jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Е. V. Nasvishchuk | R. V. Ushakov | D. N. Demydko | М. I. Prikazchikova
Source: Tsvetnye Metally (article, 2026-03-31)
Field: Chemistry — Analytical Chemistry

Application of machine learning for the calibration of pulp composition analyzers at the Talnakh Concentrator Modern production requires operational control of key parameters. These data directly affect the process management and ensure that the specified quality standards of the finished product are achieved. Any processing plant contains a variety of monitoring devices that provide operational monitoring. Enrichment is a technological process aimed at increasing the content of target metals in concentrates, exceeding their initial content in the processed ore. Using X-ray fluorescence analyzers, the contents of metals and sulfur in accumulative balance and other powder samples are determined. The cumulative nature of the samples does not allow the use of such data in operational management. An alternative is rapid analysis on X-ray multichannel pulp analyzers, which provide information about the intensity of the spectra of substances interaction. These data make it possible to reconstruct the contents of metals and sulfur through the coupling equations. It is necessary to maintain the relevance of these equations, i.e. periodic calibration of pulp analyzers. An additional complexity factor in this case is the variability of the composition of the material (ore or pulp), which leads to the need for constant monitoring of the qua lity of the contents recovery. Increasing the frequency of oscillation and quality control of calibration models requires the introduction of automated tools, in particular, based on machine learning, which can be used by personnel of X-ray spectral analysis laboratories. The article provides an example of automatic calibration implemented at the Talnakh Concentrator. It is based on the enumeration and evaluation of models of a known structure (coupling equations for recovery of the nickel and copper content with regressors of a given type). The situation of recovery of the sulfur content is considered separately: the complexity of enhancement (technological improvements) and modeling results. Using machine learning methods, the possibility of estimating the sulfur content based on the intensities of the bands of the nickel and copper spectrum has been studied. The error in restoring the operational values of the sulfur content for the method used is 3% (MAPE).Specialists of PJSC MMC Norilsk Nickel took part in the work: M. S. Datsiev,&nbsp;A. G. Aryshtaev, D. I. Ivashechkin, I. F. Zaporozhtsev, V. Yu. Ivanov, E. M. Strelet&nbsp;skaya and others. Calibration ; Pulp (tooth) ; Concentrator ; Process analytical technology ; Process (computing) ; Process control ; Data processing ; Calibration curve Spectroscopy and Chemometric Analyses ; Mineral Processing and Grinding ; Industrial Engineering and Technologies
Authors: Haftom Hagos Gebremeskel | Oliver Hensel | Abozar Nasirahmadi
Source: Potato Research (article, 2026-04-07)
Field: Chemistry — Analytical Chemistry

Efficient Band Selection for Identifying Key Infrared Bands in Hyperspectral Detection of Colorado Potato Beetles Abstract The Colorado potato beetle ( Leptinotarsa decemlineata ) is a major pest of potato crops, causing yield losses of up to 20% and challenging sustainable production due to insecticide resistance and environmental risks associated with chemical control. Current pest detection methods, like RGB-based imaging, are limited by scalability, large dataset requirements, and reduced accuracy in detecting camouflaged pests. This study investigates hyperspectral imaging (937–1718 nm) coupled with machine learning for precise CPB detection on potato leaves. Hyperspectral images of 327 beetles were captured and a band selection method was developed to identify a minimal set of informative spectral bands based on spectral slope differences between Colorado potato beetle and potato leaves. This method was compared with established feature selection techniques (forward sequential feature selection, importance-correlation, and recursive feature elimination) using support vector machine, random forest, and k-nearest neighbour classifiers. Our approach achieved over 97.3% pixel-level F1-score using only four bands, outperforming the computationally intensive recursive feature elimination (3 days) in just 0.28 s, with superior accuracy across all classifiers. This level of precision was achieved using only four bands, which is crucial for real-time field applications. The identified key informative bands were 972, 1125, 1188, and 1286 nm. The findings suggest that a narrow band multispectral camera, designed using the informative bands, could operate in snapshot mode, facilitating real-time insect detection. This study shows the possibility of developing faster, more efficient hyperspectral systems for targeted insect detection, with the potential to revolutionize pest management by enabling more precise and environmentally friendly approaches. Hyperspectral imaging ; Multispectral image ; Feature selection ; PEST analysis ; Pattern recognition (psychology) ; Insect pest ; Support vector machine ; Selection (genetic algorithm) ; Spectral bands Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Remote Sensing in Agriculture
Authors: Shuang Yu | Xiaodong Zhang | Qiang Zhan | Hao Yu | Bo Zhou
Source: Physical Chemistry Chemical Physics (article, 2026-01-01)
Field: Materials Science — Polymers and Plastics

A comparative theoretical study on the vibrational spectra of V <sub>2</sub> O <sub>5</sub> · <i>n</i> H <sub>2</sub> O The structural and vibrational properties of hydrated vanadium pentoxide (V<sub>2</sub>O<sub>5</sub>·<i>n</i>H<sub>2</sub>O) were investigated using first-principles density functional theory (DFT) calculations. Comparative analysis revealed characteristic Raman peaks arising from interlayer water molecules and elucidated the evolution of the Raman spectra as a function of the hydration level. Notably, V<sub>2</sub>O<sub>5</sub>·H<sub>2</sub>O does not adopt a strictly monoclinic lattice with <i>C</i>2/<i>m</i> symmetry. Instead, it exhibits an in-plane disordered structure that can be approximated by a triclinic unit cell closely resembling <i>C</i>2/<i>m</i> symmetry. In contrast to anhydrous α-V<sub>2</sub>O<sub>5</sub>, hydrated V<sub>2</sub>O<sub>5</sub>·<i>n</i>H<sub>2</sub>O phases display distinct vibrational spectral signatures, including a Raman-active peak at approximately 760 cm<sup>-1</sup>, corresponding to V-O<sub>3</sub>-V stretching modes, and a pronounced enhancement near 890 cm<sup>-1</sup>, associated with water-related modes. In partially hydrated systems (V<sub>2</sub>O<sub>5</sub>·0.5H<sub>2</sub>O and V<sub>2</sub>O<sub>5</sub>·1.5H<sub>2</sub>O), the framework disorder induces peak splitting. Molecular dynamics simulations, employing a machine learning-based force field applied to a supercell comprising 2560 atoms, demonstrate that the vibrational density of states (VDOS) for water molecules shifts from approximately 400 cm<sup>-1</sup> to 900 cm<sup>-1</sup> with increasing hydration. This study provides a comprehensive analysis of the vibrational modes in bilayer V<sub>2</sub>O<sub>5</sub>·<i>n</i>H<sub>2</sub>O, offering critical insights into the vibrational spectra relevant to experimental studies of V<sub>2</sub>O<sub>5</sub>-based electrode materials. Pentoxide ; Raman spectroscopy ; Molecule ; Density functional theory ; Molecular vibration ; Monoclinic crystal system ; Vibrational spectrum ; Triclinic crystal system ; Spectral line Transition Metal Oxide Nanomaterials ; Catalysis and Oxidation Reactions ; Advanced Chemical Physics Studies
Authors: Esra KAVAZ PERİŞANOĞLU | Mustafa KAVGACI | Hasan ESKALEN | Hakan Yaykaşlı | Mehmet Gök | U. Perişanoğlu
Source: Journal of Alloys and Compounds (article, 2026-01-29)
Field: Materials Science — Materials Chemistry

Comprehensive investigation of La2O3-modified borate glasses for optical and radiation shielding applications Electromagnetic shielding ; Boron ; Attenuation ; Amorphous solid ; Borate glass ; Neutron radiation ; Neutron temperature ; Thermal stability ; Effective atomic number Radiation Shielding Materials Analysis ; Glass properties and applications ; Nuclear materials and radiation effects
Authors: Zhonggui Wang | Guan-Ming Chen | Zhi Li | Zhi-Ming Li | Z. L. Li | Peng Lü
Source: Journal of the American Chemical Society (article, 2026-03-20)
Field: Chemistry — Organic Chemistry

Methylene Cycloalkene as a Reactive 1,3-Diene for Enantioselective Hydroamination Herein, we report the first enantioselective functionalization of methylene cycloalkenes─an underexplored class of reactive 1,3-dienes. The process involves a palladium-catalyzed enantioselective hydroamination of methylene cycloalkenes using pyrazole derivatives, affording a variety of chiral tertiary amines. A machine-learning model based on universal descriptors from quasi-transition-state (UD-qTS) strategy, combined with a physicochemically grounded data-augmentation protocol, has been employed to accelerate the optimization process. Deuterium-labeling experiments and density functional theory (DFT) calculations revealed that the reaction proceeded through a ligand-to-ligand hydrogen transfer (LLHT) pathway, which is both the rate-determining and enantio-determination step. Subsequent inner-sphere nucleophilic attack by the pyrazole anion enables regioselective <i>C</i><sup>3</sup> (referring to 1,3-diene)-<i>N</i><sup>1</sup> (referring to pyrazole) bond formation. Furthermore, the resulting products can be transformed into acyclic alkenes, which are equivalent to the hydroamination products of acyclic 1,3-dienes. Hydroamination ; Cycloalkene ; Enantioselective synthesis ; Methylene ; Catalysis Catalytic C–H Functionalization Methods ; Advanced Synthetic Organic Chemistry ; Asymmetric Synthesis and Catalysis
Authors: JueFei Wang | Xuan Luo
Source: RSC Advances (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

First-principles investigation of transition-metal doped Al <sub>2</sub> B <sub>2</sub> and AlB <sub>4</sub> monolayers for spintronics Two-dimensional spintronics has garnered significant attention in recent years, driven by the rapidly increasing demand for greater computational power from machine learning and AI workloads. This research investigates whether substitutional doping with the transition metals Mn, Fe, and Cr induces magnetism in two recently discovered, lightweight, two-dimensional Dirac nodal line semimetal monolayers, Al<sub>2</sub>B<sub>2</sub> and AlB<sub>4</sub>. Computations performed with density functional theory (DFT) reveal ferromagnetic ground states in nine of the twelve investigated monolayers. Defect formation energy calculations show that substitution on Al sites in both monolayers is thermodynamically preferred over B sites. The opening of gaps at band crossings is observed in band structures across all monolayers when spin-orbit coupling effects are introduced, suggesting nontrivial topologies of the pure materials are most likely retained. These findings identify doped Al<sub>2</sub>B<sub>2</sub> and AlB<sub>4</sub> monolayers as promising platforms for the future exploration of two-dimensional spintronics. Spintronics ; Monolayer ; Density functional theory ; Magnetism ; Doping ; Dirac (video compression format) ; Semimetal ; Ferromagnetism Boron and Carbon Nanomaterials Research ; Heusler alloys: electronic and magnetic properties ; MXene and MAX Phase Materials
Authors: Swapnil Pradhan, Gauri Bhamare, Dr. Manish Nungade, Kalyani Patil, Neha Patil, Suvidha Kabadi
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2026-02-28)
Field: Materials Science — Biomaterials

Smart Packaging: Innovative Packaging in Pharmaceuticals Smart packaging represents an advanced packaging approach developed to address challenges in the pharmaceutical, food, and cosmetic industries, including product safety, quality assurance, patient compliance, and supply chain traceability. This review outlines the evolution of smart packaging, its classifications, and emerging technological advancements. Smart packaging is broadly divided into two main categories: active packaging and intelligent packaging. Active packaging enhances shelf life and maintains freshness by regulating internal conditions—such as controlling oxygen and carbon dioxide levels, managing moisture, and inhibiting microbial growth. Intelligent packaging incorporates technologies including sensors, colorimetric indicators, barcodes, and RFID tags to monitor freshness, storage parameters, and product safety. Additionally, sustainable innovations such as biodegradable materials, edible packaging, and 3D-printed solutions are being investigated to reduce environmental impact. Despite its advantages, smart packaging faces limitations including high implementation costs, insufficient regulatory harmonization, and electronic waste concerns. Future advancements are expected to integrate Artificial Intelligence (AI), Machine Learning (ML), and the Internet of Things (IoT) to develop safer, more sustainable, and consumer-oriented packaging systems. Active packaging ; Supply chain ; Product (mathematics) ; Internet of Things ; Quality (philosophy) ; Packaging engineering Nanocomposite Films for Food Packaging ; Consumer Packaging Perceptions and Trends ; Material Properties and Processing
Authors: Junjie Xia | Fangjian Chen | Guangran Bai | Yimin Zhou | Changwei Bi | Hao Liang | Huahong Huang | Wei Liu
Source: Microchemical Journal (article, 2026-01-09)
Field: Chemistry — Analytical Chemistry

Nondestructive detection of carotenoids in chrysanthemum and identification of drought using hyperspectral and machine learning Hyperspectral imaging ; Feature selection ; Random forest ; Partial least squares regression ; Interpretability ; Support vector machine ; Pattern recognition (psychology) ; Preprocessor ; Carotenoid ; VNIR Spectroscopy and Chemometric Analyses ; Remote Sensing in Agriculture ; Smart Agriculture and AI
Authors: Po-Yen Chen | Teruyasu Mizoguchi
Source: arXiv (Cornell University) (preprint, 2026-01-19)
Field: Materials Science — Materials Chemistry

Effect of uniaxial compressive stress on polarization switching and domain wall formation in tetragonal phase BaTiO3 via machine learning potential Ferroelectric materials such as BaTiO3 exhibit spontaneous polarization that can be reoriented by an external electric field, forming the basis of various memory, actuator, and sensor applications. The polarization switching behavior, however, is strongly influenced by mechanical boundary conditions due to the intrinsic electromechanical coupling in ferroelectrics. In this study, we employ a machine learning interatomic potential to investigate the effect of uniaxial compressive stress on polarization switching and domain wall evolution in the tetragonal phase of BaTiO3. This study revealed a critical stress about 120 MPa which 90 degree polarization switching occurs. Beyond the critical stress, larger supercells exhibit lower activation energies for polarization switching with 180-degree domain wall formation and weaker constraints from periodic boundary conditions, thereby facilitating domain-wall formation. Besides, Increasing compressive stress reduces both the remnant polarization and the coercive field, while a double hysteresis loop emerges at a stress level of 80 MPa. These findings provide atomistic insights into stress-controlled ferroelectric switching and highlight the crucial role of mechanical loading in designing reliable ferroelectric devices. Ferroelectricity ; Tetragonal crystal system ; Polarization (electrochemistry) ; Phase boundary ; Hysteresis ; Compressive strength ; Stress (linguistics) Ferroelectric and Piezoelectric Materials ; Advanced Sensor and Energy Harvesting Materials ; Ferroelectric and Negative Capacitance Devices
Authors: Yongkuai Chen | Tao Wang | Shanshan Lin | Shuilan Liao | Songliang Wang
Source: Applied Sciences (article, 2026-01-08)
Field: Chemistry — Analytical Chemistry

Detection of Cadmium Content in Pak Choi Using Hyperspectral Imaging Combined with Feature Selection Algorithms and Multivariate Regression Models Pak choi (Brassica chinensis L.) has a strong adsorption capacity for the heavy metal cadmium (Cd), which is a big threat to human health. Traditional detection methods have drawbacks such as destructiveness, time-consuming processes, and low efficiency. Therefore, this study aimed to construct a non-destructive prediction model for Cd content in pak choi leaves using hyperspectral technology combined with feature selection algorithms and multivariate regression models. Four different cadmium concentration treatments (0 (CK), 25, 50, and 100 mg/L) were established to monitor the apparent characteristics, chlorophyll content, cadmium content, chlorophyll fluorescence parameters, and spectral features of pak choi. Competitive adaptive reweighted sampling (CARS), the successive projections algorithm (SPA), and random frog (RF) were used for feature wavelength selection. Partial least squares regression (PLSR), random forest regression (RFR), the Elman neural network, and bidirectional long short-term memory (BiLSTM) models were established using both full spectra and feature wavelengths. The results showed that high-concentration Cd (100 mg/L) significantly inhibited pak choi growth, leaf Cd content was significantly higher than that in the control group, chlorophyll content decreased by 16.6%, and damage to the PSII reaction centre was aggravated. Among the models, the FD–RF–BiLSTM model demonstrated the best prediction performance, with a determination coefficient of the prediction set (Rp2) of 0.913 and a root mean square error of the prediction set (RMSEP) of 0.032. This study revealed the physiological, ecological, and spectral response characteristics of pak choi under Cd stress. It is feasible to detect leaf Cd content in pak choi using hyperspectral imaging technology, and non-destructive, high-precision detection was achieved by combining chemometric methods. This provides an efficient technical means for the rapid screening of Cd pollution in vegetables and holds important practical significance for ensuring the quality and safety of agricultural products. Hyperspectral imaging ; Feature selection ; Partial least squares regression ; Random forest ; Cadmium ; Pattern recognition (psychology) ; Content (measure theory) ; Mean squared error Spectroscopy and Chemometric Analyses ; Remote Sensing in Agriculture ; Remote-Sensing Image Classification
Authors: Nagesh B E | Subinoy Adhikari | Jagannath Mondal
Source: bioRxiv (Cold Spring Harbor Laboratory) (article, 2026-01-22)
Field: Materials Science — Materials Chemistry

Fast Adversarial Generation of Molecular Dynamics Trajectories with Kinetic Fidelity Abstract Molecular dynamics (MD) simulations yield atomic-level insights into molecular motion but struggle to reach the long timescales needed for rare events due to prohibitive computational costs. Generative machine-learning models (e.g., diffusion models and normalizing flows) offer a promising route to accelerate sampling, yet they generate independent equilibrium snapshots without temporal correlation or kinetic information. Autoregressive sequence models can learn time evolution by producing one frame at a time, but this stepwise generation often accumulates errors and drifts from true dynamics. Here, we propose a complementary approach inspired by advances in image and video generation: we treat finite MD trajectory segments as high-dimensional objects and learn their joint distribution using Generative Adversarial Networks (GANs). Using a Wasserstein GAN with gradient penalty, we directly generate entire time-series trajectories in one shot, that remain physically coherent over time without explicitly integrating the equations of motion. We demonstrate the generality of this method on molecular systems of increasing complexity: a 2D triple-well potential energy landscape, a protein–ligand binding process (cytochrome P450), the dynamics of an intrinsically disordered protein ( α -synuclein) in a latent coordinate space, and even the conditional generation of folding trajectories for the Trp-cage mini-protein. In all cases, the GAN-generated trajectories closely reproduce the true free-energy landscapes and kinetic signatures of the systems, while enabling efficient sampling of rare events that would ordinarily require months of conventional MD simulation. Trajectory ; Autoregressive model ; Sequence (biology) ; Molecular dynamics ; Process (computing) ; Rare events ; Convergence (economics) ; Markov chain ; Frame (networking) Machine Learning in Materials Science ; Generative Adversarial Networks and Image Synthesis ; Quantum many-body systems
Authors: Gaia Meoni | Chiara Vita | Leonardo Tenori | Lorenzo Venturini | Miriam Ascolese | Alissa Mattei | Lucia Tacconi | Maria Teresa Ceccherini | Claudio Luchinat | Simone Tommasini | S. Moretti | Samuel Pelacani
Source: Food Research International (article, 2026-03-18)
Field: Chemistry — Organic Chemistry

Machine learning supported olive oil compound profiling for assessing geographic and cultivar authenticity This study aimed at developing an integrated metabolomics and machine learning (ML) framework to identify robust chemical markers of Tuscan VOO authenticity while minimizing the confounding influence of milling. A multi-platform analytical workflow, combining <sup>1</sup>H NMR, HS-SPME GC-MS, HPLC-DAD-FLD and sensory evaluation, was applied to 40 VOOs from seven cultivars (Frantoio, Leccino, Leccio del Corno, Moraiolo, Seggianese, Morcone della Valtiberina, Canino) and blends. Quantitative variables generated by the three analytical platforms were integrated into a single matrix to train ML models, including Random Forest (RF), TreeNet gradient boosting and Multivariate Adaptive Regression Splines. A two-stage RF procedure was implemented, first removing variables dominated by mill-related effects (13% of total chemical variance), then re-modelling the filtered dataset to identify mill-independent geographical markers. In summary, this approach enabled: (i) accurate geographical classification (≈80% cross-validated accuracy), with the most informative markers comprising secoiridoids, sterols, triterpenoids, aldehydes, terpenoids, and lipid-derived metabolites; (ii) the prediction of sensory attributes by linoleic and linolenic acids and their lipoxygenase-derived C6 aldehydes and alcohols, through ML models with high predictive performance (R² = 0.95 artichoke, 0.92 fruity, 0.90 tomato-like, 0.83 resinous, 0.93 heated, 0.95 rancid); iii) identification of specific molecules, notably margaric acid, strongly associated with the olive cultivars examined. These results highlight links between cultivar traits, lipid precursors and aroma expression. This study introduces the first multi-platform ML minimizing mill-related confounding, improving the reliability of VOO geographical authentication and sensory prediction, and laying the groundwork for future applications in quality assessment and regional characterization. Profiling (computer programming) ; Olive oil ; Cultivar Edible Oils Quality and Analysis ; Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies