jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Anders Kverneland | Ole Østergaard | Luisa Schmidt | Mia Ø Johansen | Steffen Ullitz Thorsen | Ruth Frikke‐Schmidt | Christina Christoffersen | Jesper V. Olsen
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2025-11-03)
Field: Chemistry — Spectroscopy

Benchmarking enrichment and depletion methods for quantitative plasma proteomics in different plasma types and the correlation to clinical routine assays. Plasma proteomics based on mass spectrometry has great potential for biomarker discovery. Plasma is challenging for mass spectrometry due to high dynamic range in protein abundance. Several workflows have been developed to overcome this, and in this study, we compare prominent workflows using platelet-poor- (PPP), platelet-rich plasma (PRP) and serum (SER). Our results show that depletion workflows including Top14 depletion and acid precipitation allow quantification of very different proteomes than methods based on enrichments of extracellular vesicles such as bead-based enrichment or ultracentrifugation. Enrichment methods are superior in terms of proteome depth and quantitative performance but may be less robust in large cohorts. There is a very high correlation between PPP and PRP samples with all methods and less to SER samples - especially with enrichment workflows.The correlation of 10 protein measurements, performed by clinical routine processes on a Cobas system, showed heterogeneous results. Low abundant proteins with biological dynamics within a healthy cohort, including c-reactive protein and lipoprotein(a), correlated very well to proteomic workflows while others, including albumin and transferrin, correlated poorly. In conclusion, the workflow for plasma proteomics should be aligned with the aim of the analysis and the setup of the sample collection. Proteome ; Proteomics ; Quantitative proteomics ; Mass spectrometry ; Extracellular vesicles ; Blood proteins ; Biomarker ; Biomarker discovery Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Advanced Biosensing Techniques and Applications
Authors: Huaiyu Li | Xixi Lu | C.I. Pang | Rong Liang | Shaoxiang Pan | Wei Fang | Xingfeng Guo
Source: Foods (article, 2025-08-23)
Field: Materials Science — Biomaterials

The Characterization of Physicochemical, Nutritional, and Flavor Properties of Bovine Hide Gelatin Prepared from Different Raw Materials Gelatin from bovine hide, especially yak hide, is valued in the food and pharmaceutical industries; however, as the most common domestic cattle in China, gelatin made from yellow cattle hide remains unexplored. Thus, the physicochemical properties, nutritional components, and flavor characteristics of gelatin produced from yellow cattle hides and yak hides, both before and after hair removal, were analyzed. It was found that yellow cattle hide gelatin (YCHG) not only had a higher protein content (68.45-71.51%) than yak hide gelatin (YHG) (66.81-67.56%) but also had a higher Fe content (86.75 ± 1.650 mg/kg). Additionally, 17 amino acids were detected in the four bovine hide gelatin samples; among them, dehaired yellow cattle hide gelatin (DYCHG) was richer in sweet-tasting amino acids than the others. Notably, non-dehaired yellow cattle hide gelatin (NDYCHG) featured 4-methyl-3-penten-2-one (with a honey aroma), whereas non-dehaired yak hide gelatin (NDYHG) featured <i>β</i>-pinene, 1-nonanal, acetic acid-D, (E)-2-pentenal, and allyl sulfide. Therefore, yellow cattle hide gelatin (YCHG) exhibits prominent nutritional and flavor properties, suggesting its potential as an alternative raw material for food industry applications. Gelatin ; Raw material ; Food science ; Flavor ; Characterization (materials science) ; Chemistry ; Chemical engineering ; Materials science ; Organic chemistry ; Nanotechnology ; Engineering Collagen: Extraction and Characterization ; Protein Hydrolysis and Bioactive Peptides ; Meat and Animal Product Quality
Authors: Bin Li | 博人 高橋 | Qianyu Zhao | Dan Yang | Rui Zhu | Zhexuan Wang | Yujie Yang
Source: Horticulturae (article, 2025-05-07)
Field: Chemistry — Analytical Chemistry

A Predictive Model of the Photosynthetic Rate of Chili Peppers Using Support Vector Regression and Environmental Multi-Factor Analysis Light intensity, CO2 concentration, and temperature are three primary environmental factors with high temporal variability, nonlinearity, and strong coupling, which directly influence the photosynthetic rate of plants. To investigate the combined influence of these factors on the photosynthetic rate of chili pepper plants, a predictive model was developed for their net photosynthetic rate (Pn) during the peak fruiting period. A multifactorial nested experimental design with irregular sampling intervals was used to systematically examine the interacting effects of light intensity, CO2 concentration, and temperature on photosynthesis. Based on the collected data, a support vector regression (SVR) algorithm was trained and its performance was compared with that of a backpropagation (BP) neural network, a radial basis function (RBF) neural network, and a random forest (RF) algorithm. To optimize performance, a grid search with five-fold cross-validation was conducted to identify optimal hyperparameters; this process yielded a cost parameter (C) of 38 and a gamma parameter (γ) of 8, which minimized the root mean square error (RMSE) on the training set. On the test set, the SVR model achieved a coefficient of determination (R2) of 0.9941 and an RMSE of 0.6988 μmol m−2 s−1 (relative to the full Pn range of −4.19 to 39.2 μmol m−2 s−1). A linear fit between measured and predicted Pn values yielded a slope of 0.992 and an intercept of 0.07, indicating near-perfect agreement and surpassing the performance of the BP, RBF, and RF models. These results demonstrate that the SVR-based model outperformed the other approaches and exhibited superior predictive ability, establishing it as a robust theoretical foundation and a practical tool for dynamic environmental optimization in controlled-environment agriculture. Regression analysis ; Statistics ; Mathematics ; Photosynthesis ; Botany ; Horticulture ; Environmental science ; Biology Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Water Quality Monitoring and Analysis
Authors: Renato Altobelli Antunes
Source: Metals (article, 2025-06-09)
Field: Materials Science — Materials Chemistry

Advances in Corrosion and Protection of Materials (Second Edition) As modern civilization, through its extensive use of metallic materials, has given rise to progressively more aggressive environmental conditions, corrosion resistance has become a prominent issue [...] Corrosion ; Forensic engineering ; Engineering ; Metallurgy ; Construction engineering ; Computer science ; Materials science Corrosion Behavior and Inhibition
Authors: Maarten Klaverdijk | M. Nemati | Marcel Ottens | Marieke E. Klijn
Source: Biotechnology Progress (article, 2025-06-16)
Field: Chemistry — Analytical Chemistry

Impact of bioreactor process parameters and yeast biomass on Raman spectra In-line Raman spectroscopy combined with chemometric modeling is a valuable process analytical technology (PAT) providing real-time quantitative information on cell culture compounds. Considering that compound quantification through chemometric models depends on pre-processing to maintain consistent changes in intensity at certain wavenumbers, all causes of signal distortion should be well understood to prevent quantification inaccuracies. This work investigated spectral distortion caused by the changing bioreactor parameters temperature, bubble quantity, and medium viscosity. In addition, the isolated spectral contribution of Saccharomyces cerevisiae cells in suspension was also determined. A temperature range from 20 to 40°C resulted in peak shifts up to 0.8 cm<sup>-1</sup> to lower wavenumbers, bubbles generated under standard bioreactor operation conditions led to signal attenuation of up to 7.93% reduction in peak intensity, and changes in liquid viscosity resulted in complex peak shift behavior. Isolated biomass concentrations reaching 5 g/L caused up to 44.6% reduction in distinct peak intensity, which was similar to spectra from batch process fermentations. Correcting for the attenuation revealed spectral features of biomass associated with proteins and lipids in the 1000-1500 cm<sup>-1</sup> region. However, the spectral contribution of yeast biomass is dominated by signal extinction, which attenuates Raman spectra in a non-linear manner as biomass accumulates. The obtained knowledge on different sources of spectral distortion aids in the development of robust pre-processing and modeling strategies to obtain chemometric models applicable across experimental setups. Bioreactor ; Raman spectroscopy ; Biomass (ecology) ; Biological system ; Viscosity ; Analytical Chemistry (journal) ; Attenuation ; Chemistry ; Distortion (music) ; Spectral line ; Materials science ; Chromatography ; Optics ; Physics ; Biology ; Optoelectronics Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Viral Infectious Diseases and Gene Expression in Insects
Authors: Wenxuan Zhong | Wei An | Jin Wang | Guanhua Ren | Zhi Ren | Shuai Li | Songtao Li | Li Li | Ruoxing Wang
Source: Journal of Applied Physics (article, 2025-09-02)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Terahertz ultra-broadband perfect absorption and absorption-to-transparency switching based on Brewster metasurfaces In this paper, we propose a non-resonant tilted anisotropic Brewster metasurface to investigate its ultra-broadband absorption characteristics in the terahertz (THz) band. It consists of periodically tilted vanadium dioxide (VO2) films arranged in a silicon dioxide substrate. Based on the anomalous Brewster effect, the metasurface can exhibit perfect broadband absorption characteristics when VO2 is in the metallic phase. Through VO2 phase transition to the insulating phase, the metasurface can present perfect broadband transmission characteristics, achieving a THz perfect broadband absorption/transmission switch. The effect of the structural parameters of the Brewster metasurface on its absorption performance is analyzed. Its broadband high absorption performance can not only almost cover the conventional full THz band, but also be extended to higher frequencies to achieve ultra-broadband high absorption ranging from 2 to 22.5 THz. Moreover, a gradient Brewster metasurface is designed to exhibit dynamical controllability toward THz waves with nearly omnidirectional wavefronts. The proposed metasurfaces have great potential for applications in THz stealth and THz switching devices. Brewster ; Terahertz radiation ; Broadband ; Absorption (acoustics) ; Materials science ; Brewster's angle ; Optoelectronics ; Transparency (behavior) ; Optics ; Physics ; Computer science Metamaterials and Metasurfaces Applications ; Plasmonic and Surface Plasmon Research ; Advanced Antenna and Metasurface Technologies
Authors: Dušan Petković | Miloš Madić | Milan Mitković
Source: Applied Sciences (article, 2025-08-21)
Field: Materials Science — Materials Chemistry

Development of a Decision Support System for Biomaterial Selection Based on MCDM Methods The material selection process can be viewed as a multi-criteria decision-making (MCDM) problem with multiple objectives, which are often conflicting and of different importance. The selection of the most suitable biomaterial is considered as a very complex, important, and responsible task that is influenced by many factors. In this paper, a procedure for biomaterial selection based on MCDM is proposed by using a developed decision support system (DSS) named MCDM Solver. Within the framework of the developed DSS, the complete procedure for selecting the criteria weights was developed. Also, in addition to the adapted standard MCDM methods such as TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) and VIKOR (VIšekriterijumsko KOmpromisno Rangiranje), an extended WASPAS (Weighted Aggregated Sum Product Assessment) method was developed, enabling its application for considering target-based criteria in solving biomaterial selection problems. The proposed MCDM Solver enables a structured decision-making process helping decision-makers rank biomaterials with respect to multiple conflicting criteria and make rational and justifiable decisions. For the validation of the developed DSS, two case studies, i.e., the selection of a plate for internal bone fixation and a hip prosthesis, were presented. Finally, lists of potential biomaterials (alternatives) in the considered case studies were ranked based on the selected criteria, where the best-ranked one presents the most suitable choice for the specific biomedical application. Multiple-criteria decision analysis ; Selection (genetic algorithm) ; Computer science ; Engineering ; Management science ; Operations research ; Artificial intelligence Material Selection and Properties ; Manufacturing Process and Optimization ; Engineering Technology and Methodologies
Authors: Yifan Wang | Mai Tan | Carlos Fernandez‐Granda | Peter A. Crozier
Source: ArXiv.org (preprint, 2025-05-20)
Field: Chemistry — Electrochemistry

Revealing Information from Weak Signal in Electron Energy-Loss Spectroscopy with a Deep Denoiser Electron energy-loss spectroscopy (EELS) coupled with scanning transmission electron microscopy (STEM) is a powerful technique to determine materials composition and bonding with high spatial resolution. Noise is often a limitation especially with the increasing sophistication of EELS experiments. The signal characteristics from direct electron detectors provide a new opportunity to design superior denoisers. We have developed a CNN based denoiser, the unsupervised deep video denoiser (UDVD), which can be applied to EELS datasets acquired with direct electron detectors. We described UDVD and explained how to adapt the denoiser to energy-loss spectral series. To benchmark the performance of the denoiser on EELS datasets, we generated and denoised a set of simulated spectra. We demonstrate the charge spreading effect associated with pixel interfaces on direct electron detectors, which leads to artifacts after denoising. To suppress such artifacts, we propose some adjustments. We demonstrate the effectiveness of the denoiser using two challenging real data examples: mapping Gd dopants in $CeO_2$ nanoparticles and characterizing vibrational modes in hexagonal boron nitride (h-BN) with atomic resolution. Spectroscopy ; Noise (video) ; Electron ; SIGNAL (programming language) ; Benchmark (surveying) ; Scanning transmission electron microscopy ; Boron nitride Electrochemical Analysis and Applications ; Electron and X-Ray Spectroscopy Techniques ; Machine Learning in Materials Science
Authors: Nicola Manca | Dhavalkumar Mungpara | L. Cichetto | Alejandro Enrique Plaza | G. Lamura | Alexander Schwarz | D. Marré | L. Pellegrino
Source: ACS Sensors (preprint, 2025-06-06)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Magnetic Trampoline Resonators Made of (La,Sr)MnO<sub>3</sub> Single-Crystal Thin Films Microelectro-mechanical resonators employing a magnetic element have been proposed for magnetic field sensing applications, but the integration of magnetic materials with standard semiconductor compounds is challenging and requires complex fabrication protocols. We present a different approach relying on (La<sub>0.7</sub>,Sr<sub>0.3</sub>)MnO<sub>3</sub> (LSMO), an oxide compound that works both as a structural element for the resonator and a functional magnetic layer. Suspended trampolines are realized in a single-step process from LSMO thin films and show a quality factor of up to 60k and <i>f</i>·<i>Q</i> products reaching 10<sup>10</sup> Hz. Their magnetic properties are probed by a SQUID magnetometer and magnetic force microscopy, showing a saturation magnetization of 240 kA/m at room temperature and in-plane magnetic domains with a coercivity of 2.5 mT. Being entirely made from a magnetic material, these resonators exhibit a larger magnetic interaction volume compared to other solutions, making them ideal candidates as building blocks for high-sensitivity magnetic field sensors. Trampoline ; Resonator ; Materials science ; Thin film ; Single crystal ; Nuclear magnetic resonance ; Condensed matter physics ; Optoelectronics ; Physics ; Engineering ; Nanotechnology ; Structural engineering Magnetic and transport properties of perovskites and related materials ; Magnetic Properties of Alloys ; Rare-earth and actinide compounds
Authors: Dakka Obulesu
Source: Journal of Information Systems Engineering & Management (article, 2025-04-21)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Supercapacitor-battery hybrid storage systems to grid-tied photovoltaic setups to improve energy management and system stability The increasing integration of renewable energy sources, particularly photovoltaic (PV) systems, into the power grid has introduced challenges related to energy variability, grid stability, and energy management. Supercapacitor-battery hybrid storage systems (SBHSS) have emerged as an effective solution to address these issues by combining the high energy density of batteries with the high-power density and fast response of supercapacitors. This paper explores the potential of SBHSS in grid-tied PV setups to enhance energy management and system stability. Batteries provide long-term energy storage and support sustained power delivery, while supercapacitors handle rapid fluctuations and transient spikes, thereby reducing the strain on batteries and extending their lifespan. A hybrid energy management strategy is proposed, which integrates real-time monitoring, adaptive power control, and dynamic load balancing to optimize energy flow between the PV array, supercapacitor, and battery. The proposed system minimizes power losses, improves grid frequency and voltage regulation, and enhances the reliability of power delivery during sudden load changes or cloud cover events. Simulation and experimental results demonstrate that the SBHSS can effectively smooth PV power output, reduce battery cycling stress, and improve overall system efficiency. The hybrid system also ensures faster response to grid disturbances, thereby improving grid resilience and reducing reliance on conventional backup sources. The combination of supercapacitors and batteries enables a more balanced and stable power supply, ensuring that renewable energy sources are efficiently integrated into the grid. This research highlights the practical advantages of SBHSS in grid-tied PV applications and provides insights into system design, control strategies, and performance improvements, paving the way for more sustainable and resilient power systems. Supercapacitor ; Photovoltaic system ; Energy storage ; Battery (electricity) ; Grid ; Computer science ; Computer data storage ; Stability (learning theory) ; Energy management ; Automotive engineering ; Electrical engineering ; Energy (signal processing) ; Engineering ; Power (physics) ; Capacitance ; Electrode ; Computer hardware ; Chemistry ; Physics Supercapacitor Materials and Fabrication ; Microgrid Control and Optimization ; Electric and Hybrid Vehicle Technologies
Authors: Katharina Ueltzen | Aakash Ashok Naik | Christina Ertural | Philipp Benner | Janine George
Source: ChemRxiv (article, 2025-08-14)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Can simple exchange heuristics guide us in predicting magnetic properties of solids? A popular heuristic derived from the Kanamori-Goodenough-Anderson rules of superexchange connects bond angles and magnetism in certain transition metal compounds. We evaluate the fulfillment of this heuristic on databases of magnetic structures. The heuristic is partly satisfied in magnetic structures from experiment, and exceptions can be rationalized. We further show that incorporating this heuristic into machine learning models for magnetism improves the prediction. Heuristics ; Simple (philosophy) ; Computer science ; Statistical physics ; Physics ; Philosophy ; Epistemology ; Operating system Magnetic Properties and Applications
Authors: Nur Hazirah Kamaruzaman | Asif Ali Siyal | Radin Maya Saphira Radin Mohamed | Wan Afnizan Bin Wan Mohamed | Nurina Fitriani
Source: International Journal of Environmental Science and Technology (article, 2025-06-30)
Field: Materials Science — Materials Chemistry

Green synthesis of zinc oxide nanoparticles for inactivation of antibiotic-resistant bacteria in real prawn farm wastewater Abstract Antibiotic-resistant bacteria are widespread in prawn farm wastewater, contaminating water resources and threatening human health. This paper describes the synthesis of zinc oxide nanoparticles through a green technique using Nymphaea leaf extract to inactivate antibiotic-resistant bacteria in the prawn farm wastewater. NLE has not been previously reported for the synthesis of zinc oxide nanoparticles. The Nymphaea leaf extract and zinc oxide nanoparticles were characterized, and the optimization of the effect of parameters of zinc oxide nanoparticles loading (0.01–0.1 g/L), pH (7–9), and reaction time (30–60 min) on the inactivation of antibiotic-resistant bacteria of gram-negative Escherichia coli and gram-positive Bacillus cereus was determined using central composite design of response surface methodology. Nymphaea leaf extract contains terpenoids, flavonoids, tannins, phenols, and glycosides. Zinc oxide nanoparticles were spherical, evenly distributed, less agglomerated with small particles (31.2–80.5 nm), and nearly homogeneous grain size distribution on the surface. It has a hexagonal wurtzite crystalline structure with a crystallite size of 42.6 nm. The p -value of &lt; 0.05 displayed the best fitting of the data to the quadratic model with a 95% confidence level. The best inactivation of 99.18% and 97.39% for Escherichia coli and Bacillus cereus in prawn farm wastewater was obtained at optimum zinc oxide loading of 0.1 g/L, pH 7, and 30 min of reaction time. Escherichia coli and Bacillus cereus were inactivated by damaging the bacterial cell wall. The green zinc oxide nanoparticles efficiently inactivated Escherichia coli and Bacillus cereus growth in prawn farm wastewater. Zinc oxide nanoparticles can be used to inactivate other bacteria in other types of wastewaters. Prawn ; Zinc ; Wastewater ; Bacteria ; Antibiotics ; Chemistry ; Environmental chemistry ; Microbiology ; Environmental science ; Environmental engineering ; Biology ; Fishery ; Organic chemistry Nanoparticles: synthesis and applications ; TiO2 Photocatalysis and Solar Cells ; Advanced Nanomaterials in Catalysis