jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: A. Borisov | J.N. Shermatov | V.A. Gafarova | И.Р. Кузеев
Source: Problems of Gathering Treatment and Transportation of Oil and Oil Products (article, 2025-11-10)
Field: Materials Science — Materials Chemistry

PREDICTING THE DEGRADATION DEGREE OF WELDED JOINTS PROPERTIES BY THE CHANGE IN THE PHYSICAL FIELDS PARAMETERS The article is devoted to forecasting the degree of degradation of welded joint properties based on the analysis of changes in the parameters of physical fields, which is an urgent task for improving the safety of pipelines and other extended structures. The authors study the relationship between accumulated damage and changes in the mechanical and magnetic properties of the material using modern diagnostic methods, including fatigue testing and non-destructive testing. The paper presents the results of fatigue tests using the pure symmetric bending scheme for samples made of 09G2S steel, both without welded joints and with them. The samples were subjected to cyclic loading, and their condition was recorded using specialized devices. Such parameters as the tangential (Hτ) and normal (Hn) components of the magnetic field strength vector, the integral acoustic parameter R were measured. Based on the data obtained, the dependences of the measured parameters on the level of accumulated stresses were constructed. The integral parameter and the tangential component of the magnetic field strength vector are informative indicators of the level of accumulated damage. This is especially noticeable when the measurement process occurs under non-stationary conditions of the system.The obtained results demonstrate a clear correlation between the level of accumulated damage (Ni/Np) and changes in the monitored parameters. The integral parameter R showed a characteristic decrease after reaching Ni/Np ≈ 0.5, indicating a loss of elastic properties. Of particular interest is the sharp increase in the integral parameter at point 11, which may indicate a local resonant sound effect in the structural components of the system before failure. Analysis of the tangential component Hτ revealed its high predictive ability - negative values at point 12 were observed already at the zero cycle, accurately predicting the location of future failure.The study confirms the high sensitivity of the proposed methods to local changes in the state of the material, which makes them promising for monitoring the limit state of welded joints. The obtained data can be used to train neural network models and create digital twins, which will improve the accuracy of forecasting and reduce the risks of operating objects with defects. Welding ; Degree (music) ; Joint (building) ; Bending ; Magnetic field ; Degradation (telecommunications) ; Fatigue limit Material Properties and Failure Mechanisms ; Engineering Diagnostics and Reliability ; Non-Destructive Testing Techniques
Authors: Quan Jing | Xinyi Huang | Jin Shao | Huiyuan Lu | Wenai Shen | Dong Pei | Duolong Di | Jun Hai
Source: Journal of Food Measurement & Characterization (article, 2025-07-26)
Field: Chemistry — Organic Chemistry

Development of a classification model for extra virgin olive oil adulteration based on squalene and phytosterol profiles Phytosterol ; Squalene ; Olive oil ; Food science ; Chemistry ; Biochemistry Edible Oils Quality and Analysis ; Cholesterol and Lipid Metabolism ; Spectroscopy and Chemometric Analyses
Authors: Nur Athirah Syafiqah Noramli | Noor Aida Syakira Ahmad Sabri | Muhammad Ikhsan Roslan | Nurlaila Ismail | Zakiah Mohd Yusoff | Mohd Nasir Taib
Source: Indonesian Journal of Electrical Engineering and Computer Science (article, 2025-05-26)
Field: Chemistry — Organic Chemistry

Unraveling the relationships among essential oil compounds in Aquilaria species using GC-MS and GC-FID techniques Agarwood, a prized non-timber resource from the Aquilaria genus, is highly valued for its aromatic and medicinal properties, playing a significant role in the healthcare, fragrance, and pharmaceutical industries. This research analyzes essential oils from four Aquilaria species-A. beccariana, A. malaccensis, A. crassna, and A. subintegra-using gas chromatography-mass spectrometry (GC-MS) and gas chromatography-flame ionization detection (GC-FID). The primary objective is to optimize classification efficiency by reducing computational time and reducing multicollinearity through feature selection. Pearson correlation analysis revealed strong relationships among six chemical compounds-β-selinene (A), dihydro-β-agarofuran (B), δguaiene (C), 10-epi-γ-eudesmol (D), γ-eudesmol (E), and pentadecanoic acid (F). Through feature selection, the three most significant compoundsdihydro-β-agarofuran (B), γ-eudesmol (D), and 10-epi-γ-eudesmol (E)-were identified, achieving a remarkable 90.02% reduction in computational time (from 0.0403 to 0.0040 seconds). These findings highlight the effectiveness of structured feature selection in refining essential oil profiling and enhancing species classification accuracy. Future research directions include exploring machine learning-based feature selection techniques to further streamline feature reduction processes and expand the scope of essential oil authentication. This study contributes to advancing the scientific understanding and practical utilization of agarwood essential oils, paving the way for more efficient and reliable analytical frameworks. Gas chromatography–mass spectrometry ; Essential oil ; Chemistry ; Chromatography ; Mass spectrometry Wood and Agarwood Research ; Identification and Quantification in Food ; Advanced Chemical Sensor Technologies
Authors: Marijana Ačanski | Marko Ilić | Djura Vujić | Kristian Pastor
Source:  (article, 2025-01-01)
Field: Chemistry — Analytical Chemistry

Chemoinformatics Approaches for Authenticating Cereal-Based Foods: From Grain to Bakery Products Authentication of cereal-based food products has become increasingly important due to the prevalence of food fraud, mislabeling, and the demand for products labeled as gluten-free, wholegrain, or originating from specific regions. These issues not only affect consumer trust but also have significant implications for health, trade, and regulatory compliance. Chemoinformatics, which integrates chemical profiling with multivariate statistical and machine learning techniques, offers a powerful solution for verifying the authenticity and integrity of cereals, pseudocereals, flours, breads, and other bakery products. This mini-review presents an overview of recent advances in chemoinformatics methodologies used for food authentication. It highlights key analytical platforms, such as gas chromatography–mass spectrometry (GC-MS), near-infrared (NIR) spectroscopy, Fourier-transform infrared (FTIR) spectroscopy, and inductively coupled plasma–mass spectrometry (ICP-MS). Data derived from these techniques are often processed using principal component analysis (PCA), partial least squares discriminant analysis (PLS- DA), support vector machines (SVM), clustering, and other classification algorithms. We further summarize representative case studies from Europe, Asia, South America, and North Africa that demonstrate the ability of chemoinformatics to differentiate botanical species, trace geographical origins, and detect adulteration in cereal-based matrices. The review outlines current challenges in method standardization and model validation, while emphasizing the future potential of portable devices and artificial intelligence in scalable food authentication strategies. Cheminformatics ; Food products ; Standardization ; Profiling (computer programming) ; Authentication (law) ; Scalability ; Partial least squares regression ; Principal component analysis Spectroscopy and Chemometric Analyses ; Identification and Quantification in Food ; Isotope Analysis in Ecology
Authors: Sofiene Achour | Zied Hosni | Bahoueddine Tangour | Ayoub Haj Saı̈d
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Chemistry — Inorganic Chemistry

A Hybrid DFT-Machine Learning Approach for Fast HOMO–LUMO Gap Prediction in Naphthalene Diimide MOF Linkers Cheminformatics ; Density functional theory ; Interpretability ; Band gap ; Hybrid functional ; Workflow ; Sensitivity (control systems) ; Metal-organic framework Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Shubham Singh Patel | Aarti Bains | Ravinder Kaushik | Sanju Bhala Dhull | Rupak Nagraik | Mohammad Fareed | Sandeep Janghu | Prince Chawla
Source: Food Control (article, 2025-11-24)
Field: Chemistry — Analytical Chemistry

Leveraging smart technologies to enhance safety in milk and milk-based products Traceability ; Food safety ; Emerging technologies ; Quality (philosophy) ; Transparency (behavior) ; Internet of Things ; Key (lock) Spectroscopy and Chemometric Analyses ; Identification and Quantification in Food ; Listeria monocytogenes in Food Safety
Authors: Fernando Fajardo-Rojas | Ryther Anderson | Mingwei Li | Remco Chang | Diego A. Gómez‐Gualdrón
Source: ChemRxiv (preprint, 2025-01-17)
Field: Chemistry — Inorganic Chemistry

Data-driven Insights on the Impact of Functionalization on Metal-Organic Framework (MOF) Free Energies Functionalization is poised to play a prominent role in MOF development as it could become the to-go strategy to bestow extant MOF with new properties, and to control MOF pore shape and size by modulating polymorph selection. Thus, to speed up MOF development through computational work, a better (predictive) understanding on how functionalization impacts MOF synthesizability is needed. Here we use a data-driven approach where molecular dynamics simulations on 5,000+ MOFs are used to shed light on how functionalization affects MOF free energy, as the latter has been largely tied to MOF synthesizability and polymorph selection. More consistently in MOFs with higher void fractions, we find that functionalization generally reduces free energy, with entropy contributing significantly to this thermodynamic stabilization. Although with some functionalizations (-CF3, -F, -Br, -SH, -OH) the role of entropy is more apparent than with others (-CN, -CH3, -NO2, -NH3). Through uneven stabilization of polymorphs, we also find functionalization (more often with -Br, -CN and -CF3) as capable of altering polymorph (topology) selection relative to original non-functionalized polymorphic families. However, no switch in polymorph stability ever occurred when the original (unfunctionalized) polymorphs were separated by more than 1.42 kJ/mol per MOF atom. We show that machine learning can predict functionalization-induced free energy change of a parent MOF with a mean absolute error of 0.16 kJ/mol per atom, using only physical properties of the parent MOF and the functional group as input. The ML-based SHAP analysis agrees with human analysis on the functionalization molecular mass and the hydrogen fraction of the parent MOF being among the factors that influence change in free energy the most. Finally, we present a publicly accessible dynamic interface to visualize and navigate the free energy data , thereby encouraging the research community to engage with and utilize the data to help uncover new insights. Surface modification ; Metal-organic framework ; Materials science ; Chemistry ; Chemical engineering ; Organic chemistry ; Engineering Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Advanced Nanomaterials in Catalysis
Authors: Long Dajun | Qin Yinluan
Source:  (article, 2025-06-28)
Field: Chemistry — Organic Chemistry

Targeting Complex Textures in Guangxi Native Tree Species Wood Defects Intelligent Diagnosis: A Multi-Feature Fusion and Multi-Task Collaborative Learning Method The wood of Guangxi native tree species exhibits unique material properties and textures. It poses challenges for intelligent defect recognition and surface quality evaluation during mechanical processing. Traditional methods perform poorly in handling complex texture features. They ignore the intrinsic relationship between defect information and surface roughness. This study proposes an intelligent diagnosis method for wood defects based on multi-feature fusion and multi-task collaborative learning. The proposed method innovatively combines multi-level texture features extracted by local binary patterns (LBP), Gabor filters, and a deep convolutional neural network (ResNet-50). It builds a robust feature representation adapted to complex texture backgrounds. It also constructs a multi-task collaborative optimization framework. It leverages shared low-level features and designs dedicated network structures for defect classification and roughness prediction. This enables synchronized and precise prediction for both tasks. Experimental results on the public WSDD dataset and a self-built dataset covering four dominant Guangxi native tree species (Dingguomu, Goushu, Kumian, and Yinhua) indicate that the proposed method achieves an accuracy of 94.5 % for defect classification. It shows improvements of approximately 16 % and 6 % over traditional methods and a single CNN model, respectively. For surface roughness prediction, it obtains an RMSE of 0.52 and an MAE of 0.41. These results are significantly better than existing methods. The study effectively addresses the challenges of intelligent diagnosis of wood defects in Guangxi native tree species under complex texture backgrounds. It also provides novel technical means and theoretical support for quality control and process optimization in wood processing. Task (project management) ; Computer science ; Artificial intelligence ; Feature (linguistics) ; Tree (set theory) ; Feature extraction ; Pattern recognition (psychology) ; Machine learning ; Engineering ; Mathematics Wood and Agarwood Research
Authors: Acharya Balkrishna | Vijay Kumar | Devendra Kumar | Dhani Arya | Vedpriya Arya | Ajay Kumar Gautam
Source: Next research. (article, 2025-11-20)
Field: Chemistry — Organic Chemistry

A critical review on agarwood: biosynthesis, therapeutic potential, trade, and conservation challenges Agarwood ; Wilderness ; Logging ; Agriculture ; Threatened species ; Quality (philosophy) ; Sustainable agriculture Wood and Agarwood Research ; Wood Treatment and Properties ; Ginger and Zingiberaceae research
Authors: Aditi Sagesh | Keerthana V | A. Mercy Latha
Source:  (article, 2025-12-06)
Field: Chemistry — Organic Chemistry

THz-based Density Prediction in Timber Wood using Stacked Ensemble Regression Strategy Density is a crucial parameter that greatly influences the mechanical properties, structural grading, and thereby, overall utilization of timber wood across diverse industrial domains. Hence, the accurate prediction of wood density is very important, which in turn enables the evaluation of defects and the estimation of moisture content in timber wood species. Although traditional density estimation techniques, such as gravimetry, X-ray densitometry, and manual inspection, are effective to some extent, they are often limited by their destructive nature, high cost, subjectivity, sensitivity, or safety concerns. To circumvent the limitations of existing techniques, terahertz (THz) technology has been explored here as a nondestructive, non-ionizing alternative for assessing the quality of timber wood. THz radiation can capture the difference in material properties arising from variations in internal structure, which in turn are correlated with wood density. In this work, a publicly available THz open dataset has been utilized, which contains the THz temporal signals of 78 samples from six diverse wood species. Here, a uniquely tailored combination of pre-processing steps, feature extraction techniques, and a stacked regression ensemble has been adopted to predict the wood density precisely. The stacked ensemble regressor sequentially integrates different ensemble learners strategically, with random forest, XGBoost, CatBoost, and the ridge regressor. This proposed approach yields a very high R<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> score of 0.9996, highlighting the advantages of integrating THz technology with machine learning techniques for precise, non-destructive estimation of wood density in the timber industry. Regression analysis ; Regression ; Linear regression ; Artificial neural network Wood and Agarwood Research ; Wood Treatment and Properties ; Face and Expression Recognition
Authors: Jia-Wei Tang (3561386) | Yong-Xuan Hong (22540635) | Jie Chen (5892) | Yun-Yun Xie (22540638) | Zhang-Wen Ma (14651323) | Qing-Hua Liu (592193) | Liang Wang (23021)
Source: Figshare (article, 2025-11-01)
Field: Chemistry — Analytical Chemistry

Ratio Quantificationof Geranium and Rose EssentialOil Mixtures via Deep Learning Analysis of Complex Raman Spectra Due to their high economic value, essential oils (EOs) are increasingly subject to adulteration, posing significant challenges for quality assessment and control. Owing to its excellent molecular specificity, Raman spectroscopy has been widely employed for the analysis and evaluation of EO products. In this study, we collected a total of 2700 Raman spectra comprising rose essential oil (REO, <i>n</i> = 900), geranium essential oil (GEO, <i>n</i> = 900), and their mixtures (<i>n</i> = 900). We first constructed a conventional convolutional neural network to distinguish spectra corresponding to varying EO mixing ratios. Interpretability analysis revealed that spectral peaks at 800, 1000, 1002, 1028, 1200, 1378, and 1668 cm<sup>–1</sup>, which show notable intensity variations in the averaged spectra of REO and GEO, also played a critical role in model decision-making, indicating that these spectral features can serve as discriminative markers for different EO ratios. Subsequently, we developed a channel attention residual feature extraction network (CARFENet), which employs spectral capturing and spectral separation modules to deconstruct mixed EO spectra into their constituent pure components. CARFENet demonstrated robust performance on the validation set and yielded predicted spectra that closely resembled the true spectra in an external test set, with similarity indices exceeding 0.99. These findings indicate that CARFENet enables the effective quantitative analysis of pure EO components within mixed Raman spectra. Raman spectroscopy ; Interpretability ; Spectral line ; Residual ; Discriminative model ; Analytical Chemistry (journal) ; Essential oil ; Principal component analysis ; Convolutional neural network Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Edible Oils Quality and Analysis
Authors: Ruimin Su | Tao Xue | Zhouqiao Wei | Hengyuan Hu | Yongbiao Mu | Jinpeng Guan | Ran Yin | Yong Yi | Limin Zang | Chao Yang | Lin Zeng
Source: Chinese Chemical Letters (article, 2025-10-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Machine learning-assisted prediction and design of biomass-derived N-doped porous carbon for zinc-ion hybrid capacitors Capacitor ; Mean squared prediction error ; Mean squared error ; Boosting (machine learning) ; Random forest ; Reliability (semiconductor) ; Porosity ; Gradient boosting ; Regression Supercapacitor Materials and Fabrication ; Advancements in Battery Materials ; Advanced Battery Technologies Research
Authors: Dilara Ickecan | Yunus Emre Okyayli | E. A. Bleda | Doğan Erbahar
Source: Computational Materials Science (article, 2025-01-20)
Field: Materials Science — Materials Chemistry

Emergent atomic environments in twisted bilayer graphene and their use in the prediction of the vibrational properties Bilayer graphene ; Graphene ; Chemical physics ; Bilayer ; Materials science ; Nanotechnology ; Condensed matter physics ; Chemistry ; Physics ; Membrane Graphene research and applications ; Force Microscopy Techniques and Applications ; Boron and Carbon Nanomaterials Research
Authors: Stephen D.; id_orcid 0000-0001-7951-4634 Cork | Alex Heater | Jiayi Zhou | T. B. Stork | Lihong; id_orcid 0000-0001-5728-4356 Zheng | Jane; id_orcid 0000-0002-9310-0291 Quinn
Source: Charles Sturt University Research Output (CRO) (article, 2025-08-14)
Field: Chemistry — Analytical Chemistry

Abscess detection on bovine livers with a commercial smart imaging system Meat processors are responsible for ensuring that animal carcasses are fit for human consumption [1]. Trained meat <br/>inspectors play a crucial role in quality control, identifying and condemning unhealthy tissue, with this process occurring <br/>at line speed. Whilst optimized for reporting of disease conditions for quality assurance processes, the time and tools <br/>currently available to inspectors, or other on-floor staff, restricts the depth of information able to be recorded due to rapid <br/>chain speeds. Impacts to the supply chain due to downgraded product can be significant. For example, abscessed <br/>condemned at around 3% of beef cattle slaughtered [2]. Line speed automated disease assessment therefore offers a <br/>promising layer of objective data for defect capture and producer feedback.Currently, Smart Imaging Systems (SIS) are <br/>used in processing plants for foreign object detection, quality assurance and quality control of meat products. One imaging <br/>modality, hyperspectral cameras, capture light reflected from the meat product, across multiple wavelengths in near <br/>infrared to visible ranges with this data being utilized via Machine learning models to identify multiple visual and <br/>biochemical tissue characteristics [3]. Current in-plant hyperspectral imaging systems include the composition analysis of <br/>meat cuts and control of foreign materials [4]. Automated assessment for abscesses and other offal health conditions, <br/>using imaging systems such as hyperspectral analysis, could therefore expand offal inspection with objective data. To <br/>investigate this, the development of hyperspectral disease detection models using commercially available smart imaging <br/>system was investigated. Hyperspectral imaging ; Quality assurance ; Quality (philosophy) ; Process (computing) ; Visual inspection ; Meat packing industry ; Image processing ; Process control Spectroscopy and Chemometric Analyses ; Microbial infections and disease research ; Spectroscopy Techniques in Biomedical and Chemical Research