jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Sarem Norouzi | Mogens Humlekrog Greve | Budiman Minasny | Marzieh Zaresourmanabad | Charles Pesch | Lucas Carvalho Gomes | Emmanuel Arthur | Trine Nørgaard | Maria Knadel | Mette Balslev Greve | Lis Wollesen de Jonge
Source:  (preprint, 2025-04-22)
Field: Chemistry — Analytical Chemistry

A Novel Physics-Informed Neural Network for Retrieving the Continuous Soil Particle Size Distribution from Vis-NIR Spectra We present a novel physics-informed neural network (PINN) for the direct estimation of the continuous soil particle size distribution curve (PSD) from visible–near infrared (vis-NIR) reflectance measurements. Unlike traditional methods that focus on predicting soil fractions (e.g., sand, silt, and clay), the PINN takes in the soil particle size as a predictor and learns a continuous, differentiable, and non-parametric representation of the cumulative PSD by integrating both measurements and physical constraints imposed during training. This approach eliminates the need for harmonization and interpolation of measurements originating from different soil texture classification systems and allows the model to be trained on datasets with varying number of measurements per sample. Performance evaluation on 30% of the 2,777 studied samples showed that the PINN achieved an RMSE of 6.77% and an R² of 0.97 in predicting the cumulative PSD fraction. For the texture fractions, the model achieved RMSE values of 4.72%, 3.06%, and 2.75% for sand, silt, and clay, respectively. A comparison with a physics-agnostic (i.e., physics-uninformed) version of the model revealed that both approaches performed similarly in terms of RMSE and R². However, the physics-agnostic model violated physical constraints even in data-rich scenarios. In contrast, the PSD obtained from the PINN maintained its physical integrity even under data sparsity conditions and consistently produced non-negative, monotonically increasing predictions that sum to 100% at the largest particle size. Artificial neural network ; Statistical physics ; Particle-size distribution ; Distribution (mathematics) ; Spectral line ; Physics ; Particle (ecology) ; Particle size ; Particle physics ; Theoretical physics ; Mathematics ; Computer science ; Artificial intelligence ; Quantum mechanics ; Biology ; Ecology ; Chemistry ; Mathematical analysis ; Physical chemistry Spectroscopy and Chemometric Analyses
Authors: Abhineet Samadhiya | Pradeep Kumar Jhinge | Kamal Kumar Kushwah
Source: Journal of Scientific & Industrial Research (article, 2025-05-01)
Field: Materials Science — Surfaces, Coatings and Films

Superhydrophobic Silica-based Nano-Coatings for Anti-Reflective and Anti-Soiling Surface: Effect of HMDS and DIPEA This study presents a novel nano-silica-HMDS (hexamethyldisilazane) surface coating, synthesized through an efficient chemical route, exhibiting anti-reflective, superhydrophobic, and anti-soiling properties suitable for solar panel top covers working in dusty environments. The synthesized surface coating enhances optical transmission, mitigating incident light reflection and performance loss from soiling. The experiment results show a 5.8% average power improvement in the nano-silica-HMDS-coated solar photovoltaic cell compared to a manually cleaned, uncoated solar cell. The cost-effective dip-coating method applied to the substrate surface at room temperature offers a practical alternative to costly surface coatings. The surface coating exhibits a finely tuned refractive index of 1.14 and 96.1% transmittance for wavelengths ranging from 400–800 nm demonstrating exceptional transparency and optical effectiveness. The Field Emission Scanning Electron Microscopy (FESEM) and Atomic Force Microscopy (AFM) results show a highly rough HMDS-modified-nano-silica surface exhibiting anti-soiling and super-hydrophobicity characteristics confirmed by a water contact angle of 156° in test results. The synthesized surface coating also showed excellent stability over two months in a dusty environment consisting of abundant particulate particles pm 2.5 and pm 10 observed in environmental stability tests conducted over 2.5 months with the help of an experimental setup. The test results confirm the nano-silica-HMDS (0.3:1) surface coating as the most optimum concentration for simultaneously achieving superhydrophobicity, anti-soiling, and antireflection properties. Nano- ; Materials science ; Chemical engineering ; Nanotechnology ; Composite material ; Engineering Surface Modification and Superhydrophobicity ; Synthesis and properties of polymers ; Aerogels and thermal insulation
Authors: Shidan Yuan | Ye Ma | Hui Yang | Zhen Ma | Lei Chen
Source: Coatings (article, 2025-04-29)
Field: Materials Science — Materials Chemistry

Comparison of Color Metallography and Electron Microscopy in Characterizing the Microstructure of H59 Brass Alloy Color metallographic samples of H59 brass alloy have been prepared by aqua regia etching. In this paper, combined with XRD (X-ray diffraction), traditional metallographic analysis, FE-SEM (field emission scanning electron microscopy), and EBSD (electron backscattering diffraction), the effect of the color metallographic method on the microstructure characterization of the H59 brass alloy was analyzed. The experimental results showed that α phases and β phases could be distinguished clearly, and the phase morphology, distribution, and content could be expressed accurately with an average error value of 5.25% for the α phase and 4.71% for the β phase. The average error rate of the phase content characterization was 4.98% (less than 5%) with the color metallographic method. In addition, it was also found in the study that the brightness and darkness of the β phase would be related to the grain orientation, and the bright grains would correspond to the low-index surface, and the dark grains to the high-index surface. As a low-cost, fast, and efficient characterization technique, the experimental results show that color metallography (CM) could replace electron backscattering diffraction (EBSD) for the analysis of the phase composition, phase distribution, phase content, and grain orientation of brass alloys, which would provide an experimental basis for optimizing alloy properties and expanding applications. Metallography ; Brass ; Microstructure ; Alloy ; Electron microscope ; Metallurgy ; Materials science ; Scanning electron microscope ; Microscopy ; Composite material ; Optics ; Copper ; Physics Metallurgy and Material Science ; Aluminum Alloy Microstructure Properties ; Metallurgy and Material Forming
Authors: Min Li | Xinzhi Qi | Rongyao Li | Rufeng Wang | Dandan Yu
Source: Scalable Computing Practice and Experience (article, 2025-07-14)
Field: Chemistry — Analytical Chemistry

Near Infrared Spectroscopy Detection of Soybean Fatty Acid Content based on Neural Network combined with Genetic Algorithm In order to solve the problem of poor performance in rapid analysis of soybean fatty acid content using traditional methods, the author proposes a near-infrared spectroscopy detection method for soybean fatty acid content based on neural network combined with genetic algorithm. The author first collected sample spectra and preprocessed them using mean centralization and Savitzky Golay smoothing differentiation method. Then, the optimal band was selected through segmented combination modeling, and genetic algorithm was used to further screen wavelength points sensitive to content prediction modeling. Finally, the spectral data was decomposed using principal component analysis (PCA), and the score matrix was input into a 3-layer neural network for training. The optimal model was established through parameter optimization. The experimental results show that the calibration relative analysis error RPD of the model based on genetic algorithm combined with neural network is, indicating that the model has good prediction accuracy and stability. To further validate the model’s reliability in practical applications, the correlation coefficient R between the predicted values and the standard values within the model’s prediction range was found to be 0.986, indicating a strong linear relationship between them. The relative deviation of each value is less than 2%, and the standard deviation of the difference is 0.0091. Paired t-test =1.2706 was performed on both methods, which is less than 2.354. This indicates that there is no significant difference between the analysis results of this method and the standard method, verifying the strong predictive ability of the model in practical applications. The model designed by the author can accurately and efficiently complete near-infrared spectroscopy detection. Computer science ; Artificial neural network ; Content (measure theory) ; Algorithm ; Genetic algorithm ; Spectroscopy ; Infrared spectroscopy ; Infrared ; Artificial intelligence ; Biological system ; Pattern recognition (psychology) ; Chemistry ; Machine learning ; Mathematics ; Biology ; Physics ; Optics ; Organic chemistry Spectroscopy and Chemometric Analyses ; Advanced Algorithms and Applications
Authors: Aoi Watanabe | Ryuhei Sato | Ikuya Kinefuchi | Yasushi Shibuta
Source: ArXiv.org (preprint, 2025-04-15)
Field: Materials Science — Materials Chemistry

douka: A universal platform of data assimilation for materials modeling A large-scale, general-purpose data assimilation (DA) platform for materials modeling, douka, was developed and applied to nonlinear materials models. The platform demonstrated its effectiveness in estimating physical properties that cannot be directly obtained from observed data. DA was successfully performed using experimental images of oxygen evolution reaction at a water electrolysis electrode, enabling the estimation of oxygen gas injection velocity and bubble contact angle. Furthermore, large-scale ensemble DA was conducted on the supercomputer Fugaku, achieving state estimation with up to 8,192 ensemble members. The results confirmed that runtime scaling for the prediction step follows the weak scaling law, ensuring computational efficiency even with increased ensemble sizes. These findings highlight the potential of douka as a new approach for data-driven materials science, integrating experimental data with numerical simulation. Data assimilation ; Scaling ; Nonlinear system ; Supercomputer ; Bubble ; Data modeling ; Ensemble forecasting Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Generative Adversarial Networks and Image Synthesis
Authors: Felix C. Mocanu | Stephen R. Elliott | Konstantinos Konstantinou
Source: physica status solidi (RRL) - Rapid Research Letters (article, 2025-04-10)
Field: Materials Science — Materials Chemistry

Partial Melting and Structural Disorder in Models of Irradiated Amorphous Ge<sub>2</sub>Sb<sub>2</sub>Te<sub>5</sub> Identifying the link between the energy landscape that arises in chemically complex amorphous solids and the evolution of the structural disorder in the host glassy matrix upon ion irradiation can provide a fundamental understanding needed for the development of functional materials with enhanced radiation tolerance. Herein, a comprehensive analysis of the structural transformations that occur when an amorphous phase‐change memory material is exposed to radiation‐damage events is presented. Different measures of atomic‐structural (dis)order—geometric, topological, and chemical—are analyzed in models of irradiated glassy . The modeling results demonstrate that the degree of chemical disorder is increased within the postirradiation recovered amorphous network of , while local atomic environments with homopolar bonds are formed in the geometry of the irradiated structures, as a result of the primary knock‐on atom simulations. The molecular dynamics trajectories of the high‐energy nonequilibrium cascades indicate partial local melting of the glass structure. The simulated system can access liquid‐like states in the energy landscape when irradiated, which lowers the energy barriers for local relaxation, leading to further atomic rearrangements and a structural recovery of the glass network. This observation provides an important insight into the radiation tolerance of these amorphous materials. Amorphous solid ; Irradiation ; Materials science ; Crystallography ; Physics ; Chemistry ; Nuclear physics Phase-change materials and chalcogenides ; Glass properties and applications ; Metallic Glasses and Amorphous Alloys
Authors: Changxin Dong | Samya Sen | Athena Kolli | Stewart G. Wilson | Eric A. Appel
Source: Advanced Engineering Materials (article, 2025-10-03)
Field: Materials Science — Polymers and Plastics

Advancing Wildfire‐Retardant Materials: Engineering Strategies for Direct and Indirect Suppression Wildfires cause widespread destruction to ecosystems and critical infrastructure, with rising frequency and severity due to climate change. These growing challenges have spurred significant innovation in fire suppression strategies and materials. Over time, direct suppression methods have progressed from inorganic chemical solutions to advanced materials such as water‐enhancing polymers and hydrogels, which have also demonstrated effectiveness in retardation and long‐term fire prevention. While prescribed burns and fuel management remain vital for wildfire management, concerns regarding the ecological sustainability and long‐term forest health impacts of traditional methods persist. This has driven increased emphasis on the development of advanced fire retardants, particularly those formulated with biodegradable and environmentally friendly materials, to enhance suppression capabilities while supporting long‐term ecosystem conservation. Despite these advancements, significant limitations persist, including scalability, cost‐effectiveness, and performance variability under diverse wildfire conditions. This review focuses on the evolution, ecological impact, and performance of current fire‐retardant materials and suppression strategies, offering an engineering perspective to address existing challenges and propose pathways for the development of more effective, scalable, and sustainable solutions to meet the demands of a changing climate. Sustainability ; Sustainable development ; Climate change ; Ecosystem ; Global warming ; Environmentally friendly Flame retardant materials and properties
Authors: João Carlos Queiroz | Ivan Silva | Eduardo Simas Filho | ÂNGELO MÁRCIO OLIVEIRA SANTANNA
Source:  (article, 2025-12-01)
Field: Materials Science — Metals and Alloys

Classificação do tempo de tratamento térmico em Aço SAF 2507 após Tratamento Térmico Utilizando SVM O aço inoxidável Superduplex SAF 2507 é amplamente utilizado em ambientes agressivos devido à sua alta resistência à corrosão e à tração. Sua microestrutura é influenciada pelas variações térmicas que podem induzir alterações que afetam o desempenho mecânico do material. A avaliação não destrutiva da microestrutura é essencial para garantir a qualidade e confiabilidade do aço. O ensaio por ultrassom promete ser uma solução, pois existe variação do sinal ultrassônico no domínio da frequência produzidas por mudanças na microestrutura. Este estudo propõe a implementação de uma máquina de vetor de suporte (SVM) para classificar a microestrutura do aço SAF 2507 após tratamento térmico a 900°C, utilizando características dos sinais de ultrassom no domínio da frequência como entradas. Foram coletados 950 sinais de ultrassom em corpos de prova de aço SAF 2507 submetidos a tratamento térmico a 900°C por diferentes tempos. Os sinais de ultrassom foram pré-processados para janelar a região de interesse e obter as características do sinal no domínio da frequência. Um algoritmo SVM foi treinado com os dados pré-processados para classificar a microestrutura em diferentes tempos de tratamento térmico. Também foi utilizado um modelo de regressão logística para comparar os resultados com o SVM. O desempenho do SVM foi avaliado utilizando métricas de precisão e o resultado da regressão logística. A SVM apresentou alto desempenho na classificação da microestrutura do aço SAF 2507, com acurácia global de 97.72%, contra 94.05% do modelo linear. Os resultados indicam que a SVM é uma ferramenta promissora para avaliação não destrutiva da microestrutura em aços superduplex. Support vector machine ; Microstructure ; Reliability (semiconductor) ; Noise (video) Hydrogen embrittlement and corrosion behaviors in metals ; Tailings Management and Properties ; Concrete and Cement Materials Research
Authors: Yuqian Wang | Gaoxiang Xu | Louzhen Fan | Runpu Shen | Song Gao | Yulu Wang | Hui‐Fang Cui | Junyang Chen
Source: Advanced Functional Materials (article, 2025-08-13)
Field: Materials Science — Materials Chemistry

Plasmonic‐Fluorescent Au Hybrids with Silver‐Regulated Optical Switching Synergized with Deep Learning Enabling Intelligent Biosensing Abstract The synergistic enhancement of signal transduction and data processing significantly improves the performance of biosensors. Herein, a deep learning assisted artificial biosensing platform is developed, featuring a dual‐mode signal response derived from silver‐modulated gold nanorods and gold nanoclusters, for detecting 2,4‐dichlorophenoxyacetic acid (2,4‐D). The platform integrates Ag‐regulated fluorescence intensity variations and localized surface plasmon resonance shifts, enabling simultaneous fluorescent and colorimetric readouts. Leveraging the reduction of silver ions by ascorbic acid (AA) and the inhibitory effect of 2,4‐D on AA production, this platform achieves high‐precision detection of 2,4‐D through complementary signal outputs. The colorimetric assay has a detection limit of 0.24 µg mL −1 in the range of 0.25–9 µg mL −1 , whereas the fluorescence assay has a detection limit of 1.29 ng mL −1 in the range of 20–1600 ng mL −1 , demonstrating their evident complementary effect. To facilitate point‐of‐care testing applications, convolutional neural network (CNN) algorithms are integrated to process dual‐mode signal images. Data analysis reveals a robust correlation (R 2 ≥ 0.97) within 5 s and exhibits excellent performance in the detection of real samples. This work bridges nanomaterial engineering with CNN‐augmented analytics, offering a versatile framework for next‐generation biosensors. Materials science ; Plasmon ; Nanotechnology ; Biosensor ; Fluorescence ; Optical sensing ; Optoelectronics ; Optics Advanced Nanomaterials in Catalysis ; Nanocluster Synthesis and Applications ; Advanced biosensing and bioanalysis techniques
Authors: Rakesh M | Manjunath B.H | Kishor Kumar S | R. Shruthi
Source: International Research Journal of Multidisciplinary Technovation (article, 2025-09-29)
Field: Chemistry — Organic Chemistry

Removal of Congo red Dye from a Synthetic Wastewater Using Parthenium Carbon as a Bio-Adsorbent- An Environmental Sustainability In this study analysis were conducted for synthetically prepared Congo red dye. Activated parthenium carbon derived from Parthenium hysterophorous weed employed as an adsorbent. Physical activated carbon (PAC) &amp; chemical activated carbon (CAC) both were used for experiential under batch studies. Dye molarity 1500 mg/l, other parameters like carbon dose, pH, &amp; time in contact were examined. Optimum adsorbent of 3g/50ml, pH 8.5, time in contact of 60 min were found effective. Column studies were carried to examine the role of dye starting concentration, rate of flow &amp; bed height on process of sorption. The optimum pH and Flow rate was 8.5 and 10 ml/min respectively. Modern techniques like IOT was used in getting real time data of parameters. SEM, FTIR were recorded which shows good surface morphology, group of functional elements (C=O, C-O, O-H). Parthenium weed can be considered as ideal material for dye sorption, the efficiency of sorption studies observed 95-99%. This model best fits for Freundlich isotherm adsorbent is heterogeneous; overall process is economical and sustainable. Sorption ; Parthenium ; Congo red ; Activated carbon ; Adsorption ; Freundlich equation Nanomaterials for catalytic reactions ; Dye analysis and toxicity ; Adsorption and biosorption for pollutant removal
Authors: Maziyar Sabet | Amalia Batrisyia Binti Ans Nawi | D.M. Reddy Prasad
Source: Polymer Engineering and Science (article, 2025-12-20)
Field: Materials Science — Electronic, Optical and Magnetic Materials

<scp>AI</scp> ‐Assisted Design of Halogen‐Free <scp>EVA</scp> /Mg( <scp>OH</scp> )2– <scp>CNT</scp> / <scp>GNP</scp> Nanocomposites for Enhanced Electrical, Thermal, and Flame‐Retardant Performance ABSTRACT Flexible and wearable electronics require polymer composites that unite electrical conductivity, thermal management, mechanical compliance, and inherent flame retardancy. Conventional halogen‐free EVA systems often optimize a single property and rely on qualitative dispersion assessment, limiting reproducibility and low‐loading multifunctionality. Here, EVA/Mg(OH) 2 nanocomposites reinforced with hybrid multi‐walled carbon nanotubes (MWCNTs) and graphene nanoplatelets (GNPs) were evaluated using an AI‐assisted FESEM image‐segmentation framework. Dispersion was quantified via network connectivity, inter‐particle distance, and agglomeration index, and these metrics correlated strongly with electrical and thermal transport. The optimized 5 wt% hybrid formulation achieved σ = 1.4 × 10 −2 S m −1 , k = 0.62 W m −1 K −1 , EMI shielding effectiveness &gt; 20 dB in the X‐band (8–12 GHz), and UL 94 V‐0 classification, while retaining flexibility. The electrical percolation threshold (~4 wt%) was markedly lower than many EVA benchmarks, confirming the efficiency of the 1D–2D hybrid network. By advancing dispersion analysis from qualitative observation to a quantifiable design variable, this work establishes a data‐driven pathway for scalable, halogen‐free nanocomposites. The results highlight routes to lightweight, multifunctional materials for flexible electronics, automotive electronics, and aerospace thermal management, underscoring the role of AI‐enabled analysis in polymer‐engineering design. Carbon nanotube ; Nanocomposite ; Percolation threshold ; Graphene ; Dispersion (optics) ; Aerospace ; Polymer nanocomposite ; Thermal conductivity ; Thermal Electromagnetic wave absorption materials ; Flame retardant materials and properties ; Natural Fiber Reinforced Composites
Authors: Ícaro Hilário Antunes Alves | Kelly F. P. Laeber | Letícia Maia Prates | Alexandre Augusto Freire Carauta | Leonardo Baptista
Source: AIDASCO Reviews (article, 2025-12-29)
Field: Chemistry — Inorganic Chemistry

Interdisciplinary Integration of Molecular Modeling and Circular Economy for Sustainable Materials and Processes The integration of Molecular Modeling (MM) and Circular Economy (CE) establishes a scientific basis for designing sustainable materials and optimizing low-impact technological processes. CE provides systemic principles for resource efficiency and material recirculation, while MM offers atomistic and electronic-structure insights that clarify mechanisms essential for circular technological development. Bibliometric data reveal a sharp increase in ECMM studies after 2018, indicating the emergence of a consolidated interdisciplinary field. Case studies demonstrate how MM supports circular strategies by predicting extraction mechanisms in aqueous two-phase systems, evaluating ion selectivity in MOFs, and elucidating adsorption pathways relevant to CO₂ capture and storage. Together, these applications show that MM enhances process predictability, reduces experimental demand, and accelerates the development of regenerative, carbon-efficient technologies aligned with CE principles. This convergence, therefore, strengthens the scientific foundation necessary for scalable circular solutions and contributes to global decarbonization efforts. Circular economy ; Process (computing) ; Sustainability ; Resource (disambiguation) ; Foundation (evidence) ; Sustainable development ; Resource efficiency Metal-Organic Frameworks: Synthesis and Applications ; Carbon Dioxide Capture Technologies ; Chemical and Physical Properties in Aqueous Solutions