jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ria Aniza | Anélie Pétrissans | Mathieu Pétrissans | Erlan Rosyadi | Hana Nabila Anindita | Tyas Puspita Rini | Zulaicha Dwi Hastuti | Nurdiah Rahmawati | Bralin Dwiratna | Ena Marlina | Akhmad Faruq Alhikami | IDAA Warmadewanthi
Source: Forests (article, 2025-12-05)
Field: Materials Science — Polymers and Plastics

Evaluation of Biomass Softwood Composites: Structural Features and Functional Properties of Advanced Engineered Wood Softwood-based composites are increasingly used in structural and nonstructural applications owing to their renewability, cost-effectiveness, and favorable strength-to-weight performance. This study applies a systematic literature review and comparative analysis, drawing on approximately 140 sources, to synthesize current knowledge on the physicochemical, mechanical, thermal, and environmental characteristics of engineered wood products derived from softwood species. The intrinsic lignocellulosic composition of softwood, comprising roughly 40%–45% cellulose, 25%–30% hemicelluloses (with mannose as the predominant sugar), and 27%–30% lignin, strongly influences hydrophilicity, stiffness, and thermal behavior. Mechanical properties vary across engineered wood product classes; for example, plywood exhibits a modulus of rupture of 33.72–42.61 MPa and a modulus of elasticity of 6.96–8.55 GPa. Microstructural and spectroscopic analyses highlight the importance of fiber–matrix interactions, chemical bonding, and surface modifications in determining composite performance. Emerging advanced materials, such as scrimber, with densities of 800–1390 kg/m3, and fluorescent transparent wood, achieving optical transmittance above 70%–85%, demonstrate the expanding functional potential of softwood-based composites. Sustainability assessments indicate that coatings, flame-retardants, and adhesives may contribute to volatile organic compound emissions, emphasizing the need for lower-emission, bio-based alternatives. Overall, the findings of this systematic review show that softwood-based composites deliver robust, quantifiable performance advantages and hold strong potential to meet the rising demand for sustainable, low-carbon engineered materials. Softwood ; Engineered wood ; Biomass (ecology) ; Lignin ; Young's modulus ; Adhesive ; Sustainability ; Nanocellulose Natural Fiber Reinforced Composites ; Wood Treatment and Properties ; Advanced Cellulose Research Studies
Authors: S. L. Pradeep Kumar | B. Deeban | D. Santhosh Kumar | P. Baranitharan | Malinee Sriariyanun | M. Gopal
Source: Discover Materials (article, 2025-10-28)
Field: Materials Science — Ceramics and Composites

Unveiling the armor: experimental study on ballistic impact resistance of silica aerogel, sorbothane, and graphite sandwich composites Ballistic armour is intended to save lives by absorbing the impact of bullets and preventing them from entering the wearer’s body. Especially for police enforcement, security, and military professionals. However, the research on armor that withstanding the impacts of shockwaves during the explosives are limited. This study evaluates a singular sandwich composite of silica aerogel, Sorbothane, and graphite (SiSG) against explosive shockwaves and ballistic impacts, marking the inaugural dual-mode assessment of lightweight structural armour. This optimised structure surpasses traditional materials in explosive and ballistic situations due to the unique amalgamation of aerogel’s exceptionally low density and thermal resistance, Sorbothane’s exceptional damping characteristics, and graphite’s elevated stiffness. To more accurately replicate real-world settings, we may get insights on item reactions upon impact near the target by conducting ballistic experiments at a confined range of 2 m, far less than the standard 55 m. The optimal, previously unreleased layer sequence—graphite strike face, aerogel intermediate, and Sorbothane backing—was established by combining these trials with extensive ANSYS finite-element simulations. The absence of apparent surface damage in shockwave-treated SiSG panels sets a new standard for the blast resistance of lightweight composites. This extensive set of testing and computer simulations establishes the foundation for next-generation multifunctional armour systems are the novelties which elicits this research. The experimental findings reveal that the sandwich composite, comprising silica aerogel, Sorbothane, and graphite layers, demonstrates superior resilience against both shockwaves and bullet impacts. The shockwave-treated materials exhibit no physical damage on their outer surfaces, affirming their ability to withstand explosions. This research underscores the critical role of material selection and design in enhancing structural resilience, contributing significantly to the advancement of material science and engineering. Armour ; Ballistic impact ; Explosive material ; Graphite ; Ballistics ; Resilience (materials science) ; Projectile ; Composite number ; Ballistic limit Advanced ceramic materials synthesis ; Graphite, nuclear technology, radiation studies ; High-Velocity Impact and Material Behavior
Authors: Yang Yang | Jiu-Ning Wang | Liang Hu | Qasim Qasim | Xuecheng Liu | Wei Xu
Source: BMC Oral Health (article, 2025-09-26)
Field: Materials Science — Materials Chemistry

Density functional theory insight into the role of Al and V in Ti–6Al–4V dental implants: structural, electronic, and mechanical properties Commercially pure titanium and Ti-6Al-4V are the most commonly used materials for dental implants owing to their balanced mechanical properties and biocompatibility. However, much of the related research has focused primarily on experimental synthesis, lacking theoretical guidance and a deeper understanding of the underlying mechanical differences. To address this, we employ density functional theory (DFT) and the special quasi-random structure (SQS) method to construct a 64-atom supercell model and systematically analyze the effects of Al (α-phase stabilizer) and V (β-phase stabilizer) on the structural, electronic, and mechanical properties of Ti-Al-V alloys with various compositions. The results show that Al stabilizes the α-phase by reducing the formation energy through significant charge transfer, whereas V promotes β-phase formation due to its inherent body-centered cubic (BCC) phase tendency. Electronic structure analysis revealed that Al enhances stability through s/p orbital hybridization at deep energy levels, whereas V's d-electrons dominate interactions near the Fermi level, weakening the bond strength. The moderate elastic modulus of α + β Ti-6Al-4V, combined with its structural isotropy and enhanced stability, results in superior tensile and yield strengths. On the basis of mechanistic insights from Ti-6Al-4V, potential alternative alloys suitable for dental implant applications are proposed. Density functional theory ; Isotropy ; Structural stability ; Ultimate tensile strength ; Electronic structure ; Elastic modulus ; Dental implant ; Phase (matter) ; Modulus Titanium Alloys Microstructure and Properties ; Magnesium Alloys: Properties and Applications ; MXene and MAX Phase Materials
Authors: Apurva Sharma | Tarandeep Singh | Neerja Mittal Garg | Dinesh Kumar | Quoc Cuong Ngo
Source: Scientific Reports (article, 2025-10-28)
Field: Chemistry — Analytical Chemistry

Estimation of protein content in wheat samples using NIR hyperspectral imaging and 1D-CNN Wheat protein content is a major determinant of its usage and value. Current methods require wet labs that may be difficult to access and are not real-time. To overcome this, Hyperspectral imaging (HSI) has been reported for estimating the protein content of wheat seeds with the advantage that it is real-time, does not require wet labs, and has high accuracy. However, these models have been developed and validated for a small range of protein content, and without considering cultivation regions. This paper reports the extension of the use of HSI for protein estimation for a wider range of protein content and for wheat cultivated in different regions. Hyperspectral images of 621 wheat samples from five regions in India were acquired in the 900-1700 nm wavelength range. The reference protein content of each sample was determined using the Kjeldahl method, with values ranging from 9.5 to 17.25%. Mean spectra were extracted from the hyperspectral images to develop deep learning and conventional machine learning methods, which were validated through 5-fold cross-validation. The experiments showed that the one-dimensional convolutional neural networks (1D-CNN) performed the best, with the coefficient of determination (R²) of 0.9972, root mean square error (RMSE) of 0.0771, and the ratio of performance to deviation (RPD) of 18.81 for the prediction set. This shows that a 1D-CNN model trained using mean spectra can accurately estimate the wheat protein content. This has the advantage of not requiring a wet lab, and being potentially real-time, which could benefit the farmers, traders, and food industry. Hyperspectral imaging ; Kjeldahl method ; Mean squared error ; Correlation coefficient ; Convolutional neural network ; Content (measure theory) ; Pattern recognition (psychology) ; Range (aeronautics) Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Cell Image Analysis Techniques
Authors: John Bussey | Natalie Smith-Gray | Nicholas Stone‐Weiss | Randall E. Youngman | Sam Karcher | Malin C. Dixon Wilkins | Daniel R. Neuville | John S. McCloy
Source: The Journal of Physical Chemistry B (article, 2025-11-24)
Field: Materials Science — Ceramics and Composites

Structure of Cl and F Incorporation in Aluminoborosilicate Glass: Sulfate Retention, Salt Formation, and Other Properties Volatile anions such as chloride (Cl<sup>-</sup>), fluoride (F<sup>-</sup>), and sulfate (SO<sub>4</sub><sup>2-</sup>) play crucial roles in determining the behavior of silicate melts across geologic, industrial, and nuclear waste vitrification systems. Their incorporation affects the melt structure, phase stability, and processing properties, often through complex interactions with modifying cations. In nuclear waste vitrification specifically, volatiles play a significant role in inducing salt formation, a waste-loading-limiting phenomenon deleterious to melter operations and waste form durability. Here, model Hanford Site Low Activity Waste peralkaline aluminoborosilicate glasses were synthesized with Cl or F additions, alongside fixed SO<sub>4</sub><sup>2-</sup> content. These glasses were examined with a combined spectroscopy-microscopy approach that included electron microscopy and dispersive X-ray spectroscopy, Raman spectroscopy, NMR spectroscopy, and X-ray nano-Computed Tomography. Cl incorporation saturated at ∼3 mol %, beyond which excess halide and SO<sub>4</sub><sup>2-</sup> partitioned to a NaCl-rich molten salt. This salt formation reduced Na<sub>2</sub>O and SO<sub>4</sub><sup>2-</sup> content in the glass and increased the glass transition temperature (<i>T</i><sub>g</sub>), molar volume, 4-coordinated boron (N<sub>4</sub>), and network polymerization. By contrast, F was retained up to ∼12 mol % before liquid-liquid phase separation, leading to submicron-scale crystallization of CaF<sub>2</sub>, and with subsequent F addition, villiaumite (NaF) and cryolite (Na<sub>3</sub>AlF<sub>6</sub>). Before phase separation, F addition decreased <i>T</i><sub>g</sub> and increased N<sub>4</sub>. F had minimal effect on SO<sub>4</sub><sup>2-</sup> retention prior to crystallization, although SO<sub>4</sub><sup>2-</sup> preferably segregated into F-rich droplets after fluoride saturation. Vitrification ; Sulfate ; Halide ; Boron ; Salt (chemistry) ; Phase (matter) ; Fluoride ; Crystallization ; Raman spectroscopy Glass properties and applications ; Nuclear materials and radiation effects ; Recycling and utilization of industrial and municipal waste in materials production
Authors: Andri Jaya Laksana | Jae-Hwan Ahn | Ji-Young Kim
Source: Journal of Food Quality (article, 2025-01-01)
Field: Chemistry — Analytical Chemistry

Partial Least Square Regression Prediction Model for Strawberry Freshness Monitoring During Storage Using Electronic Nose Strawberries produce undesirable aromas because of chemical reactions and microbial growth during deterioration. An electronic nose can identify the changes in these aromas. This study aimed to analyze the quality indexes and volatile organic compounds (VOCs) profile changes in strawberries at constant temperatures during a specific storage period. Correlation and principal component analyses (PCAs) were also employed to determine the partial least squares regression (PLSR) for predicting the screened quality indexes using the VOC data. The results showed a high correlation of quality indexes as a function of time, including weight loss, total yeast and mold growth, total color differences, and texture (firmness). PCA revealed the contribution of VOCs in the sample, suggesting that propyl nonanoate, linalool, 2‐propanol, and (Z)‐3‐hexen‐1‐ol were freshness indicators of strawberries. PLSR appropriately predicted weight loss, total yeast and mold growth, total color differences, and texture (firmness) between the observed and predicted data, which were expressed as R 2 = 81%–96%, RMSE = 21%–97%, and MAE = 15%–75%; however, the model with nonlinearity performed poorly. This finding provides important information for future development in the storage of strawberries during distribution from farm to retail at other constant temperatures or in fluctuating temperatures. Electronic nose ; Regression analysis ; Partial least squares regression ; Statistics ; Linear regression ; Regression ; Computer science ; Mathematics ; Artificial intelligence Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Advanced Chemical Sensor Technologies
Authors: Can Huang | Yuetian Jia | Min Li | Yuan Fang | Aiwen Wu | Hongsheng Chen | Chao Qian
Source: Laser & Photonics Review (article, 2025-11-24)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Attention‐Guided Transfer Learning for Robust Cross‐Frequency Metasurfaces Design ABSTRACT Recent advancements in machine learning have emerged as a transformative force to revolutionize the way of designing metasurfaces and accelerating plenty of adaptive applications in sensing, displaying, imaging, and cloaking. However, the established intelligent design agents typically work for a fixed scenario, and the associated transfer learning techniques mostly lack robust and interpretable knowledge migration. Here, we propose an attention‐guided transfer learning framework that enables adaptive cross‐frequency knowledge transfer for robust broadband metasurface inverse design. The core of attention‐guided transfer learning lies in a physics‐informed attention mechanism that evaluates source‐frequency knowledge for a target frequency, selectively emphasizing generalizable knowledge while suppressing source‐specific effects to ensure effective‐only knowledge migration. Comparative results across 5–10 GHz band demonstrate that the attention‐guided transfer learning achieves over 10% prediction accuracy enhancement compared to conventional methods, while reducing the data requirements by 20%. Moreover, visual analyses confirm the model's physical intuition, with its focus shifting from global patterns at lower frequencies to local details at higher frequencies, mirroring wavelength‐dependent electromagnetic behavior. Our work opens a new avenue for robust cross‐scenario metasurface design by leveraging selective knowledge transfer to enhance the adaptability of intelligent electromagnetic systems. Mirroring ; Knowledge transfer ; Broadband ; Transfer of learning ; Adaptability ; Focus (optics) ; Transformative learning ; Process (computing) Metamaterials and Metasurfaces Applications ; Advanced Wireless Communication Technologies ; Advanced Antenna and Metasurface Technologies
Authors: A. J. Ansari | Hamid Sharif Nia | Alireza Afshari | Sambeet Mishra
Source: Scientific Reports (article, 2025-11-21)
Field: Materials Science — Materials Chemistry

Estimating the insulation health index of XLPE cables using machine learning This paper focuses on the development of a robust machine learning-based health index model specifically for medium voltage (MV) underground cables insulated with cross-linked polyethylene (XLPE). Maintaining the health and reliability of MV cables is crucial for effective power distribution, as failures can lead to financial losses and outages. Traditionally, maintenance has been reactive, addressing issues only after failures occur, which is costly and results in unnecessary service interruptions. To address this issue, this study introduces a predictive health index model developed using machine learning (ML) techniques. The model aims to shift maintenance strategies from reactive to proactive by identifying cables at risk of failure before they become critical. The dataset used to train the model include various parameters such as environmental factors (moisture, temperature), cable characteristics (age, insulation integrity), and historical performance data (load, partial discharges). By analyzing this dataset, ML algorithms-including Support Vector Machine (SVM), K-Nearest Neighbors (K-NN), Artificial Neural Networks (ANN), and Naïve Bayes-were used to classify cables into distinct health categories, ranging from healthy to at-risk. The results indicate that ANN outperforms conventional methods, achieving 94.5% accuracy-a 12% improvement. Compared to reactive maintenance, this model improves fault prediction by 30% and reduces maintenance costs by 25%. Reliability (semiconductor) ; Artificial neural network ; Support vector machine ; Predictive maintenance ; Index (typography) ; Fault (geology) ; Predictive modelling High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Thermal Analysis in Power Transmission
Authors: Yu Zhang | Yashan Wang | Yajing Gao | Keyi Hu | Hao Gong | Haijing Jia | Xin Zhang | Xinhui Lou
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2025-02-15)
Field: Materials Science — Materials Chemistry

APV-Sankey: A Comprehensive Toolbox for Aptamer Screening and Visualization Abstract Aptamers, short single-stranded DNA or RNA molecules, have gained prominence as molecular recognition elements in diagnostics and therapeutics. Screening high-performance aptamers from SELEX process is tough due to limited library diversity, PCR bias, and low library enrichment efficiency. The enriched highest frequency sequences often do not have the highest affinity or specificity. Thus, we developed APV-Sankey, a novel and versatile toolbox designed for the rapid aptamer screening and visualization of the enrichment process. Its key feature is the innovative use of Sankey charts for interactive and informative visualizations. These charts facilitate tracing the evolution of K-mers (K-mer is just a sequence of k characters in a string) across SELEX rounds, aiding in the identification and selection of high-affinity sequences containing K-mers while excluding high-frequency sequences without affinity. Besides integrating essential functions for aptamer analysis, we also developed advanced methods including K-mer concatenation and K-mer evolution, combined with Sankey chart visualizations. Using this toolbox to analyze experimentally screened sequences, we successfully identified high-performance candidate aptamers of rapamycin and thrombin. Toolbox ; Aptamer ; Visualization ; Computer science ; Biology ; Data mining ; Genetics ; Programming language Machine Learning in Materials Science ; Graph Theory and Algorithms ; Scientific Computing and Data Management
Authors: Jian Xu | Yijun Zhang | Lin Mao | Binbin Ma | Hongpu He | Fangyi Jiang | Shu-Kun He | Chengjie Yuan
Source: Scientific Reports (article, 2025-02-20)
Field: Materials Science — Materials Chemistry

A novel MRI contrast agent NaGdF4@PEG–CLS@MMP-13 NPs for detecting articular cartilage injury Early detection of cartilage injuries is crucial due to their limited self-repair capacity and risk of joint dysfunction. Conventional contrast agents like gadolinium-diethylenetriamine-pentaacetic acid (Gd-DTPA) offer low specificity and T1 relaxivity (r1), limiting MRI application. This study introduces NaGdF<sub>4</sub> nanoparticles (NPs) modified with polyethylene glycol (PEG) and cholesterol (CLS) to enhance hydrophilicity and lipophilicity. Targeting is achieved using a matrix metalloproteinase-13 (MMP13) cartilage-binding peptide. NaGdF<sub>4</sub>@PEG-CLS@MMP13 CBP NPs demonstrate an increased r1 value (8.07 mM<sup>-1</sup> s<sup>-1</sup>) compared to NaGdF<sub>4</sub>@PEG-CLS NPs (6.65 mM<sup>-1</sup> s<sup>-1</sup>) and Gd-DTPA (3.01 mM<sup>-1</sup> s<sup>-1</sup>), enabling deeper cartilage penetration and stronger cartilage affinity. Two hours post-injection, these NPs improved the signal-to-noise ratio at injury sites by 2.4-fold over pre-injection values. Biocompatibility was confirmed with no adverse effects in blood or organs, and the NPs were metabolized in kidneys and liver, with excretion via urine. This study supports NaGdF<sub>4</sub>@PEG-CLS@MMP13 CBP NPs as an effective MRI contrast agent, enhancing early detection of cartilage injuries. CLs upper limits ; Articular cartilage ; Cartilage ; PEG ratio ; Matrix metalloproteinase ; MRI contrast agent ; Medicine ; Contrast (vision) ; Biomedical engineering ; Chemistry ; Magnetic resonance imaging ; Pathology ; Computer science ; Osteoarthritis ; Anatomy ; Radiology ; Internal medicine ; Artificial intelligence ; Ophthalmology Lanthanide and Transition Metal Complexes ; Nanoplatforms for cancer theranostics ; Medical Imaging and Pathology Studies
Authors: Ye-Cheng Luo | Qingmei Bai | Piotr Skrzypacz
Source: Frontiers in Physics (article, 2025-01-14)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Application of MEMS technology in anti-electromagnetic radiation maternity clothes: state of the art and future perspectives This review provides a comprehensive analysis of the application of Micro-Electro-Mechanical Systems (MEMS) technology in anti-electromagnetic radiation maternity wear. The review commences with an elaboration of the electromagnetic shielding principles of traditional materials and the principle of anti-electromagnetic radiation. Subsequently, the role of MEMS in maternity clothing is detailed, including the real-time monitoring of radiation via sensors, the enhancement of fabric shielding through electrospinning and material deposition, and the realization of intelligent functions such as micro-actuators and communication modules. Furthermore, the review considers the optimization of performance, taking into account factors such as electromagnetic shielding, air permeability and comfort. Furthermore, the article addresses the challenges of ensuring comfort and power supply. The article concludes by emphasizing the potential of MEMS in protecting pregnant women and fetuses and proposes future research directions, including an in-depth exploration of the working principles, technical specifications, and performance characteristics of key MEMS components (sensors and micro-actuators), as well as research on the combination and The combination of MEMS technology with existing anti-radiation technologies, such as traditional metal fiber fabrics and nanomaterials, has the potential to yield significant synergistic effects. Furthermore, an in-depth analysis of performance optimization, including durability and washing stability of maternity clothes, is essential. Additionally, the exploration of emerging technologies, such as bubble electrospinning in maternity clothes, could lead to innovative applications in this field. Clothing ; State (computer science) ; Microelectromechanical systems ; State of art ; Engineering ethics ; Engineering ; Mechanical engineering ; Computer science ; Political science ; Nanotechnology ; Materials science Electromagnetic wave absorption materials ; Dermatologic Treatments and Research ; Electromagnetic Fields and Biological Effects