation, leading to wear of gear transmissions, reduced equipment efficiency, and increased maintenance costs. The gear reducer of a pumpjack is a transmission mechanism that drives downhole pumps in oil wells. It transmits the rotational motion received from the motor, ensuring the up-and-down movement of the pumpjack. The primary function of the reducer is to decrease the high rotational speed of the motor while increasing the output torque, thereby ensuring the continuous and efficient operation of the pump. Typically, the reducer is positioned within the frame of the pumpjack, between the supports, motor, and walking beam. Its internal components include gears, shafts, and bearings. Its working principle is based on transmitting the motor shaft’s rotational energy through gear mechanisms to generate a higher torque at the output shaft. To withstand the demanding and prolonged operational conditions of the oil industry, reducers are manufactured from highly durable materials, undergo regular lubrication, and are equipped with specialized sealing systems for protection. To minimize friction and wear, optimizations have been proposed in terms of gear material selection, heat treatment methods, and lubrication systems. Additionally, modifications in gear tooth profiles aim to improve pressure distribution and reduce contact stresses. The article analyzes the required functions and considerations for selecting a gear reducer that meets operational demands. In harsh operating environments, protective coatings and surface hardening techniques such as carburizing and nitriding can be applied to enhance the durability of the reducer. As technology advances, future gear reducers are expected to integrate improved materials, smart monitoring systems, and environmentally sustainable solutions. The industry is exploring alternative eco-friendly materials for gear reducers. The implementation of artificial intelligence and sensor technology will enable real-time monitoring of reducer conditions, assessing wear levels, and providing timely maintenance alerts. These innovations will enhance the reliability and extend the service life of gear reducers in pumpjacks. Keywords: Pumpjack, gearbox, wear, protective coating, gear wheel. Wear resistance ; Materials science ; Automotive engineering ; Computer science ; Composite material ; Engineering Metal Alloys Wear and Properties ; Mechanical and Thermal Properties Analysis ; Metallurgy and Material Forming
Authors: Hui Peng (116756) | Pengfei Hou (5308403)
Source: Figshare (article, 2025-10-03)
Field: Materials Science — Materials Chemistry

Self-Powered PolarizedPhotodetection and Low-VoltageModulated Bidirectional Visual Synapse of Ta<sub>2</sub>NiS<sub>5</sub>/MoS<sub>2</sub>/Gr Vertical Heterojunction with Asymmetric InterfaceModulation Modern application scenarios increasingly demand multifunctional integrated photodetectors with high efficiency and low-power consumption. However, existing two-dimensional (2D) heterojunction-based devices face critical challenges. For instance, metal–semiconductor contacts can induce Schottky barriers and Fermi level pinning, which hinder carrier transport and degrade overall performance. To address these issues, this work proposes a vertical van der Waals heterojunction design, employing graphene (Gr) as the top electrode, tantalum nickel sulfide (Ta<sub>2</sub>NiS<sub>5</sub>) with a narrow-bandgap and anisotropy as the bottom electrodes, and molybdenum disulfide (MoS<sub>2</sub>) as the main light-absorbing layer. This architecture engineers reverse built-in electric fields at Ta<sub>2</sub>NiS<sub>5</sub>/MoS<sub>2</sub> and MoS<sub>2</sub>/Gr interfaces and replaces Schottky contacts with ohmic-like transport, leveraging Ta<sub>2</sub>NiS<sub>5</sub>’s anisotropy and interfacial asymmetry to integrate multiple functions. Under 660 nm light illumination (249.24 mW/cm<sup>2</sup>), it achieves a high photocurrent density of 171 mA/cm<sup>2</sup> and an on/off ratio of 3.7 × 10<sup>4</sup>. Under 808 nm light illumination, a self-powered anisotropy ratio of 6.03 is realized. It exhibits excitatory postsynaptic currents (EPSC) under 1064 nm light illumination with an ultralow energy consumption of 82 fJ, alongside bidirectional synaptic weight modulation via bias polarity and light pulse (duration, intensity, and number) control. This integrated design paves the way for advancing multifunctional optoelectronic devices in next-generation integrated systems and artificial intelligence applications. Photodetection ; Photocurrent ; Heterojunction ; Photodetector ; Molybdenum disulfide ; Schottky barrier ; Modulation (music) ; Plasmon ; Neuromorphic engineering 2D Materials and Applications ; Neural Networks and Reservoir Computing ; Graphene research and applications
Authors: Marvin Alberts | Nina Hartrampf | Teodoro Laino
Source: Universität Zürich, ZORA (article, 2025-07-16)
Field: Chemistry — Organic Chemistry

From Spectra to Structure: AI-Powered <sup>31</sup>P NMR Interpretation Phosphorus-31 nuclear magnetic resonance (P NMR) spectroscopy is a powerful technique for characterizing phosphorus-containing compounds in diverse chemical environments. However, spectral interpretation remains a time-consuming and expertise-dependent task, relying on reference tables and empirical comparisons. In this study, we introduce a data-driven approach that automates P NMR spectral analysis, providing rapid and accurate predictions of the local phosphorus environments. By leveraging a curated data set of experimental and synthetic spectra, our model achieves a Top-1 accuracy of 53.64% and a Top-5 accuracy of 77.69% at predicting the local environment around a phosphorus atom. Furthermore, it demonstrates robustness across different solvent conditions and outperforms expert chemists by 25% in spectral assignment tasks. The models, data sets, and architecture are openly available, facilitating seamless adoption in chemical laboratories engaged in structure elucidation, with the goal of advancing P NMR spectral analysis and interpretation. Robustness (evolution) ; Spectral line ; Interpretation (philosophy) ; Nuclear magnetic resonance spectroscopy ; NMR spectra database ; Set (abstract data type) ; Spectroscopy ; Chemical shift Organophosphorus compounds synthesis ; Machine Learning in Materials Science ; Phosphorus compounds and reactions
Authors: Varagnolo, Antonio | Romano, Giuseppe | Pestourie, Raphaël
Source: arXiv (Cornell University) (preprint, 2025-11-25)
Field: Materials Science — Materials Chemistry

Physics Enhanced Deep Surrogates for the Phonon Boltzmann Transport Equation Designing materials with controlled heat flow at the nano-scale is central to advances in microelectronics, thermoelectrics, and energy-conversion technologies. At these scales, phonon transport follows the Boltzmann Transport Equation (BTE), which captures non-diffusive (ballistic) effects but is too costly to solve repeatedly in inverse-design loops. Existing surrogate approaches trade speed for accuracy: fast macroscopic solvers can overestimate conductivities by hundreds of percent, while recent data-driven operator learners often require thousands of high-fidelity simulations. This creates a need for a fast, data-efficient surrogate that remains reliable across ballistic and diffusive regimes. We introduce a Physics-Enhanced Deep Surrogate (PEDS) that combines a differentiable Fourier solver with a neural generator and couples it with uncertainty-driven active learning. The Fourier solver acts as a physical inductive bias, while the network learns geometry-dependent corrections and a mixing coefficient that interpolates between macroscopic and nano-scale behavior. PEDS reduces training-data requirements by up to 70% compared with purely data-driven baselines, achieves roughly 5% fractional error with only 300 high-fidelity BTE simulations, and enables efficient design of porous geometries spanning 12-85 W m$^{-1}$ K$^{-1}$ with average design errors of 4%. The learned mixing parameter recovers the ballistic-diffusive transition and improves out of distribution robustness. These results show that embedding simple, differentiable low-fidelity physics can dramatically increase surrogate data-efficiency and interpretability, making repeated PDE-constrained optimization practical for nano-scale thermal-materials design. Solver ; Boltzmann equation ; Mixing (physics) ; Boltzmann constant ; Operator (biology) ; Surrogate model ; Phonon ; Flow (mathematics) ; Fourier transform Machine Learning in Materials Science ; Thermal properties of materials ; Model Reduction and Neural Networks