jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Freija De Vleeschouwer | Eline Desmedt | Mercedes Alonso
Source: Chemistry - Methods (article, 2025-09-18)
Field: Materials Science — Materials Chemistry

Exploring Aromaticity in Expanded Porphyrins: A Multidimensional Approach to Structure–Property Relationships Expanded porphyrins offer a unique platform for probing how (anti)aromaticity influences molecular properties. These macrocycles are remarkably flexible, capable of adopting diverse π ‐conjugation topologies, including Möbius strip‐like and twisted Hückel structures, which are difficult to realize with regular porphyrins. Their rich redox chemistry facilitates the formation of congeneric macrocycles with (4 n + 2) and (4 n ) π ‐electrons, making them ideal systems for testing the practical limits of Hückel, Möbius, and Baird aromaticity rules. Spectroscopic properties are commonly employed to experimentally probe the ground‐ and excited‐state aromaticity of expanded porphyrins. Nevertheless, quantifying aromaticity remains challenging from both experimental and theoretical standpoints due to the intricate interplay between local and macrocyclic ring currents, which often leads to discrepancies between descriptors based on different criteria. This review summarizes our efforts to unravel the aromaticity fingerprint on the photophysical and nonlinear optical properties of expanded porphyrins. A multidimensional framework to quantify Hückel and Möbius aromaticity is first introduced, integrating global and local descriptors derived from the energetic, reactivity, magnetic, electronic, and structural criteria. The complex structure–property relationships between aromaticity and spectroscopic features across redox‐ and topology‐controlled expanded porphyrins are examined. Lastly, innovative approaches to uncover the driving forces governing the spectroscopic properties of diverse hexaphyrin databases are introduced. Aromaticity ; Ring (chemistry) ; Measure (data warehouse) ; Fingerprint (computing) ; Ideal (ethics) Porphyrin and Phthalocyanine Chemistry ; Synthesis and Properties of Aromatic Compounds ; Photochromic and Fluorescence Chemistry
Authors: Evangelos Hristoforou
Source: Sensors (article, 2025-01-21)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Permeability Sensors for Magnetic Steel Structural Health Monitoring In this paper, magnetic permeability sensors able to perform structural health monitoring of magnetic steels, by means of determining residual strain and stress amplitude and gradient distribution, responsible for crack initiation, are presented. The good agreement between magnetic properties and residual strains and stresses is illustrated first, resulting in the determination of the magnetic stress calibration (MASC) curves and the Universal MASC curve. Having determined differential magnetic permeability as a key magnetic property, able to measure and monitor residual strain and stress distribution in magnetic steels, the paper is devoted to the presentation of the permeability instruments and sensors developed in our lab. The classic single sheet testers and the electromagnetic yokes, are compared with new, low-power-consumption permeability sensors using the Hall effect and the anisotropic magnetoresistive (AMR) effect, discussing their advantages and disadvantages in magnetic steel structural health monitoring. Permeability (electromagnetism) ; Residual stress ; Magnetoresistance ; Materials science ; Magnetic field ; Anisotropy ; Magnetic anisotropy ; Structural health monitoring ; Amplitude ; Acoustics ; Composite material ; Electrical engineering ; Engineering ; Physics ; Optics ; Chemistry ; Magnetization Magnetic Properties and Applications ; Non-Destructive Testing Techniques ; Welding Techniques and Residual Stresses
Authors: Jing Guo | Sijia Jiang | Bingjie Lu | Wei Zhang | Yinyin Zhang | Xiao Hu | Wanneng Yang | Hui Feng | Liang Xu
Source: Plant Methods (article, 2025-02-21)
Field: Chemistry — Analytical Chemistry

Exploring the potential of microscopic hyperspectral, Raman, and LIBS for nondestructive quality assessment of diverse rice samples The enhancement of rice quality stands as a pivotal focus in crop breeding research, with spectral analysis-based non-destructive quality assessment emerging as a widely adopted tool in agriculture. A prevalent trend in this field prioritizes the assessment of effectiveness of individual spectral technologies while overlooking the influence of sample type on spectral quality testing outcomes. Thus, the present study employed Microscopic Hyperspectral Imaging, Raman, and Laser-Induced Breakdown Spectroscopy (LIBS) to acquire spectral data from paddy rice, brown rice, polished rice, and rice flour. The data were then modeled and analyzed with respect to the amylopectin and protein contents of the rice samples via regression methods. Correlation analysis revealed varying degrees of correlation, both positive and negative, among the three spectral techniques and the analytes of interest. LIBS and Raman spectroscopy demonstrated stronger correlations with the analytes compared to microscopic hyperspectral imaging. Based on the selected correlation variables, feature screening and regression modeling were conducted. The modeling results indicated that microscopic hyperspectral data modeling yielded the lowest coefficient of determination of R² = 0.2, followed by Raman data modeling result was higher than it, which was about 0.5. The modeling effect of polished rice is the best. LIBS data modeling performed best, with a coefficient of determination of 0.6. The influence of different sample types on the modeling results was less than that of Raman spectroscopy, and modeling results of grains were better. The feature matching analysis of Raman and libs spectroscopy techniques showed that there were spectral variables that could match amylopectin and protein in the features obtained by multiple modeling statistics, but some modeling variables failed to match. LIBS matched more variables than Raman. These findings provide valuable insights into the application effectiveness of different spectral techniques in detecting rice contents across diverse sample types. Hyperspectral imaging ; Raman spectroscopy ; Materials science ; Nondestructive testing ; Quality (philosophy) ; Quality assessment ; Remote sensing ; Environmental science ; Analytical Chemistry (journal) ; Optics ; Environmental chemistry ; Chemistry ; Geology ; Physics ; Engineering ; Reliability engineering ; Evaluation methods Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Identification and Quantification in Food
Authors: Bo Wen | Chris Hsu | David Shteynberg | Wen‐Feng Zeng | Michael Riffle | Alexis Chang | Miranda C. Mudge | Brook L. Nunn | Brendan MacLean | Matthew D. Berg | Judit Villén | Michael J. MacCoss | William Stafford Noble
Source: Nature Communications (article, 2025-11-06)
Field: Chemistry — Spectroscopy

Carafe enables high quality in silico spectral library generation for data-independent acquisition proteomics Data-independent acquisition (DIA)-based mass spectrometry is becoming an increasingly popular mass spectrometry acquisition strategy for carrying out quantitative proteomics experiments. Most of the popular DIA search engines make use of in silico generated spectral libraries. However, the generation of high-quality spectral libraries for DIA data analysis remains a challenge, particularly because most such libraries are generated directly from data-dependent acquisition (DDA) data or are from in silico prediction using models trained on DDA data. In this study, we introduce Carafe, a tool that generates high-quality experiment-specific in silico spectral libraries by training deep learning models directly on DIA data. We demonstrate the performance of Carafe on a wide range of DIA datasets, where we observe improved fragment ion intensity prediction and peptide detection relative to existing pretrained DDA models. To make Carafe more accessible to the community, we integrate Carafe into the widely used Skyline tool. In silico ; Skyline ; Proteomics ; Data acquisition ; Quality (philosophy) ; Range (aeronautics) ; Mass spectrometry Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Machine Learning in Bioinformatics
Authors: Anna Meike Freis | Sahithya Phani Babu Vemulapalli
Source: Foods (article, 2025-02-05)
Field: Chemistry — Organic Chemistry

Analysis of the Generation of Harmful Aldehydes in Edible Oils During Sunlight Exposure and Deep-Frying Using High-Field Proton Nuclear Magnetic Resonance Spectroscopy Edible oils are essential dietary components that provide crucial micronutrients. However, their quality can deteriorate during frying-a common cooking method-and with prolonged light exposure due to chemical reactions such as hydrolysis, oxidation, and polymerization. These processes lead to the formation of harmful compounds, particularly aldehydes. This study investigates how thermal and light exposure impact the chemical composition of five widely used edible oils: olive, rapeseed, sunflower, sesame, and peanut oils. For the thermal treatment, the oils were heated to 190 ± 5 °C in a commercial fryer, with samples taken at the start and after 10 min and 60 min of heating, while intermittently frying chicken nuggets to simulate typical frying conditions. For the light exposure treatment, the oil samples were exposed to direct sunlight for 3 and 8 h, with control samples being collected beforehand. The oil composition was analyzed using an advanced 800 MHz nuclear magnetic resonance (NMR) instrument with a triple-resonance inverse cryoprobe, providing high sensitivity and resolution. The results revealed a significant increase in various aldehyde compounds in all oils under both thermal and light exposure conditions. Notably, this study identified the generation of genotoxic and cytotoxic <i>α</i>,<i>β</i>-unsaturated aldehydes, including 4-hydroperoxy-(<i>E</i>)-2-alkenals, 4-hydroxy-(<i>E</i>)-2-alkenals, and 4,5-epoxy-(<i>E</i>)-2-alkenals. Given the established association of aldehydes with health risks, including cancer, Alzheimer's, and Parkinson's diseases, these findings highlight the importance of monitoring oil degradation during cooking and the appropriate storage of oils to minimize light exposure to reduce potential health risks. Chemistry ; Sunflower oil ; Sunlight ; Food science ; Hydrolysis ; Contamination ; Organic chemistry Edible Oils Quality and Analysis ; Advanced Chemical Sensor Technologies ; Nuts composition and effects
Authors: Jiayu Gao | Xuhui Yang | Simo Liu | Yufeng Liu | Xiaofeng Ning
Source: Horticulturae (article, 2025-01-23)
Field: Chemistry — Analytical Chemistry

Detection of Tomato Leaf Pesticide Residues Based on Fluorescence Spectrum and Hyper-Spectrum In order to rapidly and nondestructively detect pesticide residues on tomato leaves, fluorescence spectroscopy and hyperspectral techniques were used to study the nondestructive detection of three different concentrations of benzyl-pyrazolyl esters on the surface of tomato leaves, respectively. In this study, fluorescence spectrum acquisition and hyperspectral imaging processing of tomato leaf samples with and without pesticides were conducted, and spectral data from regions of interest of hyperspectral images were extracted. The data in the spectral raw bands were optimized using convolutional smoothing (S-G), standard normal variable transformation (SNV), multiplicative scatter correction (MSC), and baseline calibration (baseline) algorithms, respectively. In order to improve the operating rate of discrimination, a continuous projection algorithm (SPA) was used to extract the characteristic wavelengths of the fluorescence spectra and hyperspectral data of pesticide residues, and algorithms such as the least-squares support vector machine (LSSVM) algorithm and least partial squares regression (PLSR) were used to build a quantitative model, while algorithms such as the convolutional neural network (BPNN) algorithm and decision tree algorithm (CART) were used to build a qualitative model. According to the results, R2 of the model of hyperspectral data after SG-SNV preprocessing and PLSR modeling reached 0.9974, RMSEC reached 0.0221, and RMSEP reached 0.0565. R2 of the model of fluorescence spectral data after SG-MSC preprocessing and SVM modeling reached 0.9986, RMSEC reached 0.2496, and RMSEP reached 0.4193. Qualitative analysis was established based on the characteristic wavelengths of hyper-spectrum and fluorescence spectrum extracted by the SPA algorithm, and the accuracy of the training sets of the optimal qualitative model reached 94.9% and 95.7%, respectively, and the accuracy of the test sets both reached 100%. After comparison, the quantitative model of data based on fluorescence spectrum for pesticide residue detection in tomato leaves proved to have a better effect, and the qualitative model showed higher accuracy in discrimination. Therefore, the fluorescence spectral and hyperspectral imaging techniques applied to tomato leaf pesticide detection enjoy a promising application prospect. Fluorescence ; Spectrum (functional analysis) ; Pesticide residue ; Horticulture ; Chemistry ; Pesticide ; Botany ; Biology ; Agronomy ; Physics Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis
Authors: Gustavo Henrique de Almeida Barbalho | J.J. Silva Nascimento | Lucineide Balbino da Silva | João M. P. Q. Delgado | Anderson Ferreira Vilela | Jadergudson Pereira | Ivonete B. Santos | Márcia Ramos Luíz | Larissa da Silva Santos Pinheiro | Adriano Rodrigues da Silva | Roberto Mendonça Faria | Francisco Samuel Chaves | Antônio Gilson Barbosa de Lima
Source: Journal of Composites Science (article, 2025-05-16)
Field: Materials Science — Polymers and Plastics

Moisture Absorption and Its Effects on the Mechanical Properties of Biopolymers Reinforced by Curauá Fiber and Montmorillonite Clay: A Transient Experimental Evaluation Biocomposites are defined as eco-friendly materials from an environmental point of view. Because of the importance of this class of materials, their study is important, especially in moist and heated conditions. In this sense, this work aims to evaluate the transient behavior of moisture absorption and mechanical performance of biocomposites composed of a matrix of high-density biopolyethylene (originated from ethanol produced from sugarcane) filled with curauá vegetable fiber and organophilic montmorillonite clay. For this purpose, dry biocomposites filled with organophilic montmorillonite clay and curauá fiber (1, 3, and 5 wt.%) were prepared using a hand lay-up technique and subjected to moisture absorption and mechanical (flexural and impact tests) characterizations at different times. The experiments were carried out at water bath temperatures of 30 °C and 70 °C. The results have proven the strong influence of chemical composition and temperature on the moisture absorption behavior of biocomposites across time. For a higher percentage of reinforcement on the polymeric matrix, a higher moisture migration rate was verified, reaching a higher hygroscopic equilibrium condition at 16.9% for 5 wt.% of curauá fiber and 10.25% for 5 wt.% of montmorillonite clay particles. In contrast, the mechanical properties of all of the biocomposites were strongly reduced with an increasing moisture content, especially at higher fiber content and water bath temperature conditions. The innovative aspects of this research are related to the study of a new material and its transient mechanical behavior in dry and wet conditions. Montmorillonite ; Moisture ; Materials science ; Composite material ; Transient (computer programming) ; Fiber ; Absorption (acoustics) ; Swelling ; Absorption of water Natural Fiber Reinforced Composites
Authors: Jette B. Lauridsen | Pathmanaban Ramasamy | Rachel Catzel | Vahap Canbay | Amandla Mabona | Kevin Eloff | Paul Fullwood | Jennifer Ferguson | Annekatrine Kirketerp-Moller | Ida Sofie Goldschmidt | Tine Claeys | Sam van Puyenbroeck | Nicolás López Carranza | Erwin M. Schoof | Lennart Martens | Jeroen Van Goey | Chiara Francavilla | Timothy P. Jenkins | Konstantinos Kalogeropoulos
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2025-05-18)
Field: Chemistry — Spectroscopy

InstaNovo-P: A de novo peptide sequencing model for phosphoproteomics ABSTRACT Phosphorylation, a crucial post-translational modification (PTM), plays a central role in cellular signaling and disease mechanisms. Mass spectrometry-based phosphoproteomics is widely used for system-wide characterization of phosphorylation events. However, traditional methods struggle with accurate phosphorylated site localization, complex search spaces, and detecting sequences outside the reference database. Advances in de novo peptide sequencing offer opportunities to address these limitations, but have yet to become integrated and adapted for phosphoproteomics datasets. Here, we present InstaNovo-P, a phosphorylation specific version of our transformer-based InstaNovo model, fine-tuned on extensive phosphoproteomics datasets. InstaNovo-P significantly surpasses existing methods in phosphorylated peptide detection and phosphorylated site localization accuracy across multiple datasets, including complex experimental scenarios. Our model robustly identifies peptides with single and multiple phosphorylated sites, effectively localizing phosphorylation events on serine, threonine, and tyrosine residues. We experimentally validate our model predictions by studying FGFR2 signaling, further demonstrating that InstaNovo-P uncovers phosphorylated sites previously missed by traditional database searches. These predictions align with critical biological processes, confirming the model’s capacity to yield valuable biological insights. InstaNovo-P adds value to phosphoproteomics experiments by effectively identifying biologically relevant phosphorylation events without prior information, providing a powerful analytical tool for the dissection of signaling pathways. Phosphoproteomics ; Computational biology ; Peptide ; Biology ; Chemistry ; Genetics ; Biochemistry Advanced Proteomics Techniques and Applications ; Machine Learning in Bioinformatics
Authors: Benyuan Zhu | Hao Zhou | Yibo Li | Xiaozheng Wang | Kaichen Xu
Source: Advanced Photonics Research (article, 2025-07-15)
Field: Materials Science — Surfaces, Coatings and Films

Recent Advances in Laser‐Induced Phase Separation of PEDOT:PSS for Bioelectronics Conductive hydrogels are gaining significant attention for their potential in bioelectronic applications. Among these materials, poly(3,4ethylenedioxythiophene):poly(styrene sulfonate) (PEDOT:PSS) emerges as a promising candidate due to its intrinsic conductivity, flexibility, and biocompatibility. Recently, laser‐induced phase separation (LIPS) offers a tunable approach for modifying PEDOT:PSS, significantly enhancing its electrical conductivity, wet stability, and electrochemical stability. This technique also allows for high‐spatial‐resolution patterning, rendering it suitable for fabricating on‐demand bioelectronic interfaces. The excellent biocompatibility of the laser‐treated PEDOT:PSS further broadens its potential applications in soft bioelectronic devices. After revealing the LIPS mechanism of PEDOT:PSS and summarizing its key properties, this review offers an overview of its applications in neural signal recording, stimulation, and conduction block. This review establishes a critical connection among the mechanism, properties, and applications of LIPS, thereby paving the way for future research toward advanced bioelectronic applications and beyond. Bioelectronics ; PEDOT:PSS ; Materials science ; Laser ; Nanotechnology ; Phase (matter) ; Optoelectronics ; Optics ; Chemistry ; Layer (electronics) ; Physics ; Biosensor Electron and X-Ray Spectroscopy Techniques ; Near-Field Optical Microscopy ; Molecular Junctions and Nanostructures
Authors: Rapuru Rushendran | Ankul Singh S | Rukaiah Fatma Begum | Vellapandian Chitra | Nemat Ali | Bhupendra G. Prajapati
Source: Drug Development Research (article, 2025-01-05)
Field: Chemistry — Analytical Chemistry

Bioinformatics Exploration of the Therapeutic Potential of Lotus Seed Compounds in Multiple Sclerosis: A Network Analysis of c‐Jun Pathway The central nervous system is affected by multiple sclerosis (MS), a chronic autoimmune illness characterized by axonal destruction, demyelination, and inflammation. This article summarizes the state of the field, highlighting its complexity and significant influence on people's quality of life. The research employs a network pharmacological approach, integrating systems biology, bioinformatics, and pharmacology to identify biomarkers associated with MS. Utilizing Nelumbo Nucifera (Lotus) seeds, the study involves toxicity assessments, biomolecule screening, and target prediction. Advanced computational methodologies are employed, including molecular docking and dynamic simulations, to assess potential therapeutic interactions. Biomolecule screening identifies eight active compounds from Lotus seeds, including Anonaine and Liriodenine. Target prediction reveals 264 common targets with MS-related genes. Protein-protein interaction analysis establishes a complex network, identifying central targets like SRC and AKT1. Bioinformatics enrichment analysis uncovers potential therapeutic candidates and pathways. A Biomolecule-Target-Pathway network diagram visualizes interactions, with Anonaine and Liriodenine exhibiting strong binding affinities in molecular docking studies. Molecular dynamics simulations provide insights into dynamic interactions. In conclusion, through advanced computational techniques, it unveils molecular interactions, potential therapies, and pathways, bridging predictions with practical applications. Anonaine and Liriodenine show promise in curbing MS biomarkers. Computational biology ; Lotus ; Biomolecule ; Docking (animal) ; Interaction network ; Biology ; Bioinformatics ; Chemistry ; Gene ; Medicine ; Biochemistry Chromatography in Natural Products ; Natural product bioactivities and synthesis ; Microbial Natural Products and Biosynthesis
Authors: Tomoki Hiraoka | Sandra Nestler | Wentao Zhang | Simon Rossel | Hassan A. Hafez | Savio Fabretti | H. Schlörb | Andy Thomas | Dmitry Turchinovich
Source: Nature Communications (article, 2025-06-05)
Field: Materials Science — Materials Chemistry

Terahertz field effect in a two-dimensional semiconductor Layered two-dimensional (2D) materials offer many promising avenues for advancing modern electronics, thanks to their tunable optical, electronic, and magnetic properties. Applying a strong electric field perpendicular to the layers, typically at the MV/cm level, is a highly effective way to control these properties. However, conventional methods to induce such fields employ electric circuit - based gating techniques, which are restricted to microwave response rates and face challenges in achieving device-compatible ultrafast, sub-picosecond control. Here, we demonstrate an ultrafast field effect in atomically thin MoS<sub>2</sub> embedded within a hybrid 3D-2D terahertz nanoantenna. This nanoantenna transforms an incoming terahertz electric field into a vertical ultrafast gating field in MoS<sub>2</sub>, simultaneously enhancing it to the MV/cm level. The terahertz field effect is observed as a coherent terahertz-induced Stark shift of exciton resonances in MoS<sub>2</sub>. Our results offer a promising strategy to tune and operate ultrafast optoelectronic devices based on 2D materials. Terahertz radiation ; Ultrashort pulse ; Optoelectronics ; Electric field ; Picosecond ; Materials science ; Electronics ; Semiconductor ; Microwave ; Optics ; Physics ; Laser ; Electrical engineering 2D Materials and Applications ; Plasmonic and Surface Plasmon Research ; Topological Materials and Phenomena