jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Guantian Wang | Yan Luo | Yande Liu | Yuxu Huo | Aiguo Ouyang | Dazhou Zhu | Mingmao Hu
Source: Scientific Reports (article, 2025-04-09)
Field: Chemistry — Analytical Chemistry

Relationship between optical properties and internal quality of Orah Mandarins during storage The optical properties [absorption coefficient (μ<sub>a</sub>) and reduced scattering coefficient (μ'<sub>s</sub>)] and internal qualities [moisture content (MC), and soluble solids content (SSC)] of Orah mandarin stored at 20 °C for 0-6 days were determined and and the relationship between the optical properties and different quality was explored. The SSC of Orah mandarin initially increased and then decreased, which was due to the phenomenon of low temperature saccharification. The MC remained relatively stable with a slight increase. The pulp tissue absorption coefficient (μ<sub>a</sub>) has two absorption peaks at 500 nm and 980 nm, respectively associated with pigments and water. The exocarp tissue has an additional absorption peak at 500 nm related to color, while endocarp tissue only has one absorption peak at 980 nm related to water. The μ<sub>a</sub> spectra showed significant correlations with MC and SSC. Partial Least Squares Regression (PLSR) models for predicting the SSC and MC of Orah mandarins were established based on μ<sub>a</sub> and μ'<sub>s</sub> spectra. The results showed that the prediction model based on μ<sub>a</sub> spectra had the best performance. The Correlation Coefficient of Prediction (Rp) and Root Mean Square Error of Prediction (RMSEP) for SSC were 0.921 and 0.549, respectively; for MC, Rp and RMSEP were 0.906 and 0.636 respectively. These results indicate the potential of using the optical properties of Orah mandarins to predict internal quality. Quality (philosophy) ; Computer science ; Chemistry ; Physics Spectroscopy and Chemometric Analyses ; Date Palm Research Studies ; Postharvest Quality and Shelf Life Management
Authors: Austin R. Sartori | Sandra George | Aco Radujević | Radek Pohl | Anil Prakash | Debmalya Ray | Vyacheslav S. Bryantsev | Mikhail Zamkov | Pavel Anzenbacher
Source: Chemistry - A European Journal (article, 2025-08-23)
Field: Chemistry — Spectroscopy

Pyrrole‐Imine Macrocycle: Self‐Organizing Cross‐Reactive Anion Receptor and Sensor Self-organizing macrocyclic receptor-sensors for phosphorus oxyanions, phosphates, and phosphonates comprising imine moieties were prepared by condensation of dipyrrolylmethane dicarbaldehyde with diethylene triamine. The incorporation of flexible ethylene moieties endows the macrocycle with unprecedented flexibility and ability to accommodate numerous phosphorus oxyanions from orthophosphate to large anions such as ATP or phosphonate glyphosate. The anion binding was elucidated by NMR titrations, low-temperature NMR, and NOESY NMR. The incorporation of dansyl fluorophore enables sensing of anions using the fluorescence signal, whereas the changes in fluorescence intensity, width of the fluorescence band, and position of the maxima are analyte-specific and useful in recognition and identification of eleven different P-oxyanions in water. The affinity (K<sub>assoc</sub>) for Na<sup>+</sup> salts was H<sub>2</sub>PO<sub>4</sub> <sup>-</sup> ≈ Methylphosphonate > H<sub>2</sub>P<sub>2</sub>O<sub>7</sub> <sup>2-</sup> > Phenylphosphonate<sup>-</sup> > Glyphosate<sup>2-</sup> > AMP<sup>2-</sup> > ADP<sup>2-</sup> > ATP<sup>2-</sup>. Interestingly, phosphonates, including methylphosphonate and glyphosate anions, were also found to display a strong affinity (K<sub>assoc</sub> ∼10<sup>6</sup> M<sup>-1</sup>) while halides, nitrate, carbonates, or hydrogen sulfate did not show a significant affinity. The determined fluorescence spectral parameters were used to classify the 12 analytes (11 anions and water) using Linear Discriminant Analysis (LDA). Quantification was performed using LDA and Support Vector Machine (SVM), and the phosphonate concentrations in unknown samples were determined with an error of 3.5% or lower. Imine ; Pyrrole ; Ion ; Chemistry ; Combinatorial chemistry ; Polymer chemistry ; Organic chemistry ; Catalysis Molecular Sensors and Ion Detection ; Advanced biosensing and bioanalysis techniques ; Analytical Chemistry and Sensors
Authors: Pauline Ong | Shamala Kalai Selven | See Khee Yee | Sia Chee Kiong | Kee Huong Lai
Source: Applied Fruit Science (article, 2025-09-02)
Field: Chemistry — Analytical Chemistry

Evaluating Deep Learning-Driven Feature Selection Technique and Machine Learning Models for Non-destructive Oil Palm Ripeness Assessment Using Low-Cost Electrical Impedance Spectroscopy The oil extraction rate of oil palm fruits decreases by 0.13% for every 1% of unripe fruit, emphasizing the need to harvest only ripe fresh fruit bunches (FFB). Spectroscopy has emerged as a reliable method for assessing FFB ripeness, but effective variable selection is essential due to high spectral dimensionality. This study presents a novel, non-destructive, and cost-effective method for determining oil palm fruit ripeness using electrical impedance spectroscopy. Impedance properties of 832 oil palm fruitlets at various maturity levels (unripe, ripe, and overripe) were measured within the 10–100 kHz frequency range and at 120 K resistance using a low-cost impedance analyzer. The spectra were analyzed with multivariate machine learning models, including Naïve Bayes, random forest (RF), and support vector machine, using variable selection methods of selectivity ratio, variable importance in projection, principal component analysis, and convolutional neural network (CNN). The CNN-RF approach yielded the best performance, with precision, sensitivity, specificity, and accuracy averaging 92.15%, 93.07%, 96.18%, and 92.77%, respectively. Thus, the electrical impedance spectroscopy coupled with CNN-RF multivariate calibration method offers a practical, low-cost, and reliable solution for the determination of FFB ripeness, achieving over 92% accuracy across all evaluation metrics. Ripeness ; Palm oil ; Feature selection ; Artificial intelligence ; Selection (genetic algorithm) ; Feature (linguistics) ; Computer science ; Dielectric spectroscopy ; Machine learning ; Palm ; Electrical impedance ; Pattern recognition (psychology) ; Environmental science ; Process engineering ; Engineering ; Chemistry ; Agroforestry ; Electrical engineering ; Physics Spectroscopy and Chemometric Analyses ; Oil Palm Production and Sustainability ; Industrial Vision Systems and Defect Detection
Authors: Tom E. de Vries | Hugo Meekes | Elias Vlieg | R. De Gelder
Source: ChemPhysChem (article, 2025-10-01)
Field: Chemistry — Physical and Theoretical Chemistry

Cocrystals and Solvates are Not the Same: A Network Perspective Cocrystals and solvates are two kinds of multicomponent crystals. Cocrystals consist of solid compounds, while solvates consist of a mixture of solid and liquid compounds. This work addresses the question of whether cocrystals and solvates can be treated the same or need to be treated separately. To do this, networks out of known cocrystals and solvates are created, where each node represents an individual compound, and two nodes are linked if the compounds they represent form a cocrystal or solvate. These networks are also used to predict new cocrystals and solvates using a technique called link prediction. By analyzing the structure of the cocrystal and solvate networks, differences in behaviors between cocrystals and solvates are found, due in part to a clash between chemical and steric complementarity found in the solvate network. Based on the analysis of the networks, it is found that cocrystals and solvates cannot be treated equally and that information from one network is not relevant for predictions in the other. It is also found that link prediction performs poorly when used on the solvate network, unless 14 problematic solvents are removed. Cocrystal ; Steric effects ; Complementarity (molecular biology) ; Perspective (graphical) ; Chemical nomenclature Crystallography and molecular interactions ; Computational Drug Discovery Methods ; Crystallization and Solubility Studies
Authors: Sampath Suranjan Salins | Sawan Shetty | Deepak Doreswamy | Thamme Gowda | H. K. Sachidananda
Source: Scientific Reports (article, 2025-12-22)
Field: Materials Science — Biomaterials

Recycled leather–epoxy hybrid composites exhibiting novel fracture behavior identified through multimodal characterization This study investigates the mechanical behavior and fracture mechanisms of recycled leather-epoxy composites fabricated via resin infusion and evaluated under quasi-static three-point bending. The work employs industrial post-consumer leather waste, mechanically processed into fibrous form, as a sustainable reinforcement alternative to conventional natural fibers. At a fiber volume fraction of approximately 0.3, the composites achieved a flexural strength of 100.8 ± 1.92 MPa and a modulus of 18.64 ± 0.38 GPa, showing favorable performance relative to reported jute-epoxy and flax-epoxy systems under comparable testing conditions. The bilinear viscoelastic-softening model captured the composite stress-strain response and an estimated critical energy release rate (Gc ≈ 8.89 × 10<sup>-3</sup> J/m<sup>2</sup>) reflected the onset of progressive delamination. Micro-voids and resin-deficient zones associated with collagen-based fiber morphology acted as stress concentrators that shaped local failure events. This exploratory study advances understanding of micromechanical toughening in bio-derived composites and indicates that processed post-consumer leather fibers exhibit promising flexural behavior and damage tolerance, suggesting potential for semi-structural or moderately loaded lightweight applications pending further durability and service-level validation. Flexural strength ; Composite number ; Volume fraction ; Flexural modulus ; Fracture (geology) ; Fiber ; Durability ; Characterization (materials science) ; Toughening Collagen: Extraction and Characterization ; Mechanical Behavior of Composites ; Natural Fiber Reinforced Composites
Authors: Bo-Wei Tseng | Tsun‐Hsu Chang
Source: Scientific Reports (article, 2025-12-13)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Percolation effect induced significant changes in the complex permittivity and permeability of silver-epoxy nano-composites The intricate interplay between complex permittivity and permeability constitutes the cornerstone of electromagnetic (EM) applications, enabling precise customization for various uses. This study employed silver-epoxy nano-composites to exemplify a conductor-insulator composite, leveraging silver's exceptional attributes, such as high conductivity and low reactivity. The determination of complex permittivity and permeability was conducted via the transmission/reflection method. At lower concentrations of dispersed silver particles, these nanoparticles within the epoxy resin act as modest dipoles, augmenting permittivity. This regime aligns closely with the effective medium theory (EMT) and comprises the focus of much research. However, nearing the percolation threshold, a percolation effect emerges, drastically accelerating enhancement rates beyond the predictions of EMT. Simultaneously, long-wavelength electromagnetic waves induce diamagnetic currents within loops formed by metal grains. This diamagnetic effect intensifies with increasing volume fraction, leading to a reduction in permeability. Here we report the first simultaneous extraction of complex permittivity and permeability for a metal-polymer system inside the microwave percolation window (8.2 ~ 12.4 GHz). We observe a record-high [Formula: see text] co-existing with a record-low [Formula: see text], and we show that both quantities obey 2D percolation scaling. The results constitute the first experimental verification of the Bowman-Stroud diamagnetic-loop model at GHz frequencies. Permittivity ; Permeability (electromagnetism) ; Conductivity ; Percolation (cognitive psychology) ; Percolation threshold ; Diamagnetism ; Percolation theory ; Microwave Electromagnetic wave absorption materials ; Microwave and Dielectric Measurement Techniques ; Epoxy Resin Curing Processes
Authors: Jennifer Rodríguez-Guerra | Pedro González-Mederos | Nicolás Amigó
Source: Micromachines (article, 2025-09-27)
Field: Materials Science — Biomaterials

Coupling CFD and Machine Learning to Assess Flow Properties in Porous Scaffolds for Tissue Engineering Computational fluid dynamics and machine learning (ML) models are employed to investigate the relationships between scaffold topology and key flow parameters, including permeability (<i>k</i>), average wall shear stress (WSSa), and the 25th and 75th percentiles of WSS. Statistical analysis showed that WSSa values are consistent with those found in common scaffold architectures, while percentile-based WSS properties provided insight into shear environments relevant for bone and cartilage differentiation. No significant effect of pore shape was observed on <i>k</i> and WSSa. Correlation analysis revealed that <i>k</i> was positively associated with topological features of the scaffold, whereas WSS metrics were negatively correlated with these properties. ML models trained on six topological and flow inputs achieved a performance of R2 above 0.9 for predicting <i>k</i> and WSSa, demonstrating strong predictive capability based on the topology. Their performance decreased for WSS25% and WSS75%, reflecting the difficulty in capturing more specific shear events. These findings highlight the potential of ML to guide scaffold design by linking topology to flow conditions critical for osteogenesis. Scaffold ; Topology (electrical circuits) ; Shear stress ; Computational fluid dynamics ; Coupling (piping) ; Porosity ; Tissue engineering ; Flow (mathematics) ; Microfluidics Electrospun Nanofibers in Biomedical Applications ; Bone Tissue Engineering Materials ; Additive Manufacturing and 3D Printing Technologies
Authors: Wasut Pornpatcharapong
Source: Molecules (article, 2025-06-15)
Field: Chemistry — Spectroscopy

Gaussian Process Regression for Mapping Free EnergyLandscape of Mg2+-Cl− Ion Pairing in Aqueous Solution: Molecular Insights and Computational Efficiency Free energy landscapes are pivotal for understanding molecular interactions in solution, yet their reconstruction in complex systems remains computationally demanding. In this study, we integrated Gaussian process regression (GPR) with well-tempered metadynamics (WT-MTD) to efficiently map the free energy landscape of the Mg<sup>2</sup><sup>+</sup>-Cl<sup>-</sup> ion pairing in an aqueous solution, a system central to biological processes such as magnesium hydration and ligand exchange. We compared traditional umbrella sampling (WHAM) with WT-MTD-derived free energy profiles, identifying critical discrepancies attributed to insufficient sampling in barrier regions. WT-MTD captures two distinct minima corresponding to the contact ion pair (CIP: 0.23 nm) and solvent-separated ion pair (SSIP: 0.47 nm) configurations, consistent with previous computational and experimental studies. GPR, trained on free energy gradients from WT-MTD trajectories, reconstructs smooth landscapes with small datasets (5000 points) while reducing computational costs via grid sparsification. Our results demonstrate that GPR hyperparameters can be optimized based on the insights from WT-MTD simulations, enabling accurate reconstructions even in sparse data regimes. This approach bridges computational efficiency with molecular-level resolution, offering a robust framework for studying ion solvation dynamics and hydration effects in complex systems, where this work is the first application of GPR in ionic solvation environments. The methodology's scalability to multidimensional landscapes further underscores its potential for advancing molecular simulations in biochemistry and material science. Aqueous solution ; Pairing ; Ion ; Gaussian ; Gaussian process ; Kriging ; Chemistry ; Process (computing) ; Regression ; Computational chemistry ; Molecular dynamics ; Biological system ; Chemical physics ; Statistical physics ; Computer science ; Mathematics ; Physics ; Physical chemistry ; Statistics ; Machine learning ; Organic chemistry ; Biology ; Condensed matter physics Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Spectroscopy and Quantum Chemical Studies
Authors: Iván Cortés | María Luz Tibaldi‐Bollati | Viviana E. Nicotra | Manuela E. García | Ariel M. Sarotti
Source: Tetrahedron Chem (article, 2025-03-06)
Field: Chemistry — Spectroscopy

Efficient workflow for structural elucidation applied to solidagenone derivative: Combining the speed of ML-J-DP4 screening with the precision of DP4+ The derivatization of solidagenone through ring expansion, using the "Complexity to Diversity" synthetic strategy to obtain polyketones, yielded two products upon reaction of an intermediate with in situ-generated ruthenium tetroxide.As might happen, the spectroscopic and spectrometric data were inconclusive for the structural determination of one of these products.To address the issue, a ML-J-DP4//DP4+ workflow was studied, which allowed simultaneous determination of connectivity and relative configuration with high accuracy, while saving computational resources.Furthermore, during the calculation of NMR data at the DP4+ level, abnormally large errors were observed for the beta carbon of a polyhydroxylated conjugated enone.A literature analysis revealed that this behavior could be generalized, and after careful examination, it was determined to be the result of small geometric distortions caused by an overestimated network of intramolecular hydrogen bonds.To mitigate this common source of discrepancy, a heuristic solution was introduced as a proof of concept by in silico blocking of hydroxyl groups. Derivative (finance) ; Workflow ; Computer science ; Combinatorial chemistry ; Chemistry ; Computational biology ; Biology ; Business Molecular spectroscopy and chirality ; Computational Drug Discovery Methods ; Chromatography in Natural Products
Authors: Siqiao Tan | Qiang Xie | Wenshuai Zhu | Yang‐Jun Deng | Lei Zhu | Xiaoqian Yu | Zheming Yuan | Yu Chen
Source: Frontiers in Plant Science (article, 2025-02-07)
Field: Chemistry — Analytical Chemistry

Deep learning and hyperspectral features for seedling stage identification of barnyard grass in paddy field Barnyard grass, a pernicious weed thriving in rice fields, poses a significant challenge to agricultural productivity. Detection of barnyard grass before the four-leaf stage is critical for effective control measures. However, due to their striking visual similarity, separating them from rice seedlings at early growth stages is daunting using traditional visible light imaging models. To explore the feasibility of hyperspectral identification of barnyard grass and rice in the seedling stage, we have pioneered the DeepBGS hyperspectral feature parsing framework. This approach harnesses the power of deep convolutional networks to automate the extraction of pertinent information. Initially, a sliding window-based technique is employed to transform the one-dimensional spectral band sequence into a more interpretable two-dimensional matrix. Subsequently, a deep convolutional feature extraction module, ensembled with a bilayer LSTM module, is deployed to capture both global and local correlations inherent within hyperspectral bands. The efficacy of DeepBGS was underscored by its unparalleled performance in discriminating barnyard grass from rice during the critical 2-3 leaf stage, achieving a 98.18% accuracy rate. Notably, this surpasses the capabilities of other models that rely on amalgamations of machine learning algorithms and feature dimensionality reduction methods. By seamlessly integrating deep convolutional networks, DeepBGS independently extracts salient features, indicating that hyperspectral imaging technology can be used to effectively identify barnyard grass in the early stages, and pave the way for the development of advanced early detection systems. Hyperspectral imaging ; Artificial intelligence ; Computer science ; Dimensionality reduction ; Convolutional neural network ; Pattern recognition (psychology) Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Remote Sensing in Agriculture
Authors: Ludovico Biavati | Sylvester Vogl | Klaus Drechsler
Source: Textiles (article, 2025-10-08)
Field: Chemistry — Organic Chemistry

Experimental Analysis of Ultraviolet Radiation Transmission Behavior in Fiber-Reinforced Thermoset Composites During Photopolymerization As the importance of sustainability and performance increases, new developments in the manufacturing of fiber-reinforced polymer composites (FRPC) are requested. Ultraviolet (UV) curing offers a faster, more economical, and eco-friendlier alternative to conventionally used thermal curing methods, e.g., autoclave curing, but according to extant research, also presents some shortcomings, such as limitations to thin FRPCs and transparent glass fibers (GFs). This study analyses the UV light transmission in different thermoset FRPCs by irradiating various fiber samples on one side, while a sensor on the opposite side measures the transmitted irradiance. The materials investigated include unidirectional (UD) carbon fibers (CF), UD flax fibers (FF), and six GF fabrics with different ply structures. The fiber samples are tested in a dry, non-impregnated state and a resin-impregnated state using a UV-curable vinyl-ester-based resin. The results show that up to 16 plies of five GF fabrics are fully cured within the 20 s irradiation time and still exhibit a relatively high light transmission, revealing the potential of curing thick FRPCs with UV light. Furthermore, up to three plies of non-transparent FFs are cured, which is promising for the UV curing of natural fibers. Thermosetting polymer ; Curing (chemistry) ; Ultraviolet ; Photopolymer ; Epoxy ; Irradiation ; Ultraviolet radiation ; Glass fiber Photopolymerization techniques and applications ; Additive Manufacturing and 3D Printing Technologies ; Epoxy Resin Curing Processes