jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Mona A. Kamel | Hoda M. Marzouk | Adel M. Michael | Samah S. Abbas | Christine K. Nessim
Source: Scientific Reports (article, 2025-10-31)
Field: Chemistry — Analytical Chemistry

Univariate and multivariate signal processing spectrophotometric determination of an antihypertensive combination in line with the United Nations sustainable development goals Effective management of hypertension typically involves multiple medications. This underscores the pharmaceutical industry's demand for simple, cost-effective, and environmentally sustainable analytical methods capable of handling complex, multicomponent formulations. This study's primary goal was to compare and validate univariate and multivariate spectrophotometric techniques for analyzing fixed-dose antihypertensive formulations of Telmisartan (TEL), Chlorthalidone (CHT), and Amlodipine (AML). Successive Ratio Subtraction paired with Constant Multiplication (SRS-CM) and Successive Derivative Subtraction paired with Constant Multiplication (SDS-CM) were the developed univariate methods. The cited drugs were successfully quantified at their respective maxima: 295.7 nm for TEL, 275.0 nm for CHT, and 359.5 nm for AML. On the other hand, the SDS-CM method enabled their determination using first-derivative spectra, with TEL identified at P<sub>282.5-313</sub> nm, CHT at 287.0 nm, and AML at P<sub>231-246</sub> nm. Also, Interval-Partial Least Squares (iPLS) and Genetic Algorithm-Partial Least Squares (GA-PLS) were applied as multivariate techniques. In contrast to full-spectrum modeling alone, the results showed that adding variable selection techniques greatly improved the model's performance. Following ICH guidelines, the proposed techniques were used to quantify the cited medications in tablets. The validity of the results was confirmed by statistical comparison with the reported method. The study was further expanded to assess the content uniformity of the dosage units in compliance with USP. Three environmental complementary assessment tools were employed: the Analytical Greenness Metric (AGREE), the Blue Applicability Grade Index (BAGI) and White Analytical Chemistry (RGB12). This study also aligns with several UN Sustainable Development Goals (UN-SDGs), emphasizing commitment to green pharmaceutical research. Sustainability was verified using the NQS index, confirming the method's compliance with responsible analytical practices. Univariate ; Multivariate statistics ; Sustainable development ; Multiplication (music) ; Subtraction ; Multivariate analysis ; Univariate analysis ; Least-squares function approximation Analytical Methods in Pharmaceuticals ; Spectroscopy and Chemometric Analyses ; Pesticide Residue Analysis and Safety
Authors: Arif Sikander | Barkat Ullah | Muhammad Shoaib Naseem | Hassan Raza Channar | Muhammad Aslam
Source: Journal of King Saud University - Engineering Sciences (article, 2025-07-01)
Field: Materials Science — Materials Chemistry

Analysis of dry and cryogenic drilling on hole quality in AA2024-T6 using HSS and HSS-Co tools Abstract Machining, particularly drilling, plays a significant role in various industries, including aerospace and automotive, where efficient drilling is a critical factor in their operational success. However, low-quality holes can result in structural failures and increase production costs. Achieving high-quality holes necessitates appropriate selection of drilling tools, process parameters, and machine setup conditions. This study investigates the impact of drilling parameters, specifically feed rate and spindle speed, on chip formation and hole quality, including deviations from normal size, circularity, cylindricity, perpendicularity, and surface roughness. Experiments were conducted on aluminum alloy 2024-T6 (AA2024-T6), a widely used material in the aerospace industry. This paper investigated the drilling process parameters using uncoated high-strength stainless steel (HSS) and cobalt-coated high-strength stainless steel (HSS-Co) drill bits, with a 30° helix angle and 140° point angle, under both dry and cryogenic conditions with liquid nitrogen (boiling point: − 195.8 °C). Results demonstrated that HSS drill bits, in cryogenic conditions, produce holes with lower surface roughness, minimal deviation from nominal size, and reduced errors in circularity, cylindricity, and perpendicularity compared to those made under dry conditions. Conversely, HSS-Co drill bits yielded high-quality holes under dry conditions across all feed rates and spindle speeds. Furthermore, short and segmented chips were formed at high feed rates and low spindle speeds. No significant variations were observed in the chip’s formation for both HSS and HSS-Co drills. Additionally, scanning electron microscopy (SEM) was used to examine the internal defects of the drilled holes, providing further insight into the impact of drilling conditions on hole quality. Drilling ; Quality (philosophy) ; Cryogenic treatment ; Materials science ; Metallurgy ; Manufacturing engineering ; Engineering ; Physics Metal Alloys Wear and Properties ; Aluminum Alloy Microstructure Properties ; Aluminum Alloys Composites Properties
Authors: Tong Liu | Changhong Dai | Dongyi Wang | C. T. Chan | Lei Zhou
Source: Advanced Photonics (article, 2025-08-19)
Field: Materials Science — Electronic, Optical and Magnetic Materials

High-efficiency vectorial holography based on ultra-thin metasurfaces Holography plays a crucial role in optics, yet traditional methods require complex setups and bulky devices, being unfavourable for optical integration.Although metasurface-based holograms can be ultracompact, holographic images generated by previously realized metadevices were mostly scalar ones, with a few vectorial holograms realized so far suffering from restrictions on efficiency, incident polarization, and resolution.We propose and experimentally demonstrate an efficient meta-platform to generate vectorial holographic images with high resolutions under arbitrary incident polarizations.Combining Gerchberg-Saxton algorithm and the wave-decomposition technique, we establish a generic strategy to retrieve the optical properties (e.g., reflection phases and polarization-conversion capabilities) of meta-atoms required to construct a metasurface for generating a predesigned vectorial holographic image under a predesigned incident polarization.We next design a series of high-efficiency and deep-subwavelength single-structure meta-atoms exhibiting tailored reflection phases and polarization-conversion capabilities governed by both structural resonances and the Pancharatnam-Berry effect, and experimentally characterize their optical scattering properties.We finally construct a series of ultra-thin metadevices with these meta-atoms and experimentally demonstrate that they can generate pre-designed vectorial holographic images under illuminations of circularly polarized light at 1064 nm.We provide a highly efficient and ultra-thin platform to generate predesigned vectorial holographic images under illuminations of light with arbitrary given polarization, which can inspire numerous future applications in on-chip photonics. Holography ; Optics ; Materials science ; Optoelectronics ; Physics Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Advanced Optical Imaging Technologies
Authors: Martin Mészáros | J. Sedlák | Tomáš Bílek | Aleš Vávra
Source: Algorithms (article, 2025-11-21)
Field: Chemistry — Analytical Chemistry

Evaluating LDA and PLS-DA Algorithms for Food Authentication: A Chemometric Perspective High-dimensional analytical datasets, such as those generated by inductively coupled plasma–mass spectrometry (ICP-MS), require robust computational frameworks for dimensionality reduction, classification, and model validation. This study presents a comparative evaluation of Linear Discriminant Analysis (LDA) and Partial Least Squares Discriminant Analysis (PLS-DA) algorithms applied to multivariate chemometric data for food origin authentication. The research employs a workflow that integrates Principal Component Analysis (PCA) for feature extraction, followed by supervised classification using LDA and PLS-DA. Model performance and stability were systematically assessed. The dataset comprised 28 apple samples from four geographical regions and was processed with normalization, scaling, and transformation prior to modeling. Each model was validated via leave-one-out cross-validation and evaluated using accuracy, sensitivity, specificity, balanced accuracy, detection prevalence, p-value, and Cohen’s Kappa. The results demonstrate that, as a linear projection-based classifier, LDA provides higher robustness and interpretability in small and unbalanced datasets. In contrast, PLS-DA, which is optimized for covariance maximization, exhibits higher apparent sensitivity but lower reproducibility under similar conditions. The study also emphasizes the importance of dimensionality reduction strategies, such as PCA-based variable selection versus latent space extraction in PLS-DA, in controlling overfitting and improving model generalizability. The proposed algorithmic workflow provides a reproducible and statistically sound approach for evaluating discriminant methods in chemometric classification. Linear discriminant analysis ; Interpretability ; Principal component analysis ; Overfitting ; Dimensionality reduction ; Partial least squares regression ; Pattern recognition (psychology) ; Robustness (evolution) ; Feature selection Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Isotope Analysis in Ecology
Authors: Elio López‐García | Antonio Benítez‐Cabello | Francisco Noé Arroyo‐López
Source: npj Science of Food (article, 2025-07-04)
Field: Chemistry — Analytical Chemistry

Applying machine learning to classify table olives using bacterial metataxonomic data In recent years, metataxonomic analysis has been increasingly used to characterize microbial communities in fermented foods. Moreover, advances in bioinformatics and machine learning (ML) have expanded resources for analyzing these metataxonomic data. Particularly tree-based algorithms are valuable for their interpretability. This work compares the use of three tree-based ML algorithms-Classification and Regression Tree, Random Forest (RF), and Extreme Gradient Boosting- for the analysis of a database composed of 442 samples of 16S rRNA bacterial profiles obtained from table olives. Our findings show that ML techniques can effectively classify bacterial profiles based on olive processing type, cultivar, country of origin, and isolation matrix. The RF model achieved the highest accuracy, reaching 97% in the best cases, with a kappa coefficient above 0.8 for most categories. This approach holds potential applications in the table olive sector and in other food products, where the industrial application of ML techniques could enhance traceability, authenticity, and quality control. Table (database) ; Computer science ; Artificial intelligence ; Machine learning ; Pattern recognition (psychology) ; Data mining Spectroscopy and Chemometric Analyses ; Edible Oils Quality and Analysis ; Identification and Quantification in Food
Authors: Xinmei Wang | Xiaoyong He | Hua Yang | Xu Bao | Yongjian Tang | Pinghui Wu | Yougen Yi
Source: Molecules (article, 2025-04-10)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Graphene-Based Absorber: Tunable, Highly Sensitive, Six-Frequency Due to the equipartition exciton property of graphene metamaterials, researchers have applied them to the design of absorbers and developed a series of absorbers covering different wavebands (including narrowband and broadband). In this paper, an absorber based on surface-isotropic excitations was designed with the help of graphene metamaterials and relevant simulations. The absorber exhibited six perfect absorption peaks in the mid-infrared band and had an extremely simple structure consisting of only three layers: a gold layer at the bottom, a dielectric layer made of silica in the middle, and patterned graphene at the top. This absorber possesses excellent tuning ability, and by applying an external bias to the graphene layer, the Fermi energy level of graphene can be adjusted, and thus the resonance frequency of the absorption peak can be tuned. Meanwhile, the effect of the graphene relaxation time on the absorber performance was investigated. In addition, the refractive index of the dielectric layer was found to be linearly related to the resonance frequency of the absorption peak. It is worth mentioning that the absorber structure possessed polarization insensitivity due to its central symmetry. Even when incident light with different polarizations was incident over a wide range of angles, the change in absorbance of the absorption peaks was negligible, demonstrating significant insensitivity to the angle of incidence. The sensor possesses excellent characteristics such as tunability, polarization insensitivity, incident angle insensitivity, and high sensitivity. This paper demonstrates the feasibility of a six-frequency sensor and opens up more ideas for the design of multi-frequency sensors. Materials science ; Graphene ; Metamaterial absorber ; Optoelectronics ; Polarization (electrochemistry) ; Metamaterial ; Dielectric ; Absorption (acoustics) ; Absorbance ; Refractive index ; Optics ; Ray ; Terahertz radiation ; Tunable metamaterials ; Nanotechnology ; Physics Metamaterials and Metasurfaces Applications ; Plasmonic and Surface Plasmon Research ; Graphene research and applications
Authors: Kejia Li | Clément M. Potel | Isabelle Becher | Nico Hüttmann | Martín Garrido‐Rodríguez | Jennifer Schwarz | Mira Lea Burtscher | Mikhail M. Savitski
Source: Nature Structural & Molecular Biology (article, 2025-11-05)
Field: Chemistry — Spectroscopy

High-throughput peptide-centric local stability assay extends protein–ligand identification to membrane proteins, tissues and bacteria Systematic mapping of protein-ligand interactions is essential for understanding biological processes and drug mechanisms. Peptide-centric local stability assay (PELSA) is a powerful tool for detecting these interactions and identifying potential binding sites. However, its original workflow is limited in throughput, sample compatibility and accessible protein targets. Here, we introduce a high-throughput adaptation-HT-PELSA-that increases sample processing efficiency by 100-fold while maintaining high sensitivity and reproducibility. HT-PELSA substantially extends the capabilities of the original method by enabling sensitive protein-ligand profiling in crude cell, tissue and bacterial lysates, allowing the identification of membrane protein targets in diverse biological systems. We demonstrate that HT-PELSA can precisely and accurately determine binding affinities of small molecule inhibitors, sensitively detect direct and allosteric ATP binding sites, and reveal off-target interactions of a marketed kinase inhibitor in heart tissue. By enhancing scalability, reducing costs and enabling system-wide drug screening across a wide range of sample types, HT-PELSA-when combined with next-generation mass spectrometry-may offer a powerful platform poised to accelerate both drug discovery and basic biological research. Drug discovery ; Allosteric regulation ; Binding affinities ; Affinities ; Small molecule ; Plasma protein binding ; Membrane ; Membrane protein ; Protein–protein interaction Advanced Proteomics Techniques and Applications ; Click Chemistry and Applications ; Advanced Biosensing Techniques and Applications
Authors: Wenyuan Hu | Fuping Qian | Simin Cheng | Lumin Chen | Xiao Ma | Huai-Yu Zhong
Source: Applied Sciences (article, 2025-08-05)
Field: Materials Science — Biomaterials

Numerical Study of the Filtration Performance for Electrospun Nanofiber Membranes To solve the limitations of these models for submicron materials like electrospun nanofiber membranes, a numerical simulation was used to construct a three-dimensional model closer to the actual structure to explore the filtration resistance and efficiency of these membranes. Based on the actual polydisperse electrospun nanofiber filter, the three-dimensional structure (fiber diameter 280 nm–1300 nm, thickness 0.0150 mm–0.0240 mm, and solid volume fraction 11.3–17.7%) was reconstructed by GeoDict software. The filtration resistance was simulated with the FlowDict module (surface velocity 6.89 cm/s, 20 °C), and the filtration efficiency was calculated with the FilterDict module (2.5 μm particles, tracking 20,000). The results are compared with the experimental values, Davids empirical formula, Happel model, and Kuwabara model. The results show that the simulated values of filtration resistance are generally higher than the experimental values (deviation ≤ 20%), among which the simulation and experiment have the highest consistency, followed by the Davids formula (such as the relative error of 41.62% at 9% spinning solution concentration), and the Kuwabara model has the largest error (59.86%). The simulated value of filtration efficiency is higher than the experimental value (deviation ≤ 7%), because the model assumes that the particles adhere directly after contacting the fiber, and the actual sliding off is not considered. This study confirms that numerical simulation can efficiently predict the filtration performance of electrospun nanofiber membranes. Therefore, it provides a basis for optimizing material structure by adjusting spinning parameters and promoting the engineering application of submicron filter materials. Filtration (mathematics) ; Membrane ; Nanofiber ; Materials science ; Spinning ; Composite material ; Fiber ; Electrospinning ; Mathematics ; Chemistry ; Statistics ; Polymer Electrospun Nanofibers in Biomedical Applications ; Aerosol Filtration and Electrostatic Precipitation ; Advanced Sensor and Energy Harvesting Materials
Authors: Marilena M. Bourdakou | Eleni M. Loizidou | George M. Spyrou
Source: Antioxidants (article, 2025-08-02)
Field: Chemistry — Organic Chemistry

Exploring the Impact of Bioactive Compounds Found in Extra Virgin Olive Oil on NRF2 Modulation in Alzheimer’s Disease Alzheimer's disease (AD) is a progressive neurodegenerative disorder marked by amyloid-β (Aβ) plaques, neurofibrillary tangles, blood-brain barrier dysfunction, oxidative stress (OS), and neuroinflammation. Current treatments provide symptomatic relief, but do not halt the disease's progression. OS plays a crucial role in AD pathogenesis by promoting Aβ accumulation. Nuclear factor erythroid 2-related factor 2 (NRF2) is a key regulator of the antioxidant response, influencing genes involved in OS mitigation, mitochondrial function, and inflammation. Dysregulation of NRF2 is implicated in AD, making it a promising therapeutic target. Emerging evidence suggests that adherence to a Mediterranean diet (MD), which is particularly rich in polyphenols from extra virgin olive oil (EVOO), is associated with improved cognitive function and a reduced risk of mild cognitive impairment. Polyphenols can activate NRF2, enhancing endogenous antioxidant defenses. This study employs a computational approach to explore the potential of bioactive compounds in EVOO to modulate NRF2-related pathways in AD. We analyzed transcriptomic data from AD and EVOO-treated samples to identify NRF2-associated genes, and used chemical structure-based analysis to compare EVOO's bioactive compounds with known NRF2 activators. Enrichment analysis was performed to identify common biological functions between NRF2-, EVOO-, and AD-related pathways. Our findings highlight important factors and biological functions that provide new insight into the molecular mechanisms through which EVOO consumption might influence cellular pathways associated with AD via modulation of the NRF2 pathway. The presented approach provides a different perspective in the discovery of compounds that may contribute to neuroprotective mechanisms in the context of AD. Modulation (music) ; Disease ; Alzheimer's disease ; Olive oil ; Chemistry ; Traditional medicine ; Food science ; Medicine ; Internal medicine ; Physics Edible Oils Quality and Analysis ; Phytochemicals and Antioxidant Activities ; Genomics, phytochemicals, and oxidative stress
Authors: Louis Bastogne | Philippe Ghosez
Source: Physical review. B./Physical review. B (article, 2025-10-03)
Field: Materials Science — Materials Chemistry

Ferroelectric properties and topological polar textures of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msub><mml:mi>PbTiO</mml:mi><mml:mn>3</mml:mn></mml:msub></mml:math> from a second-principles open-source interatomic potential We report large-scale atomistic simulations of inhomogeneous polar textures in ${\mathrm{PbTiO}}_{3}$ using a recently developed open-source second-principles effective interatomic potential. The model is meticulously validated against density functional theory, reproducing structural, energetic, and dynamical properties, which ensures predictive reliability beyond the training set. Building on this foundation, we uncover and characterize a rich variety of polar configurations, including Bloch-type domain-wall transition, Ising lines, skyrmions, skyrmioniums, target skyrmions, skyrmion bags in free bulk, as well as antiskyrmions, merons, and antimerons under biaxial tensile strain. These results broaden the known landscape of ferroelectric topologies and highlight ${\mathrm{PbTiO}}_{3}$ as a versatile host for emergent polar textures with potential applications in functional devices. The open-source model provides the community with a robust and efficient tool for exploring complex ferroelectric phenomena at large scales. Polar ; Ferroelectricity ; Interatomic potential ; Topology (electrical circuits) ; Ising model ; Variety (cybernetics) ; Reliability (semiconductor) Ferroelectric and Piezoelectric Materials ; Multiferroics and related materials ; Photorefractive and Nonlinear Optics