jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Iván Soriano-Díaz | Ilya D. Dergachev | Sergey A. Varganov | Enrique Ortı́ | Angelo Giussani
Source: The Journal of Physical Chemistry Letters (article, 2025-08-25)
Field: Materials Science — Electronic, Optical and Magnetic Materials

On the Role of Spin–Orbit Coupling and State-Crossing Topography in the Nonradiative Decay of Ir(III) Complexes A pillar of our current understanding of the photoluminescence of Ir(III) complexes is the assumption that the population of triplet metal-centered states determines an efficient nonradiative decay to the ground state minimum. Based on that assumption, the energy separation between the emitting state and the minimum-energy crossing point of the triplet metal-centered and ground states has been employed as a key variable for evaluating the ability of Ir(III) complexes to decay nonradiatively. We demonstrate that the strong spin-orbit coupling between the triplet metal-centered and ground state of Ir(III) complexes, together with the sloped topography of their crossing, leads to a significant energy separation between the two states, resulting in a reduced rate of nonradiative ground state recovery. Therefore, we propose that the role of metal-centered states is defined by the tendency of the excited state population to remain trapped in the metal-centered minima. Excited state ; Ground state ; Atomic physics ; Coupling (piping) ; Population ; Avoided crossing ; Physics ; Spin (aerodynamics) ; Maxima and minima ; Spin states ; Triplet state ; Metal ; Potential energy ; Chemistry ; Molecular physics ; Materials science Magnetism in coordination complexes ; Lanthanide and Transition Metal Complexes ; Organic Light-Emitting Diodes Research
Authors: Fernanda Maria Policarpo Tonelli | Christopher Santos Silva | Geicielly da Costa Pinto | Lucas Santos Azevedo | Jhenifer Cristina Carvalho Santos | Danilo Roberto Carvalho Ferreira | Pamela da Rocha Patricio | Giullya Amaral Cordeiro Lembrança | Luciana Alves Rodrigues dos Santos Lima | Clascídia Aparecida Furtado | Flávia Cristina Policarpo Tonelli | Adriano Guimarães Parreira
Source: International Journal of Molecular Sciences (article, 2025-06-16)
Field: Chemistry — Analytical Chemistry

Synthesis of Antioxidant Nano Zero-Valent Iron Using FeCl2 and Leucaena leucocephala Leaves’ Aqueous Extract and the Nanomaterial’s Potential to Promote the Adsorption of Tartrazine and Nigrosine Synthetic dyes are commonly present in industrial wastewater and when discharged in water bodies without receiving a treatment capable of removing or destroying them, they turn into concerning water pollutants. These organic contaminants threaten living beings due to their toxicity, and some of them can even damage DNA. Consequently, in order to achieve sustainable development, it is necessary to develop eco-friendly tools that can efficiently manage this kind of pollution. In the present study the aqueous extract from the leaves of <i>Leucaena leucocephala</i> (an invasive plant species native to Mexico) was used to produce green nano zero-valent iron (nZVI). The nanomaterial was characterized (TEM, UV-vis, FTIR, SEM, EDS, XRD) and assayed regarding its antioxidant potential (DPPH test) and capacity to remediate the pollution caused by two dyes. It proved to be able to adsorb nigrosine (288.30 mg/g of nanomaterial) and tartrazine (342.50 mg/g of nanomaterial), and also displayed antioxidant activity (effective concentration to discolor 50% of the DPPH solution = 286.02 μg/mL). Therefore, the biogenic antioxidant nanoparticle proved also to be a possible nanotool to be applied to remediate water contamination caused by these synthetic dyes. Leucaena leucocephala ; Nanomaterials ; Zerovalent iron ; Adsorption ; Chemistry ; Tartrazine ; Aqueous solution ; Nano- ; Antioxidant ; Nuclear chemistry ; Chemical engineering ; Organic chemistry ; Nanotechnology ; Materials science ; Biochemistry ; Botany ; Biology Dye analysis and toxicity ; Molecular Sensors and Ion Detection ; Pigment Synthesis and Properties
Authors: Minseok Choi | Hyunjung Kang | Do-Hyun Kang | Joohoon Kim | Hongyoon Kim | Junhwa Seong | Seokwoo Kim | Junsuk Rho
Source: PhotoniX (article, 2025-10-31)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Hybrid high-index composite meta-structures with atomic layer-coated nanoparticle-embedded resin Abstract Metasurfaces offer great potential to replace conventional optics by enabling multi-functionalities in compact form factors. However, their mass production remains at crossroads, as most materials compatible with scalable fabrication like nanoimprint lithography (NIL) exhibit relatively low refractive indices (~ 2), which limit metasurface performance and necessitate tall, high-aspect-ratio meta-atoms prone to bending and collapsing. To address these bottlenecks, we introduce a hybrid nanoparticle-embedded resin (nano-PER) structure that reduces meta-atom height and aspect ratio. By utilizing TiO 2 nano-PER as the core material with thin TiO 2 coatings, we can implement the optical properties of high refractive index with printable material, achieving a height reduction of over 27% and an aspect ratio reduction of more than 36% compared with conventional hybrid structures using nanoimprint resin. Despite the reduced dimensions, our meta-atoms exhibit high broadband properties, with an average conversion efficiency of over 72% across blue (450 nm), green (532 nm), and red (635 nm) wavelengths. Our design provides robustness in the fabrication process, demonstrated by producing a hyperbolic metalens via NIL and experimentally verifying its optical performance, with an average focusing efficiency of 51.23%. These findings mark an important advancement in scalable, high-performance metasurfaces, paving the way for their practical integration into optical applications. Nanoimprint lithography ; Fabrication ; Refractive index ; Composite number ; Broadband ; Lithography ; Reduction (mathematics) ; Photolithography Metamaterials and Metasurfaces Applications ; Plasmonic and Surface Plasmon Research ; Optical Coatings and Gratings
Authors: Zhifeng Liu | Zi‐Bing Jin | Yipeng Zhou | Zhenhua Wei | Huanyu Zhang
Source: Processes (article, 2025-09-21)
Field: Chemistry — Inorganic Chemistry

Research on the Prediction of Liquid Injection Volume and Leaching Rate for In Situ Leaching Uranium Mining Using the CNN–LSTM–LightGBM Model In traditional in situ leaching (ISL) uranium mining, the injection volume depends on technicians’ on-site experience. Therefore, applying artificial intelligence technologies such as machine learning to analyze the relationship between injection volume and leaching rate in ISL uranium mining, thereby reducing human factor interference, holds significant guiding importance for production process control. This study proposes a novel uranium leaching rate prediction method based on a CNN–LSTM–LightGBM fusion model integrated with an attention mechanism. Ablation experiments demonstrate that the proposed fusion model outperforms its component models across three key metrics: Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Root Mean Square Error (RMSE). Furthermore, comparative experiments reveal that this fusion model achieves superior performance on MAE, MAPE, and RMSE metrics compared to six extensively utilized machine learning methods, including Multi-Layer Perceptron, Support Vector Regression, and K-Nearest Neighbors. Specifically, the model achieves an MAE of 0.085%, an MAPE of 0.833%, and an RMSE of 0.201%. This attention-enhanced fusion model provides technical support for production control in ISL uranium mining and offers valuable references for informatization and intelligentization research in uranium mining operations. Mean squared error ; Leaching (pedology) ; Mean absolute percentage error ; Uranium ; Informatization ; Mean absolute error ; Support vector machine ; Fusion Radioactive element chemistry and processing ; Geochemistry and Geologic Mapping
Authors: Yanling Yin | Rui-Dong Wang | Yang Jiang | Yuting Suo | Yang Li | Zhentao Wang | Xihui Shen
Source: Agronomy (article, 2025-01-24)
Field: Chemistry — Analytical Chemistry

A Method for the Rapid Identification of Rice Seed Blast Using Deep Learning and Hyperspectral Imagery Rice seeds’ infection with rice blast will directly lead to rice yield reduction or even crop failure in the next year. Therefore, it is very important accurately identify infected rice seeds. In this study, deep learning and hyperspectral imaging techniques were used for that purpose. First, hyperspectral image data were collected. Then, the UeAMNet (unsupervised extraction attention-based mixed CNN) model—designed in this study—was used to analyze these data and the results compared with the 2DCNN, 3DCNN, A2DCNN, A3DCNN, Ue2DCNN, Ue3DCNN, UeA2DCNN, UeA3DCNN, MNet, AMNet and UeMNet models using different training set (Tr) sizes. The results showed that the new UeAMNet model was superior to the comparison models when using different Tr sizes, and the accuracy could reach 100%. Notably, when Tr was only 0.05, the accuracy of this model still reached 96.85%. This showed that the proposed method could successfully identify infected rice seeds. Therefore, this study provides an approach for rice germplasm management and also for the development of crop disease identification methods in other parts of the world. Hyperspectral imaging ; Remote sensing ; Identification (biology) ; Environmental science ; Artificial intelligence ; Computer science ; Pattern recognition (psychology) ; Geology ; Biology ; Botany Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; GABA and Rice Research
Authors: Jason Devers | David I. Pattison | Asger B. Hansen | Jan H. Christensen
Source: Journal of Chromatography A (article, 2025-02-24)
Field: Chemistry — Spectroscopy

New strategies for non-targeted quantification in comprehensive two-dimensional gas chromatography: The potential of reconstructed TIC response factor surfaces Comprehensive two-dimensional gas chromatography (GC × GC) offers unrivaled peak capacity and the ability to resolve and characterize hundreds to thousands of compounds within complex environmental samples. Knowledge of their concentrations is essential in assessing their toxicological and regulatory relevance; however, reliable quantification typically requires concurrent analysis of an authentic standard for each compound. Response factors (RF) may be used to estimate the concentration of a compound without the need for its respective standard, but this is not routinely employed in gas chromatography-mass spectrometry (GC-MS). This study demonstrates that GC × GC offers a new opportunity for the calculation of RFs and estimating a compounds concentration via the creation of 2D total ion current (TIC) response factor surfaces (RFS). A new method, linking a compounds retention time coordinate to its RF using a reconstructed TIC approach, was used to calculate the concentration of 27 compounds in both solvent and effluent wastewater matrix. Using both suspect screening and non-targeted methodologies, average fold change errors of 1.58 and 1.83 respectively were determined for the novel RFS approach. The approach was compared to an RF prediction model utilizing molecular descriptors, using a genetic algorithm, partial least squares regression (GA-PLS) model. The accuracy of the RFS method demonstrates its potential in the fields of suspect screening and non-targeted analysis, where realistic concentration estimates are required. The RFS approach delivers comparable accuracy in concentration prediction compared to computational approaches for determining RFs, but crucially, without the need for generating molecular descriptors or acquiring a reference standard. Additionally, a purely non-targeted approach outlined can allow for the calculation of RF for features without the need for identification or a reference spectrum. Chemistry ; Chromatography ; Gas chromatography ; Mass spectrometry ; Response factor ; Partial least squares regression ; Gas chromatography–mass spectrometry ; Effluent ; Analytical Chemistry (journal) ; Two-dimensional chromatography ; Biological system ; Computer science ; Machine learning Analytical Chemistry and Chromatography ; Advanced Chemical Sensor Technologies ; Pesticide Residue Analysis and Safety
Authors: Imade Zidane | Belkacem Meddour
Source: Mechanics & Industry (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

On modelling of the one dimensional pseudoelastic effect of shape memory alloys Last decades, a qualitative leap in materials engineering was observed, through developing new materials or discovering new properties. The shape memory alloys are a group of materials exhibiting singular behaviour under a coupled thermomechanical loading, and they are widely used in numerous applications because of their properties. To exploit these properties in engineering, several constitutive models of were developed. The aim of this work is to build a constitutive model that could describe the pseudoelastic behaviour of shape memory alloys in a simpler and more accurate way. Based on thermodynamics and mechanics laws and principles, the developed constitutive model was implemented in an algorithm and compared with experimental data in order to assess its accuracy. The obtained results indicated that the agreement between the real and computer-simulated stress-strain curves was good. This reaffirmed the model's ability to predict the pseudoelastic behaviour of the shape memory materials under various loading states. These results thus validate especially the temperature sensitivity of Nitinol alloys, which should be taken into account when designing devices that need to function reliably. Pseudoelasticity ; Shape-memory alloy ; Materials science ; Thermodynamics ; Mechanics ; Physics ; Metallurgy ; Martensite Shape Memory Alloy Transformations ; Topology Optimization in Engineering
Authors: Leiqi Zhang | Qiliang Wu | Min Liu | Hao Chen | Dianji Wang | Xuefang Li | Qingxin Ba
Source: Energies (article, 2025-01-07)
Field: Materials Science — Materials Chemistry

Hydrogen Leakage Location Prediction in a Fuel Cell System of Skid-Mounted Hydrogen Refueling Stations Hydrogen safety is a critical issue during the construction and development of the hydrogen energy industry. Hydrogen refueling stations play a pivotal role in the hydrogen energy chain. In the event of an accidental hydrogen leak at a hydrogen refueling station, the ability to quickly predict the leakage location is crucial for taking immediate and effective measures to prevent disastrous consequences. Therefore, the development of precise and efficient technologies to predict leakage locations is vital for the safe and stable operation of hydrogen refueling stations. This paper studied the localization technology of high-risk leakage locations in the fuel cell system of a skid-mounted hydrogen refueling station. The hydrogen leakage and diffusion processes in the fuel cell system were predicted using CFD simulations, and the hydrogen concentration data at various monitoring points were obtained. Then, a multilayer feedforward neural network was developed to predict leakage locations using simulated concentration data as training samples. After multiple adjustments to the network structure and hyperparameters, a final model with two hidden layers was selected. Each hidden layer consisted of 10 neurons. The hyperparameters included a learning rate of 0.0001, a batch size of 32, and 10-fold cross-validation. The Softmax classifier and Adam optimizer were used, with a training set for 1500 epochs. The results show that the algorithm can predict leakage locations not included in the training set. The accuracy achieved by the model was 95%. This approach addresses the limitations of sensor detection in accurately locating leaks and mitigates the risks associated with manual inspections. This paper provides a feasible method for locating hydrogen leakage in hydrogen energy application scenarios. Leakage (economics) ; Skid (aerodynamics) ; Hydrogen ; Hydrogen vehicle ; Fuel cells ; Environmental science ; Automotive engineering ; Engineering ; Hydrogen fuel ; Structural engineering ; Chemistry ; Chemical engineering Nuclear Materials and Properties ; Hydrogen Storage and Materials ; Combustion and Detonation Processes
Authors: Jan Sawicki | Marcin Feldo | Agnieszka Skalska-Kamińska | Ireneusz Sowa
Source: Molecules (review, 2025-07-05)
Field: Chemistry — Analytical Chemistry

Modern Bioimaging Techniques for Elemental Tissue Analysis: Key Parameters, Challenges and Medical Impact (1) Background: Elemental imaging methods such as XRF, SEM/TEM-EDS, LIBS and LA-ICP-MS are widely used in clinical diagnostics. Based on the results obtained, it is possible to assess the safety of both standard and innovative therapies, diagnose diseases, detect pathogens or determine intracellular processes. In addition to bioimaging, these techniques are used for semi-quantitative and quantitative analyses. Some of them also enable highly valuable speciation of analytes. However, the quality of information about elemental tissue composition depends on a number of different factors. Although the crucial parameters of quantitative analysis are the same for each technique, their impact varies depending on the bioimaging method. Due to the fact that imaging results are often crucial in clinical decision-making, it is important to clearly indicate and describe the parameters affecting the quality of results in each technique. Therefore, the aim of this review is to describe the influence of these crucial parameters on bioimaging results based on the methodology and results of studies published in the last ten years. (2) Methods: In order to collect relevant publications, the Scopus database was searched using the keywords "element AND imaging AND human tissue". Next, studies were selected in which methodological aspects allowed relevant conclusions to be made regarding the quality of the results obtained. (3) Results: One of the most important parameters for all techniques is measurement selectivity resulting from the complexity of human tissue. Quantitative analyses using bioimaging techniques are difficult due to the lack of suitable calibration materials. For the same reason, it is challenging to assess the accuracy of the results obtained. Particular attention should be paid to the results obtained for trace elements. (4) Conclusions: The discussed bioimaging techniques are a powerful tool in the elemental analysis of human tissues. Nevertheless, in order to obtain reliable results, a number of factors influencing the measurements must be taken into account. Key (lock) ; Elemental analysis ; Computer science ; Data science ; Nanotechnology ; Systems engineering ; Medical physics ; Risk analysis (engineering) ; Computational biology ; Chemistry ; Engineering ; Materials science ; Medicine ; Biology ; Computer security Analytical chemistry methods development ; Medical Imaging Techniques and Applications ; Advanced X-ray and CT Imaging
Authors: Dennis Semyalo | Rahul Joshi | Ye-Na Kim | Emmanuel Omia | Lorna Bridget Alal | Moon S. Kim | Insuck Baek | Byoung–Kwan Cho
Source: Foods (article, 2025-08-18)
Field: Chemistry — Analytical Chemistry

Short-Wavelength Infrared Hyperspectral Imaging and Spectral Unmixing Techniques for Detection and Distribution of Pesticide Residues on Edible Perilla Leaves Pesticide residue analysis of agricultural produce is vital because of associated health concerns, highlighting the need for effective non-destructive techniques. This study introduces a method that combines short-wavelength infrared hyperspectral imaging with spectral unmixing to detect chlorfenapyr and azoxystrobin residues on perilla leaves. Sixty-six leaves were treated with pesticides at concentrations between 0 and 0.06%. The study utilized multicurve resolution-alternating least squares (MCR-ALS), a spectral unmixing method, to identify and visualize the distribution of pesticide residues. This technique achieved lack-of-fit values of 1.03% and 1.78%, with an explained variance of 99% for both pesticides. Furthermore, a quantitative model was developed that integrates insights from MCR-ALS with Gaussian process regression to estimate chlorfenapyr residue concentrations, resulting in a root mean square error of double cross-validation (RMSEV) of 0.0012% and a double cross-validation coefficient of determination (R<sup>2</sup>v) of 0.99. Compared to other chemometric approaches, such as partial least squares regression and support vector regression, the proposed integrated method decreased RMSEV by 67.57% and improved R<sup>2</sup>v by 2.06%. The combination of hyperspectral imaging with spectral unmixing offers advancements in the real-time monitoring of agricultural product safety, supporting the delivery of high-quality fresh vegetables to consumers. Hyperspectral imaging ; Pesticide residue ; Partial least squares regression ; Pesticide ; Chemometrics ; Perilla ; Biological system ; Chlorfenapyr ; Pattern recognition (psychology) ; Environmental science ; Mathematics ; Remote sensing ; Chemistry ; Computer science ; Artificial intelligence ; Statistics ; Agronomy ; Chromatography ; Biology Spectroscopy and Chemometric Analyses ; Pesticide Residue Analysis and Safety ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Qi Wang | Ziyan Shi | Kaiyao Hou | Ning Yan | Cuiyun Wu | Xu Li
Source: Sensors (article, 2025-04-17)
Field: Chemistry — Analytical Chemistry

Smartphone-Based SPAD Value Estimation for Jujube Leaves Using Machine Learning: A Study on RGB Feature Extraction and Hybrid Modeling Chlorophyll content in date leaves is critical for fruit quality and yield. Traditional detection methods are usually complex and expensive. This study proposes a rapid detection method for chlorophyll content using smartphone images and machine learning and deep learning models. The SPAD values and RGB images of Xinjiang date palm were collected. The RGB images were preprocessed and their color features were extracted using Python and OpenCV. Through correlation analysis, 21 color features highly correlated with chlorophyll content were selected and downscaled with principal component analysis. Models including SVR, RVM, CNN, CNN-SVR, and CNN-RVM were used for prediction. Among them, the CNN-SVR model showed the most stable performance with R<sup>2</sup> values of 72.21% and 77.44% on the training and validation sets, respectively, which outperformed the other models. The proposed method is simple, cost-effective, and highly accurate, providing a novel technical approach for accurate management and health monitoring in the date industry. This method has the potential for wide application. RGB color model ; Artificial intelligence ; Computer science ; Support vector machine ; Feature extraction ; Python (programming language) ; Principal component analysis ; Pattern recognition (psychology) ; Machine learning ; Deep learning Spectroscopy and Chemometric Analyses ; Remote Sensing in Agriculture ; Date Palm Research Studies