jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Denis S. Tikhonov | Mikhail A. Kalinin | Alexander A. Maryewski | Aleksandr A. Avdoshin | Olgert L. Dallakyan | Nikita A. Vasilev | Egor A. Eliseev | Mandy Koch | Vladimir V. Rybkin | Denis G. Artiukhin
Source: Preprints.org (preprint, 2025-05-20)
Field: Chemistry — Spectroscopy

Simplistic Software for Analyzing Mass Spectra and Mixed Experimental-Theoretical Database for Identifying Poisonous and Explosive Substances A recent increase in targeted attacks using chemical warfare agents by dictators and authoritarian regimes against politicians, journalists, and other civilians is a major concern. To aid the civil investigators in identifying poisonous substances in such cases, we developed an algorithm and a lightweight and simple-to-use software, ToxicMassSceptic, with a database of 400 electron ionization mass spectra entries, which include many poisonous and explosive agents. The identification relies on a window-based reduction of the experimental spectra and four statistical metrics that are combined into a single metametric. The software also features automatic spectral background removal. Furthermore, we provide the workflow for increasing the size of this database by performing theoretical calculations of mass spectra with a molecular dynamics-based approach. The accuracy of both the theoretical prediction workflow and ToxicMassSceptic is validated on the experimental spectra. Our results demonstrate that the proposed software package can aid in the preliminary identification of traces of poisonous and explosive substances. Explosive material ; Mass spectrum ; Software ; Computer science ; Database ; Data mining ; Chemistry ; Mass spectrometry ; Chromatography ; Programming language ; Organic chemistry Mass Spectrometry Techniques and Applications ; Pesticide Residue Analysis and Safety ; Advanced Chemical Sensor Technologies
Authors: Andrea Bezze | Jessica Ponti | D. Stanco | Carlotta Mattioda | Clara Mattu
Source: Pharmaceutics (article, 2025-12-15)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Gold-Doped Hybrid Nanoparticles: A Versatile Tool for Multimodal Imaging of Cell Trafficking <b>Background:</b> Nanomedicine has demonstrated great potential to improve drug delivery across various diseases. However, accurately monitoring the real-time trafficking of organic nanoparticles (NPs) within biological systems remains a significant challenge. Current detection methods rely heavily on fluorescence, while high-resolution, label-free imaging is often precluded by the limited optical contrast of organic materials, limiting a comprehensive understanding of NP fate. Metallic doping allows simultaneous detection of carriers using multiple imaging and analysis techniques. This study presents a novel approach to prepare gold-doped hybrid NPs compatible with multimodal imaging, thus facilitating multimodal tracking. <b>Methods:</b> Gold-doped NPs were successfully synthesized via nanoprecipitation, yielding stable, monodisperse carriers with optimal size, confirmed by Dynamic Light Scattering and Nanoparticle Tracking Analysis. UV/Vis spectroscopy confirmed effective gold-doping, with doping efficiency of approximately 50%. Transmission Electron Microscopy (TEM) showed gold NP accumulation throughout the polymer core and near the lipid shell. <b>Results:</b> Although gold doping resulted in a slight increase in NP size and zeta potential, no effects on cytocompatibility or cellular uptake by glioblastoma and microglia cells were observed. Furthermore, the optical properties (i.e., the refractive index and the UV spectrum) of the NPs were successfully modified to enable tracking across complementary imaging modalities. Real-time, label-free visualization of NP accumulation in the cytoplasm of U87 cells was achieved via holotomography by exploiting the enhanced refractive index after gold-doping. This observation was confirmed through correlation with fluorescence confocal microscopy, using fluorescently labelled gold-doped NPs. Furthermore, the high electron density of the gold tracer facilitated the precise localization of NPs within intracellular compartments via TEM, bypassing the inherently low contrast of organic NPs. <b>Conclusions:</b> These findings validated the gold-doped NPs as versatile nanoplatforms for multimodal imaging, showcasing their potential for non-invasive, high-resolution tracking and more accurate quantification of intracellular accumulation using diverse analytical techniques. Nanomedicine ; Dispersity ; Dynamic light scattering ; Nanoparticle ; Confocal microscopy ; Confocal ; Fluorescence ; Colloidal gold ; Drug delivery ; Microscopy Gold and Silver Nanoparticles Synthesis and Applications ; Nanocluster Synthesis and Applications ; Luminescence and Fluorescent Materials
Authors: Feilong Tang | Rosalyn R. Porle | Hoe Tung Yew | Farrah Wong
Source: Research Square (preprint, 2025-09-26)
Field: Chemistry — Analytical Chemistry

mIT-CMCA: A Cross-Modal Category Alignment Framework for Robust Maize Disease Identification Annotation ; Robustness (evolution) ; Scalability ; Embedding ; Pattern recognition (psychology) ; Identification (biology) ; Construct (python library) ; Limiting ; Feature (linguistics) Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI
Authors: Riho Somaki | Taichi Inagaki | Miho Hatanaka
Source: Molecular Informatics (article, 2025-01-30)
Field: Materials Science — Materials Chemistry

Exploration of the Global Minimum and Conical Intersection with Bayesian Optimization Conventional molecular geometry searches on a potential energy surface (PES) utilize energy gradients from quantum chemical calculations. However, replacing energy calculations with noisy quantum computer measurements generates errors in the energies, which makes geometry optimization using the energy gradient difficult. One gradient-free optimization method that can potentially solve this problem is Bayesian optimization (BO). To use BO in geometry search, an acquisition function (AF), which involves an objective variable, must be defined suitably. In this study, we propose a strategy for geometry searches using BO and examine the appropriate AFs to explore two critical structures: the global minimum (GM) on the singlet ground state (S<sub>0</sub>) and the most stable conical intersection (CI) point between S<sub>0</sub> and the singlet excited state. We applied our strategy to two molecules and located the GM and the most stable CI geometries with high accuracy for both molecules. We also succeeded in the geometry searches even when artificial random noises were added to the energies to simulate geometry optimization using noisy quantum computer measurements. Conical intersection ; Bayesian optimization ; Energy minimization ; Intersection (aeronautics) ; Conical surface ; Geometry ; Quantum ; Optimization problem ; Singlet state ; Computer science ; Ground state ; Potential energy surface ; Algorithm ; Physics ; Mathematical optimization ; Mathematics ; Excited state ; Quantum mechanics ; Molecule ; Aerospace engineering ; Engineering Machine Learning in Materials Science ; Quantum Computing Algorithms and Architecture ; Advanced Chemical Physics Studies
Authors: Bo Liu | Yunqiang Zhang | Zhu Li | Xuetao Gan | Xin Xie
Source: Photonics (article, 2025-10-19)
Field: Materials Science — Electronic, Optical and Magnetic Materials

A Wide Field of View and Broadband Infrared Imaging System Integrating a Dispersion-Engineered Metasurface We present a compact hybrid imaging system operating in the 3–5 μm spectral band that combines refractive optics with a dispersion-engineered metasurface to overcome the longstanding trade-off between wide field of view (FOV), system size, and thermal stability. The system achieves an ultra-wide 178° FOV within a total track length of only 28.25 mm, employing just three refractive lenses and one metasurface. Through co-optimization of material selection and system architecture, it maintains the modulation transfer function (MTF) exceeding 0.54 at 33 lp/mm and the geometric (GEO) radius below 15 μm across an extended operational temperature range from –40 °C to 60 °C. The metasurface is designed using a propagation phase approach with cylindrical unit cells to ensure polarization-insensitive behavior, and its broadband dispersion-free phase profile is optimized via a particle swarm algorithm. The results indicate that phase-matching errors remain small at all wavelengths, with a mean value of 0.11068. This design provides an environmentally resilient solution for lightweight applications, including automotive infrared night vision and unmanned aerial vehicle remote sensing. Broadband ; Optical transfer function ; Field of view ; Phase (matter) ; Point spread function ; Infrared ; RADIUS ; Modulation (music) Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Optical Wireless Communication Technologies
Authors: Ali Mhammad | Gergely Dombi | Máté Dobó | B. Simon | Zoltán‐István Szabó | Gergő Tóth
Source: Journal of Chromatography A (article, 2025-07-15)
Field: Chemistry — Spectroscopy

Gradient, reversed-phase HPLC method for simultaneous determination of chemical and enantiomeric impurities of dexibuprofen in a single run In this work, a gradient reversed-phase HPLC method is presented for the simultaneous determination of four potential chemical impurities in dexibuprofen, as well as its chiral isomer, (R)-ibuprofen. Among nine polysaccharide-based chiral columns tested, enantioselectivity was observed only with the column containing cellulose tris(4-methylbenzoate) under polar organic mode conditions. To enhance enantioresolution, a reversed-phase mode was employed by adding water to the mobile phase. Examination of the water content's impact on retention time revealed a typical reversed-phase retention behavior in alcohol-water mixtures, whereas acetonitrile-water mixtures displayed HILIC-like retention characteristics. A full factorial design optimization strategy was applied to identify the ideal conditions for determining organic and enantiomeric impurities in dexibuprofen. Under the optimized conditions-Lux Cellulose-3 stationary phase using a methanol:water:acetic acid 70:30:0.1 (v/v/v) mobile phase, at a flow rate of 0.5 mL/min and temperature of 35 °C-all target compounds were effectively separated with clear baseline resolution. The initial analysis required 60 min, primarily due to the prolonged retention of highly lipophilic ester impurities. To reduce the analysis time, a gradient elution strategy was developed, successfully cutting the runtime by half. Furthermore, the stability and robustness of the gradient system were confirmed by consistent performance over one hundred consecutive injections. Following regulatory validation guidelines, the developed HPLC method was effectively applied for the quality assessment of commercially available pharmaceutical formulations. Chemistry ; Chromatography ; Enantiomer ; High-performance liquid chromatography ; Chiral stationary phase ; Impurity ; Reversed-phase chromatography ; Phase (matter) ; Stereochemistry ; Organic chemistry Analytical Chemistry and Chromatography ; Analytical Methods in Pharmaceuticals ; Advanced Chemical Sensor Technologies
Authors: Mahmoud G. Elamshity | Abdullah M. Alhamdan
Source: Foods (article, 2025-08-29)
Field: Chemistry — Analytical Chemistry

Development and Prediction of a Non-Destructive Quality Index (Qi) for Stored Date Fruits Using VIS–NIR Spectroscopy and Artificial Neural Networks This study proposes a novel non-destructive approach to assessing and predicting the quality of stored date fruits using a composite quality index (Qi) modeled via visible-near-infrared (VIS-NIR) spectroscopy and artificial neural networks (ANNs). Two leading cultivars, Sukkary and Khlass, were stored for 12 months using three temperature regimes (25 °C, 5 °C, and -18 °C) and five types of packaging. The samples were grouped into six moisture content categories (4.36-36.70% d.b.), and key physicochemical traits, namely moisture, pH, hardness, total soluble solids (TSSs), density, color, and microbial load, were used to construct a normalized, dimensionless Qi. Spectral data (410-990 nm) were preprocessed using second-derivative transformation and modeled using partial least squares regression (PLSR) and the ANNs. The ANNs outperformed PLSR, achieving the correlation coefficient (R<sup>2</sup>) values of up to 0.944 (Sukkary) and 0.927 (Khlass), with corresponding root mean square error of prediction (RMSEP) values of 0.042 and 0.049, and the relative error of prediction (REP < 5%). The best quality retention was observed in the dates stored at -18 °C in pressed semi-rigid plastic containers (PSSPCs), with minimal microbial growth and superior sensory scores. The second-order Qi model showed a significantly better fit (<i>p</i> < 0.05, AIC-reduced) over that of linear alternatives, capturing the nonlinear degradation patterns during storage. The proposed system enables real-time, non-invasive quality monitoring and could support automated decision-making in postharvest management, packaging selection, and shelf-life prediction. Mean squared error ; Partial least squares regression ; Artificial neural network ; Coefficient of determination ; Postharvest ; Biological system ; Correlation coefficient ; Mathematics ; Chemometrics ; Near-infrared spectroscopy ; Artificial intelligence ; Statistics ; Computer science ; Pattern recognition (psychology) ; Machine learning ; Botany ; Biology Spectroscopy and Chemometric Analyses ; Date Palm Research Studies ; Advanced Chemical Sensor Technologies
Authors: Muhammad Anjum Zia | Muhammad Uzair Khan | Dawar Awan | Muhammad Lais | Saadia Tabassum | Rehanullah Khan | Muhammad Awais Khan
Source: Physical Education Health and Social Sciences (article, 2025-09-30)
Field: Chemistry — Analytical Chemistry

Automated Classification of Healthy and Infected Grape Leaves Using a Deep Convolutional Neural Network This study proposes a deep learning-based method for the automated identification of grape leaf diseases, focusing on the classification of infected and healthy grape leaves. A Deep Convolutional Neural Network (CNN) model was designed and trained on a curated dataset comprising 1,180 diseased and 1,000 healthy grape leaf images. Data augmentation techniques, including rotation, flipping, scaling, noise injection, gamma correction and principal component analysis (PCA), were employed to improve model generalization and reduce overfitting. The model was trained using optimized hyperparameters such as epoch, batch size, and dropout. Experimental results achieved a classification accuracy of 97.25%, with precision, recall, and F1-score of 95.16%, 100%, and 97.52% for infected leaves, and 100%, 94%, and 96.91% for Healthy leaves, respectively. The proposed model demonstrates reliable performance and can be integrated into precision agriculture systems for early disease detection and crop management. Convolutional neural network ; Pattern recognition (psychology) ; Principal component analysis ; Deep learning ; Hyperparameter ; Artificial neural network Spectroscopy and Chemometric Analyses
Authors: Lawrence Kujur | V. B. Gupta | Abhinav Singhal
Source: Proceedings on Engineering Sciences (article, 2025-09-28)
Field: Chemistry — Analytical Chemistry

METROLOGICAL IMPACT ON ORANGE FRUIT: A COMPARATIVE ANALYSIS OF DEEP LEARNING MODELS FORPREDICTING FRUIT DISEASES This research offers a comprehensive comparative analysis of efficient deep learning models for predicting diseases in orange fruits, with a particular emphasis on the impact of meteorological factors on disease prevalence. Citrus diseases such as Blackspot, Canker, and Greening are significantly influenced by environmental conditions. Recognizing the crucial role of weather conditions in the development and spread of these diseases, we concentrate on enhancing prediction accuracy by integrating Convolutional Neural Networks (CNNs) with various classification algorithms to develop hybrid models that account for meteorological impacts. Specifically, we assess the performance of a CNN combined with Gradient Boosting (CNN-GB) and compare it against other hybrid models such as CNN integrated with Long Short-Term Memory networks (CNN-LSTM), Support Vector Machines (CNN-SVM), and Random Forest classifiers (CNN-Random Forest). These models are evaluated using different optimization algorithms to determine the most effective approach for disease prediction under varying meteorological conditions. A meticulously curated dataset comprising 1,600 training images and 300 testing images of orange fruits exhibiting a variety of disease symptoms was utilized for evaluation. The dataset reflects diverse environmental conditions to capture the meteorological impact on disease manifestation. All the models tested, the CNN-GB hybrid model with NDAM optimizer exhibited superior performance (Accuracy 98.03) in comparison of other models like CNN (Accuracy 96.03), CNN+LSTM (Accuracy 96.16), CNN + SVM (Accuracy 97.13) and CNN + Random Forest (Accuracy 97.79). The exceptional performance of the CNN-GB model suggests that integrating CNNs with powerful classification algorithms like Gradient Boosting, along with considerations of meteorological data, can significantly enhance disease detection in crops. This advancement contributes to more proactive and effective disease management strategies, ultimately reducing economic losses and increasing productivity in the agricultural sector. Random forest ; Support vector machine ; Convolutional neural network ; Deep learning ; Boosting (machine learning) ; Orange (colour) ; Gradient boosting Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Leaf Properties and Growth Measurement
Authors: S. Alonso | Christian Raeder | Kai Wieghardt | Bernhard Hoffschmidt
Source: SolarPACES Conference Proceedings (article, 2025-10-22)
Field: Materials Science — Surfaces, Coatings and Films

Real-Time Image Enhanced Data-Driven Digital Twin (Real-TImE 3DT) for Flux Density Measurements Concentrated solar power (CSP) plants are considered one of the most attractive renewable energy producers (used for green fuel and electric power). Consequently, it is essential to enhance the different elements of their power cycles, especially the central receiver and the heliostat field. To achieve this, flux density measurement (FDM) is highly recommended. In this work, a novel methodology for FDM is presented, based on the usage of real-time data-driven simulation models in parallel with the traditional camera methods for the real-time enhancement of the results. In order to improve the output, a graph neural network is used, giving as a result a Real-Time Image-Enhanced Data-Driven Digital Twin (Real-TImE 3DT). All the data are obtained from a pre-existing data platform, where the signals from the sensors of the power plant are logged and stored. This way, it is possible to operate the model manually, or to let it work automatically with these sensors' outputs. Latencies smaller than 10 seconds are achieved and the results from the digital twin showed coherence, easy handling and great inter-operability with the neural network enhancement. On the AI-enhancement’s side, the suppression of up-to 80% of the inaccuracies can be expected under parametrically semi-controlled conditions. Further work is being performed in Solar Tower Jülich (STJ) in order to contrast these results against real experimental measurements. Heliostat ; Artificial neural network ; Renewable energy ; Tower ; Solar energy ; Power (physics) ; Work (physics) ; Data acquisition ; Solar power Electron and X-Ray Spectroscopy Techniques ; Silicon and Solar Cell Technologies ; Advanced X-ray and CT Imaging
Authors: Siddhant Agarwal | Ali Can Bekar | Christian Hüttig | David S. Greenberg | Nicola Tosi
Source: Physics of Fluids (article, 2025-08-01)
Field: Materials Science — Materials Chemistry

Physics-based machine learning for mantle convection simulations Mantle convection simulations are an essential tool for understanding how rocky planets evolve. However, the poorly known input parameters to these simulations, the non-linear dependence of transport properties on pressure and temperature, and the long integration times in excess of several billion years all pose a computational challenge for numerical solvers. We propose a physics-based machine learning approach that predicts creeping flow velocities as a function of temperature while conserving mass, thereby bypassing the numerical solution of the Stokes problem. A finite-volume solver then uses the predicted velocities to advect and diffuse the temperature field to the next time step, enabling autoregressive rollout at inference. For training, our model requires temperature-velocity snapshots from a handful of simulations (94). We consider mantle convection in a two-dimensional rectangular box with basal and internal heating, and pressure- and temperature-dependent viscosity. Overall, our model is up to 89 times faster than the numerical solver. We also show the importance of different components in our convolutional neural network architecture such as mass conservation, learned paddings on the boundaries, and loss scaling for the overall rollout performance. Finally, we test our approach on unseen scenarios and find that it is able to perform thermal evolution well despite being trained on snapshots from steady-state simulations. However, when additional compressibility effects are included in the energy equation or when the initial condition is too far out of the distribution of the training data, the network fails, leaving room for future improvements. Convection ; Solver ; Artificial neural network ; Convolutional neural network ; Compressibility ; Mantle convection ; Internal heating ; Thermal ; Scaling Machine Learning in Materials Science ; High-pressure geophysics and materials ; Theoretical and Computational Physics