jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Bohan Huang | Xue Long | Jie Yang | Chaoyang Yin | Keke Liao | Muhua Liu | Jing Li | Mengmeng Shang
Source: Journal of Food Science (article, 2025-01-01)
Field: Chemistry — Analytical Chemistry

Regression study on fruit‐setting days of purple eggplant fruit based on in situ VIS‐NIRS and attention cycle neural network In the intelligent harvesting of eggplant, the lack of in situ identification technology makes it challenging to determine the maturity of purple eggplant fruit. The length of the fruit-setting date can determine when the eggplant is ready to be harvested. This study uses deep learning techniques to predict the date of fruit maturity. First, we proposed a fruit-setting days prediction method based on fruit spectroscopy and neural networks. Second, we collected the field in situ spectral data of purple eggplant fruit during 15-33 days of fruit setting using a portable spectrometer, covering 500-1000 nm. A fruit-setting time regression network combining multi-scale convolution, multi-head attention mechanism, and long short-term memory recurrent neural network was constructed using the collected in situ spectral data. The model demonstrated better fitting performance than traditional machine learning models such as backpropagation neural network, random forest, support vector machine, and partial least squares regression in the regression task of fruit-setting days. After testing various spectral preprocessing methods, the best fitting effect was found on the standard normal variate-processed dataset, with R<sup>2</sup> of 0.876 and RMSE(root mean square error) of 2.148 days. Furthermore, the feasibility of each model module was analyzed in depth through ablation experiments, confirming each component's role in improving the model's performance. The network attention weight was also analyzed, and the model has strong detail mining ability in a specific spectral interval. In summary, the combination of visible and near-infrared spectroscopy and attention cycle neural network is an effective method to predict the fruit-setting days of purple eggplant fruit. PRACTICAL APPLICATION: A prediction method of fruit-setting days based on fruit spectral characteristics and recurrent neural network regression was proposed. A novel approach to detecting and disclosing in situ surface VIS-NIRS reflectance data of eggplant fruit during ripening is presented for the first time. A set of long-term and short-term memory networks based on multi-scale convolution and multi-head attention mechanisms was constructed for spectral data fitting. Through the ablation test method and attention weight analysis, the function of each module in the network and the interpretability of feature extraction are explored. Biology ; Regression ; Regression analysis ; Horticulture ; In situ ; Artificial neural network ; Linear regression ; Botany ; Mathematics ; Chemistry ; Statistics ; Computer science ; Machine learning Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Remote Sensing and Land Use
Authors: Alexander W. Alund | Li Xia | Tao Chen
Source: Journal of Environmental Science and Health Part C (review, 2025-05-17)
Field: Materials Science — Materials Chemistry

Genotoxicity of nanoparticles evaluated using the <i>in vitro</i> micronucleus assay, a review of recent data <sup>§</sup> The <i>in vitro</i> micronucleus assay is a well-known and established component of the standard genotoxicity test battery. The growing use of nanomaterials around the world along with human exposure to them has increased the need for risk assessment with regard to safety, including potential genotoxicity. The <i>in vitro</i> micronucleus assay is one of the most used tests for evaluating the genotoxicity of nanomaterials. This review compiles studies since 2017 that performed assessments of micronucleus formation <i>in vitro</i> after cellular exposure to different nanomaterials. Genotoxicity of a broad range of nanomaterials including silver, cerium, zinc, gold, nickel, cadmium, titanium, carbon, and aluminum in different cell types were reviewed. While clear trends could be seen for some nanoparticle types like silver and cerium nanoparticles, others like gold nanoparticles showed mixed results. This review highlights the usefulness and effectiveness of the micronucleus assay for studying the genotoxicity of nanomaterials, in part, and is also careful to note that standard guidelines should be followed when conducting this assay in order to generate reliable and quality-driven data. Genotoxicity ; Micronucleus test ; Micronucleus ; In vitro ; Nanoparticle ; Chemistry ; Computational biology ; Molecular biology ; Biology ; Nanotechnology ; Materials science ; Biochemistry ; Toxicity ; Organic chemistry Nanoparticles: synthesis and applications ; Carcinogens and Genotoxicity Assessment ; Effects and risks of endocrine disrupting chemicals
Authors: Luis César Rodríguez Aliaga | Pedro Henrique Pedro Henrique Ferreira De Souza | Ivan Napoleão Bastos | Alexandre Melhorance Barboza
Source: Materials Research (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Compositional Influence on Martensitic Transformations in Ni-Ti Alloys by Molecular Dynamics Over the last decades, high-entropy refractory alloys with shape memory (SM) effect have been increasingly studied. However, identifying the optimal composition among the vast array of potential systems remains a significant challenge for the scientific community. In this work, Ni-Ti alloys are studied via molecular dynamics simulations to determine the range of martensitic phase transformations, with the goal of developing in the future medium- (MEA) and high-entropy alloys (HEA) that exhibit SM behavior. Virtual samples with compositions of Ni(100-x)Tix (45 &lt; x &lt; 55) were created using the LAMMPS software, in a system consisting of 2000 atoms that interacted under the modified embedded atom method (MEAM) interatomic potential. In addition, simulations were also performed on systems with sizes of 4000 and 8000 atoms to assess the effect of system size on the phase transformation behavior. The structural evolution and phase transformations were analyzed by applying heating and cooling rates of 1 K/ps, controlled by the isothermal-isobaric (NPT) ensemble. The compositional range, in atom percent, for martensitic transformations was found to be 47.5 to 52.5% nickel. Martensitic transformations are complex, showing either direct or two-step transformations. At lower temperatures, the phases R and B19’ can be formed as a function of Ni content. In several cases, at intermediate temperatures, the Ni4Ti3 compound forms and influences the phase transformation process. Also, the larger the system size, the lower the phases transformation temperatures. Materials science ; Molecular dynamics ; Dynamics (music) ; Martensite ; Metallurgy ; Crystallography ; Chemistry ; Computational chemistry ; Microstructure ; Psychology Shape Memory Alloy Transformations ; Intermetallics and Advanced Alloy Properties ; Microstructure and Mechanical Properties of Steels
Authors: Yanyue Huang | Hua Zhao | Qin Luo | Yulin Xu | Yin Shi | Yongjun Hu
Source: Research Square (preprint, 2025-05-15)
Field: Chemistry — Organic Chemistry

Quantitative analysis of camellia oil in blending vegetable oil based on Raman spectroscopy and deep learning models Raman spectroscopy ; Vegetable oil ; Artificial intelligence ; Oil analysis ; Spectroscopy ; Materials science ; Machine learning ; Environmental science ; Petroleum engineering ; Computer science ; Chemistry ; Engineering ; Food science ; Optics ; Physics Edible Oils Quality and Analysis ; Spectroscopy and Chemometric Analyses
Authors: 范旭菲 Fan Xufei | 薛文瑞 Xue Wenrui | 孟凡义 Meng Fanyi
Source: Acta Optica Sinica (Online) (article, 2025-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

基于串联神经网络的紫外线吸收器的逆向设计 设计出一种由氮化钛（TiN）衬底、二氧化硅（SiO2）电介质层和TiN图案层构成的紫外线超材料吸收器。采用有限元法（FEM）分析该吸收器的吸收特性并建立数据集。采用基于串联神经网络的深度学习模型对该吸收器进行逆向优化设计，极大地加快了设计过程。结果表明：采用优化后的结构参数，吸收器在200~400 nm范围内，在正入射条件下，吸收率大于94.3%，其中265~375 nm范围内的吸收率可达到97.0%；在50°入射条件下，横磁（TM）波入射后的平均吸收率达到82.6%，横电（TE）波入射后的平均吸收率达到83.0%。该吸收器的吸收机理为腔共振效应。与现有文献报道的吸收器相比，所设计的紫外线超材料吸收器不仅结构简单、易于实现，还在紫外波段展现出更高的吸收率，且具有大入射角稳定性及偏振无关性。 Computer science Metamaterials and Metasurfaces Applications ; Photochromic and Fluorescence Chemistry
Authors: Amós Freitas de Figueirêdo | Raphael Henrique Falcão de Melo
Source: Revista Principia - Divulgação Científica e Tecnológica do IFPB (article, 2025-07-07)
Field: Materials Science — General Materials Science

Bibliometric analysis of AZ31 alloy welding: trends in the use of the friction stir welding process This study presents a bibliometric analysis of research related to lightweight alloys, particularly the AZ31 magnesium alloy, and the friction stir welding (FSW) process. A structured search using the keywords “Weld* AND AZ31*” was conducted in the Web of Science database. From the 1,681 retrieved articles, titles, keywords, and abstracts were analyzed to identify prevailing themes, commonly used terms, and emerging research trends. These trends were examined in the context of macroeconomic and socio-environmental factors, especially the Environmental, Social, and Governance (ESG) agenda and the role of lightweight alloys in reducing greenhouse gas (GHG) emissions. Magnesium alloys such as AZ31 are employed to produce components with up to 30% less mass than equivalent aluminum parts, contributing to lower fuel consumption and emissions in transportation. The choice of materials must consider the interaction between material, function, process, and form. In this regard, FSW, a solid-state joining process that eliminates the need for filler materials and shielding gases, is particularly effective for joining AZ31 alloys, reducing manufacturing costs and welding defects commonly associated with fusion-based methods. The analysis indicates a growing academic interest in AZ31 alloys, driven by their low density and favorable mechanical properties. However, a notable gap remains regarding multi-pass FSW and the use of machine learning techniques to predict weld behavior. Terms like “double side” or “double pass” appear in only 0.47% of the dataset. In comparison, co-occurrences of “FSW” and “Machine Learning” are limited to 0.3%, featuring techniques such as deep neural networks, decision trees, XGBoost, and random forests. The most active research groups are based in countries with high production and consumption of magnesium alloys, including China, India, Japan, the United States, and Canada. Brazil, despite being a major magnesite producer, imports metallic magnesium, highlighting the need for national policies to support technological development. This study contributes by identifying research gaps and proposing future directions in sustainable welding and manufacturing. Friction stir welding ; Welding ; Metallurgy ; Alloy ; Process (computing) ; Materials science ; Manufacturing engineering ; Engineering ; Computer science Material Properties and Applications ; Surface Treatment and Coatings ; Advanced Welding Techniques Analysis
Authors: José Luis Ávila-Jiménez | Ann-Sophie Bülter | Ludwig Horsthemke | Francisco J. Rodriguez‐Lozano | Manuel Ortíz-López | Peter Gloesekoetter
Source: IEEE Sensors Journal (article, 2025-11-10)
Field: Materials Science — Materials Chemistry

Enhancing All-Optical NV Center Magnetometry With Machine Learning: Model Selection for Efficient and Deployable Quantum Sensors Negatively charged nitrogen–vacancy (NV) centers in diamond are promising platforms for magnetic field sensing under ambient conditions, yet practical deployment is often limited by hardware complexity, power consumption, and computational cost. All-optical approaches based on fluorescence lifetime measurements avoid microwave excitation and enable simpler, potentially more scalable sensor designs, but the nonlinear response to magnetic and thermal fields requires advanced modeling. In this study, a systematic comparison of eleven regression algorithms—including linear models, ensemble methods, support vector machines, and symbolic regression—was conducted for predicting magnetic field strength from NV fluorescence lifetime data. A dataset of 22,758 measurements was employed, and models were assessed in terms of both predictive accuracy and computational footprint (training time, model complexity, and inference latency). The results indicate that ensemble techniques such as LightGBM and XGBoost achieve near-perfect accuracy while maintaining sub-second inference times, making them suitable for real-time embedded deployment. In contrast, symbolic regression provides negligible inference cost but suffers from significantly lower accuracy. These findings offer quantitative guidance for the selection of machine learning models that balance precision and efficiency, paving the way toward compact, low-power, all-optical NV magnetometers for resource-constrained platforms. Support vector machine ; Magnetometer ; Inference ; Scalability ; Symbolic regression ; Field (mathematics) ; Model selection ; Power (physics) ; Footprint Diamond and Carbon-based Materials Research ; Magneto-Optical Properties and Applications ; Quantum optics and atomic interactions
Authors: Suryani Dyah Astuti | A. Yessica | Dezy Zahrotul Istiqomah Nurdin | Ahmad Khalil Yaqubi | Dedy Arifianto | Yunus Susilo | Andi Hamim Zaidan | Nasrul Anuar Abd Razak
Source: Brazilian Journal of Biology (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

The effect of coffee as a bio reductant in the synthesis of silver nano particles combined with laser photodynamics for bacteria inactivation Silver nanoparticles (AgNPs) have gained significant attention in antimicrobial treatments due to their potent antibacterial properties and potential applications in combating antibiotic-resistant pathogens. Green synthesis of AgNPs using natural extracts provides an eco-friendly and scalable alternative to chemical methods, leveraging plant bioactive compounds to enhance nanoparticle efficacy. Coffee extract, rich in polyphenols, effectively reduces mixed silver ions into pure silver nanoparticles at the nanoscale. This process not only provides an eco-friendly and sustainable method for AgNP synthesis but also ensures the production of high-quality nanoparticles with enhanced antibacterial properties, making it a promising alternative to traditional chemical reduction methods. This study investigates the biosynthesis of AgNPs using robusta, excelsa, and arabica coffee extracts as bioreductors, capitalizing on coffee's abundance and richness in polyphenols to produce efficient, customizable nanoparticles with improved antibacterial properties. The synthesized AgNPs were characterized using Ultraviolet-Visible (UV-Vis) spectroscopy and a Particle Size Analyzer (PSA). Antimicrobial photodynamic therapy (aPDT) with red laser exposure was employed to evaluate the effect of various energy densities on bacterial samples. Antibacterial efficacy was assessed using the diffusion well and Total Plate Count (TPC). The UV-Vis analysis revealed peak absorbance wavelengths of 425 nm, 450 nm, and 500 nm for robusta, arabica, and excelsa coffee extracts, respectively. PSA results indicated particle sizes at D50 of 73.08 nm (AgNPs-Arabica), 67.85 nm (AgNPs-Robusta), and 67.67 nm (AgNPs-Excelsa), confirming their nanoscale range (1-100 nm). Antibacterial tests showed the highest Escherichia coli bacterial death rate (95.73%) with AgNPs-Arabica and red laser treatment. Compared, Staphylococcus aureus bacterial death peaked at 94.97% with AgNPs-Excelsa and red laser treatment. These findings highlight the potential of green-synthesized AgNPs as effective antimicrobial agents, particularly when combined with laser-based therapies, offering innovative approaches for treating bacterial infections. Silver nanoparticle ; Absorbance ; Antimicrobial ; Antibacterial activity ; Materials science ; Nanoparticle ; Polyphenol ; Nuclear chemistry ; Nanotechnology ; Bacteria ; Biology ; Chemistry ; Antioxidant ; Chromatography ; Microbiology ; Biochemistry Nanoparticles: synthesis and applications
Authors: Boxue Chang | Jingyue Huang | Qingli Xie | Yinlan Ruan | Rukuan Liu
Source: Molecules (article, 2025-06-05)
Field: Chemistry — Analytical Chemistry

Identification, Geographical Traceability, and Thermal Oxidation and Photodegradation Studies of Camellia Oil Based on Raman Spectroscopy Camellia oil, rich in monounsaturated fatty acids, squalene, tocopherols, and polyphenols, is highly valued for its nutritional benefits. However, its high market value and regional variations have led to frequent adulteration, highlighting the need for rapid, non-destructive methods for authentication, geographical traceability, and quality assessment. This study employed portable Raman spectroscopy combined with Partial Least Squares Discriminant Analysis (PLS-DA) and Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) to differentiate camellia oil from other edible oils and evaluate its thermal and photo-oxidative stability. PLS-DA, based on VIP-selected spectral variables, effectively distinguished camellia oil, with Raman bands near 1250 cm<sup>-1</sup> and 1650 cm<sup>-1</sup> contributing significantly. A unique peak at 1525 cm<sup>-1</sup>, observed in samples from Gongcheng, Guangxi, was associated with carotenoids and served as a potential marker for geographical traceability. MCR-ALS modeling revealed significant reductions in the 1650 cm<sup>-1</sup> and 1525 cm<sup>-1</sup> peaks when temperatures exceeded 150 °C, indicating degradation of unsaturated fatty acids and carotenoids. Under UV exposure, the 1525 cm<sup>-1</sup> peak declined sharply and nearly disappeared after 24 h, suggesting rapid carotenoid degradation via photooxidation. Extended UV treatment also affected the 1650 cm<sup>-1</sup> peak and led to oxidative product accumulation. Overall, this study demonstrates the feasibility of integrating Raman spectroscopy with chemometric analysis for efficient oil classification, traceability, and stability monitoring, offering a valuable tool for food quality control and market supervision. Traceability ; Partial least squares regression ; Camellia oleifera ; Raman spectroscopy ; Squalene ; Camellia ; Lipid oxidation ; Carotenoid ; Food science ; Chemometrics ; Chemistry ; Environmental science ; Analytical Chemistry (journal) ; Mathematics ; Environmental chemistry ; Botany ; Chromatography ; Biology ; Organic chemistry ; Physics ; Biochemistry ; Antioxidant Spectroscopy and Chemometric Analyses ; Edible Oils Quality and Analysis ; Advanced Chemical Sensor Technologies
Authors: Shuhei Muroya | Seishi Maeda | Kei Hirose
Source: Japanese Journal of Statistics and Data Science (article, 2025-11-25)
Field: Materials Science — Materials Chemistry

Sparse multivariate regression with missing values in both predictors and responses In statistical approaches to materials science, it is often necessary to predict multiple material properties simultaneously based on a limited number of experimental observations. However, due to the cost and difficulty in collecting comprehensive data, datasets often have small sample sizes and frequently contain missing values in both predictor and response variables. These issues are further compounded when the dimensionality of the variables is moderate or high, making the analysis even more challenging. To address these challenges, we propose a novel sparse multivariate regression framework that simultaneously handles missing values and performs model estimation. The joint distribution of predictors and responses is assumed to follow a multivariate normal distribution, and the lasso penalties are applied to both the regression coefficients and the inverse covariance matrix. We demonstrate the effectiveness of our method through numerical experiments on both simulated and real datasets. Missing data ; Multivariate statistics ; Multivariate normal distribution ; Curse of dimensionality ; Lasso (programming language) ; Covariance ; Regression ; Regression analysis ; Multivariate analysis ; Sample size determination Machine Learning in Materials Science ; Stochastic Gradient Optimization Techniques ; Statistical Methods and Inference
Authors: Liang‐Wei Kang | Shao‐Song Jiang | J. Zhang | Yang Li | Xinyue Shi | Cheng‐Qian Huang | Peng Peng | Shuhui Yang | Zheng Han | Zhen Lu
Source: Rare Metals (article, 2025-12-24)
Field: Materials Science — Materials Chemistry

Thermohydrogen‐Induced α→β Phase Transition and Interfacial Aggregation: A Diffusion Bonding Mechanism for Ti65 Alloy at Low Temperatures ABSTRACT Diffusion bonding (DB) of near‐α titanium alloys requires high temperatures, which can cause grain coarsening, structural deformations, and degradation of the workpiece's mechanical characteristics. In this study, thermal hydrogen processing (THP) was applied to Ti65, combined with selective magnetron sputtering of a Ti‐Al thin film to promote hydrogen segregation, achieving DB of Ti65 at an ultralow temperature of 750°C, and the joint's shear strength reached 757.9 MPa. The typical microstructure of the hydrogenated Ti65 alloy at room temperature was composed of β‐Ti, α‐Ti, α′, and hydrides. A small fraction of hydrogen‐induced α→β (β H ) phase transformation occurred preferentially along grain boundaries, refining the grain size by 19.9%. Because of hydrogen segregation at the diffusion‐bonded interface, continuous β H ‐phase structures formed throughout the interfacial region, contributing to the stability of the joint. TEM and EBSD results revealed a high density of lattice defects in β H , whereas first‐principles simulations demonstrated an increase in vacancy concentration in β H . The increased lattice defect density significantly accelerated the atomic diffusion rate of the β H ‐phase. Overall, the hydrogen‐induced phase transformation and the stable segregation of the β H ‐phase at the interface have been confirmed as key factors in achieving low‐temperature DB of the Ti65 alloy. Microstructure ; Alloy ; Sputter deposition ; Hydrogen ; Grain size ; Vacancy defect ; Diffusion bonding ; Diffusion ; Atomic diffusion ; Sputtering Titanium Alloys Microstructure and Properties ; Hydrogen embrittlement and corrosion behaviors in metals ; Magnesium Alloys: Properties and Applications