jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Efigenia Monserrate Gonzabay Bravo | Patricia Judith Pinos Robalino | Ángela Paula Gaibor Durán | Otto Vicente Campos Mancero
Source: RECIMUNDO (article, 2025-04-10)
Field: Chemistry — Organic Chemistry

Métodos de extracción de aceites esenciales de cítricos y eucalipto: comparación de eficiencia, calidad y sostenibilidad. Una revisión sistemática Los aceites esenciales de cítricos y eucalipto tienen un sinfín de aplicaciones en las industrias farmacéutica, cosmética y alimentaria, gracias a sus propiedades terapéuticas, antimicrobianas y antioxidantes. La creciente demanda de productos naturales ha llevado a un impulso en el desarrollo y la evaluación de diferentes métodos de extracción, con el fin de mejorar la eficiencia del proceso, la calidad del aceite obtenido y su sostenibilidad ambiental. En este sentido, es crucial comparar las diversas técnicas disponibles basándose en evidencia científica. El propósito de esta revisión sistemática fue analizar y comparar la eficiencia, la calidad del aceite esencial y el impacto ambiental de los principales métodos de extracción, como la destilación por arrastre de vapor, el prensado en frío, la extracción con solventes, la extracción asistida por microondas y la hidrodestilación. Se siguieron las pautas de la declaración PRISMA para llevar a cabo la búsqueda, selección y síntesis de estudios. Se consultaron bases de datos como Scopus, Web of Science, ScienceDirect y PubMed, identificando inicialmente 187 artículos. Después de aplicar criterios de inclusión (estudios experimentales con datos comparativos entre métodos y evaluaciones de rendimiento, calidad química y sostenibilidad) y exclusión (duplicados, revisiones narrativas, artículos sin datos técnicos relevantes), se seleccionaron 30 estudios para el análisis final. Los resultados muestran que la destilación por arrastre de vapor es el método más común, gracias a su equilibrio entre rendimiento y calidad, aunque consume mucha energía. La extracción por microondas demostró ser muy eficiente, logrando resultados en menos tiempo y con un menor impacto ambiental, mientras que el prensado en frío preservó mejor los compuestos volátiles en los cítricos. Por otro lado, la extracción con solventes ofreció buenos rendimientos, pero conllevó riesgos toxicológicos y ambientales. En resumen, no hay un método que sea claramente el mejor; sin embargo, la extracción asistida por microondas se destaca por su eficiencia y sostenibilidad, lo que la convierte en una opción muy prometedora. Sería ideal realizar estudios comparativos a gran escala, centrándose en el análisis del ciclo de vida. Biology Edible Oils Quality and Analysis
Authors: Natalia E. Jiménez | M Gonzalez Espinoza | Sebastián Mejías | Sebastián N. Mendoza | Ignacia Segovia | J. Cristian Salgado | Carlos Conca | Ziomara P. Gerdtzen
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2025-06-27)
Field: Materials Science — Materials Chemistry

Selecting methods for draft GEM generation in multicellular eukaryotes: a comparative analysis Abstract Motivated by multiple strategies that have successfully implemented genome-scale models (GEMs) into their pipeline, several approaches have been developed for automatic generation of draft GEMs. However, most of these methods are not optimized for their use for multicellular eukaryotes and their performance for this task is unclear. In this work we present a comparative analysis of seven automated reconstruction tools (AuReMe, carveMe, Merlin, modelSEED, Pathway tools, Raven and Reconstructor) applied to three multicellular eukaryotes: the mosquito Aedes aegypti , the CHO (Chinese Hamster Ovary) cell line from Cricetulus griseus and the brown algae Ectocarpus siliculosus . Evaluation of these tools was based on metrics for network size, functionality, consistency, representation of organelle-specific functions and organism-specific metabolites, annotation quality and execution time. Finding that similarity of obtained metabolic networks is highly influenced by databases in which these methods base their predictions over phylogeny. Our works aims at providing a practical resource to guide researchers in selecting methods for draft generation tailored to organism characteristics and research goals. Author summary Genome-scale models (GEMs) represent all the potential biochemical transformations that a specific organism can carry out based on the information encoded in its genome. Although these models are a powerful tool for analyzing omics datasets and simulating the metabolic effects of genetic modifications or changing media composition, the process of manually reconstructing a genome-scale model is complex and time-consuming. Motivated by their potential applications, several tools have been developed for the automated generation of draft GEMs, however, most of them are oriented to simpler organisms such as bacteria or single-cell eukaryotes, while their relative performance for modeling multicellular eukaryotes is unclear. In this work, we compared seven tools for draft GEMs reconstruction of three organisms: for the mosquito Aedes aegypti , the brown algae Ectocarpus siliculosus and the CHO cell line from Cricetulus griseus. Our results showed that no tool systematically outperformed others, suggesting that method selection is influenced by several factors such as organism-specific data availability and their intended application. Multicellular organism ; Evolutionary biology ; Biology ; Computational biology ; Computer science ; Genetics ; Cell Corrosion Behavior and Inhibition
Authors: Evan McKenzie
Source: cIRcle (University of British Columbia) (article, 2025-01-01)
Field: Materials Science — Biomaterials

The mechanochemical hydrostatic organs of Chaoborus larvae : function under pressure Many aquatic animals regulate their position in the water column by controlling their buoyancy. This requires body compartments of regulated volume filled with matter that is less dense than water. However, only one group of insects has achieved this: the aquatic larvae of the midge genus Chaoborus (Diptera), which can alter the volume of four internal air-filled sacs. This thesis reveals the unique mechanochemical system underlying air-sac function, including how it is regulated, its mechanical properties, how it converts changes in pH into mechanical work, and how it has evolved to function at depth. The air-sac wall was found to contain resilin, an insect protein that swells reversibly in response to increases in pH, causing the fusiform air-sacs to expand lengthwise while maintaining a constant radius. Pharmacological investigation demonstrated that the air-sac wall is acidified by proton pumps (VHA) in a surrounding epithelium, shrinking it, while cAMP-signalling mediates alkalinisation and expansion. Finally, disrupting the air-sac epithelium in vivo revealed that the hemolymph has a pH of 7.4 while the air-sacs, without stimulation, are maintained near pH 6, providing the pH range for air-sac operation. Exposing air-sacs to hydrostatic pressure ramps revealed that those of deeper diving species and older instars could withstand more pressure before failure. Transmission electron microscopy showed that deep diving C. edulis had thicker air-sac walls relative to their radius, compared with two shallow diving species. There was no relationship between failure pressure and air-sac radius within species. In air-sacs, resilin imparts compressibility. The deepest diving species, C. edulis, possessed less resilin per length of wall, while deeper diving species, more generally, possessed stiffer air-sacs. Production of mechanical work by air-sac resilin was quantified by manipulating both pH and pressure. Dive depth correlated with work production: air-sacs of the deepest diving species, C. edulis, produced over 10-fold more work than the shallowest. With less resilin lengthwise, the greater work output by air-sacs of C. edulis must be due to differences in resilin composition. This thesis shows that Chaoborus species have adapted the pressure-tolerance and work capacity of their air-sacs to the depth of their aquatic environment. Hydrostatic pressure ; Hemolymph ; Volume (thermodynamics) ; Larva ; Compression (physics) ; Hydrostatic equilibrium ; Instar Calcium Carbonate Crystallization and Inhibition ; Neurobiology and Insect Physiology Research ; Diatoms and Algae Research
Authors: Neesha Yadav | Sandeep Sandeep | Pintu Das
Source: ArXiv.org (preprint, 2025-11-26)
Field: Materials Science — Materials Chemistry

Optical contrast-based determination of number of layers for two-dimensional van der Waals magnet Fe$_3$GeTe$_2$ Recent advances in revealing intrinsic magnetism in two-dimensional (2D) materials have highlighted their potential for future spintronic applications, driven by their novel physical properties, promising for future spintronic devices. In order to explore layer dependent magnetic behavior, in general, mechanically exfoliated flakes from high-quality single crystals are used. It is crucial to determine the number of layers of these materials accurately. In the absence of an efficient and quick method, researchers often rely on atomic force microscopy (AFM) imaging to identify their number of layers. In this work, we report an optical contrast study as a quick and cost-effective technique to determine the number of layers of Fe$_3$GeTe$_2$ (FGT). Here, we observed a linear relationship between the optical contrast (derived from optical microscopic images) observed for mechanically exfoliated FGT nano-flakes and their thickness, as measured by the AFM imaging method. This technique requires no additional equipment; it relies solely on a conventional optical microscope. Additionally, our results reveal a thickness-dependent evolution of the intensity; in contrast, the Raman frequency demonstrates no significant dependence on layer thickness. Also, our studies reveal two additional Raman modes of FGT, at the frequency of 129\,cm$^-1$ \&amp; 190\,cm$^-1$. Both modes show the intensity dependence on the thickness of FGT, same as out-of-plane (A$_{1g}$) Raman modes. Spintronics ; Raman spectroscopy ; van der Waals force ; Magnetism ; Atomic force microscopy ; Magnet ; Magnetic force microscope ; Microscopy 2D Materials and Applications ; Chemical and Physical Properties of Materials ; Heusler alloys: electronic and magnetic properties
Authors: Z. C. Tu | Xuan Fang | Robert J. Williams | Jifeng Liu
Source: ArXiv.org (preprint, 2025-01-21)
Field: Chemistry — Analytical Chemistry

PyEMILI: A New Generation Computer-aided Spectral Line Identifier Deep high-dispersion spectroscopy of Galactic photoionized gaseous nebulae, mainly planetary nebulae and HII regions, has revealed numerous emission lines. As a key step of spectral analysis, identification of emission lines hitherto has mostly been done manually, which is a tedious task, given that each line needs to be carefully checked against huge volumes of atomic transition/spectroscopic database to reach a reliable assignment of identity. Using Python, we have developed a line-identification code PyEMILI, which is a significant improvement over the Fortran-based package EMILI introduced ~20 years ago. In our new code PyEMILI, the major shortcomings in EMILI's line-identification technique have been amended. Moreover, the atomic transition database utilized by PyEMILI was adopted from Atomic Line List v3.00b4 but greatly supplemented with theoretical transition data from the literature. The effective recombination coefficients of the CII, OII, NII and NeII nebular lines are collected from the literature to form a subset of the atomic transition database to aid identification of faint optical recombination lines in the spectra of PNe and HII regions. PyEMILI is tested using the deep, high-dispersion spectra of two Galactic PNe, Hf2-2 and IC418, and gives better results of line identification than EMILI does. We also ran PyEMILI on the optical spectrum of a late-type [WC11] star UVQS J060819.93-715737.4 recently discovered in the Large Magellanic Cloud, and our results agree well with the previous manual identifications. The new identifier PyEMILI is applicable to not only emission-line nebulae but also emission stars, such as Wolf-Rayet stars. Identifier ; Computer science ; Line (geometry) ; Programming language ; Mathematics ; Geometry Spectroscopy and Chemometric Analyses
Authors: Sachi Nikhil Muni
Source: Journal of Informatics Education and Research (article, 2025-04-01)
Field: Materials Science — Materials Chemistry

Integrating Sustainable Aviation Fuels into the Aerospace Industry: Challenges and Opportunities for Industry 5.0 The integration of Sustainable Aviation Fuels (SAFs) into the aerospace industry provides an important opportunity of reducing GHGs and carbon emissions and enhancing environmental sustainability. This research paper deeply explores the various challenges and opportunities associated with SAF integration, examining technological advancements, regulatory frameworks, economic viability, and supply chain considerations keeping in mind Industry 5.0. While SAFs offer significant potential to decarbonize aviation its implementation is also met with certain obstacles such as high production costs, limited feedstock availability, and infrastructure constraints. These factors hinder large-scale implementation. The study highlights the role of Industry 5.0 in fostering digital innovation, automation, and sustainable practices to accelerate SAF deployment. Through a comprehensive analysis of current trends, policy interventions, and industry collaborations, this paper states the necessity of a multi-stakeholder approach to achieve a more sustainable, eco-friendly, and resilient aerospace sector. Aerospace ; Aviation ; Aeronautics ; Aircraft industry ; Business ; Engineering ; Manufacturing engineering ; Aerospace engineering Graphite, nuclear technology, radiation studies ; Advanced Aircraft Design and Technologies ; Nuclear and radioactivity studies
Authors: Géza I. Márk | Márton Szendrő | A. Mayer | Zoltán Simon | Péter Vancsó
Source: Carbon Trends (article, 2025-11-12)
Field: Materials Science — Materials Chemistry

Robust reflection asymmetry across rhombohedral—Bernal stacking boundaries in trilayer graphene We report a pronounced direction-dependent quantum transport phenomenon across stacking domain boundaries in trilayer graphene, revealed by large-scale wave packet dynamics simulations. Employing molecular statics with realistic interatomic force fields, we construct an ABC–ABA grain boundary geometry with structural features—such as soliton width and corrugation amplitude—that closely match experimental observations. To mimic a transport device geometry, we injected electrons from a graphene electrode into the outer layer of our ABC-ABA junction. We demonstrate that this configuration shows a striking asymmetry in transport behavior: wave packets incident from the rhombohedral (ABC) side transmit with minimal reflection, while those originating from the Bernal (ABA) side are strongly backscattered. The total reflection probability measured in the graphene electrode differs by more than a factor of 20 between the two incidence directions, and the energy-dependent transmission function reveals that the main differences are concentrated within the ± 0 . 5 eV energy range around the Fermi level. We prove that this rectification is robust across grain boundaries of varying thicknesses and morphologies, as it originates from the distinct electronic structures—effective masses, sublattice-, and layer polarizations—of the two stacking configurations. These differences in the electronic structure of the two stacking configurations are rooted in their lattice symmetries: the mirror-symmetric ABA and the inversion-symmetric ABC trilayers, which give rise to distinct reflection behavior at both the graphene–trilayer contact and the ABA–ABC grain boundary. The precise energy dependence of the reflection function, however, depends on the specific atomic structure of the domain boundary, yet, without altering the overall value of the reflection. Our results show that contacted ABC–ABA stacking domain boundaries could lead to directional quantum transport - opening a pathway toward quantum diode-like functionalities. Stacking ; Graphene ; Reflection (computer programming) ; Grain boundary ; Asymmetry ; Fermi energy ; Specular reflection ; Fermi level Graphene research and applications ; Topological Materials and Phenomena ; Quantum and electron transport phenomena
Authors: Obsa Tamiru Mekonen | F. Gebremariam | Bayisa Gemeda Shuku
Source: Research Square (preprint, 2025-10-01)
Field: Materials Science — Materials Chemistry

An investigation on the degradation of the Mechanical Properties of Mild Steel Pipeline in Simulated Soil and Tap Water Environments sourced from Bishoftu Town, Ethiopia Tap water ; Pipeline transport ; Ultimate tensile strength ; Corrosion ; Compressive strength ; Degradation (telecommunications) ; Work (physics) ; Erosion Corrosion Behavior and Inhibition ; Non-Destructive Testing Techniques ; Concrete Corrosion and Durability
Authors: Pauline Perdu-Alloy | Keller Charline | Anjali Seth | Christine Carapito
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2025-11-17)
Field: Chemistry — Spectroscopy

Fine-Tuning of Label-Free Single-Cell Proteomics Workflows ABSTRACT Mass spectrometry-based single-cell proteomics emerges as the most promising method for studying cellular heterogeneity at the global proteome level with unprecedented depth and coverage. Its widespread application remains limited due to robustness, reproducibility, and throughput requirements, still difficult to meet as analyzing large cohorts of single cells is necessary to ensure statistical confidence. In this context, we have conducted method optimizations at three levels. First, we benchmarked three distinct workflows compatible with the nanoElute2 platform using different sample collection/preparation plate supports (EVO96 oil-free, LF48 oil-based and LF48 oil-free, a streamlined automated sample resuspension and direct injection protocol). Then, we compared the optimized EVO96 workflow on nanoElute2 with EVOSEP-based separations operating at two analytical throughputs (80 and 120 samples per day). Subsequently, we evaluated digestion efficiency using a range of enzyme/protein ratios (1:1; 10:1; 20:1; 50:1) to maximize peptide recovery. Finally, the chromatographic setup was refined to determine the best compromise between throughput and robustness. Altogether, these optimizations allowed to establish a robust workflow quantifying up to 5,000 proteins in 10min gradient time per single Hela cell at a 55 samples-per-day throughput. Workflow ; Throughput ; Proteome ; Sample (material) ; Proteomics ; Range (aeronautics) ; Quantitative proteomics Advanced Proteomics Techniques and Applications ; Single-cell and spatial transcriptomics ; Advanced Biosensing Techniques and Applications
Authors: Damiano Crescini | Gabriele Mascialino | Nicola Moggia | Giordano Piubeni | Mauro Serpelloni | Emilio Sardini
Source: Sensors (article, 2025-07-04)
Field: Chemistry — Analytical Chemistry

PCA- and PLSR-Based Machine Learning Model for Prediction of Urea-N Content in Heterogeneous Soils Using Near-Infrared Spectroscopy Determining the soil's nitrogen supply accurately and quickly is essential for effective agricultural management. This study explores the use of near-infrared (NIR) spectroscopy combined with spectral pre-processing techniques (such as Savitzky-Golay filtering) and partial least squares regression (PLSR) to assess soil nitrogen levels. Six soil types of varying compositions, treated with different levels of Urea-N fertilizer, were examined. Nitrogen-specific NIR peaks were identified, and regression models were consequently developed. Through a comparison of the performance of the models, the most effective model for nitrogen detection was selected. In calibration, the models performed well, with high R<sup>2</sup> (over 0.9) and low root mean square error (RMSE) values. The second derivative-based (SD) model slightly outperformed the first derivative-based (FD) model in terms of accuracy. Both models showed minimal bias, indicating reliable performance. During validation, the FD model outperformed the SD model in terms of R<sup>2</sup>, root mean square error of prediction (RMSEP), and residual prediction deviation (RPD). Thus, the FD model demonstrated good predictive ability (R<sup>2</sup> = 0.77, RPD = 2.06), while the SD model was less effective (R<sup>2</sup> = 0.65, RPD = 1.77). Compared to previous studies, this study uniquely combines real-time online detection capability with low computational cost, unlike most prior offline approaches, and includes model validation across various soil types. Overall, NIR spectroscopy coupled with multivariate models proves to be a promising tool for the detection of nitrogen levels in various soils. Soil water ; Content (measure theory) ; Infrared spectroscopy ; Urea ; Spectroscopy ; Near-infrared spectroscopy ; Computer science ; Machine learning ; Artificial intelligence ; Chemistry ; Analytical Chemistry (journal) ; Environmental science ; Environmental chemistry ; Mathematics ; Soil science ; Physics ; Optics ; Organic chemistry Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Nhut-Thanh Tran | Huu-Phuoc Nguyen | Cat-Tuong Nguyen | Gia-Thuan Truong | Chanh-Nghiem Nguyen | Masayuki Fukuzawa
Source: CTU Journal of Innovation and Sustainable Development (article, 2025-10-16)
Field: Chemistry — Analytical Chemistry

An acoustic-mechanical sensing system with multimodal machine learning techniques for in-line quality grading of watermelons A complete assessment of both internal and external quality parameters of watermelons is essential for export. However, small and medium-sized watermelon export enterprises often face challenges in accessing cost-effective and integrated grading systems. This study proposes an acoustic-mechanical sensing system for classifying watermelons based on both sweetness and weight. By combining weight measurement and sweetness estimation through acoustic analysis at a single station, the proposed system achieves a compact design and reduces data acquisition time. Additionally, a multimodal machine learning approach is applied to classify watermelon quality accurately. Among the tested models, the K-Nearest Neighbors model achieves the highest classification performance, with an accuracy of 97.3% and a precision of 96.6%. With its strong classification ability, integrated design, and low cost, the proposed system shows great potential for automated in-line quality grading of watermelons and other agricultural products. Unlike conventional large-scale systems that cascade individual grading functions, the integrated and cost-effective design of this system is suitable for small and medium-sized watermelon export enterprises to apply at each distributed shipping facility during intensive periods. Grading (engineering) ; Cascade ; Sweetness ; Quality (philosophy) ; Machine vision Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Advanced Chemical Sensor Technologies