jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yongqing Zhang | Yadi Peng | Gege Hui
Source: Informatica (article, 2025-11-10)
Field: Chemistry — Analytical Chemistry

Aviation Gasoline Quality Detection Using RAP Feature Selection and IMPA-XGBoost Optimization Quality detection of aviation gasoline is critical to flight safety. Gasoline is a critical fuel for aircraft engines, and any quality problem may affect the performance of the engine and the safety of the aircraft. Regarding the issue of aviation gasoline detection, a RAP method combining Pearson correlation coefficient method and Relief-F algorithm is studied for gasoline mass spectrometry feature selection. Improved marine predator algorithm (MPA) introduces Logistic chaotic mapping and adaptive t-distribution operator. It is used to optimize the XGBoost model to construct an aviation gasoline quality detection model for gasoline quality detection and model classification. Among them, the RAP method is chosen because it effectively removes redundant features from mass spectrometry data while preserving the correlation between features. The use of IMPA to optimize XGBoost is because the traditional MPA is easy to fall into local optimum. Whereas the improved IMPA can find the optimal hyperparameter combination of XGBoost more effectively by enhancing the population diversity and optimizing the search strategy, thus improving the model detection performance. The results showed that the area under the receiver operating characteristic curve (AUC) of the proposed model was 0.8892, which was significantly higher than the AUC values of the particle swarm optimization-XGBoost (PSO-XGBoost) model (0.8384) and the sparrow search algorithm-XGBoost (SSA-XGBoost) model (0.8497). In the classification of gasoline models, only 4 samples were misclassified, while 122 samples were classified correctly, with an accuracy rate of 96.83%. This was a significant improvement compared to the 92.06% of the SSA XGBoost model and the 88.10% of the PSO XGBoost model. The improved marine predator and extreme gradient boosting model has shown excellent performance in gasoline quality detection. Compared to traditional chemical detection methods, such as plasma emission spectroscopy and gas chromatography with an oxygen-selective detector, the AI-based detection system proposed in this study has significant advantages in terms of detection accuracy and efficiency, and does not require expensive and complex detection equipment. This provides a strong support for AI automated system in quality detection of aviation gasoline. Particle swarm optimization ; Gasoline ; Aviation ; Feature selection ; Feature (linguistics) ; Population ; Correlation coefficient Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Fire Detection and Safety Systems
Authors: Ayşe Şen | Muhammad Zaheer Ahmed | Omar DARKAZANLI | Duygu GOKSAY-KADAIFCILER | Feyza Güzelçimen
Source: Notulae Botanicae Horti Agrobotanici Cluj-Napoca (article, 2025-02-26)
Field: Chemistry — Analytical Chemistry

Biochemical and Raman spectroscopic insights into plant-mold interactions Plants continuously interact with diverse biotic and abiotic factors in their environment, and understanding these intricate ecological relationships is crucial for advancing sustainable agriculture and biodiversity conservation. This study investigates the interactions between wheat plants and mold contaminants in in vitro cultures using Raman Spectroscopy (RS) and biochemical analyses, offering a novel approach to understanding plant responses at a molecular level. Conventional culturing methods identified Aspergillus versicolor, Penicillium sp., and Nigrospora sp. as the primary mold strains present in the cultures. Biochemical assays revealed that mold contamination led to a marked decrease in chlorophyll and carotenoid contents, along with an increase in thiobarbituric acid reactive substances (TBARS), indicating heightened oxidative stress. Antioxidant enzyme activities, including superoxide dismutase (SOD), peroxidase (POX), and catalase (CAT), were significantly elevated, though proline levels remained unchanged. Raman Spectroscopy further uncovered profound metabolomic shifts, particularly in carotenoid-related peaks, signalling impaired photosynthetic function. Additionally, RS detected increased carbohydrate-associated bands, suggesting that carbohydrate-derived osmolytes may play a more pivotal role than proline in maintaining cellular integrity under mold stress. To validate these findings, advanced multivariate techniques such as Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) were employed, achieving high accuracy in distinguishing mold-infected samples from controls. These results highlight the potential of RS as a rapid, non-invasive diagnostic tool for plant health monitoring, offering significant advantages over traditional methods. This approach not only advances our understanding of plant-microbe interactions but also offers practical applications for enhancing agricultural productivity and sustainability in the face of global food security challenges. Raman spectroscopy ; Mold ; Chemistry ; Nanotechnology ; Materials science ; Environmental chemistry ; Botany ; Biology ; Physics ; Optics Spectroscopy and Chemometric Analyses ; Plant tissue culture and regeneration ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Peng Gao | Fachen Liu | R. Le Van Mao | Zhiqiang Liu | Jinlong Du
Source: Research Square (preprint, 2025-05-07)
Field: Materials Science — Materials Chemistry

Probing phonon transport dynamics across an interface by electron microscopy Phonon ; Interface (matter) ; Dynamics (music) ; Materials science ; Condensed matter physics ; Chemical physics ; Electron microscope ; Electron ; Nanotechnology ; Physics ; Optics ; Composite material ; Quantum mechanics ; Acoustics Thermal properties of materials
Authors: S N Soni
Source: International Journal of Science and Research (IJSR) (article, 2025-04-22)
Field: Materials Science — Materials Chemistry

Technologies for Monitoring the Hermeticity of Fuel Rod Cladding in Various Types of Reactors This article explores the technologies employed for assessing the leak -tightness of fuel rod cladding in reactors of various types. The study covers both conventional non -destructive testing methods-such as ultrasonic inspection, acoustic emission, and radiography-and modern approaches based on gamma spectrometry data analysis. A comparative analysis is conducted on alloys such as AISI 316L and their modified versions obtained through tungsten and titanium alloying. The findings highlight the advantages of these new materials, particularly their enhanced mechanical strength and resistance to radiation -induced damage. The integrated approach combining advanced materials with high -precision monitoring techniques delivers a synergistic effect, enhancing the overall reliability and safety of nuclear installations while optimizing preventive maintenance protocols. The article is of particular interest to professionals in nuclear energy, engineering design, and reactor safety, as it provides a detailed analysis of modern leak -tightness control techniques for fuel rod cladding, which are critical for optimizing the operational performance of nuclear power plants. In addition, the content will be valuable to researchers and practitioners working on the development and deployment of advanced monitoring and diagnostic technologies aimed at improving the reliability and safety of reactors of various designs. Cladding (metalworking) ; Materials science ; Nuclear engineering ; Metallurgy ; Engineering Nuclear Materials and Properties ; Nuclear reactor physics and engineering ; Engineering Diagnostics and Reliability
Authors: Anna Luisa Upterworth | Daniel Sebastiani
Source: ChemRxiv (preprint, 2025-05-13)
Field: Chemistry — Organic Chemistry

Effective Interaction Strength in Liquid Mixtures: Weighting Interaction Potentials with an Effective Occurrence Factor The thermodynamics of mixing and demixing of molecular liquids poses a perpetual challenge for atomistic simulations. We provide a compu-tational tool, the effective interaction strength, which can be used to quantify the enthalpic contribution of individual species-dependent inter- actions. The effective interaction strength is configuration-dependent and thus reflects how the actual simulated state (i.e., mixed or phase separated) affects the average particle interactions. Beyond that, the effective interaction strength can be utilized to gain atomistic insight into complex phenomena, including the fundamental nature of the philicity of a compound and the contributions of individual functional groups to this philicity. Weighting ; Factor (programming language) ; Statistics ; Mathematics ; Computer science ; Physics ; Acoustics Chemical Thermodynamics and Molecular Structure
Authors: Fabian Resare | Somiya Islam Soke | Witlef Wieczorek
Source: Journal of Applied Physics (article, 2025-12-09)
Field: Materials Science — Surfaces, Coatings and Films

Modification of adhesion between microparticles and engineered silicon surfaces A key challenge in performing experiments with microparticles is controlling their adhesion to substrates. For example, levitation of a microparticle initially resting on a surface requires overcoming the surface adhesion forces to deliver the microparticle into a mechanical potential acting as a trap. By engineering the surface of silicon substrates, we aim to decrease the adhesion force between a metallic microparticle and the silicon surface. To this end, we investigate different methods of surface engineering that are based on chemical, physical, or physio-chemical modifications of the surface of silicon. We give quantitative results on the detachment force, finding a correlation between the water contact angle and the mean detachment force, indicating that hydrophobic surfaces are desired for low microparticle adhesion. We develop surface preparations decreasing the mean detachment force by more than a factor of three compared to an untreated silicon surface. Our results will enable reliable levitation of microparticles and are relevant for experiments requiring low adhesion between microparticles and a surface. Microparticle ; Adhesion ; Silicon ; Levitation ; Surface modification ; Surface force ; Surface (topology) Surface Modification and Superhydrophobicity ; Minerals Flotation and Separation Techniques ; Force Microscopy Techniques and Applications
Authors: Hedayat Haddadi | Adam Kloskowski | Piotr Mironowicz
Source: Research Square (preprint, 2025-11-18)
Field: Materials Science — Materials Chemistry

Auditing coherence through the principle of a single functional mapping Coherence (philosophical gambling strategy) ; Classifier (UML) ; Audit ; Entropy (arrow of time) ; Reliability (semiconductor) ; Variance (accounting) ; Predictability Machine Learning in Materials Science ; Bioinformatics and Genomic Networks ; Computational Drug Discovery Methods
Authors: Chongming WU
Source: Institutional Repositories DataBase (IRDB) (article, 2025-09-19)
Field: Materials Science — Polymers and Plastics

The Effects of Dehydrogenation and Stone-Wales Defect on the UV Absorption Spectra of Polycyclic Aromatic Hydrocarbons (PAHs) : A Theoretical Study Based on TDDFT Calculations UTokyo Repositoryは本学で生産されたさまざまな学術成果を電子的形態で集中的に蓄積・保存し、世界に発信することを目的としたインターネット上の発信拠点です。 The UTokyo Repository is the system to store and provide digital resources created by members of the University of Tokyo. Its main purpose is to develop digital collections, make them available online, and preserve them for long-term access. Time-dependent density functional theory ; Density functional theory ; Spectral line ; Absorption spectroscopy ; Dehydrogenation ; Absorption (acoustics) Polymer Nanocomposites and Properties ; Polymer crystallization and properties ; Epoxy Resin Curing Processes
Authors: W. Golka | Magdalena Szechyńska‐Hebda | Jolanta Kowalska | Adrian Golka | Robert Grolik | Magdalena Jakubowska | Mateusz Góra | Aleksander Bomberski
Source: Journal of Plant Protection Research (article, 2025-06-24)
Field: Chemistry — Analytical Chemistry

Selective protection of cereals using artificial neural networks Global agricultural losses due to pests and pathogens are substantial, particularly for wheat, maize, and potatoes. Addressing these challenges necessitates innovative approaches in plant protection, particularly through early detection methods. This article outlines research areas concerning the application of spectral imaging technologies in selective crop protection processes. Recent technological advancements, driven by the development of high-resolution optical sensors and data analysis methods (Pena et al. 2013), have enabled early detection of weeds, plant diseases, and pests in the field. Spectral imaging technologies, particularly hyperspectral imaging, play a pivotal role in early disease detection by capturing detailed spectral data across a wide range of wavelengths. This technology enables the detection of subtle physiological changes in plants long before visible symptoms occur. Hyperspectral imaging has proven effective in identifying diseases such as Fusarium head blight in wheat, allowing for timely interventions and potentially reducing yield losses. The integration of hyperspectral imaging with remote sensing technologies, including unmanned aerial vehicles and ground-based sensors, as well as artificial intelligence represents a significant advancement in precision agriculture. This multidisciplinary approach aims to enhance crop protection while minimizing environmental impacts. The article also explores the advantages and limitations of these technologies and strategies for reducing the reliance on chemical plant protection methods in agricultural production. It is underlined, that future research should focus on optimizing these technologies, addressing cost barriers, and exploring UAV-based applications for precision spraying and monitoring. Biology ; Artificial neural network ; Biotechnology ; Artificial intelligence ; Computer science Spectroscopy and Chemometric Analyses ; Food Industry and Aquatic Biology ; Animal Nutrition and Health
Authors: Peng Xu | Shuang Wang
Source: Advances in Engineering Research Possibilities and Challenges (article, 2025-12-09)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Architectural Choice in Inverse Design of THz Metamaterials: Local Priors vs. Global Attention under Data Scarcity The inverse design of terahertz (THz) electromagnetically induced transparency (EIT) metamaterials is complicated by high-dimensional parameter spaces and complex spectral mappings. While deep learning has emerged as a potent tool for addressing these issues, the suitability of distinct neural architectures under data-constrained conditions remains underexplored. Focusing on EIT spectra characterized by long-range frequency dependencies, this study compares the inverse design performance of the Fully Connected Residual Network (FC-ResNet), which leverages local inductive biases, against the Transformer architecture, which relies on global self-attention mechanisms. We evaluated these models using a comprehensive dataset of 20,476 samples and a restricted dataset of only 500 samples. The results demonstrate that in data-rich environ-ments, both architectures achieve exceptional accuracy (R² &gt; 0.99), indicating that architectural differences do not constitute a bottleneck when data is abun-dant. However, in small-sample regimes—simulating scenarios with scarce ex-perimental data—the FC-ResNet exhibits significantly superior generalization capabilities compared to the Transformer. Our findings suggest that while Transformers offer global modeling potential, they are prone to overfitting when data is limited; conversely, the structural priors of the FC-ResNet provide essential regularization in such contexts. This work offers empirical guidance for selecting architectures in the intelligent design of metamaterials where data availability is a limiting factor Overfitting ; Residual ; Prior probability ; Bottleneck ; Inverse problem ; Convolutional neural network ; Artificial neural network ; Bayesian probability ; Inverse Metamaterials and Metasurfaces Applications ; Terahertz technology and applications ; Plasmonic and Surface Plasmon Research
Authors: Caitlin Whitter | Alex Pothen | Aurora E. Clark
Source: Digital Discovery (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

PIL-Net: a physics-informed graph convolutional network for predicting atomic multipoles PIL-Net is a physics-informed graph convolutional network capable of predicting atomic multipole moments quickly and with high accuracy. Multipole expansion ; Graph ; Net (polyhedron) ; Computer science ; Physics ; Theoretical computer science ; Mathematics ; Quantum mechanics ; Geometry Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Protein Structure and Dynamics
Authors: Irene Donato | Juliana do Nascimento Lunz | Fernanda Davi Marques | Bráulio S. Archanjo | Francisco J. P. Lopes | Gilson A. Giraldi
Source: Revista de Informática Teórica e Aplicada (article, 2025-02-20)
Field: Materials Science — Surfaces, Coatings and Films

Deep Learning-Based Segmentation of Nanomaterial Images Acquired via Scanning Electron Microscopy This work investigates the application of deep learning techniques to analyze nanomaterial images acquired by scanning electron microscopy (SEM). The main objective is to improve the segmentation of zinc oxide (ZnO) nanoparticles and graphene oxide (GO) nanosheets. For this purpose, we developed a dedicated database for the supervised training of deep neural networks, using the U-Net architecture. Given the high spatial resolution of the original images, we investigated two approaches for image subdivision during the training and testing stages. Due to the high cost of acquiring and annotating SEM images of nanomaterials, we applied transfer learning through fine-tuning, adapting the network previously trained on ZnO images to segment GO images. The results were promising, with accuracies and recalls exceeding 95% for both ZnO and GO images. Scanning electron microscope ; Segmentation ; Nanomaterials ; Artificial intelligence ; Materials science ; Microscopy ; Electron microscope ; Computer vision ; Nanotechnology ; Computer science ; Optics ; Physics ; Composite material Electron and X-Ray Spectroscopy Techniques ; Machine Learning in Materials Science
Authors: 段雪晴 Duan Xueqing | 葛佩佩 Ge Peipei | 黎敏 Li Min
Source: Laser & Optoelectronics Progress (article, 2025-01-01)
Field: Chemistry — Organic Chemistry

NH3-H2分子二聚体中氢转移超快动力学研究（特邀） 氢转移过程广泛存在于各种物理、化学、生物反应中，涉及化学键的断裂、重组以及分子构型的变化，因此实时观测氢转移过程对理解分子动力学、精确控制物理化学反应具有非常重要的意义。近年来，分子二聚体中氢转移受到了广泛关注，其中，氢转移特征时间的测量为理解团簇中的反应路径、揭示分子间相互作用提供了关键信息。基于原子中心密度矩阵传播方法，并结合扩展的拉格朗日分子动力学模拟，研究了（NH3―H2）+解离过程中的氢转移超快动力学，得到了该分子离子的氢转移平均时间为224 fs。通过追踪氢转移过程中的演化轨迹发现，该分子离子的氢转移过程存在两种通道，分别具有不同的氢转移时间。研究结果表明，这两种通道的产生与氢转移过程中动能在不同振动自由度之间的转换密切相关，并且与两个分子间的初始间距存在显著关联。这一发现为研究更复杂分子聚合体系中的氢转移动力学提供了重要参考。 Geology Radical Photochemical Reactions ; Insect and Pesticide Research ; Oxidative Organic Chemistry Reactions
Authors: Pranjul Shrivastava | Aditya Raj Uikey | Aniket Sakharwade | Ankit Kumar | Amrit Meher | Sandip Prasad Tiwari
Source: Journal of Neonatal Surgery (article, 2025-05-24)
Field: Materials Science — Biomaterials

Nanobots in Pharmacy: A Futuristic Approach to Drug Delivery and Therapeutics Nanotechnology has played a significant role in the modern healthcare and medicine system, particularly through the development of nanobots, which have revolutionized the field of pharmaceutical drug delivery and cancer therapy. Nanobots are tiny robotic devices that, when implanted in the human body, move within biological systems to cancer cells and release therapeutic drugs with the highest accuracy. These devices allow the most targeted therapy approach, limiting the effect of drugs to healthy tissues, reducing systemic side effects, and enhancing treatment output. Furthermore, nanobots can be used to design a real-time monitoring system, early detection, and drug release, which are effective for cancer therapy. This review paper shows the design and implementation potential of such nanobot devices, highlighting their mechanisms, advancements and future prospects Medicine ; Pharmacy ; Drug ; Pharmacology ; Family medicine Nanoparticle-Based Drug Delivery ; Molecular Communication and Nanonetworks ; Advanced biosensing and bioanalysis techniques
Authors: Sarvesh Kumar | Paula Nousiainen | Donya Kamravamanesh | Rahul Mangayil
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2025-09-17)
Field: Materials Science — Biomaterials

Integrated Multi-analytical Framework for Comprehensive Characterization of Lignocellulosic Hydrolysates ABSTRACT Lignocellulosic hydrolysates are promising second-generation feedstocks for sustainable biomanufacturing. However, biomass compositional complexities and a lack of analytical framework, particularly for lignin fraction (LF), have limited holistic characterization and impeded downstream valorization. In this study, we present an integrated multi-analytical framework for in-depth compositional profiling of spruce sawdust hydrolysate (SSH). Combining high-performance anion-exchange chromatography and liquid chromatography, sugars and inhibitors were quantified, with monomeric arabinose (78.8±0.6 g/L) and acetic acid (14.9±0.1 g/L) as predominant components, respectively. Additionally, we developed a workflow to quantify and evaluate lignin structures and interlinkages, determining factors in biomanufacturing using a unique spectroscopic-chromatographic approach enabling a comprehensive understanding of the LF. UV-Vis spectroscopy quantified a total aromatic content of 58.2±0.1g/L, while quantitative 31 P NMR clarified the hydroxyl group distribution in the LF. 1 H- 13 C HSQC NMR provided structural identification of lignin units and interconnecting linkages, and gel permeation chromatography showed the molecular weight distribution. Gas chromatography-mass spectrometry identified and quantified 38 lignin-derived monomers and extractives, with benzoic acid (2.7±0.2 mg/L) as the most abundant. This integrated approach provides holistic biomass compositional insights and cross-validation across methods, overcomes the limitations of a single technique, and delivers a robust workflow to evaluate the biomass suitability for bio-valorization. ABSTRACT GRAPHIC SYNOPSIS This study develops an integrated multi-analytical workflow to comprehensively profile biomass hydrolysate for downstream valorization. Hydrolysate ; Lignin ; Lignocellulosic biomass ; Biorefining ; Biomass (ecology) ; Biorefinery ; Bioenergy ; Depolymerization ; Furfural biodegradable polymer synthesis and properties ; Biofuel production and bioconversion ; Lignin and Wood Chemistry