jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ghulam Akbar | Alessio Di Fatta | Giuseppe Rizzo | Guido Ala | Pietro Romano | Antonino Imburgia
Source: Energies (review, 2024-11-20)
Field: Materials Science — Materials Chemistry

A Detailed Review of Partial Discharge Detection Methods for SiC Power Modules Under Square-Wave Voltage Excitation Silicon carbide (SiC) power modules are increasingly being used in high-voltage and high-frequency applications due to their superior electrical and thermal qualities. However, the issue of the partial discharge (PD) phenomenon raises serious reliability difficulties resulting in insulation failure, performance degradation, and potential device collapse. This paper provides a thorough assessment of the current PD detection strategies in SiC power modules. The issues provided by SiC devices’ distinct operational features, such as high switching frequencies and higher voltage stresses, which hinder PD detection and mitigation, are widely investigated. This review compares the effectiveness, benefits, and limitations of various detection methods, emphasizing the need for better strategies to ensure long-term reliability and performance. This study gives an in-depth overview of the numerous forms of PD phenomena that occur in power modules, including internal and surface discharges, as well as how they appear under various detection systems. It examines the performance of several methods for power module technologies such as SiC. To address these PD issues, this article proposes ways to improve reliability and detection accuracy. Reliability (semiconductor) ; Silicon carbide ; Partial discharge ; Reliability engineering ; Voltage ; High voltage ; Power (physics) ; Computer science ; Electrical engineering ; Electronic engineering ; Materials science ; Engineering ; Physics High voltage insulation and dielectric phenomena ; Silicon Carbide Semiconductor Technologies ; Semiconductor materials and devices
Authors: Andreu A. de Donato | Bianca-Andreea Ghejan | Joost M. Bakker | Thorsten M. Bernhardt | Stefan T. Bromley | Sandra M. Lang
Source: ACS Earth and Space Chemistry (article, 2024-05-09)
Field: Chemistry — Spectroscopy

Gas-Phase Production of Hydroxylated Silicon Oxide Cluster Cations: Structure, Infrared Spectroscopy, and Astronomical Relevance The interaction of free cationic silicon oxide clusters, Si <sub><i>x</i></sub> O <sub><i>y</i></sub> <sup>+</sup> (<i>x</i> = 2-5, <i>y</i> ≥ <i>x</i>), with dilute water vapor, was investigated in a flow tube reactor. Product mass distributions indicate cluster size-dependent dissociative water adsorption. To probe the structure and vibrational spectra of the resulting Si <sub><i>x</i></sub> O <sub><i>y</i></sub> H<sub>2</sub> <sup>+</sup> (<i>x</i> = 2-4) clusters, we employed infrared multiple photon dissociation spectroscopy and density functional theory calculations. The planar rhombic cluster core of the disilicon oxides (<i>x</i> = 2) appears to be retained upon dissociative adsorption of one H<sub>2</sub>O unit, whereas a significant structural transformation of the tri- and tetra-silicon oxides (<i>x</i> = 3 and 4) is induced, resulting in an increased coordination of the Si atoms and more 3D cluster structures. In an astronomical context, we discuss the potential relevance of Si <sub><i>x</i></sub> O <sub><i>y</i></sub> H <sub><i>z</i></sub> <sup>+</sup> clusters as seeds for dust nucleation and catalysts for carbon-based chemistry in diffuse or translucent interstellar clouds, where all the necessary conditions for producing these species are found. In the produced clusters, the frequency of the isolated silanol Si-OH stretching vibrational mode is considerably blue-shifted compared to that in hydroxylated bulk silica and small inorganic compounds. This mode has a characteristic frequency range between 1200 cm<sup>-1</sup> (8.3 μm) and 1090 cm<sup>-1</sup> (9.2 μm) and is associated with the anomalously small Si-OH bond lengths in these ionised species. In infrared observations such high frequency Si-O stretching modes are usually associated with a pure bulk silica component of silicate cosmic dust. The presence of Si <sub><i>x</i></sub> O <sub><i>y</i></sub> H<sub>2</sub> <sup>+</sup> clusters in low silica astrophysical environments could thus potentially be detected via their signature Si-O band using the James Webb space telescope. Infrared spectroscopy ; Gas phase ; Silicon ; Infrared ; Cluster (spacecraft) ; Relevance (law) ; Spectroscopy ; Phase (matter) ; Production (economics) ; Oxide ; Materials science ; Chemistry ; Physics ; Optoelectronics ; Physical chemistry ; Optics ; Organic chemistry ; Astronomy ; Computer science Spectroscopy and Laser Applications ; Atmospheric Ozone and Climate ; Inorganic Fluorides and Related Compounds
Authors: Jin Yin | Jun Wang | Jian Jiang | Jian Xu | Liang Zhao | Anfu Hu | Qian Xia | Zhihan Zhang | Ming Cai
Source: Scientific Reports (article, 2024-12-28)
Field: Chemistry — Analytical Chemistry

Quality prediction of air-cured cigar tobacco leaf using region-based neural networks combined with visible and near-infrared hyperspectral imaging Visible and Near-infrared hyperspectral imaging (VNIR-HSI) combined with machine learning has shown its effectiveness in various detection applications. Specifically, the quality of cigar tobacco leaves undergoes subtle changes due to environmental differences during the air-curing phase. This study aims to evaluate the feasibility of deep learning methods in overcoming data limitations to develop a VNIR-HSI prediction model for the quality of cigar tobacco leaves at different air-curing levels. The moisture, chlorophyll, total nitrogen, and total sugar content in cigar tobacco leaves were predicted across various air-curing stages and light conditions. Results showed that the Diversified Region-based Convolutional Neural Network (DR-CNN) achieved the best performance, with a root mean square error of prediction for moisture at 3.109%, chlorophyll at 0.883 mg/g, total nitrogen at 0.153 mg/g, and total sugar at 0.138 mg/g. Compared to Partial Least Squares Regression and Convolutional Neural Networks, DR-CNN demonstrated superior predictive accuracy, making it a promising model for quality prediction in cigar tobacco leaves during air-curing process. Overall, VNIR-HSI based on DR-CNN can effectively predict the quality of cigar tobacco leaves at different air-curing levels. Hyperspectral imaging ; Infrared ; Remote sensing ; Air quality index ; Artificial neural network ; Tobacco leaf ; Quality (philosophy) ; Environmental science ; Computer science ; Artificial intelligence ; Meteorology ; Geology ; Optics ; Geography ; Agricultural engineering ; Physics Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Advanced Chemical Sensor Technologies
Authors: Camilo Zapata-Hernandez | Geraldine Durango-Giraldo | Miguel Gomez-Echeverri | Robison Buitrago‐Sierra | Bernardo Herrera | Karen Cacua
Source: Heliyon (article, 2024-10-30)
Field: Materials Science — Materials Chemistry

The impact of carbon quantum dots derived from spent coffee grounds on the droplet combustion of diesel/n-butanol blend As global concerns surrounding climate change mount and fossil fuel reserves diminish, the application of additives in internal combustion engines is increasingly prevalent. Butanol and carbonaceous nanomaterials, such as carbon quantum dots (CQD), are being employed as additives to increase engine efficiency and mitigate the emission of pollutants. Nevertheless, understanding the impact of these additives on combustion behavior at the droplet scale through combustion assessments before their use in engines is crucial. In this study, our main objective was to assess the impact of incorporating CQD dispersed in n-butanol as additives to conventional diesel fuel on the combustion characteristics at the droplet scale. CQD were obtained from spent coffee grounds (SCGs) using n-butanol as a solvent. The product obtained was mixed with Colombian commercial diesel (10 % vol. palm oil biodiesel), and its combustion was evaluated using the droplet combustion method. Before the CQD synthesis, SCGs were characterized by thermogravimetric analysis (TGA) and field emission scanning electron microscopy (FESEM). CQD were characterized via Fourier transform infrared (FTIR) spectroscopy, Transmission Electron Microscopy (TEM), UV-vis, and fluorescence spectroscopy. Results indicated that adding n-butanol and CQD to commercial diesel leads to a 5.4 % and 16.5 % increase in droplet ignition delay, respectively. These additives also cause droplet contraction and expansion cycles, resulting in unstable combustion. However, CQD reduces the frequency of microexplosions caused by boiling n-butanol inside the droplet, which mitigates instabilities during droplet combustion. Including CQD can enhance fuel evaporation by increasing the density of nucleation sites for bubble formation and preventing micro-explosions, thereby leading to stable combustion. These attributes can significantly influence the performance of blends in Compression Ignition (CI) Engines. Combustion ; Diesel fuel ; Butanol ; Carbon fibers ; n-Butanol ; Physics ; Materials science ; Waste management ; Chemical engineering ; Environmental science ; Engineering ; Organic chemistry ; Chemistry ; Composite material ; Ethanol Carbon and Quantum Dots Applications ; Caching and Content Delivery ; Catalytic Processes in Materials Science
Authors: Xin Gan | Yuna Wu | Min Zhu | Bo Liu | Miaomiao Kong | Zixuan Xi | Ke Li | Haibao Wang | Tiande Su | Jiali Yao | Fatehi Khushafah | Baozhu Yi | Jiabing Wang | Wulan Li | Jianzhang Wu
Source: Journal of Enzyme Inhibition and Medicinal Chemistry (article, 2024-02-22)
Field: Chemistry — Organic Chemistry

Design, synthesis, and evaluation of cyclic C7-bridged monocarbonyl curcumin analogs containing an <i>o</i> -methoxy phenyl group as potential agents against gastric cancer The structure-activity relationship (SAR) between toxicity and the types of linking ketones of C7 bridged monocarbonyl curcumin analogs (MCAs) was not clear yet. In the pursuit of effective and less cytotoxic chemotherapeutics, we conducted a SAR analysis using various diketene skeletons of C7-bridged MCAs, synthesized cyclic C7-bridged MCAs containing the identified low-toxicity cyclopentanone scaffold and an <i>o</i>-methoxy phenyl group, and assessed their anti-gastric cancer activity and safety profile. Most compounds exhibited potent cytotoxic activities against gastric cancer cells. We developed a quantitative structure-activity relationship model (<i>R</i><sup>2</sup> > 0.82) by random Forest method, providing important information for optimizing structure. An optimized compound 2 exhibited <i>in vitro</i> and <i>in vivo</i> anti-gastric cancer activity partly through inhibiting the AKT and STAT3 pathways, and displayed a favorable <i>in vivo</i> safety profile. In summary, this paper provided a promising class of MCAs and a potential compound for the development of chemotherapeutic drugs. Curcumin ; In vivo ; Chemistry ; Cancer ; Structure–activity relationship ; Stereochemistry ; In vitro ; Pharmacology ; Cytotoxic T cell ; Cancer cell ; Biochemistry ; Medicine ; Biology ; Internal medicine Synthesis and biological activity ; Multicomponent Synthesis of Heterocycles ; Curcumin's Biomedical Applications
Authors: Majid Naseri | Abdrakhman Naizabekov | Dmitry Mikhailov | Sergey Legnev | Nataliya Shaburova | Anatoliy Pellenen | Evgenii Bodrov | М. Н. Самодурова | Evgeny Trofimov | Evgeniy Panin
Source: Journal of Chemical Technology and Metallurgy (article, 2024-09-09)
Field: Materials Science — General Materials Science

EFFECT OF STRAIN PATH ON MICROSTRUCTURE AND MECHANICAL PROPERTIES IN COLD - ROLLED [FeNi]69Cr15Mn10Nb6 HIGH - ENTROPY ALLOY [FeNi]69Cr15Mn10Nb6 high - entropy alloys (HEAs) were subjected to heavy cold-rolling process at ambient temperature through two distinct routes, namely unidirectional cold-rolling (UCR) and multistep cross cold-rolling (MSCCR) routes, to investigate the effect of the strain path on the microstructure and mechanical properties of the alloys. In the MSCCR route, the specimen rotated 90° around the normal direction axis between each cycle. The as-homogenized specimen revealed a two-phase microstructure consisting of FeNiNb - rich dendrites distributed in a nearly homogenous face-centered cubic (FCC) HEA matrix. Here, FeNiNb - rich dendrites were elongated,broken down, and stretched along the rolling direction during both processing routes. In the MSCCR - processed specimen, the dendrites exhibited a smaller average size with a more uniform distribution compared to that of the UCR specimen, which could be mainly ascribed to the elongation mechanisms in the normal and transverse directions during the MSCCR route. These microstructural features, introduced through MSCCR, appeared to be most prone to shear band formation and grain refinement. The MSCCR - processed specimen shows a microhardness of 445 HV, a tensile strength of 1245 MPa, and a remarkable elongation of 16 % at ambient temperature, outperforming the UCR-processed specimen with a microhardness of 418 HV, a tensile strength of 1180 MPa, and an elongation of 14 %. The superior mechanical properties of the MSCCR - processed specimen are attributed to its refined microstructure,increased dislocation density, and uniform distribution of FeNiNb - rich dendrites within the microstructure. Microstructure ; Alloy ; Materials science ; High entropy alloys ; Metallurgy ; Strain (injury) ; Composite material ; Medicine Material Properties and Applications ; Advancements in Materials Engineering ; Aluminum Alloy Microstructure Properties
Authors: Konstantinos N. Blazakis | Danil Stupichev | Maria Kosma | Mohamad Ali Hassan El Chami | Anastasia Apodiakou | George Kostelenos | Panagiotis Kalaitzis
Source: Frontiers in Plant Science (article, 2024-08-08)
Field: Chemistry — Analytical Chemistry

Discrimination of 14 olive cultivars using morphological analysis and machine learning algorithms Traditional morphological analysis is a widely employed tool for the identification and discrimination of olive germplasm by using morphological markers which are monitored by subjective manual measurements that are labor intensive and time-consuming. Alternatively, an automated methodology can quantify the geometrical features of fruits, leaves and endocarps with high accuracy and efficiency in order to define their morphological characteristics. In this study, 24 characteristics for fruits, 16 for leaves and 25 for endocarps were determined and used in an automated way with basic classifiers combined with a meta-classsifier approach. This resulted to the discrimination of 14 olive cultivars utilizing data obtained from two consecutive olive growing periods. The cultivar classification algorithms were based on machine learning techniques. The 95% accuracy rate of the meta-classifier approach indicated that was an efficient tool to discriminate olive cultivars. The contribution of each morphological feature to cultivar discrimination was quantified, and the significance of each one was automatically detected in a quantitative way. The higher the contribution of each feature, the higher the significance for cultivar discrimination. The identification of most cultivars was guided by the features of both endocarps and fruits, while those of leaves were only efficient to identify the Kalamon cultivar. The combined use of morphological features of three olive organs might have an additive effect leading to higher capacity for discrimination of cultivars. The proposed methodology might be considered a phenomics tool for olive cultivar identification and discrimination in a wide range of applications including breeding. Cultivar ; Germplasm ; Artificial intelligence ; Classifier (UML) ; Horticulture ; Pattern recognition (psychology) ; Biology ; Mathematics ; Computer science Spectroscopy and Chemometric Analyses ; Edible Oils Quality and Analysis ; Remote Sensing in Agriculture
Authors: Gabriel E. Lima | Rayson Laroca | Eduardo Alves Portela Santos | Eduil Nascimento | David Menotti
Source:  (preprint, 2024-09-30)
Field: Chemistry — Analytical Chemistry

Toward Enhancing Vehicle Color Recognition in Adverse Conditions: A Dataset and Benchmark Vehicle information recognition is crucial in various practical domains, particularly in criminal investigations. Vehicle Color Recognition (VCR) has garnered significant research interest because color is a visually distinguishable attribute of vehicles and is less affected by partial occlusion and changes in viewpoint. Despite the success of existing methods for this task, the relatively low complexity of the datasets used in the literature has been largely overlooked. This research addresses this gap by compiling a new dataset representing a more challenging VCR scenario. The images - sourced from six license plate recognition datasets - are categorized into eleven colors, and their annotations were validated using official vehicle registration information. We evaluate the performance of four deep learning models on a widely adopted dataset and our proposed dataset to establish a benchmark. The results demonstrate that our dataset poses greater difficulty for the tested models and highlights scenarios that require further exploration in VCR. Remarkably, nighttime scenes account for a significant portion of the errors made by the best-performing model. This research provides a foundation for future studies on VCR, while also offering valuable insights for the field of fine-grained vehicle classification. Benchmark (surveying) ; Computer science ; Artificial intelligence ; Pattern recognition (psychology) ; Computer vision ; Cartography Spectroscopy and Chemometric Analyses ; Industrial Vision Systems and Defect Detection ; Video Surveillance and Tracking Methods
Authors: Rémi Marsac | Charlotte Catrouillet | Mathieu Pédrot | Marc F. Benedetti | Aline Dia | Eric D. van Hullebusch | Mélanie Davranche | Yann Sivry | Anne‐Catherine Pierson‐Wickmann | Mickaël Tharaud | Frank Heberling
Source: Current Opinion in Colloid & Interface Science (article, 2024-06-09)
Field: Chemistry — Inorganic Chemistry

Equilibrium surface complexation modeling with metastable natural colloids: The key to predict the oxidation state distribution of trace elements? Predicting the behavior and fate of redox-sensitive trace elements (TEs; e.g., As, U, Cu, Cr) in natural systems is challenging. Colloids have been reported to control TEs speciation and catalyze TEs redox reactions in many environments. We hypothesize that the lack of accurate thermodynamic models that account for the role of colloids in TEs speciation explains our inability to predict their redox state distribution in the environment. The slow evolution of the colloidal compartment in response to prevailing bio/hydro/pedo/climatological conditions needs to be decoupled from the fast TEs redox reactions promoted by colloidal surfaces. Further progress is hampered by experimental and theoretical challenges associated with capturing the extreme physical and chemical heterogeneity of colloids, their metastable structures, and their dynamic transformation behavior. TRACE (psycholinguistics) ; Metastability ; Key (lock) ; Colloid ; Chemical physics ; Oxidation state ; State (computer science) ; Surface (topology) ; Natural (archaeology) ; Chemistry ; Computer science ; Metal ; Physical chemistry ; Mathematics ; Geology ; Geometry ; Organic chemistry Radioactive element chemistry and processing ; Geochemistry and Elemental Analysis ; Mercury impact and mitigation studies
Authors: Fabrizio Rovaris | Wouter H. J. Peeters | Anna Marzegalli | Frank Glas | Laetitia Vincent | Leo Miglio | Erik P. A. M. Bakkers | Marcel A. Verheijen | Emilio Scalise
Source: ACS Applied Nano Materials (article, 2024-04-15)
Field: Materials Science — Materials Chemistry

2H–Si/Ge for Group-IV Photonics: on the Origin of Extended Defects in Core–Shell Nanowires The nucleation mechanism of ubiquitous basal stacking faults observed in hexagonal Si/Ge nanowires is still an enigma. These defects may hinder the exploitation of hexagonal Si/Ge for nano-optoelectronics and quantum technologies. In this work, the formation of the I3 basal stacking faults is investigated at the atomistic level, and results are compared to the experimental findings. We propose that these extended defects are caused by dislocation lines elongated in ⟨112̅0⟩ directions, which in turn arise from glide terminations of the step edges when two growing fronts run into each other. Stacking ; Nanowire ; Nucleation ; Hexagonal crystal system ; Materials science ; Core (optical fiber) ; Dislocation ; Condensed matter physics ; Crystallography ; Nanotechnology ; Chemistry ; Physics ; Composite material Silicon Nanostructures and Photoluminescence ; Nanowire Synthesis and Applications ; Semiconductor materials and interfaces
Authors: Abdur Rahman | Mohammad Marufuzzaman | Jason Street | James Wooten | Veera Gnaneswar Gude | Randy Buchanan | Haifeng Wang
Source:  (review, 2024-02-16)
Field: Materials Science — Polymers and Plastics

A comprehensive review on wood chip moisture content assessment and prediction Wood chips are the primary sources of raw materials for numerous industries, including pelleting mills, biorefineries, pulp-and-paper industries, and biomass-based power generation facilities. Unfortunately, when wood chips are utilized as a renewable and environmentally friendly resource, industries are constantly challenged by the consistency of the wood chip qualities (e.g., moisture/ash contents, size distributions) - a historically recognized problem on a global scale. Among other wood chip quality attributes, the moisture content is considered the most pressing one as it directly impacts the energy content, storage stability, and handling properties of the raw and finished products. Therefore, accurate wood chip moisture content prediction can help optimize the drying process and reduce energy consumption. In this review, a survey was conducted on various techniques and models employed for predicting wood chip moisture content. The advantages and limitations of these approaches, as well as their potential applications and future directions were also discussed. This review aims to provide a comprehensive overview of the current state-of-the-art in wood chip moisture content prediction and to highlight the challenges and opportunities for further research and development in this field. Water content ; Environmental science ; Renewable energy ; Process engineering ; Green wood ; Raw material ; Moisture ; Pulp and paper industry ; Computer science ; Wood drying ; Engineering ; Materials science ; Composite material Textile materials and evaluations ; Wood Treatment and Properties ; Industrial Vision Systems and Defect Detection