jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Chrysavgi Gardeli | Stavroula Sykioti | George Exarchos | Maria Koliatsou | Periklis Andritsos | Efstathios Ζ. Panagou
Source: Applied Sciences (article, 2025-01-22)
Field: Chemistry — Analytical Chemistry

The Differentiation of Extra Virgin Olive Oil from Other Olive Oil Categories Based on FTIR Spectroscopy and Random Forest The great interest in the rapid and reliable differentiation of extra virgin olive oil from other olive oil categories is directly related to its unique sensory characteristics and high market prices. The aim of the present study was to investigate the potential of FTIR as a rapid and non-invasive technique to discriminate extra virgin olive oil (EVOO) from other olive oil categories (virgin olive oil, ordinary, and lampante) based on the acquired spectral profile of olive oil. Spectral data were collected, pre-processed, and correlated by Random Forest (RF) analysis with the sensory category (EVOO vs. other) of olive oil samples, as defined by sensory analysis undertaken previously by trained panelists. The results showed that the application of Savitzky–Golay (S-G) smoothing with a second derivative (d = 2), second- and third-order polynomial (p = 2, p = 3), and window size (w) of 12 and 13 points achieved the highest accuracy (0.91) between the two classes of samples. Characteristic spectral bands of triacylglycerols related to the carbonyl groups present in triacylglycerols (C=O) located near 1744 cm−1 (specific features: 1739, 1748, and 1751 cm−1), the fingerprinting area 1250–1000 cm−1 (specific features: 1088, 1094, 1116, 1123, 1124, 1158, 1162, 1236, 1240, and 1247 cm−1), which correspond to CH bending, and 1680 cm−1, which is associated with unsaturated aldehydes were observed to constitute the main basis of the discrimination of EVOO from the “other” class. The ability of the model to achieve high classification accuracy demonstrates the robustness of the FTIR spectral data combined with advanced machine learning techniques. Due to the lower cost and more rapid analysis time afforded by FTIR, this method provides promising perspectives for industrial olive oil classification. Olive oil ; Fourier transform infrared spectroscopy ; Chemistry ; Environmental science ; Mathematics ; Food science ; Chemical engineering ; Engineering Spectroscopy and Chemometric Analyses ; Edible Oils Quality and Analysis ; Advanced Chemical Sensor Technologies
Authors: Bircan Haspulat Taymaz | Handan Kamış | Michał Dziendzikowski | Kamil Kowalczyk | Krzysztof Dragan | Volkan Eskizeybek
Source: Scientific Reports (article, 2025-01-19)
Field: Materials Science — Materials Chemistry

Enhancing structural health monitoring of fiber-reinforced polymer composites using piezoresistive Ti3C2Tx MXene fibers The anisotropic behavior of fiber-reinforced polymer composites, coupled with their susceptibility to various failure modes, poses challenges for their structural health monitoring (SHM) during service life. To address this, non-destructive testing techniques have been employed, but they often suffer from drawbacks such as high costs and suboptimal resolutions. Moreover, routine inspections fail to disclose incidents or failures occurring between successive assessments. As a result, there is a growing emphasis on SHM methods that enable continuous monitoring without grounding the aircraft. Our research focuses on advancing aerospace SHM through the utilization of piezoresistive MXene fibers. MXene, characterized by its 2D nanofiber architecture and exceptional properties, offers unique advantages for strain sensing applications. We successfully fabricate piezoresistive MXene fibers using wet spinning and integrate them into carbon fiber-reinforced epoxy laminates for in-situ strain sensing. Unlike previous studies focused on high strain levels, we adjust the strain levels to be comparable to those encountered in practical aerospace applications. Our results demonstrate remarkable sensitivity of MXene fibers within low strain ranges, with a maximum sensitivity of 0.9 at 0.13% strain. Additionally, MXene fibers exhibited high reliability for repetitive tensile deformations and low-velocity impact loading scenarios. This research contributes to the development of self-sensing composites, offering enhanced capabilities for early detection of damage and defects in aerospace structures, thereby improving safety and reducing maintenance expenses. Structural health monitoring ; Materials science ; Piezoresistive effect ; Composite material ; Aerospace ; Epoxy ; Service life ; Fiber ; Spinning ; Sensitivity (control systems) ; Strain gauge ; Aerospace engineering ; Electronic engineering MXene and MAX Phase Materials ; Smart Materials for Construction ; Advanced Sensor and Energy Harvesting Materials
Authors: Wenliang Qi | Qingqing Jiang | T. Ma | Yong Yi Tan | Yan Ren | Erihemu Erihemu
Source: Foods (review, 2025-08-27)
Field: Chemistry — Analytical Chemistry

Near-Infrared Spectroscopy Combined with Chemometrics for Liquor Product Quality Assessment: A Review China's liquor industry continues to steadily expand and develop. The industry is currently transforming, shifting its focus from scale to quality and efficiency. This transformation is significantly increasing the demand for quality and safety testing. Currently, the testing system relies mainly on manual operation or traditional mechanical equipment. Technical bottlenecks include low testing efficiency, a significant imbalance in the cost-benefit ratio, and difficulty meeting the modern industry's dual technical index requirements of testing accuracy and systematicity. In this context, the innovative research and development of new detection technology is key to promoting technological upgrades in the liquor industry. Near-infrared (NIR) spectroscopy is a core, competitive analytical method for non-destructive wine quality testing due to its technical advantages, such as non-destructive analysis, real-time online detection, and the absence of sample pretreatment requirements. This study systematically elaborates on the optical principle and detection mechanism of NIR spectroscopy and explores the application paradigm of chemometrics in spectral data analysis. This study covers the quantitative analysis of alcoholic strength, the determination of main ingredient content (sugar, acidity, esters, etc.), the construction of trace flavor substance fingerprints, the authentication and origin tracing of alcoholic products, and the monitoring of wine aging quality dynamics, among other key technology areas. Additionally, we review the fusion and innovation trends of artificial intelligence and big data technology, the R&D progress of miniaturized testing equipment, and the technical bottlenecks of spectral modeling and algorithm optimization. We also make scientific predictions about the evolution path of this technology and its industrial application prospects. Chemometrics ; Infrared spectroscopy ; Spectroscopy ; Quality (philosophy) ; Quality assessment ; Product (mathematics) ; Near-infrared spectroscopy ; Analytical Chemistry (journal) ; Chemistry ; Biochemical engineering ; Environmental science ; Materials science ; Environmental chemistry ; Chromatography ; Mathematics ; Engineering ; Organic chemistry ; Optics ; Physics ; Reliability engineering ; Evaluation methods Spectroscopy and Chemometric Analyses ; Fermentation and Sensory Analysis ; Advanced Chemical Sensor Technologies
Authors: Antonios Morellos | Xanthoula Eirini Pantazi | Muhammad Baraa Almoujahed | Zita Kriaučiūnienė | Marius Kazlauskas | Egidijus Šarauskis | Abdul Mounem Mouazen
Source: Computers and Electronics in Agriculture (article, 2025-01-20)
Field: Chemistry — Analytical Chemistry

Forecasting of Fusarium head blight spatial distribution in winter wheat using machine learning Predicting spatial distribution of Fusarium Head Blight (FHB) is essential for precision preventive site-specific fungicide application in winter wheat cultivation. The current study presents a novel approach for the prediction of the within-field spatial distribution of FHB of winter wheat using random forest (RF), least squares support vector machines (LS-SVM), and multilayer perceptron (MLP), using high resolution data on soil characteristics, meteorological data, and remote sensing derived crop growth indices. The predictive performance of the models was assessed using two distinct training approaches based on data collected from three fields in Lithuania; a cross-field validation approach (Approach 1) and a field-specific model approach (Approach 2), using data attained from three experimental fields in Lithuania. In approach 1, MLPs achieved the highest performance with coefficient of determination (R2) values reaching up to 0.71 and residual prediction deviation (RPD) values reaching 1.87. In Approach 2, MLPs have demonstrated high performances with R2 values reaching 1.00 and RPD values up to 25.63. Field-specific models significantly outperformed cross-field models, achieving Kappa coefficient values ranging from 0.91 to 0.97 across all investigated fields. The above findings indicate the potential of the effective combination of machine learning models with remote sensing and soil data for the accurate FHB prediction and for the adoption of more sustainable and targeted crop protection practices. Head (geology) ; Fusarium ; Winter wheat ; Blight ; Spatial distribution ; Distribution (mathematics) ; Agricultural engineering ; Environmental science ; Agronomy ; Artificial intelligence ; Engineering ; Computer science ; Mathematics ; Geography ; Remote sensing ; Biology ; Horticulture Spectroscopy and Chemometric Analyses ; Mycotoxins in Agriculture and Food ; Leaf Properties and Growth Measurement
Authors: Xinrui Liu | Hongmei Yuan
Source: Frontiers in Bioengineering and Biotechnology (review, 2025-02-04)
Field: Materials Science — Biomaterials

Responsive nanomaterials in biomedicine, patent path and prospect analysis In recent years, responsive nanomaterials have demonstrated tremendous potential in biomedical applications due to their unique advantages in precise drug delivery and controlled release. For complex diseases such as cancer, chronic inflammation, and genetic disorders, traditional treatment methods are often limited by insufficient targeting and significant side effects. Responsive nanotechnology, by sensing specific internal or external stimuli, has significantly enhanced the precision and efficiency of treatments. This study systematically summarizes the technological trajectory and emerging research directions of responsive nanomaterials through global patent and literature data, employing main path analysis, derivative path analysis, and keyword co-occurrence analysis. The results reveal the evolution of this field, from the optimization of early single-stimulus-responsive nano delivery systems to the rise of theranostics integration, followed by advancements in multi-stimuli-responsive synergistic therapies, and finally, the innovation in biomimetic material design. Each developmental phase has increasingly focused on adapting to complex biological environments, achieving superior targeting performance, and enhancing therapeutic efficacy. Keyword co-occurrence analysis highlights key research hotspots, including biomimetic design, multimodal synergistic therapies, and emerging response mechanisms. In the future, responsive nanomaterials are expected to play a pivotal role in personalized medicine, multifunctional carrier design, and complex disease management, providing novel insights and technological support for precision medicine. Biomedicine ; Nanotechnology ; Computer science ; Risk analysis (engineering) ; Personalized medicine ; Medicine ; Bioinformatics ; Materials science ; Biology Nanoparticle-Based Drug Delivery ; Advanced biosensing and bioanalysis techniques ; Nanoplatforms for cancer theranostics
Authors: Zhuowei Li | Ge Zhu | Heyang Li | Qi Zhu | Cong Yan | Xue Bai | Ji‐Guang Li | Bin Dong
Source: Laser & Photonics Review (article, 2025-04-02)
Field: Materials Science — Materials Chemistry

Visible Light Excited Yb<sup>3+</sup>‐Doped Phosphor Via Eu<sup>2+</sup> Bridged Energy Transfer Toward NIR‐II Spectroscopy Application Abstract Yb 3+ is regarded as an efficient near‐infrared‐II (NIR‐II) emitting activator, which has been widely used in energy conversion, up‐conversion luminescence and optical communication. However, the simple energy level structure of Yb 3+ makes it can only be excited by ultraviolet or infrared light, which limits their application in the currently booming phosphor converted NIR‐II light emitting diodes (NIR‐II pc‐LEDs). Here, “Eu 2+ bridge” strategy is presented to largely extend Yb 3+ absorption to visible range, and successfully realize efficient visible light pumped NIR‐II emission via energy transfer from Eu 2+ to Yb 3+ . Meanwhile, to balance the valence state of reduced Eu 2+ and oxidized Yb 3+ , the pre‐prepared EuS is used as precursor instead of Eu 2 O 3 , which significantly increases the NIR‐II luminescence of Yb 3+ by 6 times. Detailed energy transfer and luminescence enhancement mechanism are discussed. Finally, a NIR‐II pc‐LED is fabricated with photoelectric efficiency of 12.61%@50 mA and output power of 74.09 mW@300 mA. Subsequently, a miniaturized and real‐time test system is integrated based on the convolutional neural network technology to accurate predict organic solvents with different concentrations. This study not only introduces a new strategy to realize visible light‐excited Yb 3+ ‐doped NIR‐II emitting phosphors, but also promotes their innovative application based on NIR spectroscopy technology. Phosphor ; Excited state ; Spectroscopy ; Energy transfer ; Doping ; Materials science ; Atomic physics ; Physics ; Optoelectronics Luminescence Properties of Advanced Materials ; Radiation Detection and Scintillator Technologies ; Terahertz technology and applications
Authors: Qi Wang | Jinzhu Lu | Yuanhong Wang | Fajun Miao | Sijia Liu | Q.-Z. Shui | Junfeng Gao | Yingwang Gao
Source: Plant Methods (article, 2025-06-03)
Field: Chemistry — Analytical Chemistry

In situ nondestructive identification of citrus fruit ripeness via hyperspectral imaging technology Rapid and accurate assessment of the citrus ripening stage in the field is important for determining harvest timing and improving industrial economic efficiency; however, the lack of effective nondestructive detection methods in the current orchard leads to flaws in ripening stage assessment, which affects harvesting decisions. To solve this problem, this study utilized hyperspectral technology to collect data from 22 fruit trees in an orchard (in the range of 400-1000 nm) and explored the effectiveness of five regions of interest selection methods (x-axis, y-axis, four-quadrant, threshold segmentation, and raw) for the delineation of the citrus ripening stage. The data quality was enhanced via wavelet transform (WT)-multiple scattering correction (MSC) preprocessing, and the effective wavelengths were extracted via the successive projections algorithm (SPA). On the basis of these wavelengths, backpropagation neural network (BP) and convolutional neural network (CNN) models were built for maturity prediction. The results show that the x-axis region of interest selection method outperforms the other methods, and the SPA-BP model based on this method performs best. An accuracy of 99.19% for the correction set and 100% for the prediction set was achieved when only 0.03% of the wavelength was used. This groundbreaking study highlights the significant potential of hyperspectral technology for in situ assessment of citrus ripening stages. Furthermore, it offers crucial technical support and serves as a valuable reference for the advancement of precision agriculture. Ripeness ; Hyperspectral imaging ; In situ ; Identification (biology) ; Environmental science ; Remote sensing ; Horticulture ; Artificial intelligence ; Computer science ; Botany ; Biology ; Geography ; Ripening ; Meteorology Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Water Quality Monitoring and Analysis
Authors: Emir Akdaşçi | Furkan Eker | Hatice Duman | Mikhaël Bechelany | Sercan Karav
Source: International Journal of Molecular Sciences (review, 2025-10-19)
Field: Materials Science — Materials Chemistry

Microbial-Based Green Synthesis of Silver Nanoparticles: A Comparative Review of Bacteria- and Fungi-Mediated Approaches The growing demand for sustainable and eco-friendly technologies has driven the development of green and bio-based synthesis methods for metallic nanoparticles. Among these, the microbial synthesis of silver nanoparticles (AgNPs) has emerged as a promising alternative to conventional chemical methods, which often rely on hazardous reagents and harsh conditions. Bacteria and fungi are particularly attractive due to their ability to produce AgNPs with tunable size, shape, and surface properties through natural enzymatic and metabolic processes. This review provides a comparative analysis of bacterial and fungal synthesis routes, focusing on their distinct advantages, limitations, and optimal applications. Bacterial synthesis offers faster growth, simpler culture requirements, and greater potential for genetic manipulation, enabling precise control over nanoparticle (NP) characteristics. In contrast, fungal synthesis typically yields higher nanoparticle stability and is well suited for extracellular, scalable production. The review also summarizes key synthesis parameters (e.g., pH, temperature, reaction time), addresses reproducibility and scalability challenges, and highlights emerging research areas, including antibacterial bio-hybrid materials and bacterial-supported metallic catalysts. Overall, this comparative perspective provides a clear framework for selecting appropriate microbial systems for different technological applications and identifies future research directions to advance green nanotechnology. Scalability ; Synthetic biology ; Silver nanoparticle ; Human health ; Nanoparticle ; Green chemistry Nanoparticles: synthesis and applications
Authors: A. I. Ovcharenko | Sergey Polevoy | Oleh Yermakov
Source: Advanced Photonics Nexus (article, 2025-04-29)
Field: Materials Science — Surfaces, Coatings and Films

Forward and inverse design of single-layer metasurface-based broadband antireflective coating for silicon solar cells Almost half of the solar energy that reaches a silicon solar cell is lost due to the reflection at the silicon–air interface. Antireflective coatings aim to suppress the reflection and thereby to increase the photogenerated current. The conventional few-layer dielectric antireflective coatings may significantly boost the transmission of solar light, but only in a narrow wavelength range. Using forward and inverse design optimization algorithms, we develop the designs of antireflective coatings for silicon solar cells based on single-layer silicon metasurfaces (periodic subwavelength nanostructure arrays), leading to a broadband reflection suppression in the wavelength range from 500 to 1200 nm for the incidence angles up to 60 deg. The reflection averaged over the visible and near-infrared spectra is at the record-low level of approximately 2% and 4.4% for the normal and oblique incidence, respectively. The obtained results demonstrate the potential of machine learning–enhanced photonic nanostructures to outperform the classical antireflective coatings. Anti-reflective coating ; Broadband ; Materials science ; Silicon ; Layer (electronics) ; Silicon solar cell ; Coating ; Optoelectronics ; Inverse ; Nanotechnology ; Computer science ; Telecommunications Optical Coatings and Gratings ; Advanced Antenna and Metasurface Technologies ; Thin-Film Transistor Technologies
Authors: Kallil Zielinski | Leonardo Scabini | Lucas C. Ribas | Núbia R. da Silva | Hans Beeckman | Jan Verwaeren | Odemir Martinez Bruno | Bernard De Baets
Source: Computers and Electronics in Agriculture (article, 2025-01-20)
Field: Chemistry — Organic Chemistry

Advanced wood species identification based on multiple anatomical sections and using deep feature transfer and fusion Artificial intelligence ; Fusion ; Identification (biology) ; Feature (linguistics) ; Pattern recognition (psychology) ; Transfer of learning ; Computer science ; Engineering ; Biology ; Botany Wood and Agarwood Research ; Image Processing and 3D Reconstruction ; Microbial Metabolism and Applications
Authors: Jun Zhang | Limin Dai | Zhiwen Huang | Chuxuan Gong | Junjie Chen | Jundan Xie | Maozhen Qu
Source: Agriculture (review, 2025-02-12)
Field: Chemistry — Analytical Chemistry

Corn Seed Quality Detection Based on Spectroscopy and Its Imaging Technology: A Review The quality assurance of corn seeds is of utmost significance in all stages of production, storage, circulation, and breeding. However, the traditional detection method has some disadvantages, such as high labor intensity, strong subjectivity, low efficiency, cumbersome operation, etc. In view of this, it is of great significance to study more advanced detection methods. In this paper, the application of near-infrared spectroscopy and its imaging technology in the quality detection of corn seeds was reviewed. Firstly, the principles of these two technologies were introduced, and their components, data acquisition, and processing methods, as well as portability, were compared and discussed. Then, the application of these methods to the main quality of corn seeds (including variety and purity, vigor, internal components, mycotoxins, and other qualities such as frost damage, hardness, and maturity, etc.) was reviewed. Breakthroughs and innovations have been made in detection methods, spectral preprocessing methods and recognition algorithms. The significance of corn quality characteristics and the function of the applied algorithm were emphasized. Finally, the challenges and future research direction of spectral and its imaging technology was proposed, aiming to further enhance the accuracy, reliability, and practicability of the detection technology. With the rapid development of spectral and its imaging technology, the detection methods of corn quality are also advancing with the times. This is not just for corn, but more and more crops can be accurately detected by these technologies. It will become an important means of agricultural production inspection in the future. Quality (philosophy) ; Spectroscopy ; Agronomy ; Biology ; Physics Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Spectroscopy Techniques in Biomedical and Chemical Research