jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Muhammad Arsyad Suyuti | Djarot B. Darmadi | Winarto Winarto | Putu Hadi Setyarini
Source: Scientific Reports (article, 2025-02-05)
Field: Materials Science — Polymers and Plastics

Enhancing the tensile strength and morphology of Sansevieria trifasciata Laurentii fibers using liquid smoke and microwave treatments: an RSM approach Liquid smoke (LS), an eco-friendly substance, is effective in modifying natural fibers to enhance their mechanical properties, surface morphology, and thermal stability. This study investigates the effects of LS immersion followed by microwave treatment on the tensile strength, surface morphology, and thermal characteristics of Sansevieria trifasciata Laurentii (STL) fibers. Response surface methodology (RSM) with central composite design (CCD) was used to optimize treatment parameters, including LS immersion time, microwave heating temperature, and duration. Mechanical, morphological, structural, and thermal properties were analyzed using scanning electron microscopy (SEM), Fourier-transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), thermogravimetric analysis (TGA), and differential scanning calorimetry (DSC). Optimal treatment conditions: 120 min LS immersion and microwave heating at 40 °C for 30 min. They resulted in a tensile strength of 370.23 MPa, a 37.21% improvement compared to untreated fibers. Treated fibers exhibited enhanced thermal stability, increased crystallinity, and improved surface morphology. These findings demonstrate that LS and microwave treatments effectively enhance STL fibers' mechanical properties, surface morphology, and thermal stability, positioning them as promising reinforcement materials for sustainable composite applications. Ultimate tensile strength ; Morphology (biology) ; Microwave ; Materials science ; Composite material ; Mathematics ; Biology ; Computer science ; Zoology Natural Fiber Reinforced Composites ; Nanocomposite Films for Food Packaging ; Postharvest Quality and Shelf Life Management
Authors: Tony Trent | Justin J. Miller | Kendall Blumer | Gregory R. Bowman
Source: Journal of Molecular Biology (article, 2025-03-13)
Field: Chemistry — Spectroscopy

The G Protein Inhibitor YM-254890 is an Allosteric Glue Allosteric regulation ; GLUE ; Chemistry ; Biochemistry ; Biophysics ; Biology ; Enzyme ; Materials science Advanced Proteomics Techniques and Applications ; Growth Hormone and Insulin-like Growth Factors ; Protein Kinase Regulation and GTPase Signaling
Authors: Hasan AL Dabbas | Ibrahim Al-Adwan | Abhishek Agarwal | Masengo Ilunga | Omar Badran | Ibrahim I. Ikhries
Source: International Journal of Computational Methods and Experimental Measurements (article, 2025-03-31)
Field: Materials Science — Materials Chemistry

Comparative Investigation of Mechanical Characteristics and Microstructure in Maraging Steel Fabricated via DMLS and CNC Techniques This study investigates the mechanical properties of maraging steel MS1 produced through two distinct manufacturing processes: Which include Direct Metal Laser Sintering (DMLS) and Computer Numerical Control (CNC) machining.The goal is to investigate the influence of these methods on mechanical performance and the microstructural integrity of the produced components.The strength, ductility, and fracture behavior of the specimens were evaluated under tensile testing.Results also showed that the DMLS specimen had significantly superior mechanical properties compared to the CNC machined specimen with an ultimate tensile strength of 1145.8MPa compared to 542.45 MPa.The results indicated that the DMLS specimen withstood higher stress levels, while remaining at lower strain than that of the CNC machined specimen.Which means that the strength and coherence of the structural particles in the DMLS specimen stems from a strong degree of bonding between deposited particles of structured material.Based on fractographic analysis, the DMLS sample showed a more homogenous microstructure due to which metal atom distribution was more coherent and the CNC sample had signs of internal defects due to machining.SOLIDWORKS simulations conducted to validate the results proved to be very close to the experimental results, essentially verifying the reliability of the results.The study concludes that DMLS provides large benefits over conventional CNC machining for the production of high-performance maraging steel components and points to the feasibility of additive manufacturing in advanced engineering applications.Further, we suggest, that future research may include the following investigations to further optimize the mechanical properties of 3D printed maraging steel, including the investigation of additional processing parameters and post-processing treatments. Microstructure ; Maraging steel ; Materials science ; Metallurgy Metal Alloys Wear and Properties ; Advanced machining processes and optimization ; Advanced Machining and Optimization Techniques
Authors: Ahelijiang Saiyisan | Shihao Zeng | Huabin Zhang | Ziyan Wang | Jiawen Wang | Pei Cai | Jianpan Huang
Source: Neural Regeneration Research (article, 2025-01-29)
Field: Materials Science — Materials Chemistry

Chemical exchange saturation transfer MRI for neurodegenerative diseases: An update on clinical and preclinical studies Chemical exchange saturation transfer magnetic resonance imaging is an advanced imaging technique that enables the detection of compounds at low concentrations with high sensitivity and spatial resolution and has been extensively studied for diagnosing malignancy and stroke. In recent years, the emerging exploration of chemical exchange saturation transfer magnetic resonance imaging for detecting pathological changes in neurodegenerative diseases has opened up new possibilities for early detection and repetitive scans without ionizing radiation. This review serves as an overview of chemical exchange saturation transfer magnetic resonance imaging with detailed information on contrast mechanisms and processing methods and summarizes recent developments in both clinical and preclinical studies of chemical exchange saturation transfer magnetic resonance imaging for Alzheimer's disease, Parkinson's disease, multiple sclerosis, and Huntington's disease. A comprehensive literature search was conducted using databases such as PubMed and Google Scholar, focusing on peer-reviewed articles from the past 15 years relevant to clinical and preclinical applications. The findings suggest that chemical exchange saturation transfer magnetic resonance imaging has the potential to detect molecular changes and altered metabolism, which may aid in early diagnosis and assessment of the severity of neurodegenerative diseases. Although promising results have been observed in selected clinical and preclinical trials, further validations are needed to evaluate their clinical value. When combined with other imaging modalities and advanced analytical methods, chemical exchange saturation transfer magnetic resonance imaging shows potential as an in vivo biomarker, enhancing the understanding of neuropathological mechanisms in neurodegenerative diseases. Neuroscience ; Medicine ; Preclinical research ; Medical physics ; Psychology Lanthanide and Transition Metal Complexes ; Advanced MRI Techniques and Applications ; Electron Spin Resonance Studies
Authors: Adina Căta | Nick Samuel Țolea | Antonina Evelina Lazăr | Ioana Maria Carmen Ienașcu | Raluca Pop
Source: Sustainability (article, 2025-11-12)
Field: Materials Science — Biomaterials

Recent Advances in Sustainable Anthocyanin Applications in Food Preservation and Monitoring: A Review Anthocyanins, a group of naturally occurring flavonoid compounds, have garnered increasing attention due to their wide-ranging biological activities that suggest their considerable potential to be utilized not only as natural food colorants but also as functional additives that can enhance food preservation and contribute to the development of health-promoting functional foods. Additionally, their sensitivity to environmental factors such as pH and temperature makes anthocyanins promising candidates for use in intelligent packaging systems, particularly as natural indicators for monitoring food freshness and quality throughout storage and distribution. Despite challenges related to their stability and regulatory acceptance, continued research into anthocyanins remains crucial for advancing sustainable, clean-label food technologies and reducing reliance on synthetic additives. To fully leverage their economic and health potential, it is essential to gain a comprehensive understanding of the various plant sources of anthocyanins, their chemical composition, extraction methods, and roles in different applications. Moreover, integrating anthocyanins into food and intelligent packaging systems presents various technical and regulatory challenges that are also summarized in this review. Anthocyanin ; Food industry ; Leverage (statistics) ; Active packaging ; Food packaging ; Natural food ; Food quality ; Food preservation ; Health benefits Nanocomposite Films for Food Packaging ; Phytochemicals and Antioxidant Activities ; Potato Plant Research
Authors: Alessandro Mingotti | F Hossein Babaei | Roberto Tinarelli | Lorenzo Peretto
Source: Sensors (review, 2025-06-20)
Field: Materials Science — Materials Chemistry

Medium-Voltage AC Cable Joints: A Review of Testing Methods, Standards, and Emerging Trends Cable joints (CJs) are essential components of power systems, enabling cable network extension and repair. Their design and installation are critical to ensuring reliability. This paper reviews the international standards, state-of-the-art literature, and emerging trends in medium-voltage (MV) AC cable joint testing. It provides a comprehensive overview of existing testing methods, highlighting innovative approaches. The review covers key international standards for CJ testing, both during design and final manufacturing stages. Additionally, it examines the literature on tests developed for assessing factors affecting CJ performance, including temperature, partial discharges, and tangent delta measurements. Recent advancements in artificial intelligence for CJ testing are also discussed. This work aims to present a thorough perspective on current practices and future directions in MV cable joint testing and diagnostics. Reliability (semiconductor) ; Engineering ; Reliability engineering ; Systems engineering ; Computer science ; Mechanical engineering ; Power (physics) High voltage insulation and dielectric phenomena ; Thermal Analysis in Power Transmission ; Electrical Fault Detection and Protection
Authors: Tom Fearn | Claudia Beleites | Juan Antonio Fernández Pierna | Vincent Baeten | Martin Lagerholm | Jean‐Michel Roger | Anastasios Koidis
Source: TrAC Trends in Analytical Chemistry (article, 2025-08-07)
Field: Chemistry — Analytical Chemistry

Multivariate calibration of non-destructive spectral sensors with a particular focus on food applications: Validation issues and guidelines Multivariate calibration methods have enabled the use of non-destructive spectral sensors in a wide range of applications but carry a risk of overfitting to the available training samples. For this reason, the prediction of unseen samples plays a vital role both in tuning the prediction algorithm and in assessing its performance, two activities that need to be carefully distinguished. Methods employed include data-splitting, cross-validation, and the use of genuinely independent sets of data. These approaches are described and some common issues with them are identified. The focus is on food applications but the methods discussed are widely used in other areas. • Guidelines for the validation of multivariate calibrations. • Common mistakes in validation highlighted. • Sample splitting and cross-validation discussed. Calibration ; Focus (optics) ; Multivariate statistics ; Multivariate analysis ; Environmental science ; Computer science ; Remote sensing ; Statistics ; Mathematics ; Machine learning ; Geology ; Physics ; Optics Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Water Quality Monitoring and Analysis
Authors: Ilena Isak | Harriet L. Newson | Tripti Singh
Source: Forests (article, 2025-01-30)
Field: Chemistry — Organic Chemistry

Wood Species Differentiation: A Comparative Study of Direct Analysis in Real-Time and Chromatography Mass Spectrometry This study reports for the first time the fingerprinting extractives analysis of the indigenous wood species of Podocarpus totara from New Zealand, Eucalyptus saligna from Australia and Pinus radiata imported from California, USA and grown in New Zealand. We evaluated the use of analytical techniques for wood species discrimination. We compared the chemical fingerprinting of extractive compounds obtained using traditional chromatographic techniques with direct analysis in real-time–time of flight-mass spectrometry (DART-TOF-MS) with the auxiliary of chemometrics and principal component analysis. The traditional wet chemistry analysis of wood extracts provided a comprehensive characterisation of all extractive components. However, the more eco-friendly, sustainable and faster DART-TOF-MS technique effectively distinguished between wood species when heartwood and sapwood samples were combined. Notably, neither wet chemistry nor DART-TOF-MS could clearly differentiate between heartwood and sapwood within the same wood species. DART-TOF-MS analysis demonstrates potential as a reliable quality control tool for identifying wood species necessary in commercial and timber trading markets as well as for detecting the illicit trade of counterfeit wood products. Mass spectrometry ; Chromatography ; Chemistry Wood and Agarwood Research ; Plant biochemistry and biosynthesis ; Biological Stains and Phytochemicals
Authors: Aram Azadpour | Kaveh Mollazade | Mohsen Ramezani | Hadi Samimi-Akhijahani
Source: Scientific Reports (article, 2025-02-12)
Field: Chemistry — Analytical Chemistry

Automated grading of oleaster fruit using deep learning The agriculture sector is crucial to many economies, particularly in developing regions, with post-harvest technology emerging as a key growth area. The oleaster, valued for its nutritional and medicinal properties, has traditionally been graded manually based on color and appearance. As global demand rises, there is a growing need for efficient automated grading methods. Therefore, this study aimed to develop a real-time machine vision system for classifying oleaster fruit at various grading velocities. Initially, in the offline phase, a dataset containing video frames of four different quality classes of oleaster, categorized based on the Iranian national standard, was acquired at different linear conveyor belt velocities (ranging from 4.82 to 21.51 cm/s). The Mask R-CNN algorithm was used to segment the extracted frames to obtain the position and boundary of the samples. Experimental results indicated that, with a 100% detection rate and an average instance segmentation accuracy error ranging from 4.17 to 5.79%, the Mask R-CNN algorithm is capable of accurately segmenting all classes of oleaster at all the examined grading velocity levels. The results of the fivefold cross validation indicated that the general YOLOv8x and YOLOv8n models, created using the dataset obtained from all conveyor belt velocity levels, have a similarly reliable classification performance. Therefore, given its simpler architecture and lower processing time requirements, the YOLOv8n model was used to evaluate the grading system in real-time mode. The overall classification accuracy of this model was 92%, with a sensitivity range of 87.10-94.89% for distinguishing different classes of oleaster at a grading velocity of 21.51 cm/s. The results of this study demonstrate the effectiveness of deep learning-based models in developing grading machines for the oleaster fruit. Grading (engineering) ; Computer science ; Artificial intelligence ; Biology ; Ecology Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Advanced Chemical Sensor Technologies
Authors: Kelly C. Stewart | Benjamin W. Lamm | Takaaki Koyanagi
Source: International Journal of Applied Ceramic Technology (article, 2025-01-07)
Field: Materials Science — Ceramics and Composites

Microstructural origin of high‐strength Tyranno SA4 SiC fiber studied by comparative Raman spectroscopy <sup>#</sup> Abstract Tyranno SA4 grade SiC fiber is an emerging continuous fiber with excellent mechanical properties. The origin of the fiber's high‐performance was studied by comparative Raman spectroscopy of four different grades of SiC fibers and reference SiC monolith. Analysis of the Raman spectra of fiber cross‐sections, surface and homogeneity, and fibers under stress, revealed how differences in microstructure, such as crystalline order, size, and the presence of critical flaws, dictate mechanical properties. This information can help optimize SiC fiber manufacturing. Materials science ; Raman spectroscopy ; Microstructure ; Fiber ; Composite material ; Homogeneity (statistics) ; Spectroscopy ; Optics Advanced ceramic materials synthesis ; Silicon Carbide Semiconductor Technologies ; Diamond and Carbon-based Materials Research
Authors: Kenneth René Simonsen | Dennis Severin Hansen | Simon Pedersen
Source: Carbon Capture Science & Technology (article, 2025-06-09)
Field: Chemistry — Spectroscopy

Framework for CO2 impurity monitoring in CCUS infrastructure • CO 2 impurity monitoring throughout the CCUS chain is essential for compliance, infrastructure protection, and supports decision making. • Conceptual framework of quality compliance control points at stakeholder transition interfaces. • Detailed evaluation of the risks and impacts associated with insufficient monitoring and control of CO 2 impurities. • The best monitoring candidates are found based on the variety and quantity of impurities present in the CO 2 stream. • A prediction is carried out based on the expansion of the CCUS industry and existing CO 2 specifications. As Carbon Capture, Utilization, and Storage (CCUS) expands, the need for safe and efficient CO 2 transport becomes increasingly important. Effective monitoring of CO 2 impurities is essential for maintaining system integrity and ensuring regulatory compliance across the CCUS chain. However, no standardized approach for impurity monitoring currently exists, and specifications vary depending on the project and transport method. This study identifies key stages in the CCUS process where impurity monitoring is critical. The impurity thresholds defined by the Northern Lights and Porthos projects are assessed in terms of corrosion mitigation, minimization of energy requirements, protection of human and environmental safety, and preservation of storage integrity. A conceptual framework is proposed to underscore the importance of CO 2 quality compliance at points of ownership handover, where decisions are determined based on measured impurity concentrations. The evaluation of impurities shows that impurities can significantly impact transport safety, efficiency, and storage performance, justifying the need for defined concentration limits. The findings suggest a risk-informed monitoring approach, where the frequency and precision of measurement are guided by the potential impact of each impurity, with H 2 O highlighted as a critical case requiring high-frequency monitoring. A comparative analysis of available sensing technologies reveals that no single method can detect all impurities listed in the CO 2 specifications. Instead, a combined monitoring strategy is proposed to meet specification requirements. As CCUS deployment accelerates, effective impurity monitoring will be essential to support regulatory compliance, taxation frameworks, operational control, and the development of secure and scalable CO 2 transport networks. Impurity ; Environmental science ; Physics Spectroscopy and Laser Applications ; Gas Sensing Nanomaterials and Sensors ; Atomic and Subatomic Physics Research
Authors: Yaotang Ji | Hongli Suo | Zhen Ma | Baoxu Huang | Jianhua Liu | Lei Wang | Zili Zhang | Qiuliang Wang
Source: Metals (article, 2025-05-08)
Field: Materials Science — Materials Chemistry

Grain Rotation and Deformation Behavior in Cube-Textured Ni Polycrystalline Alloy Studied via In-Situ Tensile Testing and EBSD The cube texture in alloys shows deterioration under plastic deformation. To further observe the evolution of orientation in individual grains during deformation, in-situ tensile testing was coupled with electron backscattered diffraction (EBSD). We found that the rotation of an individual grain is not only determined by its Schmid factor and size, but also by the condition of the adjacent grains. We demonstrated the interactions between grains using the EBSD data in different models, including the crystal orientation, the kernel average misorientation, the Schmid factor, the inverse pole figure, and the grain reference orientation deviation. A systematic three-factor coupled model involving the Schmid factor, grain size, and neighboring grain states is proposed. Furthermore, the mechanism by which small-sized grains induce the splitting of adjacent larger grains through the pinning effect has not been reported in highly textured polycrystalline materials to date. This characterization allows us to better understand the changes in grain shape and crystal lattice rotation, which can be used to characterize other polycrystalline alloys. Electron backscatter diffraction ; Materials science ; Alloy ; Deformation (meteorology) ; Cube (algebra) ; Crystallite ; Rotation (mathematics) ; Ultimate tensile strength ; In situ ; Tensile testing ; Metallurgy ; Texture (cosmology) ; Composite material ; Geometry ; Microstructure Microstructure and mechanical properties ; Microstructure and Mechanical Properties of Steels ; Metal and Thin Film Mechanics
Authors: Shuyan Pan | Mingzhu Tang | Na Li | Jiapeng Zuo | Xingpeng Zhou
Source: Sensors (article, 2025-07-31)
Field: Materials Science — Materials Chemistry

Anomaly Detection of Acoustic Signals in Ultra-High Voltage Converter Valves Based on the FAVAE-AS The converter valve is the core component of the ultra-high voltage direct current (UHVDC) transmission system, and its fault detection is very important to ensure the safe and stable operation of the transmission system. However, the voiceprint signals collected by converter stations under complex operating conditions are often affected by background noise, spikes, and nonlinear interference. Traditional methods make it difficult to achieve high-precision detection due to the lack of feature extraction ability and poor noise robustness. This paper proposes a fault-aware variational self-encoder model (FAVAE-AS) based on a weak correlation between attention and self-supervised learning. It extracts probability features via a conditional variational autoencoder, strengthens feature representation using multi-layer convolution and residual connections, and introduces a weak correlation attention mechanism to capture global time point relationships. A self-supervised learning module with six signal transformations improves generalization, while KL divergence-based correlation inconsistency quantization with dynamic thresholds enables accurate anomaly detection. Experiments show that FAVAE-AS achieves 0.925 accuracy in fault detection, which is 5% higher than previous methods, and has strong robustness. This research provides critical technical support for UHVDC system safety by addressing converter valve acoustic anomaly detection. It proposes an extensible framework for industrial intelligent maintenance. Robustness (evolution) ; Computer science ; Fault detection and isolation ; Anomaly detection ; Pattern recognition (psychology) ; Artificial intelligence ; Electronic engineering ; Control theory (sociology) ; Engineering High voltage insulation and dielectric phenomena ; High-Voltage Power Transmission Systems ; Power Systems Fault Detection