jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Mehmet Doğan Elbi | Ezgi Özgören Çapraz | Emre Şahin | Mehmet Ulaş Koyuncuoğlu | Can Tuncer
Source: Pamukkale University Journal of Engineering Sciences (article, 2024-01-01)
Field: Chemistry — Analytical Chemistry

A classification based on support vector machines for monitoring avocado fruit quality Scientifically, the efficiency of a method refers to its power to best predict/calculate based on an evaluation following a certain process within the current scenario, parameter and/or data. For a good prediction, the most appropriate approach(es) to a problem should be considered and the related tests should be done reliably. Practical studies in the field of food safety and fruit quality are critical, with the accuracy, speed and economic parameters of the methods used being of particular importance. In this study, for the first time in literature an Arduino-based temperature and gas monitoring system (called e-nose) is used to monitor the decay of avocado fruit in a controlled experimental environment and support vector machines, a machine learning method, are used to detect (classification) the decay. In this study, test and validation success of over 99% was achieved with very few training-data for classification. The obtained results are encouraging in terms of the detection results of the developed e-nose and the method used to determine the level of decay in other fruit in cold storage. Support vector machine ; Quality (philosophy) ; Computer science ; Machine learning ; Artificial intelligence ; Pattern recognition (psychology) ; Physics Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Plant Physiology and Cultivation Studies
Authors: Brenna Kettlewell | Daniel Boyd
Source: Materials (article, 2024-04-28)
Field: Materials Science — Ceramics and Composites

Inside the Borate Anomaly: Leveraging a Predictive Modelling Approach to Navigate Complex Composition–Structure–Property Relationships in Oxyhalide Borate Glasses This study employs a systematic and predictive modelling approach to investigate the structure and properties of multi-component borate glasses. In particular, this work is focused on understanding the individual and interaction effects of multiple constituents on several material properties. By leveraging advanced modeling techniques, this work examines how the inclusion and variation of B<sub>2</sub>O<sub>3</sub>, CaF<sub>2</sub>, TiO<sub>2</sub>, ZnO, and Na<sub>2</sub>CO<sub>3</sub> influence the glass network, with particular attention to modifier fractions ≥ 30 mol%. This research addresses the gap in knowledge regarding the complex behavior of borate glasses in this high modifier fraction range, known as the borate anomaly, where prediction of glass structure and properties becomes particularly challenging. The use of a design of mixtures (DoM) approach facilitated the generation of polynomial equations indicating the influence of mixture components on various responses, enabling the prediction and optimization of glass properties over broad compositional ranges despite being within the anomalous region. This methodical approach not only advances our understanding of borate glass systems but also underscores the importance of predictive modelling in the accelerated design and development of novel glass materials for diverse applications. Boron ; Materials science ; Borate glass ; Work (physics) ; Anomaly (physics) ; Component (thermodynamics) ; Process engineering ; Computer science ; Biological system ; Thermodynamics ; Chemistry ; Organic chemistry ; Physics ; Engineering Glass properties and applications ; Thermal and Kinetic Analysis ; Luminescence Properties of Advanced Materials
Authors: Yongjia Zhang | Guojie Wu | Zhenfeng Gong | Liang Mei
Source: Microwave and Optical Technology Letters (article, 2024-12-01)
Field: Chemistry — Spectroscopy

A Rollar‐Type Resonant Photoacoustic Spectroscopy for Trace Gas Detection ABSTRACT This paper presents a miniature Rollar‐type resonant photoacoustic cell (RRPAC), consisting of two cylindrical resonant cavities and a buffer chamber. The finite element analysis of the acoustic field distributions for the RRPAC and T‐type resonant photoacoustic cell (TRPAC) is utilized, demonstrating that RRPAC can produce a greater photoacoustic signal than the conventional TRPAC. A high sensitivity trace C 2 H 2 photoacoustic spectroscopy (PAS) system is successfully developed by combining a cantilever beam‐based acoustic sensor as the acoustic sensing unit and a high‐speed spectrometer as the demodulation unit with the RRPAC. The experimental results indicate that the 2f signal of the RRPAC at the first‐order resonance mode is approximately 1.5 times of the TRPAC with the same C 2 H 2 concentration. The gas detection limit of the RRPAC PAS system for C 2 H 2 is 40.7 ppb at an averaging time of 100 s. In comparison with the conventional TRPAC, the RRPAC exhibits higher sensitivity, thus providing a novel solution for the advancement of the PAS system. Photoacoustic spectroscopy ; Detection limit ; Sensitivity (control systems) ; Resonance (particle physics) ; Trace gas ; Photoacoustic imaging in biomedicine ; SIGNAL (programming language) ; Photoacoustic effect ; Materials science ; Analytical Chemistry (journal) ; Spectrometer ; Demodulation ; Cantilever ; Spectroscopy ; Optics ; Chemistry ; Acoustics ; Physics ; Atomic physics ; Electronic engineering ; Electrical engineering ; Engineering Spectroscopy and Laser Applications ; Gas Sensing Nanomaterials and Sensors ; Advanced Chemical Sensor Technologies
Authors: Qiong Su | Fang Ren | Mengmeng Lu | Jinqin Zhao | Xingchen Zhu | Tao Shen | Yan Shen | Yanbin Wang | Junxi Liang
Source: Molecules (article, 2024-01-24)
Field: Materials Science — Materials Chemistry

Theoretical Study of the NO Reduction Mechanism on Biochar Surfaces Modified by Li and Na Single Adsorption and OH Co-Adsorption Theoretical and experimental investigations have shown that biochar, following KOH activation, enhances the efficiency of NO removal. Similarly, NaOH activation also improves NO removal efficiency, although the underlying mechanism remains unclear. In this study, zigzag configurations were employed as biochar models. Density functional theory (DFT) was utilized to examine how Li and Na single adsorption and OH co-adsorption affect the reaction pathways of NO reduction on the biochar surface. The rate constants for all reaction-determining steps (RDSs) within a temperature range of 200 to 1000 K were calculated using conventional transition state theory (TST). The results indicate a decrease in the activation energy for NO reduction reactions on biochar when activated by Li and Na adsorption, thus highlighting their beneficial role in NO reduction. Compared to the case with Na activation, Li-activated biochar exhibited superior performance in terms of the NO elimination rate. Furthermore, upon the adsorption of the OH functional group onto the Li-decorated and Na-decorated biochar models (LiOH-decorated and NaOH-decorated chars), the RDS energy barriers were higher than those of Li and Na single adsorption but easily overcome, suggesting effective NO reduction. In conclusion, Li-decorated biochar showed the highest reactivity due to its low RDS barrier and exothermic reaction on the surface. Biochar ; Adsorption ; Chemistry ; Exothermic reaction ; Density functional theory ; Reactivity (psychology) ; Activation energy ; Chemical engineering ; Inorganic chemistry ; Computational chemistry ; Physical chemistry ; Organic chemistry ; Pyrolysis Catalytic Processes in Materials Science ; Industrial Gas Emission Control ; Ammonia Synthesis and Nitrogen Reduction
Authors: Matthias Steurer | Paul Somers | Kristian Kraft | Lukas Grünewald | Steven J. Kraus | Florian Feist | Bastian Weinert | Erich Müller | Stefanie Dehnen | Claus Feldmann | Yolita M. Eggeler | Christopher Barner‐Kowollik | Martin Wegener
Source: Advanced Science (article, 2024-12-04)
Field: Materials Science — Materials Chemistry

Photothermal Laser Printing of Sub‐Micrometer Crystalline ZnO Structures During light-driven 3D additive manufacturing, an object represented in digital form is initially translated into a spatial distribution of light intensity (sequentially or in parallel), which then results in a spatial material distribution. To date, this process typically proceeds by photoexcitation of small functional molecules, leading to photochemically induced crosslinking of soft materials. Alternatively, thermal triggers can be employed, yet thermal processes are often slow and provide only low spatial localization. Nevertheless, sub-micrometer ZnO structures for functional microelectronic devices have recently been laser-printed. Herein, the photothermal laser-printing of ZnO is advanced by i) introducing single-crystalline rather than amorphous sub-micrometer ZnO shapes that crystallize in the hexagonal ZnO wurtzite structure, ii) employing dimethyl sulfoxide (DMSO) instead of water, enabling higher local process temperatures without micro-bubble formation, and iii) using substrates tailored for light absorption and heat management, resolving the challenge of light to heat conversion. Finally, the herein-demonstrated ZnO printing requires no post-processing and is a cleanroom-free technique for the fabrication of crystalline semiconductors. Materials science ; Photothermal therapy ; Wurtzite crystal structure ; Microelectronics ; Nanotechnology ; Optoelectronics ; Laser ; Micrometer ; Fabrication ; Optics ZnO doping and properties ; Nonlinear Optical Materials Studies ; Laser-Ablation Synthesis of Nanoparticles
Authors: Mohammad Husainy | Stéphanie Szenknect | Renaud Podor | X.F. Le Goff | Thibault Kaczmarek | Philippe Moisy | Nicolas Dacheux
Source: Journal of Nuclear Materials (article, 2024-04-15)
Field: Materials Science — Materials Chemistry

Speciation and chemical behavior of molybdenum in uranium dioxide samples prepared by hydroxide precipitation This work aims to investigate the behavior of Mo in UO2±x for being an abundant fission product with a high fission yield and a complex speciation linked to its interaction with the fuel and other fission product elements. UO2-based model compounds containing different Mo contents (between 0 and 15 mol%) were synthesized by a wet-chemistry route using hydroxide precipitation. The recovered powders were converted to oxides, pelletized, and sintered to obtain densified pellets of UO2 incorporating Mo. PXRD analyses and Rietveld refinement calculations indicate that Mo has an almost negligible solubility in the fluorite structures of both UO2 and UO2+x samples. SEM, TEM, and EDX characterizations of the produced UO2 + Mo pellets revealed the homogeneous distribution of nanosized metallic Mo particles of spherical geometry inside and outside the UO2 grains and throughout the whole sample pellets, whatever the amount of Mo added, thus confirming that the solubility of Mo in the fluorite structure is way below 0.6 mol% Mo in accordance with the PXRD results. The microstructural properties of the UO2 + Mo pellets, including density, porosity, Mo particle size distribution, and UO2 grain size variation with Mo content, were also determined. The addition of Mo to UO2 reduced the UO2 grain size as compared to UO2 grains in pure pellets, and Mo thus plays an inhibiting role in the UO2 grain growth during sintering. The produced UO2 + Mo pellets exhibit a microstructure similar to that of the real spent nuclear fuel, except that the UO2 grains are smaller. The Mo metallic nanoparticles in these simplified UO2-based model compounds of controlled microstructure could be harnessed as surrogates of the Mo-rich ε-phase metallic nanoparticles in the real spent nuclear fuel for future studies. Pellets ; Materials science ; Rietveld refinement ; Microstructure ; Solubility ; Sintering ; Molybdenum ; Nuclear chemistry ; Fission products ; Precipitation ; Uranium dioxide ; Metallurgy ; Uranium ; Chemical engineering ; Chemistry ; Radiochemistry ; Crystallography ; Physical chemistry ; Crystal structure ; Composite material Nuclear Materials and Properties ; Radioactive element chemistry and processing ; Nuclear materials and radiation effects
Authors: Mengyuan Chen | Chenfeng Lin | Yongqi Sun | Rui Yang | Xiangyu Lǚ | Weidong Lou | Xunfei Deng | Yunpeng Zhao | Fei Liu
Source: Plants (article, 2024-05-29)
Field: Chemistry — Analytical Chemistry

Ginkgo biloba Sex Identification Methods Using Hyperspectral Imaging and Machine Learning <i>Ginkgo biloba</i> L. is a rare dioecious species that is valued for its diverse applications and is cultivated globally. This study aimed to develop a rapid and effective method for determining the sex of a <i>Ginkgo biloba</i>. Green and yellow leaves representing annual growth stages were scanned with a hyperspectral imager, and classification models for RGB images, spectral features, and a fusion of spectral and image features were established. Initially, a ResNet101 model classified the RGB dataset using the proportional scaling-background expansion preprocessing method, achieving an accuracy of 90.27%. Further, machine learning algorithms like support vector machine (SVM), linear discriminant analysis (LDA), and subspace discriminant analysis (SDA) were applied. Optimal results were achieved with SVM and SDA in the green leaf stage and LDA in the yellow leaf stage, with prediction accuracies of 87.35% and 98.85%, respectively. To fully utilize the optimal model, a two-stage Period-Predetermined (PP) method was proposed, and a fusion dataset was built using the spectral and image features. The overall accuracy for the prediction set was as high as 96.30%. This is the first study to establish a standard technique framework for Ginkgo sex classification using hyperspectral imaging, offering an efficient tool for industrial and ecological applications and the potential for classifying other dioecious plants. Ginkgo biloba ; Ginkgo ; Hyperspectral imaging ; Identification (biology) ; Artificial intelligence ; Computer science ; Ginkgoales ; Pattern recognition (psychology) ; Machine learning ; Medicine ; Biology ; Traditional medicine ; Pharmacology ; Botany ; Pharmacognosy Spectroscopy and Chemometric Analyses ; Neurological Disease Mechanisms and Treatments ; GABA and Rice Research
Authors: Hussain Alessa | Nasser Algethami
Source: Journal of Umm Al-Qura University for Applied Sciences (article, 2024-01-11)
Field: Chemistry — Analytical Chemistry

Up-to-date studies regarding the determination of sertraline by different analytical methods Sertraline ; Antidepressant ; Urine ; Serotonin ; Mood ; Psychology ; Pharmacology ; Medicine ; Internal medicine ; Psychiatry ; Anxiety Analytical Methods in Pharmaceuticals ; Analytical Chemistry and Chromatography ; Treatment of Major Depression
Authors: Fatemeh Salek | Seyed Ahmad Mireei | Abbas Hemmat | Mehrnoosh Jafari | Mohammad R. Sabzalian | Majid Nazeri | Wouter Saeys
Source: Plant Stress (article, 2024-10-24)
Field: Chemistry — Analytical Chemistry

Early monitoring of drought stress in safflower (Carthamus tinctorius L.) using hyperspectral imaging: A comparison of machine learning tools and feature selection approaches • Hyperspectral imaging was examined for early detection of drought stress in safflower. • Different stress levels were distinguished using mean spectral signatures. • Pixel-wise classification was conducted using unsupervised and supervised approaches. • ANN outperformed other classifiers in spectral analysis for detecting stress levels. • The progression of stress in leaves was depicted using supervised analysis. Early detection of drought stress is essential for preventing permanent plant damage and minimizing yield loss. This study utilized hyperspectral imaging at the leaf level to visualize drought stress in safflower plants ( Carthamus tinctorius L.). Three safflower genotypes, Palenus, A82, and IL-111, were cultivated under three irrigation levels. Stress conditions were simulated by depleting 50%, 70%, and 90% of soil water content, representing unstressed (US), mild stress (MS), and severe stress (SS) conditions, respectively. Hyperspectral images of leaf samples were captured before any visible signs of water scarcity emerged. Classification analysis was performed using the full mean spectral data with partial least squares discriminant analysis, soft independent modeling of class analogy (SIMCA), support vector machines, and artificial neural network (ANN) classifiers. Feature selection methods were applied to extract the most informative wavebands, and ANN was used to build predictive models. Spatial analysis involved pixel-wise classification using both unsupervised (k-means clustering) and supervised (best classifiers) approaches. ANN outperformed other classifiers using the entire spectral data, effectively distinguishing US, MS, and SS classes in the Palenus, A82, and IL-111 genotypes, achieving F1-scores of 92.22%, 96.01%, and 96.47%, respectively. Among the feature selection methods, SIMCA-based features excelled in monitoring stress conditions in the Palenus and A82 genotypes. In supervised spatial analysis, ANN models clearly depicted the progression of stress in leaves across different genotypes. This study demonstrates the potential of hyperspectral imaging to differentiate various levels of drought stress in safflower, an important oilseed crop. Carthamus ; Hyperspectral imaging ; Feature selection ; Selection (genetic algorithm) ; Drought stress ; Feature (linguistics) ; Computer science ; Artificial intelligence ; Pattern recognition (psychology) ; Machine learning ; Biology ; Botany ; Medicine Spectroscopy and Chemometric Analyses ; Sunflower and Safflower Cultivation ; Water Quality Monitoring and Analysis
Authors: Jasna Alić | Ivor Lončarić | Martin Etter | Mirta Rubčić | Zoran Štefanić | Marina Šekutor | Krunoslav Užarević | Tomislav Stolar
Source: Physical Chemistry Chemical Physics (article, 2024-01-01)
Field: Chemistry — Physical and Theoretical Chemistry

Direct <i>in situ</i> measurement of polymorphic transition temperatures under thermo-mechanochemical conditions For the first time, we directly measured the onset and completion temperatures of polymorphic transitions under thermo-mechanochemical conditions by simultaneous <i>in situ</i> synchrotron powder X-ray diffraction and temperature monitoring. We determined the thermo-mechanochemical polymorphic transition temperature in 1-adamantyl-1-diamantyl ether to be 31 °C lower than the transition temperature determined by DSC. Our findings highlight the uniqueness of thermo-mechanochemical conditions, with potential applications in polymorph screening. In situ ; Phase transition ; Kinetic energy ; Materials science ; Polymorphism (computer science) ; Chemical physics ; Thermodynamics ; Crystallography ; Chemistry ; Physics ; Organic chemistry Crystallography and molecular interactions ; X-ray Diffraction in Crystallography ; Material Dynamics and Properties
Authors: Marina Yusoff | Darul Ehsan | Muhammad Sharif | Mohamad Taufik Mohd Sallehud-din
Source: International Journal of Advanced Computer Science and Applications (article, 2024-01-01)
Field: Chemistry — Analytical Chemistry

Topology Approach for Crude Oil Price Forecasting of Particle Swarm Optimization and Long Short-Term Memory Forecasting crude oil prices hold significant importance in finance, energy, and economics, given its extensive impact on worldwide markets and socio-economic equilibrium. Using Long Short-Term Memory (LSTM) neural networks has exhibited noteworthy achievements in time series forecasting, specifically in predicting crude oil prices. Nevertheless, LSTM models frequently depend on the manual adjustment of hyperparameters, a task that can be laborious and demanding. This study presents a novel methodology incorporating Particle Swarm Optimization (PSO) into LSTM networks to optimize the network architecture and minimize the error. This study employs historical data on crude oil prices to explore and identify optimal hyperparameters autonomously and embedded with the star and ring topology of PSO to address the local and global search capabilities. The findings demonstrate that LSTM+starPSO is superior to LSTM+ringPSO, previous hybrid LSTM-PSO, conventional LSTM networks, and statistical time series methods in its predictive accuracy. LSTM+starPSO model offers a better RMSE of about +0.16% and +22.82% for WTI and BRENT datasets, respectively. The results indicate that the LSTM model, when enhanced with PSO, demonstrates a better proficiency in capturing the patterns and inherent dynamics data changes of crude oil prices. The proposed model offers a dual benefit by alleviating the need for manual hyperparameter tuning and serving as a valuable resource for stakeholders in the energy and financial industries interested in obtaining dependable insights into fluctuations in crude oil prices. Computer science ; Term (time) ; Particle swarm optimization ; Crude oil ; Mathematical optimization ; Long short term memory ; Topology (electrical circuits) ; Topology optimization ; Artificial intelligence ; Algorithm ; Artificial neural network ; Electrical engineering ; Recurrent neural network ; Mathematics Petroleum Processing and Analysis
Authors: Liuni Yang | Qiang Liu | Haoyuan Liang | Minming Geng | Kejin Wei | Zhenrong Zhang
Source: Nanomaterials (article, 2024-08-05)
Field: Materials Science — Materials Chemistry

Design of Ultra-Compact and Multifunctional Optical Logic Gate Based on Sb2Se3-SOI Hybrid Platform Optical logic devices are essential functional devices for achieving optical signal processing. In this study, we design an ultra-compact (4.92 × 2.52 μm<sup>2</sup>) reconfigurable optical logic gate by using inverse design method with DBS algorithm based on Sb<sub>2</sub>Se<sub>3</sub>-SOI integrated platform. By selecting different amorphous/crystalline distributions of Sb<sub>2</sub>Se<sub>3</sub> via programmable electrical triggers, the designed structure can switch between OR, XOR, NOT or AND logic gate. This structure works well for all four logic functions in the wavelength range of 1540-1560 nm. Especially at the wavelength of 1550 nm, the Contrast Ratios for XOR, NOT and AND logic gate are 13.77 dB, 11.69 dB and 3.01 dB, respectively, indicating good logical judgment ability of the device. Our design is robust to a certain range of fabrication imperfections. Even if performance weakens due to deviations, improvements can be obtained by rearranging the configurations of Sb<sub>2</sub>Se<sub>3</sub> without reproducing the whole device. Silicon on insulator ; Materials science ; Optoelectronics ; Logic gate ; Nanotechnology ; Electronic engineering ; Engineering ; Silicon Phase-change materials and chalcogenides ; Photonic and Optical Devices ; Chalcogenide Semiconductor Thin Films